An interpretable mortality prediction model for COVID-19 patients

Li, Yan; Zhang, Hai Tao; Gonçalves, Jorge; Xiao, Yang; Wang, Maolin; Guo, Yuqi; Sun, Changhua; Tang, Xun; Liang, Jing; Zhang, Mingyang; Huang, Xiang; Xiao, Ying; Cao, Haosen; Chen, Yanyan; Ren, Tongxin; Wang, Fang; Xiao, Yaru; Huang, Sufang; Tan, Xi; Huang, Niannian; Jiao, Bo; Cheng, Cheng; Zhang, Yong; Luo, Ailin; Mombaerts, Laurent; Jin, Junyang; Cao, Zhiguo; Li, Shusheng; Xu, Hui; Yuan, Ye

doi:10.1038/s42256-020-0180-7

Cited by 870 publications

(1,097 citation statements)

References 17 publications

Supporting

Mentioning

1,038

Contrasting

Unclassified

Order By: Relevance

“…Compared to existing models used to predict COVID-19 prognostic outcomes, our model is more accurate and uses more readily available metrics. Yan and colleagues developed a decision tree with three laboratory factors and achieved 95% AUC [12]. However, the hs-CRP test is not typically performed, as the standard CRP is more cost-effectiveness.…”

Section: Discussionmentioning

confidence: 99%

“…We identi ed 11 laboratory measures at admission which appear to associate with the poor COVID-19 outcomes. Among the regularly measured laboratory indicators at admission, the high neutrophil count, low lymphocyte count, high neutrophil-to-lymphocyte ratio, high direct bilirubin, and elevated lactate dehydrogenase have previously been identi ed as prognostic predictors for an unfavorable outcome [12,13,22,23]. However, in our discovery set, these predictors were not ranked at the top of VIS list for all candidate factors possibly because all patients in our study had progressed to severe or critical disease and the previous predictors may be less prognostic for severity.…”

Section: Discussionmentioning

confidence: 99%

“…In the validation set, data were obtained from recently published literature [12]. Brie y, the validation population was collected in Tongji Hospital between 10 th January and 18 th February 2020 and included 375 patients, 197 with general, 27 with severe and 151 with critical COVID-19.…”

Section: Study Population Data Sources and Processingmentioning

confidence: 99%

“…The characteristics of the 375 patients in the validation set were detailed in the original literature [12]. In brief, the mean (SD) age of these patients was 58.83 (16.46), and 224 (59.7%) were male.…”

Section: Study Population Characteristicsmentioning

confidence: 99%

“…Liang et al developed a risk score incorporating 10 characteristics at admission of COVID-19 patients to predict 'the risk of developing critical illness during hospitalization, achieving an area under the receiver operating characteristic curve (AUC) of 88% in both development and validation cohorts [11]. In addition, Yan and colleagues used machine learning to select three biomarkers from patients' nal biospecimens that can predict the mortality of COVID-19 patients >10 days in advance with more than 90% accuracy [12].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning

Wei

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Novel coronavirus disease (COVID-19) is an emerging, rapidly evolving situation. At present, the prognosis of severe and critically ill patients has become an important focus of attention. We strived to develop a prognostic prediction model for severe and critically ill COVID-19 patients.MethodsTo assess the factors associated with the prognosis of those patients, we retrospectively investigated the clinical, laboratory characteristics of confirmed 112 cases of COVID-19 admitted between 21 January to 6 March 2020 from Huangshi Central Hospital, Huangshi Hospital of Traditional Chinese Medicine, and Daye People’s Hospital. We applied machine learning method (survival random forest) to select predictors for 28-day survival and taken into account the dynamic trajectory of laboratory indicators. Results Fifteen candidate prognostic features, including 11 baseline measures (including platelet count (PLT), urea, creatine kinase (CK), fibrinogen, creatine kinase isoenzyme activity, aspartate aminotransferase (AST), activation of partial thromboplastin time (APTT), albumin, standard deviation of erythrocyte distribution width (RBC-SD), neutrophils (%) and red blood cell count (RBC)) and 4 trajectory clusters (changes during hospitalization in the white blood cell (WBC), PLT large cell ratio (P-LCR), PLT distribution width (PDW) and AST), combined with covariates achieved 100% (95%CI: 99%-100%) AUC and reached 87% (95%CI: 84%-91%) AUC in an external validation set. Conclusions Taking advantage of random forest technique and laboratory dynamic measures, we developed a forest model to predict survival outcome of COVID-19 patients, which achieved 87% AUC in the external validation set. Our online tool will help to facilitate the early recognition of patients with high risk.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Study Population Data Sources and Processingmentioning

confidence: 99%

Section: Study Population Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning

Wei

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Role of AI and ML in Epidemics and Pandemics

Bezbaruah

Ghosh

Kumari

et al. 2023

Bioinformatics Tools for Pharmaceutical Drug Product Development

View full text Add to dashboard Cite

Prognostic value of serum/plasma neurofilament light chain for COVID‐19‐associated mortality

Masvekar

Kósa

Jin

et al. 2022

Ann Clin Transl Neurol

View full text Add to dashboard Cite

Objective Given the continued spread of coronavirus 2, the early predictors of coronavirus disease 19 (COVID‐19) associated mortality might improve patients' outcomes. Increased levels of circulating neurofilament light chain (NfL), a biomarker of neuronal injury, have been observed in severe COVID‐19 patients. We investigated whether NfL provides non‐redundant clinical value to previously identified predictors of COVID‐19 mortality. Methods We measured serum or plasma NfL concentrations in a blinded fashion in 3 cohorts totaling 338 COVID‐19 patients. Results In cohort 1, we found significantly elevated NfL levels only in critically ill COVID‐19 patients. Longitudinal cohort 2 data showed that NfL is elevated late in the course of the disease, following the two other prognostic markers of COVID‐19: decrease in absolute lymphocyte count (ALC) and increase in lactate dehydrogenase (LDH). Significant correlations between ALC and LDH abnormalities and subsequent rise of NfL implicate that the multi‐organ failure is the most likely cause of neuronal injury in severe COVID‐19 patients. The addition of NfL to age and gender in cohort 1 significantly improved the accuracy of mortality prediction and these improvements were validated in cohorts 2 and 3. Interpretation A substantial increase in serum/plasma NfL reproducibly enhanced COVID‐19 mortality prediction. Combined with other prognostic markers, such as ALC and LDH that are routinely measured in ICU patients, NfL measurements might be useful to identify the patients at a high risk of COVID‐19‐associated mortality, who might still benefit from escalated care.

show abstract

An interpretable mortality prediction model for COVID-19 patients

Cited by 870 publications

References 17 publications

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning

Role of AI and ML in Epidemics and Pandemics

Prognostic value of serum/plasma neurofilament light chain for COVID‐19‐associated mortality

Contact Info

Product

Resources

About