Dataset Information

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19.

ABSTRACT:

Background

The evolution of patients hospitalized with coronavirus disease 2019 (COVID-19) is still hard to predict, even after several months of dealing with the pandemic.

Aims

To develop and validate a score to predict outcomes in patients hospitalized with COVID-19.

Methods

All consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an emergency department or conventional ward, or in-hospital death. A score that estimates the risk of experiencing the primary outcome was constructed from a derivation cohort using stacked LASSO (Least Absolute Shrinkage and Selection Operator), and was tested in a validation cohort.

Results

Among 2873 patients analysed (57.9% men; 66.6±17.0 years), the primary outcome occurred in 838 (29.2%) patients: 551 (19.2%) were transferred to an intensive care unit; and 287 (10.0%) died in-hospital without transfer to an intensive care unit. Using stacked LASSO, we identified 11 variables independently associated with the primary outcome in multivariable analysis in the derivation cohort (n=2313), including demographics (sex), triage vitals (body temperature, dyspnoea, respiratory rate, fraction of inspired oxygen, blood oxygen saturation) and biological variables (pH, platelets, C-reactive protein, aspartate aminotransferase, estimated glomerular filtration rate). The Critical COVID-19 France (CCF) risk score was then developed, and displayed accurate calibration and discrimination in the derivation cohort, with C-statistics of 0.78 (95% confidence interval 0.75-0.80). The CCF risk score performed significantly better (i.e. higher C-statistics) than the usual critical care risk scores.

Conclusions

The CCF risk score was built using data collected routinely at hospital admission to predict outcomes in patients with COVID-19. This score holds promise to improve early triage of patients and allocation of healthcare resources.

SUBMITTER: Weizman O

PROVIDER: S-EPMC9595484 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19.

Weizman Orianne O Duceau Baptiste B Trimaille Antonin A Pommier Thibaut T Cellier Joffrey J Geneste Laura L Panagides Vassili V Marsou Wassima W Deney Antoine A Attou Sabir S Delmotte Thomas T Ribeyrolles Sophie S Chemaly Pascale P Karsenty Clément C Giordano Gauthier G Gautier Alexandre A Chaumont Corentin C Guilleminot Pierre P Sagnard Audrey A Pastier Julie J Ezzouhairi Nacim N Perin Benjamin B Zakine Cyril C Levasseur Thomas T Ma Iris I Chavignier Diane D Noirclerc Nathalie N Darmon Arthur A Mevelec Marine M Sutter Willy W Mika Delphine D Fauvel Charles C Pezel Théo T Waldmann Victor V Cohen Ariel A Bonnet Guillaume G

Archives of cardiovascular diseases 20221022 12

<h4>Background</h4>The evolution of patients hospitalized with coronavirus disease 2019 (COVID-19) is still hard to predict, even after several months of dealing with the pandemic.<h4>Aims</h4>To develop and validate a score to predict outcomes in patients hospitalized with COVID-19.<h4>Methods</h4>All consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an ...[more]

PMID: 36376208

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Project description:AimsAlthough some scores based on traditional statistical methods are available for risk stratification in patients hospitalized in cardiac intensive care units (CICUs), the interest of machine learning (ML) methods for risk stratification in this field is not well established. We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU.Methods and resultsIn April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluated to predict in-hospital MAE. The eight remaining centres were used as an external validation cohort. Among 1499 consecutive patients included (aged 64 ± 15 years, 70% male), 67 had in-hospital MAE (4.3%). Out of 28 clinical, biological, ECG, and echocardiographic variables, seven were selected to predict MAE in the training set (n = 844). Boosted cost-sensitive C5.0 technique showed the best performance compared with other ML methods [receiver operating characteristic area under the curve (AUROC) = 0.90, precision-recall AUC = 0.57, F1 score = 0.5]. Our ML score showed a better performance than existing scores (AUROC: ML score = 0.90 vs. Thrombolysis In Myocardial Infarction (TIMI) score: 0.56, Global Registry of Acute Coronary Events (GRACE) score: 0.52, Acute Heart Failure (ACUTE-HF) score: 0.65; all P < 0.05). Machine learning score also showed excellent performance in the external cohort (AUROC = 0.88).ConclusionThis new ML score is the first to demonstrate improved performance in predicting in-hospital outcomes over existing scores in patients admitted to the intensive care unit based on seven simple and rapid clinical and echocardiographic variables.Trial registrationClinicalTrials.gov Identifier: NCT05063097.

Project description:BackgroundFactors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic.MethodsA retrospective study was conducted at Hongqiao National Exhibition and Convention Center Fangcang shelter (Shanghai, China) from April 9, 2022 to April 25, 2022. The demographics, clinical data, inoculation history, and recovery information of the 13,162 enrolled participants were collected. A multivariable logistic regression model was used to identify independent factors associated with 7-day recovery and 14-day recovery. Machine learning algorithms (DT, SVM, RF, DT/AdaBoost, AdaBoost, SMOTEENN/DT, SMOTEENN/SVM, SMOTEENN/RF, SMOTEENN+DT/AdaBoost, and SMOTEENN/AdaBoost) were used to build models for predicting 7-day and 14-day recovery.ResultsOf the 13,162 patients in the study, the median duration of recovery was 8 days (interquartile range IQR, 6-10 d), 41.31% recovered within 7 days, and 94.83% recovered within 14 days. Univariate analysis showed that the administrative region, age, cough medicine, comorbidities, diabetes, coronary artery disease (CAD), hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were associated with a duration of recovery within 7 days. Age, gender, vaccination dose, cough medicine, comorbidities, diabetes, CAD, hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were related to a duration of recovery within 14 days. In the multivariable analysis, the receipt of two doses of the vaccination vs. unvaccinated (OR = 1.118, 95% CI = 1.003-1.248; p = 0.045), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.114, 95% CI = 1.004-1.236; p = 0.043), diabetes (OR = 0.383, 95% CI = 0.194-0.749; p = 0.005), CAD (OR = 0.107, 95% CI = 0.016-0.421; p = 0.005), hypertension (OR = 0.371, 95% CI = 0.202-0.674; p = 0.001), and ratio of N/IC (OR = 3.686, 95% CI = 2.939-4.629; p < 0.001) were significantly and independently associated with a duration of recovery within 7 days. Gender (OR = 0.736, 95% CI = 0.63-0.861; p < 0.001), age (30-70) (OR = 0.738, 95% CI = 0.594-0.911; p < 0.001), age (>70) (OR = 0.38, 95% CI = 0292-0.494; p < 0.001), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.391, 95% CI = 1.12-1.719; p = 0.0033), cough medicine (OR = 1.509, 95% CI = 1.075-2.19; p = 0.023), and symptoms (OR = 1.619, 95% CI = 1.306-2.028; p < 0.001) were significantly and independently associated with a duration of recovery within 14 days. The SMOTEEN/RF algorithm performed best, with an accuracy of 90.32%, sensitivity of 92.22%, specificity of 88.31%, F1 score of 90.71%, and AUC of 89.75% for the 7-day recovery prediction; and an accuracy of 93.81%, sensitivity of 93.40%, specificity of 93.81%, F1 score of 93.42%, and AUC of 93.53% for the 14-day recovery prediction.ConclusionAge and vaccination dose were factors robustly associated with accelerated recovery both on day 7 and day 14 from the onset of disease during the Omicron BA. 2.2 wave. The results suggest that the SMOTEEN/RF-based model could be used to predict the probability of 7-day and 14-day recovery from the Omicron variant of SARS-CoV-2 infection for COVID-19 prevention and control policy in other regions or countries. This may also help to generate external validation for the model.

Project description:BackgroundIdentification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.MethodsWe included hospitalized COVID-19 pneumonia patients from July to September 2021. K-means clustering, an unsupervised machine learning method, was performed to identify clinical phenotypes based on clinical and laboratory variables collected within 24 hours of admission. Variables were normalized before clustering to ensure equal contribution to the analysis. The optimal number of clusters was determined using the elbow method and Silhouette scores. Cox proportional hazard models were used to compare the risk of intubation and 90-day mortality across the identified clusters.ResultsThree clinically distinct clusters were identified among 538 hospitalized COVID-19 pneumonia patients. Cluster 1 (N = 27) consisted predominantly of males and showed significantly elevated serum liver enzymes and LDH levels. Cluster 2 (N = 370) was characterized by lower chest x-ray scores and higher serum albumin levels. Cluster 3 (N = 141) was characterized by older age, diabetes mellitus, higher chest x-ray scores, more severe vital signs, higher creatinine levels, lower hemoglobin levels, lower lymphocyte counts, higher C-reactive protein, higher D-dimer, and higher LDH levels. When compared to cluster 2, cluster 3 was significantly associated with increased risk of 90-day mortality (HR, 6.24; 95% CI, 2.42-16.09) and intubation (HR, 5.26; 95% CI 2.37-11.72). In contrast, cluster 1 had a 100% survival rate with a non-significant increase in intubation risk compared to cluster 2 (HR, 1.40, 95% CI, 0.18-11.04).ConclusionsWe identified three distinct clinical phenotypes of COVID-19 pneumonia patients, with cluster 3 associated with an increased risk of respiratory failure and mortality. These findings may guide tailored clinical management strategies.

Dataset Information

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19.

Background

Aims

Methods

Results

Conclusions

Publications

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19.

Similar Datasets

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