Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

Hoseini, Benyamin; Rahmatinejad, Zahra; Reihani, Hamidreza; Abu‐Hanna, Ameen; Pourmand, Ali; Tabatabaei, Seyyed Mohammad; Rahmatinejad, Fatemeh; Eslami, Saeid

doi:10.5005/jp-journals-10071-24463

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…Healthcare institutions are in high demand of tools for quick evaluation of patient status severity or resource planning. However, assessments of the severity of the condition often vary, ranging from outcomes like in-hospital death [1][2][3] or need for intensive care [4] to specific health indicators like respiratory distress [5][6][7] or organ failure markers [7,8]. Although there is an essential need to stratify patients according to their disease severity, the diversity in assessments makes it challenging to establish a standardized approach to measure and compare the severity of COVID-19 cases across different healthcare facilities and regions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the disease severity in patients hospitalized for COVID-19 based on the National Early Warning Score (NEWS) using statistical and machine learning methods: An electronic health records database analysis

Lycholip,

Puronaitė,

Skorniakov

et al. 2023

THC

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BACKGROUND: The coronavirus disease 2019 (COVID-19) was a cause of concern in the healthcare system and increased the need for disease severity indicators. However, they still vary in use to evaluate in-hospital outcomes and severity. National Early Warning Score (NEWS) is routinely used to evaluate patient health status at the hospital. Further research is needed to ensure if NEWS can be a good instrument for an overall health status assessment with or without additional information like laboratory tests, intensive care needs, and history of chronic diseases. OBJECTIVE: To evaluate if NEWS can be an indicator to measure COVID-19 patient status in-hospital. METHODS: We used the fully anonymized Electronic Health Records (EHR) characterizing patients admitted to the hospital with COVID-19. Data was obtained from Vilnius University Hospital Santaros Klinikos EHR system (SANTA-HIS) from 01-03-2020 to 31-12-2022. The study sample included 3875 patients. We created several statistical and machine learning models for discrimination between in-hospital death/discharge for evaluation NEWS as a disease severity measure for COVID-19 patients. In these models, two variable sets were considered: median NEWS and its combination with clinical parameters and medians of laboratory test results. Assessment of models’ performance was based on the scoring metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and F1-score. RESULTS: Our analysis revealed that NEWS predictive ability for describing patient health status during the stay in the hospital can be increased by adding the patient’s age at hospitalization, gender, clinical and laboratory variables (0.853 sensitivity, 0.992 specificity and F1-score – 0.859) in comparison with single NEWS (0.603, 0.995, 0.719, respectively). A comparison of different models showed that stepwise logistic regression was the best method for in-hospital mortality classification. Our findings suggest employing models like ours for advisory routine usage. CONCLUSION: Our model demonstrated incremental value for COVID-19 patient’s status evaluation.

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Section: Introductionmentioning

confidence: 99%

Assessment of the disease severity in patients hospitalized for COVID-19 based on the National Early Warning Score (NEWS) using statistical and machine learning methods: An electronic health records database analysis

Lycholip,

Puronaitė,

Skorniakov

et al. 2023

THC

View full text Add to dashboard Cite

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Development and validation of nomogram for predicting the risk of transferring to the ICU for children with influenza

Sun,

Zhang,

Hou

et al. 2024

Eur J Clin Microbiol Infect Dis

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A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

Rahmatinejad,

Dehghani,

Hoseini

et al. 2024

Sci Rep

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This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision–recall curve (AUCPR), Hosmer–Lemeshow (H–L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital’s ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802–0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787–0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

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Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department

Cited by 5 publications

References 43 publications

Assessment of the disease severity in patients hospitalized for COVID-19 based on the National Early Warning Score (NEWS) using statistical and machine learning methods: An electronic health records database analysis

Assessment of the disease severity in patients hospitalized for COVID-19 based on the National Early Warning Score (NEWS) using statistical and machine learning methods: An electronic health records database analysis

Development and validation of nomogram for predicting the risk of transferring to the ICU for children with influenza

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

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