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Hayssen H, Cires-Drouet R, Englum B, Nguyen P, Sahoo S, Mayorga-Carlin M, Siddiqui T, Turner D, Yesha Y, Sorkin JD, Lal BK. Systematic review of venous thromboembolism risk categories derived from Caprini score. J Vasc Surg Venous Lymphat Disord 2022; 10:1401-1409.e7. [PMID: 35926802 PMCID: PMC9783939 DOI: 10.1016/j.jvsv.2022.05.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/11/2022] [Accepted: 05/03/2022] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Hospital-acquired venous thromboembolism (VTE, including pulmonary embolism [PE] and deep vein thrombosis [DVT]) is a preventable cause of hospital death. The Caprini risk assessment model (RAM) is one of the most commonly used tools to assess VTE risk. The RAM is operationalized in clinical practice by grouping several risk scores into VTE risk categories that drive decisions on prophylaxis. A correlation between increasing Caprini scores and rising VTE risk is well-established. We assessed whether the increasing VTE risk categories assigned on the basis of recommended score ranges also correlate with increasing VTE risk. METHODS We conducted a systematic review of articles that used the Caprini RAM to assign VTE risk categories and that reported corresponding VTE rates. A Medline and EMBASE search retrieved 895 articles, of which 57 fulfilled inclusion criteria. RESULTS Forty-eight (84%) of the articles were cohort studies, 7 (12%) were case-control studies, and 2 (4%) were cross-sectional studies. The populations varied from postsurgical to medical patients. There was variability in the number of VTE risk categories assigned by individual studies (6 used 5 risk categories, 37 used 4, 11 used 3, and 3 used 2), and in the cutoff scores defining the risk categories (scores from 0 alone to 0-10 for the low-risk category; from ≥5 to ≥10 for high risk). The VTE rates reported for similar risk categories also varied across studies (0%-12.3% in the low-risk category; 0%-40% for high risk). The Caprini RAM is designed to assess composite VTE risk; however, two studies reported PE or DVT rates alone, and many of the other studies did not specify the types of DVTs analyzed. The Caprini RAM predicts VTE at 30 days after assessment; however, only 17 studies measured outcomes at 30 days; the remaining studies had either shorter or longer follow-ups (0-180 days). CONCLUSIONS The usefulness of the Caprini RAM is limited by heterogeneity in its implementation across centers. The score-derived VTE risk categorization has significant variability in the number of risk categories being used, the cutpoints used to define the risk categories, the outcome being measured, and the follow-up duration. This factor leads to similar risk categories being associated with different VTE rates, which impacts the clinical and research implications of the results. To enhance generalizability, there is a need for studies that validate the RAM in a broad population of medical and surgical patients, identify standardized risk categories, define risk of DVT and PE as distinct end points, and measure outcomes at standardized follow-up time points.
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Affiliation(s)
- Hilary Hayssen
- Department of Vascular Surgery, University of Maryland, Baltimore, MD; Surgery Service, VA Medical Center, Baltimore, MD
| | | | - Brian Englum
- Department of Vascular Surgery, University of Maryland, Baltimore, MD
| | - Phuong Nguyen
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD
| | - Shalini Sahoo
- Department of Vascular Surgery, University of Maryland, Baltimore, MD; Surgery Service, VA Medical Center, Baltimore, MD
| | - Minerva Mayorga-Carlin
- Department of Vascular Surgery, University of Maryland, Baltimore, MD; Surgery Service, VA Medical Center, Baltimore, MD
| | | | | | - Yelena Yesha
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD; Department of Computer Science, University of Miami, Miami, FL
| | - John D Sorkin
- Department of Medicine, Division of Gerontology and Palliative Care, University of Maryland School of Medicine, Baltimore, MD; Baltimore VA Geriatric Research, Education, and Clinical Center, Baltimore, MD
| | - Brajesh K Lal
- Department of Vascular Surgery, University of Maryland, Baltimore, MD; Surgery Service, VA Medical Center, Baltimore, MD.
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