Automating venous thromboembolism risk calculation using electronic health record data upon hospital admission: The automated Padua Prediction Score

Automated calculation of APPS using EHR data from prior encounters and the first 4 hours of admission was predictive of in-hospital VTE. APPS performed as well as traditional manual score calculation of the PPS. It was able to do so with no physician input, significantly lessening the burden of calculation and potentially increasing frequency of data-driven VTE risk assessment.

While automated calculation of certain scores is becoming more common, risk calculators that require data beyond vital signs and lab results have lagged,^16-19 in part because of uncertainty about 2 issues. The first is whether EHR data accurately represent the current clinical picture. The second is if a machine-interpretable algorithm to determine a clinical status (eg, “active cancer”) would be similar to a doctor’s perception of that same concept. We attempted to better understand these 2 challenges through developing APPS. Concerning accuracy, EHR data correctly represent the clinical scenario: designations of VTEP and hospital-acquired VTE were accurate in approximately 90% of reviewed cases. Regarding the second concern, when comparing APPS to manual calculation, we found significant differences (P < 0.001) in how often 8 of the 11 criteria were positive, yet no significant difference in overall score and similar predictive capacity. Manual calculation appeared more likely to find data in the index encounter or in structured data. For example, “active cancer” may be documented only in a physician’s note, easily accounted for during a physician’s calculation but missed by APPS looking only for structured data. In contrast, automated calculation found historic criteria, such as “prior VTE” or “known thrombophilic condition,” positive more often. If the patient is being admitted for a problem unrelated to blood clots, the physician may have little time or interest to look through hundreds of EHR documents to discover a 2-year-old VTE. As patients’ records become larger and denser, more historic data can become buried and forgotten. While the 2 scores differ on individual criteria, they are similarly predictive and able to bifurcate the at-risk population to those who should and should not receive pharmacologic prophylaxis.

There are important limitations to our study. It was done at a single academic institution using a dataset of VTE-associated, validated research that was well-known to the researchers.²⁰ Another major limitation is the dependence of the algorithm on data available within the first 4 hours of admission and earlier; thus, previous encounters may frequently play an important role. Patients presenting to our health system for the first time would have significantly fewer data available at the time of calculation. Additionally, our data could not reliably tell us the total doses of pharmacologic prophylaxis that a patient received. While most patients will maintain a consistent VTEP regimen once initiated in the hospital, 2 patients with the same LOS may have received differing amounts of pharmacologic prophylaxis. This research study did not assess how much time automatic calculation of VTE risk might save providers, because we did not record the time for each manual abstraction; however, from discussion with the main abstracter, chart review and manual calculation for this study took from 2 to 14 minutes per patient, depending on the number of previous interactions with the health system. Finally, although we chose data elements that are likely to exist at most institutions using an EHR, many institutions’ EHRs do not have EDW capabilities nor programmers who can assist with an automated risk score.

The EHR interventions to assist providers in determining appropriate VTEP have been able to increase rates of VTEP and decrease VTE-associated mortality.^16,21 In addition to automating the calculation of guideline-adherent risk scores, there is a need for wider adoption for clinical decision support for VTE. For this reason, we chose only structured data fields from some of the most common elements within our EHR’s data warehouse to derive APPS (Appendix 1). Our study supports the idea that automated calculation of scores requiring input of more complex data such as diagnoses, recent medical events, and current clinical status remains predictive of hospital-acquired VTE risk. Because it is calculated automatically in the background while the clinician completes his or her assessment, the APPS holds the potential to significantly reduce the burden on providers while making guideline-adherent risk assessment more readily accessible. Further research is required to determine the exact amount of time automatic calculation saves, and, more important, if the relatively high predictive capacity we observed using APPS would be reproducible across institutions and could reduce incidence of hospital-acquired VTE.