Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model

Peng, Hao; Hu, Chao; Deng, Wusheng; Huang, Lingmei; Zhang, Yushan; Luo, Baowei; Wang, Xingxing; Long, Xiaodan; Huang, Xiaoying

doi:10.1186/s12890-022-01986-0

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…In the validation set, a total of 25 models from 23 studies evaluated the performance of AI in predicting mortality in COVID-19 patients [ 12 – 34 ]. The overall pooled AUROC was 0.93 [1.00, 0.00].…”

Section: Resultsmentioning

confidence: 99%

The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis

Xin

Zhou

et al. 2023

BMC Med Inform Decis Mak

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Background The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. Methods The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158). Findings Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05). Interpretation Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.

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Section: Resultsmentioning

confidence: 99%

The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis

Xin

Zhou

et al. 2023

BMC Med Inform Decis Mak

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Prognostic models in COVID-19 infection that predict severity: a systematic review

et al. 2023

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Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

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Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model

Cited by 2 publications

References 36 publications

The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis

The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis

Prognostic models in COVID-19 infection that predict severity: a systematic review

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