Search

Background: Evidence from large randomized clinical trials supports the benefit of SGLT2i (sodium-glucose cotransporter-2 inhibitors) to improve cardiovascular and kidney outcomes in patients with type 2 diabetes with or at high risk for atherosclerotic cardiovascular disease or chronic kidney disease. Considering this evidence, which has been expanding since the product label indication for empagliflozin to reduce risk of cardiovascular death in 2016, clinician-level variation in the prescription of SGLT2i among US Medicare beneficiaries was evaluated. Methods: Antihyperglycemic medication prescribers were identified as those physicians and advanced practice providers prescribing metformin in Medicare part D prescriber data. In this cross-sectional study, the proportion prescribing SGLT2i was assessed overall and across specialties in 2018, with changes assessed from 2014 to 2018. SGLT2i use was compared with other second-line antihyperglycemic medication classes, sulfonylureas and DPP4is (dipeptidyl peptidase-4 inhibitors). Results: Among 232 523 unique clinicians who prescribed metformin for Medicare beneficiaries in 2018 (diabetes-treating clinicians), 45 255 (19.5%) prescribed SGLT2i. There was substantial variation across specialties—from 72% of endocrinologists to 14% of cardiologists who prescribed metformin also prescribed SGLT2i. Between 2014 and 2018, the number prescribing SGLT2i increased 5-fold from 9048 in 2014 to 45 255 in 2018. Among clinicians who prescribed both sulfonylureas and SGLT2i in 2018, SGLT2i was prescribed to a median 33 beneficiaries for every 100 prescribed sulfonylureas (interquartile range, 18–67). SGLT2i use relative to sulfonylureas increased from 19 (interquartile range, 11–34) per 100 in 2014 to 33 (interquartile range, 18–67) per 100 in 2018 ( P trend Conclusions: Eighty percent of clinicians prescribing metformin to Medicare beneficiaries did not prescribe SGLT2i in 2018. Moreover, sulfonylureas prescriptions were 3 times more frequent than those of SGLT2is, although a pattern of increasing uptake may portend future trends. These findings highlight a baseline opportunity to improve care and outcomes for patients with type 2 diabetes.

Background: Coronary artery bypass graft (CABG) surgery is a focus of bundled and alternate payment models that capture outcomes up to 90 days postsurgery. While clinical registry risk models perform well, measures encompassing mortality beyond 30 days do not currently exist. We aimed to develop a risk-adjusted hospital-level 90-day all-cause mortality measure intended for assessing hospital performance in payment models of CABG surgery using administrative data. Methods: Building upon Centers for Medicare and Medicaid Services hospital-level 30-day all-cause CABG mortality measure specifications, we extended the mortality timeframe to 90 days after surgery and developed a new hierarchical logistic regression model to calculate hospital risk-standardized 90-day all-cause mortality rates for patients hospitalized for isolated CABG. The model was derived from Medicare claims data for a 3-year cohort between July 2014 to June 2017. The data set was randomly split into 50:50 development and validation samples. The model performance was evaluated with C statistics, overfitting indices, and calibration plot. The empirical validity of the measure result at the hospital level was evaluated against the Society of Thoracic Surgeons composite star rating. Results: Among 137 819 CABG procedures performed in 1183 hospitals, the unadjusted mortality rate within 30 and 90 days were 3.1% and 4.7%, respectively. The final model included 27 variables. Hospital-level 90-day risk-standardized mortality rates ranged between 2.04% and 11.26%, with a median of 4.67%. C statistics in the development and validation samples were 0.766 and 0.772, respectively. We identified a strong positive correlation between 30- and 90-day risk-standardized mortality rates, with a regression slope of 1.09. Risk-standardized mortality rates also showed a stepwise trend of lower 90-day mortality with higher Society of Thoracic Surgeons composite star ratings. Conclusions: We present a measure of hospital-level 90-day risk-standardized mortality rates following isolated CABG. This measure complements Centers for Medicare and Medicaid Services’ existing 30-day CABG mortality measure by providing greater insight into the postacute recovery period. It offers a balancing measure to ensure efforts to reduce costs associated with CABG recovery and rehabilitation do not result in unintended consequences.

Background: It is unknown if financial pressures to reduce hospital readmission rates following passage of the Affordable Care Act (ACA) have had the unintended effect of increasing mortality rates after hospitalization. We therefore examined correlations between paired changes in hospital 30-day readmission rates and 30-day mortality rates among Medicare fee-for-service beneficiaries hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008 to 2014. Methods: We used linear regression to calculate monthly changes in hospitals’ 30-day risk-adjusted readmission rates (RARRs) and 30-day risk-adjusted mortality rates (RAMRs) after discharge for HF, AMI, and pneumonia from 2008 to 2014. Adjustment was made for patient age, sex, comorbidities, hospital length of stay, and season. We then examined the correlation of hospitals’ paired monthly changes in 30-day RARRs and monthly changes in 30-day RAMRs after discharge. Results: From 2008 to 2014, we identified 2,962,554, 1,229,939, and 2,544,530 hospitalizations for HF, AMI, and pneumonia at 5,016, 4,772, and 5,057 hospitals, respectively. Hospital 30-day RARRs declined for all three conditions from 2008 to 2014; the monthly change in RARRs was -0.053 (95% CI -0.055, -0.051) for HF, -0.044 (95% CI -0.047, -0.041) for AMI, and -0.033 (95% CI -0.035, -0.031) for pneumonia. In contrast, the monthly change in hospital 30-day RAMRs after discharge varied by admitting condition and was 0.008 (95% CI 0.007, 0.010) for HF, -0.003 (95% CI -0.006, -0.001) for AMI, and 0.001 (95% CI -0.001, 0.003) for pneumonia. The correlation between monthly changes in hospitals’ 30-day RARRs and 30-day RAMRs after discharge was 0.060 for HF (p Conclusion: Changes in hospital readmission rates for HF, AMI, and pneumonia were poorly correlated with changes in mortality rates after hospitalization between 2008 and 2014. These findings suggest that financial incentives to improve hospitals’ readmission performance have not increased mortality after hospitalization.

Background— Cardiovascular diseases are rising as a cause of death and disability in China. To improve outcomes for patients with these conditions, the Chinese government, academic researchers, clinicians, and >200 hospitals have created China Patient-Centered Evaluative Assessment of Cardiac Events (China PEACE), a national network for research and performance improvement. The first study from China PEACE, the Retrospective Study of Acute Myocardial Infarction (China PEACE-Retrospective AMI Study), is designed to promote improvements in acute myocardial infarction (AMI) quality of care by generating knowledge about the characteristics, treatments, and outcomes of patients hospitalized with AMI across a representative sample of Chinese hospitals during the past decade. Methods and Results— The China PEACE-Retrospective AMI Study will examine >18 000 patient records from 162 hospitals identified using a 2-stage cluster sampling design within economic–geographic regions. Records were chosen from 2001, 2006, and 2011 to identify temporal trends. Data quality will be monitored by a central coordinating center and will, in particular, address case ascertainment, data abstraction, and data management. Analyses will examine patient characteristics, diagnostic testing patterns, in-hospital treatments, in-hospital outcomes, and variation in results by time and site of care. In addition to publications, data will be shared with participating hospitals and the Chinese government to develop strategies to promote quality improvement. Conclusions— The China PEACE-Retrospective AMI Study is the first to leverage the China PEACE platform to better understand AMI across representative sites of care and during the past decade in China. The China PEACE collaboration among government, academicians, clinicians, and hospitals is poised to translate research about trends and patterns of AMI practices and outcomes into improved care for patients. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT01624883.

Background— National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction. Methods and Results— We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points). Conclusions— This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.

Background— The rankings of “America’s Best Hospitals” by U.S. News & World Report are influential, but the performance of ranked hospitals in caring for patients with routine cardiac conditions such as heart failure is not known. Methods and Results— Using hierarchical regression models based on medical administrative data from the period July 1, 2005, to June 30, 2006, we calculated risk-standardized mortality rates and risk-standardized readmission rates for ranked and nonranked hospitals in the treatment of heart failure. The mortality analysis examined 14 813 patients in 50 ranked hospitals and 409 806 patients in 4761 nonranked hospitals. The readmission analysis included 16 641 patients in 50 ranked hospitals and 458 473 patients in 4627 nonranked hospitals. Mean 30-day risk-standardized mortality rates were lower in ranked versus nonranked hospitals (10.1% versus 11.2%, P P =0.40). The 30-day risk-standardized mortality rates varied widely for both ranked and nonranked hospitals, ranging from 7.9% to 12.4% for ranked hospitals and from 7.1% to 17.5% for nonranked hospitals. The 30-day risk-standardized readmission rates also spanned a large range, from 18.7% to 29.3% for ranked hospitals and from 19.2% to 29.8% for nonranked hospitals. Conclusions— Hospitals ranked by U.S. News & World Report as “America’s Best Hospitals” in “Heart & Heart Surgery” are more likely than nonranked hospitals to have a significantly lower than expected 30-day mortality rate, but there was much overlap in performance. For readmission, the rates were similar in ranked and nonranked hospitals.

Background— In 2009, the Centers for Medicare & Medicaid Services is publicly reporting hospital-level risk-standardized 30-day mortality and readmission rates after acute myocardial infarction (AMI) and heart failure (HF). We provide patterns of hospital performance, based on these measures. Methods and Results— We calculated the 30-day mortality and readmission rates for all Medicare fee-for-service beneficiaries ages 65 years or older with a primary diagnosis of AMI or HF, discharged between July 2005 and June 2008. We compared weighted risk-standardized mortality and readmission rates across Hospital Referral Regions and hospital structural characteristics. The median 30-day mortality rate was 16.6% for AMI (range, 10.9% to 24.9%; 25th to 75th percentile, 15.8% to 17.4%; 10th to 90th percentile, 14.7% to 18.4%) and 11.1% for HF (range, 6.6% to 19.8%; 25th to 75th percentile, 10.3% to 12.0%; 10th to 90th percentile, 9.4% to 13.1%). The median 30-day readmission rate was 19.9% for AMI (range, 15.3% to 29.4%; 25th to 75th percentile, 19.5% to 20.4%; 10th to 90th percentile, 18.8% to 21.1%) and 24.4% for HF (range, 15.9% to 34.4%; 25th to 75th percentile, 23.4% to 25.6%; 10th to 90th percentile, 22.3% to 27.0%). We observed geographic differences in performance across the country. Although there were some differences in average performance by hospital characteristics, there were high and low hospital performers among all types of hospitals. Conclusions— In a recent 3-year period, 30-day risk-standardized mortality rates for AMI and HF varied among hospitals and across the country. The readmission rates were particularly high.

Background: The volume of percutaneous coronary intervention (PCI) in China has increased more than 20-fold over the last decade. Consequently, there is a need for national-level information to characterize indications for PCI and its long-term outcomes, including health status improvement, which is the principal goal of PCI in stable patients. Objectives: To conduct a nationwide prospective study of patients receiving PCI and follow them to 1) measure long-term clinical outcomes, patient-reported outcomes (PROs), risk factor control and adherence to medications for secondary prevention; 2) determine patient- and hospital-level factors associated with these outcomes; and 3) assess indications for and appropriateness of the PCI procedures performed. Methods: The China PEACE Prospective Study of PCI, conducted from December 2012 to December 2014, has recruited 5000 consecutive patients undergoing PCI for any indication from 36 representative hospitals across China and followed them for 1-year. The follow-up rate at 12 months was 94%. After obtaining informed consent from each patient, we abstracted details of their medical history, treatments, and in-hospital outcomes from medical charts. We conducted baseline interviews to characterize patient demographics, risk factors, clinical presentation, and healthcare utilization. In addition, we used several validated instruments to measure PROs describing quality of life, symptoms, mood and sleep. The primary outcome, a composite measure of death, myocardial infarction and/or revascularization, was assessed at follow-up interviews conducted at 1-, 6- and 12-months after discharge. In addition, we measured PROs, medication adherence and risk factor control at these interviews. Blood and urine samples were collected at the 1- and 12-month interviews and stored for future analysis. To establish the appropriateness of PCI, the angiograms of 2000 randomly selected patients were reviewed by independent “core” labs in China and the United States. To complement these patient-level data, we surveyed participating hospitals to characterize their facilities, processes and organizational learning culture. Conclusion: This study will generate novel, high-quality, and comprehensive national data characterizing the patient experience after PCI in China.

Background: The incidence of acute myocardial infarction (AMI) is growing rapidly in China, but there is limited information about the patient experience in the post-acute period. Specifically, long-term outcomes and patient-reported outcomes (PROs), including quality of life, symptoms and mood, after AMI, have not been systematically studied in China. Objectives: To conduct a nationwide prospective study following patients after AMI that 1) measures long-term clinical outcomes, PROs, cardiovascular risk factor control and adherence to medications for secondary prevention; and 2) identifies patient characteristics and hospital attributes that are associated with these outcomes. Methods: The China PEACE Prospective Study of AMI has recruited 4000 consecutive patients from 55 hospitals across China and is following them for 1-year. The first patient was enrolled in December 2012, and the last follow-up visit is scheduled for June 2015. After obtaining informed consent from patients, we abstracted details of their medical history, treatment, and in-hospital outcomes from medical charts. We conducted comprehensive baseline interviews characterizing patient demographics, risk factors, clinical presentation, and healthcare utilization. In addition, we used validated PRO instruments to measure quality of life, symptoms, mood, sleep, cognition and sexual activity. Follow-up interviews, measuring PROs, medication adherence and risk factor control were conducted at 1-, 6-, and 12-months after discharge. At these interviews, patients were asked to self-report major health events and to provide supporting materials (e.g., hospital discharge record for a readmission), which were subsequently validated by a National Coordinating Center. Blood and urine samples were obtained at baseline and 12-month follow-up, and stored for further biomarker analysis and genetic studies. To complement these patient-level data, we surveyed participating hospitals to characterize their facilities, processes and organizational learning culture. Together, these data will be used to identify factors associated with various outcomes following AMI. Conclusion: This study is uniquely positioned to generate new information regarding patient experience and determinants of outcomes after AMI in China.

Background: After hospitalization for heart failure (HF) and acute myocardial infarction (AMI), patients experience increased risk of death and hospital readmission. Defining the trajectory and timing of this period of risk may help guide interventions to improve post-discharge outcomes. Methods: We used 2008-10 Medicare data to identify patients ≥65 years discharged alive after HF or AMI hospitalization. Using hazard rates, we characterized the risks of death and first readmission on each day after discharge to describe (1) the maximum daily risks of death and readmission after discharge; (2) risks of death and readmission 1 year after discharge; (3) the time in days after discharge for the risks of death and readmission to reach their maximum daily rates and 50% of their maximum daily rates to characterize the rapidity of decline in risk. We created separate survival models for death and first readmission. Data were censored after 1 year follow up. The readmission model also censored for death prior to readmission. Results: Of 878,963 HF hospitalizations, 367,542 (41.8%) died and 618,283 (70.3%) were readmitted in 1 year. Of 350,509 AMI hospitalizations, 90,623 (25.9%) died and 177,031(50.5%) were readmitted in 1 year. The Figure shows hazard rates by time after discharge. For HF, daily risk of death was 0.0056 maximally and 0.0011 at 1 year (19% of maximum). Daily risk of readmission was 0.013 maximally and 0.002 at 1 year (16% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 11 days after discharge. Daily risk of readmission was highest 4 days after discharge and 50% less 49 days after discharge. For AMI, daily risk of death was 0.010 maximally and 0.0004 at 1 year (4% of maximum). Daily risk of readmission was 0.015 maximally and 0.0011 at 1 year (7% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 6 days after discharge. Daily risk of readmission was highest 2 days after discharge and 50% less 13 days after discharge. Conclusions: After hospitalization for HF and AMI, risk of death is highest on day 1 after discharge and then declines rapidly. In contrast, risk of readmission peaks later and declines more slowly. This extended period of risk for readmission may justify continued vigilance beyond the 30-day period used by Medicare to evaluate hospital readmission performance.

Background: Despite numerous studies of telemonitoring (TM), the use of technology to monitor patients remotely, information about patients' adoption and adherence to using such technologies is scarce. The objective of this study was to determine the patient characteristics associated with: 1) initiating use of TM and 2) adherence to using daily TM over 6 months. Methods: We evaluated all 826 patients enrolled in the TM arm of the Tele-HF multicenter RCT. TM consisted of a telephone-based interactive voice response system whereby patients reported symptoms and weight daily for 6 months. The adherence rate was calculated as the # of weeks the patient used the TM system at least 3 days/week over 6-month study period. Multivariable hierarchical regression was used to identify factors independently associated with initiation of and adherence to TM. Results: The mean ± SD age of patients was 61.1 ± 15.3 years (range 19-90), 44% were women, and 50% were of minority race. Overall, 14% (119/ 826) never initiated use of TM. In the multivariable analysis, younger patients (age < 65) and patients with higher satisfaction with care were more likely to initiate TM (p-values ≤ .03) (Figure). Among the 707 patients who initiated TM, adherence averaged 90% in week 1 and 55% in week 26. Younger patients and those with lower health literacy had lower rates of adherence to using TM over 6 months (p-values ≤ .004). Conclusion: Age, satisfaction with care and health literacy were associated with utilization of a TM system for the management of HF. Understanding more about how patient nonclinical factors impact use of new technologies such as TM, may improve the design and effectiveness of TM strategies.

Background: There is wide variation in regional admission and readmission rates and the association between the two is unknown. We sought to determine this association and whether it differs between heart failure (HF) admissions, more discretionary, and acute myocardial infarction (AMI) admissions, less discretionary. Methods: We used 2006-2008 Medicare ICD-9-CM claims data to determine AMI and HF admissions. Hospital referral region (HRR) admission rates were determined using Medicare denominator files. We estimated hospital level 30-day risk standardized readmission rates using hierarchical logistic models adjusting for age, sex and comorbidities and then aggregated them to HRRs level RSRRs. The correlation (R 2 ) obtained by weighted linear regression characterized the relationship between admission rates and RSRRs for AMI and HF. Results: Among 306 HRRs, the median AMI admission was 9.2 per 1000 enrollees (range 2.8 to 26.5) and the median HF admission rate was 19.6 (range 7.6 to 41.4). Among the 306 HRRs, the median AMI RSRR was 0.196 (range 0.163 to 0.233) and the median HF RSRR was 0.242 (range 0.201 to 0.289). Regional AMI admission rate was weakly correlated with AMI RSRR (R 2 0.05, 95% CI 0.02 - 0.11) and HF RSRR (R 2 0.07, 95% CI 0.02 - 0.13). Regional HF admission rate was modestly correlated with both AMI RSRR (R 2 0.28, 95% CI 0.20 - 0.37) and HF RSRR (R 2 0.32, 95% CI 0.24 - 0.41). Conclusion: The modest association between HF admissions, a more discretionary admission condition, and both AMI and HF readmission rates suggest a systemic propensity to admit and readmit patients. In contrast, the same was not true of AMI, a less discretionary admission condition.