Dataset Information

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

ABSTRACT:

Introduction

Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19.

Methods

We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots.

Results

A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure.

Conclusion

The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

SUBMITTER: Fleuren LM

PROVIDER: S-EPMC8711075 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

Fleuren Lucas M LM Dam Tariq A TA Tonutti Michele M de Bruin Daan P DP Lalisang Robbert C A RCA Gommers Diederik D Cremer Olaf L OL Bosman Rob J RJ Rigter Sander S Wils Evert-Jan EJ Frenzel Tim T Dongelmans Dave A DA de Jong Remko R Peters Marco M Kamps Marlijn J A MJA Ramnarain Dharmanand D Nowitzky Ralph R Nooteboom Fleur G C A FGCA de Ruijter Wouter W Urlings-Strop Louise C LC Smit Ellen G M EGM Mehagnoul-Schipper D Jannet DJ Dormans Tom T de Jager Cornelis P C CPC Hendriks Stefaan H A SHA Achterberg Sefanja S Oostdijk Evelien E Reidinga Auke C AC Festen-Spanjer Barbara B Brunnekreef Gert B GB Cornet Alexander D AD van den Tempel Walter W Boelens Age D AD Koetsier Peter P Lens Judith J Faber Harald J HJ Karakus A A Entjes Robert R de Jong Paul P Rettig Thijs C D TCD Arbous Sesmu S Vonk Sebastiaan J J SJJ Fornasa Mattia M Machado Tomas T Houwert Taco T Hovenkamp Hidde H Noorduijn Londono Roberto R Quintarelli Davide D Scholtemeijer Martijn G MG de Beer Aletta A AA Cinà Giovanni G Kantorik Adam A de Ruijter Tom T Herter Willem E WE Beudel Martijn M Girbes Armand R J ARJ Hoogendoorn Mark M Thoral Patrick J PJ Elbers Paul W G PWG

Critical care (London, England) 20211227 1

<h4>Introduction</h4>Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19.<h4>Methods</h4>We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All int ...[more]

PMID: 34961537

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Project description:ObjectivesTo evaluate the contribution of a preextubation chest X-ray (CXR) to identify the risk of extubation failure in mechanically ventilated patients.DesignRetrospective cohort study.SettingsICUs in a tertiary center (the Medical Information Mart for Intensive Care IV database).PatientsPatients greater than or equal to 18 years old who were mechanically ventilated and extubated after a spontaneous breathing trial.InterventionsNone.Measurements and main resultsAmong 1,066 mechanically ventilated patients, 132 patients (12%) experienced extubation failure, defined as reintubation or death within 48 hours of extubation. To predict extubation failure, we developed the following models based on deep learning (EfficientNet) and machine learning (LightGBM) with the training data: 1) model using only the rapid-shallow breathing index (RSBI), 2) model using RSBI and CXR, 3) model using all candidate clinical predictors (i.e., patient demographics, vital signs, laboratory values, and ventilator settings) other than CXR, and 4) model using all candidate clinical predictors with CXR. We compared the predictive abilities between models with the test data to investigate the predictive contribution of CXR. The predictive ability of the model using CXR as well as RSBI was not significantly higher than that of the model using only RSBI (c-statistics, 0.56 vs 0.56; p = 0.95). The predictive ability of the model using clinical predictors with CXR was not significantly higher than that of the model using all clinical predictors other than CXR (c-statistics, 0.71 vs 0.70; p = 0.12). Based on SHapley Additive exPlanations values to interpret the model using all clinical predictors with CXR, CXR was less likely to contribute to the predictive ability than other predictors (e.g., duration of mechanical ventilation, inability to follow commands, and heart rate).ConclusionsAdding CXR to a set of other clinical predictors in our prediction model did not significantly improve the predictive ability of extubation failure in mechanically ventilated patients.

Project description:BackgroundSince the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters.ResultsObservational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training ("training set"), while patients admitted after the 5th of March 2021 were used for external validation ("test set 1"). A further group of patients ("test set 2"), admitted to the ICU of IRCCS Ca' Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the "training set", while 124 (8%) and 199 (12%) patients were included in the "test set 1" and "test set 2," respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models.ConclusionsOur study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality.

Dataset Information

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

Introduction

Methods

Results

Conclusion

Publications

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

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