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Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study

BACKGROUND: Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triagin...

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Autores principales: Young, Dylan, Houshmand, Bita, Tan, Chunyi Christie, Kirubarajan, Abirami, Parbhakar, Ashna, Dada, Jazleen, Whittle, Wendy, Sobel, Mara L., Gomez, Luis M., Rüdiger, Mario, Pecks, Ulrich, Oppelt, Peter, Ray, Joel G., Hobson, Sebastian R., Snelgrove, John W., D’Souza, Rohan, Kashef, Rasha, Sussman, Dafna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394879/
https://www.ncbi.nlm.nih.gov/pubmed/37532986
http://dx.doi.org/10.1186/s12884-023-05679-2
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author Young, Dylan
Houshmand, Bita
Tan, Chunyi Christie
Kirubarajan, Abirami
Parbhakar, Ashna
Dada, Jazleen
Whittle, Wendy
Sobel, Mara L.
Gomez, Luis M.
Rüdiger, Mario
Pecks, Ulrich
Oppelt, Peter
Ray, Joel G.
Hobson, Sebastian R.
Snelgrove, John W.
D’Souza, Rohan
Kashef, Rasha
Sussman, Dafna
author_facet Young, Dylan
Houshmand, Bita
Tan, Chunyi Christie
Kirubarajan, Abirami
Parbhakar, Ashna
Dada, Jazleen
Whittle, Wendy
Sobel, Mara L.
Gomez, Luis M.
Rüdiger, Mario
Pecks, Ulrich
Oppelt, Peter
Ray, Joel G.
Hobson, Sebastian R.
Snelgrove, John W.
D’Souza, Rohan
Kashef, Rasha
Sussman, Dafna
author_sort Young, Dylan
collection PubMed
description BACKGROUND: Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. METHODS: An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. RESULTS: The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. CONCLUSIONS: We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05679-2.
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spelling pubmed-103948792023-08-03 Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study Young, Dylan Houshmand, Bita Tan, Chunyi Christie Kirubarajan, Abirami Parbhakar, Ashna Dada, Jazleen Whittle, Wendy Sobel, Mara L. Gomez, Luis M. Rüdiger, Mario Pecks, Ulrich Oppelt, Peter Ray, Joel G. Hobson, Sebastian R. Snelgrove, John W. D’Souza, Rohan Kashef, Rasha Sussman, Dafna BMC Pregnancy Childbirth Research BACKGROUND: Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. METHODS: An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. RESULTS: The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. CONCLUSIONS: We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-05679-2. BioMed Central 2023-08-02 /pmc/articles/PMC10394879/ /pubmed/37532986 http://dx.doi.org/10.1186/s12884-023-05679-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Young, Dylan
Houshmand, Bita
Tan, Chunyi Christie
Kirubarajan, Abirami
Parbhakar, Ashna
Dada, Jazleen
Whittle, Wendy
Sobel, Mara L.
Gomez, Luis M.
Rüdiger, Mario
Pecks, Ulrich
Oppelt, Peter
Ray, Joel G.
Hobson, Sebastian R.
Snelgrove, John W.
D’Souza, Rohan
Kashef, Rasha
Sussman, Dafna
Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
title Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
title_full Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
title_fullStr Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
title_full_unstemmed Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
title_short Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
title_sort predicting adverse outcomes in pregnant patients positive for sars-cov-2: a machine learning approach- a retrospective cohort study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394879/
https://www.ncbi.nlm.nih.gov/pubmed/37532986
http://dx.doi.org/10.1186/s12884-023-05679-2
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