The frailty-driven predictive model for failure to rescue among patients who experienced a major complication following cervical decompression and fusion: an ACS-NSQIP analysis of 3,632 cases (2011-2020).

Autor: Rumalla KC; Feinberg School of Medicine, Northwestern University, 420 E Superior St., Chicago, IL, 60611, USA., Covell MM; School of Medicine, Georgetown University, 3900 Reservoir Road NW, Washington, DC, 20007, USA., Skandalakis GP; Department of Neurosurgery, University of New Mexico Hospital, 2211 Lomas Blvd NE, Albuquerque, NM, 87106, USA., Rumalla K; Department of Neurosurgery, University of New Mexico Hospital, 2211 Lomas Blvd NE, Albuquerque, NM, 87106, USA., Kassicieh AJ; Department of Neurosurgery, University of New Mexico Hospital, 2211 Lomas Blvd NE, Albuquerque, NM, 87106, USA., Roy JM; Topiwala National Medical College, Mumbai Central, Mumbai, Maharashtra 400008, India., Kazim SF; Department of Neurosurgery, University of New Mexico Hospital, 2211 Lomas Blvd NE, Albuquerque, NM, 87106, USA., Segura A; Department of Neurosurgery, University of New Mexico Hospital, 2211 Lomas Blvd NE, Albuquerque, NM, 87106, USA., Bowers CA; Bowers Neurosurgical Frailty and Outcomes Data Science Lab, 8342 S Levine Ln, Sandy, UT, 84070, USA. Electronic address: christianbowers4@gmail.com.
Jazyk: angličtina
Zdroj: The spine journal : official journal of the North American Spine Society [Spine J] 2024 Apr; Vol. 24 (4), pp. 582-589. Date of Electronic Publication: 2023 Dec 14.
DOI: 10.1016/j.spinee.2023.12.003
Abstrakt: Background Context: Preoperative risk stratification for patients considering cervical decompression and fusion (CDF) relies on established independent risk factors to predict the probability of complications and outcomes in order to help guide pre and perioperative decision-making.
Purpose: This study aims to determine frailty's impact on failure to rescue (FTR), or when a mortality occurs within 30 days following a major complication.
Study Design/setting: Cross-sectional retrospective analysis of retrospective and nationally-representative data.
Patient Sample: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all CDF cases from 2011-2020.
Outcome Measures: CDF patients who experienced a major complication were identified and FTR was calculated as death or hospice disposition within 30 days of a major complication.
Methods: Frailty was measured by the Risk Analysis Index-Revised (RAI-Rev). Baseline patient demographics and characteristics were compared for all FTR patients. Significant factors were assessed by univariate and multivariable regression for the development of a frailty-driven predictive model for FTR. The discriminative ability of the predictive model was assessed using a receiving operating characteristic (ROC) curve analysis.
Results: There were 3632 CDF patients who suffered a major complication and 7.6% (277 patients) subsequently expired or dispositioned to hospice, the definition of FTR. Independent predictors of FTR were nonelective surgery, frailty, preoperative intubation, thrombosis or embolic complication, unplanned intubation, on ventilator for >48 hours, cardiac arrest, and septic shock. Frailty, and a combination of preoperative and postoperative risk factors in a predictive model for FTR, achieved outstanding discriminatory accuracy (C-statistic = 0.901, CI: 0.883-0.919).
Conclusion: Preoperative and postoperative risk factors, combined with frailty, yield a highly accurate predictive model for FTR in CDF patients. Our model may guide surgical management and/or prognostication regarding the likelihood of FTR after a major complication postoperatively with CDF patients. Future studies may determine the predictive ability of this model in other neurosurgical patient populations.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier Inc.)
Databáze: MEDLINE
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