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Rationale: Many patients with suspected chronic obstructive pulmonary disease (COPD) do not undergo spirometry to confirm the diagnosis. Underutilization is often attributed to barriers to accessing spirometry. Objective: Our objective wasto identify factors associated with spirometry underutilization for patients who are less likely to face access barriers related to travel, insurance, and availability of spirometry. Methods: A retrospective analysis was conducted of patients enrolled in the Veterans Health Administration and living in urban areas with a new diagnosis of COPD between 2012 to 2015, reducing out-of-pocket cost and travel barriers, respectively. We included only patients whose primary care clinic was located in an academically affiliated tertiary level facility with spirometry available. We used logistic regression to estimate associations between patient characteristics and receipt of spirometry within 2 years before or after COPD diagnosis. Results: Of 24,300 patients, 59.7% had spirometry. Compared to patients

BackgroundMortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes.ObjectivesEvaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients.Research designRetrospective cohort study.SubjectsAll ICU patients in five hospitals from October 2017 through September 2019.MeasuresWe used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots.ResultsThe cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with ConclusionsModels incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

ImportanceMany patients do not receive recommended services. Drive time to health care services may affect receipt of guideline-recommended care, but this has not been comprehensively studied.ObjectiveTo assess associations between drive time to care and receipt of guideline-recommended screening, diagnosis, and treatment interventions.Design, Setting, and ParticipantsThis cohort study used administrative data from the National Veterans Health Administration (VA) data merged with Medicare data. Eligible participants were patients using VA services between January 2016 and December 2019. Women ages 65 years or older without underlying bone disease were assessed for osteoporosis screening. Patients with new diagnosis of chronic obstructive pulmonary disease (COPD) indicated by at least 2 encounter codes for COPD or at least 1 COPD-related hospitalization were assessed for receipt of diagnostic spirometry. Patients hospitalized for ischemic heart disease were assessed for cardiac rehabilitation treatment.ExposuresDrive time from each patient’s residential address to the closest VA facility where the service was available, measured using geocoded addresses.Main Outcomes and MeasuresBinary outcome at the patient level for receipt of osteoporosis screening, spirometry, and cardiac rehabilitation. Multivariable logistic regression models were used to assess associations between drive time and receipt of services.ResultsOf 110 780 eligible women analyzed, 36 431 (32.9%) had osteoporosis screening (mean [SD] age, 66.7 [5.4] years; 19 422 [17.5%] Black, 63 403 [57.2%] White). Of 281 130 patients with new COPD diagnosis, 145 249 (51.7%) had spirometry (mean [SD] age, 68.2 [11.5] years; 268 999 [95.7%] men; 37 834 [13.5%] Black, 217 608 [77.4%] White). Of 73 146 patients hospitalized for ischemic heart disease, 11 171 (15.3%) had cardiac rehabilitation (mean [SD] age, 70.0 [10.8] years; 71 217 [97.4%] men; 15 213 [20.8%] Black, 52 144 [71.3%] White). The odds of receiving recommended services declined as drive times increased. Compared with patients with a drive time of 30 minutes or less, patients with a drive time of 61 to 90 minutes had lower odds of receiving osteoporosis screening (adjusted odds ratio [aOR], 0.90; 95% CI, 0.86-0.95) and spirometry (aOR, 0.90; 95% CI, 0.88-0.92) while patients with a drive time of 91 to 120 minutes had lower odds of receiving cardiac rehabilitation (aOR, 0.80; 95% CI, 0.74-0.87). Results were similar in analyses restricted to urban patients or patients whose primary care clinic was in a tertiary care center.Conclusions and RelevanceIn this retrospective cohort study, longer drive time was associated with less frequent receipt of guideline-recommended services across multiple components of care. To improve quality of care and health outcomes, health systems and clinicians should adopt strategies to mitigate travel burden, even for urban patients.

Objective: Multicenter data from 2 decades ago demonstrated that critically ill and injured patients spending more than 6 hours in the emergency department (ED) before transfer to the intensive care unit (ICU) had higher mortality rates. A contemporary analysis of ED length of stay in critically injured patients at American College of Surgeons’ Trauma Quality Improvement Program (ACS-TQIP) centers was performed to test whether prolonged ED length of stay is still associated with mortality. Methods: This was an observational cohort study of critically injured patients admitted directly to ICU from the ED in ACS-TQIP centers from 2010-2015. Spending more than 6 hours in the ED was defined as prolonged ED length of stay. Patients with prolonged ED length of stay were matched to those with non-prolonged ED length of stay and mortality was compared. Main Results: A total of 113,097 patients were directly admitted from the ED to the ICU following injury. The median ED length of stay was 167 minutes. Prolonged ED length of stay occurred in 15,279 (13.5%) of patients. Women accounted for 29.4% of patients with prolonged ED length of stay but only 25.8% of patients with non-prolonged ED length of stay, P < 0.0001. Mortality rates were similar after matching—4.5% among patients with prolonged ED length of stay versus 4.2% among matched controls. Multivariable logistic regression of the matched cohorts demonstrated prolonged ED length of stay was not associated with mortality. However, women had higher adjusted mortality compared to men Odds Ratio = 1.41, 95% Confidence Interval 1.28 -1.61, P < 0.0001. Conclusion: Prolonged ED length of stay is no longer associated with mortality among critically injured patients. Women are more likely to have prolonged ED length of stay and mortality.

Purpose: With decades of declining ICU mortality, we hypothesized that the outcomes and distribution of diseases cared for in the ICU have changed and we aimed to further characterize them. Study Design and Methods: A retrospective cohort analysis of 287,154 nonsurgical-critically ill adults, from 237 U.S. ICUs, using the manually abstracted Cerner APACHE Outcomes database from 2008 to 2016 was performed. Surgical patients, rare admission diagnoses (Results: The number of ICU admissions remained unchanged (IRR 0.99, 0.98-1.003) while certain organ system/disease groups increased (toxicology [25%], hematologic/oncologic [55%] while others decreased (gastrointestinal [31%], pulmonary [24%]). Overall risk-adjusted in-hospital mortality was unchanged (IRR 0.98, 0.96-1.0004). Risk-adjusted ICU LOS (Estimate −0.06 days/year, −0.07 to −0.04) decreased. Risk-adjusted mortality varied significantly by disease. Conclusion: Risk-adjusted ICU mortality rate did not change over the study period, but there was evidence of shifting disease burden across the critical care population. Our data provides useful information regarding future ICU personnel and resource needs.

Objective: Describe patient and hospital characteristics associated with Arterial Catheter (AC) or Central Venous Catheter (CVC) use among pediatric intensive care units (ICUs). Design: Hierarchical mixed effects analyses were used to identify patient and hospital characteristics associated with AC or CVC placement. The ICU adjusted median odds ratios (ICU-AMOR) for the admission ICU, marginal R2, and conditional intraclass correlation coefficient were reported. Setting: 166 PICUs in the Virtual PICU Systems (VPS, LLC) Database. Patients: 682,791 patients with unscheduled admissions to the PICU. Intervention: None. Measures and Main Results: ACs were placed in (median, [interquartile range]) 8.2% [4.9%-11.3%] of admissions, and CVCs were placed in 14.9% [10.4%-19.3%] of admissions across cohort ICUs. Measured patient characteristics explained about 25% of the variability in AC and CVC placement. Higher Pediatric Index of Mortality 2 (PIM2) illness severity scores were associated with increased odds of placement (Odds Ratio (95th% Confidence Interval)) AC: 1.88 (1.87-1.89) and CVC: 1.82 (1.81-1.83) per 1 unit increase in PIM2 score. Primary diagnoses of cardiovascular, gastrointestinal, hematology/oncology, infectious, renal/genitourinary, rheumatology, and transplant were associated with increased odds of AC or CVC placement compared to a primary respiratory diagnosis. Presence of in-house attendings 24/7 was associated with increased odds of AC placement 1.32 (1.11-1.57). Admission ICU explained 4.9% and 3.5% of the variability in AC or CVC placement, respectively. The ICU-AMOR showed a patient would have a median increase in odds of 55% and 43% for AC or CVC placement, respectively, if the same patient moved from an ICU with lower odds of placement to an ICU with higher odds of placement. Conclusions: Variation in AC or CVC use exists among PICUs. The admission ICU was more strongly associated with AC than with CVC placement. Further study is needed to understand unexplained variation in AC and CVC use.

Objective: Robust disease and syndromic surveillance tools are underdeveloped in the United States, as evidenced by limitations and heterogeneity in sociodemographic data collection throughout the COVID-19 pandemic. To monitor the COVID-19 pandemic in Minnesota, we developed a federated data network in March 2020 using electronic health record (EHR) data from 8 multispecialty health systems. Materials and Methods: In this serial cross-sectional study, we examined patients of all ages who received a COVID-19 polymerase chain reaction test, had symptoms of a viral illness, or received an influenza test from January 3, 2016, through November 7, 2020. We evaluated COVID-19 testing rates among patients with symptoms of viral illness and percentage positivity among all patients tested, in aggregate and by zip code. We stratified results by patient and area-level characteristics. Results: Cumulative COVID-19 positivity rates were similar for people aged 12-64 years (range, 15.1%-17.6%) but lower for adults aged ≥65 years (range, 9.3%-10.7%). We found notable racial and ethnic disparities in positivity rates early in the pandemic, whereas COVID-19 positivity was similarly elevated across most racial and ethnic groups by the end of 2020. Positivity rates remained substantially higher among Hispanic patients compared with other racial and ethnic groups throughout the study period. We found similar trends across area-level income and rurality, with disparities early in the pandemic converging over time. Practice Implications: We rapidly developed a distributed data network across Minnesota to monitor the COVID-19 pandemic. Our findings highlight the utility of using EHR data to monitor the current pandemic as well as future public health priorities. Building partnerships with public health agencies can help ensure data streams are flexible and tailored to meet the changing needs of decision makers.

Using vaccine data combined with electronic health records, we report that mRNA boosters provide greater protection than a two-dose regimen against SARS-CoV-2 infection and related hospitalizations. The benefit of a booster was more evident in the elderly and those with comorbidities. These results support the case for COVID-19 boosters.

Using vaccine data combined with electronic health records, we report that mRNA boosters provide greater protection than a 2-dose regimen against SARS-CoV-2 infection and related hospitalizations. The benefit of a booster was more evident in the elderly and those with comorbidities.

Background Injury is the leading cause of death in people under 45 years of age in the United States; however, how care decisions occur in critical injury is poorly understood. This exploratory study sought to generate hypotheses about how care decisions are made among interdisciplinary providers caring for patients who have been critically injured. Methods This was a qualitative study conducted at two intensive care units in a level 1 trauma center in an urban, teaching, safety-net hospital. Semistructured interviews consisted of case scenarios with competing clinical priorities presented to 25 interdisciplinary providers, elucidating how decisions are approached. Responses were recorded, transcribed, and coded. Thematic analysis was conducted to discover central themes. Category formulation and sorting was done for data reduction and thematic structuring of the data. The range and central tendency of these themes are reported. Results The central theme for how care decisions are made among interdisciplinary providers was through the distribution of shared responsibility. The distribution of shared responsibility depended on interdisciplinary communication to navigate the two subthemes of time and roles. Time had to be navigated carefully, because it was both an opportunity for data acquisition and consensus building but also a pressure to decisively progress care. Roles were distinct but interchangeable and consisted of experts, actualizers, and questioners. Conclusion Care decisions are made in the context of shared responsibility among interdisciplinary providers. Interdisciplinary communication is a means of establishing roles and navigating time to distribute shared responsibility among interdisciplinary providers.

Introduction/objectives Pneumocystis jirovecii pneumonia (PJP) is a rare but potentially fatal opportunistic infection; however, consensus varies around which conditions or medications confer a level of risk sufficient to justify antibiotic prophylaxis for PJP. We used electronic health record (EHR) data to assess the current patterns of PJP prophylaxis, PJP outcomes, and prophylaxis-related adverse events among patients with rheumatic diseases who were receiving high-risk immunosuppressant drugs. Methods Data derive from the EHR of a large health system. We included new immunosuppressant users with diagnoses of vasculitis, myositis, or systemic lupus erythematosus. We calculated the proportion of patients who received PJP prophylaxis for each diagnosis and drug combination. We also calculated the number of PJP infections and the number of antibiotic adverse drug events (ADEs) per patient-year of exposure. Results We followed 316 patients for 23.2 + /- 14.2 months. Overall, 124 (39%) of patients received prophylactic antibiotics for PJP. At least 25% of patients with the highest risk conditions (e.g. vasculitis) or highest risk immunosuppressants (e.g. cyclophosphamide) did not receive PJP prophylaxis. We found no cases of PJP infection over 640 patient-years of follow up, including among those not receiving prophylaxis, and an overall incidence rate of ADEs of 2.2% per patient-year. Conclusions PJP prophylaxis for patients with rheumatic conditions is inconsistent, with one quarter of patients who have high risk conditions or high risk immunosuppressants not receiving prophylaxis. However, given extremely low rates of PJP infection, but detectable ADEs to prophylactic antibiotics, our findings suggest that evidence to guide more personalized risk assessments are needed to inform PJP prophylaxis.

ImportanceCataract surgery in the US is routinely performed with anesthesia care, whereas anesthesia care for other elective, low-risk, outpatient procedures is applied more selectively.ObjectiveTo identify predictors of anesthesia care in Medicare beneficiaries undergoing cataract surgery and evaluate anesthesia care for cataract surgery compared with other elective, low-risk, outpatient procedures.Design, Setting, and ParticipantsThis population-based, retrospective observational cohort study included Medicare beneficiaries 66 years or older who underwent cataract surgery in 2017. The data were analyzed from August 2020 through May 2021.Interventions (for clinical trials) or Exposures (for observational studies)Anesthesia care during elective, low-risk, outpatient procedures.Main Outcomes and MeasuresPrevalence of anesthesia care during cataract surgery compared with other low-risk procedures; association of anesthesia care with patient, clinician, and health system characteristics; and proportion of patients experiencing a systemic complication within 7 days of cataract surgery compared with patients undergoing other low-risk procedures.ResultsAmong 36 652 cataract surgery patients, the mean (SD) age was 74.7 (6.1) years; 21 690 (59.2%) were female; 2200 (6.6%) were Black and 32 049 (87.4%) were White. Anesthesia care was more common among patients undergoing cataract surgery compared with patients undergoing other low-risk procedures (89.8% vs range of P = .01) nor Charlson Comorbidity Index (CCI) score (CCI of ≥3: adjusted odds ratio, 1.06; 95% CI, 0.95-1.18; P = .28; reference, CCI score of 0-1) was strongly associated with anesthesia care for cataract surgery, but a model comprising a single variable identifying the ophthalmologist predicted anesthesia care with a C statistic of 0.96. Approximately 6.0% of ophthalmologists never used anesthesia care, 76.6% always used anesthesia care, and 17.4% used it for only a subset of patients. Fewer cataract surgery patients experienced systemic complications within 7 days (2833 [7.7%]), even when limited to patients of ophthalmologists who never used anesthesia care (108 [7.4%]), than patients undergoing other low-risk procedures (range, 13.2%-52.2%).Conclusions and RelevanceThe results of this cohort study suggest that systemic complications occurred less frequently after cataract surgery compared with other elective, low-risk, outpatient procedures during which anesthesia care was less commonly used. Anesthesia care was not associated with patient characteristics, such as older age or worse health status, but with the ophthalmologists’ usual approach to cataract surgery sedation. The study findings suggest an opportunity to use anesthesia care more selectively in patients undergoing cataract surgery.

Background Opioids and multimodal analgesia are widely administered to manage postoperative pain. However, little is known on how improvements in inpatient pain control are correlated with high-risk (> 90 daily OME) discharge opioid prescriptions for opioid naïve surgical patients. Methods We conducted a retrospective observational study of adult opioid-naïve patients undergoing surgery from June 2012 through December 2018 at a large academic medical center. We used multivariate logistic regression to assess whether multimodal analgesic drugs consumed in the 24 h prior to discharge was associated with a reduction in high-risk opioid discharge prescriptions. We identified other risk factors for receiving a high-risk discharge opioid prescription. Results Among the 32,511 patients, 83% of patients were discharged with an opioid prescription. In 2013, 34.1% of patients with a discharge opioid prescription received a high-risk prescription and this declined to 17.7% by 2018. Use of multimodal analgesic agents during the final 24 h of hospitalization increased each year, with over 80% receiving at least one multimodal analgesic agent by 2018. The median OME consumed in the 24 h prior to discharge peaked in 2013 at 31 and steadily decreased to 19.8 by 2018. There was a significant association between the use of acetaminophen in the 24 h prior to discharge and a high-risk prescription at discharge (p < 0.01). OMEs consumed in the 24 h prior to discharge was a significant predictor of receiving a high-risk discharge prescription, even at low doses. Other factors associated with receipt of a high-risk discharge opioid prescription included male gender, race, history of anxiety disorder, and discharge service. Discussion Use of multimodal analgesia regimens in hospitalized surgical patients in the 24 h prior to hospital discharge increased between 2012 and 2018. Simultaneously, opioid use prior to hospital discharge decreased. Despite these gains, approximately one in five discharge prescriptions was high-risk (> 90 daily OME). In addition, we found that prescribing of discharge opioids above inpatient opioid requirements remains common in opioid naive surgical patients. Conclusion Providers should account for pre-discharge opioid consumption and use of multimodal analgesia when considering the total and daily OME’s that may be appropriate for an individual surgical patient on the discharge opioid prescription.

Supplemental Digital Content is available in the text., OBJECTIVES: To evaluate whether different approaches in note text preparation (known as preprocessing) can impact machine learning model performance in the case of mortality prediction ICU. DESIGN: Clinical note text was used to build machine learning models for adults admitted to the ICU. Preprocessing strategies studied were none (raw text), cleaning text, stemming, term frequency-inverse document frequency vectorization, and creation of n-grams. Model performance was assessed by the area under the receiver operating characteristic curve. Models were trained and internally validated on University of California San Francisco data using 10-fold cross validation. These models were then externally validated on Beth Israel Deaconess Medical Center data. SETTING: ICUs at University of California San Francisco and Beth Israel Deaconess Medical Center. SUBJECTS: Ten thousand patients in the University of California San Francisco training and internal testing dataset and 27,058 patients in the external validation dataset, Beth Israel Deaconess Medical Center. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Mortality rate at Beth Israel Deaconess Medical Center and University of California San Francisco was 10.9% and 7.4%, respectively. Data are presented as area under the receiver operating characteristic curve (95% CI) for models validated at University of California San Francisco and area under the receiver operating characteristic curve for models validated at Beth Israel Deaconess Medical Center. Models built and trained on University of California San Francisco data for the prediction of inhospital mortality improved from the raw note text model (AUROC, 0.84; CI, 0.80–0.89) to the term frequency-inverse document frequency model (AUROC, 0.89; CI, 0.85–0.94). When applying the models developed at University of California San Francisco to Beth Israel Deaconess Medical Center data, there was a similar increase in model performance from raw note text (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.72) to the term frequency-inverse document frequency model (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.83). CONCLUSIONS: Differences in preprocessing strategies for note text impacted model discrimination. Completing a preprocessing pathway including cleaning, stemming, and term frequency-inverse document frequency vectorization resulted in the preprocessing strategy with the greatest improvement in model performance. Further study is needed, with particular emphasis on how to manage author implicit bias present in note text, before natural language processing algorithms are implemented in the clinical setting.

Objective: Describe NICU admission rate variation among hospitals in infants with birthweight ≥2500g and low illness acuity and describe factors that predict NICU admission. Study Design: Retrospective study from the Vizient Clinical Data Base/Resource Manager®. Support vector machine methodology was used to develop statistical models using 1) patient characteristics 2) only the indicator for the inborn hospital and 3) patient characteristics plus indicator for the inborn hospital. Results: NICU admission rates of 427,449 infants from 154 hospitals ranged from 0–28.6%. C-statistics for the patient characteristics model: 0.64 (Confidence Interval (CI) 0.62–0.65), hospital only model: 0.81 (CI, 0.81–0.82) and patient characteristic plus hospital variable model: 0.84 (CI, 0.83–0.84). Conclusion/Relevance: There is wide variation in NICU admission rates in infants with low acuity diagnoses. In all cohorts, birth hospital better predicted NICU admission than patient characteristics alone.

This cross-sectional study uses data from the IBM MarketScan Commercial Database to examine the association between increases in wholesale list prices of branded medications and amounts paid by patients and insurers for the same drugs., Key Points Question How are increases in wholesale list prices for medications distributed among increases in patient out-of-pocket costs, insurer payments, and changes in rebates and discounts? Findings In this cross-sectional analysis of 14.4 million pharmacy claims made by 1.8 million patients for the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers in 2014, the median drug wholesale list price (as defined by Average Wholesale Price) increased by 129% from 2010-2016, while median patient out-of-pocket costs increased by 53% and median insurance payments after rebates and discounts increased by 64%. Meaning This study’s findings suggest that, after adjusting for inflation, increases in drug list prices are associated with increased patient out-of-pocket costs, which may have implications for cost-related nonadherence, and insurer payments., Importance High out-of-pocket drug costs can cause patients to skip treatment and worsen outcomes, and high insurer drug payments could increase premiums. Drug wholesale list prices have doubled in recent years. However, because of manufacturer discounts and rebates, the extent to which increases in wholesale list prices are associated with amounts paid by patients and insurers is poorly characterized. Objective To determine whether increases in wholesale list prices are associated with increases in amounts paid by patients and insurers for branded medications. Design, Setting, and Participants Cross-sectional retrospective study analyzing pharmacy claims for patients younger than 65 years in the IBM MarketScan Commercial Database and pricing data from SSR Health, LLC, between January 1, 2010, and December 31, 2016. Pharmacy claims analyzed represent claims of employees and dependents participating in employer health benefit programs belonging to large employers. Rebate data were estimated from sales data from publicly traded companies. Analysis focused on the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers nationwide in 2014. Data were analyzed from July 2017 to July 2020. Exposures Calendar year. Main Outcomes and Measures Changes in inflation-adjusted amounts paid by patients and insurers for branded medications. Results In this analysis of 14.4 million pharmacy claims made by 1.8 million patients from 2010-2016, median drug wholesale list price increased by 129% (interquartile range [IQR], 78%-133%), while median insurance payments increased by 64% (IQR, 28%-120%) and out-of-pocket costs increased by 53% (IQR, 42%-82%). The mean percentage of wholesale list price accounted for by discounts increased from 17% in 2010 to 21% in 2016, and the mean percentage of wholesale list price accounted for by rebates increased from 22% in 2010 to 24% in 2016. For specialty medications, median patient out-of-pocket costs increased by 85% (IQR, 73%-88%) from 2010 to 2016 after adjustment for inflation and 42% (IQR, 25%-53%) for nonspecialty medications. During that same period, insurer payments increased by 116% for specialty medications (IQR, 100%-127%) and 28% for nonspecialty medications (IQR, 5%-34%). Conclusions and Relevance This study’s findings suggest that drug list prices more than doubled over a 7-year study period. Despite rising manufacturer discounts and rebates, these price increases were associated with large increases in patient out-of-pocket costs and insurer payments.

Background Neurosurgical spine specialists receive considerable amounts of industry support that may impact the cost of care. The aim of this study was to evaluate the association between industry payments received by spine surgeons and the total hospital and operating room (OR) costs of an anterior cervical discectomy and fusion (ACDF) procedure among Medicare beneficiaries. Methods All ACDF cases were identified among the Medicare carrier files from January 1, 2013, to December 31, 2014, and matched to the Medicare inpatient baseline file. The total hospital and OR charges were obtained for these cases. Charges were converted to cost using year-specific cost-to-charge ratios. Surgeons were identified among the Open Payments database, which is used to quantify industry support. Analyses were performed to examine the association between industry payments received and ACDF costs. Results Matching resulting in the inclusion of 2209 ACDF claims from 2013–2014. In 2013 and 2014, the mean total cost for an ACDF was $21,798 and $21,008, respectively; mean OR cost was $5878 and $6064, respectively. Mann-Whitney U test demonstrated no significant differences in the mean total or OR cost for an ACDF based on quartile of general industry payment received (P = 0.21 and P = 0.54), and linear regression found no association between industry general payments, research support, or investments on the total hospital cost (P = 0.41, P = 0.13, and P = 0.25, respectively), or OR cost for an ACDF (P = 0.35, P = 0.24, and P = 0.40, respectively). Conclusions This study suggests that spine surgeons performing ACDF surgeries may receive industry support without impacting the cost of care.

Neal Yuan,1 Christy Boscardin,2 Nadra E Lisha,2 R Adams Dudley,3,4 Grace A Lin2 1Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; 2Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA, USA; 3Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, USA; 4School of Public Health, University of Minnesota, Minneapolis, MN, USACorrespondence: Neal YuanSmidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Davis 1015, Los Angeles, CA 90048, USAEmail Neal.Yuan@cshs.orgBackground: In stable coronary artery disease (CAD), shared decision-making (SDM) is encouraged when deciding whether to pursue percutaneous coronary intervention (PCI) given similar cardiovascular outcomes between PCI and medical therapy. However, it remains unclear whether improving patient-provider communication and patient knowledge, the main tenets of SDM, changes patient preferences or the treatment chosen. We explored the relationships between patient-provider communication, patient knowledge, patient preferences, and the treatment received.Methods: We surveyed stable CAD patients referred for elective cardiac catheterization at seven hospitals from 6/2016 to 9/2018. Surveys assessed patient-provider communication, medical knowledge, and preferences for treatment and decision-making. We verified treatments received by chart review. We used linear and logistic regression to examine relationships between patient-provider communication and knowledge, knowledge and preference, and preference and treatment received.Results: Eighty-seven patients completed the survey. More discussion of the benefits and risks of both medical therapy and PCI associated with higher patient knowledge scores (β=0.28, p< 0.01). Patient knowledge level was not associated with preference for PCI (OR=0.78, 95% CI 0.57– 1.03, p=0.09). Black patients had more than four times the odds of preferring medical therapy to PCI (OR=4.49, 1.22– 18.45, p=0.03). Patients preferring medical therapy were not significantly less likely to receive PCI (OR=0.67, 0.16– 2.52, p=0.57).Conclusions: While communicating the risks of PCI may improve patient knowledge, this knowledge may not affect patient treatment preferences. Rather, other factors such as race may be significantly more influential on a patient’s treatment preferences. Furthermore, patient preferences are still not well reflected in the treatment received. Improving shared decision-making in stable CAD therefore may require not only increasing patient education but also better understanding and including a patient’s background and pre-existing beliefs.Keywords: stable coronary artery disease, stable angina, shared decision-making, percutaneous coronary intervention, stent, optimal medical therapy

BackgroundThere is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes.ObjectiveIdentify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.Design, Settings, and ParticipantsRetrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020.MethodsEnsemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes.Main outcomes and measuresPhenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality.ResultsThe database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III.When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (pConclusionIn this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.

Importance Despite past and ongoing efforts to achieve health equity in the United States, persistent disparities in socioeconomic status along with multilevel racism maintain disparate outcomes and appear to be amplified by COVID-19. Objective Measure socioeconomic factors and primary language effects on the risk of COVID-19 severity across and within racial/ethnic groups. Design Retrospective cohort study. Setting Health records of 12 Midwest hospitals and 60 clinics in the U.S. between March 4, 2020 to August 19, 2020. Participants PCR+ COVID-19 patients. Patients missing race or ethnicity data or those diagnosed with COVID-19 during a hospitalization were excluded. Exposures Main exposures included race/ethnicity, area deprivation index (ADI), and primary language. Main Outcomes and Measures The primary outcome was COVID-19 severity using hospitalization within 45 days of diagnosis. Logistic and competing-risk regression models (censored at 45 days and accounting for the competing risk of death prior to hospitalization) assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race effects of ADI and primary language were measured using logistic regression. Results 5,577 COVID-19 patients were included, 866 (n=15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 years, IQR: 45.7-75.9 vs. 40.4 years, IQR: 25.6-58.3, p

This study examines cost sharing for novel second-line diabetes treatment agents under Medicare Part D.

BackgroundDespite past and ongoing efforts to achieve health equity in the United States, persistent disparities in socioeconomic status along with multilevel racism maintain disparate outcomes and appear to be amplified by COVID-19.ObjectiveMeasure socioeconomic factors and primary language effects on the risk of COVID-19 severity across and within racial/ethnic groups.DesignRetrospective cohort study.SettingHealth records of 12 Midwest hospitals and 60 clinics in the U.S. between March 4, 2020 to August 19, 2020.PatientsPCR+ COVID-19 patients.ExposuresMain exposures included race/ethnicity, area deprivation index (ADI), and primary language.Main Outcomes and MeasuresThe primary outcome was COVID-19 severity using hospitalization within 45 days of diagnosis. Logistic and competing-risk regression models (censored at 45 days and accounting for the competing risk of death prior to hospitalization) assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race effects of ADI and primary language were measured using logistic regression.Results5,577 COVID-19 patients were included, 866 (n=15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 vs. 40.4 years, pIndependent of ADI, minority race/ethnicity was associated with COVID-19 severity; Hispanic patients (OR 3.8, 95% CI 2.72–5.30), Asians (OR 2.39, 95% CI 1.74–3.29), and Blacks (OR 1.50, 95% CI 1.15–1.94). ADI was not associated with hospitalization. Non-English speaking (OR 1.91, 95% CI 1.51–2.43) significantly increased odds of hospital admission across and within minority groups.ConclusionsMinority populations have increased odds of severe COVID-19 independent of neighborhood deprivation, a commonly suspected driver of disparate outcomes. Non-English-speaking accounts for differences across and within minority populations. These results support the continued concern that racism contributes to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity across and within minority groups.Key PointsQuestionDoes socioeconomic factors or primary language account for racial disparities in COVID-19 disease severity?FindingsIn this observational study of 5,577 adults, race/ethnicity minorities and non-English as a primary language, independent of neighborhood-level deprivation, are associated with increased risk of severe COVID-19 disease.MeaningSocioeconomic factors do not account for racial/ethnic disparities related to COVID-19 severity which supports further investigation into the racism and highlights the need to focus on our non-English speaking populations.

To develop a method for calculating rates of testing for breast cancer recurrence in patients who have already undergone initial treatment for breast cancer, we calculated rates in a cohort of Medicare breast cancer patients and an age-matched non-cancer cohort. We first used only tests with claims including diagnosis codes indicating invasive breast cancer and then used all tests regardless of diagnosis code. For each method, we calculated testing rates in the breast cancer cohort above the background rate in the non-cancer population. The two methods provided similar estimates of testing prevalence and frequency, with exception of prevalence of CT.

OBJECTIVES To examine the association between geography and utilization rates of contralateral prophylactic mastectomy (CPM) relative to patient-level factors in patients with early stage breast cancer. MATERIALS AND METHODS Using the Truven Health MarketScan Commercial Database, a descriptive analysis of geographic variation in genetic testing and CPM rates of 38,108 women ages 18 to 64 years treated with surgery for invasive unilateral breast cancer between 2010 and 2012 was conducted. Multivariate hierarchical analysis was used to examine the relationship between CPM likelihood and patient characteristics, with metropolitan statistical area (MSA) serving as a random effect. Patient characteristics included age group, BRCA1/2 carrier status, family history of breast cancer, breast surgery type, radiation therapy, drug therapy, and payer type. All MSAs in the United States were included, with areas outside MSAs within a given state aggregated into a single area for analytical purposes. RESULTS Descriptive analysis demonstrated significant geographic variation in rates of CPM irrespective of risk inferred from primary malignancy treatment course (P

Although the Affordable Care Act’s Medicaid expansion reduced uninsurance, less is known about its impact on mortality, especially in the context of the opioid epidemic. We conducted a difference-in-differences study comparing trends in mortality between expansion and nonexpansion states from 2011 to 2016 using the Centers for Disease Control and Prevention mortality data. We analyzed all-cause deaths, health care amenable deaths, drug overdose deaths, and deaths from causes other than drug overdose among adults aged 20 to 64 years. Medicaid expansion was associated with a 2.7% reduction ( p = .020) in health care amenable mortality, and a 1.9% reduction ( p = .042) in mortality not due to drug overdose. However, the expansion was not associated with any change in all-cause mortality (0.2% reduction, p = .84). In addition, drug overdose deaths rose more sharply in expansion versus nonexpansion states. The absence of all-cause mortality reduction until drug overdose deaths were excluded indicate that the opioid epidemic had a mitigating impact on any potential lives saved by Medicaid expansion.

Evidence suggests that hydroxychloroquine (HCQ) retinal toxicity is more common than previously thought. Adhering to careful weight-based dosing can significantly reduce the risk of this adverse event and is recommended in recent guidelines. We used electronic health record data from a large health system to examine HCQ dosing over a 5-year period and identify risk factors associated with higher dosage of HCQ. We constructed a longitudinal, retrospective cohort of patients with HCQ prescriptions (1681 patients with 3490 prescribing events) between 2012 and 2016. We measured HCQ dosing patterns relative to guidelines (

Supplemental Material, 14._S_Tab3_Admit_Discharge_Table for Recent Trends in Admission Diagnosis and Related Mortality in the Medically Critically Ill by Nicholas E. Ingraham, Victor Vakayil, Kathryn M. Pendleton, Alexandria J. Robbins, Rebecca L. Freese, Elise F. Palzer, Anthony Charles, R. Adams Dudley and Christopher J. Tignanelli in Journal of Intensive Care Medicine

Supplemental Material, Supplementary_Materials_9.2.20 for Variation in Arterial and Central Venous Catheter Use in Pediatric Intensive Care Units by Malini Mahendra, Patrick McQuillen, R. Adams Dudley and Martina A. Steurer in Journal of Intensive Care Medicine

Supplemental material, MCRR_Revised_Appendix_1 for The Opioid Epidemic Blunted the Mortality Benefit of Medicaid Expansion by Brandon W. Yan, Frank A. Sloan, W. John Boscardin, Felicia Guo and R. Adams Dudley in Medical Care Research and Review

Supplemental material, Appendix_7_Figures for The Opioid Epidemic Blunted the Mortality Benefit of Medicaid Expansion by Brandon W. Yan, Frank A. Sloan, W. John Boscardin, Felicia Guo and R. Adams Dudley in Medical Care Research and Review

OBJECTIVE: Delaying cataract surgery is associated with an increased risk of falls, but it is unknown whether routine preoperative testing delays cataract surgery long enough to cause clinical harm. We sought to determine whether the use of routine preoperative testing leads to harm in the form of delayed surgery and falls in Medicare beneficiaries awaiting cataract surgery. DESIGN: Retrospective observational cohort study using 2006-2014 Medicare claims PARTICIPANTS: Medicare beneficiaries age 66+ with a CPT claim for ocular biometry METHODS: We measured the mean and median number of days between ocular biometry and cataract surgery, calculated the proportion of patients waiting >30 days or >90 days for surgery, and determined the odds of having a fall within 90 days of biometry among patients of high-testing physicians (testing performed in ≥75% of their patients) compared to patients of low-testing physicians. We estimated the number of days of delay attributable to high-testing physicians relative to other factors that may determine surgical scheduling. MAIN OUTCOME MEASURES: Incidence of falls occurring between biometry and surgery, odds of falling within 90 days of biometry, and estimated delay in days associated with physician testing behavior . RESULTS: Of 248,345 beneficiaries, 16.4% were patients of high-testing physicians. More patients of high-testing physicians waited >30 days and >90 days to have cataract surgery (31.4% and 8.2% versus 25.0% and 5.5%, respectively, p

Introduction: Opioids are routinely prescribed to manage acute postoperative pain, but changes in postoperative opioid prescribing associated with the marketing of long-acting opioids such as OxyContin have not been described in the surgical cohort. Methods: Using a large commercial claims data set, we studied postoperative opioid prescribing after selected common surgical procedures between 1994 and 2014. For each procedure and year, we calculated the mean postoperative morphine milligram equivalents (MME) filled on the index prescription and assessed the proportion of patients who filled a high-dose prescription (≥350 MME). We reported changes in postoperative opioid prescribing over time and identified predictors of filling a high-dose postoperative opioid prescription. Results: We identified 1,321,264 adult patients undergoing selected common surgical procedures between 1994 and 2014, of whom 80.3% filled a postoperative opioid prescription. One in five surgery patients filled a high-dose postoperative opioid prescription. Between 1994 and 2014, the mean MME filled increased by 145%, 84%, and 85% for lumbar laminectomy/laminotomy, total knee arthroplasty, and total hip arthroplasty, respectively. The procedures most likely to be associated with a high-dose opioid fill were all orthopaedic procedures (AOR 5.20 to 7.55, P < 0.001 for all). Patients whose postoperative opioid prescription included a long-acting formulation had the highest odds of filling a prescription that exceeded 350 MME (AOR 32.01, 95% CI, 30.23–33.90). Discussion: After the US introduction of long-acting opioids such as OxyContin, postoperative opioid prescribing in commercially insured patients increased in parallel with broader US opioid-prescribing trends, most notably among patients undergoing orthopaedic surgical procedures. The increase in the mean annual MME filled starting in the late 1990s was driven in part by the higher proportion of long-acting opioid formulations on the index postoperative opioid prescription filled by orthopaedic surgery patients.

This study examines the insurance coverage and out-of-pocket costs to Medicare beneficiaries for the angiotensin receptor–neprilysin inhibitor sacubitril/valsartan.