Automating venous thromboembolism risk calculation using electronic health record data upon hospital admission: The automated padua prediction score

Pierre Elias, Raman Khanna, Adams Dudley, Jason Davies, Ronald Jacolbia, Kara McArthur, Andrew D. Auerbach

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


BACKGROUND: Venous thromboembolism (VTE) risk scores assist providers in determining the relative benefit of prophylaxis for individual patients. While automated risk calculation using simpler electronic health record (EHR) data is feasible, it lacks clinical nuance and may be less predictive. Automated calculation of the Padua Prediction Score (PPS), requiring more complex input such as recent medical events and clinical status, may save providers time and increase risk score use. OBJECTIVE: We developed the Automated Padua Prediction Score (APPS) to auto-calculate a VTE risk score using EHR data drawn from prior encounters and the first 4 hours of admission. We compared APPS to standard practice of clinicians manually calculating the PPS to assess VTE risk. DESIGN: Cohort study of 30,726 hospitalized patients. APPS was compared to manual calculation of PPS by chart review from 300 randomly selected patients. MEASUREMENTS: Prediction of hospital-acquired VTE not present on admission. RESULTS: Compared to manual PPS calculation, no significant difference in average score was found (5.5 vs. 5.1, P = 0.073), and area under curve (AUC) was similar (0.79 vs. 0.76). Hospital-acquired VTE occurred in 260 (0.8%) of 30,726 patients. Those without VTE averaged APPS of 4.9 (standard deviation [SD], 2.6) and those with VTE averaged 7.7 (SD, 2.6). APPS had AUC = 0.81 (confidence interval [CI], 0.79-0.83) in patients receiving no pharmacologic prophylaxis and AUC = 0.78 (CI, 0.76-0.82) in patients receiving pharmacologic prophylaxis. CONCLUSION: Automated calculation of VTE risk had similar ability to predict hospital-acquired VTE as manual calculation despite differences in how often specific scoring criteria were considered present by the 2 methods.

Original languageEnglish (US)
Pages (from-to)231-237
Number of pages7
JournalJournal of hospital medicine
Issue number4
StatePublished - Apr 2017
Externally publishedYes

Bibliographical note

Funding Information:
Disclosures: Dr. Auerbach was supported by NHLBI K24HL098372 during the period of this study. Dr. Khanna, who is an implementation scientist at the University of California San Francisco Center for Digital Health Innovation, is the principal inventor of CareWeb, and may benefit financially from its commercialization. The other authors report no financial conflicts of interest.


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