Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China

Hu, Ling; Chen, Shaoqiu; Fu, Yuanyuan; Gao, Zhi‐Hui; Long, Hui; Ren, Hanyun; Zuo, Yi; Wang, Jie; Li, Huan; Xu, Qingbang; Yu, Wen-xiong; Liu, Jia; Shao, Chen; Hao, Junjie; Wang, Chuan-Zhen; Ma, Yao; Wang, Zhanwei; Yanagihara, Richard; Deng, Youping

doi:10.1093/cid/ciaa539

Cited by 320 publications

(252 citation statements)

References 36 publications

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“…Previous studies found that advanced age and presence of comorbidity (e.g., cardiovascular disease or hypertension) were risk factors associated with an adverse outcome such as admission to intensive care unit (ICU), need for mechanical ventilation, or death (7,8). In addition, some laboratory indicators e.g., elevated hypersensitive troponin I, leukocytosis, neutrophilia, lymphopenia, and elevated D-dimer were found to be linked with unfavorable clinical outcomes (7)(8)(9). Presence of consolidation on computed tomography (CT) was also considered to be predictive of poor outcome in COVID-19 (10).…”

Section: Introductionmentioning

confidence: 99%

A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia

Jin

Tian

Wang

et al. 2020

Front. Public Health

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Background: As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival. This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia. Methods: One hundred and sixty-five patients with COVID-19 (91 men, 4-89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pattern 0 (negative), Pattern 1 (bronchopneumonia pattern), Pattern 2 (organizing pneumonia pattern), Pattern 3 (progressive organizing pneumonia pattern), and Pattern 4 (diffuse alveolar damage pattern). Clinical findings were compared across different categories. Time-dependent progression of CT patterns and correlations with clinical outcomes, i.e." discharge or adverse outcome (admission to ICU, requiring mechanical ventilation, or death), with pulmonary sequelae (complete absorption or residuals) on CT after discharge were analyzed. Results: Of 94 patients with outcome, 81 (86.2%) were discharged, 3 (3.2%) were admitted to ICU, 4 (4.3%) required mechanical ventilation, 6 (6.4%) died. 31 (38.3%) had complete absorption at median day 37 after symptom onset. Significant differences between pattern-categories were found in age, disease severity, comorbidity and laboratory results (all P < 0.05). Remarkable evolution was observed in Pattern 0-2 and Pattern 3-4 within 3 and 2 weeks after symptom-onset, respectively; most of patterns remained thereafter. After controlling for age, CT pattern significantly correlated with adverse outcomes [Pattern 4 vs. Pattern 0-3 [reference]; hazard-ratio [95% CI], 18.90 [1.91-186.60], P = 0.012]. CT pattern [Pattern 3-4 vs. Pattern 0-2 [reference]; 0.26 [0.08-0.88], P = 0.030] and C-reactive protein [>10 vs. ≤10 mg/L [reference]; 0.31 [0.13-0.72], P = 0.006] were risk factors associated with pulmonary residuals. Jin et al. CT Pattern Categorization of COVID-19 Conclusion: CT pattern categorization allied with clinical characteristics within 2 weeks after symptom onset would facilitate early prognostic stratification in COVID-19 pneumonia.

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Section: Introductionmentioning

confidence: 99%

A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia

Jin

Tian

Wang

et al. 2020

Front. Public Health

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“…Hence, in our study, we analyzed the effect and revealed that COVID-19 patients with persistent negative PCR results tended to have disease progression. This might be explained by older age, accompanying hypertension, more severe status upon admission and elevated CRP, NLR, IL-6, D-dimer, or troponin in these patients to some extent, since other studies previously reported effects of these factors on disease progression [32][33][34]. However, after excluding the effect of age, only serum levels of IL-6 and D-dimer were still associated with disease progression, implying that more severe cytokine reactions and microvascular thromboembolism might be the cause of progression in these patients.…”

Section: Discussionmentioning

confidence: 94%

More severe hypercoagulation status, cytokine storm, and disease progression in coronavirus disease 2019 with persistent RT-PCR negative results: a multicenter prospective study

Shi

Zhou

et al. 2020

Preprint

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Purpose: Persistent negative results (at least 3 times) of reverse transcription–polymerase chain reaction (RT-PCR) from pharyngeal swabs are not rare in coronavirus disease 2019 (COVID-19) patients, but their characteristics have not yet been well studied.Methods: PCR confirmed, serum antibody confirmed with persistent negative PCR results, and clinically diagnosed patients hospitalized in two medical centers during February and March 2020 were included. Differences in clinical, imaging and laboratory characteristics as well as factors affecting their prognosis were analyzed.Results: There were 114 PCR confirmed, 17 serology confirmed and 21 clinically diagnosed patients included. Time from onset of disease to the first PCR and admission were similar among the groups. Compared with PCR-confirmed patients, serology-confirmed patients were older and likely to have hypertension, vomiting, or symptoms of chest pain and dyspnea. Regarding imaging manifestations, serology-confirmed patients were more prone to pleural effusion. In addition, higher levels of C-reactive protein, neutrophil-to-lymphocyte ratio, total bilirubin, D-dimer, fibrinogen, troponin, interleukin-6 and IL-8 were also found. Although with similar mortality, serology confirmed patients were more likely to have disease progression. High levels of D-dimer and IL-6 were possibly the underlying factors leading to their worse prognosis. On the other hand, clinically diagnosed patients were more similar to PCR-confirmed patients.Conclusion: Serology confirmed COVID-19 patients with at least three negative PCR results had different clinical characteristics and were likely to have disease progression, possibly due to more severe hypercoagulation status and cytokine storm.

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“…Its predictive role is even more relevant considering the low prevalence of obesity in elderly, in our region (9.8% in Piedmont vs. 14.0% in Italy) (22). The real impact of smoking is more debated and less de ned; while some authors advocate its detrimental impact on the patient's prognosis (23), others conversely postulate a protective effect deriving from the down-regulation of Angiotensin Converting Enzyme-2 (ACE-2) expression in lungs (24). According to our data, current smokers are at increased risk of mortality, although we should acknolwedge that this information, as well as details about history of smoking, could not be retrieved for around one-hundred patients, making it challenging to accurately evaluate the effect of this risk factor.…”

Section: Discussionmentioning

confidence: 99%

Fatality rate and predictors of mortality in a large Italian cohort of hospitalized COVID-19 patients

Team

Sainaghi

2020

Preprint

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Substantial discrepancies are evident in the clinical features and natural history of coronavirus disease 2019 (COVID-19), among different countries, so we aimed to evaluate the fatality rate and to identify predictors of mortality in a cohort of hospitalized patients. We performed a retrospective multicenter cohort study on medical records of patients admitted between 1st March and 28th April 2020, involving three hospitals in Northern Italy. We included 1697 patients older than 18 years of age and with a confirmed diagnosis of SARS-CoV-2 infection by reverse-transcriptase polymerase chain reaction (RT-PCR). During the study period we observed 504 deaths, with a CFR of 29.7%. We further looked for predictors of mortality in a subset of 486 patients (239 males, 59%); the median age of the study population was 71 [58-80] years. Among demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were independent predictors of mortality. During the COVID-19 outbreak, the CFR of hospitalized patients in Northern Italy was high, approaching 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.

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Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China

Cited by 320 publications

References 36 publications

A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia

A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia

More severe hypercoagulation status, cytokine storm, and disease progression in coronavirus disease 2019 with persistent RT-PCR negative results: a multicenter prospective study

Fatality rate and predictors of mortality in a large Italian cohort of hospitalized COVID-19 patients

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