Nomograms

BackgroundConventional intracerebral hemorrhage (ICH) functional scores are typically developed based on clinical factors for assessing mortality risk, and their applicability in evaluating functional outcomes in patients with the specific hemorrhagic subtype of mild to moderate ICH(MTM-ICH) remains unclear. Therefore, we aimed to construct a nomogram model to predict early functional dependence in this patient population and to validate three previous general ICH functional scores.MethodsWe conducted a retrospective analysis of data from 575 patients with MTM-ICH (GCS score ≥ 9) treated at our hospital between January 2018 and July 2025. The baseline demographic information, clinical characteristics, and clinical outcomes were collected. Early functional dependence was defined as a modified Rankin Scale (MRS) score ≥ 3 at discharge or 30 days after ICH. Least absolute shrinkage and selection operator (LASSO) and stepwise logistic regression analyses were used to screen for risk factors for early functional dependence and construct a predictive model. The model was visualized using a nomogram, and its predictive performance was compared with three existing ICH functional scores (FUNC, ICH-GS and ICH-FOS). Internal validation of the nomogram was performed using a 10-fold cross-validation combined with bootstrap resampling.ResultsAmong 575 patients with MTM-ICH, 198 (34.4%) developed early functional dependence. LASSO and stepwise logistic regression analyses identified six independent predictors: age, admission GCS score, hematoma volume, thalamic hemorrhage, basal ganglia hemorrhage, and mechanical ventilation. The nomogram based on these factors demonstrated good calibration and superior predictive performance compared to three existing ICH functional scores (AUC = 0.856 for our nomogram, 0.579 for FUNC, 0.601 for ICH-GS, and 0.714 for ICH-FOS, respectively). The decision curve analysis further demonstrated that the model had greater net benefit than the three previous general ICH functional scores. Internal validation confirmed the model's stability and generalizability (10-fold cross-validation mean: 0.846; bootstrap-corrected C-index: 0.844).ConclusionAmong patients with MTM-ICH, advanced age, lower admission GCS score, larger hematoma volume, thalamic or basal ganglia hemorrhage, and mechanical ventilation were associated with a higher risk of early functional dependence. The nomogram developed based on these factors demonstrated high predictive performance. In contrast, conventional ICH functional scores demonstrate certain limitations when assessing this specific hemorrhage subtype.

AimsMiddle-aged and older adults with overweight or obesity are at increased risk of cardiometabolic multimorbidity (CMM), yet validated prediction tools tailored to this population remain limited. This study aimed to develop and validate a multidimensional nomogram integrating metabolic, functional and psychological predictors.Materials and methodsUsing the China Health and Retirement Longitudinal Study (CHARLS) data (2011 and 2015), 3965 overweight/obese adults with a body mass index (BMI) ≥ 24.0 kg/m2 were randomly split into development (n = 2775) and validation (n = 1190) sets. Candidate predictors spanned socio-demographic, lifestyle, functional, psychological and biochemical factors. External validation included 8180 overweight/obese participants from the US Health and Retirement Study (HRS). Least absolute shrinkage and selection operator and multivariable logistic regression were applied to build a nomogram. Performance was assessed by area under the curve (AUC), calibration and decision curve analysis.ResultsCMM prevalence was 3.91%. Eight variables were retained: residence, Activities of daily living (ADL) score, the Center for Epidemiologic Studies Depression Scale (CESD-10) score, haemoglobin A1c (HbA1c), hypertension, arthritis, dyslipidaemia and memory disorder. AUCs were 0.871 (95% CI: 0.838-0.905) (development), 0.817 (95% CI: 0.768-0.867) (validation) and 0.733 (95% CI: 0.714-0.751) in HRS external validation, with acceptable calibration in CHARLS and evidence of underestimation in HRS that improved after intercept recalibration, and clear net benefit. Positive predictive values were additionally reported to support clinical interpretation under the low CMM prevalence.ConclusionsThis nomogram integrating metabolic, functional and psychological predictors demonstrated strong discrimination in the CHARLS cohort and maintained adequate discriminative performance in a cross-national external validation using the US HRS cohort despite differences in population characteristics and measurement instruments, supporting its potential utility for early CMM risk stratification in overweight/obese older adults. The external HRS validation showed fair discrimination, suggesting that model recalibration may be necessary before broad cross-national implementation.

BackgroundTo enhance risk stratification in metastatic castrate-resistant prostate cancer (mCRPC), we developed clinical and integrated clinico-genomic prognostic nomograms combining clinical prognostic factors with copy number alteration-based risk scores (RSs) in circulating tumor DNA (ctDNA) and metastatic tissue to predict 1-, 2-, and 3-year overall survival (OS) probabilities.MethodsA clinical prognostic nomogram was developed from publicly accessed Yale University Open Data Access (YODA) data-sciences project (N = 1088 patients). A second publicly available independent dataset (N = 82) with an 11-gene prognostic RS developed concurrently obtained ctDNA and matched metastatic tissue specimens (gains: AR, MYC, COL22A1, PIK3CA, PIK3CB, NOTCH1; losses: TMPRSS2, NCOR1, ZBTB18, TP53, NKX3-1) was used to develop an integrated clinico-genomic survival nomogram. Independent predictors for survival were identified in each dataset using univariate and multivariate Cox proportional hazard. Multivariate regression coefficients with statistical significance (P ResultsThe YODA dataset t-AUCs for the prognostic nomogram for estimating OS at 1, 2, and 3 years were 0.74, 0.70, and 0.74. In the independent second dataset, the integrated clinico-genomic prognostic nomogram was observed to have higher t-AUCs for predicting 3-year mCRPC survival at 0.807 compared with 0.714 using clinical factors alone.ConclusionsAn integrated clinico-genomic nomogram with ctDNA RSs achieved the highest t-AUC for 3-year survival in mCRPC and may enable precision in identification of poor-prognosis subgroups based on somatic alterations.