ArticleApplied clinical informatics2018
Automated versus Manual Data Extraction of the Padua Prediction Score for Venous Thromboembolism Risk in Hospitalized Older Adults.
Article in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Utilization of the Caprini risk assessment model(RAM) to predict venous thromboembolism after primary hip and knee arthroplasty: an analysis of the Healthcare Cost and Utilization Project(HCUP).Thrombosis journal · 2024Article
- A real-time prognostic model for venous thromboembolic events among hospitalized adults.Research and practice in thrombosis and haemostasis · 2024Article
- Provider Response to a Venous Thromboembolism Risk Assessment and Prophylaxis Ordering Tool: Observational Study.Applied clinical informatics · 2022Observational
- Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study.JMIR formative research · 2022Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveVenous 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.
methodsThis 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.
resultsPPS 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%.
conclusionAPPS 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.
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