Machine learning models for predicting severe COVID-19 outcomes in hospitals

Wendland, Philipp; Schmitt, Vanessa; Zimmermann, Jörg; Häger, Lukas; Göpel, Siri; Schenkel-Häger, Christof; Kschischo, Maik

doi:10.1016/j.imu.2023.101188

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…Four major benefits of a ML based (or data driven) triage approach may be identified: risk stratification ability, scalability, continuous integration of newly acquired knowledge and accuracy. Indeed, ML models can be trained to predict outcome events such as mortality, hospitalization, or readmission [ 40 , 41 ]. This can help prioritizing patients who need urgent medical attention and ensure that resources are optimally allocated.…”

Section: Discussionmentioning

confidence: 99%

Validation of a pre-established triage protocol for critically ill patients in a COVID-19 outbreak under resource scarcity: A retrospective multicenter cohort study

Donat

Mellati²,

Frumento

et al. 2023

PLoS ONE

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Introduction In case of COVID-19 related scarcity of critical care resources, an early French triage algorithm categorized critically ill patients by probability of survival based on medical history and severity, with four priority levels for initiation or continuation of critical care: P1 –high priority, P2 –intermediate priority, P3 –not needed, P4 –not appropriate. This retrospective multi-center study aimed to assess its classification performance and its ability to help saving lives under capacity saturation. Methods ICU patients admitted for severe COVID-19 without triage in spring 2020 were retrospectively included from three hospitals. Demographic data, medical history and severity items were collected. Priority levels were retrospectively allocated at ICU admission and on ICU day 7–10. Mortality rate, cumulative incidence of death and of alive ICU discharge, length of ICU stay and of mechanical ventilation were compared between priority levels. Calculated mortality and survival were compared between full simulated triage and no triage. Results 225 patients were included, aged 63.1±11.9 years. Median SAPS2 was 40 (IQR 29–49). At the end of follow-up, 61 (27%) had died, 26 were still in ICU, and 138 had been discharged. Following retrospective initial priority allocation, mortality rate was 53% among P4 patients (95CI 34–72%) versus 23% among all P1 to P3 patients (95CI 17–30%, chi-squared p = 5.2e-4). The cumulative incidence of death consistently increased in the order P3, P1, P2 and P4 both at admission (Gray’s test p = 3.1e-5) and at reassessment (p = 8e-5), and conversely for that of alive ICU discharge. Reassessment strengthened consistency. Simulation under saturation showed that this two-step triage protocol could have saved 28 to 40 more lives than no triage. Conclusion Although it cannot eliminate potentially avoidable deaths, this triage protocol proved able to adequately prioritize critical care for patients with highest probability of survival, hence to save more lives if applied.

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Section: Discussionmentioning

confidence: 99%

Validation of a pre-established triage protocol for critically ill patients in a COVID-19 outbreak under resource scarcity: A retrospective multicenter cohort study

Donat

Mellati²,

Frumento

et al. 2023

PLoS ONE

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“…Twenty-nine markers were considered, and the local weighted algorithm obtained a maximum accuracy of 97.86%. Wendland et al 12 used classifiers to predict severe COVID-19 cases. They were able to predict the severity status with an AUC of 0.918.…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Chadaga,

Prabhu,

Sampathila

et al. 2024

Sci Rep

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The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.

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The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19

Iglesias,

Gálvez,

Suárez

2023

Mathematical Modeling and Intelligent Control for Combating Pandemics

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Machine learning models for predicting severe COVID-19 outcomes in hospitals

Cited by 7 publications

References 39 publications

Validation of a pre-established triage protocol for critically ill patients in a COVID-19 outbreak under resource scarcity: A retrospective multicenter cohort study

Validation of a pre-established triage protocol for critically ill patients in a COVID-19 outbreak under resource scarcity: A retrospective multicenter cohort study

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19

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