Clinical diagnosis of severe COVID-19:A derivation and validation of a prediction rule

BACKGROUND The widespread coronavirus disease 2019(COVID-19)has led to high morbidity and mortality.Therefore,early risk identification of critically ill patients remains crucial.AIM To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit(ICU)care.METHODS This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19,2020,and March 14,2020 in Shenzhen Third People’s Hospital.Multivariate logistic regression was applied to develop the predictive model.The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020,by area under the receiver operating curve(AUROC),goodness-of-fit and the performance matrix including the sensitivity,specificity,and precision.A nomogram was also used to visualize the model.RESULTS Among the patients in the derivation and validation datasets,38 and 9 participants(10.5%and 2.54%,respectively)developed severe COVID-19,respectively.In univariate analysis,21 parameters such as age,sex(male),smoker,body mass index(BMI),time from onset to admission(>5 d),asthenia,dry cough,expectoration,shortness of breath,asthenia,and Rox index<18(pulse oxygen saturation,SpO2)/(FiO2×respiratory rate,RR)showed positive correlations with severe COVID-19.In multivariate logistic regression analysis,only six parameters including BMI[odds ratio(OR)3.939;95%confidence interval(CI):1.409-11.015;P=0.009],time from onset to admission(≥5 d)(OR 7.107;95%CI:1.449-34.849;P=0.016),fever(OR 6.794;95%CI:1.401-32.951;P=0.017),Charlson index(OR 2.917;95%CI:1.279-6.654;P=0.011),PaO2/FiO2 ratio(OR 17.570;95%CI:1.117-276.383;P=0.041),and neutrophil/lymphocyte ratio(OR 3.574;95%CI:1.048-12.191;P=0.042)were found to be independent predictors of COVID-19.These factors were found to be significant risk factors for severe patients confirmed with COVID-19.The AUROC was 0.941(95%CI:0.901-0.981)and 0.936(95%CI:0.886-0.987)in both datasets.The calibration properties were good.CONCLUSION The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU.It assisted the ICU clinicians in making timely decisions for the target population.

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

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.

Clinical features of 162 fatal cases of COVID-19:a multi-center retrospective study

Background:The coronavirus disease 2019(COVID-19)has affected approximately 2 million individuals worldwide;however,data regarding fatal cases have been limited.Objective:To report the clinical features of 162 fatal cases of COVID-19 from 5 hospitals in Wuhan between December 30,2019 and March 12,2020.Methods:The demographic data,signs and symptoms,clinical course,comorbidities,laboratory findings,computed tomographic(CT)scans,treatments,and complications of the patients with fatal cases were retrieved from electronic medical records.Results:The median patient age was 69.5(interquartile range:63.0–77.25)years,and 80%of the patients were over 61 years.A total of 112(69.1%)patients were men.Hypertension(45.1%)was the most common comorbidity,while 59(36.4%)patients had no comorbidity.At admission,131(81.9%)patients had severe or critical COVID-19,whereas 39(18.1%)patients with hypertension or chronic lung disease had moderate COVID-19.In total,126(77.8%)patients received antiviral treatment,while 132(81.5%)patients received glucocorticoid treatment.A total of 116(71.6%)patients were admitted to the intensive care unit(ICU),and 137(85.1%)patients received mechanical ventilation.Most patients received mechanical ventilation before ICU admission.Approximately 93.2%of the patients developed respiratory failure or acute respiratory distress syndrome.There were no significant differences in the inhospital survival time among the hospitals(P=0.14).Conclusion:Young patients with moderate COVID-19 without comorbidity at admission could also develop fatal outcomes.The in-hospital survival time of the fatal cases was similar among the hospitals of different levels in Wuhan.