Maniaci, Michael J

Background/Objectives: Hospital-at-home (HaH) programs have emerged as an alternative model to brick-and-mortar (BaM) hospital management of acute conditions such as heart failure, pneumonia, pyelonephritis, and chronic obstructive pulmonary disease exacerbation among the others. However, the clinical management of acute pancreatitis in the HaH setting has never been described in the literature. The primary objective of this study was to describe the demographic and clinical characteristics of patients with acute pancreatitis managed in a Mayo Clinic Advanced Care at home (ACH) program. The secondary objective was to describe feasibility and short-term clinical outcomes. Methods: This multistate, multicenter, retrospective descriptive study included 40 unique patients with acute pancreatitis managed at ACH between 13 October 2020 and 1 June 2024. Repeat admissions were excluded from the primary analysis. Results: The most common etiologies were idiopathic (42.5%), gallstone-related (22.5%), and alcohol-related (12.5%). Peripancreatic fluid collections occurred in 17.5%, necrotizing pancreatitis in 10.0%, pleural effusion in 12.5%, and infected pancreatitis in 7.5%. Intravenous antiemetics were administered to 22.5%, intravenous fluids to 62.5%, and intravenous opioids to 7.5%. Median total hospital length of stay was 5.0 days (IQR, 3.0–8.25), including a median of 3.0 days (IQR, 2.0–4.0) in ACH. Four patients (10.0%; exact 95% CI, 2.8–23.7%) were transferred from ACH to BaM care. Seven-day and 30-day readmission rates were 10% (95% CI: 2.8%, 23.7%) and 5% (95%CI: 0.6%, 16.9%), respectively. The 30-day emergency department visit was 0%. Although 30-day mortality was 2.5% (95% CI: 0.0%, 13.2%), in-program mortality was 0%. Conclusions: Among a highly selected group of clinically stable patients meeting the program’s eligibility criteria, management of acute pancreatitis in ACH was feasible and was associated with low observed rates of unplanned escalations and short-term adverse outcomes.

Abstract Background Outcomes of patients with acute pyelonephritis (AP) treated in a hospital-at-home setting have not been comprehensively evaluated in the United States. Methods We performed a multicenter, retrospective cohort study of adults diagnosed with and managed for AP in Mayo Clinic's Advanced Care at Home (ACH) program between July 2020 and January 2025. Collected data included demographics, Charlson Comorbidity Index (CCI), genitourinary comorbidities, severity of illness (SOI), and risk of mortality (ROM) scores, as well as pyelonephritis-related complications. Outcomes included length of stay (LOS), escalation of care, and 30-day postdischarge emergency department (ED) visits, readmissions, and mortality. Results A total of 165 patients met inclusion criteria. Median age was 67 years; SOI scores were moderate in 33.3%, major in 52.1%, and extreme in 8.5%. ROM scores were moderate in 30.3%, major in 38.2%, and extreme in 6.7%. Median CCI score was 5, and all patients had preexisting genitourinary conditions. On admission, 30.9% met sepsis criteria, acute kidney injury was present in 47.3%, and bacteremia developed in 33.3%. Median LOS in the ACH program was 3.1 days. Only 4.8% required escalation to a brick-and-mortar hospital. Readmission occurred in 17.0%, and 4.8% had ED visits. No in-program deaths occurred. Conclusions This multicenter retrospective study shows that AP, including cases with high illness severity and complex comorbidities, can be managed safely and effectively in a hospital-at-home setting with careful patient selection and robust infrastructure to support timely escalation when needed.

Abstract Antimicrobial use among hospital-at-home (HaH) patients across three Mayo Clinic sites was evaluated from 2023 to 2024. Nearly three-quarters of patients were administered an antibiotic during HaH admission. Ceftriaxone was the most commonly used antibiotic and respiratory infections were the most common antimicrobial indication. Further investigation into stewardship opportunities is warranted.

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.

The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.

AbstractBackgroundHospital‐at‐home has become a more recognized way to care for patients requiring inpatient hospitalization. At times, these patients may require escalation of care (transfer from home back to the brick‐and‐mortar (BAM) hospital for ongoing hospitalization care needs), a process that has not been extensively studied.ObjectiveTo evaluate what patient factors contribute to escalations of care in the hospital‐at‐home delivery model.Designs, Settings, and ParticipantsWe conducted a retrospective review of all patients admitted to Mayo Clinic's Advanced Care at Home (ACH) program from January 1, 2022 to December 31, 2022.InterventionNone.Main Outcomes and MeasuresPatient information was collected via electronic health record including demographic, socioeconomic, and clinical status. The primary outcome was the of occurrence of an escalation.ResultsA total of 904 patients were included, of whom 80 (8.8%) required an escalation of care. In multivariable analysis, risk of an escalation was significantly higher for patients who were married or had a life partner (HR: 1.82, 95% CI: 1.05–3.23, p = .033) for patients admitted with procedure‐related disorders (HR: 2.61, 95% CI: 1.35–5.05, p = .005) and patients with an increased mortality risk score (HR [per each 1–category increase] = 1.86, 95% CI: 1.39–2.50, p < .001).

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.