EClinicalMedicine

ImportanceRecent reports have highlighted an intense influenza activity related to the circulation of the influenza A(H3N2) subclade k variant. There is no data available on the impact of the emergence of H3N2 subclade k on the severity of the 2025-2026 epidemic or on the clinical phenotype of patients requiring admission to the intensive care unit (ICU). ObjectiveTo compare the clinical presentation, hospital mortality and virological characteristics of patients with laboratory-confirmed influenza infection included in French intensive care units during the 2025-2026 epidemic season with those of patients admitted during the 2024-2025 season. We also aimed at measuring the impact of the A(H3N2) subtype on hospital mortality during the 2025-2026 season. DesignProspective, multicenter, observational SEVARVIR cohort study including patients admitted during the 2024-2025 and 2025-2025 influenza seasons. SettingForty-two French ICUs ParticipantsAdult patients with laboratory-confirmed influenza infection Interventionsnone Main Outcomes and MeasuresThe primary outcome measure was in-hospital mortality. ResultsPatients admitted in intensive care units for influenza in 2024-2025 (n=360) and 2025-2026 (n=325) were included in the French nationwide prospective multicentre SEVARVIR study. There was no significant difference in day-28 mortality between the seasons (12.7%, n=45/355 vs 16.5% n=28/170; p=0.28). In the 2025-26 season, 49% had the A(H1N1) subtype and 51% the A(H3N2) subtype (k subclade: 77%). The univariable Cox analysis revealed that patients infected with A(H3N2) viruses were at greater risk of death over time. Multivariable Cox analysis revealed that during the 2025-2026 season, age (adjusted hazard ratio, aHR=1.05 [1.00;1.11]; p=0.046) and the clinical frailty scale (aHR=1.82 [1.26;2.72]; p=0.001) were associated with an increased risk of death. The A(H3N2) subtype was not associated with an increased risk of death (aHR=1.13 [0.32;4.51]; p=0.85). Phylogenetic analyses from our ICU cohort together with 300 contextual sequences from community-acquired influenza cases collected during the same period showed no clustering according to severity. Conclusions and RelevanceThis French national prospective observational study, found that the emergence of the influenza A(H3N2) subclade K was associated with an increased risk of death in univariable but not multivariable analysis, adjusting for host-related factors. Trial RegistrationNCT051625 Key PointsQuestion: What impact did the 2025-26 influenza epidemic and the A(H3N2) variant have on the mortality of patients admitted to intensive care units? Findings: In this prospective, nationwide cohort study of 685 patients admitted to intensive care units with severe influenza during the 2024-25 or 2025-26 seasons, no difference in hospital mortality was observed between the two seasons. Patients infected with the A(H3N2) virus, 77% of which corresponded to clade k, were at higher risk of death in univariable but not in multivariable analysis after adjusting for age and clinical frailty scale. Meaning: Patients in intensive care units with severe A(H3N2) infection during the 2025/2026 season were not at higher risk of death after adjusting for confounding variables.

Background: How post-COVID-19 condition (PCC) differs from post-acute infection syndromes (PAIS) caused by other respiratory viruses remains uncertain. Comparing these conditions may clarify whether post-acute symptoms reflect specific consequences of SARS-CoV-2 infection or broader post-viral mechanisms. Methods: We conducted a systematic review and meta-analysis of cohort studies comparing persistent symptoms or conditions in adults after SARS-CoV-2 infection with those following other acute respiratory viral infections. PubMed, Embase, and Scopus were searched. Random-effects models were used to estimate pooled risks. Results: Among 9,371 records screened, 22 studies were included and 14 contributed to the meta-analysis. Increased risk after SARS-CoV-2 infection was observed for pulmonary embolism, abnormal breathing, fatigue, hemorrhagic stroke, memory loss/brain fog, and palpitations; heart rate abnormalities showed borderline significance. For most other outcomes pooled estimates were inconclusive. Conclusions: Only a subset of outcomes appears more frequent after SARS-CoV-2 infection, suggesting many symptoms attributed to PCC may reflect broader post-viral syndromes.

Background: Long COVID presents with one or multiple symptoms or diagnosable conditions after SARS-CoV-2 infection. To study whether use of the antiviral remdesivir in persons hospitalized with acute COVID-19 is associated with reduced Long COVID, we created a computational phenotype for Long COVID. Methods: In electronic health records (EHR) from a multistate healthcare system (US), hospital admissions from 5/1/20 - 9/30/22 were reviewed. The study group was hospitalized with acute COVID-19 and the control group was hospitalized for other reasons without prior SARS-CoV-2 infection. The populations were balanced with overlap weights based on a high-dimensional propensity score of pre-specified variables and the top 100 comorbidities differing between the groups. Hazard ratios (HR) were calculated for the combined primary outcome: U09.9 (Post-Covid Conditions) or any incident secondary outcome from 90 to 365 days after admission. Secondary outcomes included 27 individual incident diagnoses, corrected for multiplicity with Holm-Bonferroni. Results: Admissions included 45,540 with, and 409,186 without COVID-19 during the study period, evaluable for the primary outcome. After weighting, standardized difference was < 0.01 for all measured confounders including demographic and clinical features. In the COVID+ and non-COVID groups 38.0% and 29.3% met the combined primary outcome, respectively. Weighted HR (95%CI) for the primary outcome was 1.37 (1.35, 1.40), p < 0.0001. All secondary outcomes were associated with the COVID+ group, when adjusted for multiplicity. Incident diagnoses with strong associations (HR > 2) included thromboembolism, hair loss, diabetes mellitus, obesity, and hypoxia. Anosmia/dysgeusia was associated with COVID, but wide confidence intervals reflected few charted diagnoses. Conclusions: Manifestations of Long COVID at population scale are detectable as part of routine symptoms and clinical diagnoses in the EHR after admissions for COVID-19, compared with all other hospital admissions. This a prior computational phenotype for Long COVID will be used to assess whether remdesivir use is associated with decreased Long COVID.

BackgroundMulti-system impacts of long COVID remain unknown. We compared multi-system deficits between people with long COVID and controls. MethodsA case-control study recruited from the Avon Longitudinal Study of Parents and Children and TwinsUK population cohorts. Cases (141) had long COVID (evidence of COVID-19 infection and persistent symptoms [&ge;]4 weeks post infection); controls (280) included people making a full recovery in <4 weeks, people self-reporting long COVID like symptoms but without wild-type SARS-CoV-2 virus antibodies, and people without symptoms or history of COVID-19 infection. Participants underwent multi-system MRI, (cardiac, brain, lung, kidney), measurement of blood pressure and autonomic function, tests of exercise performance, spirometry, renal function, strength and physical capability. System-specific deficits were summed to a total potential score of 27. FindingsParticipants attended clinic between 2021-23. Overall deficit score in cases was 0.22 (95% CI -0.44,0.88) units greater than controls, adjusted for age, sex, ethnicity, cohort membership and relatedness. This estimate was little changed (0.32 (-0.34, 0.98)) when additionally adjusted for educational status, index of multiple deprivation, physical activity, smoking and co-morbidity. Restricting cases to those reporting at least fatigue (46) increased the excess deficit score to 0.81 (-0.19,1.81) units in the minimally adjusted model. A difference was only observed in the vascular domain, largely attributable to elevated blood pressure, showing a 1.76 (1.04,2.97) multivariable adjusted odds ratio excess in cases, and 3.04 (1.36,6.80) when restricted to cases with fatigue. InterpretationPeople with community-based long COVID should be reassured that there is not marked residual deficit across multiple systems. However, blood pressure measurement and control should be included in clinical follow-up. FundingJointly funded by the National Institute for Health and Care Research and UK Research and Innovation (CONVALESCENCE, COV-LT-0009, MC_PC_20051).

BackgroundTo date, accessible diagnostic tools to identify whether a patients pneumonia is a bacterial, or viral infection, are not accurate or timely enough to prevent preemptive antibiotic administration. Relying on single biomarkers or clinical presentations has been insufficient. We aimed to incorporate a wide range of novel biomarkers and clinical presentations in a multivariable model and validate its capacity to differentiate cases of bacterial and viral pneumonia. MethodsData from 457 children aged 2-59 months, admitted to Kilifi County Referral Hospital, Kenya, with bacterial (n = 229) and viral (n = 228) infections, were used to develop and validate a predictive multivariable Poisson regression model to differentiate pneumonia etiology. The Receiver Operating Characteristic curve was used to assess biomarker performance and validate the model internally. ResultsSixty-three percent (63%) of the children presented with severe pneumonia. 72% with viral pneumonia had severe pneumonia, compared to 54% with bacterial pneumonia who had severe pneumonia. In crude analyses, chest-wall indrawing, cough, convulsions, crackles, angiotensinogen, and Serpin Family A Member 1 were significantly associated with pneumonia etiology, controlling for age. However, only chest-wall indrawing remained significant in multivariable analyses after controlling for age. The model demonstrated fair, but inadequate, discrimination, with an Area Under the Curve of 0.61. ConclusionAmong the children admitted to hospital with WHO defined pneumonia, a wide range of biomarkers and clinical presentations still failed to distinguish bacterial from viral pneumonia.

IntroductionNeurocognitive impairment (NCI) remains common among people living with HIV (PWH), particularly in low- and middle-income countries where accurate diagnostic tools are limited. In Peru, the lack of locally validated neuropsychological (NP) normative data in Spanish poses a major barrier to diagnosing HIV-associated NCI, especially among PWH who develop NCI at younger ages. This study aimed to develop regression-based NP norms for young and middle-aged Spanish-speaking adults in Lima, Peru and validate the norms in demographically similar PWH to improve diagnostic precision of HIV-associated NCI. MethodsA total of 164 healthy adults without HIV from Lima completed a comprehensive NP battery assessing memory, attention, executive function, and language, which are commonly affected in HIV-associated NCI. Multiple regression models were used to consider the influence of age, years of education, and sex on raw scores, yielding standardized demographically-adjusted norms for the population. The resulting norms were then applied to 310 PWH from Lima and then compared with previously published norms for Spanish speaking adults to evaluate performance differences. ResultsAge and education were the strongest predictors of performance across tests, while sex had minimal influence. Compared to people without HIV, PWH had significantly lower educational attainment (mean 12.6 vs. 13.7 years) and exhibited significantly worse performance on normed scores of Benson Figure Copy, Benson Figure Delayed Recall, Color Trails 1 and 2, Hopkins Verbal Learning Test - Revised, and WAIS-III Digit Symbol Coding, Digit Span, and Symbol Search. There were statistically significant differences between T-scores on nearly all tests between our population-specific norms and previously published norms in both directions, indicating potential over- and under-detection errors when applying norms from non-local samples. DiscussionOur findings highlight the utility of locally derived norms in detecting subtle cognitive changes among young and middle-aged PWH compared with previously published norms for Spanish-speakers. Application of these norms reveals significant between-group differences that may go undetected using non-local normative data or raw scores. Future efforts should focus on rural norm development and inclusion of individuals with lower educational backgrounds in Peru and other Latin American countries.

BackgroundLong Covid, also referred to as post-acute sequelae of SARS-CoV-2 infection (PASC), is characterized by symptoms that persist or emerge weeks to months following acute COVID-19 illness. Cognitive impairments affecting attention, memory, and executive functioning--commonly described as "brain fog"--are frequently reported. Currently, limited evidence-based cognitive rehabilitation interventions specifically target these impairments. ObjectivesThis pilot randomized controlled trial aims to evaluate the feasibility and acceptability, and to develop preliminary data regarding efficacy of Attention Process Training-3 (APT-3), a computerized attention training program, for individuals with Long Covid-related brain fog. MethodsThis study uses a three-arm randomized controlled design. Participants are randomized to (1) Immediate Attention Training, (2) Delayed Attention Training, or (3) Music Activity. Participants complete comprehensive cognitive assessments at baseline, post-intervention, and one-month follow-up. The Immediate Attention Training group completes a 4-week APT-3 intervention, while the Music Activity group engages in a 4-week music-based listening activity. The Delayed Attention Training group dont receive any intervention for 4 weeks. Following completion of the final assessment, participants in the Music Activity and Delayed Attention Training groups are offered the APT-3 intervention. Feasibility and acceptability outcomes include recruitment, retention, and adherence numbers, and participant satisfaction. Preliminary data regarding efficacy will be determined using objective cognitive tests and subjective self-report measures. ConclusionsThis pilot trial will inform the feasibility and acceptability of APT-3 and generate preliminary efficacy data to guide the design of a future fully powered randomized controlled trial targeting brain fog associated with Long Covid.

ObjectiveBiologically defined sepsis subtypes have been identified in low- and middle-income countries (LMICs), but limited access to molecular diagnostics challenges broader evaluation and implementation in resource-limited settings. We assessed whether models including bedside clinical and rapid microbiologic data could accurately stratify Ugandan adults with sepsis by molecular subtype. DesignSecondary analysis of two prospective observational sepsis cohorts, testing bedside-adaptable classifier models against transcriptomic and proteomic subtype assignments. SettingEntebbe Regional Referral Hospital (urban) and Tororo General Hospital (rural), Uganda. PatientsAdults ([&ge;]18 years) hospitalized with sepsis, with available transcriptomic (N=355) and/or proteomic (N=495) profiling enabling subtype assignment. InterventionsNone. Measurements and Main ResultsUsing data from two prospective cohorts (RESERVE-U-2-TOR and RESERVE-U-1-EBB), we evaluated bedside-adaptable models against Uganda-derived molecular sepsis subtypes, and, secondarily, against molecular subtypes and axes derived in high-income countries. In RESERVE-U-2-TOR, clinical models including demographics and bedside physiological variables demonstrated moderate discrimination for transcriptomic and proteomic subtype assignment (AUROC 0.75 [95% CI, 0.69-0.81] and 0.73 [0.66-0.80], respectively) with strong calibration (Integrated Calibration Index [Eavg] [&le;]0.015 for both models). Adding rapid diagnostic results for HIV, malaria, and tuberculosis produced similar performance (AUROC 0.76 and 0.74; Eavg [&le;]0.016). In RESERVE-U-1-EBB, discrimination for clinical and clinico-microbiological models was more variable (AUROC range 0.63-0.75) while calibration remained acceptable (Eavg [&le;]0.053). Performance was similar when models were evaluated against molecular sepsis frameworks derived in high-income countries, with acceptable calibration and moderate discrimination. ConclusionsBedside-adaptable clinical models, with or without rapid microbiologic testing, demonstrated acceptable calibration but only modest discrimination for molecular sepsis subtype assignment in Uganda. Expanding laboratory capacity and access to scalable, low-cost molecular biomarker assays will be necessary to advance precision sepsis care in LMIC settings. Key PointsO_ST_ABSQuestionC_ST_ABSAmong adults hospitalized with sepsis in a resource-limited setting, can bedside clinical variables, alone or combined with rapid pathogen diagnostics, accurately stratify molecular sepsis subtype assignments? FindingsIn two prospective Ugandan sepsis cohorts, bedside clinical and clinico-microbiologic models showed robust calibration but only modest discrimination for classifying Uganda-derived transcriptomic and proteomic subtypes. Models also achieved moderate performance for stratifying high-income-country-derived transcriptomic subtypes and immune dysfunction axes, suggesting bedside variables reflect illness severity but incompletely capture underlying molecular signatures. MeaningBedside-adaptable models can support reasonably calibrated risk estimation for molecular sepsis stratification in resource-limited settings but lack sufficient discriminatory power to serve as stand-alone tools. These findings support efforts to improve acute-care laboratory capacity and access to scalable molecular biomarker panels, with the goal of enabling precision sepsis care in low- and middle-income countries.

Background: The 2024-25 influenza season was the most severe in the United States (US) since 2017-18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. Methods: Using electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged 18+ years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. Results: Analyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED/UC encounters) among adults. Among children across care settings, 17.0% (6,097/35,765) of cases versus 29.4% (21,449/72,853) of controls were vaccinated. Among adults, 28.2% (21,832/77,477) of cases versus 44.2% (102,560/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza-associated ED/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza-associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED/UC encounters among adults. Conclusions: Influenza vaccination provided protection against influenza-associated hospitalizations and ED/UC encounters among children and adults in the US during the severe 2024-25 influenza season. These findings support influenza vaccination as an important tool to reduce influenza-associated disease.

PurposeVentilator-associated pneumonia (VAP) remains one of the most serious hospital-acquired infections in the intensive care unit (ICU), with high morbidity and mortality. Early identification of patients at risk for developing VAP could enable timely diagnostics and intervention. However, current clinical tools are limited in their ability to detect early physiologic signals preceding VAP onset. We aimed to build supervised machine learning models to predict short term onset of VAP. MethodsWe analyzed electronic health record data from a prospective observational cohort of ICU patients, where VAP was adjudicated using a standardized published protocol by a panel of critical care physicians. Clinical features (including vital signs, ventilator settings, laboratory values, and support devices) were extracted for each patient-ICU-day. We explored unsupervised clustering to characterize feature dynamics associated with VAP onset. We built multiple machine learning models across different prediction windows (3, 5, 7 days before VAP). We examined model performance in two external cohorts, MIMIC-IV and secondary analysis of the AMIKINHAL trial. Results were evaluated with discrimination metrics such as AUROC. ResultsThe internal cohort included 507 patients with BAL-confirmed diagnoses: 261 developed VAP and 246 did not have VAP. Visualization using clustering identified distinct physiologic states enriched for VAP-labeled days. The best-performing model achieved an AUROC of 0.866 in predicting VAP up to seven days before clinical diagnosis. Temporal model probability trajectories showed rising model confidence in the days leading up to VAP. On external validation in MIMIC-IV, the best model achieved an AUROC of 0.817 for forecasting VAP within five days. There was low feature overlap with the AMIKINHAL trial data, leading to poor model performance. Feature analysis revealed that platelet count, positive end-expiratory pressure (PEEP), ventilator duration, and inflammatory markers were key drivers of model predictions. ConclusionsMachine learning models trained on routinely collected ICU data with careful labeling can anticipate VAP onset up to a week in advance with strong predictive performance. Model performance generalized to data from an entirely different hospital system despite differences in practice and labeling patterns, but did not perform well when there was poor feature overlap. Future work should focus on real-time prospective evaluation.

Background Outcome after stroke varies according to stroke subtype by location, but healthcare systems data studies do not include subtyping information. We linked natural language processing (NLP) of brain imaging reports to routinely collected data to estimate risk of death and other outcomes after stroke subtypes in a nationwide dataset. Methods We applied a previously validated NLP algorithm to all CT and MRI head scan reports in Scotland between 2010 and 2018. We linked the reports to hospital readmissions, prescriptions and death data to identify and characterize people with stroke, and to categorize into deep and cortical ischemic stroke, deep and lobar intracerebral hemorrhage (ICH), subarachnoid hemorrhage, and subdural hemorrhage. We used a matched cohort design, and age- and sex-matched four controls per case who never had a stroke. By subtype, we estimated rehospitalization with stroke, myocardial infarction (MI), cancer, dementia, epilepsy and death, accounting for confounders and competing risk of death. Results From 785,331 people with a head scan, we identified 64,219 with clinical stroke phenotypes (mean age 73.4yrs, 49.5% male), and subtyped 12,616 with deep ischaemic stroke; 14,103 with cortical ischaemic stroke; 1,814 with deep ICH; and 1,456 with lobar ICH. There was higher absolute rate of 1-year hospital readmission for lobar compared with deep ICH (4.9% [95%CI 3.9% - 6.1%] vs 3.4% [2.6% - 4.3%]), higher risk of dementia beyond 6 months after lobar ICH compared to controls than for other stroke subtypes (aHR 3.5 [2.3-5.3]); and higher risk of MI within 6 months of cortical ischemic stroke than for other stroke subtypes (aHR 4.6 [3.4-6.3]). Conclusions NLP of free-text reports linked to coded data successfully subtyped stroke at scale, and we estimated risk of clinically relevant outcomes. Future work should use free text to enable large-scale audit and epidemiology of people with stroke.

Objectives: Among individuals attending stroke rehabilitation, we aimed to determine the proportion who participated in cardiorespiratory exercise, identify patient characteristics predicting participation, and describe exercise characteristics. Design, setting, and participants: This was an observational cohort study involving all patients admitted to four stroke rehabilitation centres in Ontario, Canada, during March or October 2019, or over 12 months starting in 2021. Main measures: Patient characteristics extracted during chart review included age, sex, marital status, employment status, date of stroke, time post-stroke at admission, length of stay for rehabilitation, past medical history that could affect exercise participation, Functional Independence Measure, Functional Ambulation Category, mobility aid use, Chedoke-McMaster Stroke Assessment, Montreal Cognitive Assessment, National Institutes of Health Stroke Scale, and details describing cardiorespiratory exercise completed. Results: 40.1% of stroke patients participated in cardiorespiratory exercise, with 26.4% having it included in their treatment plan. Diagnosed cardiac disease (OR=0.74), poor left ventricular function (OR=0.09), history of mental health conditions (OR=0.69), lower functional ambulation ability (OR=0.74), and wheelchair use at rehabilitation admission (OR=0.46) were associated with lower odds of participating in cardiorespiratory exercise after stroke (p-values<0.05). Use of a walker or rollator at rehabilitation admission (OR=3.22), having a cardiorespiratory exercise goal (OR=2.13), and longer lengths of stay (OR=1.01) were associated with higher odds of participating in cardiorespiratory exercise after stroke (p-values<0.05). Only 1.5% of patients (N=9/601) who participated in cardiorespiratory exercise completed it with recommended intensity and duration. Conclusion: Improving participation in cardiorespiratory exercise during stroke rehabilitation may require addressing cardiovascular, mental health, and mobility-related barriers.

BACKGROUNDRespiratory viral infections can trigger acute myocardial infarction (AMI). However, the proportion of AMI events attributable to viral respiratory pathogens is unclear. METHODSThis ecological study used time-series and spatiotemporal analyses to examine population-level patterns in Victoria, Australia, from 2010 to 2022. Independent statewide admissions and laboratory datasets were obtained. Generalized additive modelling was used to analyze the temporal association between respiratory viral circulation captured by polymerase chain reaction (PCR) testing and weekly counts of AMI admissions. A Bayesian hierarchical model was used to explore spatiotemporal variation in AMI associated with respiratory viruses. RESULTSOur study included 164 283 AMI hospital admissions and 6 180 896 PCR-tested samples. An increase in any respiratory virus detection rate was significantly associated with an increased incidence of AMI (incidence rate ratio [IRR] 1.0041; 95% confidence interval 1.0015-1.0067), after adjusting for seasonality, cold temperature, and fine particulate matter air pollution. An estimated 8.7% of total AMI events may be attributable to respiratory viral triggers, constituting an average annual incidence of 16.2 per 100,000 population. Significant associations were found with specific respiratory viruses; the fractions of AMI attributable to enterovirus, influenza, and respiratory syncytial virus were 5.2%, 1.5%, and 0.9%, respectively, with figures increased during peak viral seasons. Spatiotemporal analysis revealed that the association was more pronounced in outer-metropolitan areas. CONCLUSIONSRespiratory viral triggers contribute to the incidence of AMI. Population-level infection prevention strategies, such as vaccination, may reduce the impact of respiratory viral outbreaks during peak seasons. CLINICAL PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABSO_LIUsing time-series analysis and modern spatiotemporal techniques, we analyzed data from Victoria, Australia, to model population-level associations between AMI and respiratory viral activity and found that a recent laboratory-confirmed respiratory viral infection is associated with a higher incidence of AMI. C_LIO_LIAn estimated 8.7% of total AMI events may be attributable to respiratory viral triggers, constituting an average annual incidence of 16.2 per 100,000 population. C_LI What Are the Clinical Implications?O_LISome respiratory viral infections temporarily increase the risk of acute MI. C_LIO_LIWith the existing vaccines available against influenza and respiratory syncytial virus (RSV), public health policy actions for influenza and RSV vaccination, particularly in high-growth urban areas, may help reduce the acute cardiovascular burden and health system strain. C_LI

BackgroundCystoscopy is a core component of haematuria investigations but is invasive and resource-intensive. GALEAS Bladder is a DNA-based diagnostic urine test that measures alterations in 23 bladder cancer-associated genes. ObjectiveTo assess the diagnostic performance and clinical utility of GALEAS Bladder as a molecular triage tool in real-world haematuria investigation pathways. MethodsPatients referred for urgent investigation of haematuria were prospectively enrolled across seven UK NHS Urology Departments between October 2024 and June 2025. Urine samples were collected prior to cystoscopy and analysed using the GALEAS Bladder assay (Nonacus Clinical Services, UK). Assay results were compared with cystoscopy findings. Key Findings and LimitationsCystoscopic findings and GALEAS Bladder results were available for 964 participants, including 77 (8.0%) newly-diagnosed with pathology-confirmed BC. The assay demonstrated an overall sensitivity of 92.2% (95% CI: 84.0-96.4%), specificity of 92.0% (95% CI: 90.0-93.6%), and negative predictive value (NPV) of 99.3% (95% CI: 98.4-99.7%) for the diagnosis of BC. For the diagnosis of high-grade BCs, sensitivity was 97.2% (95% CI: 85.8-99.5%) with an NPV of 99.9% (95% CI:99.3-100.0%). Limitations include an absence of subsequent diagnoses for participants with positive GALEAS Bladder test results in the absence of cystoscopically-visible tumour. Conclusions and Clinical ImplicationsGALEAS Bladder is a clinically implementable molecular urine test with very high sensitivity and specificity for the diagnosis of new cases of BC in patients undergoing urgent investigation of haematuria, especially for high-grade BCs. Clinical adoption could permit the molecular triage of haematuria patients to immediate or deferred cystoscopy.

BackgroundReliable identification of early predictors of adverse outcomes was essential during the pre-vaccination phase of the COVID-19 pandemic. Few studies have comprehensively integrated clinical presentation, laboratory parameters including arterial blood gas analysis, and chest computed tomography (CT) findings within a single well-characterized cohort, particularly in underrepresented regions of Brazil. MethodsThis retrospective cohort study included 482 consecutive adults (median age 61 years [IQR 49-73]; 64.3% men) with RT-PCR-confirmed SARS-CoV-2 infection hospitalized at a tertiary cardiac center in Central-West Brazil between March 2020 and January 2021. Demographic, clinical, laboratory (including arterial blood gas analysis), and chest CT data obtained within 48 hours of admission were analyzed. Univariable logistic regression was performed for 76 variables. Multivariable models were constructed using an a priori variable selection strategy based on clinical relevance, representation of distinct pathophysiological domains, and adherence to events-per-variable principles. Complete-case analyses were performed without imputation. ResultsIn-hospital mortality was 9.3% (45/482). Invasive mechanical ventilation was required in 74 patients (15.4%), with a mortality rate of 58.1% among those ventilated. In univariable analysis, 42 variables were associated with mortality (p < 0.05). In multivariable analysis (n = 438), five independent predictors of death were identified: age (adjusted OR 1.66 per 10 years; 95% CI 1.19-2.32; p = 0.003), arterial pH (adjusted OR 0.47 per 0.1-unit increase; 95% CI 0.25-0.89; p = 0.021), neutrophil-to-lymphocyte ratio (adjusted OR 1.30; 95% CI 1.18-1.44; p < 0.001), number of comorbidities (adjusted OR 1.59; 95% CI 1.25-2.02; p < 0.001), and serum creatinine (adjusted OR 1.37; 95% CI 1.05-1.77; p = 0.019). The model demonstrated good calibration (Hosmer-Lemeshow p > 0.05) and moderate-to-high explanatory power (Nagelkerke R{superscript 2} = 0.43). For the composite outcome of death or invasive mechanical ventilation (74 events; 15.4%), four predictors remained independently associated; serum creatinine showed a non-significant trend (p = 0.069). On chest CT (n = 424), analyzed descriptively and in univariable models only, pulmonary involvement > 50% was associated with increased odds of death (OR 2.87; 95% CI 1.42-5.79; p = 0.003). ConclusionsFive admission variables representing distinct pathophysiological domains--age, arterial pH, neutrophil-to-lymphocyte ratio, comorbidity burden, and serum creatinine--were independently associated with in-hospital mortality in this pre-vaccination cohort. Arterial pH provided independent prognostic information beyond inflammatory and renal markers. These findings support early risk stratification using routinely available clinical data.

BackgroundOut-of-hospital cardiac arrest (OHCA) survival depends on timely bystander cardiopulmonary resuscitation (CPR) and quick defibrillation via automated external defibrillator (AED). However, access to CPR education and willingness to intervene are not equitably distributed. Within the Muslim community, intersecting religious identity, language, immigration-related concerns, and other social determinants of health may affect CPR/AED education, bystander response, and ultimately OHCA outcomes, underscoring the need for culturally responsive, faith-based training models. MethodsA survey based cross sectional study was conducted to evaluate the perceived barriers to emergency response and lay rescuer cardiopulmonary resuscitation (CPR). Individuals aged 13 years and older were recruited between January and June 2025 through convenience sampling at free, non-certification public CPR/AED classes, where participants self-reported demographic characteristics and barriers to calling 9-1-1 or initiating CPR. Analyses compared Muslim and non-Muslim participants using Fisher exact tests and multivariable logistic regression models adjusted for demographic and socioeconomic factors, with results reported as odds ratios (OR) and 95% confidence intervals (CI). ResultsOf the 651 surveys collected, 33% of participants identified as Muslim, and 46% reported no prior CPR/AED training, with a higher proportion among Muslim respondents (57% vs 41%). Religion was significantly associated with some perceived barriers, with Muslim participants more likely to report law enforcement as a barrier to calling 9-1-1 (OR: 0.53 for non-Muslims vs Muslims, p=0.04) and less likely to report "no problem" starting CPR (OR: 0.91, p=0.04). Race and gender also influenced barriers, with non-white and female participants more likely to report immigration status, language, cost, and concern for violence as barriers to initiating CPR or calling 9-1-1. ConclusionMuslim participants were more confident in performing CPR, but reported less confidence in calling 9-1-1, revealing gaps in emergency response readiness. This emphasizes the importance of culturally adapted CPR/AED training that addresses specific barriers within faith-based communities and to strengthen all links of the chain of survival.

Background The ageing of incarcerated populations is accelerating across high-income countries, yet dementia remains absent from routine correctional mental health statistics. We investigated whether correctional data systems in Japan, the United States, the United Kingdom, and Australia are structurally capable of detecting dementia in their prison populations. Methods We conducted a cross-national descriptive analysis of publicly available, aggregate-level correctional data. Japanese data comprised all newly admitted sentenced prisoners from 2006 to 2024 (approximately 390,000 individuals) from the Ministry of Justice Correctional Statistics Annual, including mental disorder classifications and CAPAS-derived work aptitude scores (used as a proxy for cognitive functioning; not clinical IQ measurements). US data were drawn from the Bureau of Justice Statistics Survey of Prison Inmates (2016). UK data were obtained from the Ministry of Justice Offender Management Statistics Quarterly (2015-2025). Australian data were sourced from the Australian Institute of Health and Welfare National Prisoner Health Data Collection (2022, n = 371). All analyses were descriptive; no inferential statistics were conducted. Findings Three distinct mechanisms rendered dementia statistically invisible across all four countries. First, in the United States and Australia, reliance on self-report instruments produced a paradox in which self-reported mental disorder prevalence declined with age: among US state prisoners, reported prevalence fell from 44.9% in the 35-44 age group to 31.9% among those aged 65 and older - the opposite of community epidemiological patterns. Second, in Japan - the only country with systematic cognitive assessment at prison admission - 35.0% of female theft offenders had work aptitude scores below 70, yet the classification system contains no dementia category; 43-52% of all detected mental disorders were absorbed into a residual "other" category even after a 2023 classification revision that added four new diagnostic categories but not dementia. Third, the United Kingdom lacks routine mental health prevalence data collection in prisons altogether. None of the four countries includes dementia as a standard correctional classification category. Interpretation Correctional mental health statistics across four high-income countries are structurally incapable of detecting dementia - not through clinical ignorance but by design: systems built for younger populations that have not been updated as prison demographics have changed. Japan's ageing female theft offender profile (39.4% aged 60 or older, 35.0% with low cognitive scores) represents a potential sentinel population for undetected cognitive impairment. Targeted interventions - cognitive screening at admission in the United States and Australia, introduction of a dementia classification category in Japan, and routine mental health data publication in the United Kingdom - are feasible with existing infrastructure. As prison populations continue to age, the statistical invisibility of dementia constitutes an escalating failure of health surveillance with direct consequences for clinical care, sentencing, and human rights.

IntroductionBuilding on our previously published methodology for claims-based pathway mapping, we extended the analysis by incorporating disease staging. The aim of this study was to develop and evaluate quality indicators (QIs) in patients with non-small cell lung cancer (NSCLC). MethodsThis retrospective, longitudinal cohort study spanned 2017-2023, with follow-up data extending to September 2025. Data were obtained from the National Cancer Registry (NCR), the National Registry of Reimbursed Health Services (NRRHS), which is organized through seven health insurance funds providing nationwide coverage. The index date was defined as the date of the first biopsy (BX) followed by a histopathological examination (HP), along with the ICD-10 code C34. Incident patients aged [&ge;]18 years were included if no prior malignancy was reported, and the presence of PET/CT or CT examination was mandatory in the final verified cohort. The presence of multidisciplinary team (MDT) discussion, time to treatment, availability of care in a Complex Oncology Center (COC), and completeness of predictive biomarker testing were considered key QIs. ResultsWe analyzed the care pathways of 15,886 patients with NSCLC; 3,380 (21.3%) were not treated, and 1,837 (11.6%) were excluded due to the absence of (PET) CT prior to biopsy (BX). The final verified cohort included 10,669 patients with a median age of 69 years (interquartile range, 64-74). The incident stage distribution comprised of stage I/II (27.6%), stage III/IV (67.9%), and 4.5% unknown. Multidisciplinary team (MDT) review was reported in 53.9% of patients, with a median time to MDT discussion of 37 days. Surgery (SX) was performed in 81.0% of stage I and 68.4% of stage II patients. Fewer than 50% of patients initiated treatment within 8 weeks, regardless of disease stage. Centralization of care in COCs and implementation of MDT review showed a positive temporal trend, although disparities across disease stages and regions persisted. PD-L1 testing was documented in 70.0% of stage IV and 65.2% of stage III patients. ConclusionsAdministrative claims data linked with the NCR enabled stage-stratified monitoring of NSCLC care pathways and the identification of actionable QIs, which were implemented as a national tool for continuous quality evaluation of cancer care in the Czech Republic. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIPatient pathway monitoring and quality indicators (QIs) for lung cancer care -- including timeliness of treatment, multidisciplinary team discussion (MDT), and centralization in specialized centers (COCs) -- have been established in several European countries. C_LIO_LIPopulation-level data integrating administrative claims with cancer registry staging data to evaluate QIs across disease stages remain limited. C_LI What this study addsO_LIStage-stratified analysis of 10,669 NSCLC patients revealed that fewer than 50% initiated treatment within 8 weeks, with a declining trend over time despite improvements in MDT utilization and care centralization in COCs. C_LIO_LIPD-L1 testing rates in stages III-IV increased over 2021-2023 but showed substantial regional variability, highlighting opportunities for improving equity of access to biomarker-guided therapy. C_LI How this study might affect research, practice or policyO_LIThe methodology has been implemented as a national tool for continuous quality evaluation of cancer care in the Czech Republic, with PD-L1 testing completeness proposed as an additional OI alongside MDT discussion, time to treatment, and COC centralization. C_LI

BackgroundSedentary behavior is highly prevalent following traumatic brain injury (TBI) and compounds existing risks for cardiovascular, neurodegenerative, and affective disorders. The cognitive and behavioral sequelae of TBI, including impaired decision-making, blunted reward processing, and cognitive fatigue, create particular barriers to adopting and maintaining an active lifestyle. Despite this, effective behavior change interventions targeting physical activity in community-dwelling TBI survivors remain scarce. Here, we evaluated the feasibility, compliance, and preliminary efficacy of a 12-week remotely delivered walking intervention combining planning, behavioral reminders, and monetary micro-incentives. MethodsFifty-six adults aged 40-80 years with a mild-to-moderate TBI diagnosed between 3 months and 15 years prior were randomized to either a planning, reminders, and micro-incentives intervention (n=23) or a health advice control condition (n=25). Participants wore a Fitbit Inspire 3 continuously throughout the study. Intervention participants completed weekly phone calls to plan five 30-minute walks for the following week, received daily text message or email reminders on planned walk days, and earned small monetary incentives upon walk completion. Control participants received weekly health education calls. Feasibility was assessed through recruitment, retention, and adverse event rates. Compliance was assessed via phone call completion rates and Fitbit wear time. Efficacy outcomes included weekly walk counts, walking duration, and step counts, modeled using Poisson generalized linear mixed models and linear mixed-effects models over 12 weeks. ResultsForty-eight participants completed the study (retention rate: 84.2%), with high phone call compliance in both groups (intervention: 98.4%; control: 98.1%). Intervention participants completed significantly more walks than controls from week 1 onward (aIRR = 5.33, 95% CI: 2.27-12.5, p < 0.001), with the group difference growing over time (interaction aIRR = 1.09 per week, 95% CI: 1.01-1.17, p = 0.029). Estimated marginal means indicated that intervention participants completed 5.5 times more walks than controls at week 1, increasing to 15.5 times more by week 12. The intervention group also walked significantly longer at week 1 (b = 62.14 min, 95% CI: 1.05-123.23, p = .046), with the advantage growing over time; by week 12, intervention participants walked 5.3 times longer than controls. Similarly, the intervention group accumulated significantly more steps during walks at week 1 (b = 4,779 steps, 95% CI: 45.50-9,513.00, p = .048), accumulating 3.1 times more steps than controls by week 12. ConclusionsA remotely delivered, multicomponent walking intervention targeting planning, behavioral reminders, and micro-incentives was feasible, well-tolerated, and produced meaningful increases in walking activity in community-dwelling adults with TBI. With high retention and compliance, and consistent effects on walk counts, duration, and steps across the intervention period, these findings provide compelling support for a larger, fully powered trial.

ObjectivesTo quantify patterns of preventable death in Australian coronial findings, measure government compliance with coroner recommendations across jurisdictions, and identify case characteristics associated with recommendation issuance and acceptance. DesignCross-sectional computational text analysis of publicly available coronial findings using unsupervised topic modelling and rule-based classification. SettingAustralian coronial system, all eight state and territory jurisdictions, findings published on the Australasian Legal Information Institute (AustLII) database, 2000-2024. Participants9833 coronial findings and 2040 linked government responses. Main outcome measuresDeath-type topic prevalence, recommendation rate by jurisdiction, government response acceptance rate. ResultsTwenty-six death-type topics were identified, with medical/surgical deaths (12.5%), men-tal health (10.4%), and deaths in custody (8.6%) most prevalent. Overall, 45.6% of published find-ings contained formal recommendations (95% CI, 44.6-46.5%). Of 2040 government responses, 43.0% were unclassifiable (predominantly Victorian administrative cover letters). Among classifi-able responses, 53.2% were accepted (implemented, already implemented, or partially accepted), ranging from 26.0% (Western Australia) to 88.0% (Queensland). Multivariable logistic regression showed that jurisdiction was the strongest measured predictor of acceptance (pseudo R2 0.13 vs 0.14 with all covariates), though most variance remained unexplained. Among published findings, In-digenous Australians were represented in 10.1% (2.7 times the 3.8% population share).12 Findings involving medication errors had the highest recommendation rate (55.1%) but among the lowest acceptance rates (26.4%). ConclusionsAmong publicly available coronial findings, fewer than half contain formal recom-mendations. Government acceptance is low and structurally determined by jurisdiction rather than case characteristics, suggesting that legislative reform is needed to improve the systems preventive effectiveness.