Dataset Information

Socioeconomic Distressed Communities Index Predicts Risk-Adjusted Mortality After Cardiac Surgery.

ABSTRACT: BACKGROUND:The effects of socioeconomic factors other than insurance status and race on outcomes after cardiac operations are not well understood. We hypothesized that the Distressed Communities Index (DCI), a comprehensive socioeconomic ranking by zip code, would predict operative mortality after coronary artery bypass grafting (CABG). METHODS:All patients who underwent isolated CABG (2010 to 2017) in the Virginia Cardiac Services Quality Initiative database were analyzed. The DCI accounts for unemployment, education level, poverty rate, median income, business growth, and housing vacancies, with scores ranging from 0 (no distress) to 100 (severe distress). Patients were stratified by DCI quartiles (I: 0 to 24.9, II: 25 to 49.9, III: 50 to 74.9, IV: 75 to 100) and compared. Hierarchical linear regression modeled the association between the DCI and mortality. RESULTS:A total of 19,756 CABG patients were analyzed, with mean predicted risk of mortality of 2.0% ± 3.5%. Higher DCI scores were associated with increasing predicted risk of mortality. Overall operative mortality was 2.1% (n = 424) and increased with increasing DCI quartile (I: 1.6% [n = 95], II: 2.1% [n = 77], III: 2.4% [n = 114], IV: 2.6% [n = 138]; p = 0.0009). The observed-to-expected ratio for mortality increased as level of socioeconomic distress increased. After risk adjustment for The Society of Thoracic Surgeons predicted risk of mortality, year of surgical procedure, and hospital, the DCI remained predictive of operative mortality after CABG (odds ratio, 1.14 for each 25-point increase in DCI; 95% confidence interval 1.04 to 1.26; p = 0.007). CONCLUSIONS:The DCI independently predicts risk-adjusted operative mortality after CABG. Socioeconomic status, although not part of traditional risk calculators, should be considered when building risk models, evaluating resource utilization, and comparing hospitals.

SUBMITTER: Charles EJ

PROVIDER: S-EPMC6535341 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Socioeconomic Distressed Communities Index Predicts Risk-Adjusted Mortality After Cardiac Surgery.

Charles Eric J EJ Mehaffey J Hunter JH Hawkins Robert B RB Fonner Clifford E CE Yarboro Leora T LT Quader Mohammed A MA Kiser Andy C AC Rich Jeffrey B JB Speir Alan M AM Kron Irving L IL Tracci Margaret C MC Ailawadi Gorav G

The Annals of thoracic surgery 20190122 6

<h4>Background</h4>The effects of socioeconomic factors other than insurance status and race on outcomes after cardiac operations are not well understood. We hypothesized that the Distressed Communities Index (DCI), a comprehensive socioeconomic ranking by zip code, would predict operative mortality after coronary artery bypass grafting (CABG).<h4>Methods</h4>All patients who underwent isolated CABG (2010 to 2017) in the Virginia Cardiac Services Quality Initiative database were analyzed. The DC ...[more]

PMID: 30682354

Similar Datasets

Project description:OBJECTIVE:To develop a postoperative mortality case-mix adjustment model to facilitate assessment of cardiac ICU quality of care, and to describe variation in adjusted cardiac ICU mortality across hospitals within the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Multicenter Pediatric Cardiac Critical Care Consortium clinical registry. PARTICIPANTS:All surgical cardiac ICU admissions between August 2014 and May 2016. The analysis included 8,543 admissions from 23 dedicated cardiac ICUs. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We developed a novel case-mix adjustment model to measure postoperative cardiac ICU mortality after congenital heart surgery. Multivariable logistic regression was performed to assess preoperative, intraoperative, and immediate postoperative severity of illness variables as candidate predictors. We used generalized estimating equations to account for clustering of patients within hospital and obtain robust SEs. Bootstrap resampling (1,000 samples) was used to derive bias-corrected 95% CIs around each predictor and validate the model. The final model was used to calculate expected mortality at each hospital. We calculated a standardized mortality ratio (observed-to-expected mortality) for each hospital and derived 95% CIs around the standardized mortality ratio estimate. Hospital standardized mortality ratio was considered a statistically significant outlier if the 95% CI did not include 1. Significant preoperative predictors of mortality in the final model included age, chromosomal abnormality/syndrome, previous cardiac surgeries, preoperative mechanical ventilation, and surgical complexity. Significant early postoperative risk factors included open sternum, mechanical ventilation, maximum vasoactive inotropic score, and extracorporeal membrane oxygenation. The model demonstrated excellent discrimination (C statistic, 0.92) and adequate calibration. Comparison across Pediatric Cardiac Critical Care Consortium hospitals revealed five-fold difference in standardized mortality ratio (0.4-1.9). Two hospitals had significantly better-than-expected and two had significantly worse-than-expected mortality. CONCLUSIONS:For the first time, we have demonstrated that variation in mortality as a quality metric exists across dedicated cardiac ICUs. These findings can guide efforts to reduce mortality after cardiac surgery.

Project description:BACKGROUND:Coronary artery disease (CAD) is influenced by genetic variation and traditional risk factors. Polygenic risk scores (PRS), which can be ascertained before the development of traditional risk factors, have been shown to identify individuals at elevated risk of CAD. Here, we demonstrate that a genome-wide PRS for CAD predicts all-cause mortality after accounting for not only traditional cardiovascular risk factors but also angiographic CAD itself. METHODS:Individuals who underwent coronary angiography and were enrolled in an institutional biobank were included; those with prior myocardial infarction or heart transplant were excluded. Using a pruning-and-thresholding approach, a genome-wide PRS comprised of 139?239 variants was calculated for 1503 participants who underwent coronary angiography and genotyping. Individuals were categorized into high PRS (hiPRS) and low-PRS control groups using the maximally selected rank statistic. Stratified analysis based on angiographic findings was also performed. The primary outcome was all-cause mortality following the index coronary angiogram. RESULTS:Individuals with hiPRS were younger than controls (66 years versus 69 years; P=2.1×10-5) but did not differ by sex, body mass index, or traditional risk-factor profiles. Individuals with hiPRS were at significantly increased risk of all-cause mortality after cardiac catheterization, adjusting for traditional risk factors and angiographic extent of CAD (hazard ratio, 1.6; 95% CI, 1.2-2.2; P=0.004). The strongest increase in risk of all-cause mortality conferred by hiPRS was seen among individuals without angiographic CAD (hazard ratio, 2.4; 95% CI, 1.1-5.5; P=0.04). In the overall cohort, adding hiPRS to traditional risk assessment improved prediction of 5-year all-cause mortality (area under the receiver-operating curve 0.70; 95% CI, 0.66-0.75 versus 0.66; 95% CI, 0.61-0.70; P=0.001). CONCLUSIONS:A genome-wide PRS improves risk stratification when added to traditional risk factors and coronary angiography. Individuals without angiographic CAD but with hiPRS remain at significantly elevated risk of mortality.

Dataset Information

Socioeconomic Distressed Communities Index Predicts Risk-Adjusted Mortality After Cardiac Surgery.

Publications

Socioeconomic Distressed Communities Index Predicts Risk-Adjusted Mortality After Cardiac Surgery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure