Development of a Novel Preoperative Venous Thromboembolism Risk Assessment Model. Issue 9 (September 2020)

• Record Type: Journal Article
• Authors: Mlaver, Eli; Lynde, Grant C.; Gallion, Claire; Sweeney, John F.; Sharma, Jyotirmay
• Is Part Of: American surgeon. Volume 86:Issue 9(2020)
• Page Start: 1098
• Page End: 1105
• Publication Date: 2020-09
• Subjects: venous thromboembolism -- risk assessment model -- quality improvement; Surgery -- Periodicals; Surgery -- United States -- Periodicals; 617.0973
• DOI: 10.1177/0003134820943556
• Languages: English
• ISSNs: 0003-1348

Abstract

Introduction: Standardization of preoperative venous thromboembolism (VTE) risk assessment remains challenging due to variation in risk assessment models (RAMs) and the cumbersome workflow addition that most RAMs represent. We aimed to develop a parsimonious RAM that is automatable and actionable within the preoperative workflow. Methods: We performed a case-controlled review of all 18 VTE cases reported over a 12-month period and 171 matched controls included in an institutional National Surgical Quality Improvement Project (NSQIP) data set. We examined the predictive value of the Caprini, Padua, and NSQIP RAMs. We identified the 5 most impactful risk factors in VTE development by contribution to the known RAMs. We compared the predictive ability of cancer, age, body mass index, black race, and American Society of Anesthesiologists Physical Status (ASA-PS) score, to the Caprini, Padua, and NSQIP RAMs for VTE outcomes. Finally, we evaluated concordance between each of the models. Results: The Caprini Score was found to be 88.9% sensitive and 32.7% specific using a threshold of 5. The Padua score was found to be 61.1% sensitive and 47.4% specific using a threshold of 4. The novel 5-factor RAM was found to be 94.4% sensitive and 38.0% specific using a threshold of 4. The Caprini and Padua models were discordant in 26% of patients. Discussion: Cumbersome manual data entry contributes to the ongoing challenge of standardized VTE risk assessment and prophylaxis. Universally documented information and patient demographics can be utilized to create clinical decision support tools that can improve the efficiency of perioperative workflow and improve the quality of care.