Sneha Subramaniam1, Jeffrey J Aalberg1, Rainier P Soriano2, Celia M Divino3. 1. Division of General Surgery, Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY. 2. Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. 3. Division of General Surgery, Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY. Electronic address: celia.divino@mountsinai.org.
Abstract
BACKGROUND: The modified frailty index (mFI-11) is a NSQIP-based 11-factor index that has been proven to adequately reflect frailty and predict mortality and morbidity. These 11 factors, made of 16 variables, map to the original 70-item Canada Study of Health and Aging Frailty Index. In past years, certain NSQIP variables have been removed from the database; as of 2015, only 5 of the original 11 factors remained. The predictive power and usefulness of these 5 factors in an index (mFI-5) have not been proven in past literature. The goal of our study was to compare the mFI-5 to the mFI-11 in terms of value and predictive ability for mortality, postoperative infection, and unplanned 30-day readmission. STUDY DESIGN: The mFI was calculated by dividing the number of factors present for a patient by the number of available factors for which there were no missing data. Spearman's rho was used to assess correlation between the mFI-5 and mFI-11. Predictive models, using both unadjusted and adjusted logistic regressions, were created for each outcome for 9 surgical sub-specialties using 2012 NSQIP data, the last year all mFI-11 variables existed. RESULTS: Correlation between the mFI-5 and mFI-11 was above 0.9 across all surgical specialties except for cardiac and vascular surgery. Adjusted and unadjusted models showed similar c-statistics for mFI-5 and mFI-11, and strong predictive ability for mortality and postoperative complications. CONCLUSIONS: The mFI-5 and the mFI-11 are equally effective predictors in all sub-specialties and the mFI-5 is a strong predictor of mortality and postoperative complications. It has credibility for future use to study frailty within the NSQIP database. It also has potential in other databases and for clinical use.
BACKGROUND: The modified frailty index (mFI-11) is a NSQIP-based 11-factor index that has been proven to adequately reflect frailty and predict mortality and morbidity. These 11 factors, made of 16 variables, map to the original 70-item Canada Study of Health and Aging Frailty Index. In past years, certain NSQIP variables have been removed from the database; as of 2015, only 5 of the original 11 factors remained. The predictive power and usefulness of these 5 factors in an index (mFI-5) have not been proven in past literature. The goal of our study was to compare the mFI-5 to the mFI-11 in terms of value and predictive ability for mortality, postoperative infection, and unplanned 30-day readmission. STUDY DESIGN: The mFI was calculated by dividing the number of factors present for a patient by the number of available factors for which there were no missing data. Spearman's rho was used to assess correlation between the mFI-5 and mFI-11. Predictive models, using both unadjusted and adjusted logistic regressions, were created for each outcome for 9 surgical sub-specialties using 2012 NSQIP data, the last year all mFI-11 variables existed. RESULTS: Correlation between the mFI-5 and mFI-11 was above 0.9 across all surgical specialties except for cardiac and vascular surgery. Adjusted and unadjusted models showed similar c-statistics for mFI-5 and mFI-11, and strong predictive ability for mortality and postoperative complications. CONCLUSIONS: The mFI-5 and the mFI-11 are equally effective predictors in all sub-specialties and the mFI-5 is a strong predictor of mortality and postoperative complications. It has credibility for future use to study frailty within the NSQIP database. It also has potential in other databases and for clinical use.
Authors: Samuel Torres Landa; Jordana B Cohen; Robert A Swendiman; Chris Wirtalla; Daniel T Dempsey; Kristoffel R Dumon Journal: J Gastrointest Surg Date: 2018-07-31 Impact factor: 3.452
Authors: Katherine C Lee; Anne M Walling; Steven S Senglaub; Amy S Kelley; Zara Cooper Journal: J Pain Symptom Manage Date: 2019-08-09 Impact factor: 3.612
Authors: Brendan M Finnerty; Katherine D Gray; Timothy M Ullmann; Rasa Zarnegar; Thomas J Fahey; Toni Beninato Journal: World J Surg Date: 2020-06 Impact factor: 3.352
Authors: Khodayar Goshtasbi; Mehdi Abouzari; Sina Soltanzadeh-Zarandi; Brooke Sarna; Ariel Lee; Frank P K Hsu; Hamid R Djalilian Journal: Clin Neurol Neurosurg Date: 2020-08-28 Impact factor: 1.876