Files and links (1)
CC BY V4.0, Open Access
Journal article
An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: Development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems
Respiratory research, Vol.24(1), Art. 79
2023
PMID: 36915107
Abstract
Background
We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores.
Methods
This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis.
Results
Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849–0.856, calibration slopes 0.911–1.173, and Hosmer–Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores.
Conclusion
The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.
Details
- Title
- An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: Development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems
- Authors/Creators
- S.W.H. Kwok - Hong Kong Polytechnic UniversityGuanjin Wang - Murdoch University, School of Information TechnologyFerdous A Sohel - Murdoch University, Centre for Crop and Food InnovationKianoush B. KashaniY. ZhuZ. WangEduardo AntpackKanika KhandelwalSandeep R. PagaliSanjeev NandaAhmed D. AbdalrhimUmesh M. SharmaSumit BhagraSagar DuganiPaul Y. TakahashiMohammad H. MuradMohammed Yousufuddin
- Publication Details
- Respiratory research, Vol.24(1), Art. 79
- Publisher
- BioMed Central
- Identifiers
- 991005567869807891
- Copyright
- © 2023 The Author(s) 2023.
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Information Technology
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Metrics
13 File views/ downloads
100 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.104 Virology - General
- 1.104.1353 Coronavirus Research
- Web Of Science research areas
- Respiratory System
- ESI research areas
- Clinical Medicine