Although frailty is strongly associated with mortality in patients with heart failure (HF), the risk of which specific cause of death is associated with being complicated with frailty is unclear. We aimed to clarify the association between multidomain frailty and the causes of death in elderly patients hospitalized with HF. We analyzed data from the FRAGILE-HF cohort, where patients aged 65 years and older, hospitalized with HF, were prospectively registered between 2016 and 2018 in 15 Japanese hospitals before discharge and followed up for 2 years. All patients were assessed for physical, social, and cognitive dysfunction, and categorized into 3 groups based on their number of frailty domains (FDs, 0-1, 2, and 3). Kaplan-Meier survival analysis was used to evaluate the association between the number of FDs and all-cause mortality, whereas Fine-Gray competing risk regression analysis was used for assessing the impact on cause-specific mortality. We analyzed 1181 patients with HF (81 years old in median, 57.4% were male), 530 (44.9%), 437 (37.0%), and 214 (18.1%) of whom were categorized into the FD 0 to 1, FD 2, and FD 3 groups, respectively. During the 2-year follow-up, 240 deaths were observed (99 HF deaths, 34 cardiovascular deaths, and 107 noncardiovascular deaths), and an increase in the number of FD was significantly associated with mortality (Log-rank: P<0.001). The Fine-Gray competing risk analysis adjusted for age and sex showed that FDs 2 (subdistribution hazard ratio, 1.77 [95% CI, 1.11-2.81]) and 3 (2.78, [95% CI, 1.69-4.59]) groups were associated with higher incidence of noncardiovascular death but not with HF and other cardiovascular deaths. Although multidomain frailty is strongly associated with mortality in older patients with HF, it is mostly attributable to noncardiovascular death and not cardiovascular death, including HF death. URL: https://www.clinicaltrials.gov; Unique identifier: UMIN000023929.

The US health care industry has broadly adopted performance and quality measures that are extracted from electronic health records and connected to payment incentives that hope to improve declining life expectancy and health status and reduce costs. While the development of a quality measurement infrastructure based on electronic health record data was an important first step in addressing US health outcomes, these metrics, reflecting the average performance across diverse populations, do not adequately adjust for population demographic differences, social determinants of health, or ecosystem vulnerability. Like society as a whole, health care must confront the powerful impact that social determinants of health, race, ethnicity, and other demographic variations have on key health care performance indicators and quality metrics. Tools that are currently available to capture and report the health status of Americans lack the granularity, complexity, and standardization needed to improve health and address disparities at the local level. In this article, we discuss the current and future state of electronic clinical quality measures through a lens of equity.

In heart failure (HF) trials, there has been an emphasis on utilizing more patient-centered outcomes, including quality of life (QoL) and days alive and out of hospital. We aimed to explore the impact of QoL adjusted days alive and out of hospital as an outcome in 2 HF clinical trials. Using data from 2 trials in HF (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure [GUIDE-IT] and Study of Dietary Intervention under 100 mmol in Heart Failure [SODIUM-HF]), we determined treatment differences using percentage days alive and out of hospital (%DAOH) adjusted for QoL at 18 months as the primary outcome. For each participant, %DAOH was calculated as a ratio between days alive and out of hospital/total follow-up. Using a regression model, %DAOH was subsequently adjusted for QoL measured by the Kansas City Cardiomyopathy Questionnaire Overall Summary Score. In the GUIDE-IT trial, 847 participants had a median baseline Kansas City Cardiomyopathy Questionnaire Overall Summary Score of 59.0 (interquartile range, 40.8-74.3), which did not change over 18 months. %DAOH was 90.76%±22.09% in the biomarker-guided arm and 88.56%±25.27% in the usual care arm. No significant difference in QoL adjusted %DAOH was observed (1.09% [95% CI, -1.57% to 3.97%]). In the SODIUM-HF trial, 796 participants had a median baseline Kansas City Cardiomyopathy Questionnaire Overall Summary Score of 69.8 (interquartile range, 49.3-84.3), which did not change over 18 months. %DAOH was 95.69%±16.31% in the low-sodium arm and 95.95%±14.76% in the usual care arm. No significant difference was observed (1.91% [95% CI, -0.85% to 4.77%]). In 2 large HF clinical trials, adjusting %DAOH for QoL was feasible and may provide complementary information on treatment effects in clinical trials.

Since 2016, hospitals have been able to document International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for the National Institutes of Health Stroke Scale (NIHSS). As of 2023, the Centers for Medicare & Medicaid Services uses NIHSS as a risk adjustment variable. We assessed associations between patient- and hospital-level variables and contemporary NIHSS reporting. We performed a retrospective cross-sectional analysis of 2019 acute ischemic stroke admissions using deidentified, national 100% inpatient Medicare Fee-For-Service data sets. We identified index acute ischemic stroke admissions using the ICD-10-CM code I63.x and abstracted demographic information, medical comorbidities, hospital characteristics, and NIHSS. We linked Medicare and Mount Sinai Health System (New York, NY) registry data from 2016 to 2019. We calculated NIHSS documentation at the patient and hospital levels, predictors of documentation, change over time, and concordance with local data. There were 231 383 index acute ischemic stroke admissions in 2019. NIHSS was documented in 44.4% of admissions and by 66.5% of hospitals. Hospitals that documented ≥1 NIHSS were more commonly teaching hospitals (39.0% versus 5.5%; standardized mean difference score, 0.88), stroke certified (37.2% versus 8.0%; standardized mean difference score, 0.75), higher volume (mean, 80.8 [SD, 92.6] versus 6.33 [SD, 14.1]; standardized mean difference score, 1.12), and had intensive care unit availability (84.9% versus 23.2%; standardized mean difference score, 1.57). Adjusted odds of documentation were lower for patients with inpatient mortality (odds ratio, 0.64 [95% CI, 0.61-0.68]; P<0.0001), in nonmetropolitan areas (odds ratio, 0.49 [95% CI, 0.40-0.61]; P<0.0001), and male sex (odds ratio, 0.95 [95% CI, 0.93-0.97]; P<0.0001). NIHSS was documented for 52.9% of Medicare cases versus 93.1% of registry cases, and 74.7% of Medicare NIHSS scores equaled registry admission NIHSS. Missing ICD-10-CM NIHSS data remain widespread 3 years after the introduction of the ICD-10-CM NIHSS code, and there are systematic differences in reporting at the patient and hospital levels. These findings support continued assessment of NIHSS reporting and caution in its application to risk adjustment models.

Patients with atrial fibrillation have a high mortality rate that is only partially attributable to vascular outcomes. The competing risk of death may affect the expected anticoagulant benefit. We determined if competing risks materially affect the guideline-endorsed estimate of anticoagulant benefit. We conducted a secondary analysis of 12 randomized controlled trials that randomized patients with atrial fibrillation to vitamin K antagonists (VKAs) or either placebo or antiplatelets. For each participant, we estimated the absolute risk reduction (ARR) of VKAs to prevent stroke or systemic embolism using 2 methods-first using a guideline-endorsed model (CHA2DS2-VASc) and then again using a competing risk model that uses the same inputs as CHA2DS2-VASc but accounts for the competing risk of death and allows for nonlinear growth in benefit. We compared the absolute and relative differences in estimated benefit and whether the differences varied by life expectancy. A total of 7933 participants (median age, 73 years, 36% women) had a median life expectancy of 8 years (interquartile range, 6-12), determined by comorbidity-adjusted life tables and 43% were randomized to VKAs. The CHA2DS2-VASc model estimated a larger ARR than the competing risk model (median ARR at 3 years, 6.9% [interquartile range, 4.7%-10.0%] versus 5.2% [interquartile range, 3.5%-7.4%]; P<0.001). ARR differences varied by life expectancies: for those with life expectancies in the highest decile, 3-year ARR difference (CHA2DS2-VASc model - competing risk model 3-year risk) was -1.3% (95% CI, -1.3% to -1.2%); for those with life expectancies in the lowest decile, 3-year ARR difference was 4.7% (95% CI, 4.5%-5.0%). VKA anticoagulants were exceptionally effective at reducing stroke risk. However, VKA benefits were misestimated with CHA2DS2-VASc, which does not account for the competing risk of death nor decelerating treatment benefit over time. Overestimation was most pronounced when life expectancy was low and when the benefit was estimated over a multiyear horizon.