scholarly journals A nomogram prediction of pressure injury in critical ill patients: A retrospective cohort study

2021 ◽  
Author(s):  
Wen Xu ◽  
Xueshu Yu ◽  
Hao Jiang ◽  
Yumin Wang ◽  
Yincai Ye
Keyword(s):  
Cohort Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Critical Ill Patients ◽  
Pressure Injury ◽  
Critical Ill

Predictors of pressure injury development in critically ill adults: A retrospective cohort study

2021 ◽  
Vol 62 ◽  
pp. 102924 ◽  
Author(s):  
Jennifer J. Sala ◽  
Anoop Mayampurath ◽  
Susan Solmos ◽  
Susan C. Vonderheid ◽  
Marianne Banas ◽  
...  
Keyword(s):  
Cohort Study ◽  
Critically Ill ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Pressure Injury ◽  
Critically Ill Adults ◽  
Ill Adults

scholarly journals Early predictive factors of progression from severe type to critical ill type in patients with Coronavirus Disease 2019: A retrospective cohort study

2020 ◽  
Author(s):  
Nan Li ◽  
Hao Kong ◽  
Xi-Zi Zheng ◽  
Xue-Ying Li ◽  
Jing Ma ◽  
...  
Keyword(s):  
Creatine Kinase ◽  
Cohort Study ◽  
Critical Illness ◽  
Hospital Admission ◽  
Neutrophil Count ◽  
Retrospective Cohort Study ◽  
Lymphocyte Count ◽  
Retrospective Cohort ◽  
Creatine Kinase Mb ◽  
Critical Ill

Abstract Background: The current worldwide pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health, and the mortality rate of critical ill patients remains high. The purpose of this study was to identify factors that early predict the progression of COVID-19 from severe to critical illness.Methods: This retrospective cohort study included adult patients with severe or critical ill COVID-19 who were consecutively admitted to the Zhongfaxincheng campus of Tongji Hospital (Wuhan, China) from February 8 to 18, 2020. Baseline variables, data at hospital admission and during hospital stay, as well as clinical outcomes were collected from electronic medical records system. The primary endpoint was the development of critical illness. A multivariable logistic regression model was used to identify independent factors that were associated with the progression from severe to critical illness.Results: A total of 138 patients were included in the analysis; of them 119 were diagnosed as severe cases and 16 as critical ill cases at hospital admission. During hospital stay, 19 more severe cases progressed to critical illness. For all enrolled patients, longer duration from diagnosis to admission (odds ratio [OR] 1.108, 95% CI 1.022-1.202; P=0.013), pulse oxygen saturation at admission <93% (OR 5.775, 95% CI 1.257-26.535; P=0.024), higher neutrophil count (OR 1.495, 95% CI 1.177-1.899; P=0.001) and higher creatine kinase-MB level at admission (OR 2.449, 95% CI 1.089-5.511; P=0.030) were associated with a higher risk, whereas higher lymphocyte count at admission (OR 0.149, 95% CI 0.026-0.852; P=0.032) was associated with a lower risk of critical illness development. For the subgroup of severe cases at hospital admission, the above factors except creatine kinase-MB level were also found to have similar correlation with critical illness development.Conclusions: Higher neutrophil count and lower lymphocyte count at admission were early independent predictors of progression to critical illness in severe COVID-19 patients.

scholarly journals Early predictive factors of progression from severe type to critical ill type in patients with Coronavirus Disease 2019: A retrospective cohort study

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243195 ◽  
Author(s):  
Nan Li ◽  
Hao Kong ◽  
Xi-Zi Zheng ◽  
Xue-Ying Li ◽  
Jing Ma ◽  
...  
Keyword(s):  
Creatine Kinase ◽  
Cohort Study ◽  
Critical Illness ◽  
Hospital Admission ◽  
Neutrophil Count ◽  
Retrospective Cohort Study ◽  
Lymphocyte Count ◽  
Retrospective Cohort ◽  
Creatine Kinase Mb ◽  
Critical Ill

Background The current worldwide pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health, and the mortality rate of critical ill patients remains high. The purpose of this study was to identify factors that early predict the progression of COVID-19 from severe to critical illness. Methods This retrospective cohort study included adult patients with severe or critical ill COVID-19 who were consecutively admitted to the Zhongfaxincheng campus of Tongji Hospital (Wuhan, China) from February 8 to 18, 2020. Baseline variables, data at hospital admission and during hospital stay, as well as clinical outcomes were collected from electronic medical records system. The primary endpoint was the development of critical illness. A multivariable logistic regression model was used to identify independent factors that were associated with the progression from severe to critical illness. Results A total of 138 patients were included in the analysis; of them 119 were diagnosed as severe cases and 16 as critical ill cases at hospital admission. During hospital stay, 19 more severe cases progressed to critical illness. For all enrolled patients, longer duration from diagnosis to admission (odds ratio [OR] 1.108, 95% CI 1.022–1.202; P = 0.013), pulse oxygen saturation at admission <93% (OR 5.775, 95% CI 1.257–26.535; P = 0.024), higher neutrophil count (OR 1.495, 95% CI 1.177–1.899; P = 0.001) and higher creatine kinase-MB level at admission (OR 2.449, 95% CI 1.089–5.511; P = 0.030) were associated with a higher risk, whereas higher lymphocyte count at admission (OR 0.149, 95% CI 0.026–0.852; P = 0.032) was associated with a lower risk of critical illness development. For the subgroup of severe cases at hospital admission, the above factors except creatine kinase-MB level were also found to have similar correlation with critical illness development. Conclusions Higher neutrophil count and lower lymphocyte count at admission were early independent predictors of progression to critical illness in severe COVID-19 patients.

scholarly journals Clinical characteristics of COVID-19 and the model for predicting the occurrence of critically ill patients: a retrospective cohort study.

2020 ◽  
Author(s):  
Jing Ouyang ◽  
Xuefeng Shan ◽  
Xin Wang ◽  
Xue Zhang ◽  
Yaling Chen ◽  
...  
Keyword(s):  
Cohort Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Clinical Characteristics ◽  
Feature Fusion ◽  
Medical Center ◽  
Receiver Operating Curve ◽  
Imaging Features ◽  
Laboratory Findings ◽  
Critical Ill

Background: The present study aim to comprehensively report the epidemiological and clinical characteristics of the COVID-19 patients and to develop a multi-feature fusion model for predicting the critical ill probability. Methods: It was a retrospective cohort study that incorporating the laboratory-confirmed COVID-19 patients in the Chongqing Public Health Medical Center. The prediction model was constructed with least absolute shrinkage and selection operator (LASSO) logistic regression method and the model was further tested in the validation cohort. The performance was evaluated by the receiver operating curve (ROC), calibration curve and decision curve analysis (DCA). Results: A total of 217 patients were included in the study. During the treatment, 34 patients were admitted to intensive care unit (ICU) and no developed death. A model incorporating the demographic and clinical characteristics, imaging features and laboratory findings were constructed to predict the critical ill probability and it was proved to have good calibration, discrimination ability and clinic use. Conclusions: The prevalence of critical ill was relatively high and the model may help the clinicians to identify the patients with high risk for developing the critical ill, thus to conduct timely and targeted treatment to reduce the mortality rate.

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