Dataset Information

A risk score based on baseline risk factors for predicting mortality in COVID-19 patients.

ABSTRACT:

Background

To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources.

Methods

6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses.

Results

Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93-23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts.

Conclusions

The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources.

SUBMITTER: Chen Z

PROVIDER: S-EPMC8054492 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundEarly and accurate prognosis prediction of the patients was urgently warranted due to the widespread popularity of COVID-19. We performed a meta-analysis aimed at comprehensively summarizing the clinical characteristics and laboratory abnormalities correlated with increased risk of mortality in COVID-19 patients.MethodsPubMed, Scopus, Web of Science, and Embase were systematically searched for studies considering the relationship between COVID-19 and mortality up to 4 June 2020. Data were extracted including clinical characteristics and laboratory examination.ResultsThirty-one studies involving 9407 COVID-19 patients were included. Dyspnea (OR = 4.52, 95%CI [3.15, 6.48], P < 0.001), chest tightness (OR = 2.50, 95%CI [1.78, 3.52], P<0.001), hemoptysis (OR = 2.00, 95%CI [1.02, 3.93], P = 0.045), expectoration (OR = 1.52, 95%CI [1.17, 1.97], P = 0.002) and fatigue (OR = 1.27, 95%CI [1.09, 1.48], P = 0.003) were significantly related to increased risk of mortality in COVID-19 patients. Furthermore, increased pretreatment absolute leukocyte count (OR = 11.11, 95%CI [6.85,18.03], P<0.001) and decreased pretreatment absolute lymphocyte count (OR = 9.83, 95%CI [6.72, 14.38], P<0.001) were also associated with increased mortality of COVID-19. We also compared the mean value of them between survivors and non-survivors, and found that non-survivors showed significantly raise in pretreatment absolute leukocyte count (WMD: 3.27×109/L, 95%CI [2.34, 4.21], P<0.001) and reduction in pretreatment absolute lymphocyte count (WMD = -0.39×109/L, 95%CI [-0.46, -0.33], P<0.001) compared with survivors. The results of pretreatment lactate dehydrogenase (LDH), procalcitonin (PCT), D-Dimer and ferritin showed the similar trend with pretreatment absolute leukocyte count.ConclusionsAmong the common symptoms of COVID-19 infections, fatigue, expectoration, hemoptysis, dyspnea and chest tightness were independent predictors of death. As for laboratory examinations, significantly increased pretreatment absolute leukocytosis count, LDH, PCT, D-Dimer and ferritin, and decreased pretreatment absolute lymphocyte count were found in non-survivors, which also have an unbeneficial impact on mortality among COVID-19 patients. Motoring these indicators during the hospitalization plays a very important role in predicting the prognosis of patients.

Project description:ObjectivesPredictive algorithms to inform risk management decisions are needed for patients with COVID-19, although the traditional risk scores have not been adequately assessed in Asian patients. We aimed to evaluate the performance of a COVID-19-specific prediction model, the 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score, along with other conventional critical care risk models in Japanese nationwide registry data.DesignRetrospective cohort study.Setting and participantsHospitalised patients with COVID-19 and cardiovascular disease or coronary risk factors from January to May 2020 in 49 hospitals in Japan.Main outcome measuresTwo different types of outcomes, in-hospital mortality and a composite outcome, defined as the need for invasive mechanical ventilation and mortality.ResultsThe risk scores for 693 patients were tested by predicting in-hospital mortality for all patients and composite endpoint among those not intubated at baseline (n=659). The number of events was 108 (15.6%) for mortality and 178 (27.0%) for composite endpoints. After missing values were multiply imputed, the performance of the 4C Mortality Score was assessed and compared with three prediction models that have shown good discriminatory ability (RISE UP score, A-DROP score and the Rapid Emergency Medicine Score (REMS)). The area under the receiver operating characteristic curve (AUC) for the 4C Mortality Score was 0.84 (95% CI 0.80 to 0.88) for in-hospital mortality and 0.78 (95% CI 0.74 to 0.81) for the composite endpoint. It showed greater discriminatory ability compared with other scores, except for the RISE UP score, for predicting in-hospital mortality (AUC: 0.82, 95% CI 0.78 to 0.86). Similarly, the 4C Mortality Score showed a positive net reclassification improvement index over the A-DROP and REMS for mortality and over all three scores for the composite endpoint. The 4C Mortality Score model showed good calibration, regardless of outcome.ConclusionsThe 4C Mortality Score performed well in an independent external COVID-19 cohort and may enable appropriate disposition of patients and allocation of medical resources.Trial registration number UMIN000040598.