Net Reclassification Improvement Research Articles

Determinants of serious health outcome-free status in middle-aged and older people with dysglycaemia: Exploratory analysis of the ORIGIN trial.

To assess clinical and biochemical measurements that can identify people with dysglycaemia (i.e. diabetes or pre-diabetes) who remain free of serious outcomes during follow-up. We conducted exploratory analyses using data from the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) study to identify independent determinants of outcome-free status in 12 537 middle-aged and older adults with prediabetes and early type 2 diabetes from 40 countries. Serious outcome-free status was defined as the absence of major cardiovascular outcomes, kidney or retinal outcomes, peripheral artery disease, dementia, cancer, any hospitalization, or death during follow-up. In total, 3328 (26.6%) participants remained free of serious outcomes during a median follow-up of 6.2 years (IQR 5.8, 6.7). Independent clinical determinants of outcome-free status included younger age, female sex, non-White ethnicity, shorter diabetes duration, absence of previous cardiovascular disease, current or former smokers, higher grip strength, Mini-Mental State Examination score, and ankle-brachial index, lower body mass index and kidney disease index, and non-use of renin-angiotensin system drugs and beta-blockers. In a subset of 8401 people with baseline measurements of 238 biomarkers, growth differentiation factor 15, kidney injury molecule-1, N-terminal pro-brain natriuretic peptide, uromodulin, C-reactive protein, factor VII and ferritin were independent determinants. The combination of clinical determinants and biomarkers best identified participants who remained outcome-free (C-statistics 0.71, 95% confidence interval 0.70-0.73; net reclassification improvement 0.55, 95% confidence interval 0.48-0.58). A set of routinely measured clinical characteristics and seven protein biomarkers identify middle-aged and older people with prediabetes or early type 2 diabetes as least likely to experience serious outcomes during follow-up.

In Vivo Classification and Characterization of Carotid Atherosclerotic Lesions with Integrated 18F-FDG PET/MRI

Background: The aim of this study was to exploit integrated PET/MRI to simultaneously evaluate the morphological, component, and metabolic features of advanced atherosclerotic plaques and explore their incremental value. Methods: In this observational prospective cohort study, patients with advanced plaque in the carotid artery underwent 18F-FDG PET/MRI. Plaque morphological features were measured, and plaque component features were determined via MRI according to AHA lesion-types. Maximum standardized uptake values (SUVmax) and tissue to background ratio (TBR) on PET were calculated. Area under the receiver-operating characteristic curve (AUC) and net reclassification improvement (NRI) were used to compare the incremental contribution of FDG uptake when added to AHA lesion-types for symptomatic plaque classification. Results: A total of 280 patients with advanced plaque in the carotid artery were recruited. A total of 402 plaques were confirmed, and 87 of 402 (21.6%) were symptomatic plaques. 18F-FDG PET/MRI was performed a mean of 38 days (range 1–90) after the symptom. Increased stenosis degree (61.5% vs. 50.0%, p < 0.001) and TBR (2.96 vs. 2.32, p < 0.001) were observed in symptomatic plaques compared with asymptomatic plaques. The performance of the combined model (AHA lesion type VI + stenosis degree + TBR) for predicting symptomatic plaques was the best among all models (AUC = 0.789). The improvement of the combined model (AHA lesion type VII + stenosis degree + TBR) over AHA lesion type VII model for predicting symptomatic plaques was the highest (AUC = 0.757/0.454, combined model/AHA lesion type VII model), and the NRI was 50.7%. Conclusions: Integrated PET/MRI could simultaneously evaluate the morphological component and inflammation features of advanced atherosclerotic plaques and provide supplementary optimization information over AHA lesion-types for identifying vulnerable plaques in atherosclerosis subjects to achieve further stratification of stroke risk.

Clinical predictive value of the age, creatinine, and ejection fraction score in patients in acute type A aortic dissection after total arch replacement

The age, creatinine, and ejection fraction (ACEF) score has been accepted as a predictor of poor outcome in elective operations. This study aimed to investigate the predictive value of ACEF score in acute type A aortic dissection (AAAD) patients after total arch replacement. A total of 227 AAAD patients from July 2021 and June 2022 were enrolled and divided into Tertiles 1 (ACEF ≤ 0.73), Tertiles 2 (0.73 < ACEF ≤ 0.95), and Tertiles 3 (ACEF > 0.95). Using inverse probability processing weighting (IPTW) to balance the baseline characteristics and compare the outcomes. Cox logistic regression was used to further evaluate the survival prediction ability of ACEF score. The in-hospital mortality was 9.8%. After IPTW, in the baseline characteristics reached an equilibrium, a higher ACEF score before operation still associated with higher in-hospital mortality. After 1 year follow-up, 184 patients (90.6%) survival. Multivariable analysis revealed that ACEF score (adjusted hazard ratio 1.68; 95% confidence interval 1.34–4.91; p = 0.036) and binary ACEF score (adjusted HR 2.26; 95% CI 1.82–6.20; p < 0.001) was independently associated with 1-year survival. In addition, net reclassification improvement (NRI) and integrated differentiation improvement (IDI) verified that the ACEF score and binary ACEF score is an accurate predictive tool in clinical settings. In conclusions, ACEF score could be considered as a useful tool to risk stratification in patients with AAAD before operation in daily clinical work.

Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits.

Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits. We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture. The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.

Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study.

Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI. This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI). CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2. HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.

European and US Guideline-Based Statin Eligibility, Genetically Predicted Coronary Artery Disease, and the Risk of Major Coronary Events.

A study was designed to investigate whether the coronary artery disease polygenic risk score (CAD-PRS) may guide lipid-lowering treatment initiation as well as deferral in primary prevention beyond established clinical risk scores. Participants were 311 799 individuals from the UK Biobank free of atherosclerotic cardiovascular disease, diabetes, chronic kidney disease, and lipid-lowering treatment at baseline. Participants were categorized as statin indicated, statin indication unclear, or statin not indicated as defined by the European and US guidelines on statin use. For a median of 11.9 (11.2-12.6) years, 8196 major coronary events developed. CAD-PRS added to European-Systematic Coronary Risk Evaluation 2 (European-SCORE2) and US-Pooled Cohort Equation (US-PCE) identified 18% and 12% of statin-indication-unclear individuals whose risk of major coronary events were the same as or higher than the average risk of statin-indicated individuals and 16% and 12% of statin-indicated individuals whose major coronary event risks were the same as or lower than the average risk of statin-indication-unclear individuals. For major coronary and atherosclerotic cardiovascular disease events, CAD-PRS improved C-statistics greater among statin-indicated or statin-indication-unclear than statin-not-indicated individuals. For atherosclerotic cardiovascular disease events, CAD-PRS added to the European evaluation and US equation resulted in a net reclassification improvement of 13.6% (95% CI, 11.8-15.5) and 14.7% (95% CI, 13.1-16.3) among statin-indicated, 10.8% (95% CI, 9.6-12.0) and 15.3% (95% CI, 13.2-17.5) among statin-indication-unclear, and 0.9% (95% CI, 0.6-1.3) and 3.6% (95% CI, 3.0-4.2) among statin-not-indicated individuals. CAD-PRS may guide statin initiation as well as deferral among statin-indication-unclear or statin-indicated individuals as defined by the European and US guidelines. CAD-PRS had little clinical utility among statin-not-indicated individuals.

Associations between in-hospital daily protein intake and adverse clinical outcomes in older patients with heart failure.

The adverse effects of low daily protein intake (DPI) on clinical outcomes in patients with heart failure (HF) are known; however, an optimal DPI to predict event adverse outcomes remains undetermined. Moreover, whether protein restriction therapy for chronic kidney disease is applicable in patients with HF and renal dysfunction remains unclear. In this single-centre, ambispective cohort study, we included 405 patients with HF aged ≥65years (mean age, 78.6±7.5years; 50% women). DPI was estimated from consumption over three consecutive days before discharge and normalized relative to the ideal body weight [IBW, 22kg/m2×height (m)2]. The primary outcome was a composite of all-cause mortality and HF-related readmission within the 2year post-discharge period. During an average follow-up period of 1.49±0.74years, 100 patients experienced composite events. Kaplan-Meier survival curves revealed a significantly lower composite event-free rate in patients within the lowest quartile of DPI than in the upper quartiles (log-rank test, P=0.02). A multivariate Cox proportional hazards analysis after adjusting for established prognostic markers and non-proteogenic energy intake revealed that patients in the lowest DPI quartile faced a two-fold higher risk of composite events than those in the highest quartile [hazard ratio (HR), 2.03; 95% confidence interval (CI), 1.08-3.82; P=0.03]. The composite event risk linearly increased as DPI decreased (P for nonlinearity=0.90), with each standard deviation (0.26g/kg IBW/day) decrease in DPI associated with a 32% increase in composite event risk (HR, 1.32; 95% CI, 1.10-1.71; P=0.04). There was significant heterogeneity in the effect of DPI, with the possible disadvantage of lower DPI in patients with HF with cystatin C-based estimated glomerular filtration rate <30mL/min/1.73m2. The cutoff value of DPI for predicting the occurrence of composite events calculated from the Youden index was 1.12g/kg IBW/day. Incorporating a DPI<1.12g/kg IBW/day into the baseline model significantly improved the prediction of post-discharge composite events (continuous net reclassification improvement, 0.294; 95% CI, 0.072-0.516; P=0.01). Lower DPI during hospitalization is associated with an increased risk of mortality and HF readmission independent of non-proteogenic energy intake, and the possible optimal DPI for predicting adverse clinical outcomes is >1.12g/kg IBW/day in older patients with HF. Caution is warranted when protein restriction therapy is administered to older patients with HF and renal dysfunction.

Hematocrit drift and outcomes in surgical patients with aneurysmal subarachnoid hemorrhage.

There is a paucity of conclusive evidence regarding the impact of downward drift in hematocrit levels among patients who have undergone surgical clipping for aneurysmal subarachnoid hemorrhage (aSAH). This study endeavors to explore the potential association between hematocrit drift and mortality in this specific patient population. A cohort study was conducted, encompassing adult patients diagnosed with aSAH at a university hospital. The primary endpoint was follow-up mortality. Propensity score matching was employed to align patients based on their baseline characteristics. Discrimination capacity across various models was assessed and compared using net reclassification improvement (NRI). Among the 671 patients with aSAH in the study period, 118 patients (17.6%) experienced an in-hospital hematocrit drift of more than 25%. Following adjustment with multivariate regression analysis, patients with elevated hematocrit drift demonstrated significantly increased odds of mortality (aOR: 2.12, 95% CI: 1.14 to 3.97; P = 0.019). Matching analysis yielded similar results (aOR: 2.07, 95% CI: 1.05 to 4.10; P = 0.036). The inclusion of hematocrit drift significantly improved the NRI (P < 0.0001) for mortality prediction. When in-hospital hematocrit drift was served as a continuous variable, each 10% increase in hematocrit drift corresponded to an adjusted odds ratio of 1.31 (95% CI 1.08-1.61; P = 0.008) for mortality. In conclusion, the findings from this comprehensive cohort study indicate that a downward hematocrit drift exceeding 25% independently predicts mortality in surgical patients with aSAH. These findings underscore the significance of monitoring hematocrit and managing anemia in this patient population.

White matter hyperintensity, parent artery steno-occlusion, and neurological deterioration in anterior circulation single subcortical infarction patients.

The evidence for the association between white matter hyperintensity (WMH) severity and neurological deterioration (ND) in patients with single subcortical infarction (SSI) remains unclear and whether the association between them is modified by anterior circulation parent artery steno-occlusion (PAS) is unknown. Herein, we aimed to prospectively investigate the internal relevance. In this prospective study, the severity of WMH and PAS were assessed in 288 consecutive patients with anterior circulation SSI arriving at our hospital, a tertiary teaching hospital affiliated with Fudan University, 24h after onset from January 2017 to December 2018. The multivariable logistic regression model was used to estimate the association between WMH severity and the risk of ND within 7 days after stroke onset as well as the interactive effect between WMH severity and PAS on ND among patients with SSI. PAS modified the association between WMH severity and ND among patients with SSI (pinteraction=.029). After multivariate adjustment, the odds ratios of moderate-severe WMH associated with ND were 1.61 (95% CI, 0.50-5.19; ptrend=.428) for patients with PAS, and 0.37 (95% CI, 0.14-0.97; ptrend=.043) for those without PAS. Adding WMH severity to conventional risk factors improved risk prediction for ND in patients without PAS (net reclassification improvement: 48.2%, p=.005; integrated discrimination index: 2.5%, p=.004) but not in those with PAS. There was a modified effect of PAS on the association between WMH severity and ND within 7 days after stroke onset among patients with anterior circulation SSI, which deserves more research attention. WMH was negatively associated with ND in anterior circulation SSI patients without PAS.

Proteomic biomarkers for noninvasive left atrial appendage thrombus prediction in patients with atrial fibrillation

IntroductionThe CHA2DS2-VASc score, used to assess the risk of left atrial appendage thrombus (LAAT) formation in patients with atrial fibrillation (AF), has limited predictive value. Moreover, transesophageal echocardiography imaging, the gold standard diagnostic method to identify thrombi, is semi-invasive. Consequently, there is a need for alternative and noninvasive diagnostic methods for LAAT risk assessment. MethodsDeep proteomic analysis was conducted in plasma samples from 8 patients with nonvalvular AF, divided into thrombus and control groups (4 patients in each group) based on the presence or absence of LAAT. Biomarkers associated with LAAT were validated using an enzyme-linked immunosorbent assay in a cohort of 179 patients with available clinical, transthoracic, and transesophageal echocardiography data. Predictive models were developed to assess the improvement in LAAT identification. ResultsThe LAAT group had higher CHA2DS2-VASc scores, larger LA diameter, and lower LAA flow velocities. Deep proteomic analysis identified 30 differentially expressed proteins, including myosin light chain 4, prenylcysteine oxidase 1 (PCYOX1), and decorin as potential diagnostic biomarkers of LAAT. The model showed that PCYOX1 and decorin provided an area under the curve (AUC) of 0.970 for LAAT prediction compared with 0.672 in a model including the CHA2DS2-VASc score and LAA cauliflower morphology. The incremental value of proteomic biomarkers for LAAT in patients with nonvalvular AF was further confirmed with the net reclassification improvement and integrated discrimination improvement indices. ConclusionsProtein levels of PCYOX1 and decorin improve the predictive performance for LAAT in patients with nonvalvular AF.

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Iron deficiency and all-cause mortality after myocardial infarction

BackgroundData on the clinical significance of iron deficiency (ID) in patients with myocardial infarction (MI) are conflicting. This may be related to the use of various ID criteria.We aimed to compare the association of different ID criteria with all-cause mortality after MI. MethodsConsecutive patients hospitalized for their first MI at a large tertiary heart center were included. We evaluated the association of different iron metabolism parameters measured on the first day after hospital admission with all-cause mortality. ResultsFrom the 1,156 patients included (aged 64±12 years, 25 % women), 194 (16.8 %) patients died during the median follow-up of 3.4 years. After multivariate adjustment, iron level ≤13 µmol/L (HR 1.67, 95 % CI 1.19–2.34) and the combination of iron level ≤12.8 µmol/L and soluble transferrin receptor (sTfR) ≥3 mg/L (HR 2.56, 95 % CI 1.64–3.99) termed as PragueID criteria were associated with increased mortality risk and had additional predictive value to the GRACE score. Compared to the model including iron level, the addition of sTfR improved risk stratification (net reclassification improvement 0.61, 95 % CI 0.52–0.69) by reclassifying patients into a higher-risk group. No association between ferritin level and mortality was found. 51 % of patients had low iron levels, and 58 % fulfilled the PragueID criteria. ConclusionIron deficiency is common among patients with the first MI. The PragueID criteria based on iron and soluble transferrin receptor levels provide the best prediction of mortality and should be evaluated in future interventional studies for the identification of patients potentially benefiting from intravenous iron therapy.

Pre-Transplant Calcimimetic Use and Dose Information Improves the Accuracy of Prediction of Tertiary Hyperparathyroidism after Kidney Transplantation: A Retrospective Cohort Study

Tertiary hyperparathyroidism (THPT) is characterized by elevated parathyroid hormone and serum calcium levels after kidney transplantation (KTx). To ascertain whether pre-transplant calcimimetic use and dose information would improve THPT prediction accuracy, this retrospective cohort study evaluated patients who underwent KTx between 2010 and 2022. The primary outcome was the development of clinically relevant THPT. Logistic regression analysis was used to evaluate pre-transplant calcimimetic use as a determinant of THPT development. Participants were categorized into four groups according to calcimimetic dose, developing two THPT prediction models (with or without calcimimetic information). Continuous net reclassification improvement (CNRI) and integrated discrimination improvement (IDI) were calculated to assess ability to reclassify the degree of THPT risk by adding pre-transplant calcimimetic information. Of the 554 patients, 87 (15.7%) developed THPT, whereas 139 (25.1%) received pre-transplant calcimimetic treatment. Multivariate logistic regression analysis revealed that pre-transplant calcimimetic use was significantly associated with THPT development. Pre-transplant calcimimetic information significantly improved the predicted probability accuracy of THPT (CNRI and IDI were 0.91 [p &amp;lt; 0.001], and 0.09 [p &amp;lt; 0.001], respectively). The THPT prediction model including pre-transplant calcimimetic information as a predictive factor can contribute to the prevention and early treatment of THPT in the era of calcimimetics.

Estimated pulse wave velocity as a predictor of all-cause and cardiovascular mortality in patients with hypertension in China: a prospective cohort study.

Whether the estimated pulse wave velocity (ePWV) in Chinese patients with hypertension can serve as an independent predictor of cardiovascular and all-cause mortality remains unknown. Therefore, this study investigated the associations between ePWV and cardiovascular and all-cause mortalities and explored potential effect modifiers influencing these relationships. Finally, we compared the ePWV with the brachial-ankle pulse wave velocity (baPWV) to determine which parameter better predicts mortality. The population of this longitudinal cohort study was selected from the China H-type Hypertension Registry Study. The exposure and outcome variables were ePWV and all-cause and cardiovascular mortalities, respectively. The Cox proportional hazard regression model was applied to assess the associations between ePWV and all-cause and cardiovascular mortalities. The performances of ePWV and baPWV in predicting death were compared using the receiver operating characteristic (ROC) curve area, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). This prospective study enrolled 14,232 patients with hypertension. Following an average follow-up of 48 months, 806 individuals succumbed to all-cause mortality, with 397 cases specifically attributed to cardiovascular diseases. The Cox proportional regression analysis revealed a significant association between a 1 m/s increase in ePWV and a 37% higher risk of all-cause mortality (hazard ratio [HR]: 1.37, 95% confidence interval [CI]: 1.31-1.43) as well as a 52% higher risk of cardiovascular mortality (HR: 1.52, 95% CI: 1.43-1.62) in the fully adjusted model. The findings for ePWV according to quartile demonstrated hazard ratios for all-cause mortality for Q2 (10.25 < ePWV < 11.32), Q3 (11.32 < ePWV < 12.40), and Q4 (ePWV ≥ 12.40) of 1.50 (HR: 1.50, 95% CI: 1.07-2.10), 2.34 (HR: 2.34, 95% CI: 1.73-3.18), and 4.09 (HR: 4.09, 95% CI: 3.05-5.49), respectively, compared with Q1 (ePWV < 10.25). The risk of cardiovascular death also increased in proportion to the rise in ePWV. The results of the area under the ROC curve, NRI, and IDI all indicated that ePWV outperformed baPWV in predicting mortality. The results of the subgroup analysis demonstrated that body mass index (BMI) and hypoglycemic drug use modified the association between ePWV and mortality. The performance of ePWV in predicting all-cause and cardiovascular mortalities was superior to that of baPWV alone. Patients who were overweight or obese with higher ePWV values exhibited a significantly increased risk of all-cause death. The correlation between elevated ePWV and the risk of cardiovascular death was more pronounced in patients who had not received hypoglycemic drugs.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study.

Perineural invasion (PNI) has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer (RC). Preoperative prediction of PNI status is helpful for individualized treatment of RC. Recently, several radiomics studies have been used to predict the PNI status in RC, demonstrating a good predictive effect, but the results lacked generalizability. The preoperative prediction of PNI status is still challenging and needs further study. To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients. This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers. The patients underwent pre-operative high-resolution magnetic resonance imaging (MRI) between May 2019 and August 2022. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI) and contrast-enhanced T1WI (T1CE) sequences. The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared (T2WI, T1CE and T2WI + T1CE fusion sequences). A clinical-radiomics (CR) model was established by combining the radiomics features and clinical risk factors. The internal and external validation groups were used to validate the proposed models. The area under the receiver operating characteristic curve (AUC), DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the model performance. Among the radiomics models, the T2WI + T1CE fusion sequences model showed the best predictive performance, in the training and internal validation groups, the AUCs of the fusion sequence model were 0.839 [95% confidence interval (CI): 0.757-0.921] and 0.787 (95%CI: 0.650-0.923), which were higher than those of the T2WI and T1CE sequence models. The CR model constructed by combining clinical risk factors had the best predictive performance. In the training and internal and external validation groups, the AUCs of the CR model were 0.889 (95%CI: 0.824-0.954), 0.889 (95%CI: 0.803-0.976) and 0.894 (95%CI: 0.814-0.974). Delong test, NRI, and IDI showed that the CR model had significant differences from other models (P < 0.05). Calibration curves demonstrated good agreement, and DCA revealed significant benefits of the CR model. The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively, which facilitates individualized treatment of RC patients.

Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis

BackgroundUrinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations.MethodsData of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis.ResultsThis study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram.ConclusionsThe nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.

Development of a Better Nomogram for Prediction of Preoperative Microvascular Invasion and Postoperative Prognosis in Hepatocellular Carcinoma Patients: A Comparison Study.

Personalized precision medicine can be facilitated by clinically available preoperative microvascular invasion (MVI) prediction models that are reliable and postoperative MVI pathological grade-related recurrence prediction models that are accurate. In this study, we aimed to compare different mathematical models to derive the best preoperative prediction and postoperative recurrence prediction models for MVI. A total of 143 patients with hepatocellular carcinoma (HCC) whose clinical, laboratory, imaging, and pathological data were available were included in the analysis. Logistic regression, Cox proportional hazards regression, LASSO regression with 10-fold cross-validation, stepwise regression, and random forest methods were used for variable screening and predictive modeling. The accuracy and validity of seven preoperative MVI prediction models and five postoperative recurrence prediction models were compared in terms of C-index, net reclassification improvement, and integrated discrimination improvement. Multivariate logistic regression analysis revealed that a preoperative nomogram model with the variables cirrhosis diagnosis, alpha-fetoprotein > 400, and diameter, shape, and number of lesions can predict MVI in patients with HCC reliably. Postoperatively, a nomogram model with MVI grade, number of lesions, capsule involvement status, macrovascular invasion, and shape as the variables was selected after LASSO regression and 10-fold cross-validation analysis to accurately predict the prognosis for different MVI grades. The number and shape of the lesions were the most common predictors of MVI preoperatively and recurrence postoperatively. Our study identified the best statistical approach for the prediction of preoperative MVI as well as postoperative recurrence in patients with HCC based on clinical, imaging, and laboratory tests results. This could expedite preoperative treatment decisions and facilitate postoperative management.

Serum YKL-40 as a Predictive Biomarker of Cerebral Amyloid Angiopathy-Related Intracerebral Hemorrhage Recurrence.

Neuroinflammation is a major cause of secondary brain injury in intracerebral hemorrhage (ICH). To date, the prognostic value of YKL-40 (chitinase-3-like-1 protein), a biomarker of neuroinflammation, in cerebral amyloid angiopathy-related intracerebral hemorrhage (CAA-ICH) remains undiscovered. To evaluate the relationships between serum YKL-40 and CAA-ICH recurrence. Clinical and imaging information of 68 first-onset probable CAA-ICH cases and 95 controls were collected at baseline. Serum YKL-40 was measured by Luminex assay. Cox proportional hazards model was used to analyze the associations between YKL-40 level and CAA-ICH recurrence. Serum YKL-40 level was significantly higher in CAA-ICH cases than healthy controls (median [interquartile range, IQR], 46.1 [19.8, 93.4] versus 24.4 [13.9, 59.0] ng/mL, p = 0.004). Higher level of YKL-40 predicted increased risk of CAA-ICH recurrence adjusted for age, ICH volume and enlarged perivascular space score (ePVS) (above versus below 115.5 ng/ml, adjusted hazard ratios 4.721, 95% confidence intervals 1.829-12.189, p = 0.001) within a median follow-up period of 2.4 years. Adding YKL-40 to a model of only MRI imaging markers including ICH volume and ePVS score improved the discriminatory power (concordance index from 0.707 to 0.772, p = 0.001) and the reclassification power (net reclassification improvement 28.4%; integrated discrimination index 11.0%). Serum YKL-40 level might be a candidate prognostic biomarker for CAA-ICH recurrence.

Exploring the optimal indicator of short-term peridiagnosis weight dynamics to predict cancer survival: A multicentre cohort study.

Body weight and its changes have been associated with cancer outcomes. However, the associations of short-term peridiagnosis weight dynamics in standardized, clinically operational time frames with cancer survival remain largely unknown. This study aimed to screen for and evaluate the optimal indicator of short-term peridiagnosis weight dynamics to predict overall survival (OS) in patients with cancer. This multicentre cohort study prospectively collected data from 7460 patients pathologically diagnosed with cancer between 2013 and 2019. Body weight data were recorded 1month before, at the time of and 1month following diagnosis. By permuting different types (point value in kg, point height-adjusted value in kg/m2, absolute change in kg or relative change in percentage) and time frames (prediagnosis, postdiagnosis or peridiagnosis), we generated 12 different weight-related indicators and compared their prognostic performance using Harrell's C-index, integrated discrimination improvement, continuous net reclassification improvement and time-dependent C-index. We analysed associations of peridiagnosis relative weight change (RWC) with OS using restricted cubic spine (RCS), Kaplan-Meier analysis and multivariable-adjusted Cox regression models. The study enrolled 5012 males and 2448 females, with a median age of 59years. During a median follow-up of 37months, 1026 deaths occurred. Peridiagnosis (1month before diagnosis to 1month following diagnosis) RWC showed higher prognostic performance (Harrell's C-index=0.601, 95% confidence interval [CI]=[0.583, 0.619]) than other types of indicators including body mass index (BMI), absolute weight change, absolute BMI change, prediagnosis RWC and postdiagnosis RWC in the study population (all P<0.05). Time-dependent C-index analysis also indicated that peridiagnosis RWC was optimal for predicting OS. The multivariable-adjusted RCS analysis revealed an N-shaped non-linear association between peridiagnosis RWC and OS (PRWC<0.001, Pnon-linear<0.001). Univariate survival analysis showed that the peridiagnosis RWC groups could represent distinct mortality risk stratifications (P<0.001). Multivariable survival analysis showed that, compared with the maintenance group (weight change<5%), the significant (gain >10%, hazard ratio [HR]=0.530, 95% CI=[0.413, 0.680]) and moderate (gain 5-10%, HR=0.588, 95% CI=[0.422, 0.819]) weight gain groups were both associated with improved OS. In contrast, the moderate (loss 5-10%, HR=1.219, 95% CI=[1.029, 1.443]) and significant (loss >10%, HR=1.280, 95% CI=[1.095, 1.497]) weight loss groups were both associated with poorer OS. The prognostic performance of peridiagnosis RWC is superior to other weight-related indicators in patients with cancer. The findings underscore the importance of expanding the surveillance of body weight from at diagnosis to both past and future, and conducting it withinclinically operational time frames, in order to identify and intervene with patients who are at risk of weight change-related premature deaths.

Association Between Life's Essential 8 Cardiovascular Health Metrics With Cardiovascular Events in the Cardiovascular Disease Lifetime Risk Pooling Project.

The American Heart Association recently launched updated cardiovascular health metrics, termed Life's Essential 8 (LE8). Compared with Life's Simple 7 (LS7), the new approach added sleep health as an eighth metric and updated the remaining 7 health factors and behaviors. The association of the updated LE8 score with long-term cardiovascular disease (CVD) outcomes and death is unknown. We pooled individual-level data from 6 contemporary US-based cohorts from the Cardiovascular Lifetime Risk Pooling Project. Total LE8 score (0-100 points), LE8 score without sleep (0-100 points), and prior LS7 scores (0-14 points) were calculated separately. We used multivariable-adjusted Cox models to evaluate the association of LE8 with CVD, CVD subtypes, and all-cause mortality among younger, middle, and older adult participants. Net reclassification improvement analysis was used to measure the improvement in CVD risk classification with the addition of LS7 and LE8 recategorization based on score quartile rankings. Our sample consisted of 32 896 US adults (7836 [23.8%] Black; 14 941 [45.4%] men) followed for 642 000 person-years, of whom 9391 developed CVD events. Each 10-point higher overall LE8 score was associated with lower risk by 22% to 40% for CVD, 24% to 43% for congenital heart disease, 17% to 34% for stroke, 23% to 38% for heart failure, and 17% to 21% for all causes of mortality events across age strata. LE8 score provided more granular differentiation of the related CVD risk than LS7. Overall, 19.5% and 15.5% of the study participants were recategorized upward and downward based on LE8 versus LS7 categories, respectively, and the recategorization was significantly associated with CVD risk in addition to LS7 score. The addition of recategorization between LE8 and LS7 categories improved CVD risk reclassification across age groups (clinical net reclassification improvement, 0.06-0.12; P<0.01). These findings support the improved utility of the LE8 algorithm for assessing overall cardiovascular health and future CVD risk.