Automated versus Manual Data Extraction of the Padua Prediction Score for Venous Thromboembolism Risk in Hospitalized Older Adults

Abstract

Objective Venous thromboembolism (VTE) prophylaxis is an important consideration for hospitalized older adults, and the Padua Prediction Score (PPS) is a risk prediction tool used to prioritize patient selection. We developed an automated PPS (APPS) algorithm using electronic health record (EHR) data. This study examines the accuracy of APPS and its individual components versus manual data extraction. Methods This is a retrospective cohort study of hospitalized general internal medicine patients, aged 70 and over. Fourteen clinical variables were collected to determine their PPS; APPS used EHR data exports from health system databases, and a trained abstractor performed manual chart abstractions. We calculated sensitivity and specificity of the APPS, using manual PPS as the gold standard for classifying risk category (low vs. high). We also examined performance characteristics of the APPS for individual variables. Results PPS was calculated by both methods on 311 individuals. The mean PPS was 3.6 (standard deviation, 1.8) for manual abstraction and 2.8 (1.4) for APPS. In detecting patients at high risk for VTE, the sensitivity and specificity of the APPS algorithm were 46 and 94%, respectively. The sensitivity for APPS was poor (range: 6-34%) for detecting acute conditions (i.e., acute myocardial infarction), moderate (range: 52-74%) for chronic conditions (i.e., heart failure), and excellent (range: 94-98%) for conditions of obesity and restricted mobility. Specificity of the automated extraction method for each PPS variable was > 87%. Conclusion APPS as a stand-alone tool was suboptimal for classifying risk of VTE occurrence. The APPS accurately identified high risk patients (true positives), but lower scores were considered indeterminate.