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

Patient Inclusion

Adult patients admitted to a medical or surgical service between July 1, 2012 and April 1, 2014 were included in the study if they were candidates for VTEP, defined as: length of stay (LOS) greater than 2 days, not on hospice care, not pregnant at admission, no present on admission VTE diagnosis, no known contraindications to prophylaxis (eg, gastrointestinal bleed), and were not receiving therapeutic doses of warfarin, low molecular weight heparins, heparin, or novel anticoagulants prior to admission.

Data Sources

Clinical variables were extracted from the EHR’s enterprise data warehouse (EDW) by SQL Server query (Microsoft, Redmond, Washington) and deposited in a secure database. Chart review was conducted by a trained researcher (Mr. Jacolbia) using the EHR and a standardized protocol. Findings were recorded using REDCap (REDCap Consortium, Vanderbilt University, Nashville, Tennessee). The specific ICD-9, procedure, and lab codes used to determine each criterion of APPS are available in the Appendix.

Creation of the Automated Padua Prediction Score (APPS)

We developed APPS from the original 11 criteria that comprise the Padua Prediction Score: active cancer, previous VTE (excluding superficial vein thrombosis), reduced mobility, known thrombophilic condition, recent (1 month or less) trauma and/or surgery, age 70 years or older, heart and/or respiratory failure, acute myocardial infarction and/or ischemic stroke, acute infection and/or rheumatologic disorder, body mass index (BMI) 30 or higher, and ongoing hormonal treatment.¹³ APPS has the same scoring methodology as PPS: criteria are weighted from 1 to 3 points and summed with a maximum score of 20, representing highest risk of VTE. To automate the score calculation from data routinely available in the EHR, APPS checks pre-selected structured data fields for specific values within laboratory results, orders, nursing flowsheets and claims. Claims data included all ICD-9 and procedure codes used for billing purposes. If any of the predetermined data elements are found, then the specific criterion is considered positive; otherwise, it is scored as negative. The creators of the PPS were consulted in the generation of these data queries to replicate the original standards for deeming a criterion positive. The automated calculation required no use of natural language processing.

Characterization of Study Population

We recorded patient demographics (age, race, gender, BMI), LOS, and rate of hospital-acquired VTE. These patients were separated into 2 cohorts determined by the VTE prophylaxis they received. The risk profile of patients who received pharmacologic prophylaxis was hypothesized to be inherently different from those who had not. To evaluate APPS within this heterogeneous cohort, patients were divided into 2 major categories: pharmacologic vs. no pharmacologic prophylaxis. If they had a completed order or medication administration record on the institution’s approved formulary for pharmacologic VTEP, they were considered to have received pharmacologic prophylaxis. If they had only a completed order for usage of mechanical prophylaxis (sequential compression devices) or no evidence of any form of VTEP, they were considered to have received no pharmacologic prophylaxis. Patients with evidence of both pharmacologic and mechanical were placed in the pharmacologic prophylaxis group. To ensure that automated designation of prophylaxis group was accurate, we reviewed 40 randomly chosen charts because prior researchers were able to achieve sensitivity and specificity greater than 90% with that sample size.¹⁴

The primary outcome of hospital-acquired VTE was defined as an ICD-9 code for VTE (specific codes are found in the Appendix) paired with a “present on admission = no” flag on that encounter’s hospital billing data, abstracted from the EDW. A previous study at this institution used the same methodology and found 212/226 (94%) of patients with a VTE ICD-9 code on claim had evidence of a hospital-acquired VTE event upon chart review.¹⁴ Chart review was also completed to ensure that the primary outcome of newly discovered hospital-acquired VTE was differentiated from chronic VTE or history of VTE. Theoretically, ICD-9 codes and other data elements treat chronic VTE, history of VTE, and hospital-acquired VTE as distinct diagnoses, but it was unclear if this was true in our dataset. For 75 randomly selected cases of presumed hospital-acquired VTE, charts were reviewed for evidence that confirmed newly found VTE during that encounter.

Validation of APPS through Comparison to Manual Calculation of the Original PPS

To compare our automated calculation to standard clinical practice, we manually calculated the PPS through chart review within the first 2 days of admission on 300 random patients, a subsample of the entire study cohort. The largest study we could find had manually calculated the PPS of 1,080 hospitalized patients with a mean PPS of 4.86 (standard deviation [SD], 2.26).¹⁵ One researcher (Mr. Jacolbia) accessed the EHR with all patient information available to physicians, including admission notes, orders, labs, flowsheets, past medical history, and all prior encounters to calculate and record the PPS. To limit potential score bias, 2 authors (Drs. Elias and Davies) assessed 30 randomly selected charts from the cohort of 300. The standardized chart review protocol mimicked a physician’s approach to determine if a patient met a criterion, such as concluding if he/she had active cancer by examining medication lists for chemotherapy, procedure notes for radiation, and recent diagnoses on problem lists. After the original PPS was manually calculated, APPS was automatically calculated for the same 300 patients. We intended to characterize similarities and differences between APPS and manual calculation prior to investigating APPS’ predictive capacity for the entire study population, because it would not be feasible to manually calculate the PPS for all 30,726 patients.