Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.
Adolescent
Adult
Aged
Aged, 80 and over
Clinical Decision-Making
Female
Humans
Lung
/ surgery
Machine Learning
Male
Middle Aged
Pneumonectomy
/ adverse effects
Postoperative Complications
Respiratory Insufficiency
/ etiology
Risk Assessment
/ methods
Risk Factors
Sensitivity and Specificity
Young Adult
Journal
Surgery
ISSN: 1532-7361
Titre abrégé: Surgery
Pays: United States
ID NLM: 0417347
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
06
12
2019
revised:
20
05
2020
accepted:
22
05
2020
pubmed:
19
7
2020
medline:
12
11
2020
entrez:
19
7
2020
Statut:
ppublish
Résumé
When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and recognize at-risk thoracic patients to avoid respiratory failure and standardize outcome measures. The National (Nationwide) Inpatient Sample for 2015 was used to establish our model. We identified 417 respiratory failure from a total of 4,062 patients who underwent pulmonary lobectomy. Risk factors for respiratory failure were identified, analyzed, and used in novel machine learning models to predict respiratory failure. Factors that contributed to increased odds of respiratory failure, such as preexisting chronic diseases, and intraoperative and postoperative events during hospitalization were identified. Two machine learning-based prediction models were generated and optimized by the knowledge accrued from the clinical course of postlobectomy patients. The first model, with high accuracy and specificity, is suited for performance evaluation, and the second model, with high sensitivity, is suited for clinical decision making. We identified risk factors for respiratory failure after lobectomy and introduced 2 machine learning-based techniques to predict respiratory failure for quality review and clinical decision-making settings. Such techniques can be used to not only provide targeted support but also standardize quality peer review measures.
Sections du résumé
BACKGROUND
When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and recognize at-risk thoracic patients to avoid respiratory failure and standardize outcome measures.
METHODS
The National (Nationwide) Inpatient Sample for 2015 was used to establish our model. We identified 417 respiratory failure from a total of 4,062 patients who underwent pulmonary lobectomy. Risk factors for respiratory failure were identified, analyzed, and used in novel machine learning models to predict respiratory failure.
RESULTS
Factors that contributed to increased odds of respiratory failure, such as preexisting chronic diseases, and intraoperative and postoperative events during hospitalization were identified. Two machine learning-based prediction models were generated and optimized by the knowledge accrued from the clinical course of postlobectomy patients. The first model, with high accuracy and specificity, is suited for performance evaluation, and the second model, with high sensitivity, is suited for clinical decision making.
CONCLUSION
We identified risk factors for respiratory failure after lobectomy and introduced 2 machine learning-based techniques to predict respiratory failure for quality review and clinical decision-making settings. Such techniques can be used to not only provide targeted support but also standardize quality peer review measures.
Identifiants
pubmed: 32680748
pii: S0039-6060(20)30331-7
doi: 10.1016/j.surg.2020.05.032
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
743-752Commentaires et corrections
Type : ErratumIn
Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.