Publications by authors named "Harlan M Krumholz"

1,162 Publications

  • Page 1 of 1

Investigating Lipid-Modulating Agents for Prevention or Treatment of COVID-19: JACC State-of-the-Art Review.

J Am Coll Cardiol 2021 10;78(16):1635-1654

Clinical Trials Center, Cardiovascular Research Foundation, New York, New York, USA; Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, Connecticut, USA; Division of Cardiovascular Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address:

Coronavirus disease-2019 (COVID-19) is associated with systemic inflammation, endothelial activation, and multiorgan manifestations. Lipid-modulating agents may be useful in treating patients with COVID-19. These agents may inhibit viral entry by lipid raft disruption or ameliorate the inflammatory response and endothelial activation. In addition, dyslipidemia with lower high-density lipoprotein cholesterol and higher triglyceride levels portend worse outcomes in patients with COVID-19. Upon a systematic search, 40 randomized controlled trials (RCTs) with lipid-modulating agents were identified, including 17 statin trials, 14 omega-3 fatty acids RCTs, 3 fibrate RCTs, 5 niacin RCTs, and 1 dalcetrapib RCT for the management or prevention of COVID-19. From these 40 RCTs, only 2 have reported preliminary results, and most others are ongoing. This paper summarizes the ongoing or completed RCTs of lipid-modulating agents in COVID-19 and the implications of these trials for patient management.
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http://dx.doi.org/10.1016/j.jacc.2021.08.021DOI Listing
October 2021

Scope of Practice of US Interventional Cardiologists from an Analysis of Medicare Billing Data.

Am J Cardiol 2021 Oct 2. Epub 2021 Oct 2.

Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut.

The contemporary scope of practice of interventional cardiologists (ICs) in the United States and recent trends are unknown. Using Medicare claims from 2013 to 2017, we categorized ICs into 4 practice categories (only percutaneous coronary intervention [PCI], PCI with noninvasive imaging, PCI with specialized interventions [peripheral/structural], and all 3 services) and evaluated associations with region, hospital bed size and teaching status, gender, and graduation year. Of 6,083 ICs in 2017, 10.9% performed only PCI, 68.3% PCI with noninvasive imaging, 5.7% PCI with specialized interventions, and 15.1% all 3 services. A higher proportion of Northeast ICs (vs South ICs) were performing only PCI (24.8% vs 7.3%) and PCI with specialized interventions (12% vs 3.4%), but lower PCI and noninvasive imaging (53.8% vs 71.7%) and all 3 services (9.3% and 17.6%). Regarding ICs at larger hospitals (bed size >575 vs <218), a higher proportion was performing only PCI (23.8% vs 5.2%) or PCI with specialized interventions (13.5% vs 1.7%) and lower proportion was performing PCI with noninvasive imaging (48.8% vs 78%), similar to teaching hospitals. Female ICs (vs male ICs) more frequently performed only PCI (18.9% vs 10.6%) and less frequently all 3 services (8.3% vs 15.4%). A lower proportion of recent graduates (2001 to 2016) performed only PCI (9.8% vs 13.8%) and PCI with noninvasive imaging (66.3% vs 72.6%) but a higher proportion performed all 3 services (18% vs 8.4%) than earlier graduates (1959 to 1984). From 2013 to 2017, only PCI and PCI with noninvasive imaging decreased, whereas PCI and specialized interventions and all 3 services increased (all p <0.001). In conclusion, there is marked heterogeneity in practice responsibilities among ICs, which has implications for training and competency assessments.
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http://dx.doi.org/10.1016/j.amjcard.2021.08.041DOI Listing
October 2021

Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning.

Circ Cardiovasc Qual Outcomes 2021 Oct 4;14(10):e007526. Epub 2021 Oct 4.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (C.H., S.-X.L., C.C., H.M.K.).

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.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.120.007526DOI Listing
October 2021

Changes in ST segment elevation myocardial infarzzction hospitalisations in China from 2011 to 2015.

Open Heart 2021 09;8(2)

National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, People's Republic of China

Objective: Access to acute cardiovascular care has improved and health services capacity has increased over the past decades. We assessed national changes in (1) patient characteristics, (2) in-hospital management and (3) patient outcomes among patients presenting with ST segment elevation myocardial infarction (STEMI) in 2011-2015 in China.

Methods: In a nationally representative sample of hospitals in China, we created two random cohorts of patients in 2011 and 2015 separately. We weighted our findings to estimate nationally representative numbers and assessed changes from 2011 to 2015. Data were abstracted from medical charts centrally using standardised definitions.

Results: While the proportion of patients with STEMI among all patients with acute myocardial infarction decreased over time from 82.5% (95% CI 81.7 to 83.3) in 2011 to 68.5% (95% CI 67.7 to 69.3) in 2015 (p<0.0001), the weighted national estimate of patients with STEMI increased from 210 000 to 380 000. The rate of reperfusion eligibility among patients with STEMI decreased from 49.3% (95% CI 48.1 to 50.5) to 42.2% (95% CI 41.1 to 43.4) in 2015 (p<0.0001); ineligibility was principally driven by larger proportions with prehospital delay exceeding 12 hours (67.4%-76.7%, p<0.0001). Among eligible patients, the proportion receiving reperfusion therapies increased from 54% (95% CI 52.3 to 55.7) to 59.7% (95% CI 57.9 to 61.4) (p<0.0001). Crude and risk-adjusted rates of in-hospital death did not differ significantly between 2011 and 2015.

Conclusions: In this most recent nationally representative study of STEMI in China, the use of acute reperfusion increased, but no significant improvement occurred in outcomes. There is a need to continue efforts to prevent cardiovascular diseases, to monitor changes in in-hospital treatments and outcomes, and to reduce prehospital delay.
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http://dx.doi.org/10.1136/openhrt-2021-001666DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488733PMC
September 2021

Prevalence of Dyslipidemia and Availability of Lipid-Lowering Medications Among Primary Health Care Settings in China.

JAMA Netw Open 2021 Sep 1;4(9):e2127573. Epub 2021 Sep 1.

National Clinical Research Center for Cardiovascular Diseases, National Health Commission Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China.

Importance: Dyslipidemia, the prevalence of which historically has been low in China, is emerging as the second leading yet often unaddressed factor associated with the risk of cardiovascular diseases. However, recent national data on the prevalence, treatment, and control of dyslipidemia are lacking.

Objective: To assess the prevalence, treatment, and control of dyslipidemia in community residents and the availability of lipid-lowering medications in primary care institutions in China.

Design, Setting, And Participants: This cross-sectional study used data from the China-PEACE (Patient-Centered Evaluative Assessment of Cardiac Events) Million Persons Project, which enrolled 2 660 666 community residents aged 35 to 75 years from all 31 provinces in China between December 2014 and May 2019, and the China-PEACE primary health care survey of 3041 primary care institutions. Data analysis was performed from June 2019 to March 2021.

Exposures: Study period.

Main Outcomes And Measures: The main outcome was the prevalence of dyslipidemia, which was defined as total cholesterol greater than or equal to 240 mg/dL, low-density lipoprotein cholesterol (LDL-C) greater than or equal to 160 mg/dL, high-density lipoprotein cholesterol (HDL-C) less than 40 mg/dL, triglycerides greater than or equal to 200 mg/dL, or self-reported use of lipid-lowering medications, in accordance with the 2016 Chinese Adult Dyslipidemia Prevention Guideline.

Results: This study included 2 314 538 participants with lipid measurements (1 389 322 women [60.0%]; mean [SD] age, 55.8 [9.8] years). Among them, 781 865 participants (33.8%) had dyslipidemia. Of 71 785 participants (3.2%) who had established atherosclerotic cardiovascular disease (ASCVD) and were recommended by guidelines for lipid-lowering medications regardless of LDL-C levels, 10 120 (14.1%) were treated. The overall control rate of LDL-C (≤70 mg/dL) among adults with established ASCVD was 26.6% (19 087 participants), with the control rate being 44.8% (4535 participants) among those who were treated and 23.6% (14 552 participants) among those not treated. Of 236 579 participants (10.2%) with high risk of ASCVD, 101 474 (42.9%) achieved LDL-C less than or equal to 100 mg/dL. Among participants with established ASCVD, advanced age (age 65-75 years, odds ratio [OR], 0.63; 95% CI, 0.56-0.70), female sex (OR, 0.56; 95% CI, 0.53-0.58), lower income (reference category), smoking (OR, 0.89; 95% CI, 0.85-0.94), alcohol consumption (OR, 0.87; 95% CI, 0.83-0.92), and not having diabetes (reference category) were associated with lower control of LDL-C. Among participants with high risk of ASCVD, younger age (reference category) and female sex (OR, 0.58; 95% CI, 0.56-0.59) were associated with lower control of LDL-C. Of 3041 primary care institutions surveyed, 1512 (49.7%) stocked statin and 584 (19.2%) stocked nonstatin lipid-lowering drugs. Village clinics in rural areas had the lowest statin availability.

Conclusions And Relevance: These findings suggest that dyslipidemia has become a major public health problem in China and is often inadequately treated and uncontrolled. Statins were available in less than one-half of the primary care institutions. Strategies aimed at detection, prevention, and treatment are needed.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.27573DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482054PMC
September 2021

Assessing Performance of Machine Learning-Reply.

JAMA Cardiol 2021 Sep 29. Epub 2021 Sep 29.

Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.

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http://dx.doi.org/10.1001/jamacardio.2021.3715DOI Listing
September 2021

Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study.

JMIR Public Health Surveill 2021 09 28;7(9):e29413. Epub 2021 Sep 28.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, United States.

Background: Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time.

Objective: This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States.

Methods: We used natural language processing (NLP) algorithms to identify symptom- and medical condition-related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics.

Results: Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition-related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05).

Conclusions: COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.
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http://dx.doi.org/10.2196/29413DOI Listing
September 2021

Prior Authorization, Copayments, and Utilization of Sacubitril/Valsartan in Medicare and Commercial Plans in Patients With Heart Failure With Reduced Ejection Fraction.

Circ Cardiovasc Qual Outcomes 2021 Sep 1;14(9):e007665. Epub 2021 Sep 1.

Department of Pharmacy Practice and Administration, Western University of Health Sciences, Pomona, CA (A.F.O., T.T.T., Q.T.L., M.Y., C.A.J.).

Background: Slow uptake of sacubitril/valsartan in patients with heart failure with reduced ejection fraction has been reported, which may negatively impact clinical outcomes. We characterized prior authorization (PA) burden, prescription copayment, and utilization of sacubitril/valsartan by insurance plan type to identify potential barriers to its use.

Methods: We conducted a national population-level, cross-sectional study using PA data from an insurance coverage website accessed in March 2019 and IQVIA National Prescription Audit data from August 2018 to July 2019. Primary outcomes were proportion of plans requiring PA, frequency of specific PA criteria, number of sacubitril/valsartan prescriptions, and copayments per insurance plan type.

Results: Overall, 48.1% (1394/2896) of insurance plans required PA for sacubitril/valsartan. Fewer Medicare (27.7%) than commercial (57.2%) plans required PA (<0.001). For both plan types, the most frequently required PA criteria were ejection fraction (71.6%, 90.9%) and New York Heart Association class (60.4%, 90.8%) for Medicare and commercial plans, respectively. Copayment amounts varied by plan type, with more sacubitril/valsartan prescriptions for commercial plans not requiring a patient copayment (32.4%) compared with Medicare plans (19.3%; <0.001). There were 814 437 sacubitril/valsartan prescriptions for Medicare and 822 292 for commercial plans dispensed from August 2018 to July 2019. Based on estimated heart failure with reduced ejection fraction populations for each plan type, 4-fold more sacubitril/valsartan prescriptions were dispensed in commercial than in Medicare plans (820 versus 215 prescriptions/1000 individuals in the heart failure with reduced ejection fraction population). The estimated proportion of heart failure with reduced ejection fraction patients prescribed sacubitril/valsartan was 3.6% (1.5%-6.8%) for Medicare and 13.7% (4.9%-31.8%) for commercial plan populations.

Conclusions: Despite commercial plans having greater PA requirements than Medicare, population-adjusted use of sacubitril/valsartan was higher in commercial plans. Given that commercial plans had more prescriptions with low copayments than Medicare, copayment policies may be more influential on sacubitril/valsartan use than its PA policies. Low sacubitril/valsartan use in both plan types highlights the multifactorial nature of medication underutilization that includes factors beyond the drug policies that we evaluated.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.120.007665DOI Listing
September 2021

Disparities in Excess Mortality Associated with COVID-19 - United States, 2020.

MMWR Morb Mortal Wkly Rep 2021 Aug 20;70(33):1114-1119. Epub 2021 Aug 20.

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.
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http://dx.doi.org/10.15585/mmwr.mm7033a2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375709PMC
August 2021

Characteristics of available studies and dissemination of research using major clinical data sharing platforms.

Clin Trials 2021 Aug 18:17407745211038524. Epub 2021 Aug 18.

Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.

Background/aims: Over the past decade, numerous data sharing platforms have been launched, providing access to de-identified individual patient-level data and supporting documentation. We evaluated the characteristics of prominent clinical data sharing platforms, including types of studies listed as available for request, data requests received, and rates of dissemination of research findings from data requests.

Methods: We reviewed publicly available information listed on the websites of six prominent clinical data sharing platforms: Biological Specimen and Data Repository Information Coordinating Center, ClinicalStudyDataRequest.com, Project Data Sphere, Supporting Open Access to Researchers-Bristol Myers Squibb, Vivli, and the Yale Open Data Access Project. We recorded key platform characteristics, including listed studies and available supporting documentation, information on the number and status of data requests, and rates of dissemination of research findings from data requests (i.e. publications in a peer-reviewed journals, preprints, conference abstracts, or results reported on the platform's website).

Results: The number of clinical studies listed as available for request varied among five data sharing platforms: Biological Specimen and Data Repository Information Coordinating Center (n = 219), ClinicalStudyDataRequest.com (n = 2,897), Project Data Sphere (n = 154), Vivli (n = 5426), and the Yale Open Data Access Project (n = 395); Supporting Open Access to Researchers did not provide a list of Bristol Myers Squibb studies available for request. Individual patient-level data were nearly always reported as being available for request, as opposed to only Clinical Study Reports (Biological Specimen and Data Repository Information Coordinating Center = 211/219 (96.3%); ClinicalStudyDataRequest.com = 2884/2897 (99.6%); Project Data Sphere = 154/154 (100.0%); and the Yale Open Data Access Project = 355/395 (89.9%)); Vivli did not provide downloadable study metadata. Of 1201 data requests listed on ClinicalStudyDataRequest.com, Supporting Open Access to Researchers-Bristol Myers Squibb, Vivli, and the Yale Open Data Access Project platforms, 586 requests (48.8%) were approved (i.e. data access granted). The majority were for secondary analyses and/or developing/validating methods (ClinicalStudyDataRequest.com = 262/313 (83.7%); Supporting Open Access to Researchers-Bristol Myers Squibb = 22/30 (73.3%); Vivli = 63/84 (75.0%); the Yale Open Data Access Project = 111/159 (69.8%)); four were for re-analyses or corroborations of previous research findings (ClinicalStudyDataRequest.com = 3/313 (1.0%) and the Yale Open Data Access Project = 1/159 (0.6%)). Ninety-five (16.1%) approved data requests had results disseminated via peer-reviewed publications (ClinicalStudyDataRequest.com = 61/313 (19.5%); Supporting Open Access to Researchers-Bristol Myers Squibb = 3/30 (10.0%); Vivli = 4/84 (4.8%); the Yale Open Data Access Project = 27/159 (17.0%)). Forty-two (6.8%) additional requests reported results through preprints, conference abstracts, or on the platform's website (ClinicalStudyDataRequest.com = 12/313 (3.8%); Supporting Open Access to Researchers-Bristol Myers Squibb = 3/30 (10.0%); Vivli = 2/84 (2.4%); Yale Open Data Access Project = 25/159 (15.7%)).

Conclusion: Across six prominent clinical data sharing platforms, information on studies and request metrics varied in availability and format. Most data requests focused on secondary analyses and approximately one-quarter of all approved requests publicly disseminated their results. To further promote the use of shared clinical data, platforms should increase transparency, consistently clarify the availability of the listed studies and supporting documentation, and ensure that research findings from data requests are disseminated.
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http://dx.doi.org/10.1177/17407745211038524DOI Listing
August 2021

Trends in Differences in Health Status and Health Care Access and Affordability by Race and Ethnicity in the United States, 1999-2018.

JAMA 2021 Aug;326(7):637-648

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut.

Importance: The elimination of racial and ethnic differences in health status and health care access is a US goal, but it is unclear whether the country has made progress over the last 2 decades.

Objective: To determine 20-year trends in the racial and ethnic differences in self-reported measures of health status and health care access and affordability among adults in the US.

Design, Setting, And Participants: Serial cross-sectional study of National Health Interview Survey data, 1999-2018, that included 596 355 adults.

Exposures: Self-reported race, ethnicity, and income level.

Main Outcomes And Measures: Rates and racial and ethnic differences in self-reported health status and health care access and affordability.

Results: The study included 596 355 adults (mean [SE] age, 46.2 [0.07] years, 51.8% [SE, 0.10] women), of whom 4.7% were Asian, 11.8% were Black, 13.8% were Latino/Hispanic, and 69.7% were White. The estimated percentages of people with low income were 28.2%, 46.1%, 51.5%, and 23.9% among Asian, Black, Latino/Hispanic, and White individuals, respectively. Black individuals with low income had the highest estimated prevalence of poor or fair health status (29.1% [95% CI, 26.5%-31.7%] in 1999 and 24.9% [95% CI, 21.8%-28.3%] in 2018), while White individuals with middle and high income had the lowest (6.4% [95% CI, 5.9%-6.8%] in 1999 and 6.3% [95% CI, 5.8%-6.7%] in 2018). Black individuals had a significantly higher estimated prevalence of poor or fair health status than White individuals in 1999, regardless of income strata (P < .001 for the overall and low-income groups; P = .03 for middle and high-income group). From 1999 to 2018, racial and ethnic gaps in poor or fair health status did not change significantly, with or without income stratification, except for a significant decrease in the difference between White and Black individuals with low income (-6.7 percentage points [95% CI, -11.3 to -2.0]; P = .005); the difference in 2018 was no longer statistically significant (P = .13). Black and White individuals had the highest levels of self-reported functional limitations, which increased significantly among all groups over time. There were significant reductions in the racial and ethnic differences in some self-reported measures of health care access, but not affordability, with and without income stratification.

Conclusions And Relevance: In a serial cross-sectional survey study of US adults from 1999 to 2018, racial and ethnic differences in self-reported health status, access, and affordability improved in some subgroups, but largely persisted.
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http://dx.doi.org/10.1001/jama.2021.9907DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371573PMC
August 2021

Atherosclerotic Cardiovascular Disease, Cancer, and Financial Toxicity Among Adults in the United States.

JACC CardioOncol 2021 Jun 15;3(2):236-246. Epub 2021 Jun 15.

Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA.

Background: Financial toxicity (FT) is a well-established side-effect of the high costs associated with cancer care. In recent years, studies have suggested that a significant proportion of those with atherosclerotic cardiovascular disease (ASCVD) experience FT and its consequences.

Objectives: This study aimed to compare FT for individuals with neither ASCVD nor cancer, ASCVD only, cancer only, and both ASCVD and cancer.

Methods: From the National Health Interview Survey, we identified adults with self-reported ASCVD and/or cancer between 2013 and 2018, stratifying results by nonelderly (age <65 years) and elderly (age ≥65 years). We defined FT if any of the following were present: any difficulty paying medical bills, high financial distress, cost-related medication nonadherence, food insecurity, and/or foregone/delayed care due to cost.

Results: The prevalence of FT was higher among those with ASCVD when compared with cancer (54% vs. 41%; p < 0.001). When studying the individual components of FT, in adjusted analyses, those with ASCVD had higher odds of any difficulty paying medical bills (odds ratio [OR]: 1.22; 95% confidence interval [CI]: 1.09 to 1.36), inability to pay bills (OR: 1.25; 95% CI: 1.04 to 1.50), cost-related medication nonadherence (OR: 1.28; 95% CI: 1.08 to 1.51), food insecurity (OR: 1.39; 95% CI: 1.17 to 1.64), and foregone/delayed care due to cost (OR: 1.17; 95% CI: 1.01 to 1.36). The presence of ≥3 of these factors was significantly higher among those with ASCVD and those with both ASCVD and cancer when compared with those with cancer (23% vs. 30% vs. 13%, respectively; p < 0.001). These results remained similar in the elderly population.

Conclusions: Our study highlights that FT is greater among patients with ASCVD compared with those with cancer, with the highest burden among those with both conditions.
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http://dx.doi.org/10.1016/j.jaccao.2021.02.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352280PMC
June 2021

Falls in older adults after hospitalization for acute myocardial infarction.

J Am Geriatr Soc 2021 Aug 12. Epub 2021 Aug 12.

Section of General Internal Medicine, Department of Internal Medicine, School of Medicine, Yale University, New Haven, Connecticut, United States.

Background: After hospitalization for acute myocardial infarction (AMI), older adults may be at increased risk for falls due to deconditioning, new medications, and worsening health status. Our primary objective was to identify risk factors for falls after AMI hospitalization among adults over age 75.

Methods: We used data from the Comprehensive Evaluation of Risk Factors in Older Patients with AMI (SILVER-AMI) study, a prospective cohort study of 3041 adults age 75 and older hospitalized with AMI at 94 community and academic medical centers across the United States. In-person interviews and physical assessments, as well as medical record review, were performed to collect demographic, clinical, functional, and psychosocial data. Falls were self-reported in telephone interviews and medically serious falls (those associated with emergency department use or hospitalization) were determined by medical record adjudication. Backward selection was used to identify predictors of fall risk in logistic regression analysis.

Results: A total of 554 (21.6%) participants reported a fall and 191 (6.4%) had a medically serious fall within 6 months of discharge. Factors independently associated with self-reported falls included impaired mobility, prior fall history, longer hospital stay, visual impairment, and weak grip. Factors independently associated with medically serious falls included older age, polypharmacy, impaired functional mobility, prior fall history, and living alone.

Conclusions: Among older patients hospitalized for AMI, falls are common in the 6 months following discharge and associated with demographic, functional, and clinical factors that are readily identifiable. Fall risk should be considered in post-AMI clinical decision-making and interventions to prevent falls should be evaluated.
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http://dx.doi.org/10.1111/jgs.17398DOI Listing
August 2021

National Trends in the Use of Sacubitril/Valsartan.

J Card Fail 2021 Aug;27(8):839-847

Department of Pharmacy Practice and Administration, Western University of Health Sciences, Pomona, California; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California; ICES, Toronto, Canada; Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada. Electronic address:

Background: Better understanding of recent sacubitril/valsartan prescription patterns may help identify factors that influence its use. The aim of the study was to characterize sacubitril/valsartan use and dosage patterns nationally.

Methods And Results: We conducted a population-level cohort study using IQVIA Inc. National Prescription Audit™ data in the United States from August 2016 to July 2019. Over 3 years, there was a 5.6-fold increase in the number of sacubitril/valsartan prescriptions dispensed per month, totaling 3.3 million prescriptions. For the most recent year, this extrapolates to a best-case scenario of 13.8% of patients with heart failure with reduced ejection fraction using sacubitril/valsartan, representing at most one-half of those eligible for sacubitril/valsartan use. During the most recent year, 48.7% of dispensed prescriptions were for the lowest strength (24/26 mg) and only 20.6% for the target strength (97/103 mg). A greater proportion of the target strength was used in younger patients (< 65years: 24.6%; ≥ 85: 11.1%; P<0.0001). Cardiologists prescribed 59.0% of all dispensed prescriptions, and noncardiologists showed a greater increase (7.5-fold vs 4.9-fold; P<0.0001) over time.

Conclusions: Recent use of sacubitril/valsartan has increased greatly in the United States; however, a substantial proportion of eligible patients with heart failure with reduced ejection fraction did not receive treatment, and only 1 in 5 prescriptions dispensed were for the target strength. Further exploration of barriers to the use of sacubitril/valsartan and dosing uptitration and their clinical implications warrant further evaluation.
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http://dx.doi.org/10.1016/j.cardfail.2021.05.015DOI Listing
August 2021

Association of COVID-19 Hospitalization Volume and Case Growth at US Hospitals with Patient Outcomes.

Am J Med 2021 Jul 31. Epub 2021 Jul 31.

Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Conn; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Conn.

Background: Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions.

Methods: We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR).

Results: There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03).

Conclusions: An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.
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http://dx.doi.org/10.1016/j.amjmed.2021.06.034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325555PMC
July 2021

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.

ACM Trans Comput Healthc 2021 Jan 30;2(1). Epub 2020 Dec 30.

Texas A&M University, USA.

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.
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http://dx.doi.org/10.1145/3417958DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320445PMC
January 2021

Trajectories of Pain After Cardiac Surgery: Implications for Measurement, Reporting, and Individualized Treatment.

Circ Cardiovasc Qual Outcomes 2021 Aug 26;14(8):e007781. Epub 2021 Jul 26.

Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT (M.M., Y.L., E.S.S., H.M.K.).

Background: Postoperative pain after cardiac surgery is a significant problem, but studies often report pain value as an average of the study cohort, obscuring clinically meaningful differences in pain trajectories. We sought to characterize heterogeneity in postoperative pain experiences.

Methods: We enrolled patients undergoing a cardiac surgery at a tertiary care center between January 2019 and February 2020. Participants received an electronically-delivered questionnaire every 3 days for 30 days to assess incision site pain level. We evaluated the variability in pain trajectories over 30 days by the cohort-level mean with confidence band and latent classes identified by group-based trajectory model. Group-based trajectory model estimated the probability of belonging to a specific trajectory of pain.

Results: Of 92 patients enrolled, 75 provided ≥3 questionnaire responses. The cohort-level mean showed a gradual and consistent decline in the mean pain level, but the confidence bands covered most of the pain score range. The individual-level trajectories varied substantially across patients. Group-based trajectory model identified 4 pain trajectories: persistently low (n=9, 12%), moderate declining (initially mid-level, followed by decline; n=26, 35%), high declining (initially high-level, followed by decline; n=33, 44%), and persistently high pain (n=7, 9%). Persistently high pain and high declining groups did not seem to be clearly distinguishable until approximately postoperative day 10. Patients in persistently low pain trajectory class had a numerically lower median age than the other 3 classes and were below the lower confidence band of the cohort-level approach. Patients in the persistently high pain trajectory class had a longer median length of hospital stay than the other 3 classes and were often higher than the upper confidence band of the cohort-level approach.

Conclusions: We identified 4 trajectories of postoperative pain that were not evident from a cohort-level mean, which has been a common way of reporting pain level. This study provides key information about the patient experience and indicates the need to understand variation among sites and surgeons and to investigate determinants of different experience and interventions to mitigate persistently high pain.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.120.007781DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366534PMC
August 2021

Multicentre methodological study to create a publicly available score of hospital financial standing in the USA.

BMJ Open 2021 07 23;11(7):e046500. Epub 2021 Jul 23.

Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA.

Objectives: To create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital.

Design: Methodological study.

Setting: Multicentre study.

Participants: All hospitals and health systems reporting the required financial metrics in the USA in 2017 were included for a total of 1075 participants.

Interventions: We examined a list of 232 hospital financial indicators and used existing models and financial literature to select 30 metrics that sufficiently describe hospital operations. In a set of hospital financial data from 2017, we used principal coordinate analysis to assess collinearity among variables and eliminated redundant variables. We isolated 10 unique variables, each assigned a weight equal to the share of its coefficient in a regression onto Moody's Credit Rating, our predefined gold standard. The sum of weighted variables is a single composite score named the Yale Hospital Financial Score (YHFS).

Primary Outcome Measures: Ability to reproduce both financial trends from a 'gold-standard' metric and known associations with non-fiscal data.

Results: The validity of the YHFS was evaluated by: (1) cross-validating it with previously excluded data; (2) comparing it to existing models and (3) replicating known associations with non-fiscal data. Ten per cent of the initial dataset had been reserved for validation and was not used in creating the model; the YHFS predicts 96.7% of the variation in this reserved sample, demonstrating reproducibility. The YHFS predicts 90.5% and 88.8% of the variation in Moody's and Standard and Poor's bond ratings, respectively, supporting its validity. As expected, larger hospitals had higher YHFS scores whereas a greater share of Medicare discharges correlated with lower YHFS scores.

Conclusions: We created a reliable and publicly available composite score of hospital financial stability.
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http://dx.doi.org/10.1136/bmjopen-2020-046500DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8311305PMC
July 2021

Association of STEMI regionalization of care with de facto NSTEMI regionalization.

Am Heart J 2021 Jul 15;242:1-5. Epub 2021 Jul 15.

Department of Emergency Medicine, University of California, San Francisco; Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco.

The regionalization of care for ST elevation myocardial infarction (STEMI) may unintentionally concentrate patients with non-ST elevation myocardial infarction (NSTEMI) into percutaneous coronary intervention (PCI) capable hospitals. This could lead to benefits such as increased access to PCI-capable hospitals, but could cause harms such as crowding in some hospitals with decreased patient volume and revenue in others. We set out to assess whether STEMI regionalization programs concentrated patients with NSTEMI at STEMI-receiving hospitals.
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http://dx.doi.org/10.1016/j.ahj.2021.07.002DOI Listing
July 2021

Trends and geographical variation in population thriving, struggling and suffering across the USA, 2008-2017: a retrospective repeated cross-sectional study.

BMJ Open 2021 07 14;11(7):e043375. Epub 2021 Jul 14.

The Institute for Integrative Health, Baltimore, Maryland, USA.

Objectives: Well-being is a holistic, positively framed conception of health, integrating physical, emotional, social, financial, community and spiritual aspects of life. High well-being is an intrinsically worthy goal for individuals, communities and nations. Multiple measures of well-being exist, yet we lack information to identify benchmarks, geographical disparities and targets for intervention to improve population life evaluation in the USA.

Design: Using data from the Gallup National Health and Well-Being Index, we conducted retrospective analyses of a series of cross-sectional samples.

Setting/participants: We summarised select well-being outcomes nationally for each year, and by county (n=599) over two time periods, 2008-2012 and 2013-2017.

Main Outcome Measures: We report percentages of people thriving, struggling and suffering using the Cantril Self-Anchoring Scale, percentages reporting high or low current life satisfaction, percentages reporting high or low future life optimism, and changes in these percentages over time.

Results: Nationally, the percentage of people that report thriving increased from 48.9% in 2008 to 56.3% in 2017 (p<0.05). The percentage suffering was not significantly different over time, ranging from 4.4% to 3.2%. In 2013-2017, counties with the highest life evaluation had a mean 63.6% thriving and 2.3% suffering while counties with the lowest life evaluation had a mean 49.5% thriving and 6.5% suffering, with counties experiencing up to 10% suffering, threefold the national average. Changes in county-level life evaluation also varied. While counties with the greatest improvements experienced 10%-15% increase in the absolute percentage thriving or 3%-5% decrease in absolute percentage suffering, most counties experienced no change and some experienced declines in life evaluation.

Conclusions: The percentage of the US population thriving increased from 2008 to 2017 while the percentage suffering remained unchanged. Marked geographical variation exists indicating priority areas for intervention.
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http://dx.doi.org/10.1136/bmjopen-2020-043375DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281074PMC
July 2021

Obesity Prevalence and Risks Among Chinese Adults: Findings From the China PEACE Million Persons Project, 2014-2018.

Circ Cardiovasc Qual Outcomes 2021 06 10;14(6):e007292. Epub 2021 Jun 10.

National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.).

Background: China has seen a burgeoning epidemic of obesity in recent decades, but few studies reported nationally on obesity across socio-demographic subgroups. We sought to assess the prevalence and socio-demographic associations of obesity nationwide.

Methods: We assessed the prevalence of overall obesity (body mass index ≥28 kg/m) and abdominal obesity (waist circumference ≥85/90 cm for women/men) among 2.7 million community-dwelling adults aged 35 to 75 years in the China PEACE Million Persons Project from 2014 to 2018 and quantified the socio-demographic associations of obesity using multivariable mixed models.

Results: Age-standardized rates of overall and abdominal obesity were 14.4% (95% CI, 14.3%-14.4%) and 32.7% (32.6%-32.8%) in women and 16.0% (15.9%-16.1%) and 36.6% (36.5%-36.8%) in men. Obesity varied considerably across socio-demographic subgroups. Older women were at higher risk for obesity (eg, adjusted relative risk [95% CI] of women aged 65-75 versus 35-44 years: 1.29 [1.27-1.31] for overall obesity, 1.76 [1.74-1.77] for abdominal obesity) while older men were not. Higher education was associated with lower risk in women (eg, adjusted relative risk [95% CI] of those with college or university education versus below primary school: 0.47 [0.46-0.48] for overall obesity, 0.61 [0.60-0.62] for abdominal obesity) but higher risk in men (1.07 [1.05-1.10], 1.17 [1.16-1.19]).

Conclusions: In China, over 1 in 7 individuals meet criteria for overall obesity, and 1 in 3 for abdominal obesity. Wide variation exists across socio-demographic subgroups. The associations of age and education with obesity are significant and differ by sex. Understanding obesity in contemporary China has broad domestic policy implications and provides a valuable international reference.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.120.007292DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8204767PMC
June 2021

Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft: Improving Risk Prediction With Intraoperative Events Using Gradient Boosting.

Circ Cardiovasc Qual Outcomes 2021 06 3;14(6):e007363. Epub 2021 Jun 3.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (M.M., T.J.S.D., C.H., B.J.M., R.A.J, A.C., W.L.S., H.M.K).

Background: Intraoperative data may improve models predicting postoperative events. We evaluated the effect of incorporating intraoperative variables to the existing preoperative model on the predictive performance of the model for coronary artery bypass graft.

Methods: We analyzed 378 572 isolated coronary artery bypass graft cases performed across 1083 centers, using the national Society of Thoracic Surgeons Adult Cardiac Surgery Database between 2014 and 2016. Outcomes were operative mortality, 5 postoperative complications, and composite representation of all events. We fitted models by logistic regression or extreme gradient boosting (XGBoost). For each modeling approach, we used preoperative only, intraoperative only, or pre+intraoperative variables. We developed 84 models with unique combinations of the 3 variable sets, 2 variable selection methods, 2 modeling approaches, and 7 outcomes. Each model was tested in 20 iterations of 70:30 stratified random splitting into development/testing samples. Model performances were evaluated on the testing dataset using the C statistic, area under the precision-recall curve, and calibration metrics, including the Brier score.

Results: The mean patient age was 65.3 years, and 24.7% were women. Operative mortality, excluding intraoperative death, occurred in 1.9%. In all outcomes, models that considered pre+intraoperative variables demonstrated significantly improved Brier score and area under the precision-recall curve compared with models considering pre or intraoperative variables alone. XGBoost without external variable selection had the best C statistics, Brier score, and area under the precision-recall curve values in 4 of the 7 outcomes (mortality, renal failure, prolonged ventilation, and composite) compared with logistic regression models with or without variable selection. Based on the calibration plots, risk restratification for mortality showed that the logistic regression model underestimated the risk in 11 114 patients (9.8%) and overestimated in 12 005 patients (10.6%). In contrast, the XGBoost model underestimated the risk in 7218 patients (6.4%) and overestimated in 0 patients (0%).

Conclusions: In isolated coronary artery bypass graft, adding intraoperative variables to preoperative variables resulted in improved predictions of all 7 outcomes. Risk models based on XGBoost may provide a better prediction of adverse events to guide clinical care.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.120.007363DOI Listing
June 2021

The association of neighborhood walkability with health outcomes in older adults after acute myocardial infarction: The SILVER-AMI study.

Prev Med Rep 2021 Sep 30;23:101391. Epub 2021 Apr 30.

Section of General Internal Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, United States.

Physical activity and social support are associated with better outcomes after surviving acute myocardial infarction (AMI), and greater walkability has been associated with activity and support. We used data from the SILVER-AMI study (November 2014-June 2017), a longitudinal cohort of community-living adults ≥ 75 years hospitalized for AMI to assess associations of neighborhood walkability with health outcomes, and to assess whether physical activity and social support mediate this relationship, if it exists. We included data from 1345 participants who were not bedbound, were discharged home, and for whom we successfully linked walkability scores (from Walk Score®) for their home census block. Our primary outcome was hospital-free survival time (HFST) at six months after discharge; secondary outcomes included physical and mental health at six months, assessed using SF-12. Physical activity and social support were measured at baseline. Covariates included cognition, functioning, comorbidities, participation in rehabilitation or physical therapy, and demographics. We employed survival analysis to examine associations between walkability and HFST, before and after adjustment for covariates; we repeated analyses using linear regression with physical and mental health as outcomes. In adjusted models, walkability was not associated with physical health (ß = 0.010; 95% CI: -0.027, 0.047), mental health (ß = -0.08; 95% CI: -0.175, -0.013), or HFST (ß = 0.008; 95% CI: -0.023, 0.009). Social support was associated with mental health in adjusted models. Neighborhood walkability was not predictive of outcomes among older adults with existing coronary disease, suggesting that among older adults, mobility limitations may supercede neighborhood walkability.
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http://dx.doi.org/10.1016/j.pmedr.2021.101391DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141908PMC
September 2021

The association of neighborhood walkability with health outcomes in older adults after acute myocardial infarction: The SILVER-AMI study.

Prev Med Rep 2021 Sep 30;23:101391. Epub 2021 Apr 30.

Section of General Internal Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, United States.

Physical activity and social support are associated with better outcomes after surviving acute myocardial infarction (AMI), and greater walkability has been associated with activity and support. We used data from the SILVER-AMI study (November 2014-June 2017), a longitudinal cohort of community-living adults ≥ 75 years hospitalized for AMI to assess associations of neighborhood walkability with health outcomes, and to assess whether physical activity and social support mediate this relationship, if it exists. We included data from 1345 participants who were not bedbound, were discharged home, and for whom we successfully linked walkability scores (from Walk Score®) for their home census block. Our primary outcome was hospital-free survival time (HFST) at six months after discharge; secondary outcomes included physical and mental health at six months, assessed using SF-12. Physical activity and social support were measured at baseline. Covariates included cognition, functioning, comorbidities, participation in rehabilitation or physical therapy, and demographics. We employed survival analysis to examine associations between walkability and HFST, before and after adjustment for covariates; we repeated analyses using linear regression with physical and mental health as outcomes. In adjusted models, walkability was not associated with physical health (ß = 0.010; 95% CI: -0.027, 0.047), mental health (ß = -0.08; 95% CI: -0.175, -0.013), or HFST (ß = 0.008; 95% CI: -0.023, 0.009). Social support was associated with mental health in adjusted models. Neighborhood walkability was not predictive of outcomes among older adults with existing coronary disease, suggesting that among older adults, mobility limitations may supercede neighborhood walkability.
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http://dx.doi.org/10.1016/j.pmedr.2021.101391DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141908PMC
September 2021

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record.

medRxiv 2021 May 13. Epub 2021 May 13.

Objective: Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations.

Methods: Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations.

Results: Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19.

Conclusions: COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.
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http://dx.doi.org/10.1101/2021.03.16.21253770DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132274PMC
May 2021

Out-of-Pocket Annual Health Expenditures and Financial Toxicity From Healthcare Costs in Patients With Heart Failure in the United States.

J Am Heart Assoc 2021 07 16;10(14):e022164. Epub 2021 May 16.

Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven CT.

Background Heart failure (HF) poses a major public health burden in the United States. We examined the burden of out-of-pocket healthcare costs on patients with HF and their families. Methods and Results In the Medical Expenditure Panel Survey, we identified all families with ≥1 adult member with HF during 2014 to 2018. Total out-of-pocket healthcare expenditures included yearly care-specific costs and insurance premiums. We evaluated 2 outcomes of financial toxicity: (1) high financial burden-total out-of-pocket healthcare expense to postsubsistence income ratio of >20%, and (2) catastrophic financial burden with the ratio of >40%-a bankrupting expense defined by the World Health Organization. There were 788 families in the Medical Expenditure Panel Survey with a member with HF representing 0.54% (95% CI, 0.48%-0.60%) of all families nationally. The overall mean annual out-of-pocket healthcare expenses were $4423 (95% CI, $3908-$4939), with medications and health insurance premiums representing the largest categories of cost. Overall, 14% (95% CI, 11%-18%) of families experienced a high burden and 5% (95% CI, 3%-6%) experienced a catastrophic burden. Among the two-fifths of families considered low income, 24% (95% CI, 18%-30%) experienced a high financial burden, whereas 10% (95% CI, 6%-14%) experienced a catastrophic burden. Low-income families had 4-fold greater risk-adjusted odds of high financial burden (odds ratio [OR] , 3.9; 95% CI, 2.3-6.6), and 14-fold greater risk-adjusted odds of catastrophic financial burden (OR, 14.2; 95% CI, 5.1-39.5) compared with middle/high-income families. Conclusions Patients with HF and their families experience large out-of-pocket healthcare expenses. A large proportion encounter financial toxicity, with a disproportionate effect on low-income families.
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http://dx.doi.org/10.1161/JAHA.121.022164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8483501PMC
July 2021

Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models.

JAMA Netw Open 2021 May 3;4(5):e218512. Epub 2021 May 3.

Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut.

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.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.8512DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116982PMC
May 2021
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