Incorporating Acute Conditions into Risk-Adjustment for Provider Profiling: The Case of the US News and World Report Best Hospitals Rankings Methodology.

Several years ago, the US News and World Report changed their risk-adjustment methodology, now relying almost exclusively on chronic conditions for risk adjustment. The impacts of adding selected acute conditions like pneumonia, sepsis, and electrolyte disorders ("augmented") to their current risk models ("base") for 4 specialties-cardiology, neurology, oncology, and pulmonology-on estimates of hospital performance are reported here. In the augmented models, many acute conditions were associated with substantial risks of mortality. Compared to the base models, the discrimination and calibration of the augmented models for all specialties were improved. While estimated hospital performance was highly correlated between the 2 models, the inclusion of acute conditions in risk-adjustment models meaningfully improved the predictive ability of those models and had noticeable effects on hospital performance estimates. Measures or conditions that address disease severity should always be included when risk-adjusting hospitalization outcomes, especially if the goal is provider profiling.

The Effect of Social Drivers of Health on 90-Day Readmission Rates and Costs After Primary Total Hip and Total Knee Arthroplasty.

INTRODUCTION: The effect of social drivers of health (SDOH) on readmissions and costs after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is poorly understood. Policies such as the Hospital Readmissions Reduction Program have targeted overall readmission reduction, using value-based strategies to improve healthcare quality. However, the implications of SDOH on these outcomes are not yet understood. We hypothesized that the area deprivation index (ADI) as a surrogate for SDOH would markedly influence readmission rates and healthcare costs in the 90-day postprocedural period for THA and TKA. METHODS: We used the 100% US fee-for-service Medicare claims data from 2019 to 2021. Patients were identified using diagnosis-related groups. Our primary outcomes included 90-day unplanned readmission after hospital discharge and cost of care, treated as "high cost" if > 1 standard deviation above the mean. The relationships between ADI and primary outcomes were estimated with logistic regression models. RESULTS: A total of 628,399 patients were included in this study. The mean age of patients was 75.6, 64% were female, and 7.8% were dually eligible for Medicaid. After full covariate adjustment, readmission was higher for patients in more deprived areas (high Area Deprivation Index (ADI)) (low socioeconomic status (SES) group OR: 1.30 [95% confidence intervals 1.23, 1.38]). ADI was associated with high cost before adjustment (low SES group odds ratio 1.08 [95% confidence intervals 1.04, 1.11], P < 0.001), although, after adjustment, this association was lost. DISCUSSION: This analysis highlights the effect of SDOH on readmission rates after THA and TKA. A nuanced understanding of neighborhood-level disparities may facilitate targeted strategies to reduce avoidable readmissions in orthopaedic surgery. Regarding cost, although there is some association between ADI and cost, this study may illustrate that ADI for THA and TKA is not sufficiently granular to identify the contribution of social drivers to elevated costs.

A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records.

Objective: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods. Methods: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null treatment effect, including the COI in the outcome generation model, and four missingness mechanisms for the COI were simulated: completely at random (MCAR), at random (MAR), and two not at random (MNAR) mechanisms, where missingness was dependent on an unmeasured confounder and on the value of the COI itself. We evaluated the ability of three groups of diagnostics to differentiate between mechanisms: 1)-differences in characteristics between patients with or without the observed COI (using averaged standardized mean differences [ASMD]), 2)-predictive ability of the missingness indicator based on observed covariates, and 3)-association of the missingness indicator with the outcome. We then compared analytic methods including "complete case", inverse probability weighting, single and multiple imputation in their ability to recover true treatment effects. Results: The diagnostics successfully identified characteristic patterns of simulated missingness mechanisms. For MAR, but not MCAR, the patient characteristics showed substantial differences (median ASMD 0.20 vs 0.05) and consequently, discrimination of the prediction models for missingness was also higher (0.59 vs 0.50). For MNAR, but not MAR or MCAR, missingness was significantly associated with the outcome even in models adjusting for other observed covariates. Comparing analytic methods, multiple imputation using a random forest algorithm resulted in the lowest root-mean-squared-error. Conclusion: Principled diagnostics provided reliable insights into missingness mechanisms. When assumptions allow, multiple imputation with nonparametric models could help reduce bias.

Neighborhood Socioeconomic Disadvantage and 30-Day Outcomes for Common Cardiovascular Conditions.

BACKGROUND: Understanding the relationship between neighborhood environment and cardiovascular outcomes is important to achieve health equity and implement effective quality strategies. We conducted a population-based cohort study to determine the association of neighborhood socioeconomic deprivation and 30-day mortality and readmission rate for patients admitted with common cardiovascular conditions. METHODS AND RESULTS: We examined claims data from fee-for-service Medicare beneficiaries aged ≥65 years between 2017 and 2019 admitted for heart failure, valvular heart disease, ischemic heart disease, or cardiac arrhythmias. The primary exposure was the Area Deprivation Index; outcomes were 30-day all-cause death and unplanned readmission. More than 2 million admissions were included. After sequential adjustment for patient characteristics (demographics, dual eligibility, comorbidities), area health care resources (primary care clinicians, specialists, and hospital beds per capita), and admitting hospital characteristics (ownership, size, teaching status), there was a dose-dependent association between neighborhood socioeconomic deprivation and 30-day mortality rate for all conditions. In the fully adjusted model for death, estimated effect sizes of residence in the most disadvantaged versus least disadvantaged neighborhoods ranged from adjusted odds ratio 1.29 (95% CI, 1.22-1.36) for the heart failure group to adjusted odds ratio 1.63 (95% CI, 1.36-1.95) for the valvular heart disease group. Neighborhood deprivation was associated with increased adjusted 30-day readmission rates, with estimated effect sizes from adjusted odds ratio 1.09 (95% CI, 1.05-1.14) for heart failure to adjusted odds ratio 1.19 (95% CI, 1.13-1.26) for arrhythmia. CONCLUSIONS: Neighborhood socioeconomic disadvantage was associated with 30-day mortality rate and readmission for patients admitted with common cardiovascular conditions independent of individual demographics, socioeconomic status, medical risk, care access, or admitting hospital characteristics.

smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.

Objectives: Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions. Materials and methods: We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR. Results: smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data. Conclusions: The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.

Age and Aspirin Dosing in Secondary Prevention of Atherosclerotic Cardiovascular Disease.

BACKGROUND: In patients with atherosclerotic cardiovascular disease, increasing age is concurrently associated with higher risks of ischemic and bleeding events. The objectives are to determine the impact of aspirin dose on clinical outcomes according to age in atherosclerotic cardiovascular disease. METHODS AND RESULTS: In the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial, patients with atherosclerotic cardiovascular disease were randomized to daily aspirin doses of 81 mg or 325 mg. The primary effectiveness end point was death from any cause, hospitalization for myocardial infarction, or hospitalization for stroke. The primary safety end point was hospitalization for bleeding requiring transfusion. A total of 15 076 participants were randomized to aspirin 81 mg (n=7540) or 325 mg (n=7536) daily (median follow-up: 26.2 months; interquartile range: 19.0-34.9 months). Median age was 67.6 years (interquartile range: 60.7-73.6 years). Among participants aged <65 years (n=5841 [38.7%]), a primary end point occurred in 226 (7.54%) in the 81 mg group, and in 191 (6.80%) in the 325 mg group (adjusted hazard ratio [HR], 1.23 [95% CI, 1.01-1.49]). Among participants aged ≥65 years (n=9235 [61.3%]), a primary end point occurred in 364 (7.12%) in the 81 mg group, and in 378 (7.96%) in the 325 mg group (adjusted HR, 0.95 [95% CI, 0.82-1.10]). The age-dose interaction was not significant (P=0.559). There was no significant interaction between age and the randomized aspirin dose for the secondary effectiveness and the primary safety bleeding end points (P>0.05 for all). CONCLUSIONS: Age does not modify the impact of aspirin dosing (81 mg or 325 mg daily) on clinical end points in secondary prevention of atherosclerotic cardiovascular disease.

Contribution of Clinical Trial Event Data by Data Source: A Prespecified Analysis of the ADAPTABLE Randomized Clinical Trial.

Importance: Pragmatic randomized clinical trials (RCTs) often use multiple data sources to examine clinical events, but the relative contribution of data sources to clinical end-point rates is understudied. Objective: To assess the contribution of data sources (electronic health records [EHRs], public/private insurance claims, and/or participant-reported data) to clinical end points among ADAPTABLE participants who had available data. Design, Setting, and Participants: The ADAPTABLE study was an open-label, pragmatic RCT from April 2016 through June 2019 conducted in research networks within clinical practice. Participants had existing atherosclerotic cardiovascular disease and available data to analyze. The characteristics of patients by combinations of data source availability were compared to examine the contribution of each of the data sources to end-point ascertainment. Data for this prespecified analysis were examined from January 2022 to June 2023. Exposures: Randomized exposure to 81 mg or 325 mg of aspirin daily. Main Outcomes and Measures: Number of events for the primary end point (composite of death, hospitalization for myocardial infarction, and hospitalization for stroke) that were contributed by EHR or claims data and then number of events contributed by each additional data source. Results: Of 15 006 participants randomized with at least 1 other source of data available beyond participant-reported data, there were 8756 (58.3%) with participant-reported and EHR data; 4291 (28.6%) with participant-reported, EHR, and claims data; 1412 (9.4%) with EHR-only data; 262 (1.7%) with participant-reported and claims data; 202 (1.3%) with EHR and claims data; and 83 (0.6%) with claims-only data. Participants with EHR-only data were younger (median age, 63.7 years; IQR, 55.8-71.4) compared with the other groups (range, 65.6-71.9 years). Among participants with both EHR and claims data, with or without participant-reported data (n = 4493), for each outcome, most events (92%-100%) were identified in the EHR or in claims data. For all clinical end points, participant-reported data contributed less than 10% of events not otherwise available from claims or EHR data. Conclusions and Relevance: In this analysis of a pragmatic RCT, claims and EHR data provided the most clinical end-point data when compared with participant-reported events. These findings provide a framework for collecting end points in pragmatic clinical trials. Further work is needed to understand the data source combinations that most effectively provide clinical end-point data in RCTs.