Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters
Abstract Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively c...
Main Authors: | , , , , , , , , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2021-02-01
|
Series: | Journal of Intensive Care |
Subjects: | |
Online Access: | https://doi.org/10.1186/s40560-021-00531-1 |
id |
doaj-234c0774f4184812bd5f6ba43eb83015 |
---|---|
record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yue Gao Lingxi Chen Jianhua Chi Shaoqing Zeng Xikang Feng Huayi Li Dan Liu Xinxia Feng Siyuan Wang Ya Wang Ruidi Yu Yuan Yuan Sen Xu Chunrui Li Wei Zhang Shuaicheng Li Qinglei Gao |
spellingShingle |
Yue Gao Lingxi Chen Jianhua Chi Shaoqing Zeng Xikang Feng Huayi Li Dan Liu Xinxia Feng Siyuan Wang Ya Wang Ruidi Yu Yuan Yuan Sen Xu Chunrui Li Wei Zhang Shuaicheng Li Qinglei Gao Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters Journal of Intensive Care COVID-19 Critical illness Machine learning Immune-inflammatory parameters Online model |
author_facet |
Yue Gao Lingxi Chen Jianhua Chi Shaoqing Zeng Xikang Feng Huayi Li Dan Liu Xinxia Feng Siyuan Wang Ya Wang Ruidi Yu Yuan Yuan Sen Xu Chunrui Li Wei Zhang Shuaicheng Li Qinglei Gao |
author_sort |
Yue Gao |
title |
Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters |
title_short |
Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters |
title_full |
Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters |
title_fullStr |
Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters |
title_full_unstemmed |
Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters |
title_sort |
development and validation of an online model to predict critical covid-19 with immune-inflammatory parameters |
publisher |
BMC |
series |
Journal of Intensive Care |
issn |
2052-0492 |
publishDate |
2021-02-01 |
description |
Abstract Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. Results Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979–1.000) in internal validation cohort and 0.999 (95% CI 0.998–1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. Conclusions The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). Graphical abstracthelper lymphocytve vv |
topic |
COVID-19 Critical illness Machine learning Immune-inflammatory parameters Online model |
url |
https://doi.org/10.1186/s40560-021-00531-1 |
work_keys_str_mv |
AT yuegao developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT lingxichen developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT jianhuachi developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT shaoqingzeng developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT xikangfeng developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT huayili developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT danliu developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT xinxiafeng developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT siyuanwang developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT yawang developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT ruidiyu developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT yuanyuan developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT senxu developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT chunruili developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT weizhang developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT shuaichengli developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters AT qingleigao developmentandvalidationofanonlinemodeltopredictcriticalcovid19withimmuneinflammatoryparameters |
_version_ |
1724258351465889792 |
spelling |
doaj-234c0774f4184812bd5f6ba43eb830152021-02-21T12:09:59ZengBMCJournal of Intensive Care2052-04922021-02-019111210.1186/s40560-021-00531-1Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parametersYue Gao0Lingxi Chen1Jianhua Chi2Shaoqing Zeng3Xikang Feng4Huayi Li5Dan Liu6Xinxia Feng7Siyuan Wang8Ya Wang9Ruidi Yu10Yuan Yuan11Sen Xu12Chunrui Li13Wei Zhang14Shuaicheng Li15Qinglei Gao16National Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Computer Science, City University of Hong KongNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologySchool of Software, Northwestern Polytechnical UniversityNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Computer Science, City University of Hong KongNational Medical Center for Major Public Health Events, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyAbstract Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. Results Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979–1.000) in internal validation cohort and 0.999 (95% CI 0.998–1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. Conclusions The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). Graphical abstracthelper lymphocytve vvhttps://doi.org/10.1186/s40560-021-00531-1COVID-19Critical illnessMachine learningImmune-inflammatory parametersOnline model |