Aug 2026· Clinical Interventions in Aging· Vol 21, pp. 1-11· 0 citations· 30 references
Medicine
TL;DR
The RRD prediction model incorporating age, ASA classification, preoperative hypoalbuminemia, intraoperative hypothermia, intraoperative hypothermia, and hypotension demonstrated promising discriminatory ability and calibration in this single-center cohort.
Abstract
Purpose To identify independent risk factors for recovery room delirium (RRD) in patients undergoing transurethral resection of the prostate (TURP) and to develop a predictive model for early identification of high-risk patients. Patients and Methods We retrospectively reviewed 1166 adult patients who underwent TURP under general anesthesia between January 2020 and December 2024. RRD was assessed using the 3-minute Diagnostic Interview for CAM-defined Delirium (3D-CAM). Patients were randomly assigned to the training set (n = 816) and the validation set (n = 350). Independent risk factors were identified by multivariate logistic regression, and a nomogram was constructed. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results The incidence of RRD was 19.5% in the training set and 20% in the validation set. Multivariable logistic regression identified older age, higher ASA physical status classification, preoperative hypoalbuminemia, intraoperative hypothermia, and intraoperative hypotension as independent predictors associated with an increased risk of RRD. Based on these predictors, a nomogram was established. The model demonstrated good discrimination, with an AUC of 0.841 (95% CI, 0.806–0.876) in the training cohort and 0.858 (95% CI, 0.810–0.906) in the validation cohort. Calibration analysis showed good agreement between the predicted and observed probabilities, and decision curve analysis demonstrated favorable clinical utility across a wide range of threshold probabilities. Conclusion The RRD prediction model incorporating age, ASA classification, preoperative hypoalbuminemia, intraoperative hypothermia, and hypotension demonstrated promising discriminatory ability and calibration in this single-center cohort. However, prospective external validation is required before widespread clinical implementation.
AIM
Elderly patients admitted to the intensive care unit (ICU) after emergency surgery represent a uniquely high-risk population for postoperative delirium (POD). However, the key risk factors specific to this group have not been comprehensively integrated, and a corresponding clinical prediction tool is lacking. This study aimed to elucidate the clinical features, identify independent risk factors for POD, and develop and validate a prediction model tailored for elderly patients in the emergency surgical ICU.
METHODS
This retrospective study included 496 elderly patients (≥60 years) admitted post-emergency surgery. Delirium was assessed using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Patients were randomly divided in a 7:3 ratio into training (n = 347) and validation (n = 149) sets. Logistic regression identified independent predictors, which were used to construct a nomogram. Model performance was evaluated via receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA).
RESULTS
POD incidence was 33.1%, predominantly the hypoactive subtype (64.6%), with peak onset on postoperative day 2 (57.9%). Five independent risk factors were identified: higher Sequential Organ Failure Assessment (SOFA) score (Odds Ratio [OR] = 3.818), elevated Procalcitonin (PCT) (OR = 1.594), longer mechanical ventilation (OR = 1.283), intraoperative hypotension (OR = 2.666), and physical restraint use (OR = 3.003). A nomogram was developed using these variables, which demonstrated excellent discriminative performance, with area under the curves (AUCs) of 0.937 and 0.913, in the training and validation datasets, respectively. DCA confirmed the clinical utility of our proposed model.
CONCLUSIONS
This study identified five key and modifiable risk factors for POD in elderly emergency surgical ICU patients and successfully developed and validated a high-performance nomogram prediction tool. This model aids in the early identification of high-risk patients, providing a basis for implementing targeted bundled prevention strategies, which holds promise for improving clinical outcomes in this vulnerable population.
Xiaojiao Liu, F. Pan, Liqin Hu et al.· Annali italiani di chirurgia· 0 citations
BACKGROUND
Cardiac arrest (CA) is a critical clinical event associated with extremely high mortality and long-term disability. This study aimed to identify risk factors associated with ICU mortality and develop a practical nomogram for risk prediction to support prognostic assessment and risk stratification among patients with CA.
METHODS
A retrospective analysis was conducted using two large public critical care databases. The MIMIC-IV database was used as the training cohort for model development, while the eICU-CRD database served as the external validation cohort. Variables were initially screened using least absolute shrinkage and selection operator (LASSO) regression, and a predictive nomogram was subsequently established using binary logistic regression analysis. The discriminative performance of the model was evaluated using the receiver operating characteristic (ROC) curve. Calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test. Clinical applicability and net benefit were further analyzed using decision curve analysis (DCA).
RESULTS
A total of 3,029 patients from the MIMIC-IV database and 2,805 patients from the eICU-CRD database were enrolled in this study. After variable selection by LASSO regression, eight predictors were included in the nomogram. The model was compared with conventional ICU scoring systems, including SAPS II, SOFA, and CCI. In the training cohort, the AUROC values were 0.802 for the nomogram, 0.725 for SAPS II, 0.709 for SOFA, and 0.509 for CCI. In the external validation cohort, the corresponding AUROC values were 0.733, 0.596, 0.701, and 0.517, respectively. The Hosmer-Lemeshow test yielded P-values of 0.976 in the training set and 0.905 in the validation set, indicating favorable goodness-of-fit and satisfactory calibration. Moreover, DCA demonstrated that the nomogram provided greater net benefit compared with traditional scoring systems.
CONCLUSION
The established nomogram for predicting ICU mortality in patients with CA demonstrated acceptable predictive performance and calibration. As a quantitative risk stratification tool, it may provide clinicians with useful individualized prognostic information for this vulnerable patient population.
Wei Shi, Xuemei Hu, Hui Li et al.· BMC Cardiovascular Disorders· 0 citations
Objective Hypoxemia is a common and potentially harmful complication in patients undergoing major abdominal surgery (MAS). We aimed to develop and validate a nomogram prediction model for hypoxemia in the post-anesthesia care unit (PACU) after MAS. Patients and methods A total of 378 patients who underwent MAS and were admitted into the PACU were selected as the training group from June 1, 2024 to December 31, 2024. From April 1, 2025 to May 31, 2025, 189 patients who underwent MAS and were admitted to the PACU were enrolled as the validation group for temporal validation. Logistic regression analysis was employed to develop the nomogram model using age, body mass index (BMI), the ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO₂/FiO₂), and hemoglobin (HGB) as variables. The area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate the model accuracy. The primary outcome was defined as hypoxemia that occurred after tracheal extubation in the PACU. Results In the training group (n = 378), hypoxemia occurred in 112 patients (29.6%). The final nomogram included four variables: age >60.5 years, BMI ≥ 24.0 kg/m2, PaO₂/FiO₂ < 311.5 mmHg, and HGB < 106.5 g/L (all p < 0.05). The model showed good calibration (Hosmer-Lemeshow test, p = 0.244) and discrimination (AUC = 0.88, 95% CI: 0.83–0.92). At the optimal cutoff of 0.242 (Youden index), sensitivity was 0.76 and specificity 0.85. Decision curve analysis demonstrated a positive net benefit across threshold probabilities of 0.03–1.00, confirming clinical utility. In the validation group (n = 189), the predictive accuracy was 78.3%, indicating that the model maintained excellent performance in the validation group. Conclusion The nomogram model based on age, BMI, PaO₂/FiO₂, and HGB shows good predictive performance for hypoxemia. This model may help anesthesia staff identify patients at high risk of hypoxemia in the PACU.
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.
Xi Zhu, Xujie Wang, Rongfei Xie et al.· Journal of clinical neurosci...· 0 citations
Urinary dysfunction is a relatively common functional complication after laparoscopic radical surgery for rectal cancer and may affect postoperative recovery and quality of life. This study aimed to identify risk factors for urinary dysfunction after laparoscopic radical rectal cancer surgery and to develop a nomogram prediction model to facilitate early identification of high-risk patients.
A total of 392 patients who underwent laparoscopic radical surgery for rectal cancer between January 2021 and December 2025 were retrospectively included. Patients were grouped according to the occurrence of postoperative urinary dysfunction and randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Demographic, tumor-related, and perioperative variables were collected. Univariable and multivariable logistic regression analyses were performed to identify independent predictors, and a nomogram model was constructed. Model performance was evaluated using receiver operating characteristic curves, calibration curves, the Hosmer-Lemeshow test, and decision curve analysis.
Among the 392 patients, 84 developed postoperative urinary dysfunction, with an incidence of 21.4%. Multivariable logistic regression analysis showed that age ≥65 years, male sex,
t
umor distance from the anal verge ≤5 cm, intraoperative fluid rate ≥10 mL/kg/h, neoadjuvant chemoradiotherapy, documented incomplete pelvic autonomic nerve preservation, and postoperative urinary tract infection were independent factors associated with postoperative urinary dysfunction. A nomogram was constructed based on these predictors. The area under the curve was 0.864 (95% CI: 0.803–0.926) in the training cohort and 0.856 (95% CI: 0.774–0.938) in the validation cohort. Calibration curves and the Hosmer-Lemeshow test indicated good model fit, and decision curve analysis showed favorable clinical net benefit.
Age, sex, tumor distance from the anal verge, intraoperative fluid rate, neoadjuvant chemoradiotherapy, pelvic autonomic nerve preservation, and postoperative urinary tract infection were closely associated with urinary dysfunction after laparoscopic radical surgery for rectal cancer. The nomogram based on these factors showed good predictive performance and may help identify high-risk patients, guide urinary catheter management, and support postoperative follow-up.
Background Postoperative nausea and vomiting (PONV) is a common and distressing complication following liver cancer surgery. This study aimed to develop and validate a risk prediction model for PONV in patients undergoing hepatectomy for hepatocellular carcinoma. Methods This prospective study enrolled patients who underwent liver resection between February 2024 and December 2024. Based on risk factors identified through univariate and binary logistic regression analyses, a nomogram prediction model was constructed. The model’s discrimination was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) and the consistency index (C-index). Calibration was assessed with calibration curves, and internal validation was performed via the bootstrap method. Results A total of 512 patients were included in the modeling cohort. The incidence of PONV was 47.5%. Significant predictors incorporated into the nomogram included age, gender, duration of surgery (min), time of hepatic portal vein occlusion during operation (min), and history of PONV. The model demonstrated an AUC of 0.717 (95% CI: 0.673–0.761), with a sensitivity of 69.9% and a specificity of 62.0% at the optimal cut–off value of 0.413. Bootstrap internal validation yielded a C-index of 0.717, and the calibration curve indicated good agreement between predicted and observed outcomes. Conclusion The developed risk warning model shows an acceptable predictive effect in identifying the risk of PONV in patients with liver cancer. It can assist clinical medical staff in early risk assessment and individualized intervention, and has certain predictive value.
Dan-Dan Geng, Jun Wang, Yan Zhu et al.· Journal of Hepatocellular Ca...· 0 citations