Canadian Medical Association Journal

Background and ObjectivesSkilled nursing facility (SNF) hospitalization rates vary substantially across facilities serving comparable patient populations, yet the organizational factors underlying high performance remain poorly characterized. This study examines whether faith or mission-driven organizational identity is associated with lower-than-expected hospitalization rates in a national sample of Medicare-certified SNFs. DesignCross-sectional analysis of a stratified random sample of 618 Medicare-certified SNFs, drawn from a national cohort of 13,419 facilities with claims-based quality data. Facilities were classified by organizational identity (faith-affiliated, purpose-driven, or secular) using publicly available records. Performance was measured using CMS claims-based hospitalization and emergency department transfer rates adjusted for expected rates given patient case mix. Setting and ParticipantsMedicare-certified skilled nursing facilities in the United States, February 2026 CMS release. MethodsWe computed a composite performance gap as the mean of four z-scored observed-minus-expected measures (short-stay and long-stay hospitalization and ED transfer rates). We tested the association between faith affiliation and performance using Fishers exact test, logistic regression, OLS regression, propensity score matching, and causal mediation analysis. ResultsFaith-affiliated or purpose-driven facilities constituted 14.7% of significant overperformers (95% CI: 7.0-23.5%) and 0% of significant underperformers (95% CI: 0.0-4.4%), a monotonic gradient confirmed across all five performance zones. After propensity score matching on facility size, ownership type, and urbanicity (n=49 matched pairs), faith-affiliated facilities achieved 18.2% short-stay rehospitalization compared to 21.7% for matched secular facilities (3.5 percentage points fewer, p=0.019), and 1.30 long-stay hospitalizations per 1,000 resident-days compared to 1.71 (0.41 fewer per 1,000 days, p=0.019). Faith affiliation was associated with 61% more RN staffing hours per resident per day (0.96 vs. 0.60 hours, p<0.001), and formal mediation analysis confirmed that RN staffing hours substantially mediated the relationship between faith affiliation and hospitalization performance. Conclusions and ImplicationsFaith and mission-driven organizational identity is associated with superior hospitalization performance in a national SNF sample, mediated by elevated RN staffing intensity. These findings suggest that organizational culture and values are modifiable upstream determinants of nursing home quality, with implications for quality improvement, workforce policy, and value-based payment design.

Importance: Sickle cell disease (SCD) affects approximately 100,000 people in the United States, causes life-threatening complications, and shortens life expectancy by decades. Adults with SCD routinely encounter undertreated pain, provider bias, and structural barriers in hospital settings. Objective: To describe patterns of leave against medical advice (LAMA) among adults hospitalized for SCD. Design, Setting, and Participants: Retrospective analysis of inpatient discharge records among adults ages 18 and older in New York City hospitals, 2022-2023, hospitalized for SCD or any reason. Main Outcomes and Measures: The primary outcome was hospital-level LAMA, measured by crude rates and rates adjusting for patient characteristics using Bayesian hierarchical models. The secondary outcome was 30-day all-cause readmissions, stratified by LAMA status. Results: LAMA discharges comprised 14% of SCD hospitalizations and 4% of all-cause hospitalizations. Adjusted hospital-level SCD LAMA ranged from under 5% to 30% (IQR: 10-20%) and was higher than all-cause LAMA in most facilities. Crude SCD LAMA rates exceeded 30% in several hospitals, including those with more than 100 SCD hospitalizations during the study period. Patients with 10 or more SCD hospitalizations accounted for 40% of total SCD volume. Sensitivity analyses accounting for this concentration showed attenuated but persistent variation in SCD LAMA. Over 50% of SCD LAMA discharges were followed by a 30-day readmission compared to 38% of non-LAMA discharges. LAMA was associated with higher adjusted odds of readmissions in both SCD and all-cause hospitalizations. Conclusions: Our findings challenge the assumption that patients are solely responsible for early departures. Leaving against medical advice should be monitored as a signal of unmet care needs in SCD.

Background: Advance care planning (ACP) documentation, including living wills and durable power of attorney (DPOA), is intended to support goal concordant end of life care. However, it is unknown if comprehensive documentation confers additional benefits, and how these associations vary across clinical contexts. Methods: We used 2010 to 2022 Health and Retirement Study exit interview data to examine associations between ACP documentation and end of life care among U.S. adults aged 50 years and older. Documentation was categorized as none, one document (living will or DPOA), or two documents (both). Outcomes included intensive care unit (ICU) use, life sustaining treatment, hospice enrollment, and out-of-hospital death. Modified Poisson regression models were used to estimate adjusted risk ratios (aRRs), and temporal trends in documentation were assessed using joinpoint regression. Results: Among 5,622 decedents representing 23.2 million individuals, 42.7% had two documents and 28.9% had none, documentation increased substantially around 2014. Compared with no documentation, having any documentation was associated with lower likelihood of life-sustaining treatment (aRR=0.85, 95% CI: 0.74 to 0.98) and higher likelihood of hospice enrollment (aRR=1.43, 95% CI: 1.28 to 1.60) and out-of-hospital death (aRR=1.11, 95% CI: 1.06 to 1.18), but not ICU use. Having two documents showed similar patterns, with modest differences compared with one document after adjustment. Associations were stronger among decedents with expected death and attenuated among those with unexpected death. Conclusions: Comprehensive ACP documentation is associated with less aggressive end of life care and greater hospice use, though the incremental benefits of two documents are modest. Findings highlight the importance of documentation within care planning processes and the clinical context.

PurposeTo characterize maternal transport patterns in Iowa, a state with levels of maternal care and without formal perinatal regions, and assess whether transport decisions reflect efficient, risk-appropriate coordination. MethodsWe analyzed 2010-2023 Iowa birth records, which included 2,251 maternal transports between obstetric facilities across 106 unique routes. We characterized transport patterns and applied a community detection algorithm to identify "communities" of obstetric facilities that disproportionately transport among themselves. FindingsSuburban and rural counties have elevated transport rates compared to urban counties. 2,189 transports (97%) were from lower-to higher-level facilities. Among these, 2,037 (93%) were to Level III tertiary care centers. 567 transports (25.2%) bypassed a closer facility offering an equivalent or higher level of care than its destination facility. Health system affiliation was associated with bypassing transport, indicating potential organizational rather than purely geographic drivers of transport decisions. Three "communities" of obstetric facilities largely shaped by geographic proximity were identified. ConclusionsAlthough Iowa does not have formal perinatal regions, patterns of maternal transport are mostly in line with three de facto regions. Some potential inefficiencies were identified, such as obstetric facilities transporting to a farther facility when a closer facility offered the same level of care or higher. These findings may help identify opportunities to enhance care coordination among obstetric facilities, optimize maternal transport networks, and improve regionalization of maternal care.

Introduction Antibiotic use drives antimicrobial resistance, and optimizing prescribing in skilled nursing facilities (SNFs) - which care for medically complex residents in congregate settings characterized by frequent care transitions and diagnostic uncertainty - presents unique challenges. Antimicrobial stewardship (AMS) in SNFs has therefore become a focus of quality improvement efforts by federal and state health agencies. We aimed to identify factors that facilitate and hinder AMS implementation in SNFs. Methods A qualitative study of AMS implementation was conducted in Southern Arizona SNFs randomly sampled to represent urban/suburban, border, and rural regions. Semi-structured interviews were conducted with administrators, clinicians, and nonclinical staff within participating facilities. Interview transcripts were analyzed using constant comparative analysis, with both directed and emergent coding, facilitated by NVivo 12 software. Findings From 04/13/2019 through 12/13/2019, 57 interviews were conducted with 9 administrators, 38 clinical providers, and 10 nonclinical staff across 6 urban/suburban, 2 border, and 2 rural facilities. Analysis identified two thematic categories: "influencer themes," which describe specific barriers and facilitators to AMS implementation, and "system themes," which characterize SNFs as complex adaptive systems shaped by interacting staff roles, care transition challenges, and differing perceptions of AMS practices within the same facility. Key facilitators included effective internal communication, ongoing AMS education, and clinician AMS champions. Primary barriers included poor interfacility communication during care transitions, limited access to diagnostic resources, enculturated prescribing norms, and tension between immediate infection control priorities and long-term AMS goals. Conclusions Findings suggest that AMS implementation in Arizona SNFs is best understood as a systems-level process emerging from interactions among staff roles, organizational workflows, and care transitions, rather than solely from individual prescribing decisions. Recognizing SNFs as complex adaptive systems highlights the importance of communication structures, local champions, and feedback mechanisms. It underscores the need for coordination strategies within and across SNFs to sustain AMS interventions.

Background: Health-related social needs (HRSNs), particularly housing instability, are significant drivers of poor health outcomes among Medicaid populations. New York State's Social Care Networks (SCNs) aim to systematically connect members to housing services through coordinated referral systems. However, limited systematic analysis of referral patterns hinders quality improvement efforts. We analyzed housing referral outcomes and workflows to identify barriers to successful service connections. Methods: We conducted a mixed-methods quality improvement study at Public Health Solutions' WholeYouNYC SCN Coordination Center. Quantitative analysis examined 4,258 housing referrals submitted between June 2025 and January 2026, extracted from the Unite Us platform via Power BI dashboard. We calculated acceptance rates, analyzed time metrics, and examined outcomes by receiving organization. Qualitative data were collected through structured consultations with 7 staff members (5 navigators, 2 supervisors) and review of internal workflow documentation. Process mapping identified workflow bottlenecks. Results: Of 4,258 housing referrals, only 45% (n=1,936) were accepted by receiving organizations, while 19% (n=815) were rejected and 32% (n=1,382) remained awaiting response with no recorded action. Average time to acceptance was 8 days for accepted referrals. Acceptance rates were consistent across top receiving organizations (44-46%), suggesting systemic rather than partner-specific barriers. Analysis of unresolved referrals revealed prolonged cases, with the longest pending 271 days. Three critical workflow bottlenecks were identified: CBO response delays, missing housing documentation, and challenges with client engagement. Conclusions: Low housing connection rates (45%) and prolonged unresolved referrals (up to 271 days) indicate systemic barriers requiring interventions at multiple levels. Recommendations include establishing CBO response time benchmarks, implementing automated follow-up protocols, standardizing documentation requirements, and enhancing real-time data monitoring. These findings provide an evidence-based framework for quality improvement in social care coordination programs.

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in behavior management (PTBM) as first-line treatment. However, assessing guideline adherence through manual chart review is time-consuming and costly, limiting scalable and timely quality-of-care measurement. ObjectiveTo evaluate the accuracy and explainability of large language models (LLMs) in identifying PTBM recommendations in pediatric electronic health record (EHR) notes as a scalable alternative to manual chart review. Design, Setting, and ParticipantsThis retrospective cohort study was conducted in a community-based pediatric healthcare network in California consisting of 27 primary care clinics. The study cohort included children aged 4-6 years with [&ge;] 2 primary care visits between 2020-2024 and ICD-10 diagnoses of ADHD or ADHD symptoms (n=542 patients). Clinical notes from the first ADHD-related visit were included. A stratified subset of 122 notes, including all cases with model disagreement, was manually annotated to assess model performance in identifying PTBM recommendations and rank model explanations. ExposuresAssessment and plan sections of clinical notes were analyzed using three generative large language models (Claude-3.5, GPT-4o, and LLaMA-3.3-70B) to identify the presence of PTBM recommendations and generate explanatory rationales and documentation evidence. Main Outcomes and MeasuresModel performance in identifying PTBM recommendations (measured by sensitivity, positive predictive value (PPV), and F1-score) and qualitative explainability ratings of model-generated rationales (based on the QUEST framework). ResultsAll three models demonstrated high performance compared to expert chart review. Claude-3.5 showed balanced performance (sensitivity=0.89, PPV=0.95, and F1-score=0.92) and ranked highest in explainability. LLaMA3.3-70B achieved sensitivity=0.91, PPV=0.89, and F1-score=0.90, ranking second for explainability. GPT-4o had the highest PPV [0.97] but lowest sensitivity [0.82], with an F1-score of 0.89 and the lowest explainability ranking. Based on classifications from the best-performing model, Claude-3.5, 26.4% (143/542) of patients had documented PTBM recommendations at their first ADHD-related visit. Conclusions and RelevanceLLMs can accurately extract guideline-concordant clinician recommendations for non-pharmacological ADHD treatment from unstructured clinical notes while providing clear explanations and supporting evidence. Evaluating model explainability as part of LLM implementation for medical chart review tasks can promote transparent and scalable solutions for quality-of-care measurement.

BackgroundGuidelines recommend vascular specialist evaluation and revascularization consideration before major amputation in chronic limb-threatening ischemia (CLTI). Whether patients consistently receive pre-amputation vascular workup is poorly characterized nationally. MethodsWe conducted a retrospective cohort study of Medicare fee-for-service beneficiaries [&ge;]66 years with CLTI undergoing incident major lower-extremity amputation (2021-2022) with [&ge;]12 months continuous enrollment. Using claims in the 180 days preceding hospitalization for amputation, we classified patients into mutually exclusive pathway phenotypes: (A) no specialist, no imaging, no revascularization attempt; (B) specialist only, no revascularization attempt; (C) imaging, no revascularization attempt; or (D) revascularization attempted. Mixed-effects multinomial regression with hospital random intercepts identified predictors of phenotype membership. Post-amputation outcomes were compared across phenotypes. ResultsAmong 10,666 patients (mean age 76.6 years; 35% female; 70% White, 21% Black), phenotype distribution was: A, 9.4%; B, 7.1%; C, 50.7%; D, 32.7%. Thus, 16.6% had no vascular imaging before amputation. Dementia (OR 2.0; 95% CI, 1.61-2.52), paralysis (OR 4.1; 2.62-6.34), and dual eligibility (OR 1.2; 1.01-1.42) were independently associated with phenotype A. Higher comorbidity burden was inversely associated with A (OR 0.49 for >6 vs 0-3 Elixhauser comorbidities). Phenotype A patients had lower 1-year mortality (40% vs 51% for D), fewer readmissions (90-day OR 0.54; 0.47-0.64), and lower costs (adjusted 50% lower at 180 days). Results were robust to acuity adjustment, exclusion of early deaths, and propensity-score matching (n=824 pairs). Phenotype A prevalence varied widely across hospital referral regions, ranging from 3% (Boston, Atlanta) to 16% (Little Rock) among regions with >100 patients. ConclusionsOne in six CLTI amputees had no vascular imaging before amputation. Patients without evaluation were characterized by cognitive impairment, functional limitation, lower healthcare engagement, and socioeconomic disadvantage rather than extreme medical complexity. Hospital-level variation suggests system-level interventions could address these gaps. WHAT IS KNOWNO_LIPrior studies have shown that 50-63% of Medicare patients with chronic limb-threatening ischemia undergo major amputation without receiving revascularization, with substantial racial and geographic disparities in pre-amputation vascular care. C_LI WHAT THE STUDY ADDSO_LIThis study documents the extent to which CLTI patients proceed with amputation without first being evaluated by a vascular specialist, which suggests lack of guideline-recommended care. C_LIO_LIAbout 1 in 10 Medicare CLTI amputees had no vascular specialist contact and no vascular imaging in the 6 months before amputation. C_LIO_LIPatients reaching amputation without evaluation were characterized not by extreme medical complexity but by dementia, paralysis, depression, and dual eligibility--suggesting populations unable to self-advocate within the healthcare system. C_LIO_LISubstantial hospital-level and geographic variation (3-16% phenotype A prevalence across large hospital referral regions) indicates that system-level factors, not just patient characteristics, drive these gaps. C_LI

BackgroundBenchmarking has been used to target clinically unwarranted variation in antimicrobial prescribing in UK primary care. However, variation in antibiotic prescribing between general practices may be partly explained by differences in case-mix. We aimed to quantify how much variation in antibiotic prescribing for common infections was attributable to differences in prescribing between practices after accounting for case mix. MethodsWe used the UK Clinical Practice Research Datalink (CPRD) Aurum database to identify GP consultations for 11 common infections. Three-way variance decomposition quantified the proportions of total variance attributable to patient case-mix, between practice variation, and residual unexplained variance using variables recorded in electronic medical records, across three models (no, minimal (age/sex), and full case mix adjustment). For lower respiratory tract infection (LRTI) and sore throat, external data to impute illness severity were used to estimate the potential effect of unmeasured infection severity. FindingsWe identified 3,820,806 consultations in 2019. There was clear variability in antibiotic prescribing across practices for most conditions. In fully adjusted models, between practice variation explained 5.8-32.6% of total variance, exceeding variation attributed to case-mix in 9 out of 11 infections. Compared to no adjustment, full case-mix adjustment reduced between-practice differences, lowering their contribution to total explained variation by more than 20% in 6 of 11 infections and by 10-20% in 4 others. Minimal age-sex adjustment had little impact, with changes below 5% in 8 of 11 infections. Imputing infection severity in addition to full case-mix adjustment further reduced contribution of between-practice variance to the total variance (by 25.9% for LRTI and 8.5% for sore throat). InterpretationDifferences in practice-level prescribing, beyond patient case-mix, call for targeted interventions and highlight the value of providing feedback at the practice level. Comprehensive case-mix adjustment, including imputed infection severity, improves the assessment of prescribing variation and supports fairer performance comparisons. FundingWellcome Trust; NIHR RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe searched MEDLINE for articles published between 1 January 2005 and 31 July 2025, using a combination of key terms including "antibiotic" (or "antimicrobial" or "antibacterial"), "prescribing" (or "prescription" or "use" or "utilisation" or "utilization"), "primary health care" (or "primary care" or "general practice" or "general practitioner" or "GP"), and "United Kingdom" (or "UK" or "England"). We focused on studies using patient-level data to compare antibiotic prescribing between general practices (GP practices). Most studies assessed overall prescribing or focused on a small subset of infections. Only a few examined condition-specific measures across a broader range of infections. We found no studies that decomposed variance into that caused by patient case-mix versus practice performance adjusting for case-mix across a wide range of infections. Added value of this studyUsing individual-level data from 3.8 million consultations for eleven common infections in the UK Clinical Practice Research Datalink (CPRD) Aurum, we applied three-way variance decomposition to quantify the proportions of total variance attributable to patient case-mix, between-practice differences after adjusting for case-mix, and residual variation under three adjustment strategies (non, age-sex only, and full case-mix adjustment). There was clear variability in antibiotic prescribing across practices for most conditions. The total variance attributable to between-practice differences exceeded that attributed to case-mix in 9 out of 11 infections according, according to the fully adjusted case-mix models. Fully adjusting for case-mix based on routinely collected data substantially reduced between-practice differences, lowering their contribution to explained variance (the sum of the patient case-mix variance and between-practice variance) by more than 20% in 6 of 11 infections and by 10-20% in 4 others, whereas minimal age-sex adjustment had little impact. Between-practice differences were reduced further incorporating external information to simulate unmeasured infection severity. Implications of all the available evidenceDifferences in practice-level prescribing, beyond patient case-mix, call for targeted interventions and highlight the value of providing feedback at the practice level. Full case-mix adjustment substantially reduces the risk of overstating between-practice differences, performing far better than adjusting for age/sex alone. Condition-specific indicators with sufficient case-mix adjustment may be more effective benchmarks of practice performance than aggregated total antibiotic use levels as general practitioners (GPs) are more likely to respond positively to comparisons they perceive as fair. In particular, acute otitis media and upper respiratory tract infection, conditions with substantial variability in antibiotic prescribing across GP practices and the highest variance attributable to adjusted between-practice differences (12.6% and 10.3%, respectively), are promising candidates for fair prescribing indicators.

BackgroundEvidence to guide the choice of injectable antibiotics and supportive care for children with severe pneumonia is limited and may not reflect changes in epidemiology associated with vaccination and antimicrobial resistance. MethodsIn this pragmatic, open-label, factorial, randomized trial conducted in 16 hospitals in Kenya, children aged 2-59 months with World Health Organization-defined severe pneumonia were assigned to receive one of three injectable antibiotic regimens: benzylpenicillin plus gentamicin (standard care), ceftriaxone, or amoxicillin-clavulanic acid. Eligible children were also randomly assigned to receive nasogastric tube feeding or intravenous fluids. The primary outcome was death from any cause by day 5 after enrollment. ResultsA total of 4393 children underwent randomization to the antibiotic groups, and 1064 to the supportive care groups. By day 5, deaths occurred in 87/1463 children (6.0%) receiving benzylpenicillin plus gentamicin, 82/1458 (5.6%) receiving amoxicillin-clavulanic acid (adjusted risk ratio [aRR], 0.94; 97.5% confidence interval [CI], 0.67 to 1.31), and 81/1462 (5.5%) receiving ceftriaxone (aRR vs. benzylpenicillin plus gentamicin, 0.95; 97.5% CI, 0.68 to 1.33). Death by day 5 occurred in 30/531 children (5.7%) receiving nasogastric tube feeding and 35/532 (6.7%) receiving intravenous fluids (aRR, 1.13; 97.5% CI, 0.71 to 1.79). Secondary outcomes were similar across groups. ConclusionsAmong children hospitalized with severe pneumonia, outcomes with benzylpenicillin plus gentamicin were similar to those with ceftriaxone or amoxicillin-clavulanic acid, and nasogastric tube feeding was similar to intravenous fluids with respect to mortality and safety.

ObjectivesTo evaluate rates of treatment for depression and identify resident- and facility-level predictors of pharmacotherapy among long-term care (LTC) residents in the United States. DesignRetrospective, observational study. Setting and ParticipantsElectronic health record data from 1,675,873 LTC residents in the PointClickCare Life Sciences database (January-April 2025) were reviewed and 358,425 skilled nursing facility residents with a documented depression diagnosis were identified. MethodsResidents were classified as treated/untreated based on having a medication order for pharmacological depression treatment within medication classes recommended by the American Psychological Association. Descriptive analyses incorporated demographic and clinical characteristics, and multivariable logistic regression estimated odds of treatment. ResultsOverall, 81.7% of residents diagnosed with depression had [&ge;]1 pharmacological depression treatment order. Selective serotonin reuptake inhibitors (59.8%) and miscellaneous antidepressants (42.3%) were the most frequently used classes. Treatment rates were similar across depression diagnoses. Higher odds of receiving treatment were observed among residents also diagnosed with vascular dementia and those with hyperlipidemia medication orders. Lower odds were noted among residents who were Black or African American, had diabetes or hyperlipidemia diagnoses, or resided in facilities located in areas with poor socioeconomic status. Conclusions and ImplicationsMost residents with depression had at least one recommended pharmacologic therapy, although important disparities remain. Racial differences, comorbid conditions, and facility context continue to influence treatment access. These findings support the need for improved screening practices, greater attention to equity in prescribing, and strengthened clinical resources in socially vulnerable settings to enhance the quality of depression care in LTC facilities. Brief SummaryDepression is common in long-term care (LTC) and is associated with poor functional and clinical outcomes, however recent treatment patterns are not well understood. Using electronic health record data from 1,675,873 U.S. LTC residents between January and April 2025, 358,425 skilled nursing facility residents were identified with a documented depression diagnosis. The use of antidepressant medication was assessed based on medication order history and was aligned with American Psychological Association recommendations. Overall, 81.7% had at least one pharmacologic treatment order for depression; selective serotonin reuptake inhibitors (59.8%) and miscellaneous antidepressants (42.3%) were most frequently used. After adjusting for covariates, lower odds of treatment were observed among Black or African American residents and among residents in facilities located in more socioeconomically vulnerable areas. These findings highlight persistent inequities in depression pharmacotherapy in LTC and support efforts to strengthen depression assessment and ensure equitable access to evidence-informed treatment across facilities.

Objective: Readmissions to the PICU are associated with increased morbidity and mortality. A prediction model that can identify children at risk of readmission at the time of transfer can allow providers to intervene and potentially improve patient outcomes. The objective of this study was to derive and validate machine learning models to predict PICU readmission at the time of transfer. Design: Retrospective observational cohort study Setting: Three quaternary care PICUs in the city of Chicago Patients: All children admitted to the PICU between 2012 and 2019. Measurements: The primary outcome was unplanned readmission to the PICU within 48 hours of transfer to the inpatient ward. Predictor variables included vital signs, patient characteristics, and laboratory results. We developed and externally validated four models to predict PICU readmission: logistic regression, elastic net, random forest, and XGBoost. Main Results: This study included 35,601 patients, with readmission rates ranging from 2.2-3.7% by site. The performance of models during internal validation was consistent at the three sites, with the area under the receiver operating characteristic (AUC) values between 0.70 and 0.73 and no difference across the four models. Model performance decreased significantly during external validation (AUCs of 0.60-0.69). The variables most important to the prediction differed at each site. Conclusion: Machine learning models for predicting readmissions to the PICU have limited generalizability. Locally derived models demonstrated modest performance in our study and could potentially inform provider decision-making if prospectively validated. Externally developed models are unlikely to perform well at predicting PICU readmissions.

Background Cardiovascular-kidney-metabolic (CKM) syndrome integrates adiposity, metabolic risk, kidney dysfunction, and cardiovascular disease in a prevention-oriented framework. National estimates across 1999-2023 NHANES and future burden remain limited. Methods We analyzed US adults aged 20 years from 11 NHANES cycles, 1999-2000 through August 2021-August 2023. CKM stage 0-4 was assigned using harmonized examination, laboratory, medication, and questionnaire data. Prevalence was survey-weighted and standardized to the 2010 US Census adult population. Decade trends used survey-weighted logistic regression adjusted for age, sex, and race and ethnicity. Exploratory 2040 and 2050 projections combined NHANES prevalence models with US Census projections under population-aging-only, trend-continuation, and risk-improvement scenarios. Results Among 62,890 eligible adults, 62,888 had sufficient CKM data. In 2021-2023, age-standardized prevalence was 87.9% (95% CI, 86.5%-89.4%) for CKM stage 1 and 62.0% (95% CI, 60.1%-63.8%) for stages 2-4. Stage 2 accounted for 50.1% (95% CI, 48.2%-51.9%) and stages 3-4 for 11.9% (95% CI, 11.0%-12.7%). From 1999-2000 to 2021-2023, any CKM increased by 4.6 percentage points (95% CI, 2.4 to 6.9; P<0.001), whereas stages 2-4 changed by 2.1 percentage points (95% CI, 5.1 to 0.8; P=0.156). In adjusted decade models, any CKM increased (OR, 1.28; 95% CI, 1.19-1.38; P<0.001), while stages 2-4 showed no significant linear trend (OR, 0.95; 95% CI, 0.89-1.01; P=0.084). Excess adiposity and diabetes increased, dyslipidemia declined, and hypertension, chronic kidney disease, and clinical cardiovascular disease were stable. With population aging alone, projected stages 2-4 burden rose from 164.8 million adults in 2023 to 193.7 million in 2050; under risk improvement, it was 147.7 million. Conclusions CKM syndrome remained highly prevalent among US adults. Although later stages did not increase significantly, population aging may expand the absolute care burden unless broad risk improvement occurs.

ObjectiveCerebral palsy (CP) affects approximately 1 million Americans and 18 million individuals worldwide, yet contemporary US epidemiologic data remains limited. We aimed to use Cerebral Palsy Research Network (CPRN) clinical registry to describe demographics and clinical characteristics of individuals with CP across the US and determine associations with gross motor function and genetic etiology. MethodsRegistry subjects were included if they had clinician-confirmed CP and prospectively entered data for Gross Motor Function Classification System (GMFCS) Level, gestational age, genetic etiology, CP distribution, and tone/movement types. Logistic regression was used to determine which of these variables plus race, sex, ethnicity, and age were associated with GMFCS level and genetic etiology. ResultsA total of 9,756 children and adults with CP from 22 CPRN sites met inclusion criteria. Participants were predominantly White (73.0%), male (57.3%), non-Hispanic (87.8%), and younger than 18 years (73.7%). Most were classified as GMFCS levels I-III (55.6%), born preterm (52.8%), had spasticity (83.8%), and had quadriplegia (41.9%); 12.2% were identified as having a genetic etiology. Tone/movement types, CP distribution, and gestational age were significantly associated with both GMFCS level and genetic etiology (p<0.001). Compared to White individuals, Black individuals were more likely to have greater gross motor impairment (p<0.001). ConclusionIn this large US cohort, clinical and demographic factors, including race, were associated with gross motor function and genetic etiology in CP. These findings highlight persistent disparities and demonstrate the value of a national clinical registry for informing prognostication, quality improvement efforts, and targeted genetic testing strategies.

Importance Missed outpatient appointments, including no-shows and cancellations, may disrupt continuity of care and be associated with worse outcomes, but long-term system-wide patterns and clinical implications are not well characterized. Objective To characterize variation in missed appointment rates in the Veterans Health Administration (VHA) over time and by geographic location, visit modality, and preexisting conditions, and to evaluate associations between missed appointment rates and adverse outcomes among veterans with posttraumatic stress disorder (PTSD) or traumatic brain injury (TBI). Design Cohort study using VHA Corporate Data Warehouse outpatient appointment data from January 1, 2000, through December 31, 2024. Setting National integrated health care system of the VHA. Participants System analysis includes all scheduled outpatient appointments with a valid status, and outcome analysis includes veterans with PTSD (n = 1 429 890) or TBI (n = 554 553), diagnosed before 2023. Exposures For system -level analyses, missed appointment rates were calculated. In outcome analyses, 2023 missed appointment rates were categorized into tertiles within the cohort and appointment type. Main Outcomes and Measures One year risks of all-cause hospitalization, all-cause mortality, and hospitalization or death beginning January 1, 2024. Results Among 2,162,520,880 outpatient appointments from 2000 to 2024, 6.5% were no-shows and 25.4% were canceled. Across facilities, no-show rates ranged from 3.5% to 14.1%, patient-initiated cancellation rates from 9.7% to 26.0%, and clinic-initiated cancellation rates from 8.5% to 17.9%. In 2023, veterans with amputation, Parkinson disease, PTSD, or TBI had higher missed appointment rates than veterans without these conditions. Among veterans with PTSD, the highest no-show tertile, compared with none, was associated with higher mortality (HR, 1.91; 95% CI, 1.84-1.98) and hospitalization or death (HR, 1.07; 95% CI, 1.05-1.08). Among veterans with TBI, the highest no-show tertile was associated with hospitalization or death (HR, 1.65; 95% CI, 1.61-1.69). Conclusions and Relevance Missed outpatient appointments were common in the VHA and varied substantially across facilities and over time. Among veterans with PTSD or TBI, higher missed appointment rates, particularly no-shows, were associated with increased risks of hospitalization and mortality, suggesting that these patterns may help identify high-risk veterans for targeted outreach.

BackgroundAlthough virtual consultations are increasingly used in healthcare, mode affects attendance patterns remains limited, particularly across demographic groups. Within NHS secondary care, telephone consultations have been the most widely adopted form of telephone care; however, few studies have examined non-attendance (commonly termed Did Not Attend [DNA]) patterns specifically for telephone consultations and fewer still have explored how patient characteristics influence attendance differently across consultation modes. Understanding these patterns is essential for equitable service planning. ObjectiveTo compare non-attendance rates between telephone and in-person secondary care consultations among adults with type 2 diabetes (T2D), and to identify patient characteristics associated with non-attendance under each mode. MethodsData from 853,693 secondary care consultations (January 2020-August 2024) for 45,618 patients with T2D in Northwest London were analysed. Telephone consultations in this dataset consisted exclusively of telephone consultations; we therefore refer to them as telephone consultations throughout. Patient-level consultations were aggregated into patient-mode strata for regression modelling. Zero-inflated Negative Binomial regression assessed factors associated with missed consultation rates by mode (in-person or telephone). Propensity-score balance diagnostics (inverse probability of treatment weighting) were conducted to assess measured confounding by mode assignment. Specialty-stratified non-attendance rates were examined across 34 major specialties. ResultsIn-person consultations had higher unadjusted non-attendance rates than telephone consultations (9.1% vs 7.2%, p<0.001). This pattern was consistent for both first consultations (9.3% vs 6.2%, p<0.001) and follow-up consultations (9.0% vs 7.50%, p<0.001). For in-person consultations, higher non-attendance was associated with younger age (18-39: 12.2%, 40-59: 11.1% vs 60-79: 9.9%, p<0.001), Black or Black British ethnicity (18.9% vs White: 7.6%, p<0.001), and greater deprivation (most deprived IMD1: 10.3% vs least deprived IMD5: 7.0%, p<0.001). For telephone consultations, higher non-attendance was associated with male gender (7.3% vs female: 7.0%, p<0.01), younger age (18-39: 11.3%, 40-59: 9.5% vs 60-79: 6.1%, 80+: 5.6%, p<0.001), and greater socioeconomic deprivation (most deprived: 8.3% vs least deprived: 4.7%, p<0.001). Interaction analyses revealed that demographic disparities were amplified for telephone relative to in-person consultations. Specialty-stratified analysis showed that in-person non-attendance exceeded telephone non-attendance in the majority of high-volume specialties. ConclusionsIn-person consultations had higher non-attendance rates than telephone consultations. Various demographic factors influenced non-attendance rates, with younger age and greater socioeconomic deprivation consistently associated with non-attendance for both in-person and telephone consultations. These findings suggest that a personalised, equity-informed approach to consultation mode selection is needed. Findings apply to telephone consultations and may not generalise to video-based modalities.

BackgroundCase-based infectious disease surveillance is subject to ascertainment bias when testing intensity varies across time and population subgroups. We previously developed a regression-based test adjustment methodology using Standardized Testing Ratios (STRs) to correct for differential testing patterns in COVID-19 surveillance data. Wastewater-based surveillance (WWS) measures viral burden in the community independently of diagnostic testing behavior, making it a valuable external validation tool for test-adjusted case estimates. MethodsWe analyzed 111 weeks of paired wastewater and case surveillance data from Ontario, Canada (July 19, 2020 to August 28, 2022). Wastewater SARS-CoV-2 signals from 107 sewersheds across 34 public health units were normalized within sewersheds and aggregated using population-weighted averages. We compared wastewater correlations with crude reported and test-adjusted case counts using Spearman rank correlations, linear regression, and negative binomial distributed lag nonlinear models (DLNM), stratified by epidemic period. ResultsTest-adjusted cases correlated substantially more strongly with wastewater signals than crude reported cases overall (Spearman {rho} = 0.849 vs. 0.679; linear R{superscript 2} = 0.609 vs. 0.191). The advantage of test adjustment was greatest during the Omicron wave, when population-level diagnostic testing contracted sharply following PCR eligibility restrictions ({rho} = 0.924 vs. 0.604; R{superscript 2} = 0.815 vs. 0.470). DLNM incorporating the wastewater signal explained substantially more variance in test-adjusted than crude reported cases (McFadden pseudo-R{superscript 2} 0.898 vs. 0.776), despite similar lag-response structure for both outcomes. ConclusionsWastewater surveillance provides compelling independent validation of a previously described test adjustment methodology for COVID-19 case surveillance. The agreement between wastewater signals and test-adjusted cases was strongest precisely when testing scarcity was most severe, supporting the use of test adjustment to recover accurate infection dynamics from case surveillance data during periods of changing testing access and policy.

Background Non-vitamin K antagonist oral anticoagulants (NOACs) are the guideline-recommended standard for stroke prevention in atrial fibrillation (AF), yet bleeding risks limit real-world adherence. Percutaneous left atrial appendage closure (LAAC) offers a mechanical alternative without definitive comparative synthesis. Objectives To evaluate percutaneous LAAC versus NOAC therapy by synthesizing all contemporary NOAC-era randomized controlled trials (RCTs). Methods Five databases and registries (PubMed, MEDLINE, Embase, Cochrane CENTRAL, ClinicalTrials.gov) were searched from inception to 8 May 2026 for RCTs comparing percutaneous LAAC against NOACs in adults with non-valvular AF. Risk of bias was assessed using Cochrane RoB 2. Ischemic stroke was pooled using a random-effects DerSimonian-Laird model; primary efficacy composite and non-procedural bleeding were evaluated via pre-specified narrative synthesis. Results Four RCTs (CHAMPION-AF, OPTION, PRAGUE-17, CLOSURE-AF) comprising 5,890 patients were included. LAAC achieved noninferiority for the primary efficacy composite in three trials and demonstrated a statistically significant 45-56% reduction in non-procedural bleeding across the three moderate-risk trials. CLOSURE-AF did not meet noninferiority but retained a directionally consistent bleeding reduction. Pooled ischemic stroke analysis (HR 1.31; 95% CI 0.96-1.80; I^2=0%) showed no statistically significant increase in stroke risk, though a consistent directional trend toward more ischemic events was observed. Conclusions LAAC significantly reduces non-procedural bleeding in moderate-risk AF patients, though this benefit attenuates in very high-risk populations. A consistent, statistically nonsignificant ischemic stroke trend and population-dependent efficacy establish LAAC as a shared decision-making alternative to NOACs rather than a universal replacement, pending 5-year CHAMPION-AF data.

BackgroundDischarge destination after acute ischemic stroke has implications for functional recovery and healthcare costs. Individuals discharged to inpatient rehabilitation facilities (IRFs) achieve better outcomes than those discharged to skilled nursing facilities (SNFs); however, many patients discharged to IRFs and SNFs have similar clinical profiles. We examined non-clinical factors associated with discharge location after acute ischemic stroke. MethodsPopulation: 236 adults hospitalized with acute ischemic stroke, living independently in the community prior to admission, and discharged to either an IRF (n=171) or SNF (n=65). Clinical variables: NIHSS, Charlson Comorbidity Index (CCI), acute care length of stay (LOS), functional status (AM-PAC "6-Clicks"), and neglect. Non-clinical variables: age, sex, race, marital status, insurance, home layout, living status, and available assistance. Associations with discharge location were evaluated using univariable and multivariable logistic regression and reported as odds ratios (OR) with 95% confidence intervals (CI). ResultsIndividuals discharged to IRFs were younger, more likely to cohabitate, and had shorter LOS than those discharged to SNFs. Functional status (AM-PAC) and comorbidity burden (CCI) did not differ significantly between groups despite differences in discharge destination. In univariable models, younger age, cohabitating marital status, living with family, available assistance, shorter LOS, private insurance, and higher NIHSS were associated with greater odds of IRF discharge. In multivariable analysis, younger age (OR 0.94, 95% CI 0.91-0.98), cohabitating marital status (OR 2.46, 95% CI 1.13-5.48), and shorter LOS (OR 0.88, 95% CI 0.82-0.93) remained independently associated with IRF discharge. ConclusionsIndividuals with comparable pre-stroke independence and similar clinical severity, discharge to IRF versus SNF was independently associated with non-clinical factors; age, marital status, and LOS, whereas stroke severity and functional status were not significant predictors. These findings underscore the importance of evidence-informed discharge criteria integrating clinical indicators and social context to support equitable access to intensive rehabilitation after stroke.

PurposeWhen emulating trials of medication initiation using real-world data, there may be ambiguity regarding the most suitable time zero event for the research question of interest. The time zero event must be strongly associated with the clinical indication for treatment, confer a high probability of actual treatment initiation, and be measurable with sufficient temporal precision in the source data. When it is uncertain whether a candidate event will satisfy these three conditions simultaneously, empirical identification of predictors of medication initiation can provide valuable guidance. The objective of this study was to empirically identify predictors of incident atorvastatin initiation to inform the definition of time zero for future target trial emulations. MethodsA retrospective cohort study was conducted using Medicare claims data (study period January 1, 2018 - December 31, 2019). The cohort included statin naive beneficiaries aged [&ge;] 65 years with [&ge;] 12 months of continuous enrollment, as of the study period start date, and at least one new or incident prescription claim after study period start date. Atorvastatin initiation was defined by the first dispensing (index date). Non-atorvastatin initiators (reference group) were sampled at 25%; their index date was a randomly selected date of a new medication dispensing. Candidate predictor variables were ascertained in the 6 months pre-index and included demographics, comorbidities (classified separately from inpatient and outpatient claims), healthcare utilization, and pharmacotherapy. We developed and applied an eight-step procedure to identify independent predictors of incident atorvastatin initiation. ResultsThe study cohort comprised 481,742 incident atorvastatin initiators and 896,575 non-atorvastatin initiators (25% random sample). The strongest predictors of atorvastatin initiation were inpatient admission for cerebral infarction (OR 11.51, 95% CI 10.79-12.27) and myocardial infarction (OR 5.32, 95% CI 5.03-5.62). For example, a White male with a recent inpatient diagnosis of cerebral infarction had a predicted probability of atorvastatin initiation of 82% (95% CI 81-83%). ConclusionThe empirically identified predictors of atorvastatin initiation (acute cardio/cerebrovascular events) align with ACC/AHA guidelines recommending prompt statin therapy for secondary prevention. These predictors satisfy the three key requirements for a valid time zero event and should mitigate selection bias, channeling bias, and residual confounding in future target trial emulations. KEY POINTSO_LIFindings: Acute myocardial infarction and cerebral infarction recorded during an inpatient admission were the strongest predictors of incident atorvastatin initiation among statin-naive Medicare beneficiaries age 65 years and older. C_LIO_LIClinical Context: These findings align with current American College of Cardiology/American Heart Association guidelines that recommend prompt statin therapy for secondary prevention after these acute cardiovascular events. C_LIO_LIImplications for Future Research: Anchoring the time zero event to an inpatient admission for myocardial/cerebral infarction satisfies the three key requirements for a valid time zero event when studying medication initiation: it is strongly associated with the clinical indication for treatment, carries a high probability of actual statin initiation, and can be identified with sufficient temporal precision in administrative data. This approach should reduce channeling bias, selection bias (e.g., immortal time bias) and residual confounding in future target trial emulations. C_LIO_LIBroader Significance: The study provides an empirically derived, high-probability time zero event that can strengthen future target trial emulations using real-world data to assess the safety of commonly used medicines in older adults, a population often underrepresented in randomized trials to obtain regulatory approval. C_LI PLAIN LANGUAGE SUMMARYThis study aimed to identify a clear starting point for future research on the safety of atorvastatin in older adults. Using Medicare claims data from 2018-2019, researchers examined more than 1.3 million beneficiaries aged 65 and older who had not previously taken statins in the last year. They developed a predictive model to determine which patient characteristics were most strongly linked to starting atorvastatin. The strongest predictors were a recent hospital admission for heart attack (myocardial infarction) or stroke (cerebral infarction). These events were associated with a much higher chance of promptly receiving atorvastatin, which aligns with American College of Cardiology and American Heart Association guidelines recommending statin therapy soon after such events for secondary prevention. By using hospital discharge after these acute events as the starting point for future studies, researchers can create comparisons that reduce bias and allow more reliable estimates of atorvastatins effects on potential harms in this vulnerable elderly population.