Clinical and Laboratory Predictors of In-Hospital Mortality in 305 Patients with COVID-19: A Cohort Study in Wuhan, China

Wang, Kun; Zuo, Peiyuan; Liu, Yuwei; Zhang, Meng; Zhao, Xiaofang; Xie, Songpu; Zhang, Hao; Chen, Xinglin; Liu, Chengyun

doi:10.2139/ssrn.3546115

Cited by 69 publications

(102 citation statements)

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“…Table 1 summarizes the most relevant characteristics of the selected studies. Sixteen studies were peer-reviewed [5,6,[12][13][14][15][16][17][18][19][20][21][22][23][24][25] , nineteen were non-peer-reviewed [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] . Not surprisingly, quality assessment revealed a non-high study quality (Supplemental Table 1).…”

Section: Resultsmentioning

confidence: 99%

Prognostic Impact of Coagulopathy in Patients with COVID-19: a Meta-analysis of 35 Studies and 6427 Patients

Polimeni¹,

Leo²,

Spaccarotella³

et al. 2020

Preprint

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Coronavirus Disease 2019 (COVID-19) is a highly contagious disease that appeared in China in December 2019. Several patients with severe COVID-19 infection can develop a coagulopathy according to the ISTH-criteria for disseminated intravascular coagulopathy (DIC). We conducted a meta-analysis of all available studies on COVID-19 to explore the impact of coagulopathy on severe illness and mortality. An electronic search was performed within PubMed, Google Scholar and Scopus electronic databases. The primary endpoint was the difference of D-dimer values between Non-Severe vs Severe disease and Survivors vs Non-Survivors. The primary analysis showed that mean d-dimer is significantly higher in COVID-19 patients with severe disease than in those without (SMD -2.15 [-2.73 to -1.56], I2 98%, P <0.0001). Additional analysis of platelet count showed lower levels of mean PLT in Severe patients than those observed in the Non-Severe patients (SMD 0.77 [0.32 to 1.22], I2 96%, P <0.001). Interestingly, longer mean PT was found in Severe group (SMD -1.34 [-2.06 to -0.62], I2 98%, P <0.0002) compared to Non-Severe group. In conclusion, the results of the present meta-analysis, the largest and most comprehensive to date, demonstrate that Severe COVID-19 infection is associated with higher D-dimer values, lower platelet count and prolonged PT.

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Section: Resultsmentioning

confidence: 99%

Prognostic Impact of Coagulopathy in Patients with COVID-19: a Meta-analysis of 35 Studies and 6427 Patients

Polimeni¹,

Leo²,

Spaccarotella³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Most patients with COVID-19 have mild disease with a good prognosis, but some patients may develop severe respiratory distress syndrome and have a poor prognosis [19]. To mitigate the burden on the healthcare system and provide the best care for patients, it is necessary to effectively predict the prognosis of the disease [20]. A predictive model that combines multiple variables or features to estimate the risk of poor outcomes of an infected person can assist the healthcare staff in classifying the patient's disease severity when allocating limited medical resources [21].…”

Section: Discussionmentioning

confidence: 99%

“…Chen et al constructed a diagnosis prediction model with 10 clinical factors based on 136 participants [22]. Wang et al enrolled 296 in-hospital patients with COVID-19 and developed a clinical model to predict the mortality of such patients [20]. Dong et al developed a scoring model to predict the progression risk with COVID-19 pneumonia on the basis of 209 patients [23].…”

Section: Discussionmentioning

confidence: 99%

Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

Zhou

Yang

et al. 2020

Preprint

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Background Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).Methods Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyses were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.Results The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

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“…Most COVID-19 cases have mild disease with good prognosis, but some patients may develop severe respiratory distress syndrome and poor prognosis [16]. To mitigate the burden on the healthcare system and provide the best care for patients, it is necessary to effectively predict the prognosis of the disease [17]. A predictive model that combines multiple variables or features to estimate the risk of an infected person's poor outcomes can help healthcare staff classify patients' severity when allocating limited medical resources [18].…”

Section: Discussionmentioning

confidence: 99%

“…Chen et al constructed a diagnosis prediction models with 10 clinical factors based on 136 participants [19]. Wang et al enrolled 296 in-hospital COVID-19 patients and developed a clinical model for predicting the mortality of in-hospital COVID-19 patients [17]. Dong et al developed a scoring model to predict the progression risk with COVID-19 pneumonia base on 209 patients [20].…”

Section: Discussionmentioning

confidence: 99%

Exploiting an Early Warning Nomogram for Predicting the Risk of ICU Admission in COVID-19 patients: A Multi-Center Study in China

Zhou

Yang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Novel corona virus disease 2019 (COVID-19) is an urgent event in the worldwide. We aimed to develop and validate a practical model for early identifying and predicting which patients will be admitted to intensive care unit (ICU) based on a multi-center cohort in China. Methods Data from 1087 patients of laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28 2020 in Sichuan and Wuhan. Patients were randomly divided into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis were employed for the development account. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. The nomogram was further assessed in a different cohort as external validation. Results The individualized prediction nomogram included 6 predictors, including age, respiratory rate, systolic blood pressure, smoking status, fever and chronic kidney disease. The model showed high discrimination ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for prediction the risk of ICU admission. Decision curve analysis showed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission even in community health center. This model can be conveniently used to facilitate predicting the individual risk for ICU admission of COVID-19 patients and optimizing use of limited resources.

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Clinical and Laboratory Predictors of In-Hospital Mortality in 305 Patients with COVID-19: A Cohort Study in Wuhan, China

Cited by 69 publications

References 0 publications

Prognostic Impact of Coagulopathy in Patients with COVID-19: a Meta-analysis of 35 Studies and 6427 Patients

Prognostic Impact of Coagulopathy in Patients with COVID-19: a Meta-analysis of 35 Studies and 6427 Patients

Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

Exploiting an Early Warning Nomogram for Predicting the Risk of ICU Admission in COVID-19 patients: A Multi-Center Study in China

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