The Journal of surgical research · 2025
Derivation of FAST CLOTS: A Multivariable Risk Assessment Model for Postoperative Venous Thromboembolism.
СтатьяПоддержка службы здравоохранения СШАНазвание и аннотация: Перевод в очереди
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Mlaver Eli, Sharma Jyotirmay, Hechenbleikner Elizabeth M, Kempker Jordan A
Аннотация
INTRODUCTION: Venous thromboembolism (VTE) remains a leading preventable cause of postoperative morbidity and mortality in part due to failure of consistent, standardized risk assessment. Available risk assessment models (RAMs) are burdensome and lack procedural specificity or actionable thresholds for intervention. A parsimonious, clinically oriented VTE RAM has the potential to increase adherence to risk assessment.
METHODS: We applied multivariable logistic regression modeling with a clinically guided forward selection process to the 2019 National Surgical Quality Improvement Project public user file. Considered predictors included patient demographics, comorbidities, and elements of the preoperative assessment. Procedural specificity was introduced by grouping Current Procedural Terminology codes and capturing minimally invasive techniques. Model performance was internally compared to three currently available RAMs: the Caprini score, cancer, old age, BMI, race, ASA model, and American College of Surgeons risk calculator.
RESULTS: VTE occurred in 8161 (0.76%) of 1,079,441 patients. The following eleven variables were chosen for model inclusion: age, body mass index, functional status, American Society of Anesthesiologists Physical Status classification; history of steroid use, ascites, or cancer; preoperative sepsis or blood transfusion; and Current Procedural Terminology group and minimally invasive surgery. The new FAST CLOTS model has a c-statistic of 0.753 and an 89% sensitivity for VTE outcomes at the chosen cut-off of 6 out of a maximum possible total of 24 points.
CONCLUSIONS: As it was derived with an emphasis on biological plausibility and face validity to clinicians, the FAST CLOTS model addresses many of the limitations of currently available RAMs. If further validated and refined, adoption may improve care quality and patient outcomes.
METHODS: We applied multivariable logistic regression modeling with a clinically guided forward selection process to the 2019 National Surgical Quality Improvement Project public user file. Considered predictors included patient demographics, comorbidities, and elements of the preoperative assessment. Procedural specificity was introduced by grouping Current Procedural Terminology codes and capturing minimally invasive techniques. Model performance was internally compared to three currently available RAMs: the Caprini score, cancer, old age, BMI, race, ASA model, and American College of Surgeons risk calculator.
RESULTS: VTE occurred in 8161 (0.76%) of 1,079,441 patients. The following eleven variables were chosen for model inclusion: age, body mass index, functional status, American Society of Anesthesiologists Physical Status classification; history of steroid use, ascites, or cancer; preoperative sepsis or blood transfusion; and Current Procedural Terminology group and minimally invasive surgery. The new FAST CLOTS model has a c-statistic of 0.753 and an 89% sensitivity for VTE outcomes at the chosen cut-off of 6 out of a maximum possible total of 24 points.
CONCLUSIONS: As it was derived with an emphasis on biological plausibility and face validity to clinicians, the FAST CLOTS model addresses many of the limitations of currently available RAMs. If further validated and refined, adoption may improve care quality and patient outcomes.