CIRPMC: An online model with simplified inflammatory signature to predict the occurrence of critical illness in patients with COVID-19.
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ABSTRACT:
SUBMITTER: Gao Y
PROVIDER: S-EPMC7577323 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
CIRPMC: An online model with simplified inflammatory signature to predict the occurrence of critical illness in patients with COVID-19.
Gao Yue Y Chen Lingxi L Zeng Shaoqing S Feng Xikang X Chi JianHua J Wang Ya Y Li Huayi H Jiang Tengping T Yu Yang Y Jiao XiaoFei X Liu Dan D Feng XinXia X Wang SiYuan S Yu RuiDi R Yuan Yuan Y Xu Sen S Cai Guangyao G Xiong Xiaoming X Chen Pingbo P Mo Qingqing Q Jin Xin X Wu Yuan Y Ma Ding D Li Chunrui C Li Shuai Cheng SC Gao Qinglei Q
Clinical and translational medicine 20201001 6
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Project description:BackgroundImmune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission.MethodsWe retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset.ResultsSix features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979-1.000) in internal validation cohort and 0.999 (95% CI 0.998-1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance.ConclusionsThe developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients.Trial registrationThis study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). vv.
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Project description:Pathologic immune hyperactivation is emerging as a key feature of critical illness in COVID-19, but the mechanisms involved remain poorly understood. We carried out proteomic profiling of plasma from cross-sectional and longitudinal cohorts of hospitalized patients with COVID-19 and analyzed clinical data from our health system database of more than 3300 patients. Using a machine learning algorithm, we identified a prominent signature of neutrophil activation, including resistin, lipocalin-2, hepatocyte growth factor, interleukin-8, and granulocyte colony-stimulating factor, which were the strongest predictors of critical illness. Evidence of neutrophil activation was present on the first day of hospitalization in patients who would only later require transfer to the intensive care unit, thus preceding the onset of critical illness and predicting increased mortality. In the health system database, early elevations in developing and mature neutrophil counts also predicted higher mortality rates. Altogether, these data suggest a central role for neutrophil activation in the pathogenesis of severe COVID-19 and identify molecular markers that distinguish patients at risk of future clinical decompensation.
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Project description:ImportanceEarly identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources.ObjectiveTo develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China.Design, setting, and participantsCollaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020.Main outcomes and measuresAmong patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death.ResultsThe development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/).Conclusions and relevanceIn this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.
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Project description:Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77-0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77-0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the "first wave" of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78-0.87]; for full follow-up: 0.82 [95% CI: 0.78-0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.
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Project description:The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges in critical care medicine, including extreme demand for intensive care unit (ICU) resources and rapidly evolving understanding of a novel disease. Up to one-third of hospitalized patients with COVID-19 experience critical illness. The most common form of organ failure in COVID-19 critical illness is acute hypoxemic respiratory failure, which clinically presents as acute respiratory distress syndrome (ARDS) in three-quarters of ICU patients. Noninvasive respiratory support modalities are being used with increasing frequency given their potential to reduce the need for intubation. Determining optimal patient selection for and timing of intubation remains a challenge. Management of mechanically ventilated patients with COVID-19 largely mirrors that of non-COVID-19 ARDS. Organ failure is common and portends a poor prognosis. Mortality rates have improved over the course of the pandemic, likely owing to increasing disease familiarity, data-driven pharmacologics, and improved adherence to evidence-based critical care.
| S-EPMC8795486 | biostudies-literature
Project description: Not available
| S-BSST416 | biostudies-other
Project description:ObjectivesTo evaluate the presence of chronic critical illness (CCI) in COVID-19 patients and compare clinical characteristics and prognosis of patients with and without CCI admitted to intensive care unit (ICU).MethodsIt was a retrospective, observational study at a university hospital ICU. Patients were accepted as CCI if they had prolonged ICU stay (≥14 days) and got ≥1 score for cardiovascular sequential organ failure assessment (SOFA) score and ≥2 score in other parameters on day 14 of ICU admission which was described as persistent organ dysfunction.Results131 of 397 (33%) patients met CCI criteria. CCI patients were older (p = 0.003) and frailer (p < 0.001). Their Acute Physiology and Chronic Health Evaluation (APACHE) II and SOFA scores were higher, PaO2/FiO2 ratio was lower (p < 0.001). Requirement of invasive mechanical ventilation (IMV), steroid use, and septic shock on admission were higher in the CCI group (p < 0.001). CCI patients had higher ICU and hospital mortality than other patients (54.2% vs. 19.9% and 55.7% vs. 22.6%, p < 0.001, respectively). Regression analysis revealed that IMV (OR: 8.40, [5.10-13.83], p < 0.001) and PaO2/FiO2 < 150 on admission (OR: 2.25, [1.36-3.71], p = 0.002) were independent predictors for CCI.DiscussionOne-third of the COVID-19 patients admitted to the ICU were considered as CCI with significantly higher ICU and hospital mortality.
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Project description: Not available
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Project description:ObjectiveRecent cohort studies have identified obesity as a risk factor for poor outcomes in coronavirus disease 2019 (COVID-19). To further explore the relationship between obesity and critical illness in COVID-19, the association of BMI with baseline demographic and intensive care unit (ICU) parameters, laboratory values, and outcomes in a critically ill patient cohort was examined.MethodsIn this retrospective study, the first 277 consecutive patients admitted to Massachusetts General Hospital ICUs with laboratory-confirmed COVID-19 were examined. BMI class, initial ICU laboratory values, physiologic characteristics including gas exchange and ventilatory mechanics, and ICU interventions as clinically available were measured. Mortality, length of ICU admission, and duration of mechanical ventilation were also measured.ResultsThere was no difference found in respiratory system compliance or oxygenation between patients with and without obesity. Patients without obesity had higher initial ferritin and D-dimer levels than patients with obesity. Standard acute respiratory distress syndrome management, including prone ventilation, was equally distributed between BMI groups. There was no difference found in outcomes between BMI groups, including 30- and 60-day mortality and duration of mechanical ventilation.ConclusionsIn this cohort of critically ill patients with COVID-19, obesity was not associated with meaningful differences in respiratory physiology, inflammatory profile, or clinical outcomes.
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