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Objective: To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter- and hospital-submitted inpatient claims., Data Sources and Study Setting: Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges., Study Design: We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim., Data Collection/extraction Methods: We combined hospital- and MAO-submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO-submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures., Principal Findings: After removing void and duplicate claims, identifying overlapped claims between the hospital- and MAO-submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital-submitted claims were submitted within 3 months after a one-year performance period, versus 85.2% of MAO-submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code., Conclusions: Inpatient MA data are feasible for use in CMS claims-based hospital outcome measures. We recommend prioritizing hospital-submitted over MAO-submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors., (© 2024 Health Research and Educational Trust.)

Importance: Medicare Advantage (MA) enrollment is rapidly expanding, yet Centers for Medicare & Medicaid Services (CMS) claims-based hospital outcome measures, including readmission rates, have historically included only fee-for-service (FFS) beneficiaries., Objective: To assess the outcomes of incorporating MA data into the CMS claims-based FFS Hospital-Wide All-Cause Unplanned Readmission (HWR) measure., Design, Setting, and Participants: This cohort study assessed differences in 30-day unadjusted readmission rates and demographic and risk adjustment variables for MA vs FFS admissions. Inpatient FFS and MA administrative claims data were extracted from the Integrated Data Repository for all admissions for Medicare beneficiaries from July 1, 2018, to June 30, 2019. Measure reliability and risk-standardized readmission rates were calculated for the FFS and MA cohort vs the FFS-only cohort, overall and within specialty subgroups (cardiorespiratory, cardiovascular, medicine, surgery, neurology), then changes in hospital performance quintiles were assessed after adding MA admissions., Main Outcome and Measure: Risk-standardized readmission rates., Results: The cohort included 11 029 470 admissions (4 077 633 [37.0%] MA; 6 044 060 [54.8%] female; mean [SD] age, 77.7 [8.2] years). Unadjusted readmission rates were slightly higher for MA vs FFS admissions (15.7% vs 15.4%), yet comorbidities were generally lower among MA beneficiaries. Test-retest reliability for the FFS and MA cohort was higher than for the FFS-only cohort (0.78 vs 0.73) and signal-to-noise reliability increased in each specialty subgroup. Mean hospital risk-standardized readmission rates were similar for the FFS and MA cohort and FFS-only cohorts (15.5% vs 15.3%); this trend was consistent across the 5 specialty subgroups. After adding MA admissions to the FFS-only HWR measure, 1489 hospitals (33.1%) had their performance quintile ranking changed. As their proportion of MA admissions increased, more hospitals experienced a change in their performance quintile ranking (147 hospitals [16.3%] in the lowest quintile of percentage MA admissions; 408 [45.3%] in the highest). The combined cohort added 63 hospitals eligible for public reporting and more than 4 million admissions to the measure., Conclusions and Relevance: In this cohort study, adding MA admissions to the HWR measure was associated with improved measure reliability and precision and enabled the inclusion of more hospitals and beneficiaries. After MA admissions were included, 1 in 3 hospitals had their performance quintile changed, with the greatest shifts among hospitals with a high percentage of MA admissions.

Background: Individuals with long COVID lack evidence-based treatments and have difficulty participating in traditional site-based trials. Our digital, decentralized trial investigates the efficacy and safety of nirmatrelvir/ritonavir, targeting viral persistence as a potential cause of long COVID., Methods: The PAX LC trial (NCT05668091) is a Phase 2, 1:1 randomized, double-blind, superiority, placebo-controlled trial in 100 community-dwelling, highly symptomatic adult participants with long COVID residing in the 48 contiguous US states to determine the efficacy, safety, and tolerability of 15 days of nirmatrelvir/ritonavir compared with placebo/ritonavir. Participants are recruited via patient groups, cultural ambassadors, and social media platforms. Medical records are reviewed through a platform facilitating participant-mediated data acquisition from electronic health records nationwide. During the drug treatment, participants complete daily digital diaries using a web-based application. Blood draws for eligibility and safety assessments are conducted at or near participants' homes. The study drug is shipped directly to participants' homes. The primary endpoint is the PROMIS-29 Physical Health Summary Score difference between baseline and Day 28, evaluated by a mixed model repeated measure analysis. Secondary endpoints include PROMIS-29 (Mental Health Summary Score and all items), Modified GSQ-30 with supplemental symptoms questionnaire, COVID Core Outcome Measures for Recovery, EQ-5D-5L (Utility Score and all items), PGIS 1 and 2, PGIC 1 and 2, and healthcare utilization. The trial incorporates immunophenotyping to identify long COVID biomarkers and treatment responders., Conclusion: The PAX LC trial uses a novel decentralized design and a participant-centric approach to test a 15-day regimen of nirmatrelvir/ritonavir for long COVID., (Copyright © 2024. Published by Elsevier Inc.)

Background: The digital transformation of medical data enables health systems to leverage real-world data from electronic health records to gain actionable insights for improving hypertension care., Methods and Results: We performed a serial cross-sectional analysis of outpatients of a large regional health system from 2010 to 2021. Hypertension was defined by systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or recorded treatment with antihypertension medications. We evaluated 4 methods of using blood pressure measurements in the electronic health record to define hypertension. The primary outcomes were age-adjusted prevalence rates and age-adjusted control rates. Hypertension prevalence varied depending on the definition used, ranging from 36.5% to 50.9% initially and increasing over time by ≈5%, regardless of the definition used. Control rates ranged from 61.2% to 71.3% initially, increased during 2018 to 2019, and decreased during 2020 to 2021. The proportion of patients with a hypertension diagnosis ranged from 45.5% to 60.2% initially and improved during the study period. Non-Hispanic Black patients represented 25% of our regional population and consistently had higher prevalence rates, higher mean systolic and diastolic blood pressure, and lower control rates compared with other racial and ethnic groups., Conclusions: In a large regional health system, we leveraged the electronic health record to provide real-world insights. The findings largely reflected national trends but showed distinctive regional demographics and findings, with prevalence increasing, one-quarter of the patients not controlled, and marked disparities. This approach could be emulated by regional health systems seeking to improve hypertension care.

Objectives: The extent to which care quality influenced outcomes for patients hospitalised with COVID-19 is unknown. Our objective was to determine if prepandemic hospital quality is associated with mortality among Medicare patients hospitalised with COVID-19., Design: This is a retrospective observational study. We calculated hospital-level risk-standardised in-hospital and 30-day mortality rates (risk-standardised mortality rates, RSMRs) for patients hospitalised with COVID-19, and correlation coefficients between RSMRs and pre-COVID-19 hospital quality, overall and stratified by hospital characteristics., Setting: Short-term acute care hospitals and critical access hospitals in the USA., Participants: Hospitalised Medicare beneficiaries (Fee-For-Service and Medicare Advantage) age 65 and older hospitalised with COVID-19, discharged between 1 April 2020 and 30 September 2021., Intervention/exposure: Pre-COVID-19 hospital quality., Outcomes: Risk-standardised COVID-19 in-hospital and 30-day mortality rates (RSMRs)., Results: In-hospital (n=4256) RSMRs for Medicare patients hospitalised with COVID-19 (April 2020-September 2021) ranged from 4.5% to 59.9% (median 18.2%; IQR 14.7%-23.7%); 30-day RSMRs ranged from 12.9% to 56.2% (IQR 24.6%-30.6%). COVID-19 RSMRs were negatively correlated with star rating summary scores (in-hospital correlation coefficient -0.41, p<0.0001; 30 days -0.38, p<0.0001). Correlations with in-hospital RSMRs were strongest for patient experience (-0.39, p<0.0001) and timely and effective care (-0.30, p<0.0001) group scores; 30-day RSMRs were strongest for patient experience (-0.34, p<0.0001) and mortality (-0.33, p<0.0001) groups. Patients admitted to 1-star hospitals had higher odds of mortality (in-hospital OR 1.87, 95% CI 1.83 to 1.91; 30-day OR 1.46, 95% CI 1.43 to 1.48) compared with 5-star hospitals. If all hospitals performed like an average 5-star hospital, we estimate 38 000 fewer COVID-19-related deaths would have occurred between April 2020 and September 2021., Conclusions: Hospitals with better prepandemic quality may have care structures and processes that allowed for better care delivery and outcomes during the COVID-19 pandemic. Understanding the relationship between pre-COVID-19 hospital quality and COVID-19 outcomes will allow policy-makers and hospitals better prepare for future public health emergencies., Competing Interests: Competing interests: S-XL, YW, JZ, JG, KM, EN, ZL, AV, LS and EWT receive salary support from the Centers for Medicare and Medicaid Services to develop, implement and maintain hospital performance outcome measures, including the methodology for the Overall Hospital Star Ratings, that are publicly reported. SB and MS are employed by CMS. LAF is employed by the Perelman School of Medicine at the University of Pennsylvania. DP is a subcontractor to Yale/CORE., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Data on ethnic and racial differences in symptoms and health-related impacts following SARS-CoV-2 infection are limited. We aimed to estimate the ethnic and racial differences in symptoms and health-related impacts 3 and 6 months after the first SARS-CoV-2 infection., Methods: Participants included adults with SARS-CoV-2 infection enrolled in a prospective multicenter US study between 12/11/2020 and 7/4/2022 as the primary cohort of interest, as well as a SARS-CoV-2-negative cohort to account for non-SARS-CoV-2-infection impacts, who completed enrollment and 3-month surveys ( N  = 3,161; 2,402 SARS-CoV-2-positive, 759 SARS-CoV-2-negative). Marginal odds ratios were estimated using GEE logistic regression for individual symptoms, health status, activity level, and missed work 3 and 6 months after COVID-19 illness, comparing each ethnicity or race to the referent group (non-Hispanic or white), adjusting for demographic factors, social determinants of health, substance use, pre-existing health conditions, SARS-CoV-2 infection status, COVID-19 vaccination status, and survey time point, with interactions between ethnicity or race and time point, ethnicity or race and SARS-CoV-2 infection status, and SARS-CoV-2 infection status and time point., Results: Following SARS-CoV-2 infection, the majority of symptoms were similar over time between ethnic and racial groups. At 3 months, Hispanic participants were more likely than non-Hispanic participants to report fair/poor health (OR: 1.94; 95%CI: 1.36-2.78) and reduced activity (somewhat less, OR: 1.47; 95%CI: 1.06-2.02; much less, OR: 2.23; 95%CI: 1.38-3.61). At 6 months, differences by ethnicity were not present. At 3 months, Other/Multiple race participants were more likely than white participants to report fair/poor health (OR: 1.90; 95% CI: 1.25-2.88), reduced activity (somewhat less, OR: 1.72; 95%CI: 1.21-2.46; much less, OR: 2.08; 95%CI: 1.18-3.65). At 6 months, Asian participants were more likely than white participants to report fair/poor health (OR: 1.88; 95%CI: 1.13-3.12); Black participants reported more missed work (OR, 2.83; 95%CI: 1.60-5.00); and Other/Multiple race participants reported more fair/poor health (OR: 1.83; 95%CI: 1.10-3.05), reduced activity (somewhat less, OR: 1.60; 95%CI: 1.02-2.51; much less, OR: 2.49; 95%CI: 1.40-4.44), and more missed work (OR: 2.25; 95%CI: 1.27-3.98)., Discussion: Awareness of ethnic and racial differences in outcomes following SARS-CoV-2 infection may inform clinical and public health efforts to advance health equity in long-term outcomes., Competing Interests: JGE is Editor in Chief of Adult Primary Care topics for UpToDate. MG reports grant funding from the Rush Center for Emerging Infectious Diseases Research Grant, Biomedical Advanced Research and Development Authority Research Grant, Emergency Medicine Foundation/Council of Residency Directors in Emergency Medicine Education Research Grant, Emergency Medicine: Reviews and Perspectives Medical Education Research Grant, University of Ottawa Department of Medicine Education Grant; and Society of Directors of Research in Medical Education Grant. KLR reports research grant funding from Abbott Diagnostics, DermTech, MeMed, Prenosis, and Siemens Healthcare Diagnostics. RMR reports research funding for PROCOVAXED funded by NIAID R01AI166967-01 (PI: Rodriquez). KK reports HECAP funded by RWJF (contract number: 79308 PI: Ansell); Chicago Department of Public Health Order 2020–4 COVID-19 Data Sharing for Patient Safety and Capacity Management funded by CDC (contract number: 6NU50CK000556-01-04 PI: Saldanha). GN reports funding through National Institutes of Health. PROCOVAXED Trial, Site PI. Centers for Disease Control and Prevention. Clinical Core, INSPIRE Registry, PI. Patient-Centered Outcomes Research Institute, Washington, DC. University of Washington PCORNet Expansion Award, Joint PI. Abiomed Inc., Danvers, MA. Emergency Care Core for Trial of Impella in Patients with STEMI and Cardiogenic Shock (RECOVER IV), PI. ZOLL Medical Corp., Chelmsford, MA, Multidimensional Study of Oxygenation in Early Post-Resuscitation (MOSER), PI. Vapotherm Inc., Exeter, NH. Vapotherm Device for Rapid Cooling Study (VOS), Co-PI. ZOLL Circulation Inc., San Jose, CA. Better Resuscitation with Supersaturated Oxygen (BASSO) Study, Co-PI. Powerful Medical Inc., Bratislava, Slovakia, US Validation Study of AI-Enhanced Diagnosis of Occluding Myocardial Infarction, PI. CPR Therapeutics Inc., Putney, VT. Consultant. Heartbeam Inc., Santa Clara, CA. Consultant. Invero Health LLC, Montville, NJ. Consultant. Kestra Medical Technologies Inc., Kirkland, WA. Consultant. Orixha Inc., Saint Cyr Au Mont d’Or, France. Consultant. BrainCool AB, Lund, Sweden. Consultant Patent for measurement of blood flow during CPR; non-provisional patent pending for blood flow measurement during CPR using signal gating; non-provisional patent pending for reperfusion-injury modifying device; all assigned to University of Washington. KNO reports research grant funding for PROCOVOXED funded by NIAID R01 AI166967 (PI: Rodriguez). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 O’Laughlin, Klabbers, Ebna Mannan, Gentile, Geyer, Zheng, Yu, Li, Chan, Spatz, Wang, L’Hommedieu, Weinstein, Plumb, Gottlieb, Huebinger, Hagen, Elmore, Hill, Kelly, McDonald, Rising, Rodriguez, Venkatesh, Idris, Santangelo, Koo, Saydah, Nichol, Stephens and the INSPIRE Group.)

To clarify the alleviation effect of exogenous melatonin (MT) on Agropyron mongolicum under drought stress, we examined the response of A. mongolicum 'Yanchi' seedlings to simulated drought stress with polyethylene glycol 6000 (PEG-6000), by investigating the effects of exogenous addition of different concentrations (0, 1, 10, 50, 100, 150 and 200 mg·L -1 ) of MT on seedlings growth and physiological characteristics under drought stress. The results showed that drought stress significantly inhibited the growth of A. mongolicum seedlings, and that exogenous addition of different concentrations of MT could alleviate the growth inhibition caused by drought stress, with the strongest mitigation effect observed at MT concentration of 100 mg·L -1 . Compared with the drought stress treatment alone, exogenous addition of 100 mg·L -1 MT under drought stress increased plant height, aboveground dry weight, and leaf relative water content by 58.2%, 121.2% and 48.1%. The contents of chlorophyll a, chlorophyll b, carotenoids increased by 48.7%, 80.8% and 38.3%, superoxide dismutase, peroxidase and root activity increased by 12.6%, 33.9% and 39.1%, and the contents of ascorbic acid and glutathione increased by 19.5% and 18.3%, respectively. The contents of proline, soluble sugar and soluble protein were increased by 16.2%, 32.6% and 14.3%, while that of malondialdehyde, hydrogen peroxide and superoxide anion radical were decreased by 45.8%, 65.8% and 30.8%, respectively. In summary, exogenous addition of 100 mg·L -1 MT could improve drought tolerance of A. mongolicum seedlings by promoting growth, enhancing antioxidant capacity, increasing the content of osmoregulation substances, inhibiting the excessive production of reactive oxygen, and reducing membrane peroxide level.

Background: The delivery and financing of health care services were altered in unprecedented ways by COVID-19 and subsequent policy responses. We estimated reimbursement losses to emergency physicians in 2020 compared to 2019 related to shifting acute care utilization during COVID-19., Methods: This was an observational analysis of the Clinical Emergency Department Registry (CEDR) and the Nationwide Emergency Department Sample (NEDS). Study sample included all ED visits from a sample of 214 emergency department (ED) sites in the CEDR in 2019 and 2020 as well as all ED visits in the NEDS in 2019. We identified level of service billing code for evaluation and management (E&M) services, insurance payer, and geographic location of ED visits across sites in the CEDR and linked these to fee schedules to estimate total professional reimbursement across sites. Our primary analysis was to estimate reimbursement in 2020 compared to 2019 across the CEDR sites. In our secondary analysis, we linked sites in the CEDR to those in NEDS to estimate nationwide reimbursement., Results: Total E&M reimbursement for emergency physicians in the CEDR was $1.6 billion in 2019 and $1.3 billion in 2020, reflecting a 19.7% decline year over year ($308 million loss). In our secondary analysis, we estimate nationwide losses of $6.6 billion, a -19.4% decline year over year. If emergency physicians had received maximum allowable federal relief funds via CARES Act Phases 1 to 3 (2% of 2019 revenue) this would sum to $680 million (2% of the $34 billion) or 10.3% of the estimated $6.6 billion pandemic-related losses., Conclusions: Our analyses provide an estimate of the scale of economic impacts of the COVID-19 pandemic. These findings warrant consideration for policymaker relief and future redesign of emergency care financing. Ultimately, the COVID-19 pandemic likely expanded known cracks in the financing of health care into steep fault lines., (© 2023 The Authors. Academic Emergency Medicine published by Wiley Periodicals LLC on behalf of Society for Academic Emergency Medicine.)

Background: The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection., Methods: This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms., Results: Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months., Conclusions: We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized. Clinical Trials Registration.  NCT04610515., Competing Interests: Potential conflicts of interest. M. G. reports funding from the Rush Center for Emerging Infectious Diseases Research Grant, Emergency Medicine Foundation/Council of Residency Directors in Emergency Medicine Education Research Grant, Emergency Medicine: Reviews and Perspectives Medical Education Research Grant, University of Ottawa Department of Medicine Education Grant, and Society of Directors of Research in Medical Education Grant. E. S. S. receives grant funding from the National Institute on Minority Health and Health Disparities (U54MD010711-01), the US Food and Drug Administration to support projects within the Yale-Mayo Clinic Center of Excellence in Regulatory Science and Innovation (CERSI, U01FD005938), the National Institute of Biomedical Imaging and Bioengineering (R01EB028106-01), and the National Heart, Lung, and Blood Institute (R01HL151240). J. G. E. reports serving as Editor-in-Chief of Adult Primary Care topics for UpToDate. N. L. G. receives grant funding from the CDC (BAA75D301-20-75D30121C10207). A. V. reports funding for COVID-19–related studies from the Society of Academic Emergency Medicine Foundation Emerging Infectious Disease and Preparedness Grant, the Agency for Healthcare Research and Quality (R01 HS 28340-01), the Food and Drug Administration (ID: 75F40120C00174), and the Centers for Medicare and Medicaid Services. G. N. reports the following: funding for COVID-19–related studies from the National Institute of Allergy and Infectious Diseases (NIAID) (1R01AI66967), research funding from Abiomed, Vapotherm, and ZOLL Medical; consultant to CPR Therapeutics, Heartbeam Inc, Invero Health LLC, Kestra Medical Technologies, Orixha, and ZOLL Circulation; and reports a patent (Method for non-imaging ultrasound to measure blood flow during CPR) and nonprovisional patent (Method for modifying cell injury associated with reduced blood flow). K. N. O. reports funding for COVID-19–related studies from NIAID (1R01AI66967). K. L. R. reports funding for COVID-19–related studies from NIAID (1R01AI66967) and the Philadelphia Department of Public Health. All other authors report no potential conflicts., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Background: While prior work examining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern focused on hospitalization and death, less is known about differences in clinical presentation. We compared the prevalence of acute symptoms across pre-Delta, Delta, and Omicron., Methods: We conducted an analysis of the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE), a cohort study enrolling symptomatic SARS-CoV-2-positive participants. We determined the association between the pre-Delta, Delta, and Omicron time periods and the prevalence of 21 coronavirus disease 2019 (COVID-19) acute symptoms., Results: We enrolled 4113 participants from December 2020 to June 2022. Pre-Delta vs Delta vs Omicron participants had increasing sore throat (40.9%, 54.6%, 70.6%; P < .001), cough (50.9%, 63.3%, 66.7%; P < .001), and runny noses (48.9%, 71.3%, 72.9%; P < .001). We observed reductions during Omicron in chest pain (31.1%, 24.2%, 20.9%; P < .001), shortness of breath (42.7%, 29.5%, 27.5%; P < .001), loss of taste (47.1%, 61.8%, 19.2%; P < .001), and loss of smell (47.5%, 55.6%, 20.0%; P < .001). After adjustment, those infected during Omicron had significantly higher odds of sore throat vs pre-Delta (odds ratio [OR], 2.76; 95% CI, 2.26-3.35) and Delta (OR, 1.96; 95% CI, 1.69-2.28)., Conclusions: Participants infected during Omicron were more likely to report symptoms of common respiratory viruses, such as sore throat, and less likely to report loss of smell and taste., Trial Registration: NCT04610515., Competing Interests: Potential conflicts of interest. Dr. Elmore reports serving as Editor in Chief of Adult Primary Care topics for UpToDate. Dr. Venkatesh reports funding for COVID-19-related studies from the Society of Academic Emergency Medicine Foundation Emerging Infectious Disease and Preparedness Grant, the Agency for Healthcare Research and Quality (R01 HS 28340-01) the Food and Drug Administration (ID: 75F40120C00174), and the Emergency Medicine Health Policy Institute/Emergency Medicine Foundation. Dr. Wang reports funding from CDC for research on N95 respirators. Dr. Gottlieb reports grant funding from the Rush Center for Emerging Infectious Diseases Research Grant, Emergency Medicine Foundation/Council of Residency Directors in Emergency Medicine Education Research Grant, Emergency Medicine: Reviews and Perspectives Medical Education Research Grant, University of Ottawa Department of Medicine Education Grant; and Society of Directors of Research in Medical Education Grant. Dr. Nichol reports the following: Vapotherm Inc, Exeter, NH- Research funding; ZOLL Medical, Chelmsford, MA- Research funding; Abiomed Inc., Danvers, MA- Consultant; CellPhire Inc., Rockville, MD- Consultant; CPR Therapeutics, Putney, VT- Consultant; ZOLL Circulation, San Jose, CA- Consultant; Patent- Method for non-imaging ultrasound to measure blood flow during CPR; Non-provisional patent- Method for modifying cell injury associated with reduced blood flow; Heartbeam Inc., Santa Clara, CA- Consultant; Invero Health, LLC., Montvale, NJ- Consultant; Orixha Inc., Saint Cyr Au Mont d’Or, France- Consultant; Kestra Medical Technologies, Kirkland, WA- Consultant; Medic One Foundation, Seattle, WA- Salary Support via Univ. Washington (UW). Dr. Elmore reports serving as Editor in Chief of Adult Primary Care topics for UpToDate. Dr. Venkatesh reports funding for COVID-19-related studies from the Society of Academic Emergency Medicine Foundation Emerging Infectious Disease and Preparedness Grant, the Agency for Healthcare Research and Quality (R01 HS 28340-01), the Food and Drug Administration (ID: 75F40120C00174), and the Emergency Medicine Health Policy Institute/Emergency Medicine Foundation. Dr. Wang reports funding from CDC for research on N95 respirators. All other authors report no potential conflicts., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Background: Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood., Methods: We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID-), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email., Results: Among the first 1000 participants, 722 were COVID+ and 278 were COVID-. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID- group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID- groups at 3 months., Conclusions: Approximately half of COVID+ participants, as compared with one-quarter of COVID- participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID., Clinical Trials Registration: NCT04610515., Competing Interests: Potential conflicts of interest . All authors: No potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: The aim of the present study was to detect the association between platelet glycoprotein-specific autoantibodies and the patient response to short-term high-dose dexamethasone (HD-DXM) + prednisone maintenance treatment., Methods: The data from 112 adult patients newly diagnosed with ITP who were administered first-line HD-DXM + prednisone maintenance therapy between January 2016 and January 2021 were retrospectively analyzed., Results: A total of 72 patients positive for platelet glycoprotein-specific antibodies were enrolled in the antibody-positive group, and 40 patients not positive for platelet glycoprotein-specific antibodies were enrolled in the antibody-negative group. In the antibody-positive group, six platelet glycoprotein-specific antibody types were found: 41.67% of the patients were anti-GP IIb/IIIa-positive only, 5.56% were anti-GP Ib/IX-positive only, 5.56% were anti-P-selectin-positive only, 19.44% were anti-GP IIb/IIIa- and anti-GP Ib/IX-positive, 16.67% were anti-GP Ib/IX- and P-selectin-positive and 11.11% were positive for all three antibodies. There was no significant difference in the overall response rate between the antibody-positive group and the antibody-negative group (94.44 versus 80.00%, p  = .221). However, the CR rate was significantly higher in the antibody-positive group than in the antibody-negative group (69.44% versus 40.00%, p  = .032). The logistic regression analysis revealed that platelet glycoprotein-specific antibody positivity and age were two factors that could affect patient response., Conclusions: The present study discovered that adult patients newly diagnosed with ITP who had positive platelet glycoprotein-specific antibody test results were likely to achieve a better response after treatment with HD-DXM + prednisone maintenance.

Importance: An increasing proportion of people in the US hospitalized for acute myocardial infarction (AMI) are younger than 55 years, with the largest increase in young women. Effective prevention requires an understanding of risk factors associated with risk of AMI in young women compared with men., Objectives: To assess the sex-specific associations of demographic, clinical, and psychosocial risk factors with first AMI among adults younger than 55 years, overall, and by AMI subtype., Design, Setting, and Participants: This study used a case-control design with 2264 patients with AMI, aged 18 to 55 years, from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study and 2264 population-based controls matched for age, sex, and race and ethnicity from the National Health and Nutrition Examination Survey from 2008 to 2012. Data were analyzed from April 2020 to November 2021., Exposures: A wide range of demographic, clinical, and psychosocial risk factors., Main Outcomes and Measures: Odds ratios (ORs) and population attributable fractions (PAF) for first AMI associated with demographic, clinical, and psychosocial risk factors., Results: Of the 4528 case patients and matched controls, 3122 (68.9%) were women, and the median (IQR) age was 48 (44-52) years. Seven risk factors (diabetes [OR, 3.59 (95% CI, 2.72-4.74) in women vs 1.76 (1.19-2.60) in men], depression [OR, 3.09 (95% CI, 2.37-4.04) in women vs 1.77 (1.15-2.73) in men], hypertension [OR, 2.87 (95% CI, 2.31-3.57) in women vs 2.19 (1.65-2.90) in men], current smoking [OR, 3.28 (95% CI, 2.65-4.07) in women vs 3.28 (2.65-4.07) in men], family history of premature myocardial infarction [OR, 1.48 (95% CI, 1.17-1.88) in women vs 2.42 (1.71-3.41) in men], low household income [OR, 1.79 (95% CI, 1.28-2.50) in women vs 1.35 (0.82-2.23) in men], hypercholesterolemia [OR, 1.02 (95% CI, 0.81-1.29) in women vs 2.16 (1.49-3.15) in men]) collectively accounted for the majority of the total risk of AMI in women (83.9%) and men (85.1%). There were significant sex differences in risk factor associations: hypertension, depression, diabetes, current smoking, and family history of diabetes had stronger associations with AMI in young women, whereas hypercholesterolemia had a stronger association in young men. Risk factor profiles varied by AMI subtype, and traditional cardiovascular risk factors had higher prevalence and stronger ORs for type 1 AMI compared with other AMI subtypes., Conclusions and Relevance: In this case-control study, 7 risk factors, many potentially modifiable, accounted for 85% of the risk of first AMI in young women and men. Significant differences in risk factor profiles and risk factor associations existed by sex and by AMI subtype. These findings suggest the need for sex-specific strategies in risk factor modification and prevention of AMI in young adults. Further research is needed to improve risk assessment of AMI subtypes.

Background: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics., Methods and Results: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics., Conclusions: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models.

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged <25 years (-2.9 to 14.1). Among persons aged <65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of >1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Zhenqiu Lin reports contract support from the Centers for Medicare & Medicaid Services (CMS) to develop and maintain measures of hospital performance that are publicly reported. Harlan M. Krumholz reports the following outside the current work: honoraria for presentations at various educational events; grants from Medtronic and the Food and Drug Administration, Medtronic and Johnson & Johnson, Shenzhen Center for Health Information, Foundation for a Smoke-Free World, and Connecticut Department of Public Health and CMS; payment from law firms Martin/Baughman, Arnold & Porter, and Siegfried & Jensen for expert testimony; chairmanship or member of United Healthcare cardiac scientific advisory board, IBM Watson Health life sciences board, Element Science scientific advisor, Aetna health care advisory board, and Facebook advisory board; and ownership of Hugo Health and Refractor Health. No other potential conflicts of interest were disclosed.

Objective: Although timely administration of antibiotics has an established benefit in serious bacterial infection, the majority of studies evaluating antibiotic delay focus only on the first dose. Recent evidence suggests that delays in redosing may also be associated with worse clinical outcome. In light of the increasing burden of boarding in Emergency Departments (ED) and subsequent need to redose antibiotic in the ED, we examined the association between delayed second antibiotic dose administration and mortality among patients admitted from the ED with a broad array of infections and characterized risk factors associated with delayed second dose administration., Methods: We performed a retrospective cohort study of patients admitted through five EDs in a single healthcare system from 1/2018 through 12/2018. Our study included all patients, aged 18 years or older, who received two intravenous antibiotic doses within a 30-h period, with the first dose administered in the ED. Patients with end stage renal disease, cirrhosis and extremes of weight were excluded due to a lack of consensus on antibiotic dosing intervals for these populations. Delay was defined as administration of the second dose at a time-point greater than 125% of the recommended interval. The primary outcome was in-hospital mortality., Results: A total of 5605 second antibiotic doses, occurring during 4904 visits, met study criteria. Delayed administration of the second dose occurred during 21.1% of visits. After adjustment for patient characteristics, delayed second dose administration was associated with increased odds of in-hospital mortality (OR 1.50, 95%CI 1.05-2.13). Regarding risk factors for delay, every one-hour increase in allowable compliance time was associated with a 18% decrease in odds of delay (OR 0.82 95%CI 0.75-0.88). Other risk factors for delay included ED boarding more than 4 h (OR 1.47, 95%CI 1.27-1.71) or a high acuity presentation as defined by emergency severity index (ESI) (OR 1.54, 95%CI 1.30-1.81 for ESI 1-2 versus 3-5)., Conclusions: Delays in second antibiotic dose administration were frequent in the ED and early hospital course, and were associated with increased odds of in-hospital mortality. Several risk factors associated with delays in second dose administration, including ED boarding, were identified., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Infection fatality rate and infection hospitalization rate, defined as the proportion of deaths and hospitalizations, respectively, of the total infected individuals, can estimate the actual toll of coronavirus disease 2019 (COVID-19) on a community, as the denominator is ideally based on a representative sample of a population, which captures the full spectrum of illness, including asymptomatic and untested individuals., Objective: To determine the COVID-19 infection hospitalization rate and infection fatality rate among the non-congregate population in Connecticut between March 1 and June 1, 2020., Methods: The infection hospitalization rate and infection fatality rate were calculated for adults residing in non-congregate settings in Connecticut prior to June 2020. Individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies were estimated using the seroprevalence estimates from the recently conducted Post-Infection Prevalence study. Information on total hospitalizations and deaths was obtained from the Connecticut Hospital Association and the Connecticut Department of Public Health, respectively., Results: Prior to June 1, 2020, nearly 113,515 (90% confidence interval [CI] 56,758-170,273) individuals were estimated to have SARS-CoV-2 antibodies, and there were 7792 hospitalizations and 1079 deaths among the non-congregate population. The overall COVID-19 infection hospitalization rate and infection fatality rate were estimated to be 6.86% (90% CI, 4.58%-13.72%) and 0.95% (90% CI, 0.63%-1.90%), respectively, and there was variation in these rate estimates across subgroups; older people, men, non-Hispanic Black people, and those belonging to 2 of the counties had a higher burden of adverse outcomes, although the differences between most subgroups were not statistically significant., Conclusions: Using representative seroprevalence estimates, the overall COVID-19 infection hospitalization rate and infection fatality rate were estimated to be 6.86% and 0.95%, respectively, among community residents in Connecticut., (Published by Elsevier Inc.)

Importance: Present-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting., Objective: To assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS)., Design, Setting, and Participants: This comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020., Main Outcomes and Measures: Changes in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment., Results: Data from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure)., Conclusions and Relevance: The findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.

Background: A seroprevalence study can estimate the percentage of people with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in the general population; however, most existing reports have used a convenience sample, which may bias their estimates., Methods: We sought a representative sample of Connecticut residents, ages ≥18 years and residing in noncongregate settings, who completed a survey between June 4 and June 23, 2020, and underwent serology testing for SARS-CoV-2-specific immunoglobulin G (IgG) antibodies between June 10 and July 29, 2020. We also oversampled non-Hispanic black and Hispanic subpopulations. We estimated the seroprevalence of SARS-CoV-2-specific IgG antibodies and the prevalence of symptomatic illness and self-reported adherence to risk-mitigation behaviors among this population., Results: Of the 567 respondents (mean age 50 [± 17] years; 53% women; 75% non-Hispanic white individuals) included at the state level, 23 respondents tested positive for SARS-CoV-2-specific antibodies, resulting in weighted seroprevalence of 4.0 (90% confidence interval [CI] 2.0-6.0). The weighted seroprevalence for the oversampled non-Hispanic black and Hispanic populations was 6.4% (90% CI 0.9-11.9) and 19.9% (90% CI 13.2-26.6), respectively. The majority of respondents at the state level reported following risk-mitigation behaviors: 73% avoided public places, 75% avoided gatherings of families or friends, and 97% wore a facemask, at least part of the time., Conclusions: These estimates indicate that the vast majority of people in Connecticut lack antibodies against SARS-CoV-2, and there is variation by race and ethnicity. There is a need for continued adherence to risk-mitigation behaviors among Connecticut residents to prevent resurgence of COVID-19 in this region., (Copyright © 2020. Published by Elsevier Inc.)

Background: To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia., Methods: The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays., Results: These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia., Conclusions: This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

Importance: Determining the association of contrast volume during percutaneous coronary intervention (PCI) with the risk of acute kidney injury (AKI) is important for optimizing PCI safety., Objective: To quantify how the risk of AKI is associated with contrast volume, accounting for the possibility of nonlinearity and heterogeneity among different baseline risks., Design, Setting, and Participants: This prognostic study used data from the American College of Cardiology National Cardiovascular Data Registry CathPCI Registry for 1694 US hospitals. Derivation analysis included 2 076 694 individuals who underwent PCI from July 1, 2011, to June 30, 2015. Validation analysis included 961 863 individuals who underwent PCI from July 1, 2015, to March 31, 2017. Data analysis took place from July 2018 to May 2019., Exposure: Contrast volume during PCI., Main Outcomes and Measures: Acute kidney injury was defined using 3 thresholds for preprocedure to postprocedure creatinine level increase (ie, ≥0.3 mg/dL, ≥0.5 mg/dL, and ≥1.0 mg/dL). A model quantifying the association of contrast volume with AKI was developed, and the existence of nonlinearity and heterogeneity were examined by likelihood ratio tests. The model was derived in the training set (a random 50% of the derivation cohort), and performance was evaluated in the test set (the remaining 50% of the derivation cohort) and an independent validation set by area under the receiver operating characteristic curve (AUC) and calibration slope of observed vs predicted risks., Results: The 2 076 694 patients in the derivation set had a mean (SD) age of 65.1 (12.1) years, and 662 525 (31.9%) were women; 133 306 (6.4%) had creatinine level increases of at least 0.3 mg/dL, 66 626 (3.2%) had creatinine level increases of at least 0.5 mg/dL, and 28 378 (1.4%) had creatinine level increases of at least 1.0 mg/dL. In the validation set of 961 843 patients (mean [SD] age, 65.7 [12.1] years; 305 577 [31.8%] women), these rates were 62 913 (6.5%), 34 229 (3.6%), and 15 555 (1.6%), respectively. The association of contrast volume and AKI risk was nonlinear (χ226 = 1436.2; P < .001) and varied by preprocedural risk (χ220 = 105.6; P < .001). In the test set, the model yielded an AUC of 0.777 (95% CI, 0.775-0.779) for predicting risk of a creatinine level increase of at least 0.3 mg/dL, 0.839 (95% CI, 0.837-0.841) for predicting risk of a creatinine level increase of at least 0.5 mg/dL, and 0.870 (95% CI, 0.867-0.873) for predicting risk of a creatinine level increase of at least 1.0 mg/dL; it achieved a calibration slope of 0.998 (95% CI, 0.989-1.007), 0.999 (95% CI, 0.989-1.008), and 0.986 (95% CI, 0.973-0.998), respectively, for the AKI severity levels. The model had similar performance in the validation set (creatinine level increase of ≥0.3 mg/dL: AUC, 0.794; 95% CI, 0.792-0.795; calibration slope, 1.039; 95% CI, 1.030-1.047; creatinine level increase of ≥0.5 mg/dL: AUC, 0.845; 95% CI, 0.843-0.848; calibration slope, 1.063; 95% CI, 1.054-1.074; creatinine level increase of ≥1.0 mg/dL: AUC, 0.872; 95% CI, 0.869-0.875; calibration slope, 1.103; 95% CI, 1.089-1.117)., Conclusions and Relevance: The association of contrast volume with AKI risk is complex, varies by baseline risk, and can be predicted by a model. Future research to evaluate the effect of the model on AKI is needed.

Importance: Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models., Objective: To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia., Design, Setting, and Participants: This comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019., Main Outcomes and Measures: The models' goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2., Results: Among the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions., Conclusions and Relevance: Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.

Importance: Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement., Objective: To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures., Design, Setting, and Participants: This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018., Main Outcomes and Measures: The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes., Results: There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers., Conclusions and Relevance: Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

The grey wolf optimizer (GWO) is a novel type of swarm intelligence optimization algorithm. An improved grey wolf optimizer (IGWO) with evolution and elimination mechanism was proposed so as to achieve the proper compromise between exploration and exploitation, further accelerate the convergence and increase the optimization accuracy of GWO. The biological evolution and the "survival of the fittest" (SOF) principle of biological updating of nature are added to the basic wolf algorithm. The differential evolution (DE) is adopted as the evolutionary pattern of wolves. The wolf pack is updated according to the SOF principle so as to make the algorithm not fall into the local optimum. That is, after each iteration of the algorithm sort the fitness value that corresponds to each wolf by ascending order, and then eliminate R wolves with worst fitness value, meanwhile randomly generate wolves equal to the number of eliminated wolves. Finally, 12 typical benchmark functions are used to carry out simulation experiments with GWO with differential evolution (DGWO), GWO algorithm with SOF mechanism (SGWO), IGWO, DE algorithm, particle swarm algorithm (PSO), artificial bee colony (ABC) algorithm and cuckoo search (CS) algorithm. Experimental results show that IGWO obtains the better convergence velocity and optimization accuracy.

Importance: Studies of public hospitals have reported increasing incidence of emergency department (ED) transfers of uninsured patients for hospitalization, which is perceived to be associated with financial incentives., Objective: To examine the differences in risk-adjusted transfer and discharge rates by patient insurance status among hospitals capable of providing critical care., Design, Setting, and Participants: A cross-sectional analysis of the 2015 National Emergency Department Sample was conducted, including visits between January 2015 and December 2015. Adult ED visits throughout 2015 (n = 215 028) for the 3 common medical conditions of pneumonia, chronic obstructive pulmonary disease, and asthma, at hospitals with intensive care capabilities were included. Only hospitals with advanced critical care capabilities for pulmonary care were included., Main Outcomes and Measures: The primary outcomes were patient-level and hospital-level risk-adjusted ED discharges, ED transfers, and hospital admissions. Adjusted odds of discharge or transfer compared with admission among uninsured patients, Medicaid and Medicare beneficiaries, and privately insured patients are reported. Hospital ownership status was used for the secondary analysis., Results: Of the 30 542 691 ED visits to 953 hospitals included in the 2015 National Emergency Department Sample, 215 028 visits (0.7%) were for acute pulmonary diseases to 160 intensive care-capable hospitals. These visits were made by patients with a median (interquartile range [IQR]) age of 55 (40-71) years and who were predominantly female (124 931 [58.1%]). Substantial variation in unadjusted and risk-standardized ED discharge, ED transfer, and hospital admission rates was found across EDs. Compared with privately insured patients, uninsured patients were more likely to be discharged (odds ratio [OR], 1.66; 95% CI, 1.57-1.76) and transferred (adjusted OR [aOR], 2.41; 95% CI, 2.08-2.79). Medicaid beneficiaries had comparable odds of discharge (aOR, 1.00; 95% CI, 0.97-1.04) but higher odds of transfer (aOR, 1.19; 95% CI, 1.05-1.33)., Conclusions and Relevance: After accounting for hospital critical care capability and patient case mix, the study found that uninsured patients and Medicaid beneficiaries with common medical conditions appeared to have higher odds of interhospital transfer.

Background: Efforts to decrease hospitalization costs could increase post-acute care costs. This effect could undermine initiatives to reduce overall episode costs and have implications for the design of health care under alternative payment models., Methods: Among Medicare fee-for-service beneficiaries aged ≥65 years hospitalized with acute myocardial infarction (AMI) between July 2010 and June 2013 in the Premier Healthcare Database, we studied the association of in-hospital and post-acute care resource utilization and outcomes by in-hospital cost tertiles., Results: Among patients with AMI at 326 hospitals, the median (range) of each hospital's mean per-patient in-hospital risk-standardized cost (RSC) for the low, medium, and high cost tertiles were $16,257 ($13,097-$17,648), $18,544 ($17,663-$19,875), and $21,831 ($19,923-$31,296), respectively. There was no difference in the median (IQR) of risk-standardized post-acute payments across cost-tertiles: $5014 (4295-6051), $4980 (4349-5931) and $4922 (4056-5457) for the low (n = 90), medium (n = 98), and high (n = 86) in-hospital RSC tertiles (p = 0.21), respectively. In-hospital and 30-day mortality rates did not differ significantly across the in-hospital RSC tertiles; however, 30-day readmission rates were higher at hospitals with higher in-hospital RSCs: median = 17.5, 17.8, and 18.0% at low, medium, and high in-hospital RSC tertiles, respectively (p = 0.005 for test of trend across tertiles)., Conclusions: In our study of patients hospitalized with AMI, greater resource utilization during the hospitalization was not associated with meaningful differences in costs or mortality during the post-acute period. These findings suggest that it may be possible for higher cost hospitals to improve efficiency in care without increasing post-acute care utilization or worsening outcomes.

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI., Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors., Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SSD is supported by the Department of Veterans Affairs. WLS is a consultant for Hugo, a personal health information platform. CIM is a consultant for Cook, Bard, Medtronic, Abbott and Cardinal Health. FPW is supported by the National Science Foundation grant R01DK113191. JSR is the Chief Innovation Officer for the American College of Cardiology. HMK is a recipient of research agreements from Medtronic and from Johnson & Johnson (Janssen), through Yale University, to develop methods of clinical trial data sharing; was the recipient of a grant from the Food and Drug Administration and Medtronic to develop methods for postmarket surveillance of medical devices; works under contract with the Centers for Medicare and Medicaid Services to develop and maintain performance measures; chairs a cardiac scientific advisory board for UnitedHealth; is a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna; is a participant/participant representative of the IBM Watson Health Life Sciences Board; and is the founder of Hugo, a personal health information platform. JAS is supported by grants from Gilead, Genentech, Lilly, Amorcyte, and EvaHeart and has a patent for the Seattle Angina Questionnaire with royalties paid. He also owns the copyright to the Seattle Angina Questionnaire. He is the PI of an Analytic Center for the American College of Cardiology Foundation and has an equity interest in Health Outcomes Sciences. FAM has a contract (through his primary institution) for his role as Chief Science Officer of the NCDR. BJM is an associate editor for PLOS ONE, which is involved in this special issue. He has a relationship with the American College of Cardiology in selecting and pursuing innovative research based upon their registry data (unrelated to this paper). He has a pending patent application for an EHR-based prediction tool in Yale New Haven Health, as well as two funded studies, one by the DoD-Advanced Research Projects Agency and one with the NSF to support student travel to conferences in the body sensor networks field. American College of Cardiology may incorporate this work, or future iterations, into its registry. No other organisation named above has a competing interest in relation to this work. The other authors report no potential competing interests.

Background: Measurement of cancer-related fatigue and nutrition in the same colorectal cancer patient group using fast-track surgery has never been examined previously. The association between fatigue and nutritional status in the same patient group is thus worthwhile to be investigated., Objective: The aim of this study was to evaluate the relationship between fatigue and nutrition risk factors in colorectal cancer patients with fast-track surgery., Methods: This is a single-arm, observational study. Seventy eligible postoperative patients with colorectal cancer fast-track surgery were enrolled in this study. Patients completed the Cancer Fatigue Scale and the Patient-Generated Subjective Global Assessment (PG-SGA) besides routine perioperative laboratory examination., Results: In this study, all patients were found to have cancer-related fatigue; 20% of the patients had severe fatigue. Furthermore, 94.29% of the patients were malnourished according to the PG-SGA score; the average was 15.585.18. Fatigue severity was significantly, positively correlated with nutrition status. White blood cells and serum calcium were significantly, positively related to both Cancer Fatigue Scale and PG-SGA scores., Conclusion: Fatigue and malnutrition commonly exist in patients with colorectal cancer experiencing fast-track surgery. Fatigue may reflect the nutritional status in this group of patients., Implications for Practice: Clinical nursing staff need to evaluate patients' fatigue status and nutritional status to provide the suitable clinical intervention when needed.

Purpose: To estimate medical device utilization needed to detect safety differences among implantable cardioverter defibrillators (ICDs) generator models and compare these estimates to utilization in practice., Methods: We conducted repeated sample size estimates to calculate the medical device utilization needed, systematically varying device-specific safety event rate ratios and significance levels while maintaining 80% power, testing 3 average adverse event rates (3.9, 6.1, and 12.6 events per 100 person-years) estimated from the American College of Cardiology's 2006 to 2010 National Cardiovascular Data Registry of ICDs. We then compared with actual medical device utilization., Results: At significance level 0.05 and 80% power, 34% or fewer ICD models accrued sufficient utilization in practice to detect safety differences for rate ratios <1.15 and an average event rate of 12.6 events per 100 person-years. For average event rates of 3.9 and 12.6 events per 100 person-years, 30% and 50% of ICD models, respectively, accrued sufficient utilization for a rate ratio of 1.25, whereas 52% and 67% for a rate ratio of 1.50. Because actual ICD utilization was not uniformly distributed across ICD models, the proportion of individuals receiving any ICD that accrued sufficient utilization in practice was 0% to 21%, 32% to 70%, and 67% to 84% for rate ratios of 1.05, 1.15, and 1.25, respectively, for the range of 3 average adverse event rates., Conclusions: Small safety differences among ICD generator models are unlikely to be detected through routine surveillance given current ICD utilization in practice, but large safety differences can be detected for most patients at anticipated average adverse event rates., (Copyright © 2018 John Wiley & Sons, Ltd.)

Background: SPRINT (Systolic Blood Pressure Intervention Trial) and the ACCORD (Action to Control Cardiovascular Risk in Diabetes) blood pressure trial used similar interventions but produced discordant results. We investigated whether differences in systolic blood pressure (SBP) response contributed to the discordant trial results., Methods and Results: We evaluated the distributions of SBP response during the first year for the intensive and standard treatment groups of SPRINT and ACCORD using growth mixture models. We assessed whether significant differences existed between trials in the distributions of SBP achieved at 1 year and the treatment-independent relationships of achieved SBP with risks of primary outcomes defined in each trial, heart failure, stroke, and all-cause death. We examined whether visit-to-visit variability was associated with heterogeneous treatment effects. Among the included 9027 SPRINT and 4575 ACCORD participants, the difference in mean SBP achieved between treatment groups was 15.7 mm Hg in SPRINT and 14.2 mm Hg in ACCORD, but SPRINT had significantly less between-group overlap in the achieved SBP (standard deviations of intensive and standard groups, respectively: 6.7 and 5.9 mm Hg in SPRINT versus 8.8 and 8.2 mm Hg in ACCORD; P <0.001). The relationship between achieved SBP and outcomes was consistent across trials except for stroke and all-cause death. Higher visit-to-visit variability was more common in SPRINT but without treatment-effect heterogeneity., Conclusions: SPRINT and ACCORD had different degrees of separation in achieved SBP between treatment groups, even as they had similar mean differences. The greater between-group overlap of achieved SBP may have contributed to the discordant trial results., (© 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.)

Background: Machine learning methods may complement traditional analytic methods for medical device surveillance., Methods and Results: Using data from the National Cardiovascular Data Registry for implantable cardioverter-defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%-20.9%; nonfatal ICD-related adverse events, 19.3%-26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%-37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k =0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k =-0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k =-0.042)., Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance., Competing Interests: Disclosure JSR receives support from the US FDA as part of the Centers for Excellence in Regulatory Science and Innovation program and from the Laura and John Arnold Foundation to support the Collaboration on Research Integrity and Transparency at Yale. JSR, NRD, HMK, and GMG receive research support through Yale University from Johnson and Johnson to develop methods of clinical trial data sharing. JSR and GMG receive research support from the Blue Cross Blue Shield Association to better understand medical technology evidence generation. JSR, JPC, NRD, SXL, SLTM, IR, HMK, and CSP work under contract to the Centers for Medicare and Medicaid Services to develop and maintain performance measures that are used for public reporting. JVF receives salary support from the American College of Cardiology NCDR, and modest consulting fees from Janssen Pharmaceuticals. RK is an employee of Medtronic Inc. DMD is an employee of the FDA. HMK chairs a cardiac scientific advisory board for United Health, is a participant/participant representative of the IBM Watson Health Life Sciences Board, is a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna, and is the founder of Hugo, a personal health-information platform. The authors report no other conflicts of interest in this work.

Randomized trials of hypertension have seldom examined heterogeneity in response to treatments over time and the implications for cardiovascular outcomes. Understanding this heterogeneity, however, is a necessary step toward personalizing antihypertensive therapy. We applied trajectory-based modeling to data on 39 763 study participants of the ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) to identify distinct patterns of systolic blood pressure (SBP) response to randomized medications during the first 6 months of the trial. Two trajectory patterns were identified: immediate responders (85.5%), on average, had a decreasing SBP, whereas nonimmediate responders (14.5%), on average, had an initially increasing SBP followed by a decrease. Compared with those randomized to chlorthalidone, participants randomized to amlodipine (odds ratio, 1.20; 95% confidence interval [CI], 1.10-1.31), lisinopril (odds ratio, 1.88; 95% CI, 1.73-2.03), and doxazosin (odds ratio, 1.65; 95% CI, 1.52-1.78) had higher adjusted odds ratios associated with being a nonimmediate responder (versus immediate responder). After multivariable adjustment, nonimmediate responders had a higher hazard ratio of stroke (hazard ratio, 1.49; 95% CI, 1.21-1.84), combined cardiovascular disease (hazard ratio, 1.21; 95% CI, 1.11-1.31), and heart failure (hazard ratio, 1.48; 95% CI, 1.24-1.78) during follow-up between 6 months and 2 years. The SBP response trajectories provided superior discrimination for predicting downstream adverse cardiovascular events than classification based on difference in SBP between the first 2 measurements, SBP at 6 months, and average SBP during the first 6 months. Our findings demonstrate heterogeneity in response to antihypertensive therapies and show that chlorthalidone is associated with more favorable initial response than the other medications., (© 2017 American Heart Association, Inc.)

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008-2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition's seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.

Background: The number of percutaneous coronary interventions (PCI) in China has increased more than 20-fold over the last decade. Consequently, there is a need for national-level information to characterize PCI indications and long-term patient outcomes, including health status, to understand and improve evolving practice patterns., Objectives: This nationwide prospective study of patients receiving PCI is to: (1) measure long-term clinical outcomes (including death, acute myocardial infarction [AMI], and/or revascularization), patient-reported outcomes (PROs), cardiovascular risk factor control and adherence to medications for secondary prevention; (2) determine patient- and hospital-level factors associated with care process and outcomes; and (3) assess the appropriateness of PCI procedures., Methods: The China Patient-centered Evaluative Assessment of Cardiac Events (PEACE) Prospective Study of PCI has enrolled 5,000 consecutive patients during 2012-2014 from 34 diverse hospitals across China undergoing PCI for any indication. We abstracted details of patient's medical history, treatments, and in-hospital outcomes from medical charts, and conducted baseline, 1-, 6-, and 12-month interviews to characterize patient demographics, risk factors, clinical presentation, healthcare utilization, and health status using validated PRO measures. The primary outcome, a composite measure of death, AMI and/or revascularization, as well as PROs, medication adherence and cardiovascular risk factor control, was assessed throughout the 12-month follow-up. Blood and urine samples were collected at baseline and 12 months and stored for future analyses. To validate reports of coronary anatomy, 2,000 angiograms are randomly selected and read by two independent core laboratories. Hospital characteristics regarding their facilities, processes and organizational characteristics are assessed by site surveys., Conclusion: China PEACE Prospective Study of PCI will be the first study to generate novel, high-quality, comprehensive national data on patients' socio-demographic, clinical, treatment, and metabolic/genetic factors, and importantly, their long-term outcomes following PCI, including health status. This will build the foundation for PCI performance improvement efforts in China. © 2016 The Authors. Catheterization and Cardiovascular Interventions. Published by Wiley Periodicals, Inc., (© 2016 The Authors. Catheterization and Cardiovascular Interventions. Published by Wiley Periodicals, Inc.)

Background: The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions., Methods and Results: Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively)., Conclusions: Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates., (© 2016 American Heart Association, Inc.)

Objectives: To characterize hospital phenotypes by their combined utilization pattern of percutaneous coronary interventions (PCI), coronary artery bypass grafting (CABG) procedures, and intensive care unit (ICU) admissions for patients hospitalized for acute myocardial infarction (AMI)., Research Design: Using the Premier Analytical Database, we identified 129,138 hospitalizations for AMI from 246 hospitals with the capacity for performing open-heart surgery during 2010-2013. We calculated year-specific, risk-standardized estimates of PCI procedure rates, CABG procedure rates, and ICU admission rates for each hospital, adjusting for patient clinical characteristics and within-hospital correlation of patients. We used a mixture modeling approach to identify groups of hospitals (ie, hospital phenotypes) that exhibit distinct longitudinal patterns of risk-standardized PCI, CABG, and ICU admission rates., Results: We identified 3 distinct phenotypes among the 246 hospitals: (1) high PCI-low CABG-high ICU admission (39.2% of the hospitals), (2) high PCI-low CABG-low ICU admission (30.5%), and (3) low PCI-high CABG-moderate ICU admission (30.4%). Hospitals in the high PCI-low CABG-high ICU admission phenotype had significantly higher risk-standardized in-hospital costs and 30-day risk-standardized payment yet similar risk-standardized mortality and readmission rates compared with hospitals in the low PCI-high CABG-moderate ICU admission phenotype. Hospitals in these phenotypes differed by geographic region., Conclusions: Hospitals differ in how they manage patients hospitalized for AMI. Their distinctive practice patterns suggest that some hospital phenotypes may be more successful in producing good outcomes at lower cost.

Background: The availability of hospital cardiac services may vary between hospitals and influence care processes and outcomes. However, data on available cardiac services are restricted to a limited number of services collected by the American Hospital Association (AHA) annual survey. We developed an alternative method to identify hospital services using individual patient discharge data for acute myocardial infarction (AMI) in the Premier Healthcare Database., Methods and Results: Thirty-five inpatient cardiac services relevant for AMI care were identified using American Heart Association/American College of Cardiology guidelines. Thirty-one of these services could be defined using patient-level administrative data codes, such as International Classification of Diseases, Ninth Revision, Clinical Modification and Current Procedural Terminology codes. A hospital was classified as providing a service if it had ≥5 instances for the service in the Premier database from 2009 to 2011. Using this system, the availability of these services among 432 Premier hospitals ranged from 100% (services such as chest X-ray) to 1.2% (heart transplant service). To measure the accuracy of this method using administrative data, we calculated agreement between the AHA survey and Premier for a subset of 16 services defined by both sources. There was a high percentage of agreement (≥80%) for 11 of 16 (68.8%) services, moderate agreement for 3 of 16 (18.8%) services, and low agreement (≤50%) for 2 of 16 services (12.5%)., Conclusions: The availability of cardiac services for AMI care varies widely among hospitals. Using individual patient discharge data is a feasible method to identify these cardiac services, particularly for those services pertaining to inpatient care., (© 2016 The Authors and Premier Inc. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.)

Objectives: To determine how often hospitalized older adults principally diagnosed with pneumonia, chronic obstructive pulmonary disease (COPD), or heart failure (HF) are concurrently treated for two or more of these acute cardiopulmonary conditions., Design: Retrospective cohort study., Setting: 368 U.S. hospitals in the Premier research database., Participants: Individuals aged 65 and older principally hospitalized with pneumonia, COPD, or HF in 2009 or 2010., Measurements: Proportion of diagnosed episodes of pneumonia, COPD, or HF concurrently treated for two or more of these acute cardiopulmonary conditions during the first 2 hospital days., Results: Of 91,709 diagnosed pneumonia hospitalizations, 32% received treatment for two or more acute cardiopulmonary conditions (18% for HF, 18% for COPD, 4% for both). Of 41,052 diagnosed COPD hospitalizations, 19% received treatment for two or more acute cardiopulmonary conditions (all of which involved additional HF treatment). Of 118,061 diagnosed HF hospitalizations, 38% received treatment for two or more acute cardiopulmonary conditions (34% for pneumonia, 9% for COPD, 5% for both)., Conclusion: Hospitalized older adults diagnosed with pneumonia, COPD, or HF are frequently treated for two or more acute cardiopulmonary conditions, suggesting that clinical syndromes often fall between traditional diagnostic categories. Research is needed to evaluate the risks and benefits of real-world treatment for the many older adults whose presentations elicit diagnostic uncertainty or concern about coexisting acute conditions., (© 2016, Copyright the Authors Journal compilation © 2016, The American Geriatrics Society.)

Background: Despite the rapid growth in the incidence of acute myocardial infarction (AMI) in China, there is limited information about patients' experiences after AMI hospitalization, especially on long-term adverse events and patient-reported outcomes (PROs)., Methods: The China Patient-centered Evaluative Assessment of Cardiac Events (PEACE)-Prospective AMI Study will enroll 4000 consecutive AMI patients from 53 diverse hospitals across China and follow them longitudinally for 12 months to document their treatment, recovery, and outcomes. Details of patients' medical history, treatment, and in-hospital outcomes are abstracted from medical charts. Comprehensive baseline interviews are being conducted to characterize patient demographics, risk factors, presentation, and healthcare utilization. As part of these interviews, validated instruments are administered to measure PROs, including quality of life, symptoms, mood, cognition, and sexual activity. Follow-up interviews, measuring PROs, medication adherence, risk factor control, and collecting hospitalization events are conducted at 1, 6, and 12 months after discharge. Supporting documents for potential outcomes are collected for adjudication by clinicians at the National Coordinating Center. Blood and urine samples are also obtained at baseline, 1- and 12-month follow-up. In addition, we are conducting a survey of participating hospitals to characterize their organizational characteristics., Conclusion: The China PEACE-Prospective AMI study will be uniquely positioned to generate new information regarding patient's experiences and outcomes after AMI in China and serve as a foundation for quality improvement activities.

Introduction: The aim of this study was to detect the survivin, carcinoembryonic antigen (CEA) and ErbB2 in the saliva, serum and local tumor-exfoliated cells of oral squamous cell carcinoma (OSCC) patients, for providing reliable tumor markers for the early detection of oral malignant cancer., Materials and Methods: The saliva, serum, and local tumor-exfoliated cell samples of 26 OSCC patients without chemotherapy and 10 non-cancer patients were collected in Department of Oral and Maxillofacial Surgery, School of Stomatology, Peking University. The contents of survivin, CEA and ErbB2 using were detected usingenzyme-linked immunosorbent assay., Results: The survivin and CEA levels in saliva and local tumor-exfoliated cells of OSCC patients were significantly higher than those in the non-cancer patients (P < 0.05), but there was no significant difference in the content of the above factors in the serum sample between two groups. There was no significant difference in the ErbB2 content in the saliva, serum or local tumor-exfoliated cells between two groups., Conclusion: Survivin and CEA levels are significantly increased in the saliva and local tumor-exfoliated cells in OSCC patients, and they can be used as reliable markers for the early detection of oral malignant cancer.

Background: The treatment for patients with acute myocardial infarction (AMI) was transformed by the introduction of intensive care units (ICUs), yet we know little about how contemporary hospitals use this resource-intensive setting and whether higher use is associated with better outcomes., Methods: We identified 114,136 adult hospitalizations for AMI from 307 hospitals in the 2009 to 2010 Premier database using codes from the International Classification of Diseases, Ninth Revision, Clinical Modification. Hospitals were stratified into quartiles by rates of ICU admission for AMI patients. Across quartiles, we examined in-hospital risk-standardized mortality rates and usage rates of critical care therapies for these patients., Results: Rates of ICU admission for AMI patients varied markedly among hospitals (median 48%, Q1-Q4 20%-71%, range 0%-98%), and there was no association with in-hospital risk-standardized mortality rates (6% all quartiles, P = .7). However, hospitals admitting more AMI patients to the ICU were more likely to use critical care therapies overall (mechanical ventilation [from Q1 with lowest rate of ICU use to Q4 with highest rate 13%-16%], vasopressors/inotropes [17%-21%], intra-aortic balloon pumps [4%-7%], and pulmonary artery catheters [4%-5%]; P for trend < .05 in all comparisons)., Conclusions: Rates of ICU admission for patients with AMI vary substantially across hospitals and were not associated with differences in mortality, but were associated with greater use of critical care therapies. These findings suggest uncertainty about the appropriate use of this resource-intensive setting and a need to optimize ICU triage for patients who will truly benefit., (Copyright © 2015. Published by Elsevier Inc.)