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Nevin Manimala Statistics

Policy Spotlight Effects on Critical Time-Sensitive Diseases: Nationwide Retrospective Cohort Study on Taiwan’s Hospital Emergency Capability Categorization Policy

Interact J Med Res. 2025 Mar 25;14:e54651. doi: 10.2196/54651.

ABSTRACT

BACKGROUND: Taiwan’s categorization of hospital emergency capability (CHEC) policy is designed to regionalize and dispatch critical patients. The policy was designed in 2009 to improve the quality of emergency care for critical time-sensitive diseases (CTSDs). The CHEC policy primarily uses time-based quality surveillance indicators.

OBJECTIVE: We aimed to investigate the impact of Taiwan’s CHEC policy on CTSDs.

METHODS: Using Taiwan’s 2005 Longitudinal Health Insurance Database, this nationwide retrospective cohort study examined the CHEC policy’s impact from 2005 to 2011. Propensity score matching and difference-in-differences analysis within a generalized estimating equation framework were used to compare pre- and postimplementation periods. The study focused on acute ischemic stroke (AIS), ST-segment elevation myocardial infarction (STEMI), septic shock, and major trauma. AIS and STEMI cases, monitored with time-based indicators, were evaluated for adherence to diagnostic and treatment guidelines as process quality measures. Mortality and medical use served as outcome indicators. Major trauma, with evolving guidelines and no time-based monitoring, acted as a control to test for policy spotlight effects.

RESULTS: In our cohort of 9923 patients, refined through 1:1 propensity score matching, 5566 (56.09%) were male and were mostly older adults. Our analysis revealed that the CHEC policy effectively improved system efficiency and patient outcomes, resulting in significant reductions in medical orders (-7.29 items, 95% CI -10.09 to -4.48; P<.001), short-term mortality rates (-0.09%, 95% CI -0.17% to -0.02%; P=.01) and long-term mortality rates (-0.09%, 95% CI -0.15% to -0.04%; P=.001), and total medical expenses (-5328.35 points per case, 95% CI -10,387.10 to -269.60; P=.04), despite a modest increase in diagnostic fees (376.37 points, 95% CI 92.42-660.33; P=.01). The CHEC policy led to notable increases in diagnostic fees, major treatments, and medical orders for AIS and STEMI cases. For AIS cases, significant increases were observed in major treatments (β=0.77; 95% CI 0.21-1.33; P=.007) and medical orders (β=15.20; 95% CI 5.28-25.11; P=.003) compared to major trauma. In STEMI cases, diagnostic fees significantly increased (β=1983.75; 95% CI 84.28-3883.21; P=.04), while upward transfer rates significantly decreased (β=-0.59; 95% CI -1.18 to -0.001; P=.049). There were also trends toward increased major treatments (β=0.30; 95% CI -0.03 to 0.62, P=.07), medical orders (β=11.92; 95% CI -0.90 to 24.73; P=.07), and medical expenses (β=24,275.54; 95% CI -640.71 to 4,991,991.78; P=.06), although these were not statistically significant. In contrast, no significant changes were identified in process or outcome quality indicators for septic shock. These findings suggest policy spotlight effects, reflecting a greater emphasis on diseases directly prioritized under the CHEC policy.

CONCLUSIONS: The CHEC policy demonstrated the dual benefits of reducing costs and improving patient outcomes. We observed unintended consequences of policy spotlight effects, which led to a disproportionate improvement in guideline adherence and process quality for CTSDs with time-based surveillance indicators.

PMID:40132185 | DOI:10.2196/54651

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Nevin Manimala Statistics

Development and Validation of the Digital Health Literacy Questionnaire for Stroke Survivors: Exploratory Sequential Mixed Methods Study

J Med Internet Res. 2025 Mar 25;27:e64591. doi: 10.2196/64591.

ABSTRACT

BACKGROUND: In China, there is limited research on digital health literacy (DHL) among patients with stroke. This is mainly due to the lack of validated tools, which hinders the precision and sustainability of our country’s digital transformation.

OBJECTIVE: This study aimed to develop and validate a DHL scale specifically for stroke survivors in China.

METHODS: We used a sequential, exploratory, mixed methods approach to develop a DHL questionnaire for stroke survivors. This study comprised 418 patients with stroke aged 18 years and older. To evaluate the questionnaire’s psychometric qualities, we randomly assigned individuals to 2 groups (subsample 1: n=118, subsample 2: n=300). Construct validity was evaluated through internal consistency analysis, exploratory and confirmatory factor analyses, hypothesis testing for structural validity, measurement invariance assessments using the eHealth Literacy Scale, and Rasch analyses to determine the questionnaire’s validity and reliability.

RESULTS: This study underwent 4 stages of systematic development. The initial pool of items contained 25 items, 5 of which were eliminated after content validity testing; 19 items were subsequently retained through cognitive interviews. After an interitem correlation analysis, 2 more items were excluded, leaving 17 items for exploratory factor analysis. Finally, 2 items were excluded by Rasch analysis, resulting in a final version of the questionnaire containing 15 items. The total score range of the scale was 15-75, with higher scores indicating greater DHL competence. Results showed that principal component analysis confirmed the theoretical structure of the questionnaire (69.212% explained variance). The factor model fit was good with χ24=1.669; root mean square error of approximation=0.047; Tucker-Lewis Index=0.973; and Comparative Fit Index=0.977. In addition, hypothesis-testing construct validity with the eHealth Literacy Scale revealed a strong correlation (r=0.853). The internal consistency (Cronbach α) coefficient was 0.937. The retest reliability coefficient was 0.941. Rasch analysis demonstrated the item separation index was 3.81 (reliability 0.94) and the individual separation index was 2.91 (reliability 0.89).

CONCLUSIONS: The DHL Questionnaire for Stroke Survivors is a reliable and valid measure to assess DHL among stroke survivors in China.

PMID:40132183 | DOI:10.2196/64591

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Nevin Manimala Statistics

Detection of Clinically Significant Drug-Drug Interactions in Fatal Torsades de Pointes: Disproportionality Analysis of the Food and Drug Administration Adverse Event Reporting System

J Med Internet Res. 2025 Mar 25;27:e65872. doi: 10.2196/65872.

ABSTRACT

BACKGROUND: Torsades de pointes (TdP) is a rare yet potentially fatal cardiac arrhythmia that is often drug-induced. Drug-drug interactions (DDIs) are a major risk factor for TdP development, but the specific drug combinations that increase this risk have not been extensively studied.

OBJECTIVE: This study aims to identify clinically significant, high-priority DDIs to provide a foundation to minimize the risk of TdP and effectively manage DDI risks in the future.

METHODS: We used the following 4 frequency statistical models to detect DDI signals using the Food and Drug Administration Adverse Event Reporting System (FAERS) database: Ω shrinkage measure, combination risk ratio, chi-square statistic, and additive model. The adverse event of interest was TdP, and the drugs targeted were all registered and classified as “suspect,” “interacting,” or “concomitant drugs” in FAERS. The DDI signals were identified and evaluated using the Lexicomp and Drugs.com databases, supplemented with real-world data from the literature.

RESULTS: As of September 2023, this study included 4313 TdP cases, with 721 drugs and 4230 drug combinations that were reported for at least 3 cases. The Ω shrinkage measure model demonstrated the most conservative signal detection, whereas the chi-square statistic model exhibited the closest similarity in signal detection tendency to the Ω shrinkage measure model. The κ value was 0.972 (95% CI 0.942-1.002), and the Ppositive and Pnegative values were 0.987 and 0.985, respectively. We detected 2158 combinations using the 4 frequency statistical models, of which 241 combinations were indexed by Drugs.com or Lexicomp and 105 were indexed by both. The most commonly interacting drugs were amiodarone, citalopram, quetiapine, ondansetron, ciprofloxacin, methadone, escitalopram, sotalol, and voriconazole. The most common combinations were citalopram and quetiapine, amiodarone and ciprofloxacin, amiodarone and escitalopram, amiodarone and fluoxetine, ciprofloxacin and sotalol, and amiodarone and citalopram. Although 38 DDIs were indexed by Drugs.com and Lexicomp, they were not detected by any of the 4 models.

CONCLUSIONS: Clinical evidence on DDIs is limited, and not all combinations of heart rate-corrected QT interval (QTc)-prolonging drugs result in TdP, even when involving high-risk drugs or those with known risk of TdP. This study provides a comprehensive real-world overview of drug-induced TdP, delimiting both clinically significant DDIs and negative DDIs, providing valuable insights into the safety profiles of various drugs, and informing the optimization of clinical practice.

PMID:40132181 | DOI:10.2196/65872

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Nevin Manimala Statistics

Flourishing in nursing: positive factors that contributed to mental wellbeing of nursing students in Thailand

Int J Nurs Educ Scholarsh. 2025 Mar 26;22(1). doi: 10.1515/ijnes-2024-0076. eCollection 2025 Jan 1.

ABSTRACT

OBJECTIVES: To explore post-pandemic mental wellbeing status and identify positive factors influencing mental wellbeing among nursing students.

METHODS: A cross-sectional survey of undergraduate nursing students from three public colleges in Thailand was conducted. A convenience sample of 983 participants completed a paper questionnaire.

RESULTS: The mental wellbeing mean score was 43.67 (SD=6.75, possible range of 10-60). Mental wellbeing was negatively associated with participant’s age and class level while positively associated with income, BMI, exercise hours/week, sleep hours/day, academic support, perceived social support, community involvement, and grit. Using hierarchical multiple regression, six significant predictors were identified: income, sleep hours/day, academic support, perceived social support, community involvement, and grit. These predictors combined explained 44 % of the variance, F(11, 722)=55.97, p<0.001, adjusted R2=0.44.

CONCLUSIONS: To promote mental wellbeing of nursing students, colleges should explore how to increase academic support, encourage healthy habits in students, and enhance their community involvement.

PMID:40132175 | DOI:10.1515/ijnes-2024-0076

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Nevin Manimala Statistics

Intimate partner violence and physical health in England: Gender stratified analyses of a probability sample survey

Womens Health (Lond). 2025 Jan-Dec;21:17455057251326419. doi: 10.1177/17455057251326419. Epub 2025 Mar 25.

ABSTRACT

BACKGROUND: Gender differences in the associated health outcomes of different forms of intimate partner violence (IPV) are understudied. The long-term effects of IPV on specific physical health conditions are also under-researched in comparison to the effects on general health and mental health.

OBJECTIVES: To examine gender differences in the association between IPV and specific physical health conditions, accounting for differences in the types and number of types of IPV experienced.

DESIGN: We used data from the 2014 Adult Psychiatric Morbidity Survey, a cross-sectional survey using a stratified, multistage random sampling design to cover the household population of England aged 16 years and older.

METHODS: Descriptive and multivariable regression analyses of 4120 women and 2764 men who had ever had a partner. Lifetime IPV by types (physical, sexual, psychological, and economic), any lifetime and recent IPV, the number of IPV types experienced, and multiple chronic health conditions experienced over the past 12 months were included in the analyses.

RESULTS: Gender differences were observed in both the prevalence of IPV and associated health conditions. Women were more likely to experience any type and a higher number of IPV types than men. Women’s exposure to any lifetime and 12-month IPV were significantly associated with an increased likelihood of reporting 12 and 11 conditions, respectively, while men’s exposure to any lifetime and 12-month IPV were significantly associated with 4 and 1 conditions, respectively. Specific IPV types had varied health impacts, particularly among women. A cumulative association was evident for women but not for men.

CONCLUSION: Healthcare systems need to be mobilised to address IPV as a priority health issue for the female population. Our findings highlight the need for gender-informed approaches in IPV intervention strategies and healthcare provision, emphasising the development of IPV-responsive healthcare systems and comprehensive IPV curricula in medical and health training.

PMID:40132162 | DOI:10.1177/17455057251326419

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Nevin Manimala Statistics

The Relationship Between Chronic Postoperative Pain and Circulating Inflammatory Biomarkers (CC-Chemokine Ligand 5, Adiponectin, and Resistin) After Fracture-Related Surgery in Pain Chronification

Anesth Analg. 2025 Mar 25. doi: 10.1213/ANE.0000000000007504. Online ahead of print.

ABSTRACT

BACKGROUND: After fracture-related surgery, chronic posttraumatic and/or postsurgical pain (CPSP) has a high incidence rate of up to 43% a year after surgery. Yet the underlying mechanisms are poorly understood. Murine and clinical evidence suggest immunological modulation of postsurgical pain. However, the specific cytokine profiles of patients who develop CPSP after fracture-related surgery remain to be determined. Therefore, we analyzed in an exploratory manner cytokines, chemokines and adipocytokines in patients with and without CPSP up to 1 year after fracture-related surgery.

METHODS: A prospective longitudinal serum profiling of 30 patients with traumatic fractures that required osteosynthesis was conducted on the first day (D1), at 6 weeks (W6) and 1 year after surgery (Y1). Patients with CPSP at Y1 were compared to those who did not develop CPSP. A total of 22 pro- and anti-inflammatory serum cytokines, including adipocytokines, were quantified using Luminex technology. Statistical analyses included χ² test, t test, and Mann-Whitney U test, Spearman’s rank correlations, and repeated-measures mixed models with Bonferroni correction for cytokine differences between patients with and without CPSP. Receiver-operating characteristic (ROC) curves evaluated the discriminatory ability of specific cytokines regarding the development of CPSP.

RESULTS: Patients with CPSP 1 year after surgery (n = 12/30, 40%) exhibited elevated resistin levels at Y1 (CPSP: 1.04 ± 1.04 vs no-CPSP: 0.41 ± 0.31 pg/mL; P < .001) as well as higher adiponectin levels at Y1 (CPSP: 9.37 ± 8.23 vs no-CPSP: 5.57 ± 2.75 μg/mL; P = .008). Patients with CPSP had higher Rantes/CCL5 (CC-chemokine ligand 5) levels immediately after surgery on D1 than patients without CPSP (mean difference [MD] = 5.5, confidence interval [CI], 1.7-9.3 ng/mL; P = .014). At W6 and Y1, adiponectin and CCL5 levels correlated with pain intensity in patients with CPSP (adiponectin: r = 0.50, P = .03; CCL5: r = -0.50, P = .03). Across the entire patient population, resistin levels were correlated with pain intensity (r = 0.34, P < .001; D1-Y1).

CONCLUSIONS: Our explorative cytokine analysis uncovered an imbalance in serum cytokines and chemokines during the chronification process in patients who developed CPSP 1 year after surgically treated fractures. In particular, adiponectin and resistin were noted to be novel biomarkers for CPSP development. These data provide preliminary insight into a potential unexplored crosstalk between chronic postoperative pain and adipocytokines in the chronification of CPSP, which remains to be further analyzed.

PMID:40132159 | DOI:10.1213/ANE.0000000000007504

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Nevin Manimala Statistics

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials

Neurology. 2025 Apr 22;104(8):e213490. doi: 10.1212/WNL.0000000000213490. Epub 2025 Mar 25.

ABSTRACT

BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We aimed to develop machine learning-based predictive models to identify persons unlikely to show decline on placebo treatment over 80 weeks.

METHODS: We used the data from the placebo arm of EXPEDITION3 AD clinical trial and a subpopulation from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants in the EXPEDITION3 trial were patients with mild dementia and biomarker evidence of amyloid burden. For this study, participants were identified as those who demonstrated clinically meaningful cognitive decline (CMCD) or cognitively stable (CS) at final visit of the trial (week 80). Machine learning-based classifiers were trained to classify participants into CMCD vs CS groups using combinations of demographics, APOE genotype, neuropsychological tests, and biomarkers (volumetric MRI). The results were developed in 70% of the EXPEDITION3 placebo sample using 5-fold cross-validation. Trained models were then used to classify the participants in an internal validation sample and an external matched sample ADNIAD.

RESULTS: Eight hundred ninety-four of the 1,072 participants in the placebo arm of the EXPEDITION3 trial had necessary follow-up data, who were on average aged 72.7 (±7.7) years and 59% female. 55.8% of those participants showed CMCD (∼2 years younger than those without) at the final visit. In the independent validation sample within the EXPEDITION3 data, all the models showed high sensitivity and modest specificity. Positive predictive values (PPVs) of models were at least 11% higher than base prevalence of CMCD observed at the end of the trial. The subset of matched ADNI participants (ADNIAD, N = 105) were aged 74.5 (±6.4) years and 46% female. The models that were validated in ADNIAD also showed high sensitivity, modest specificity, and PPVs of at least 15% higher than the base prevalence in ADNIAD.

DISCUSSION: Our results indicate that predictive models have the potential to improve the design of AD trials through selective inclusion and exclusion criteria based on expected cognitive decline. Such predictive models need further validation across data from different AD clinical trials.

PMID:40132145 | DOI:10.1212/WNL.0000000000213490

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Nevin Manimala Statistics

Proton density fat fraction for diagnosis of metabolic dysfunction associated steatotic liver disease

Hepatology. 2025 Mar 25. doi: 10.1097/HEP.0000000000001318. Online ahead of print.

ABSTRACT

BACKGROUND AIMS: Prior work has shown that magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF) can diagnose metabolic dysfunction-associated steatotic liver disease (MASLD) noninvasively, but there is a paucity of data on the performance of PDFF to classify more advanced forms of the MASLD spectrum. The purpose of this study was to assess the diagnostic performance of PDFF for the diagnoses of MASLD, metabolic dysfunction-associated steatohepatitis (MASH), and fibrotic MASH in adults with obesity undergoing bariatric surgery, using contemporaneous intraoperative liver biopsy as reference.

APPROACH RESULTS: PDFF was evaluated alone and with other potential classifiers (imaging, serum and anthropometric), using Bayesian Information Criterion-based stepwise logistic regression models. Areas under the receiver operating characteristic (ROC) curves (AUC) were computed for all models and single classifiers. Cross-validated sensitivity and specificity were calculated at Youden-based PDFF classification thresholds. Data analysis from 140 patients demonstrated that PDFF was the most accurate single classifier, with high AUC for MASLD (0.95), MASH (0.85), and fibrotic MASH (0.82) (all p<0.001). Multivariable models including PDFF outperformed those without PDFF. The Youden-based threshold for PDFF was 4.4% for MASLD (sensitivity: 87%, specificity: 86%), 6.9% for MASH (sensitivity: 77%, specificity: 66%), and 13.5% for fibrotic MASH (sensitivity: 67%, specificity: 85%).

CONCLUSIONS: PDFF was the most accurate single classifier for diagnosing MASLD, MASH, and fibrotic MASH. The most accurate multivariable classification models for MASLD, MASH, and fibrotic MASH included PDFF, demonstrating the central importance of PDFF for noninvasive assessment of the MASLD spectrum.

PMID:40132140 | DOI:10.1097/HEP.0000000000001318

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Nevin Manimala Statistics

Multiformula Prediction Range: a univariate predictor of IOL Power Calculation Accuracy

J Cataract Refract Surg. 2025 Mar 24. doi: 10.1097/j.jcrs.0000000000001658. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study was to evaluate the influence of range between the predictions of 5 different calculation formulas in IOL power calculation accuracy.

SETTINGS: General University Hospital of Elche, Spain.

DESIGN: Retrospective Sequential Cohort.

METHODS: In this retrospective sequential cohort, the LenStar LS900 (Haag-Streit, Koeniz, Switzerland) was used for the preoperative biometry. The predicted spherical equivalent refraction of the implanted IOL were calculated for 5 different formulas: Barrett Universal II, Emmetropia Verifying Optical (EVO) 2.0, Hill RBF-3.0, Kane, PEARL-DGS. Multiformula Prediction Range was defined as the range of the refractive error predicted by the 5 formulas. According to the median of the Multiformula Prediction Range the sample was divided into a low and high spread group (LS and HS respectively).

RESULTS: 278 eyes were included. The standard deviation of the prediction error was significantly lower in the LS group for all included formulas. For the Barrett Universal II, EVO 2.0, RBF-3.0, Kane and PEARL-DGS formulae, the median absolute error (MdAE) was significantly lower in the LS group compared to the HS group (p-values of 0.001, 0.027, 0.004, 0.028 and 0.035, respectively). The percentage of eyes within the ±0.50D PE range was significantly higher in the LS group for all five analyzed formulas.

CONCLUSIONS: Multiformula Prediction Range can be a novel univariate predictor of IOL power calculation accuracy and a potential determinant for identifying patient suitable for immediate sequential cataract surgery. Accuracy was consistently higher for all five included formulas in the Low Spread group.

PMID:40132121 | DOI:10.1097/j.jcrs.0000000000001658

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Nevin Manimala Statistics

Long-term Tomographic, Refractive, and Visual Analysis of Keratoconus Eyes With Extreme Corneal Flattening After Corneal Cross-linking

Cornea. 2025 Mar 25. doi: 10.1097/ICO.0000000000003861. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the long-term tomographic, refractive, and visual characteristics of eyes with extreme corneal flattening after corneal cross-linking (CXL) for progressive keratoconus.

METHODS: A retrospective observational study included eyes that underwent corneal CXL with epithelial removal between June 2006 and March 2017 and had extreme keratometric flattening [greater than 5 diopters (D)] and a minimum follow-up of 5 years. Visual, tomographic, pachymetric, and refractive characteristics were evaluated.

RESULTS: Mean follow-up time was 7.6 ± 2.6 years (range 5-13 years). Fifteen eyes were included in the study. Mean maximum keratometric (Kmax) flattening was -7.58 ± 2.63 D [range 5.0-12.2 D, (P <0.001)]. Approximately 56.25% (9/15) of the eyes experienced progressive flattening over the years. And 40% (6/15) presented an improvement of one or more lines of corrected distance visual acuity (CDVA), and 26.6% (5/16) of the eyes showed a worsening of CDVA. Logistic regression analysis revealed that postoperative Kmax flattening greater than 2 D at the first year postop (odds ratio 17.7, 95% confidence interval, 4.4-71.2) and preoperative Kmax greater than 55 D (odds ratio 8.8, 95% confidence interval, 2.7-28.3) were significant risk factors for extreme postop keratometric flattening.

CONCLUSIONS: Progressive extreme corneal flattening when accompanied with a decrease of CDVA was a late complication of CXL that may have required corneal transplantation for visual rehabilitation. Preoperative steeper corneas and keratometric flattening greater than 2 D at the first year postoperative period were risk factors associated with long-term extreme postoperative corneal flattening.

PMID:40132089 | DOI:10.1097/ICO.0000000000003861