Walczak S, Velanovich V. Predicting Elective Surgical Patient Outcome Destination Based on the Preoperative Modified Frailty Index and Laboratory Values.
J Surg Res 2022;
275:341-351. [PMID:
35339003 DOI:
10.1016/j.jss.2022.02.029]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 01/29/2022] [Accepted: 02/14/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION
To determine the accuracy of preoperative modified frailty index (mFI) with or without laboratory values (mFI-labs or labs-continuous) in predicting postoperative discharge destination. Discharge destination is important to providers and patients. The ability to accurately predict discharge destination preoperatively can improve hospital resource utilization and help set patient and family expectations.
METHODS
Cohort analysis of the 2018 American College of Surgeon National Surgical Quality Improvement Project (ACS-NSQIP) Participant Use File of patients undergoing operations with complete data point sets: age, sex, operation work relative-value units; mFI-clinical based on 12 clinical findings, mFI-labs based on seven laboratory values. The nine hierarchical destinations: home, home with assistance, multi-level community, unskilled-care facility, rehabilitation facility, skilled-nursing facility, acute care hospital, hospice, or death, from best to worst outcome. Data were analyzed using univariate analysis, multiple logistic regression and supervised learning artificial neural networks.
RESULTS
Univariate and multivariate in general showed that patients with higher mFI-clinical and mFI-lab scores, as well as older age and more complex operations were more likely to be discharged to facilities other than home. However, these statistical techniques could not predict the exact destination. An artificial neural network analysis demonstrated perfect location prediction in 64.9% of cases and within one level of prefect prediction is 87.4%.
CONCLUSIONS
Using a limited number of preoperative factors, combining the mFI-clinical with laboratory values significantly improves the destination prediction performance significantly better than using the values separately. Preoperative knowledge of the likely discharge destination can benefit postoperative care planning and delivery.
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