A preoperative logistic regression model using six routine variables predicted in-hospital MACE after hip fracture surgery with an AUC of 0.814 in internal testing and 0.760 in external validation.
Cohort (n=1,103)
Yes
Can a simple preoperative prediction model accurately predict postoperative in-hospital MACE after hip fracture surgery in older adults?
A simple preoperative logistic regression model based on routinely available clinical variables showed moderate discrimination for predicting postoperative in-hospital MACE after hip fracture surgery in older adults.
Effect estimate: AUC 0.814 (95% CI 0.748-0.876)
Background Older adults with hip fracture are vulnerable to postoperative major adverse cardiovascular events (MACE) during hospitalization. However, early risk stratification remains difficult because existing assessments may require complex or late-available information. A simple model based on variables routinely available before surgery may support perioperative decision-making. Methods We conducted a retrospective two-center cohort study of patients ≥ 60 years who underwent surgery for traumatic hip fracture at 2 hospitals in China. The development cohort included 935 patients treated between January 2024 and December 2025, and the external validation cohort included 168 patients treated between January 2020 and December 2025. Candidate preoperative predictors were screened using penalized regression. Six candidate prediction models were developed and compared using the same preprocessing pipeline. The final model was calibrated using Platt scaling. Model performance was assessed in an internal test set and an external validation cohort. Results In the development cohort, 122 of 935 patients developed postoperative in-hospital MACE. The final model was a Platt-calibrated logistic regression model including age, baseline electrocardiographic abnormality, chronic heart failure, serum potassium, albumin, and hemoglobin. In the internal test set, the model had an area under the receiver operating characteristic curve of 0.814, with a 95% CI of 0.748–0.876, and a Brier score of 0.102. At the prespecified threshold of 0.147, sensitivity was 0.710 and specificity was 0.754. In the external validation cohort, the area under the receiver operating characteristic curve was 0.760, with a 95% CI of 0.657–0.849. The Brier score was 0.141, sensitivity was 0.788, specificity was 0.630, and negative predictive value was 0.924. Conclusions A simple preoperative logistic regression model based on routinely available clinical variables showed moderate discrimination in internal testing and provided preliminary external validation evidence for predicting postoperative in-hospital MACE after hip fracture surgery in older adults. The model may support early perioperative risk stratification and postoperative surveillance, although prospective multicenter validation and recalibration are needed before wider clinical use.
Luo et al. (Mon,) conducted a cohort in Traumatic hip fracture (n=1,103). Preoperative prediction model (age, baseline ECG abnormality, chronic heart failure, serum potassium, albumin, hemoglobin) was evaluated on Postoperative in-hospital major adverse cardiovascular events (MACE) (AUC 0.814, 95% CI 0.748-0.876). A preoperative logistic regression model using six routine variables predicted in-hospital MACE after hip fracture surgery with an AUC of 0.814 in internal testing and 0.760 in external validation.