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10.1093/cid/ciaa414

http://scihub22266oqcxt.onion/10.1093/cid/ciaa414
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32271369!7184473!32271369
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suck abstract from ncbi

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  • Prediction for Progression Risk in Patients With COVID-19 Pneumonia: The CALL Score #MMPMID32271369
  • Ji D; Zhang D; Xu J; Chen Z; Yang T; Zhao P; Chen G; Cheng G; Wang Y; Bi J; Tan L; Lau G; Qin E
  • Clin Infect Dis 2020[Sep]; 71 (6): 1393-1399 PMID32271369show ga
  • BACKGROUND: We aimed to clarify high-risk factors for coronavirus disease 2019 (COVID-19) with multivariate analysis and establish a predictive model of disease progression to help clinicians better choose a therapeutic strategy. METHODS: All consecutive patients with COVID-19 admitted to Fuyang Second People's Hospital or the Fifth Medical Center of Chinese PLA General Hospital between 20 January and 22 February 2020 were enrolled and their clinical data were retrospectively collected. Multivariate Cox regression was used to identify risk factors associated with progression, which were then were incorporated into a nomogram to establish a novel prediction scoring model. ROC was used to assess the performance of the model. RESULTS: Overall, 208 patients were divided into a stable group (n = 168, 80.8%) and a progressive group (n = 40,19.2%) based on whether their conditions worsened during hospitalization. Univariate and multivariate analyses showed that comorbidity, older age, lower lymphocyte count, and higher lactate dehydrogenase at presentation were independent high-risk factors for COVID-19 progression. Incorporating these 4 factors, the nomogram achieved good concordance indexes of .86 (95% confidence interval [CI], .81-.91) and well-fitted calibration curves. A novel scoring model, named as CALL, was established; its area under the ROC was .91 (95% CI, .86-.94). Using a cutoff of 6 points, the positive and negative predictive values were 50.7% (38.9-62.4%) and 98.5% (94.7-99.8%), respectively. CONCLUSIONS: Using the CALL score model, clinicians can improve the therapeutic effect and reduce the mortality of COVID-19 with more accurate and efficient use of medical resources.
  • |*Betacoronavirus[MESH]
  • |*Clinical Decision Rules[MESH]
  • |*Severity of Illness Index[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |China/epidemiology[MESH]
  • |Coronavirus Infections/blood/*diagnosis/mortality[MESH]
  • |Disease Progression[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Lymphocyte Count[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Multivariate Analysis[MESH]
  • |Nomograms[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/blood/*diagnosis/mortality[MESH]
  • |Prognosis[MESH]
  • |Proportional Hazards Models[MESH]
  • |Retrospective Studies[MESH]
  • |Risk Assessment[MESH]
  • |Risk Factors[MESH]


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  • suck abstract from ncbi

    1393 6.71 2020