Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data.

BACKGROUND: Patients with type 2 diabetes are at risk of heart failure hospitalization. As social determinants of health are rarely included in risk models, we validated and recalibrated the WATCH-DM score in a diverse patient-group using their social deprivation index (SDI). METHODS: We identified US Veterans with type 2 diabetes without heart failure that received outpatient care during 2010 at Veterans Affairs medical centers nationwide, linked them to their SDI using residential ZIP codes and grouped them as SDI <20%, 21% to 40%, 41% to 60%, 61% to 80%, and >80% (higher values represent increased deprivation). Accounting for all-cause mortality, we obtained the incidence for heart failure hospitalization at 5 years follow-up; overall and in each SDI group. We evaluated the WATCH-DM score using the C statistic, the Greenwood Nam D'Agostino test χ2 test and calibration plots and further recalibrated the WATCH-DM score for each SDI group using a statistical correction factor. RESULTS: In 1 065 691 studied patients (mean age 67 years, 25% Black and 6% Hispanic patients), the 5-year incidence of heart failure hospitalization was 5.39%. In SDI group 1 (least deprived) and 5 (most deprived), the 5-year heart failure hospitalization was 3.18% and 11%, respectively. The score C statistic was 0.62; WATCH-DM systematically overestimated heart failure risk in SDI groups 1 to 2 (expected/observed ratios, 1.38 and 1.36, respectively) and underestimated the heart failure risk in groups 4 to 5 (expected/observed ratios, 0.95 and 0.80, respectively). Graphical evaluation demonstrated that the recalibration of WATCH-DM using an SDI group-based correction factor improved predictive capabilities as supported by reduction in the χ2 test results (801-27 in SDI groups I; 623-23 in SDI group V). CONCLUSIONS: Including social determinants of health to recalibrate the WATCH-DM score improved risk prediction highlighting the importance of including social determinants in future clinical risk prediction models.

Validating the SMART2 Score in a Racially Diverse High-Risk Nationwide Cohort of Patients Receiving Coronary Artery Bypass Grafting.

Background We tested the potential of the Secondary Manifestations of Arterial Disease (SMART2) risk score for use in patients undergoing coronary artery bypass grafting. Methods and Results We conducted an external validation of the SMART2 score in a racially diverse high-risk national cohort (2010-2019) that underwent isolated coronary artery bypass grafting. We calculated the preoperative SMART2 score and modeled the 5-year major adverse cardiovascular event (cardiovascular mortality+myocardial infarction+stroke) incidence. We evaluated SMART2 score discrimination at 5 years using c-statistic and calibration with observed/expected ratio and calibration plots. We analyzed the potential clinical benefit using decision curves. We repeated these analyses in clinical subgroups, diabetes, chronic kidney disease, and polyvascular disease, and separately in White and Black patients. In 27 443 (mean age, 65 years; 10% Black individuals) US veterans undergoing coronary artery bypass grafting (2010-2019) nationwide, the 5-year major adverse cardiovascular event rate was 25%; 27% patients were in high predicted risk (>30% 5-year major adverse cardiovascular events). SMART2 score discrimination (c-statistic: 64) was comparable to the original study (c-statistic: 67) and was best in patients with chronic kidney disease (c-statistic: 66). However, it underpredicted major adverse cardiovascular event rates in the whole cohort (observed/expected ratio, 1.45) as well as in all studied subgroups. The SMART2 score performed better in White than Black patients. On decision curve analysis, the SMART2 score provides a net benefit over a wide range of risk thresholds. Conclusions The SMART2 model performs well in a racially diverse coronary artery bypass grafting cohort, with better predictive capabilities at the upper range of baseline risk, and can therefore be used to guide secondary preventive pharmacotherapy.

Neighbourhood-level social deprivation and the risk of recurrent heart failure hospitalizations in type 2 diabetes.

BACKGROUND: The importance of type 2 diabetes mellitus (T2D) in heart failure hospitalizations (HFH) is acknowledged. As information on the prevalence and influence of social deprivation on HFH is limited, we studied this issue in a racially diverse cohort. METHODS: Linking data from US Veterans with stable T2D (without prevalent HF) with a zip-code derived population-level social deprivation index (SDI), we grouped them according to increasing SDI as follows: SDI: group I: ≤20; II: 21-40; III: 41-60; IV: 61-80; and V (most deprived) 81-100. Over a 10-year follow-up period, we identified the total (first and recurrent) number of HFH episodes for each patient and calculated the age-adjusted HFH rate [per 1000 patient-years (PY)]. We analysed the incident rate ratio between SDI groups and HFH using adjusted analyses. RESULTS: In 1 012 351 patients with T2D (mean age 67.5 years, 75.7% White), the cumulative incidence of first HFH was 9.4% and 14.2% in SDI groups I and V respectively. The 10-year total HFH rate was 54.8 (95% CI: 54.5, 55.2)/1000 PY. Total HFH increased incrementally from SDI group I [43.3 (95% CI: 42.4, 44.2)/1000 PY] to group V [68.6 (95% CI: 67.8, 69.9)/1000 PY]. Compared with group I, group V patients had a 53% higher relative risk of HFH. The negative association between SDI and HFH was stronger in Black patients (SDI × Race pinteraction < .001). CONCLUSIONS: Social deprivation is associated with increased HFH in T2D with a disproportionate influence in Black patients. Strategies to reduce social disparity and equalize racial differences may help to bridge this gap.

Disparities in PCSK9 Initiation Among US Veterans with Peripheral Arterial Disease or Cerebrovascular Disease.

BACKGROUND: Effective lipid lowering is essential in patients with peripheral arterial disease (PAD) and cerebrovascular disease (CeVD). Proprotein convertase subtilsin/kexin type 9 inhibitors (PCSK9i) efficiently lower low-density lipoprotein (LDL) levels; however, use in PAD and CeVD patients is limited. Therefore, our aim was to evaluate the use of PCSK9i among US Veterans and compare rates between patients with PAD, CeVD, and coronary artery disease (CAD). METHODS: We evaluated PCSK9i initiation (2016-2019) in US Veterans with CAD, PAD, or CeVD treated at 124 Veterans Affairs (VA) hospitals. We fit a hierarchical logistic regression model to evaluate the association of the patient's primary diagnosis, baseline low-density lipoprotein cholesterol (LDL-C) levels, socioeconomic indicators, and the Department of Veterans Affairs medical center enrollment with PCSK9i initiation. RESULTS: Of 519,566 patients with atherosclerotic vascular disease, 337,766 (65%), 79,926 (15%) and 101,874 (20%) had CAD, PAD, and CeVD, respectively. Among 2115/519,566 (0.4%) initiated on PCSK9i therapy, 84.3% had CAD, while only 7.2% and 8.5% had PAD and CeVD, respectively. Compared with CAD patients, PAD {odds ratio [OR] 0.50 (0.36-0.70)} and CeVD [OR 0.24 (0.15-0.37)] patients were less likely to receive PCSK9i. Relative to under $40K per year, PCSK9i initiation was higher if earning $40,000-$80,000 [OR 1.13 (1.01-1.27)] or > $80,000 [OR 1.41 (1.14-1.75)]. Even moderate community deprivation [OR 0.87 (0.77-0.97)] was associated with lower PCSK9i therapy. CONCLUSIONS: Adjusted for LDL-C levels, PAD and CeVD patients are much less likely to receive PCSK9i therapy. Despite low co-pay, PCSK9i initiation rates among US veterans, nationwide, is low, with household income and community deprivation appearing to predict PCSK9i use.

Risk Calculator to Predict 30-Day Readmission After Coronary Artery Bypass: A Strategic Decision Support Tool.

BACKGROUND: Re-admission is an important source of patient dissatisfaction and increased hospital costs. A simple calculator to determine the probability of re-admission may help guide patient dismissal planning. METHODS: Using the national readmissions database (NRD), we identified admissions for isolated primary coronary artery bypass (CABG) and stratified them according to 30-day readmission. Including pre, intra and postoperative variables, we prepared a logistic regression model to determine the probability for re-admission. The model was tested for reliability with boot-strapping and 10-fold cross-validation. RESULTS: From 135,699 procedures, 19,355 were readmitted at least once within 30days of dismissal. Patients who were readmitted were older (67±10 vs 65 ± 10 years, p<0.01), females (32% vs 24%; p<0.01) and had a higher Elixhauser comorbidity score (1.5±1.4 vs 1.1±1.2; p<0.01). Our final model (c- statistic=0.65) consisted of 16 pre and three postoperative factors. End-stage renal disease (OR 1.79 [1.57-2.04]) and length of stay>9days (OR 1.60 [1.52-1.68]) were most prominent indicators for readmission. Compared to Medicaid beneficiaries, those with private insurance (OR 0.62 [0.57-0.68]) and Medicare (OR 0.85 [0.79-0.92]) coverage were less likely to be readmitted. CONCLUSIONS: Our simple 30-days CABG readmission calculator can be used as a strategic tool to help reduce readmissions after coronary artery bypass surgery.