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Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China.

ABSTRACT: 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.

SUBMITTER: Chen R

PROVIDER: S-EPMC7158802 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China.

Chen Ruchong R Liang Wenhua W Jiang Mei M Guan Weijie W Zhan Chen C Wang Tao T Tang Chunli C Sang Ling L Liu Jiaxing J Ni Zhengyi Z Hu Yu Y Liu Lei L Shan Hong H Lei Chunliang C Peng Yixiang Y Wei Li L Liu Yong Y Hu Yahua Y Peng Peng P Wang Jianming J Liu Jiyang J Chen Zhong Z Li Gang G Zheng Zhijian Z Qiu Shaoqin S Luo Jie J Ye Changjiang C Zhu Shaoyong S Liu Xiaoqing X Cheng Linling L Ye Feng F Zheng Jinping J Zhang Nuofu N Li Yimin Y He Jianxing J Li Shiyue S Zhong Nanshan N

Chest 20200415 1

<h4>Background</h4>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.<h4>Research question</h4>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.<h4>Study des ...[more]

PMID: 32304772

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Project description:AimsIn patients with coronavirus disease 2019 (COVID-19), the involvement of the cardiovascular system significantly relates to poor prognosis. However, the risk factors for acute myocardial injury have not been sufficiently studied. Thus, we aimed to determine the characteristics of myocardial injury and define the association between routine blood markers and cardiac troponin I, in order to perform a predictive model.Methods and resultsThis retrospective cohort study included patients with confirmed COVID-19 from Wuhan Tongji Hospital (Wuhan, China). Data were compared between those with and without myocardial injury. Kaplan-Meier analysis and Cox regression models were used to describe the association between myocardial injury and poor prognosis. Simple correlation analyses were used to find factors associated with high-sensitivity cardiac troponin I levels. Univariate and multivariate logistic regression methods were used to explore the risk factors associated with myocardial injury. The area under the receiver operating characteristic curve was used to determine the predictive value of the model. Of 353 patients included in the study, 79 presented myocardial injury. Patients with myocardial injury had higher levels of inflammation markers, poorer liver and kidney function, and more complications compared with patients without myocardial injury. High-sensitivity cardiac troponin I levels were significantly associated with neutrophil/lymphocyte ratio, creatinine, d-dimer, lactate dehydrogenase, and inflammatory cytokines and negatively associated with oxygen saturation. It was significantly associated with poor prognosis after adjusting for age, sex, and complications. Multivariate regression showed that myocardial injury was associated with a high neutrophil/lymphocyte ratio (odds ratio 2.30, 95% CI 1.11-4.75, per standard deviation increase, P = 0.02), creatinine (3.58, 1.35-8.06, P = 0.01), and lactate dehydrogenase (3.39, 1.42-8.06, P = 0.01) levels. Using a predictive model, the area under the receiver operating characteristic curve was 0.92 (0.88-0.96).ConclusionsIn patients with COVID-19, neutrophil/lymphocyte ratio, creatinine, and lactate dehydrogenase are blood markers that could help identify patients with a high risk of myocardial injury at an early stage.

Project description:ImportanceThe outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, is serious and has the potential to become an epidemic worldwide. Several studies have described typical clinical manifestations including fever, cough, diarrhea, and fatigue. However, to our knowledge, it has not been reported that patients with COVID-19 had any neurologic manifestations.ObjectiveTo study the neurologic manifestations of patients with COVID-19.Design, setting, and participantsThis is a retrospective, observational case series. Data were collected from January 16, 2020, to February 19, 2020, at 3 designated special care centers for COVID-19 (Main District, West Branch, and Tumor Center) of the Union Hospital of Huazhong University of Science and Technology in Wuhan, China. The study included 214 consecutive hospitalized patients with laboratory-confirmed diagnosis of severe acute respiratory syndrome coronavirus 2 infection.Main outcomes and measuresClinical data were extracted from electronic medical records, and data of all neurologic symptoms were checked by 2 trained neurologists. Neurologic manifestations fell into 3 categories: central nervous system manifestations (dizziness, headache, impaired consciousness, acute cerebrovascular disease, ataxia, and seizure), peripheral nervous system manifestations (taste impairment, smell impairment, vision impairment, and nerve pain), and skeletal muscular injury manifestations.ResultsOf 214 patients (mean [SD] age, 52.7 [15.5] years; 87 men [40.7%]) with COVID-19, 126 patients (58.9%) had nonsevere infection and 88 patients (41.1%) had severe infection according to their respiratory status. Overall, 78 patients (36.4%) had neurologic manifestations. Compared with patients with nonsevere infection, patients with severe infection were older, had more underlying disorders, especially hypertension, and showed fewer typical symptoms of COVID-19, such as fever and cough. Patients with more severe infection had neurologic manifestations, such as acute cerebrovascular diseases (5 [5.7%] vs 1 [0.8%]), impaired consciousness (13 [14.8%] vs 3 [2.4%]), and skeletal muscle injury (17 [19.3%] vs 6 [4.8%]).Conclusions and relevancePatients with COVID-19 commonly have neurologic manifestations. During the epidemic period of COVID-19, when seeing patients with neurologic manifestations, clinicians should suspect severe acute respiratory syndrome coronavirus 2 infection as a differential diagnosis to avoid delayed diagnosis or misdiagnosis and lose the chance to treat and prevent further transmission.

Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic dramatically increased the number of patients requiring treatment in an intensive care unit or invasive mechanical ventilation worldwide. Delirium is a well-known neuropsychiatric complication of patients with acute respiratory diseases, representing the most frequent clinical expression of acute brain dysfunction in critically ill patients, especially in those undergoing invasive mechanical ventilation. Among hospitalized patients with COVID-19, delirium incidence ranges from 11% to 80%, depending on the studied population and hospital setting.ObjectiveTo determine risk factors for the development of delirium in hospitalized patients with COVID-19 pneumonia.MethodsWe retrospectively studied consecutive hospitalized adult (≥18 y) patients with confirmed COVID-19 pneumonia from March 15 to July 15, 2020, in a tertiary-care hospital in Mexico City. Delirium was assessed by the attending physician or trained nurse, with either the Confusion Assessment Method for the Intensive Care Unit or the Confusion Assessment Method brief version, according to the appropriate diagnostic tool for each hospital setting. Consultation-liaison psychiatrists and neurologists confirmed all diagnoses. We calculated adjusted hazard ratios (aHR) with 95% confidence interval (CI) using a Cox proportional-hazards regression model.ResultsWe studied 1017 (64.2% men; median age, 54 y; interquartile range 44-64), of whom 166 (16.3%) developed delirium (hyperactive in 75.3%); 78.9% of our delirium cases were detected in patients under invasive mechanical ventilation. The median of days from admission to diagnosis was 14 (interquartile range 8-21) days. Unadjusted mortality rates between delirium and no delirium groups were similar (23.3% vs. 24.1; risk ratio 0.962, 95% CI 0.70-1.33). Age (aHR 1.02, 95% CI 1.01-1.04; P = 0.006), an initial neutrophil-to-lymphocyte ratio ≥9 (aHR 1.81, 95% CI 1.23-2.65; P = 0.003), and requirement of invasive mechanical ventilation (aHR 3.39, 95% CI 1.47-7.84; P = 0.004) were independent risk factors for in-hospital delirium development.ConclusionsDelirium is a common in-hospital complication of patients with COVID-19 pneumonia, associated with disease severity; given the extensive number of active COVID-19 cases worldwide, it is essential to detect patients who are most likely to develop delirium during hospitalization. Improving its preventive measures may reduce the risk of the long-term cognitive and functional sequelae associated with this neuropsychiatric complication.

Project description:ObjectivesThis study aims to determine similarities and differences in clinical characteristics between the patients from two waves of severe acute respiratory syndrome coronavirus-2 infection at the time of hospital admission, as well as to identify risk biomarkers of coronavirus disease 2019 severity.DesignRetrospective observational study.SettingA single tertiary-care center in Madrid.PatientsCoronavirus disease 2019 adult patients admitted to hospital from March 4, 2020, to March 25, 2020 (first infection wave), and during July 18, 2020, and August 20, 2020 (second infection wave).InterventionsTreatment with a hospital-approved drug cocktail during hospitalization.Measurements and main resultsDemographic, clinical, and laboratory data were compared between the patients with moderate and critical/fatal illness across both infection waves. The median age of patients with critical/fatal coronavirus disease 2019 was 67.5 years (interquartile range, 56.75-78.25 yr; 64.5% male) in the first wave and 59.0 years (interquartile range, 48.25-80.50 yr; 70.8% male) in the second wave. Hypertension and dyslipidemia were major comorbidities in both waves. Body mass index over 25 and presence of bilateral pneumonia were common findings. Univariate logistic regression analyses revealed an association of a number of blood parameters with the subsequent illness progression and severity in both waves. However, some remarkable differences were detected between both waves that prevented an accurate extrapolation of prediction models from the first wave into the second wave. Interleukin-6 and d-dimer concentrations at the time of hospital admission were remarkably higher in patients who developed a critical/fatal condition only during the first wave (p < 0.001), although both parameters significantly increased with disease worsening in follow-up studies from both waves. Multivariate analyses from wave 1 rendered a predictive signature for critical/fatal illness upon hospital admission that comprised six blood biomarkers: neutrophil-to-lymphocyte ratio (≥ 5; odds ratio, 2.684 [95% CI, 1.143-6.308]), C-reactive protein (≥ 15.2 mg/dL; odds ratio, 2.412 [95% CI, 1.006-5.786]), lactate dehydrogenase (≥ 411.96 U/L; odds ratio, 2.875 [95% CI, 1.229-6.726]), interleukin-6 (≥ 78.8 pg/mL; odds ratio, 5.737 [95% CI, 2.432-13.535]), urea (≥ 40 mg/dL; odds ratio, 1.701 [95% CI, 0.737-3.928]), and d-dimer (≥ 713 ng/mL; odds ratio, 1.903 [95% CI, 0.832-4.356]). The predictive accuracy of the signature was 84% and the area under the receiver operating characteristic curve was 0.886. When the signature was validated with data from wave 2, the accuracy was 81% and the area under the receiver operating characteristic curve value was 0.874, albeit most biomarkers lost their independent significance. Follow-up studies reassured the importance of monitoring the biomarkers included in the signature, since dramatic increases in the levels of such biomarkers occurred in critical/fatal patients over disease progression.ConclusionsMost parameters analyzed behaved similarly in the two waves of coronavirus disease 2019. However, univariate logistic regression conducted in both waves revealed differences in some parameters associated with poor prognosis in wave 1 that were not found in wave 2, which may reflect a different disease stage of patients on arrival to hospital. The six-biomarker predictive signature reported here constitutes a helpful tool to classify patient's prognosis on arrival to hospital.

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Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China.

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Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China.

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