Cowart, Jennifer B

BACKGROUND: Publicly reported 30-day acute myocardial infarction (AMI) mortality rates are widely used to benchmark hospital performance. However, these measures may lack validity when they include deaths among patients with do-not-resuscitate (DNR) orders or those receiving palliative care—groups for whom mortality may be expected and goal-concordant. METHODS: We analyzed the national 100% Medicare Inpatient Standard Analytic File and Medicare Beneficiary Summary file to assess 30-day AMI mortality rates stratified by DNR status (absent, present-on-admission, and postadmission) and palliative care involvement. We then conducted a retrospective chart review of all 30-day AMI mortalities at a single academic medical center (2019–2025), applying Global Registry of Acute Coronary Events and a Palliative Performance Scale scores to identify unpreventable deaths, which we defined as a Global Registry of Acute Coronary Events >190.5 or Palliative Performance Scale ≤40%. Global Registry of Acute Coronary Events is a risk-stratification tool using clinical and laboratory variables whereas the Palliative Performance Scale is a bedside functional status and palliative care assessment quantifying patient’s self-care ability. RESULTS: Across n=467 259 total AMI encounters, mean (SD) age was 75.1 (10.5), and the population was 44% female. Thirty-day mortality was 10.4% (39 112/376 062) among patients without DNRs, 43.1% (26 181/60 719) in those with present-on-admission DNR, and 72.1% (21 984/30 478) in those with postadmission DNR. Among 9.2% (43 090/467 259) of encounters with palliative care, there was a 76.9% mortality rate (33 151/43 090). Hazard ratios for mortality were 5.25 (95% CI, 5.17–5.33) for present-on-admission DNR and 10.73 (95% CI, 10.55–10.91) for postadmission DNR as compared with encounters without DNRs. Among n=24 AMI mortalities at our institution, 71% (17/24) had DNRs, and 33% (8/24) met the unpreventable death criteria. CONCLUSIONS: The validity of 30-day AMI mortality metrics may be undermined by their inclusion of high-acuity, end-of-life patients, which may disincentivize hospitals from delivering appropriate, patient-centered care. Removal of DNR or palliative cases from performance measures should be explored to better identify opportunities for reducing preventable deaths.

Abstract Background Hospital‐at‐home programs (HaH) in the United States have evolved to include a virtual‐hybrid delivery model, where all physician encounters are virtual and partnered with a home care team. Objective To examine whether a virtual hybrid HaH program enabled by technology has similar clinical outcomes to traditional brick‐and‐mortar (B&M) hospital care. Methods We conducted a pragmatic trial at three hospitals, randomizing 1150 acutely ill patients requiring hospital care between July 10, 2023, and October 31, 2023 one‐to‐one into two groups: intervention (HaH) and control (B&M). The primary analysis was an intention‐to‐treat non‐inferiority analysis of the primary outcome, which was a composite of 30‐day all‐cause mortality and unplanned readmissions. Secondary outcomes included 30‐day readmission, all‐cause mortality, and patient experience. Results The mean age was 67.8 (standard deviation [SD] 16.3) years, and 52.2% were female. The primary outcome occurred in 99 (17.3%) HaH patients and 114 (19.8%) B&M patients (odds ratio [OR] 0.85, 95% confidence interval [CI] 0.63–1.14, p = .28), meeting the non‐inferiority criterion. Thirty‐day unplanned readmission occurred in 84 (14.7%) HaH patients and 101 (17.5%) B&M patients (OR 0.81, 95% CI 0.59–1.11, p = .19). Thirty‐day all‐cause mortality occurred in 25 (4.4%) HaH patients and 19 (3.3%) B&M patients (OR 1.34, 95% CI 0.73–2.46, p = .35). No HaH patients died while receiving their hospital care at home. HaH program was associated with a higher likelihood of patients reporting feeling extremely comfortable or very comfortable (84.4% HaH; 60.9% B&M, p = .001). Conclusion A hybrid HaH model is a safe and comfortable alternative to traditional B&M hospital care.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being.

AbstractBackgroundPatient Safety Indicator (PSI)‐12, a hospital quality measure designed by Agency for Healthcare Research and Quality (AHRQ) to capture potentially preventable adverse events, captures perioperative venous thromboembolism (VTE). It is unclear how COVID‐19 has affected PSI‐12 performance.ObjectiveWe sought to compare the cumulative incidence of PSI‐12 in patients with and without acute COVID‐19 infection.Design, Setting, and ParticipantsThis was a retrospective cohort study including PSI‐12‐eligible events at three Mayo Clinic medical centers (4/1/2020‐10/5/2021).Exposure, Main Outcomes, and MeasuresWe compared the unadjusted rate and adjusted risk ratio (aRR) for PSI‐12 events among patients with and without COVID‐19 infection using Fisher's exact χ2 test and the AHRQ risk‐adjustment software, respectively. We summarized the clinical outcomes of COVID‐19 patients with a PSI‐12 event.ResultsOur cohort included 50,400 consecutive hospitalizations. Rates of PSI‐12 events were significantly higher among patients with acute COVID‐19 infection (8/257 [3.11%; 95% confidence interval {CI}, 1.35%–6.04%]) compared to patients without COVID‐19 (210/50,143 [0.42%; 95% CI, 0.36%–0.48%]) with a PSI‐12 event during the encounter (p < .001). The risk‐adjusted rate of PSI‐12 was significantly higher in patients with acute COVID‐19 infection (1.50% vs. 0.38%; aRR, 3.90; 95% CI, 2.12–7.17; p < .001). All COVID‐19 patients with PSI‐12 events had severe disease and 4 died. The most common procedure was tracheostomy (75%); the mean (SD) days from surgical procedure to VTE were 0.12 (7.32) days.ConclusionPatients with acute COVID‐19 infection are at higher risk for PSI‐12. The present definition of PSI‐12 does not account for COVID‐19. This may impact hospitals' quality performance if COVID‐19 infection is not accounted for by exclusion or risk adjustment.