Predictive Utility Research Articles

Integration of Apolipoprotein B into the SCORE2 Framework: Implications for Cardiovascular Risk Prediction.

To evaluate whether integrating Apolipoprotein B (ApoB) into the SCORE2 cardiovascular risk prediction framework improves its predictive accuracy and clinical applicability within the UK Biobank population. A 10-year prospective cohort study was conducted with 448,303 UK Biobank participants eligible for SCORE2 calculation. Three approaches were employed: (1) threshold analysis to determine the optimal ApoB cutoff for cardiovascular disease (CVD) risk prediction using Youden's Index, (2) assessment of the synergistic effect of SCORE2 and ApoB through concordant and discordant classifications, and (3) recalibration of the SCORE2 model by incorporating ApoB as an additional predictor. Each 0.2 g/L increase in ApoB was associated with an increased subdistribution hazard for CVD events (SHR: 1.13; 95% CI: 1.11-1.14, p < 0.001), accounting for non-cardiovascular death as a competing risk. Threshold analysis identified an optimal ApoB cutoff at 1.18 g/L; however, it demonstrated limited discriminatory performance (area under the curve 0.54), with low sensitivity (32.4%) and moderate specificity (74.4%). Individuals with both low ApoB (<1.18 g/L) and low SCORE2 risk (<5%) had a lower CVD incidence rate (232.51 per 100,000 person-years) compared to those identified as low risk by SCORE2 alone (253.69 per 100,000 person-years). Integration of ApoB into the SCORE2 model did not significantly improve the model discrimination, calibration and net reclassification improvement. ApoB exhibited a dose-response relationship with cardiovascular risk but had limited standalone predictive utility within the UK Biobank population. However, combining ApoB with SCORE2 thresholds improved the identification of low-risk individuals, suggesting a complementary role for ApoB in refining cardiovascular risk stratification.

Development and validation of a prognostic nomogram for predicting of patients with acute sedative-hypnotic overdose admitted to the intensive care unit

To develop and evaluate a predictive model for intensive care unit (ICU) admission among patients with acute sedative-hypnotic overdose. We conducted a retrospective analysis of patients admitted to the emergency department of West China Hospital, Sichuan University, between October 11, 2009, and December 31, 2023. Patients were divided into ICU and non-ICU groups based on admission criteria including the need for blood purification therapy, organ support therapy (ventilatory support, vasoactive drugs, renal replacement therapy, artificial liver), or post-cardiopulmonary resuscitation. Patients were randomly split into a training set and a validation set in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to optimize variables, followed by a multivariate logistic regression analysis to identify independent risk factors for ICU admission. A nomogram model was constructed and assessed using receiver operating characteristic (ROC) curves, calibration curves, Decision Curve Analysis (DCA), and Clinical Impact Curve (CIC). Predictors in the nomogram included barbiturate overdose, Glasgow Coma Scale (GCS) score, and anion gap at admission. The nomogram demonstrated strong predictive performance with an area under the curve (AUC) of 0.858 (95% CI: 0.788–0.927) in the training set and 0.845 (95% CI: 0.757–0.933) in the validation set. Calibration curves showed the model closely matched the ideal curve, and DCA and CIC indicated high clinical applicability and utility. Barbiturate overdose, initial decreased GCS score and decreased anion gap were identified as independent risk factors for ICU admission in acute sedative-hypnotic overdose. The nomogram model based on these indicators demonstrates good predictive accuracy, discrimination, and clinical utility.

A Nomogram utilizing ECG P-wave parameters to predict recurrence risk following catheter ablation in paroxysmal atrial fibrillation

The objective of this study is to assess the predictive utility of perioperative P-wave parameters in patients with paroxysmal atrial fibrillation (PAF) undergoing catheter ablation, and to develop a predictive model using these parameters. A total of 213 patients with PAF undergoing catheter ablation were retrospectively analyzed. P-wave parameters were measured within 3 days preoperatively and on the day postoperatively to determine their predictive significance for postoperative PAF recurrence. Post-ablation, PAF did not recur in 168 patients, while 45 experienced recurrence. Significant differences were observed in preoperative P-wave parameters as Maximum P Wave Duration(Pmax), absolute value of P Wave Terminal Force of V1 (PtfV1) and P Wave Dispersion(Pd), postoperative P-wave parameters as P Wave Duration (PWDII, III, aVF), Pmax, P Wave Area(P-area), absolute value of PtfV1 and Pd, and changes in perioperative P-wave parameters (Delta-Pmax, Delta-PtfV1 absolute value, Delta-Pd, Delta-PWDII, III, aVF). Univariate logistic regression, receiver operating characteristic (ROC) curve analysis, and hazard ratio assessment identified predictive indicators for postoperative recurrence, including Pmax, PtfV1 absolute value, Pd, post-P area, post-PWDII, III, aVF and Delta-pwdII, III, aVF). A personalized nomogram model based on these P-wave parameters was developed. Calibration curve assessment demonstrated that the predictive performance of the nomogram for PAF recurrence following catheter ablation closely matched actual observed outcomes. ROC curve analysis indicated a sensitivity of 89.3% for the model, and decision curve analysis confirmed its significantly favorable predictive use and clinical benefits. P-wave parameters like PWDШ, PWDaVF, Pmax, Pd, and PtfV1 serve as predictors of PAF recurrence following catheter ablation. The nomogram model constructed using these P-wave parameters demonstrates robust predictive performance.

Gated myocardial perfusion SPECT for evaluating left ventricular dyssynchrony in cardiac resynchronization therapy candidates: a systematic review.

Cardiac resynchronization therapy (CRT) is an intervention for heart failure patients with reduced ejection fraction who exhibit specific electrocardiographic indicators of electrical dyssynchrony. However, electrical dyssynchrony does not universally correspond to left ventricular mechanical dyssynchrony (LVMD). Gated single-photon emission computed tomography (SPECT) myocardial perfusion allows for the assessment of LVMD, yet its role in the CRT selection process remains debated. We conducted a systematic literature review to critically evaluate the evidence for the prediction and prognostic utility of SPECT for LVMD in assessing LVMD among CRT candidates. The review adhered to PRISMA 2020 Statement criteria and included articles from PubMed, Embase, and Cochrane databases. The quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. From an initial pool of 1055 records, 33 met the inclusion criteria and provided original data on the predictive value of myocardial perfusion SPECT for LVMD. Most of them measured LVMD according to established recommendations, focusing on phase histogram bandwidth (HBW) and phase histogram standard deviation (PSD). Out of 2066 patients from 27 studies, 62% (n = 1214) were qualified as CRT responders. Five studies reported SPECT-based cutoffs for predicting CRT response (HBW ranging 55°-152° and for PSD 20°-54°). Only five studies assessed the prognostic implications of baseline SPECT-measured LVMD, indicating that elevated baseline HBW and PSD values are associated with poorer outcomes. The objective and reproducible measurement of LVMD provided by SPECT underscores its potential as a valuable tool. Such assessment seems to be emerging as a promising adjunctive technique with potential to enhance CRT outcomes.

Constructing and Validating Models for Predicting Gleason Grade Group Upgrading Following Radical Prostatectomy in Localized Prostate Cancer: A Comparison between Machine Learning Algorithms and Conventional Logistic Regression.

The occurrence of Gleason grade group upgrading (GGU) significantly impacts both treatment strategy development. We aim to develop an optimal predictive model to assess the risk of GGU in patients with localized prostate cancer (PCa), by comparing traditional logistic regression (LR) with seven machine learning algorithms. A retrospective collection of clinical data was conducted on patients who underwent RP at Wuhan Central Hospital (January 2017 to December 2023, n=177) and Jiangxi Cancer Hospital (July 2019 to February 2024, n=87). The least absolute shrinkage and selection operator (LASSO) regression was employed to filter the clinical characteristics of patients. Subsequently, models were conducted using multivariate LR, along with seven diverse machine learning algorithms: eXtreme Gradient Boosting, Decision Tree, Multilayer Perceptron, Naive Bayes, k-Nearest Neighbors, Random Forest, and Support Vector Machine. By employing the receiver operating characteristic curve, accuracy, brier score, recall, calibration curve, and decision curve analysis, we compared the predictive capabilities and clinical utility of eight models to identify the optimal one. In the evaluation of eight models, the LR model demonstrated superior performance. In the modeling set, it achieved an AUC of 0.826 (95% CI: 0.808 - 0.845), accuracy of 0.765, and a brier score of 0.167. In the validation set, it kept good results with an AUC of 0.819 (95% CI: 0.758 - 0.880), accuracy of 0.725, and a brier score of 0.180. The calibration curve, brier score， and DCA also demonstrated the excellent calibration and net benefit of the LR model. After conducting a comprehensive multi-model comparison, we concluded that the LR model was optimal for predicting GGU, which was confirmed by external validation. Our study also revealed percent free prostate-specific antigen density as a predictive factor for GGU, offering a novel approach for managing localized PCa patients.

D-dimer cutoff values for predicting functional prognosis in patients with severe head trauma: a multi-centre prospective observational study.

D-dimer, a fibrinolysis indicator, may predict functional and life outcomes in traumatic brain injury (TBI) patients. We aimed to identify optimal D-dimer cutoff values for poor functional outcomes in severe TBI. We used data from a multi-centre prospective observational cohort study that included patients with TBI with a Glasgow Coma Scale (GCS) score ≤ 8 within 48h after injury or required neurosurgical procedures. We selected patients admitted ≤ 1h after injury. Neurological function at discharge was assessed using the modified Rankin Scale (mRS). The association between D-dimer levels at admission and neurological function (mRS ≥ 4 or < 4), adjusted for age, GCS, systolic blood pressure, and head abbreviated injury scale (AIS) score, was determined by logistic regression analysis. The predictive utility of D-dimer levels was assessed using the area under the receiver operating characteristic curve (AUC), with the cutoff value being determined using Youden's index. Among the 336 patients, most were male; approximately half had experienced motor vehicle accidents. The median (interquartile range) of age, GCS scores, and head AIS scores were 65 (40.25 - 78.75) years, 7 (4 - 9), and 5 (4 - 5), respectively. Overall, 214 (63.7%) patients had poor neurological function (mRS ≥ 4). D-dimer levels > 28µg/mL predicted poor functional prognosis (odds ratio = 3.84 [95% confidence interval 1.62 - 9.11]); the AUC and cutoff values were 0.73 and 27.2µg/mL, respectively. A cutoff value of 27.2µg/mL for early-stage D-dimer levels could predict the functional prognosis of patients with severe isolated TBI.

P0260 Scoring indices grading radiologic disease activity severity on magnetic resonance imaging strongly reflect clinical healing of Crohn’s perianal fistulas

Abstract Background Magnetic resonance imaging (MRI) is the gold-standard for evaluating Crohn’s perianal fistulas. Multiple scoring indices to grade radiologic disease activity severity have been established, with limited comparative data and a lack of consensus regarding which index correlates most accurately with clinical findings. In a prospective MRI series, we explored the relationship between established radiologic indices and clinical healing. Methods Serial MRI scans from 60 adults with Crohn’s perianal fistulas enrolled in a prospective study were acquired. The prospective study evaluated the efficacy of a protocolised treatment strategy optimising care delivery at a single tertiary centre. Patients had ≥12 months follow-up with serial MRI performed annually. Each MRI scan had a paired physical examination, with clinical healing defined as the absence of externally draining perianal fistulas and no seton. MRI scans were reviewed by a specialist radiologist blinded to clinical findings. Radiologic disease activity severity was graded using the Van Assche Index (VAI), Magnetic Resonance Index for Assessing Fistulas in Patients with Crohn’s Disease (MAGNIFI-CD) and Fibrosis Score (FS). The primary endpoint was the relationship between radiologic indices and clinical healing. Secondary endpoints included the optimal index scores for clinical healing and the relationship between individual scoring criteria and clinical healing. Results A total of 135 MRI scans were acquired. All patients had baseline imaging, with 88% and 37% having serial imaging at 1 and 2 years, respectively. The median interval between baseline and final MRI was 16 months. Clinical healing significantly correlated with less severe radiologic disease activity, reflected by lower VAI (OR 0.70, P&amp;lt;0.001), lower MAGNIFI-CD (OR 0.76, P&amp;lt;0.001) and higher FS (OR 3.14, P&amp;lt;0.001). On ROC curve analysis, all radiologic indices exhibited a similarly high degree of correlation with clinical healing; AUROC of 0.91, 0.84 and 0.92, respectively (Figure 1). The optimal index scores for clinical healing were VAI ≤6 (sensitivity 83%, specificity 94%, AUROC 0.88), MAGNIFI-CD ≤8 (sensitivity 78%, specificity 73%, AUROC 0.75) and FS ≥5 (sensitivity 83%, specificity 94%, AUROC 0.88). On multivariable logistic regression analysis, increasing degree of fibrosis on FS (OR 2.68, P&amp;lt;0.001) and increasing fistula tract extension on VAI (OR 0.09, P&amp;lt;0.001) were independent positive and negative indicators of clinical healing, respectively. Conclusion Radiologic disease activity severity using established scoring indices have equally strong correlations with clinical healing of Crohn’s perianal fistulas. Further studies are needed to explore the predictive utility of individual radiologic parameters.

Neutrophil to platelet ratio predicts in-hospital mortality in patients with acute myocardial infarction.

Acute myocardial infarction (AMI) is a critical medical emergency worldwide and a leading cause of mortality. This study aims to investigate the predictive utility of the neutrophil-to-platelet ratio (NPR) in identifying AMI patients at an increased risk of in-hospital mortality. We enrolled 664 patients, including 421 with ST-elevation myocardial infarction (STEMI) and 243 with non-ST-elevation myocardial infarction (NSTEMI), at Zhongshan Hospital, Fudan University, from January 2020 to September 2023. NPR was calculated as the neutrophil count divided by the platelet count. The primary outcome was defined as in-hospital mortality. The overall in-hospital mortality among AMI patients was 6.78%. Mortality was notably higher in the high NPR group compared to the low NPR group. Univariate analysis identified several variables significantly associated with in-hospital mortality, including age, left ventricular ejection fraction (LVEF), neutrophil-to-lymphocyte ratio (NLR), and NPR. NPR demonstrated a strong independent association with in-hospital mortality following adjustment for potential confounders. Receiver operating characteristic (ROC) curve analyses were performed to assess the discriminative power of NPR and NLR in predicting in-hospital mortality. NPR exhibited an area under the curve (AUC) of 0.755 (95% CI, 0.682-0.829, p < 0.001), indicating good discriminative ability. Similarly, NLR showed a discriminative AUC of 0.674 (95% CI, 0.586-0.762, p < 0.001). The optimal cutoff values for predicting mortality were determined as 0.042 for NPR (sensitivity 80%, specificity 62.2%) and 8.02 for NLR (sensitivity 62.2%, specificity 67.5%). Bootstrap validation with 1000 iterations confirmed the robustness of these findings, with validated AUCs of 0.755 (95% CI, 0.681-0.826) for NPR and 0.674 (95% CI, 0.587-0.766) for NLR. This study identifies NPR as an independent and valuable predictor of in-hospital mortality among AMI patients, The findings underscore NPR's potential utility in clinical practice for risk stratification and early intervention strategies aimed at reducing mortality rates in this high-risk patient population.

Ds-FCRN: three-dimensional dual-stream fully convolutional residual networks and transformer-based global-local feature learning for brain age prediction.

The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks. The gray matter (GM) density maps obtained from T1 MRI data of 16,377 healthy participants aged 45 to 82 years from the UKB database were included in this study (mean age, , 7811 men). We propose an innovative deep learning architecture for predicting brain age based on GM density maps. The architecture combines a 3D dual-stream fully convolutional residual network (ds-FCRN) with a Transformer-based global-local feature learning paradigm to enhance prediction accuracy. Moreover, we employed Shapley values to elucidate the influence of various brain regions on prediction precision. On a test set of 3,276 healthy subjects (mean age, , 1561 men), our 3D ds-FCRN model achieved a mean absolute error of 2.2 years in brain age prediction, outperforming existing models on the same dataset. The posterior interpretation revealed that the temporal lobe plays the most significant role in the brain age prediction process, while frontal lobe aging is associated with the greatest number of lifestyle factors. Our designed 3D ds-FCRN model achieved high predictive accuracy and high decision transparency. The brain age vectors constructed using Shapley values provided brain region-level insights into life factors associated with abnormal brain aging.

Salivary cortisol measures across the clinical stages of psychosis: An individual participant data (IPD) meta-analysis.

Studies of salivary cortisol levels in psychosis have yielded inconsistent findings, which may be attributable to heterogeneity in cortisol measurement, illness stage, and approaches to dealing with sampling factors and potential confounders. To address these issues, we performed an individual participant data (IPD) meta-analysis comparing individuals at different stages of psychosis to controls using five different salivary cortisol measures and explored potential effect modifiers. Salivary cortisol data from five London-based cohorts were used to derive the cortisol awakening response, total daytime cortisol output, basal cortisol, and diurnal slope measures (wake-to-evening and peak-to-evening). Linear regression models were first performed to obtain standardised beta coefficients (β), representing the difference in each cortisol metric between each clinical stage group (cases) and healthy individuals (controls) after accounting for relevant sampling factors; we then used random-effects meta-analyses and meta-regression models to investigate the effect of psychosis stage and sample characteristics on effect sizes. Data were available for 352 individuals distributed across psychosis clinical stages (1a - distress disorder: N = 35; 1b - clinical high-risk for psychosis: N = 90; 2a - first-episode psychosis: N = 197; 2b - single episode remitted: N = 5; 3 - relapsing/remitting illness: N = 18; 4 - severe and persistent illness: N = 7) and 292 controls. A significant overall main effect of clinical stage on peak-to-evening diurnal slope was observed (χ2=12.83, p = 0.025), with both the clinical high-risk (β=0.21, 95 %CI: 0.06, 0.36) and first-episode psychosis (β=0.20, 95 %CI: 0.10, 0.31) groups characterised by flatter slopes than controls. The clinical stage groups and controls did not differ on any other cortisol measure. Several sample characteristics were significantly associated with diurnal slope effect sizes, but after accounting for clinical stage, only the association between mean age in cases and wake-to-evening diurnal slope retained significance. Clinical high-risk and first-episode psychosis participants differed from healthy controls in the peak-to-evening diurnal cortisol slope. This measure has not been examined in these populations before, and its potential predictive and prognostic utility for psychotic disorders merits further investigation.

Sex-specific associations between body composition and depression among U.S. adults: a cross-sectional study

BackgroundDepression presents sexual dimorphism, and one important factor that increases the frequency of depression and contributes to sex-specific variations in its presentation is obesity. The conventional use of Body Mass Index (BMI) as an indicator of obesity is inherently limited due to its inability to distinguish between fat and lean mass, which limits its predictive utility for depression risk. Implementation of dual-energy X-ray absorptiometry (DXA) investigated sex-specific associations between body composition (fat mass, appendicular lean mass) and depression.MethodsData from the NHANES cycles between 2011 and 2018 were analyzed, including 3,637 participants (1,788 males and 1,849 females). Four body composition profiles were identified in the subjects: low adiposity-low muscle (LA-LM), low adiposity-high muscle (LA-HM), high adiposity-low muscle (HA-LM) and high adiposity-high muscle (HA-HM). After accounting for confounding variables, the associations between fat mass index (FMI), appendicular skeletal muscle mass index (ASMI), body fat percentage (BFP), body composition phenotypes, and depression risk were assessed using restricted cubic spline (RCS) curves and multivariable logistic regression models. We further conducted interaction analyses for ASMI and FMI in females.ResultsRCS curves indicated a U-shaped relationship between ASMI and the risk of depression in males. Logistic regression analysis revealed that in males, the second (OR = 0.43, 95%CI:0.22–0.85) and third (OR = 0.35, 95%CI:0.14–0.86) quartile levels of ASMI were significantly negatively associated with depression risk. In females, increases in BFP (OR = 1.06, 95%CI:1.03–1.09) and FMI (OR = 1.08, 95% CI:1.04–1.12) were significantly associated with an increased risk of depression. Additionally, compared to females with a low-fat high-muscle phenotype, those with LA-LM (OR = 3.97, 95%CI:2.16–7.30), HA-LM (OR = 5.40, 95%CI:2.34–12.46), and HA-HM (OR = 6.36, 95%CI:3.26–12.37) phenotypes were more likely to develop depression. Interestingly, further interaction analysis of ASMI and FMI in females revealed an interplay between height-adjusted fat mass and muscle mass (OR = 4.67, 95%CI: 2.04–10.71).ConclusionThe findings demonstrate how important it is to consider body composition when estimating the risk of depression, particularly in females. There is a substantial correlation between the LA-LM, HA-LM, and HA-HM phenotypes in females with a higher prevalence of depression. It is advised to use a preventative approach that involves gaining muscle mass and losing fat.

Utility of Parent and Teacher Behavior Ratings for Self-Regulatory Outcomes of Preschool Children: A Multi-Study Examination.

Despite frequent reliance on teacher and parent ratings of children's behavior for multi-informant assessment, agreement between teachers' and parents' ratings is low. This study examined the predictive utility of teacher and parent ratings for children's self-regulatory outcomes (i.e., executive function, continuous performance task) in four studies. Study 1 included 163 children ranging from 31 to 84 months of age (M = 55.58 months, SD = 8.43). Study 2 included 1,088 children ranging from 48 to 63 months of age (M = 55.15, SD = 3.65). Study 3 included 246 bilingual Spanish-speaking children ranging from 40 to 72 months of age (M = 56.66, SD = 6.06). Study 4 included 280 children ranging from 38 to 75 months of age (M = 55.92, SD = 4.16). Across studies, parents and teachers rated children's externalizing behaviors on the Conners' Rating Scale, the Strength and Weaknesses of ADHD-Symptoms and Normal Behavior Scale, or both; children completed a variety of performance-based self-regulation tasks. The strength of associations of parent and teacher ratings with self-regulatory outcomes was compared, and regression analyses determined the unique and overlapping variance accounted for by different raters. Teachers' ratings had larger associations with self-regulation than did parents' ratings across outcomes and studies-except for two instances in Study 4 where the associations for teachers' and parents' ratings were equal. These findings indicate that teachers supply more useful information than parents, possibly because teachers have better-informed expectations of children's behavior, and they raise questions about the utility of multi-informant assessment, at least with preschool children.

A physics informed neural network approach to quantify antigen presentation activities at single cell level using omics data.

Antigen processing and presentation via major histocompatibility complex (MHC) molecules are central to immune surveillance. Yet, quantifying the dynamic activity of MHC class I and II antigen presentation remains a critical challenge, particularly in diseases like cancer, infection and autoimmunity where these pathways are often disrupted. Current methods fall short in providing precise, sample-specific insights into antigen presentation, limiting our understanding of immune evasion and therapeutic responses. Here, we present PSAA (PINN-empowered Systems Biology Analysis of Antigen Presentation Activity), which is designed to estimate sample-wise MHC class I and class II antigen presentation activity using bulk, single-cell, and spatially resolved transcriptomics or proteomics data. By reconstructing MHC pathways and employing pathway flux estimation, PSAA offers a detailed, stepwise quantification of MHC pathway activity, enabling predictions of gene-specific impacts and their downstream effects on immune interactions. Benchmarked across diverse omics datasets and experimental validations, PSAA demonstrates a robust prediction accuracy and utility across various disease contexts. In conclusion, PSAA and its downstream functions provide a comprehensive framework for analyzing the dynamics of MHC antigen presentation using omics data. By linking antigen presentation to immune cell activity and clinical outcomes, PSAA both elucidates key mechanisms driving disease progression and uncovers potential therapeutic targets.

Development and Validation of a Nomogram for Predicting Long-Term Net Adverse Clinical Events in High Bleeding Risk Patients Undergoing Percutaneous Coronary Intervention.

Patients with a high risk of bleeding undergoing percutaneous coronary intervention (PCI-HBR) were provided consensus-based criteria by the Academic Research Consortium for High Bleeding Risk (ARC-HBR). However, the prognostic predictors in this group of patients have yet to be fully explored. Thus, an effective prognostic prediction model for PCI-HBR patients is required. We prospectively enrolled PCI-HBR patients from May 2022 to April 2024 at West China Hospital of Sichuan University. The cohort was randomly divided into training and internal validation sets in a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to select variables in the training set. Subsequently, a prediction model for 1-year net adverse clinical events (NACEs)-free survival was developed using a multivariable Cox regression model, and a nomogram was constructed. The outcome of the NACEs is defined as a composite endpoint that includes death, myocardial infarction, ischemic stroke, and Bleeding Academic Research Consortium (BARC) grade 3-5 major bleeding. Validation was conducted exclusively using the internal validation cohort, assessing the discrimination, calibration, and clinical utility of the nomogram. This study included 1512 patients with PCI-HBR, including 1058 in the derivation cohort and 454 in the validation cohort. We revealed five risk factors after LASSO regression, Cox regression, and clinical significance screening. These were then utilized to construct a prognostic prediction nomogram, including chronic kidney disease, left main stem lesion, multivessel disease, triglycerides (TG), and creatine kinase-myocardial band (CK-MB). The nomogram exhibited strong predictive ability (the area under the curve (AUC) to predict 1-year NACE-free survival was 0.728), displaying favorable levels of accuracy, discrimination, and clinical usefulness in the internal validation cohort. This study presents a nomogram to predict 1-year NACE outcomes in PCI-HBR patients. Internal validation showed strong predictive capability and clinical utility. Future research should validate the nomogram in diverse populations and explore new predictors for improved accuracy. The data for this study were obtained from the PPP-PCI registry, NCT05369442 (https://clinicaltrials.gov/study/NCT05369442).

Integrating the A2DS2 Score with 24-Hour ASPECTS and red cell distribution width for enhanced prediction of stroke-associated pneumonia following intravenous thrombolysis: model development and internal validation

IntroductionStroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The A2DS2 score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the A2DS2 score and to develop a novel prediction model for SAP following thrombolysis.MethodsThis retrospective cohort study included thrombolyzed AIS patients at Saraburi Hospital, Thailand. The combined A2DS2-MFP model incorporated 24-h NCCT-ASPECTS and RDW, along with non-linear continuous predictors, using multivariable fractional polynomial (MFP) regression. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AuROC), calibration plots, and decision curve analysis (DCA), comparing it with the traditional A2DS2 model and a model with continuous predictors. The goodness of fit for logistic regression models in relation to the observed data was determined through the Hosmer–Lemeshow method, and the accuracy of the probability predictions was examined using a calibration curve. Internal validation was performed using a bootstrapping approach. The predicted probability equation obtained from the final model after optimism correction was developed into a web-based application for predicting the risk of SAP, using PHP and JavaScript.ResultsOf 345 AIS patients, 20.3% developed SAP. The combined A2DS2-MFP model demonstrated excellent discriminative performance (AuROC: 0.917) compared to the traditional A2DS2 model (AuROC: 0.868) and the model with continuous predictors (AuROC: 0.888). Both the calibration curve and the Hosmer–Lemeshow test indicated that the predicted probabilities and observed frequencies were in acceptable agreement. Incorporating 24-h NCCT-ASPECTS and RDW significantly improved risk prediction and clinical utility, as shown by improved reclassification indices and DCA. The model was internally validated with a C-statistic of 0.912, confirming its robustness.ConclusionsThe combined A2DS2-MFP calculation showed superior performance, enabling early SAP detection and improving survival outcomes. This novel model offers a practical tool for resource-limited settings, supporting better SAP risk stratification and clinical management.

Population-Level SARS-CoV-2 RT-PCR Cycle Threshold Values and Their Relationships with COVID-19 Transmission and Outcome Metrics: A Time Series Analysis Across Pandemic Years.

This study investigates the relationship between SARS-CoV-2 RT-PCR cycle threshold (Ct) values and key COVID-19 transmission and outcome metrics across five years of the pandemic in Jalisco, Mexico. Utilizing a comprehensive time-series analysis, we evaluated weekly median Ct values as proxies for viral load and their temporal associations with positivity rates, reproduction numbers (Rt), hospitalizations, and mortality. Cross-correlation and lagged regression analyses revealed significant lead-lag relationships, with declining Ct values consistently preceding surges in positivity rates and hospitalizations, particularly during the early phases of the pandemic. Granger causality tests and vector autoregressive modeling confirmed the predictive utility of Ct values, highlighting their potential as early warning indicators. The study further observed a weakening association in later pandemic stages, likely influenced by the emergence of new variants, hybrid immunity, changes in human behavior, and diagnostic shifts. These findings underscore the value of Ct values as scalable tools for public health surveillance and highlight the importance of contextualizing their analysis within specific epidemiological and temporal frameworks. Integrating Ct monitoring into surveillance systems could enhance pandemic preparedness, improve outbreak forecasting, and strengthen epidemiological modeling.

Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model

BackgroundUnplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility. This study aims to compare the predictive accuracy of the SRC with a novel artificial neural network (ANN) algorithm for 30-day readmission after primary TKA, using the same set of clinical variables from a large national database.MethodsPatients undergoing primary TKA between 2013 and 2020 were identified from the ACS-NSQIP database and randomly stratified into training and validation cohorts. The ANN was developed using data from the training cohort with fivefold cross-validation performed five times. ANN and SRC performance were subsequently evaluated in the distinct validation cohort, and predictive performance was compared on the basis of discrimination, calibration, accuracy, and clinical utility.ResultsThe overall cohort consisted of 365,394 patients (trainingN = 362,559; validationN = 2835), with 11,392 (3.1%) readmitted within 30 days. While the ANN demonstrated good discrimination and calibration (area under the curve (AUC)ANN = 0.72, slope = 1.32, intercept = −0.09) in the validation cohort, the SRC demonstrated poor discrimination (AUCSRC = 0.55) and underestimated readmission risk (slope = −0.21, intercept = 0.04). Although both models possessed similar accuracy (Brier score: ANN = 0.03; SRC = 0.02), only the ANN demonstrated a higher net benefit than intervening in all or no patients on the decision curve analysis. The strongest predictors of readmission were body mass index (> 33.5 kg/m2), age (> 69 years), and male sex.ConclusionsThis study demonstrates the superior predictive ability and potential clinical utility of the ANN over the conventional SRC when constrained to the same variables. By identifying the most important predictors of readmission following TKA, our findings may assist in the development of novel clinical decision support tools, potentially improving preoperative counseling and postoperative monitoring practices in at-risk patients.

The predictive value of the serum creatinine-to-albumin ratio (sCAR) and lactate dehydrogenase-to-albumin ratio (LAR) in sepsis-related persistent severe acute kidney injury

Background/objectivesSepsis-related acute kidney injury (SA-AKI) is a severe condition characterized by high mortality rates. The utility of the sCAR (secrum creatinine/albumin) and LAR (Lactate dehydrogenase/albumin) as diagnostic markers for persistent severe SA-AKI remains unclear.MethodsWe acquired training set data from the MIMIC-IV database and validation set data from the First Affiliated Hospital of Harbin Medical University. Logistic regression analysis was used to identify key predictors of persistent severe SA-AKI, considering factors such as sCAR, LAR, PAR (Platelet/albumin), BAR (BUN/albumin), and LAO (Lactic/albumin). Independent predictors, sCAR and LAR, were combined into a composite Log(sCAR)_Log(LAR) score, denoted as the Log(sCAR)_Log(LAR) score. Possible confounding factors were screened out by univariate logistic regression, and multivariable logistic regression was applied to evaluate the association of Log (sCAR) _Log (LAR) score with persistent severe sepsis and other secondary clinical outcomes. The ROC curve was utilized to obtain the best cutoff value of the Log(sCAR)_Log(LAR) score. The Kaplan–Meier curve was used to evaluate the prognosis predictive ability of the risk model.ResultsLogistic regression analysis indicated that sCAR and LAR independently predicted persistent severe SA-AKI. This led to the creation of Log(sCAR)_Log(LAR) score on the base of logarithms of sCAR and LAR. ROC curve analysis showed that the Log(sCAR)_Log(LAR) score was more effective in predicting persistent severe SA-AKI (AUC = 0.71) than Log(sCAR) (AUC = 0.69), Log(LAR) (AUC = 0.65), SOFA score (AUC = 0.66) and Δ Scr (AUC = 0.70). Multivariate regression identified that the SOFA score, PT, ΔScr, Tbil, chronic liver disease, and Vasopressor use as independent risk factors for persistent severe SA-AKI (P < 0.05). A basic clinical prediction model was created using these variables, and its predictive ability, recognition capability, and clinical utility improved with the inclusion of the Log(sCAR)_Log(LAR) score. The model's predictive ability for secondary outcomes, such as renal replacement therapy (RRT), also improved with the addition of the Log(sCAR)_Log(LAR) score. The sensitivity analysis further corroborated the stability of the Log(sCAR)_Log(LAR) score in predicting persistent severe SA-AKI and secondary outcomes, such as RRT.ConclusionsThe Log(sCAR)_Log(LAR) score effectively predicted persistent severe SA-AKI, potentially aiding intensive care physicians in risk assessment.

Predictive Utility of the HALP and Modified HALP Score for the Assessment of Operative Complications in Patients Undergoing Laparoscopic Cholecystectomy for Acute Cholecystitis.

Background and Objectives: The aim of the present study was to calculate HALP and modified HALP scores for patients diagnosed with acute cholecystitis (AC) and to determine the predictive utility of these scores for surgical timing and morbidity in patients who underwent surgery for AC. Materials and Methods: This study included data from 641 patients who underwent surgery for AC between January 2010 and May 2023. The HALP score was calculated using the formula hemoglobin (g/L) × albumin (g/L) × lymphocyte (109/L)/platelets (109/L). The modified HALP score was calculated using the formula hemoglobin (g/L) × albumin (g/L) × lymphocyte (109/L) × platelets (109/L). Results: The mean HALP score was 46.81, and the mean modified HALP score was 2,758,401.21. Laparoscopic cholecystectomy (LC) was successfully completed in 582 (90.8%) patients. When examining the relationship between HALP and modified HALP scores and the procedure type, a statistically significant difference was found in the distribution of median HALP and modified HALP scores between the LC and laparoscopic and open cholecystectomy (LTOC) groups. For patients with a HALP score < 34.43 and modified HALP score < 2,077,019, the likelihood of conversion to open surgery increased, with a sensitivity of 65.5% vs. 58.8% and a specificity of 66.1% vs. 59.3%. In patients who underwent surgery, there was a significant difference in the LTOC between the HALP score and modified HALP score cut-off groups (p < 0.001 and, p = 0.007). Conclusions: Evaluation of the HALP score is a promising and valuable assessment method for designing appropriate treatment and management strategies for patients with AC.

Magnetic topological Weyl fermions in half-metallic In2CoSe4

Magnetic Weyl semimetals (WSMs) have recently attracted much attention due to their potential in realizing strong anomalous Hall effects. Yet, how to design such systems remains unclear. Based on first-principles calculations, we show here that the ferromagnetic half-metallic compound In2CoSe4has several pairs of Weyl points and is hence a good candidate for magnetic WSM. These Weyl points would approach the Fermi level gradually as the HubbardUincreases, and finally disappear after a critical valueUc. The range of the HubbardUthat can realize the magnetic WSM state can be expanded by pressure, manifesting the practical utility of the present prediction. Moreover, by generating two surface terminations at Co or In atom after cleaving the compound at the Co-Se bonds, the nontrivial Fermi arcs connecting one pair of Weyl points with opposite chirality are discovered in surface states. Furthermore, it is possible to observe the nontrivial surface state experimentally, e.g. angle-resolved photoemission spectroscopy measurements. As such, the present findings imply strongly a new magnetic WSM which may host a large anomalous Hall conductivity.