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01.12.2018 | Research | Ausgabe 1/2018 Open Access

Perioperative Medicine 1/2018

Self-reported mobility as a preoperative risk assessment tool in older surgical patients compared to the American College of Surgeons National Surgical Quality Improvement Program

Zeitschrift:
Perioperative Medicine > Ausgabe 1/2018
Autoren:
Sunghye Kim, Rebecca Neiberg, W. Jack Rejeski, Anthony P. Marsh, Stephen B. Kritchevsky, Xiaoyan I. Leng, Leanne Groban

Abstract

Background

The American College of Surgeons National Surgical Quality Improvement Program (NSQIP®) developed a surgical risk calculator using data from 1.4 million patients and including 1557 unique Current Procedural Terminology (CPT) codes. Although this calculator demonstrated excellent performance in predicting postoperative mortality, morbidity, and six surgical complications, it was not developed specifically for use in older surgical patients who have worse surgical outcomes and additional unique risk factors compared to younger adults. We aimed to test the ability of a simple self-reported mobility tool to predict postoperative outcomes in the older surgical population compared to the NSQIP.

Methods

We used data from a prospective cohort study that enrolled 197 older surgical patients (≥ 69 years) undergoing various elective surgeries and assessed 30-day surgical outcomes. Statistical models included data from the Mobility Assessment Tool-short form (MAT-sf) alone, covariates alone, and MAT-sf data and covariates. We used leave-one-out (LOO) cross-validation of the models within our cohort and compared their performance for predicting postoperative outcomes against the NSQIP calculator based on receiver operating characteristic area under the curve (ROC AUC).

Results

Patients with poor self-reported mobility experienced higher rates of postoperative complications and nursing home placement. There was no difference in performance between any of our models and the NSQIP calculator (p > 0.1), with AUC between 0.604 and 0.697 for predicting postoperative complications and 0.653 and 0.760 for predicting nursing home placement. All models also predicted a length of stay (LOS) similar to the actual LOS.

Conclusion

Mobility assessment alone using MAT-sf can predict postoperative complications, nursing home placement, and LOS for older surgical patients, with accuracy comparable to that of the NSQIP calculator. The simplicity of this noninvasive risk assessment tool makes it an attractive alternative to the NSQIP calculator that requires 20 patient predictors and the planned procedure, or CPT code to predict the chance that patients will have 15 different adverse outcomes following surgery.
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