Development and validation of the OUTCoV score to predict the risk of hospitalisation among patients with SARS-CoV-2 infection in ambulatory settings: a prospective cohort study (original) (raw)
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BMC Infectious Diseases, 2021
Background Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) constitutes a major health burden worldwide due to high mortality rates and hospital bed shortages. SARS-CoV-2 infection is associated with several laboratory abnormalities. We aimed to develop and validate a risk score based on simple demographic and laboratory data that could be used on admission in patients with SARS-CoV-2 infection to predict in-hospital mortality. Methods Three cohorts of patients from different hospitals were studied consecutively (developing, validation, and prospective cohorts). The following demographic and laboratory data were obtained from medical records: sex, age, hemoglobin, mean corpuscular volume (MCV), platelets, leukocytes, sodium, potassium, creatinine, and C-reactive protein (CRP). For each variable, classification and regression tree analysis were used to establish the cut-off point(s) associated with in-hospital mortality outcom...
Research Square (Research Square), 2020
Background. In order to rapidly identifying patients with a low probability of being infected by COVID19 to quickly refer them to specialized departments, the objective of our study was to develop a clinical predictive model of infection by COVID19 in patients attending the ED for respiratory symptoms or unexplained fever. Methods. We included all patients over 15 years old, admitted in one of the 2 emergency departments of Toulouse University Hospital between March 13 and March 31 for respiratory symptoms (dyspnea, cough), or fever (or sensation of fever) of unknown origin, and potentially requiring hospitalization. COVID19 infection was assessed by CT-SCAN and RT-PCR. All the candidate predictors were variables collected during the rst clinical examination. Internal validation of the nal model was performed using the bootstrap procedure. We performed a temporal validation in the same way on patients included between April 1 and April 13. Results. patients were included. The prevalence of COVID19 was 25.5%. There were 19 predictors in the nal model. The corrected-by-optimism area under the curve was 0.86 (95%CI = [0.83;0.89]). For a threshold at 10%, the sensitivity was 92%., the speci city was 56%, and the false negative rate was 5%. In secondary data, including 387 patients, the prevalence of COVID19 was 15%. The area under the curve was 0.73 (95%CI = [0.63;0.83]). For the same threshold, the sensitivity was 78%, the speci city was 48%, and the false negative rate was 7% Conclusion. We have developed a predictive tool of COVID19 infection for patients attending the ED. It could safely reduce admission in COVID19 dedicated unit in ED and prevent its overcrowding.
Internal and Emergency Medicine, 2022
The objectives of this study are to develop a predictive model of hospital admission for COVID-19 to help in the activation of emergency services, early referrals from primary care, and the improvement of clinical decision-making in emergency room services. The method is the retrospective cohort study of 49,750 patients with microbiological confirmation of SARS-CoV-2 infection. The sample was randomly divided into two subsamples, for the purposes of derivation and validation of the prediction rule (60% and 40%, respectively). Data collected for this study included sociodemographic data, baseline comorbidities, baseline treatments, and other background data. Multilevel analyses with generalized estimated equations were used to develop the predictive model. Male sex and the gradual effect of age were the main risk factors for hospital admission. Regarding baseline comorbidities, coagulopathies, cancer, cardiovascular diseases, diabetes with organ damage, and liver disease were among t...
BackgroundAn urgent need exists for an early detection of cases with a high-risk of SARS-CoV-2 infection, particularly in high-flow and -risk settings, such as emergency departments (EDs). The aim of this work is to develop and validate a predictive model for the evaluation of SARS-CoV-2 infection risk, with the rationale of using this tool to manage ED patients.MethodsA retrospective study was performed by cross-sectionally reviewing the electronical case records of patients admitted to Niguarda Hospital or referred to its ED in the period 15 March to 24 April 2020.Derivation sample was composed of non-random inpatients hospitalized on 24 April and admitted before 22 April 2020. Validation sample was composed of consecutive patients who visited the ED between 15 and 25 March 2020. The association between the dichotomic outcome and each predictor was explored by univariate analysis with logistic regression models.ResultsA total of 113 patients in the derivation sample and 419 in the...
Indian Journal of Critical Care Medicine, 2023
Background: The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA). Materials and methods: A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation. Results: The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration. Conclusion: The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors.
Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021
PLOS ONE, 2021
Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted a...
Clinical Microbiology and Infection, 2020
We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19). We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from February 22 to April 3 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: SpO2<93% with 100% FiO2, respiratory rate (RR)>30bpm, or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, β-coefficients were used to develop a risk score. Trial Registration NCT04316949. We analyzed 1113 patients (644 derivation, 469 validation cohort). Mean (±standard deviation)age was 65.7(±15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in derivation and validation cohort, respectiv...
Open Journal of Respiratory Diseases, 2021
Objectives: Early identification of patients with the novel coronavirus induced-disease 2019 (COVID-19) and pneumonia is currently challenging. Few data are available on validated scores predictive of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection. The Portuguese Society of Intensive Care (PSIC) proposed a risk score whose main goals were to predict a higher probability of COVID-19 and optimize hospital resources, adjusting patients' intervention. This study aimed to validate the PSIC risk score applied to inpatients with pneumonia. Methods: A retrospective analysis of 207 patients with pneumonia admitted to a suspected/confirmed SARS-CoV-2 infection specialized ward (20/03 to 20/05/2020) was performed. Score variables were analyzed to determine the significance of the independent predictive variables on the probability of a positive SARS-CoV-2 rRT-PCR test. The binary logistic regression modeling approach was selected. The best cutoff value was obtained with the Receiver Operating Characteristic (ROC) curve together with the evaluation of the discriminatory power through the Area Under the Curve (AUC). Results: The validation cohort included 145 patients. Typical chest computed-tomography features (OR, 12.16; 95% CI, 3.32-44.50) and contact with a positive SARS-CoV-2 patient (OR, 6.56; 95% CI, 1.33-32.30) were the most significant independent predictive variables. A score ≥ 10 increased suspicion for SARS-CoV-2 pneumonia. The AUC was 0.82 (95% CI, 0.73-0.91) demonstrating the good discriminating power for COVID-19 probability stratification in inpatients with pneumonia. Conclusions: The application of the PSIC score to inpatients with pneumonia may be of value in predicting the risk of COVID-19. Further studies from other centers are needed to validate this score widely.
Clinical Microbiology and Infection, 2020
Objectives: We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19). Methods: We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: SpO 2 <93% with 100% FiO 2 , respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, b-coefficients were used to develop a risk score. Trial Registration NCT04316949.