Predicting length of stay for trauma and emergency general surgery patients.
Length of stay
NSQIP risk calculator
Prediction
Trauma and emergency general surgery
Journal
American journal of surgery
ISSN: 1879-1883
Titre abrégé: Am J Surg
Pays: United States
ID NLM: 0370473
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
02
11
2019
revised:
29
01
2020
accepted:
31
01
2020
pubmed:
23
2
2020
medline:
26
11
2020
entrez:
22
2
2020
Statut:
ppublish
Résumé
Predicting length of stay (LOS) is difficult for trauma and emergency general surgery (TEGS) patients. Our aim was to determine the accuracy of LOS predictions by TEGS team members and the NSQIP Risk Calculator and the patient factors associated with inaccurate predictions. LOS for 200 TEGS patients were predicted. Full-model univariate and multivariable linear regressions were used to determine associations between patient characteristics and inaccurate predictions. There were 1,518 predictions of LOS. LOS predictions were rarely correct (TEGS team: 30.7% all patients, 35.6% surgical; NSQIP: 33.0% surgical). No individual group nor NSQIP was significantly better at predicting LOS. Inaccurate predictions were associated with female patients, longer LOS, trauma, frailty, higher comorbidity and injury severity scores, and lesser disposition. Both the TEGS team and NSQIP are poor at predicting LOS for TEGS patients. Further work helping to guide LOS predictions for TEGS patients is warranted.
Sections du résumé
BACKGROUND
Predicting length of stay (LOS) is difficult for trauma and emergency general surgery (TEGS) patients. Our aim was to determine the accuracy of LOS predictions by TEGS team members and the NSQIP Risk Calculator and the patient factors associated with inaccurate predictions.
METHODS
LOS for 200 TEGS patients were predicted. Full-model univariate and multivariable linear regressions were used to determine associations between patient characteristics and inaccurate predictions.
RESULTS
There were 1,518 predictions of LOS. LOS predictions were rarely correct (TEGS team: 30.7% all patients, 35.6% surgical; NSQIP: 33.0% surgical). No individual group nor NSQIP was significantly better at predicting LOS. Inaccurate predictions were associated with female patients, longer LOS, trauma, frailty, higher comorbidity and injury severity scores, and lesser disposition.
CONCLUSION
Both the TEGS team and NSQIP are poor at predicting LOS for TEGS patients. Further work helping to guide LOS predictions for TEGS patients is warranted.
Identifiants
pubmed: 32081410
pii: S0002-9610(20)30067-2
doi: 10.1016/j.amjsurg.2020.01.055
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
757-764Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest No author has a conflict of interest to report.