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  • Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China #MMPMID32304772
  • Chen R; Liang W; Jiang M; Guan W; Zhan C; Wang T; Tang C; Sang L; Liu J; Ni Z; Hu Y; Liu L; Shan H; Lei C; Peng Y; Wei L; Liu Y; Hu Y; Peng P; Wang J; Liu J; Chen Z; Li G; Zheng Z; Qiu S; Luo J; Ye C; Zhu S; Liu X; Cheng L; Ye F; Zheng J; Zhang N; Li Y; He J; Li S; Zhong N
  • Chest 2020[Jul]; 158 (1): 97-105 PMID32304772show ga
  • BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has become a global health emergency. The cumulative number of new confirmed cases and deaths are still increasing out of China. Independent predicted factors associated with fatal outcomes remain uncertain. RESEARCH QUESTION: The goal of the current study was to investigate the potential risk factors associated with fatal outcomes from COVID-19 through a multivariate Cox regression analysis and a nomogram model. STUDY DESIGN AND METHODS: A retrospective cohort of 1,590 hospitalized patients with COVID-19 throughout China was established. The prognostic effects of variables, including clinical features and laboratory findings, were analyzed by using Kaplan-Meier methods and a Cox proportional hazards model. A prognostic nomogram was formulated to predict the survival of patients with COVID-19. RESULTS: In this nationwide cohort, nonsurvivors included a higher incidence of elderly people and subjects with coexisting chronic illness, dyspnea, and laboratory abnormalities on admission compared with survivors. Multivariate Cox regression analysis showed that age >/= 75 years (hazard ratio [HR], 7.86; 95% CI, 2.44-25.35), age between 65 and 74 years (HR, 3.43; 95% CI, 1.24-9.5), coronary heart disease (HR, 4.28; 95% CI, 1.14-16.13), cerebrovascular disease (HR, 3.1; 95% CI, 1.07-8.94), dyspnea (HR, 3.96; 95% CI, 1.42-11), procalcitonin level > 0.5 ng/mL (HR, 8.72; 95% CI, 3.42-22.28), and aspartate aminotransferase level > 40 U/L (HR, 2.2; 95% CI, 1.1-6.73) were independent risk factors associated with fatal outcome. A nomogram was established based on the results of multivariate analysis. The internal bootstrap resampling approach suggested the nomogram has sufficient discriminatory power with a C-index of 0.91 (95% CI, 0.85-0.97). The calibration plots also showed good consistency between the prediction and the observation. INTERPRETATION: The proposed nomogram accurately predicted clinical outcomes of patients with COVID-19 based on individual characteristics. Earlier identification, more intensive surveillance, and appropriate therapy should be considered in patients at high risk.
  • |*Coronavirus Infections/blood/diagnosis/mortality/physiopathology[MESH]
  • |*Dyspnea/epidemiology/etiology[MESH]
  • |*Pandemics[MESH]
  • |*Pneumonia, Viral/blood/diagnosis/mortality/physiopathology[MESH]
  • |Aged[MESH]
  • |Aspartate Aminotransferases/*blood[MESH]
  • |Betacoronavirus/isolation & purification[MESH]
  • |COVID-19[MESH]
  • |Cardiovascular Diseases/*epidemiology[MESH]
  • |China/epidemiology[MESH]
  • |Correlation of Data[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Male[MESH]
  • |Nomograms[MESH]
  • |Procalcitonin/*blood[MESH]
  • |Prognosis[MESH]
  • |Risk Assessment/methods[MESH]
  • |Risk Factors[MESH]
  • |SARS-CoV-2[MESH]
  • |Survival Analysis[MESH]

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  • suck abstract from ncbi

    97 1.158 2020