[Development and Validation of a Risk Prediction Model for Secondary Pulmonary Infarction in Elderly Patients With Acute Pulmonary Embolism].

Alcohol consumption was identified as an independent risk factor for secondary pulmonary infarction in elderly PE patients.

• OR = 8.586 (95% CI: 2.430–30.361), P < 0.05
• Identified via stepwise regression analysis in a cohort of 147 elderly PE patients
• Patients were divided into secondary PI group (n = 44) and non-secondary PI group (n = 103)

Chronic bronchitis was identified as an independent risk factor for secondary pulmonary infarction in elderly PE patients.

• OR = 9.831 (95% CI: 2.701–35.782), P < 0.05
• Identified via stepwise regression analysis
• Among the stronger risk factors identified in the model

Emphysema was identified as an independent risk factor for secondary pulmonary infarction in elderly PE patients.

• OR = 6.990 (95% CI: 1.987–24.582), P < 0.05
• Identified via stepwise regression analysis in the modeling cohort of 147 elderly PE patients

Coronary heart disease was the strongest independent risk factor for secondary pulmonary infarction in elderly PE patients.

• OR = 15.603 (95% CI: 3.470–41.144), P < 0.05
• Had the highest odds ratio among all identified risk factors
• Identified via stepwise regression analysis

Diabetes was identified as an independent risk factor for secondary pulmonary infarction in elderly PE patients.

• OR = 11.955 (95% CI: 1.097–130.860), P < 0.05
• Notable for a wide confidence interval suggesting variability in the estimate
• Identified via stepwise regression analysis

Elevated D-dimer level was identified as an independent risk factor for secondary pulmonary infarction in elderly PE patients.

• OR = 1.021 (95% CI: 1.002–1.037), P < 0.05
• The only continuous laboratory variable among the identified independent risk factors
• Identified via stepwise regression analysis

The risk prediction model demonstrated high discriminatory power in both the modeling and validation cohorts.

• AUC = 0.936 (95% CI: 0.901–0.976) for the modeling group (n = 147)
• AUC = 0.917 (95% CI: 0.852–0.990) for the validation group (n = 63)
• The validation cohort consisted of elderly PE patients admitted between September 2022 and December 2023
• ROC curve analysis was used to assess discriminatory power

The risk prediction model demonstrated high calibration accuracy and clinical utility in both cohorts.

• Calibration curve results indicated high accuracy in both the modeling and validation cohorts
• Clinical decision curve analysis showed the model has high clinical utility
• The model was constructed using R software as a nomogram