Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients
Copyright © 2024 Elsevier España, S.L.U. and SEMICYUC. All rights reserved..
OBJECTIVE: To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves.
DESIGN: Observational, retrospective, multicentre study.
SETTING: Intensive Care Unit (ICU).
PATIENTS: Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves.
INTERVENTIONS: None.
MAIN VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC.
RESULTS: A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08).
CONCLUSION: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Medicina intensiva - (2024) vom: 09. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rodríguez, Alejandro [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 10.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1016/j.medine.2024.02.006 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM369521269 |
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| 041 | |a eng | ||
| 100 | 1 | |a Rodríguez, Alejandro |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
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| 500 | |a Date Revised 10.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Copyright © 2024 Elsevier España, S.L.U. and SEMICYUC. All rights reserved. | ||
| 520 | |a OBJECTIVE: To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves | ||
| 520 | |a DESIGN: Observational, retrospective, multicentre study | ||
| 520 | |a SETTING: Intensive Care Unit (ICU) | ||
| 520 | |a PATIENTS: Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves | ||
| 520 | |a INTERVENTIONS: None | ||
| 520 | |a MAIN VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC | ||
| 520 | |a RESULTS: A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08) | ||
| 520 | |a CONCLUSION: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Aprendizaje automático | |
| 650 | 4 | |a Factores de riesgo | |
| 650 | 4 | |a Fenotipos | |
| 650 | 4 | |a Infección grave por SARS-CoV-2 | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Phenotypes | |
| 650 | 4 | |a Prognosis | |
| 650 | 4 | |a Pronóstico | |
| 650 | 4 | |a Risk factors | |
| 650 | 4 | |a Severe SARS-CoV-2 infection | |
| 650 | 4 | |a Validación | |
| 650 | 4 | |a Validation | |
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| 700 | 1 | |a Franquet, Álvaro |e verfasserin |4 aut | |
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| 700 | 1 | |a Canabal Berlanga, Alfonso |e verfasserin |4 aut | |
| 700 | 1 | |a Del Valle Ortíz, María |e verfasserin |4 aut | |
| 700 | 1 | |a Carlos Ballesteros, Juan |e verfasserin |4 aut | |
| 700 | 1 | |a López Amor, Lucía |e verfasserin |4 aut | |
| 700 | 1 | |a Sancho Chinesta, Susana |e verfasserin |4 aut | |
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| 700 | 1 | |a Martín-Loeches, Ignacio |e verfasserin |4 aut | |
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