Automating venous thromboembolism risk calculation using electronic health record data upon hospital admission: The automated Padua Prediction Score
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. Journal of Hospital Medicine 2017;12:231-237. © 2017 Society of Hospital Medicine
© 2017 Society of Hospital Medicine
Hospital-acquired venous thromboembolism (VTE) continues to be a critical quality challenge for U.S. hospitals,1 and high-risk patients are often not adequately prophylaxed. Use of VTE prophylaxis (VTEP) varies as widely as 26% to 85% of patients in various studies, as does patient outcomes and care expenditures.2-6 The 9th edition of the American College of Chest Physicians (CHEST) guidelines7 recommend the Padua Prediction Score (PPS) to select individual patients who may be at high risk for venous thromboembolism (VTE) and could benefit from thromboprophylaxis. Use of the manually calculated PPS to select patients for thromboprophylaxis has been shown to help decrease 30-day and 90-day mortality associated with VTE events after hospitalization to medical services.8 However, the PPS requires time-consuming manual calculation by a provider, who may be focused on more immediate aspects of patient care and several other risk scores competing for his attention, potentially decreasing its use.
Other risk scores that use only discrete scalar data, such as vital signs and lab results to predict early recognition of sepsis, have been successfully automated and implemented within electronic health records (EHRs).9-11 Successful automation of scores requiring input of diagnoses, recent medical events, and current clinical status such as the PPS remains difficult.12 Data representing these characteristics are more prone to error, and harder to translate clearly into a single data field than discrete elements like heart rate, potentially impacting validity of the calculated result.13 To improve usage of guideline based VTE risk assessment and decrease physician burden, we developed an algorithm called Automated Padua Prediction Score (APPS) that automatically calculates the PPS using only EHR data available within prior encounters and the first 4 hours of admission, a similar timeframe to when admitting providers would be entering orders. Our goal was to assess if an automatically calculated version of the PPS, a score that depends on criteria more complex than vital signs and labs, would accurately assess risk for hospital-acquired VTE when compared to traditional manual calculation of the Padua Prediction Score by a provider.
METHODS
Site Description and Ethics
The study was conducted at University of California, San Francisco Medical Center, a 790-bed academic hospital; its Institutional Review Board approved the study and collection of data via chart review. Handling of patient information complied with the Health Insurance Portability and Accountability Act of 1996.
