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Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019.

ABSTRACT:

Abstract

Coronavirus disease 2019 (COVID-19) has been a rampant worldwide health threat and we aimed to develop a model for early prediction of disease progression.This retrospective study included 124 adult inpatients with COVID-19 who presented with severe illness at admission and had a definite outcome (recovered or progressed to critical illness) during February 2020. Eighty-four patients were used as training cohort and 40 patients as validation cohort. Logistic regression analysis and receiver operating characteristic curve (ROC) analysis were used to develop and evaluate the prognostic prediction model.In the training cohort, the mean age was 63.4?±?1.5?years, and male patients (48, 57%) were predominant. Forty-three (52%) recovered, and 41 (49%) progressed to critical. Decreased lymphocyte count (LC, odds ratio [OR]?=?4.40, P?=?.026), elevated lactate dehydrogenase levels (LDH, OR?=?4.24, P?=?.030), and high-sensitivity C-reactive protein (hsCRP, OR?=?1.01, P?=?.025) at admission were independently associated with higher odds of deteriorated outcome. Accordingly, we developed a predictive model for disease progression based on the levels of the 3 risk factors (LC, LDH, and hsCRP) with a satisfactory performance in ROC analysis (area under the ROC curve [AUC]?=?0.88, P?

SUBMITTER: Liu L

PROVIDER: S-EPMC7909133 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019.

Liu Li L Dong Lei L Zhang Benping B Chen Xi X Song Xiaoqing X Li Shengzhong S Wei Wang W

Medicine 20210201 8

<h4>Abstract</h4>Coronavirus disease 2019 (COVID-19) has been a rampant worldwide health threat and we aimed to develop a model for early prediction of disease progression.This retrospective study included 124 adult inpatients with COVID-19 who presented with severe illness at admission and had a definite outcome (recovered or progressed to critical illness) during February 2020. Eighty-four patients were used as training cohort and 40 patients as validation cohort. Logistic regression analysis ...[more]

PMID: 33663123

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Project description:BackgroundThe 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.MethodsThis 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.ResultsA 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.ConclusionsHigher neutrophil count and lower lymphocyte count at admission were early independent predictors of progression to critical illness in severe COVID-19 patients.

Project description:BackgroundAvailable data suggest that the obstetric population is particularly vulnerable to severe respiratory syndrome coronavirus 2 infection, with a variable clinical course leading to severe respiratory failure. However, established early warning scores designed to identify patients at risk of clinical deterioration were never validated in the obstetric population.ObjectiveThis retrospective cohort study sought to evaluate the initial clinical characteristics of pregnant patients diagnosed with severe acute respiratory syndrome coronavirus 2 infection and to develop a pregnancy-specific early warning score to identify patients at risk for clinical deterioration and requiring advanced respiratory support.Study designThis was a single center, retrospective cohort study of pregnant patients diagnosed with severe acute respiratory syndrome coronavirus 2 infection between April 2020 and December 2020. A total of 50 patients with severe acute respiratory syndrome coronavirus 2 infection between April 2020 and November 2020 were used to create the prediction model. Initial clinical characteristics identified at the time of diagnosis were compared between patients who required advanced respiratory support and those who were asymptomatic or had mild symptoms for those diagnosed during the period of April 2020 to November 2020. Risk factors associated with a requirement for advanced respiratory support were used to create the Obstetric Warning Score system. The Obstetric Warning Score system was then validated using 30 patients diagnosed with severe acute respiratory syndrome coronavirus 2 infection in December 2020. A receiver operating characteristic curve was generated to evaluate the test characteristics of the Obstetric Warning Score system compared with other scoring systems including the Early Warning Score, the National Early Warning Score 2, and the Maternal Early Warning Criteria.ResultsWomen who required advanced respiratory support were more likely to present with dyspnea (100% vs 33.3%; P<.001), have a higher heart rate (113.4 beats per minute vs 93 beats per minute; P<.001), respiratory rate (23.5 breaths per minute vs 17.7 breaths per minute; P<.001), temperature (99.1°F vs 98.3°F; P=.004), and C-reactive protein level (7.4 mg/dL vs 2.4 mg/dL; P<.001). Furthermore, 88.2% of patients requiring advanced respiratory support showed chest x-ray findings consistent with pneumonia, compared with 20.0% of the patients not requiring advanced respiratory support (P<.001). All patients requiring advanced respiratory support presented with at least 1 coronavirus disease 2019 symptom, whereas only 51.5% of patients not requiring advanced respiratory support were symptomatic (P<.001). The Obstetrical Warning Score model allocated 1 point each for a hazard ratio of >100 beats per minute, temperature of >99.0°F, C-reactive protein level of >2.0 mg/dL, respiratory rate between 20 and 24 breaths per minute, complaints of dyspnea, and a positive chest x-ray. A respiratory rate of >24 breaths per minute was assigned 2 points. The area under the curve for the Obstetric Warning Score system was 0.97 compared with 0.72 for the Early Warning Score system, 0.92 for the National Early Warning Score 2 system, and 0.85 for the Maternal Early Warning Criteria system. An Obstetric Warning Score of ≥3 was predictive of a requirement for advanced respiratory support with a sensitivity of 100%, specificity 64%, and a positive predictive value of 36%.ConclusionThe Obstetric Warning Score system presents a validated method for providers to identify pregnant patients who are at risk for respiratory failure and a requirement for advanced respiratory support.

Project description:Recent studies have highlighted multiple immune perturbations related to severe acute respiratory syndrome coronavirus 2 infection-associated respiratory disease [coronavirus disease 2019 (COVID-19)]. Some of them were associated with immunopathogenesis of severe COVID-19. However, reports on immunological indicators of severe COVID-19 in the early phase of infection in patients with comorbidities such as cancer are scarce. We prospectively studied about 200 immune response parameters, including a comprehensive immune-cell profile, inflammatory cytokines and other parameters, in 95 patients with COVID-19 (37 cancer patients without active disease and intensive chemo/immunotherapy, 58 patients without cancer) and 21 healthy donors. Of 95 patients, 41 had severe disease, and the remaining 54 were categorized as having a nonsevere disease. We evaluated the association of immune response parameters with severe COVID-19. By principal component analysis, three immune signatures defining characteristic immune responses in COVID-19 patients were found. Immune cell perturbations, in particular, decreased levels of circulating dendritic cells (DCs) along with reduced levels of CD4 T-cell subsets such as regulatory T cells (Tregs ), type 1 T helper (Th1) and Th9; additionally, relative expansion of effector natural killer (NK) cells were significantly associated with severe COVID-19. Compared with patients without cancer, the levels of terminal effector CD4 T cells, Tregs , Th9, effector NK cells, B cells, intermediate-type monocytes and myeloid DCs were significantly lower in cancer patients with mild and severe COVID-19. We concluded that severely depleted circulating myeloid DCs and helper T subsets in the initial phase of infection were strongly associated with severe COVID-19 independent of age, type of comorbidity and other parameters. Thus, our study describes the early immune response associated with severe COVID-19 in cancer patients without intensive chemo/immunotherapy.

Dataset Information

Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019.

Abstract

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Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019.

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