Melissa A Hornor1, Meixi Ma2, Lynn Zhou3, Mark E Cohen3, Ronnie A Rosenthal4, Marcia M Russell5, Clifford Y Ko6. 1. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL; Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH. 2. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL; Department of Surgery, University of Alabama at Birmingham Medical Center, Birmingham, AL. Electronic address: mma@facs.org. 3. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL. 4. Department of Surgery, Yale University, New Haven, CT. 5. Department of Surgery, University of California-Los Angeles, Los Angeles, CA. 6. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL; Department of Surgery, University of California-Los Angeles, Los Angeles, CA.
Abstract
BACKGROUND: The American College of Surgeons (ACS) NSQIP Surgical Risk Calculator (SRC) plays an important role in risk prediction and decision-making. We sought to enhance the existing ACS NSQIP SRC with functionality to predict geriatric-specific outcomes and assess the predictive value of geriatric-specific risk factors by comparing performance in outcomes prediction using the traditional ACS NSQIP SRC with models that also included geriatric risk factors. STUDY DESIGN: Data were collected from 21 ACS NSQIP Geriatric Surgery Pilot Project hospitals between 2014 and 2017. Hierarchical regression models predicted 4 postoperative geriatric outcomes (ie pressure ulcer, delirium, new mobility aid use, and functional decline) using the traditional 21-variable ACS NSQIP SRC models and 27-variable models that included 6 geriatric risk factors (ie living situation, fall history, mobility aid use, cognitive impairment, surrogate-signed consent, and palliative care on admission). RESULTS: Data from 38,048 patients 65 years or older undergoing 197 unique operations across 10 surgical subspecialties were used. Stable model discrimination and calibration between developmental and validation datasets confirmed predictive validity. Models with and without geriatric risk factors demonstrated excellent performance (C statistic >0.8) with inclusion of geriatric risk factors improving performance. Of the 21 ACS NSQIP variables, CPT code, COPD, age, functional dependence, sex, disseminated cancer, diabetes, and sepsis were the strongest risk predictors, and impaired cognition, fall history, and mobility aid use were the strongest geriatric predictors. CONCLUSIONS: The ACS NSQIP SRC can predict 4 unique outcomes germane to geriatric surgical patients, with improvement of predictive capability after accounting for geriatric risk factors. Augmentation of ACS NSQIP SRC can enhance shared decision-making to improve the quality of surgical care in older adults.
BACKGROUND: The American College of Surgeons (ACS) NSQIP Surgical Risk Calculator (SRC) plays an important role in risk prediction and decision-making. We sought to enhance the existing ACS NSQIP SRC with functionality to predict geriatric-specific outcomes and assess the predictive value of geriatric-specific risk factors by comparing performance in outcomes prediction using the traditional ACS NSQIP SRC with models that also included geriatric risk factors. STUDY DESIGN: Data were collected from 21 ACS NSQIP Geriatric Surgery Pilot Project hospitals between 2014 and 2017. Hierarchical regression models predicted 4 postoperative geriatric outcomes (ie pressure ulcer, delirium, new mobility aid use, and functional decline) using the traditional 21-variable ACS NSQIP SRC models and 27-variable models that included 6 geriatric risk factors (ie living situation, fall history, mobility aid use, cognitive impairment, surrogate-signed consent, and palliative care on admission). RESULTS: Data from 38,048 patients 65 years or older undergoing 197 unique operations across 10 surgical subspecialties were used. Stable model discrimination and calibration between developmental and validation datasets confirmed predictive validity. Models with and without geriatric risk factors demonstrated excellent performance (C statistic >0.8) with inclusion of geriatric risk factors improving performance. Of the 21 ACS NSQIP variables, CPT code, COPD, age, functional dependence, sex, disseminated cancer, diabetes, and sepsis were the strongest risk predictors, and impaired cognition, fall history, and mobility aid use were the strongest geriatric predictors. CONCLUSIONS: The ACS NSQIP SRC can predict 4 unique outcomes germane to geriatric surgical patients, with improvement of predictive capability after accounting for geriatric risk factors. Augmentation of ACS NSQIP SRC can enhance shared decision-making to improve the quality of surgical care in older adults.
Authors: Christopher G Sinon; Amy Ottensmeyer; Austin N Slone; Dan C Li; Rachael S Allen; Machelle T Pardue; Paul S García Journal: Neurosci Lett Date: 2021-03-08 Impact factor: 3.046