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6 recordsLinked to original sources
PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline：A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗
PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗
PubMed2026

Transcranial Photobiomodulation Variables Assessment Battery: Development and Validation.

Transcranial photobiomodulation (tPBM) response variability is partly driven by biophysical characteristics such as skin tone and hair properties that attenuate photon penetration, and by lifestyle factors including sleep quality, alcohol use, and nicotine consumption that disrupt the mitochondrial and vascular pathways on which tPBM acts. To date, no validated self-report tool exists to capture these moderators systematically. To address this gap, the tPBM Variables Assessment Battery was developed and psychometrically evaluated. It integrates adapted versions of established measures (Brief Pittsburgh Sleep Quality Index, E-cigarette Dependence Scale, Hair Scale Assessment PRO, Monk Skin Tone Scale, and Heaviness of Smoking Index), validated wellbeing evaluators (Ryff's Psychological Wellbeing), and custom measures (Hairstyle Classification, Hair Color Classification). Face and content validity met recommended expert thresholds, internal consistency was acceptable across adapted subscales, and criterion validity analyses confirmed meaningful associations between the lifestyle components and PROMIS-10 global health outcomes. The battery is low-burden, digitally deployable, and psychometrically defensible, offering a practical tool for characterizing the variables most likely to moderate tPBM response in home-use studies.

Humans↗
PubMed2026

Low-burden metrics for monitoring healthy diets among nonpregnant females aged 15 to 49 years: a multicountry validation analysis using quantitative 24-hour dietary intake data.

BACKGROUND: Limited nationally representative quantitative dietary intake data and a lack of consensus on lower-burden tools and metrics hinder high-frequency monitoring of healthy diets globally. OBJECTIVES: This study aimed to evaluate the comparative construct validity and potential complementarity of low-burden metrics of a healthy diet among nonpregnant females aged 15 to 49 y. METHODS: Quantitative 24-h dietary intake data collected from 77,118 adolescent and adult females across 27 countries were used to construct low-burden metrics and reference metrics of dietary intake. Associations between mean-standardized low-burden measures or indicators and reference metrics were assessed using linear and logistic mixed-effect models, with Spearman's &#x3c1; used for survey-level rank correlations. Test characteristics identified low-burden indicators best differentiated adherence to reference indicators. RESULTS: An indicator reflecting nonconsumption of sweet foods and/or sweet beverages was most robustly associated with greater adherence to <10% energy from free sugars in upper-middle-income countries {odds ratio [OR] [95% confidence interval (CI)]: 5.35 [5.05, 5.66]}. Food group diversity score (FGDS) was most strongly associated with and differentiated higher mean adequacy ratio of micronutrients [&#x3b2; of 1-standard deviation (SD) change: &#x223c;11 percentage points (9, 12); &#x3c1;: 0.79], whereas noncommunicable disease-Protect score best reflected consumption of &#x2265;400 g/d of fruits and vegetables [range OR of 1-SD changes (95% CI): 2.56-3.01 (2.40, 3.13) in lower-middle and high-income countries, respectively; &#x3c1;: 0.56]. FGDS and Global Diet Quality Score Positive were most consistently associated with achieving &#x2265;25 g/d of fiber and &#x2265;3510 mg/d of potassium across contexts. CONCLUSIONS: Low-burden data collection tools yield valid metrics, enabling high-frequency monitoring of healthy diets across contexts. Specifically, avoiding sweet foods and/or sweet beverages is an indicator for adherence to WHO free sugar guidelines among nonpregnant females in upper-middle-income countries, whereas metrics reflecting nutritious food group diversity strongly reflect better micronutrient adequacy and adherence to WHO guidelines for fruits and vegetables, fiber, and potassium intakes within and across contexts.

Humans↗
PubMed2026

Metformin Adherence and Risk of Polyneuropathy in Type 2 Diabetes Mellitus: An International Matched Cohort Study with Independent Validation.

BACKGROUND: Metformin is a popular first-line glucose-lowering medication for type 2 diabetes mellitus (T2DM). Although metformin reduces the risks of various complications of diabetes, its potential to cause polyneuropathy by depleting vitamin B12 levels is concerning. This study investigated whether the adherence or discontinuation of metformin after adding-on a second-line antiglycemic agent increases the risk of polyneuropathy in patients with T2DM. METHODS: Data from TriNetX were obtained, and patients with T2DM who were receiving second-line antiglycemic agents were divided into metformin-adherent and metformin-nonadherent groups based on prescription claims data. Neuropathy incidence was evaluated using diagnostic claims and nerve conduction examinations. For independent confirmation and external validation of the primary findings, we used data from the National Health Insurance Research Database (NHIRD) of Taiwan. RESULTS: After matching, 58,027 patients were included in each group. Compared with metformin adherent patients, metformin nonadherent patients had a higher risk of polyneuropathy (adjusted hazard ratios [aHR] 1.26; 95% confidence interval [CI] 1.23-1.29; P < 0.001). Risks of diabetic foot ulcer, amputation, neuropathy-related medication use, and bone fracture were also higher among nonadherent patients. Sensitivity analyses confirmed the robustness of findings. In the validation NHIRD cohort (31,384 matched pairs), metformin nonadherence remained associated with increased polyneuropathy risk (aHR 1.25; 95% CI 1.10-1.42; P < 0.001). CONCLUSIONS: Metformin adherence in patients with T2DM who require second-line treatment may reduce the risk of polyneuropathy; vitamin B supplementation may enhance this benefit.

Humans↗
PubMed2026

Could the preoperative urethral curve be used to predict immediate urinary continence following Retzius-sparing robot-assisted radical prostatectomy? A retrospective multi-center study.

PURPOSE: Immediate urinary continence (UC) recovery following Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) remains highly variable, highlighting the need for reliable preoperative prediction. We aimed to develop and validate models to identify patients likely to achieve immediate UC recovery following RS-RARP. MATERIALS AND METHODS: A total of 580 prostate cancer patients who underwent RS-RARP from four medical centers were assigned to a training set (n=348), an internal validation set (n=103) and an external validation set (n=129). Independent predictors were identified through univariate analysis and LASSO regression. A nomogram was constructed using multivariate logistic regression. Its performance was evaluated with receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. RESULTS: Immediate UC recovery was observed in 84.5% (294/348) of patients in the training cohort, 80.6% (83/103) in the internal validation cohort, and 81.4% (105/129) in the external validation cohort, respectively. Multivariate analysis identified membranous urethral length (MUL) (OR=1.23, P=0.029) and urethral curvature (OR=2.84, P<0.001) as independent predictors, while prostate volume (PV) (OR=0.84, P <0.001) as a protective factor. The nomogram integrating MUL, PV, and urethral curvature demonstrated superior predictive accuracy, with an AUC of 0.87 (95% CI, 0.83-0.91) in the training cohort. The bootstrap-corrected calibration slope was 0.96, and the Brier score was 0.08.&#xa0;Calibration curves and decision curve analysis confirmed the predictive accuracy and clinical utility of the nomogram. CONCLUSIONS: Our study introduces a novel quantitative method for assessing urethral curvature. The mpMRI-based model, integrating urethral curvature and prostate spatial configuration, offers enhanced predictive accuracy for postoperative immediate UC recovery.

Humans↗