Determinants of Prosthetic Referral and Functional Mobility for Vascular Patients after Major Lower Extremity Amputation.

Although obesity has been widely recognized as a risk factor for cardiovascular morbidity and mortality, distinguishing metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) introduces a nuanced perspective in managing cancer-related pulmonary embolism (PE) among older people. Therefore, this study aims to investigate whether MHO or MUHO phenotypes have a differential impact on in-hospital outcomes. Using the National Inpatient Sample (NIS) (2016 to 2020) and ICD-10 CM codes, we identified cancer-related PE hospitalizations in elderly (≥65 years) patients. The study evaluated the demographics, comorbidities, and outcomes of cancer-related PE hospitalizations in two groups: patients without obesity and obese patients with MHO and MUHO. The study assessed the adjusted odds of all-cause mortality and cardiac arrest as primary outcomes and healthcare resource utilization as a secondary outcome using multivariable regression analyses. Of 211,070 cancer-related pulmonary embolism (PE) admissions, 87.5% were non-obese, 1.4% were MHO, and 11% were MUHO. MHO patients were primarily female (64.2%), younger (median age 71), and white (85.5%). Medicare was the primary payer, and care was mainly provided in urban teaching hospitals (74%) and the South region (28.9%). MUHO's prevalence significantly rose from 9.8% in 2016 to 11.9% in 2020 compared to patients with MHO. Patients with MUHO had higher rates of comorbidities compared to other groups. Multivariable regression analysis revealed that patients with metabolically healthy obesity (MHO) demonstrate a significantly lower risk of all-cause mortality (OR: 0.67, 95% confidence interval [CI]: 0.59-0.76) compared to non-obese patients. Conversely, patients with metabolically unhealthy obesity (MUHO) did not have a significant impact on mortality risk (OR: 0.88, 95% CI: 0.67-1.15), all with P < 0.001. Additionally, no significant differences in the rates of cardiac arrest were observed among MHO (OR: 1.14, 95% CI: 0.66-1.98, P = 0.892) or MUHO (OR: 1.01, 95% CI: 0.80-1.26, P = 0.892) patients when compared to non-obese individuals. Notably, patients with MHO experienced prolonged hospital stays (6 days) and incurred higher hospital expenses ($63,228) than other groups. Moreover, patients with MHO exhibited a higher likelihood of being discharged to a skilled nursing facility in comparison with patients with MUHO and those without obesity (28.6% vs 25.6% vs 23.2%). In conclusion, despite the rise in the prevalence of metabolically unhealthy (MUHO) and healthy obesity (MHO), our study focusing on the elderly cohort of cancer patients complicated by PE found a paradoxical effect of obesity on all-cause mortality rates with both metabolically healthy and unhealthy obesity, suggesting a potential protective effect. These findings highlight the need for further research to understand better the mechanisms underlying these associations.

Skilled Nursing Facilities (SNF) hospital readmissions continue to be a significant issue in terms of healthcare quality, patient safety and cost management in the Centres for Medicare and Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP). A large number of SNFs do not have sophisticated analytical software to integrate clinical and social data to determine high-risk residents of early readmission. By training and testing a machine learning model that is interpretable and based on interoperable Fast Healthcare Interoperability Resources (FHIR) data, this study will fulfill this gap and predict 30-day hospital readmissions among SNF residents. The analysis was based on de-identified, FHIR-mapped data of 14,250 SNF residents, namely medications, vital sign, functional status, prior utilisation and social risk indicators. The gradient-boosted machine (GBM) model was constructed and compared to a basis of logistic regression. The performance of the models was assessed in terms of the AUROC, AUPRC, calibration analysis, and the decision curve analysis. The explainability was done by SHapley Additive exPlanations (SHAP) which allowed transparent understanding of the individual risk factors. SHAP analysis gave easily understandable, clinically significant explanations, which justified actionable care planning. The unmanned pilot ensured stable performance over a period of time with slight drift. On the whole, this paper proves that interoperable FHIR data combined with explainable machine learning can help to make SNFs predict readmission risks ethically, transparently, and effectively. The strategy complies with policy, privacy and quality improvement objectives, and provides value to work conveniently to clinicians, administrators and policymakers aiming to minimize preventable hospital readmissions.

BackgroundArt has been increasingly recognized as a valuable tool in enhancing the well‐being of individuals in memory care settings. Research indicates that engaging in creative activities can significantly benefit residents with Alzheimer's disease or related dementia (ADRD) by stimulating cognitive functions, fostering social connections, and providing avenues for self‐expression. This community‐based art project was designed to explore these benefits within a skilled nursing facility, where residents could collaboratively create artwork that honors their past experiences and memories.MethodOver several weeks, a group of memory care residents (n = 6) at a skilled nursing facility participated in creating a piece of art titled “Nature and People: A Beautiul Mynd.” This collaborative effort involved utilizing various objects from nature including dried fruit, flowers and leaves. The process encouraged residents to tap into their long‐term memories and express themselves through the creation of nurturing and meaningful artwork.ResultA Beautiful Mynd, Memorializing Yesterdays Nurturing Dreams culminated into a visually stunning piece. The artwork is a vibrant representation of the residents' memories and experiences, as it integrates elements from nature that evoke emotional connections and reminiscence. By working together, the residents not only fostered a sense of community but also created lasting expressions of their collective memories.ConclusionThrough this project, we highlight the significant impact that community‐based art initiatives can have on enriching the lives of residents with dementia, fostering both creativity and connection.

BackgroundThe Minimum Data Set (MDS) is a tool developed by the Centers for Medicare & Medicaid Services (CMS) to standardize assessment for facilitating care management in long‐term care facilities. The integration of biosensor data into the assessment could provide clinicians with real‐time and longitudinal information that allow for an unprecedented insight into the physical and cognitive health of the individual.MethodAn example of an area where biosensor data could be integrated into the MDS in Section G0300. Balance During Transitions and Walking. Currently, the assessment is based on eye‐witness of the clinician and uses the following coding scores. 0. Steady at all times.1. Not steady, but able to stabilize without staff assistance. 2. Not steady, only able to stabilize with staff assistance. 8. Activity did not occur. For the following activities A. Moving from seated to standing position. B. Walking (with assistive device if used). C. Turning around and facing the opposite direction while walking. D. Moving on and off toilet. E. Surface‐to‐surface.An individual using a biosensor for gait analysis could both reduce the amount of time for assessment and provide more accurate and objective information. Continuous monitoring is feasible with this approach along with more sophisticated detection of movement and gait anomalies. Several solutions may be appropriate for further research including inertial measurement units, pressure‐sensitive insoles, radar‐based monitoring, and depth‐sensing cameras.ResultThe biosensing method combined with MDS codes could utilize the following data driven criteria. 0. No significant deviation in step symmetry, sway velocity, or transition stability. 1. Minor deviations detected but corrected within a threshold time (e.g., <1.5s delay in stability) 2. High sway, excessive corrective steps, prolonged stabilization time (>2s). 8. No sensor data recorded for the movement.ConclusionIntegrating gait analysis sensors into MDS Section G0300 would enhance assessment accuracy, reduce subjectivity, and provide real‐time fall risk insights. This could be particularly beneficial in skilled nursing facilities, rehabilitation centers, and fall prevention programs.

Diabetes Medications in Post-Acute Care: Association With Emergency Department Visits and Hospitalization.

Medicaid eligibility for nursing home care is determined in part by an individual's (or a couples', if married) financial resources, including income and assets. To qualify, individuals must "spend down" their resources to meet states' Medicaid eligibility asset thresholds. Little empirical work has examined the rate of Medicaid spend-down in nursing homes over the past 2 decades. To identify the rate of spend-down in nursing homes, defined as the share of total residents who began their stay as non-Medicaid enrolled (after accounting for Medicare-covered skilled nursing facility [SNF] days, where applicable) and became Medicaid enrolled before discharge or death. This cohort study used a combination of administrative, enrollment, and claims data from 2018 to 2022 to build a panel of 191 416 US nursing home residents enrolled in traditional Medicare-including those admitted for postacute and long-term care-who newly entered a facility in 2018 and either stayed beyond their Medicare SNF days or did not have any Medicare-covered SNF days. Statistical analysis was performed from July 2024 to October 2025. Newly entering a nursing home in 2018 as non-Medicaid enrolled. The main outcome was whether an individual spent down their assets and became enrolled in Medicaid during their nursing home stay. Multivariate regression was used to identify factors associated with spend-down. The study included 191 416 individuals (mean [SD] age at time of admission, 81.0 [11.4] years; 58.0% women; mean [SD] time in nursing home, 331.0 [485.8] days) with traditional Medicare who newly entered a nursing home in 2018, of whom 33.9% either began their stay as Medicaid enrolled or enrolled in Medicaid after the completion of their Medicare-covered SNF days. The remaining 66.2% of individuals were initially not enrolled in Medicaid on admission or after the completion of their Medicare-covered SNF days. Of those who were initially not Medicaid enrolled, 16.4% spent down their assets during their stay and enrolled in Medicaid (mean [SD] time to spend-down, 6.1 [7.9] months). The likelihood of spend-down increased with length of stay and was higher among Black, Hispanic, North American Native, and younger residents. In this cohort study of nursing home residents, those who entered a nursing home as initially non-Medicaid enrolled, especially those with longer stays, were at risk of spending down their assets and enrolling in Medicaid. This finding raises concerns both about individuals impoverishing themselves because of the high cost of care and the long-term financial sustainability of the Medicaid program.

Patient demographics, diagnoses, and care needs in a Norwegian Community Virtual Ward versus Skilled Nursing Facility: a longitudinal comparative cohort study.

Comparing Post-Acute Care Outcomes Between Home Health Care and Skilled Nursing Facilities: A Scoping Review.

Education for Certified Nursing Assistants in Skilled Nursing Facilities: A Needs Assessment and Pilot Intervention.

Frailty Status and Dysphagia Trajectory Among Hospitalized Nursing Home Residents With Advanced Dementia.

Clinical outcomes and discharge disposition in nonagenarians with ST-elevation myocardial infarction.

Flap reconstruction for pressure ulcers remains a surgical challenge, requiring careful selection of suitable patients to optimise outcomes. Postoperative care, especially pressure offloading, is critical for the prevention of complications, which include failure of the skin flap and wound recurrence. While the rates and risks of postoperative complications have been acknowledged, the challenges of postoperative care have been largely unaddressed. A 10-year retrospective review of all patients undergoing flap reconstruction for pressure ulcers at the College of Medicine (University of Florida, Florida, US) was conducted to elucidate factors that affected delays to discharge, post-discharge disposition (where the patient is discharged to), postoperative care and associated complications. Among patients who underwent multiple separate reconstructions, disposition to home versus a skilled nursing facility (SNF) or long-term acute care hospital (LTACH) had a significant association with a higher risk of complications. In these high-risk patients, a multidisciplinary approach that would ideally be put in place preoperatively should be used to optimise postoperative care and prevent unnecessary prolonged hospitalisation.

Algorithmic decision-making in health care: Evidence from post-acute care in Medicare Advantage.

Changing Patient Medical Complexity After Skilled Nursing Facility Payment Reform: An Interrupted Time Series Analysis.

Changes in Patient Medical Complexity After Skilled Nursing Facility Payment Reform: A Qualitative Study.

Hospitalized patients with metastatic non-small cell lung cancer (mNSCLC) and poor performance status are commonly discharged to a skilled nursing facility (SNF) to undergo rehabilitation, with a plan for further systemic therapy as an outpatient. The success rate of this strategy is unknown. Patients with mNSCLC between 2016 and 2023 discharged to a SNF were retrospectively reviewed, to find the proportion of patients who received further systemic therapy, quantify disease response, and characterize end-of-life care. Multivariable logistic regression was used to identify characteristics associated with receiving further therapy. Kaplan-Meier method was used to estimate overall survival (OS). Of 427 patients, 131 (30.7%) received further therapy, with 40/54 (74.1%) of those planned for a tyrosine kinase inhibitor, 42/130 (32.3%) of those planned for an immune checkpoint inhibitor, and 49/243 (20.2%) of those planned for chemotherapy (P < .001). On multivariable analysis, the planned therapy type (P < .001) and 6-Clicks score (P < .001) were significantly associated with receiving further therapy. For those 131 patients, 51 (38.9%) died or enrolled in hospice without response assessment, 33 (25.2%) had progression at first assessment, and 48 (35.8%) had stable disease or a partial response. Systemic treatment was given in the last 30 days of life for 44 (33.6%) patients. Median OS was 1.7 months. Of the 410 patients who died, 97 (23.7%) died inpatient and 110 (26.8%) died at a facility without ever returning home. The number of patients who return to intended oncologic therapy is low, varying by therapy type and performance status. Few have subsequent disease response or stability, with many patients receiving therapy in the last 30 days of life or dying in the hospital.

Variation in Post-Acute Care Transitions for Medicare Beneficiaries With Stroke.

Wastewater surveillance at healthcare facilities for carbapenem-resistant (CR) bacteria harboring carbapenemase genes could result in earlier detection of circulating or emerging antimicrobial resistance (AR) threats. However, knowledge gaps exist regarding the CR bacterial community and carbapenemase genes present in facility wastewater and plumbing, which need to be addressed. Wastewater effluent samples from three skilled nursing facilities (SNFs) (Georgia, USA) were collected weekly over six months. Selective chromogenic media were examined for their performance in screening the samples for targeted CR bacteria. Culture-dependent and culture-independent methods (ie, PCR) were applied to enriched wastewater samples (n = 73) to screen isolates and pooled samples for the most common carbapenemase genes circulating in the United States (blaKPC, blaNDM, blaVIM, blaOXA-48-like, and blaIMP), and method sensitivity was compared. Whole genome sequencing (WGS) was used to identify all AR genes present in isolates. Bacteria (n = 209 isolates) were identified using MALDI-TOF and included >20 different genera. Clinically relevant (Acinetobacter baumannii complex, Enterobacter cloacae complex, Enterococcus faecium, Stenotrophomonas spp.) and environmental/opportunistic (Comamonas spp., Pandoraea spp., Pseudomonas putida group) CR bacteria were identified in each of the three SNFs. In Facility A and C, only blaKPC was detected in 9 (26.5%) of 34 and 14 (11.4%) of 123 recovered isolates, respectively. There were no PCR-positive results in Facility B. WGS confirmed the presence or absence of all genes screened by PCR (100% concordance). These data provide a proof-of-concept and insights into CR bacteria present in healthcare facility wastewater using culture, PCR, and sequencing methods.

Discharge to the home/community following a skilled nursing facility (SNF) stay is a key metric of high-quality care. However, achieving this in this domain remains challenging, especially for distinctly complex patients. Little research to date has examined within-group variation in discharge outcomes for persons with SMI, a population that reflects a growing proportion of SNF consumers in the U.S. We leveraged a 4-year (2016-2019) 100% sample of Medicare claims data to examine individual- and organization-level predictors of SNF discharge location for persons with SMI. We first describe within-group differences for persons with SMI at the bivariate level. We then test linear probability models fully adjusted for individual and organization-level covariates, allowing for calculation of post-estimation marginal effects. We identified 118,325 unique SNF stays for people with SMI; 54% ended in discharge to the home/community. Patients with SMI who were discharged to the home/community (versus not) were significantly younger, more likely to be female, and were less likely to be dual-eligible or to have co-occurring ADRD. SMI patients discharged to the home/community were also significantly more likely to receive care in SNFs that were more integrated, higher quality, and saw a smaller share of SMI patients overall. These findings were reinforced by our fully adjusted regression analyses. This work finds within-group differences in characteristics associated with SNF discharge outcomes among the population of patients with SMI at both the person- and organization-levels. Policymakers should consider how to leverage value-based payment (VBP) programs, including new SNF-VBP requirements, in a way that more realistically accounts for the resources (e.g., time, staffing) required to coordinate care for this population. Similarly, an explicit focus on investments along the continuum should center around services that facilitate community retention (e.g., home- and community-based services).