Risk Prediction for Poor Outcome and Death in Hospital In-Patients with COVID-19: Derivation in Wuhan, China and External Validation in London, UK

Zhang, Huayu; Shi, Ting; Wu, Xiaodong; Zhang, Xin; Wang, Kun; Bean, Daniel; Dobson, Richard; Teo, James; Sun, Jiaxing; Zhao, Pei; Li, Chenghong; Dhaliwal, Kevin; Wu, Honghan; Li, Qiang; Guthrie, Bruce

doi:10.2139/ssrn.3590468

Cited by 22 publications

(21 citation statements)

References 20 publications

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“…First, we noted that most of the proposed approaches avoid, or do not even mention, any pre-processing phase for data normalization/standardization, missing value imputation, or feature selection, which would surely increase robustness and improve performances. Moreover, while some works only report descriptive statistics obtained by univariate [79] or multivariate [80] analysis, the majority of the approaches exploit logistic regression classifiers [81] – [97] . The remaining methods use RF classifiers [85] , [87] , [91] , [92] , [97] – [101] or XGBoost [102] , [103] , SVMs [87] , [91] , [97] , [100] , [101] , K-Nearest Neighbor classifiers [87] , [91] , [100] , Cox regression models [104] , [105] , or artificial neural networks [106] .…”

Section: Related Workmentioning

confidence: 99%

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments

et al. 2020

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Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in-or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.

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Section: Related Workmentioning

confidence: 99%

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments

et al. 2020

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“…Due to the rapidly expanding number of patients and the limited resources in the ICU, prediction models for COVID-19 are crucial in clinical decision-making and medical resource micro-allocation. However, although approximately 50 prognostic models have been built so far, including eight models to predict progression to severe or critical disease[ 8 ], only four of the models predicted ICU admission[ 9 - 12 ]. Among the four studies, only two calibrated their models, resulting in underestimation of the risk of poor outcomes and miscalibration risks during external validation[ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, although approximately 50 prognostic models have been built so far, including eight models to predict progression to severe or critical disease[ 8 ], only four of the models predicted ICU admission[ 9 - 12 ]. Among the four studies, only two calibrated their models, resulting in underestimation of the risk of poor outcomes and miscalibration risks during external validation[ 11 , 12 ]. Several prognostic predictive models mainly based on laboratory tests have been developed to predict disease progression to a severe or critical state, and the estimated C index of model performance was approximately 0.85[ 13 - 15 ].…”

Section: Introductionmentioning

confidence: 99%

Validated tool for early prediction of intensive care unit admission in COVID-19 patients

Huang¹,

Liu²,

Li³

et al. 2021

WJCC

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BACKGROUND The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2. AIM To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission. METHODS The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models. RESULTS There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort (median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar (49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods (0.90 vs 0.82 and 0.75). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A. CONCLUSION Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.

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“…An accurate means to predict risk for speci c organ injury in severe COVID-19 would greatly assist clinical decision-making. Studies have attempted to assess such risks by grouping several outcomes of interest together and building a predictive model [13][14][15][16]. Despite the success of this kind of model, grouping the outcomes together is less useful for resource allocation and triage, as patients will require different equipment and sta ng expertise depending on their disease course and complications [3,17].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19

Trivedi

Becker

et al. 2021

Preprint

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BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

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Risk Prediction for Poor Outcome and Death in Hospital In-Patients with COVID-19: Derivation in Wuhan, China and External Validation in London, UK

Cited by 22 publications

References 20 publications

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments

Validated tool for early prediction of intensive care unit admission in COVID-19 patients

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19

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