PRCTC: a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients

Gao, Yuexiang; Xiong, Xiaoming; Jiao, Xue; Yu, Yang; Chi, Jianhua; Zhang, Wei; Chen, Lingxi; Li, Shuai Cheng; Gao, Qinglei

doi:10.18632/aging.203819

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Murakami et al proposed severe respiratory failure and high-soluble IL-2 receptor, lactate dehydrogenase, and CRP levels as factors associated with adverse outcomes [ 27 ]. A study using deep learning algorithms in predicting corticosteroid responsiveness also included laboratory results such as lymphocyte percentage, PCT, and tumor necrosis factor α, IL-1β, IL-2 receptor, IL-6, IL-8, IL-10, and CRP levels [ 28 ]. Our study is different from these studies in that we attempted to use CXR imaging as the main tool for outcome prediction.…”

Section: Discussionmentioning

confidence: 99%

Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs

Kim,

Shin,

Kim

et al. 2023

JCM

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The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case–control study involving mild-to-moderate COVID-19 patients treated with corticosteroids was conducted from 4 September 2021, to 30 August 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from the onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6–9 days from onset of symptoms (adjusted odds ratio of (aOR): 1.022, 95% confidence interval (CI): 1.003–1.042, p = 0.020) and consolidation scores up to 9 days from onset of symptoms (0–2 days: aOR: 1.025, 95% CI: 1.006–1.045, p = 0.010; 3–5 days: aOR: 1.03 95% CI: 1.011–1.051, p = 0.002; 6–9 days: aOR; 1.052, 95% CI: 1.015–1.089, p = 0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them.

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Section: Discussionmentioning

confidence: 99%

Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs

Kim,

Shin,

Kim

et al. 2023

JCM

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Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data

Liu,

Durbin‐Johnson,

Paciotti

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONThe incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development.METHODSThe analyses included patients ≥ 65 years with AD diagnosis in six University of California hospitals between January 2012 and October 2023. The controls were race/ethnicity, sex, and age matched without dementia. Data analyses used the Cox proportional hazards model and machine learning (ML).RESULTSHispanic/Latino and Native Hawaiian/Pacific Islander, but not Black subjects, had increased AD risk compared to White subjects. Non‐infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men. While underweight increased AD risk, overweight or obesity reduced risk. ML confirmed the importance of metabolic laboratory tests in predicting AD development.DISCUSSIONThe data stress the significance of metabolism in AD development and the need for racial/ethnic‐ and sex‐specific preventive strategies.Highlights Hispanics/Latinos and Native Hawaiians/Pacific Islanders show increased hazards of Alzheimer's disease (AD) compared to White subjects. Underweight individuals demonstrate a significantly higher hazard ratio for AD compared to those with normal body mass index. The association between obesity and AD hazard differs among racial groups, with elderly Asian subjects showing increased risk compared to White subjects. Alcohol consumption and non‐infectious hepatitis are significant hazards for AD. Machine learning approaches highlight the potential of metabolic panels for AD prediction.

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Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19

et al. 2023

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Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.

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PRCTC: a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients

Cited by 3 publications

References 49 publications

Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs

Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs

Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data

Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19

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