Nevin Manimala

Foot Ankle Spec. 2025 Nov 1:19386400251383440. doi: 10.1177/19386400251383440. Online ahead of print.

ABSTRACT

BackgroundThis study aims to analyze the effect of preoperative 5-factor modified frailty index (mFI-5) on 30-day complication, readmission, reoperation, and mortality rates following ankle fracture open reduction and internal fixation (ORIF).MethodsThe American College of Surgeons (ACS NSQIP) database was queried to identify 44 838 patients undergoing ankle fracture ORIF. Patients were stratified into groups based on preoperative mFI-5 scores.ResultsThe cohort was predominantly male (59.0%), and the mean age was 49.52 (range = 16-89) years. The mFI-5 score was statistically significantly predictive of any complication (P < .001), serious medical complication (P < .001), surgical site infection (P <.001), readmission (P <.001), reoperation (P <.001), mortality (P <.001), adverse discharge (P <.001), and increased hospital length of stay (LOS) (P <.001).ConclusionOur results indicate that mFI-5 score is a useful predictive measure for postoperative complications, adverse discharge, readmission, reoperation, mortality, and increased LOS in patients undergoing ankle fracture ORIF.Levels of Evidence:Level III, Retrospective cohort study.

PMID:41175018 | DOI:10.1177/19386400251383440

Ther Adv Respir Dis. 2025 Jan-Dec;19:17534666251390672. doi: 10.1177/17534666251390672. Epub 2025 Nov 1.

ABSTRACT

BACKGROUND: Interstitial lung disease (ILD) leads to progressive lung function decline and significant respiratory symptoms. Although antifibrotic agents preserve lung function and reduce mortality in ILD, their impact on health-related quality of life (HRQoL) remains unclear.

OBJECTIVES: We aimed to evaluate whether antifibrotic agents improve HRQoL and their effectiveness in treating HRQoL-related symptoms in patients with ILD.

DESIGN: Systematic review and meta-analysis.

DATA SOURCES AND METHODS: A literature search was conducted using MEDLINE, EMBASE, and the Cochrane Library from inception to August 25, 2025. The search included terms related to ILD, antifibrotic agents, and measures of HRQoL. HRQoL outcomes were assessed using the St. George’s Respiratory Questionnaire (SGRQ), including total and domain scores. Data were pooled using a random-effects model, with outcomes reported as mean differences (MD) or relative risks (RR) and heterogeneity evaluated using the I² statistic.

RESULTS: A total of 13 randomized controlled trials were included. Antifibrotic agents showed significant improvement in SGRQ scores, particularly in the symptom (MD: -2.59, 95% confidence interval [CI]: -4.56 to -0.61; I² = 32%) and activity (MD: -2.88, 95% CI: -4.82 to -0.94; I² = 34%) domains. Antifibrotics reduced the rate of cough (RR: 0.77, 95% CI: 0.64-0.94; I² = 0%) and dyspnea (RR: 0.71, 95% CI: 0.56 to 0.89; I² = 0%). However, fatigue was frequently observed in patients treated with antifibrotics (RR: 1.48, 95% CI: 1.20-1.83; I² = 0%) compared with the non-antifibrotic group. Most trials were judged to have low-to-moderate risk of bias, and the certainty of evidence was rated very low for total SGRQ scores but low to moderate for domain-specific outcomes and symptoms.

CONCLUSION: Antifibrotic agents may improve HRQoL and reduce dyspnea and cough in patients with ILD, but the certainty of evidence is low, and they may increase fatigue, requiring careful monitoring.Trial registration:The study protocol was registered in PROSPERO (CRD42023450917).

PMID:41174997 | DOI:10.1177/17534666251390672

Inquiry. 2025 Jan-Dec;62:469580251385397. doi: 10.1177/00469580251385397. Epub 2025 Nov 1.

ABSTRACT

Stroke is a leading cause of morbidity and disability, with limited data on in-hospital mortality from low-resource settings. This study aimed to identify predictors of in-hospital mortality among stroke patients at a tertiary care hospital in Nepal. A prospective cohort study was conducted among 120 stroke patients aged ≥ 18 years, enrolled between November 2023 and April 2024. The primary outcome was in-hospital mortality following admission. Data was analysed using SAS version 9.4. Kaplan-Meier survival analysis and Cox proportional hazards regression were employed to identify predictors of in-hospital mortality. A p-value < .05 was considered statistically significant. The cohort comprised 68.3% ischemic and 31.7% haemorrhagic strokes, with an overall in-hospital mortality rate of 9.0%. Multivariate analysis revealed that a Glasgow Coma (GCS) score < 8 (AHR: 12.36; 95% CI: 2.73-56.00), National Institutes of Health Stroke Scale (NIHSS) ≥12 (AHR: 14.75; 95% CI: 3.01-72.28), moderate to severe disability (mRS ≥ 3; AHR: 9.92; 95% CI: 1.10-89.24), hemiplegia (AHR: 6.70; 95% CI: 1.835-53.748), territorial infarcts (AHR: 26.33; 95% CI: 2.093-331.203), capsuloganglionic infarcts (AHR: 14.6; 95% CI: 1.819-160.877), presence of chronic obstructive pulmonary disease (COPD) (AHR: 2.48; 95% CI: 1.317-45.091), and alcohol use (AHR: 3.87; 95% CI: 1.014-18.478) were significant predictors of in-hospital mortality. Neurological impairment at admission, specific infarct locations, hemiplegia, COPD, and alcohol use are significant predictors of in-hospital mortality among stroke patients. These findings underscore the importance of early neurological assessment, systematic risk stratification, and targeted interventions to improve stroke outcomes in resource-constrained settings.

PMID:41174984 | DOI:10.1177/00469580251385397

Inquiry. 2025 Jan-Dec;62:469580251382395. doi: 10.1177/00469580251382395. Epub 2025 Nov 1.

ABSTRACT

Non-suicidal self-injury (NSSI) and suicidal ideation (SI) represent significant mental health challenges among university students. In low- and middle-income contexts like Bangladesh, there is limited understanding of how these behaviors differentially affect students with and without mental illness. This study addresses these gaps by investigating the prevalence and risk factors of NSSI and SI, with stratified analyses by mental illness status, to predict these behaviors. This cross-sectional study recruited 1401 university students between December 2024 and January 2025. Data was collected via a self-administered questionnaire assessing socio-demographics, and psychological factors. Traditional statistical analyses, including chi-square tests and logistic regression, were conducted in SPSS 27. The prevalence of NSSI and SI was 21.4% and 17.2%, respectively. Both NSSI and SI were more common among students with symptoms of depression or anxiety (mental illness) than those without. Multivariable analyses identified smoking, cyberbullying, and probable eating disorder as significant predictors of both NSSI and SI, with these associations persisting after stratification by mental illness status. Subgroup analyses showed that among students without mental illness, female gender, older age, smoking, cyberbullying, and eating disorder symptoms significantly predicted NSSI, while smoking, cyberbullying, eating disorder, and older age predicted SI. In students with mental illness, smoking and cyberbullying remained robust predictors of both NSSI and SI, while eating disorder was significantly associated with NSSI but not SI. The regression models explained 12.9% of the variance in NSSI and 16.6% in SI. The findings highlight the necessity to adopt interventions that address modifiable risk factors, with a strong emphasis on behavioral and mental health variables, to effectively reduce self-harming and suicidal behaviors in young adults.

PMID:41174978 | DOI:10.1177/00469580251382395

Int J Gynaecol Obstet. 2025 Nov 1. doi: 10.1002/ijgo.70634. Online ahead of print.

NO ABSTRACT

PMID:41174961 | DOI:10.1002/ijgo.70634

Pediatr Allergy Immunol. 2025 Nov;36(11):e70234. doi: 10.1111/pai.70234.

ABSTRACT

BACKGROUND: Patients with chronic granulomatous disease (CGD) can develop hemophagocytic lymphohistiocytosis (HLH), exacerbating mortality risk. Despite its clinical significance, data on HLH in CGD from international cohorts remain limited. This study aims to describe the occurrence of HLH in a cohort of patients with CGD, providing clinical insight into this association and emphasizing the need for early recognition and effective management.

METHODS: The records of 60 patients with CGD were reviewed. Those meeting the diagnostic criteria for HLH based on the HScore were included in the analysis. Both descriptive and inferential statistics were employed to evaluate the data.

RESULTS: Eleven patients (18.3%) fulfilled the HLH diagnostic criteria. The median interval between CGD genetic diagnosis and HLH onset was 36 months, with a median age at HLH diagnosis of 67 months. Infectious triggers were identified in eight cases, with Salmonella and Aspergillus species being the most common. One case involved an inflammatory trigger-multisystem inflammatory syndrome in children (MIS-C) following SARS-CoV-2 infection. Mortality was high: 72.7% of the patients with HLH died. No significant difference (p = .338) was observed between those who died after receiving only immunosuppressive therapy (n = 2) and those who received both intravenous immunoglobulin and immunosuppressive therapy (n = 6).

CONCLUSION: HLH in CGD is associated with a high mortality rate. Notably, MIS-C can present as an inflammatory trigger for HLH in this population. Careful evaluation of HLH parameters is recommended for all patients with CGD admitted with infection or inflammation to facilitate early diagnosis and guide management.

PMID:41174960 | DOI:10.1111/pai.70234

Int J Biostat. 2025 Nov 3. doi: 10.1515/ijb-2025-0011. Online ahead of print.

ABSTRACT

Competing risks models are survival models with several events of interest acting in competition and whose occurrence is only observed for the event that occurs first in time. This paper presents a Bayesian approach to these models in which the issue of model selection is treated in a special way by proposing generalizations of some of the Bayesian procedures used in univariate survival analysis. This research is motivated by a study on the survival of patients with heart failure undergoing cardiac resynchronization therapy, a procedure which involves the implant of a device to stabilize the heartbeat. Two different causes of death have been considered: cardiovascular and non-cardiovascular, and a set of baseline covariates are examined in order to better understand their relationship with both causes of death. Model selection, model checking, and model comparison procedures have been implemented and assessed. The posterior distribution of some relevant outputs such as the overall survival function, cumulative incidence functions, and transition probabilities have been computed and discussed.

PMID:41174955 | DOI:10.1515/ijb-2025-0011

Int J Biostat. 2025 Nov 3. doi: 10.1515/ijb-2025-0016. Online ahead of print.

ABSTRACT

The binary classification problem (BCP) aims to correctly allocate subjects in one of two possible groups. The groups are frequently defined as having or not one characteristic of interest. With this goal, we are allowed to use different types of information. There is a huge number of methods dealing with this problem; including standard binary regression models, or complex machine learning techniques such as support vector machine, boosting, or perceptron, among others. When this information is summarized in a continuous score, we have to define classification regions (or subsets) which will determine whether the subjects are classified as positive, with the characteristic under study, or as negative, otherwise. The standard (or regular) receiver-operating characteristic (ROC) curve assumes that higher values of the marker are associated with higher probabilities of being positive and considers as positive those patients with values within the intervals [c, ∞) $\left(c\in R\right)$ , and plots the true- against the false- positive rates (sensitivity against one minus specificity) for all potential c. The so-called generalized ROC curve, gROC, allows that both higher and lower values of the score are associated with higher probabilities of being positive. The efficient ROC curve, eROC, considers the best ROC curve based on a transformation of the score. In this manuscript, we are interested in studying, comparing and approximating the transformations leading to the eROC and to the gROC curves. We will prove that, when the optimal transformation does not have relative maximum, both curves are equivalent. Besides, we investigate the use of the gROC curve on some theoretical models, explore the relationship between the gROC and the eROC curves, and propose two non-parametric procedures for approximating the transformation leading to the gROC curve. The finite-sample behavior of the proposed estimators is explored through Monte Carlo simulations. Two real-data sets illustrate the practical use of the proposed methods.

PMID:41174954 | DOI:10.1515/ijb-2025-0016

Psychiatr Serv. 2025 Nov 1;76(11):944-950. doi: 10.1176/appi.ps.20250086. Epub 2025 Aug 26.

ABSTRACT

OBJECTIVE: This study aimed to evaluate whether three popular chatbots powered by large language models (LLMs)-ChatGPT, Claude, and Gemini-provided direct responses to suicide-related queries and how these responses aligned with clinician-determined risk levels for each question.

METHODS: Thirteen clinical experts categorized 30 hypothetical suicide-related queries into five levels of self-harm risk: very high, high, medium, low, and very low. Each LLM-based chatbot responded to each query 100 times (N=9,000 total responses). Responses were coded as “direct” (answering the query) or “indirect” (e.g., declining to answer or referring to a hotline). Mixed-effects logistic regression was used to assess the relationship between question risk level and the likelihood of a direct response.

RESULTS: ChatGPT and Claude provided direct responses to very-low-risk queries 100% of the time, and all three chatbots did not provide direct responses to any very-high-risk query. LLM-based chatbots did not meaningfully distinguish intermediate risk levels. Compared with very-low-risk queries, the odds of a direct response were not statistically different for low-risk, medium-risk, or high-risk queries. Across models, Claude was more likely (adjusted odds ratio [AOR]=2.01, 95% CI=1.71-2.37, p<0.001) and Gemini less likely (AOR=0.09, 95% CI=0.08-0.11, p<0.001) than ChatGPT to provide direct responses.

CONCLUSIONS: LLM-based chatbots’ responses to queries aligned with experts’ judgment about whether to respond to queries at the extremes of suicide risk (very low and very high), but the chatbots showed inconsistency in addressing intermediate-risk queries, underscoring the need to further refine LLMs.

PMID:41174947 | DOI:10.1176/appi.ps.20250086

Psychiatr Serv. 2025 Nov 1;76(11):988-996. doi: 10.1176/appi.ps.20250042.

ABSTRACT

OBJECTIVE: People with serious mental illness (i.e., disabling psychotic, mood, and other disorders) develop chronic medical diseases early in life. This study aimed to examine the effects of integrating primary care into community mental health centers (CMHCs; reverse integrated care) on service use among young adults with serious mental illness who may benefit from early intervention.

METHODS: This retrospective cohort analysis used Medicaid claims of 945 people with serious mental illness (ages 18-40) in CMHC care from 2020 to 2022-315 in reverse integrated care and 630 propensity score matched participants in comparison care (i.e., not reverse integrated care). Logistic regression, adjusted for participant characteristics, enrollment quarter, and past service use, assessed outcomes in the 6 months after enrollment.

RESULTS: Participants’ mean±SD age was 32.56 ± 7.84 years; 29% had a diagnosis of schizophrenia, 40% had a co-occurring substance use disorder, 33% had a medical emergency department (ED) visit in the 6 months before enrollment, and all were enrolled in CMHC care at baseline. During follow-up, participants in reverse integrated care were more likely to have an outpatient medical visit (65% vs. 58%; adjusted odds ratio [AOR]=1.54, p=0.005) and were less likely to have a medical ED visit (26% vs. 33%; AOR=0.70, p=0.035) than those in comparison care.

CONCLUSIONS: Integrating primary care into CMHC services may increase access to outpatient medical care and reduce ED visits for medical reasons among young adults with serious mental illness. Future research should confirm these findings, assess longer-term outcomes, and examine implementation facilitators and barriers.

PMID:41174946 | DOI:10.1176/appi.ps.20250042