Journal of the American College of Surgeons · 2026
Prospective Evaluation of Association Between American College of Surgeons NSQIP Risk Estimates and Multidisciplinary Operability Decisions in Complex Surgical Patients.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Pavel Mihai-Calin, Mohedano Alba, Ferre Ana, Garcia-Huete Lucia, Oliva Iban, Tomas Ignacio, Jorba-Martin Rosa
Аннотация
BACKGROUND: Complex surgical patients often present with frailty, comorbidities, and limited physiological reserve, making operability decisions highly challenging. We hypothesized that parameters derived from the American College of Surgeons NSQIP (ACS-NSQIP) Surgical Risk Calculator could reflect multidisciplinary assessments of operability and support preoperative decision-making in this population.
STUDY DESIGN: A single-center prospective observational cohort study included 134 consecutive complex surgical patients evaluated by a multidisciplinary committee between November 2022 and April 2025. All patients underwent standardized preoperative assessment, including ACS-NSQIP estimation, but operability decisions were made independently of the ACS-NSQIP results. The primary endpoint was the ability of the NSQIP-derived risk deviation to discriminate between operable and nonoperable patients. This variable was analyzed using receiver operating characteristic curves, and findings were used to develop a Nonoperability Risk Score.
RESULTS: Of the 134 patients, 98 (73.1%) were evaluated as operable and 35 (26.1%) as nonoperable. The "risk deviation" variable showed moderate to good discriminatory power (area under the curve range 0.678 to 0.740, all p < 0.01) for outcomes such as serious complications, readmission, or death. The Nonoperability Risk Score, derived from 6 ACS-NSQIP outputs, correlated strongly with the committee's operability decisions (area under the curve 0.805, p < 0.001) and demonstrated a significant trend: higher scores were associated with increased likelihood of nonoperability. Among the 80 patients who underwent operation, complication rates did not correlate with the Nonoperability Risk Score.
CONCLUSIONS: ACS-NSQIP, particularly the derived Nonoperability Risk Score, is a useful tool to support multidisciplinary operability decisions in complex surgical patients. Their use may help standardize preoperative discussions, though further multicenter validation is needed to confirm generalizability.
STUDY DESIGN: A single-center prospective observational cohort study included 134 consecutive complex surgical patients evaluated by a multidisciplinary committee between November 2022 and April 2025. All patients underwent standardized preoperative assessment, including ACS-NSQIP estimation, but operability decisions were made independently of the ACS-NSQIP results. The primary endpoint was the ability of the NSQIP-derived risk deviation to discriminate between operable and nonoperable patients. This variable was analyzed using receiver operating characteristic curves, and findings were used to develop a Nonoperability Risk Score.
RESULTS: Of the 134 patients, 98 (73.1%) were evaluated as operable and 35 (26.1%) as nonoperable. The "risk deviation" variable showed moderate to good discriminatory power (area under the curve range 0.678 to 0.740, all p < 0.01) for outcomes such as serious complications, readmission, or death. The Nonoperability Risk Score, derived from 6 ACS-NSQIP outputs, correlated strongly with the committee's operability decisions (area under the curve 0.805, p < 0.001) and demonstrated a significant trend: higher scores were associated with increased likelihood of nonoperability. Among the 80 patients who underwent operation, complication rates did not correlate with the Nonoperability Risk Score.
CONCLUSIONS: ACS-NSQIP, particularly the derived Nonoperability Risk Score, is a useful tool to support multidisciplinary operability decisions in complex surgical patients. Their use may help standardize preoperative discussions, though further multicenter validation is needed to confirm generalizability.