Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients

Cheng, Fu-Yuan; Joshi, Himanshu; Tandon, Pranai; Freeman, Robert; Reich, David L.; Mazumdar, Madhu; Kohli‐Seth, Roopa; Levin, Matthew A.; Timsina, Prem; Kia, Arash

doi:10.3390/jcm9061668

Cited by 168 publications

(187 citation statements)

References 36 publications

(38 reference statements)

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“…Thus, recent studies using big data and machine learning have explored the prognostic factors of the disease, including ICU transfer, discharge, and mortality [ 8 - 11 ]. In line with our results, respiratory rate has also been identified as an important predictor of ICU transfer in patients with COVID-19 [ 9 ].…”

Section: Discussionsupporting

confidence: 90%

“…To our knowledge, this is one of the first attempts to combine NLP and machine learning to access and analyze unstructured, free-text real-world data from EHRs in a large series of patients with COVID-19. Although recent studies have used machine learning to predict ICU admission in patients with COVID-19 [ 9 ], our approach takes this methodology one step further by applying NLP to exclusively analyze unstructured information. Indeed, our state-ot-the-art methodology enabled rapid analysis of the unstructured free-text narratives in the EHRs of 1 million patients from the general population of the region of Castilla-La Mancha (Spain).…”

Section: Discussionmentioning

confidence: 99%

“…Considering the unprecedented spread and severity of the ongoing COVID-19 outbreak, focus has been given to hospitals’ unmet needs, particularly their ICU requirements [ 8 , 9 , 12 ]. Indeed, health systems have been or currently are near collapse, and independent modelling efforts have aimed at forecasting a number of epidemiological estimators, including ICU use [ 13 - 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Alonso¹,

Ancochea²,

Soriano³

2020

J Med Internet Res

108

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Background Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Objective Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. Methods We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. Results A total of 10,504 patients with a clinical or polymerase chain reaction–confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Conclusions Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Alonso¹,

Ancochea²,

Soriano³

2020

J Med Internet Res

108

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“…Most models use more than one variable and most models predict composite outcomes. Of the 30 models, 23 were trained on patients in China , 2 were trained on patients in the United States 30,31 , and 5 were trained on patients in South Korea 32 or Europe [33][34][35][36] . Only 8 of the models underwent validation on either held-out or external datasets 7,10,14,18,19,24,26,36 , and 1 underwent prospective validation .…”

Section: Introductionmentioning

confidence: 99%

A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients

et al. 2020

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The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

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“…[177]. A study by Cheng et al [133] developed an ML-based model to predict ICU transfers within 24 hours of hospital admission. .…”

mentioning

confidence: 99%

The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review

Syeda

Syed

Sexton

et al. 2020

Preprint

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Background: The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. Methods: A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. Results: The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. Conclusions: In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

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Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients

Cited by 168 publications

References 36 publications

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients

The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review

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