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Research Article
Priyadip Ray
Lawrence Livermore National Laboratory
Corresponding Author
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Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML based tool for predicting adverse outcomes based on EHR data to optimize clinical utility under a given cost structure. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Healthcare in northwest Ohio and southeastern Michigan.
Methods: We tested performance of various ML approaches for predicting either increasing ventilatory support or mortality and the set of model features under a budget constraint was optimized via exhaustive search across all combinations of features.
Results: The optimal sets of features for predicting ventilation under any budget constraint included demographics and comorbidities (DCM), basic metabolic panel (BMP), D-dimer, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), CRP, brain natriuretic peptide (BNP), and procalcitonin and for mortality included DCM, BMP, complete blood count, D-dimer, LDH, CRP, BNP, procalcitonin and ferritin.
Conclusions: This study presents a quick, accurate and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.
Keywords
COVID-19, machine learning, outcomes
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The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 08 Jun, 2021
No community comments so far
Editorial decision: Major revision
On 04 Aug, 2021
Reviews received
Received 04 Aug, 2021
Reviewers agreed
On 31 Jul, 2021
Reviewers invited
Invitations sent on 20 Jul, 2021
Editor assigned
On 19 Jul, 2021
Editor invited
On 23 Jun, 2021
Submission checks complete
On 22 Jun, 2021
First submitted
On 05 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
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A new preprint design is coming!
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Priyadip Ray
Lawrence Livermore National Laboratory
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 08 Jun, 2021
No community comments so far
Editorial decision: Major revision
On 04 Aug, 2021
Reviews received
Received 04 Aug, 2021
Reviewers agreed
On 31 Jul, 2021
Reviewers invited
Invitations sent on 20 Jul, 2021
Editor assigned
On 19 Jul, 2021
Editor invited
On 23 Jun, 2021
Submission checks complete
On 22 Jun, 2021
First submitted
On 05 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML based tool for predicting adverse outcomes based on EHR data to optimize clinical utility under a given cost structure. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Healthcare in northwest Ohio and southeastern Michigan.
Methods: We tested performance of various ML approaches for predicting either increasing ventilatory support or mortality and the set of model features under a budget constraint was optimized via exhaustive search across all combinations of features.
Results: The optimal sets of features for predicting ventilation under any budget constraint included demographics and comorbidities (DCM), basic metabolic panel (BMP), D-dimer, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), CRP, brain natriuretic peptide (BNP), and procalcitonin and for mortality included DCM, BMP, complete blood count, D-dimer, LDH, CRP, BNP, procalcitonin and ferritin.
Conclusions: This study presents a quick, accurate and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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