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Predictors for extubation failure in COVID-19 patients using a machine learning approach.
Fleuren, Lucas M; Dam, Tariq A; Tonutti, Michele; de Bruin, Daan P; Lalisang, Robbert C A; Gommers, Diederik; Cremer, Olaf L; Bosman, Rob J; Rigter, Sander; Wils, Evert-Jan; Frenzel, Tim; Dongelmans, Dave A; de Jong, Remko; Peters, Marco; Kamps, Marlijn J A; Ramnarain, Dharmanand; Nowitzky, Ralph; Nooteboom, Fleur G C A; de Ruijter, Wouter; Urlings-Strop, Louise C; Smit, Ellen G M; Mehagnoul-Schipper, D Jannet; Dormans, Tom; de Jager, Cornelis P C; Hendriks, Stefaan H A; Achterberg, Sefanja; Oostdijk, Evelien; Reidinga, Auke C; Festen-Spanjer, Barbara; Brunnekreef, Gert B; Cornet, Alexander D; van den Tempel, Walter; Boelens, Age D; Koetsier, Peter; Lens, Judith; Faber, Harald J; Karakus, A; Entjes, Robert; de Jong, Paul; Rettig, Thijs C D; Arbous, Sesmu; Vonk, Sebastiaan J J; Fornasa, Mattia; Machado, Tomas; Houwert, Taco; Hovenkamp, Hidde; Noorduijn Londono, Roberto; Quintarelli, Davide; Scholtemeijer, Martijn G; de Beer, Aletta A.
  • Fleuren LM; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands. l.fleuren@amsterdamumc.nl.
  • Dam TA; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Tonutti M; , Pacmed, Amsterdam, The Netherlands.
  • de Bruin DP; , Pacmed, Amsterdam, The Netherlands.
  • Lalisang RCA; , Pacmed, Amsterdam, The Netherlands.
  • Gommers D; Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Cremer OL; Department of Intensive Care, UMC Utrecht, Utrecht, The Netherlands.
  • Bosman RJ; ICU, OLVG, Amsterdam, The Netherlands.
  • Rigter S; Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands.
  • Wils EJ; Department of Intensive Care, Franciscus Gasthuis and Vlietland, Rotterdam, The Netherlands.
  • Frenzel T; Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Dongelmans DA; Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, The Netherlands.
  • de Jong R; Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands.
  • Peters M; Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands.
  • Kamps MJA; Intensive Care, Catharina Ziekenhuis Eindhoven, Eindhoven, The Netherlands.
  • Ramnarain D; Department of Intensive Care, ETZ Tilburg, Tilburg, The Netherlands.
  • Nowitzky R; Intensive Care, HagaZiekenhuis, Den Haag, The Netherlands.
  • Nooteboom FGCA; Intensive Care, Laurentius Ziekenhuis, Roermond, The Netherlands.
  • de Ruijter W; Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands.
  • Urlings-Strop LC; Intensive Care, Reinier de Graaf Gasthuis, Delft, The Netherlands.
  • Smit EGM; Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands.
  • Mehagnoul-Schipper DJ; Intensive Care, VieCuri Medisch Centrum, Venlo, The Netherlands.
  • Dormans T; Intensive Care, Zuyderland MC, Heerlen, The Netherlands.
  • de Jager CPC; Department of Intensive Care, Jeroen Bosch Ziekenhuis, Den Bosch, The Netherlands.
  • Hendriks SHA; Intensive Care, Albert Schweitzerziekenhuis, Dordrecht, The Netherlands.
  • Achterberg S; ICU, Haaglanden Medisch Centrum, Den Haag, The Netherlands.
  • Oostdijk E; ICU, Maasstad Ziekenhuis Rotterdam, Rotterdam, The Netherlands.
  • Reidinga AC; ICU, SEH, BWC, Martiniziekenhuis, Groningen, The Netherlands.
  • Festen-Spanjer B; Intensive Care, Ziekenhuis Gelderse Vallei, Ede, The Netherlands.
  • Brunnekreef GB; Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands.
  • Cornet AD; Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands.
  • van den Tempel W; Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands.
  • Boelens AD; Antonius Ziekenhuis Sneek, Sneek, The Netherlands.
  • Koetsier P; Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands.
  • Lens J; ICU, IJsselland Ziekenhuis, Capelle Aan Den IJssel, The Netherlands.
  • Faber HJ; ICU, WZA, Assen, The Netherlands.
  • Karakus A; Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands.
  • Entjes R; Department of Intensive Care, Adrz, Goes, The Netherlands.
  • de Jong P; Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands.
  • Rettig TCD; Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands.
  • Arbous S; Department of Intensive Care, LUMC, Leiden, The Netherlands.
  • Vonk SJJ; , Pacmed, Amsterdam, The Netherlands.
  • Fornasa M; , Pacmed, Amsterdam, The Netherlands.
  • Machado T; , Pacmed, Amsterdam, The Netherlands.
  • Houwert T; , Pacmed, Amsterdam, The Netherlands.
  • Hovenkamp H; , Pacmed, Amsterdam, The Netherlands.
  • Noorduijn Londono R; , Pacmed, Amsterdam, The Netherlands.
  • Quintarelli D; , Pacmed, Amsterdam, The Netherlands.
  • Scholtemeijer MG; , Pacmed, Amsterdam, The Netherlands.
  • de Beer AA; , Pacmed, Amsterdam, The Netherlands.
Crit Care ; 25(1): 448, 2021 12 27.
Article in English | MEDLINE | ID: covidwho-1632299
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Treatment Failure / Airway Extubation / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Adult / Humans Language: English Journal: Crit Care Year: 2021 Document Type: Article Affiliation country: S13054-021-03864-3

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Treatment Failure / Airway Extubation / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Adult / Humans Language: English Journal: Crit Care Year: 2021 Document Type: Article Affiliation country: S13054-021-03864-3