Abstract
BACKGROUND: The prognosis of elderly patients with acute myocardial infarction (AMI) is poor, and this study aimed to investigate the characteristics and predictors of preoperative AMI in elderly hip fracture patients and to propose a valid clinical prediction model. METHODS: We collected clinical data of older hip fracture patients from January 2019 to December 2020. The data collected include demographic and clinical characteristics, underlying diseases and laboratory results. In AMI patients, we further collected type of myocardial infarctions, clinical symptoms, electrocardiogram (ECG), Killip class and diagnosis method. The prediction model was constructed by using Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses. In addition, the constructed prediction model was transformed into a nomogram. The performance of the model was evaluated using the area under receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Bootstrapping was used for validation. RESULTS: There are 40 (4.2%) cases developed AMI in all 958 patients. There were 685 (71.5%) female patients and 273 (28.5%) male patients. Among 40 AMI patients, 38 (95.0%) had Type 2 myocardial infarction (T2MI) and 2 (5.0%) had Type 1 myocardial infarction (T1MI). The majority of these ECG were ST segment depression (57.5%). Most elderly AMI patients (67.5%) presented asymptomatic. Predictors for preoperative AMI were higher age (OR 2.386, 95% CI 1.126-5.057), diabetes (OR 5.863, 95% CI 2.851-12.058), Hb≤100 g/L (OR 3.976, 95% CI 1.478-10.695), CRP≥40 mg/L (OR 6.998, 95% CI 2.875-17.033), and ALB≤35 g/L (OR 2.166, 95% CI 1.049-4.471). Good discrimination and calibration effect of the model was showed. Interval validation could still achieve the C-index value of 0.771. DCA demonstrated this nomogram has good clinical utility. CONCLUSION: This model has a good predictive effect on preoperative AMI in elderly patients with hip fracture, which can help to better plan clinical evaluation.