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Inflammatory biomarkers as independent prognosticators of 28-day mortality for COVID-19 patients admitted to general medicine or ICU wards: a retrospective cohort study.

ABSTRACT: Inflammatory biomarkers may be associated with disease severity and increased mortality in COVID-19 patients but have not been studied in North American populations. We sought to determine whether a set of commonly ordered inflammatory biomarkers can predict 28-day mortality. We analyzed a multi-centered (four) COVID-19 registry cohort from March 4th to December 7th, 2020. This cohort included COVID-19-positive patients admitted to medical wards or intensive care units. Patients presenting to the emergency department for COVID-19 symptoms and then subsequently discharged were also included. We performed Cox-regression analysis to measure whether commonly used biomarkers were associated with an increased 28-day mortality. Of 336 COVID-19-positive patients, 267 required hospital admission, and 69 were seen in the emergency room and discharged. The median age was 63 years (IQR 80-50) and the female-to-male ratio was 49:51. Derivation of internally validated cut-offs suggested that C-reactive protein???78.4 mg/L, neutrophil-to-lymphocyte ratio???6.1, lymphocyte-to-white blood cell ratio?

SUBMITTER: Pitre T

PROVIDER: S-EPMC7836340 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Inflammatory biomarkers as independent prognosticators of 28-day mortality for COVID-19 patients admitted to general medicine or ICU wards: a retrospective cohort study.

Pitre Tyler T Jones Aaron A Su Johnny J Helmeczi Wryan W Xu Grace G Lee Catherine C Shamsuddin Adib A Mir Adhora A MacGregor Sarah S Duong MyLinh M Ho Terence T Beauchamp Marla K MK Costa Andrew P AP Kruisselbrink Rebecca R

Internal and emergency medicine 20210126 6

Inflammatory biomarkers may be associated with disease severity and increased mortality in COVID-19 patients but have not been studied in North American populations. We sought to determine whether a set of commonly ordered inflammatory biomarkers can predict 28-day mortality. We analyzed a multi-centered (four) COVID-19 registry cohort from March 4th to December 7th, 2020. This cohort included COVID-19-positive patients admitted to medical wards or intensive care units. Patients presenting to th ...[more]

PMID: 33496923

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Project description:Background and aimThe efficacy of Sequential Organ Failure Assessment (SOFA) score as predictor of clinical outcomes among ICU-admitted COVID-19 patients is still controversial. We aimed to assess whether SOFA-score in different time intervals could predict 28-day mortality compared with other well-acknowledged risk factors of COVID-19 mortality.MethodsThis observational prospective cohort was conducted on 1057 patients from March 2020 to March 2022 at Masih Daneshvari Hospital, Iran. The univariate and multivariate Cox proportional analysis were performed to assess the hazards of SOFA-score models. Receiver operating characteristic (ROC) curves were designed to estimate the predictive values.ResultsMean SOFA-score during first 96 h (HR: 3.82 [CI: 2.75-5.31]), highest SOFA-score (HR: 2.70 [CI: 1.93-3.78]), and initial SOFA-score (HR: 1.65 [CI: 1.30-2.11]) had strongest association with 28-day mortality (p < .0001). In contrast, SOFA scores at 48 and 96 h as well as Δ-SOFA: 48-0 h and Δ-SOFA: 96-0 h did not show significant correlations. Among them, merely mean SOFA-score (HR: 2.28 [CI: 2.21-3.51]; p < .001) remained as independent prognosticator on multivariate regression analysis; though having less odds of predicting value compared with age (HR: 3.81 [CI: 1.98-5.21]), hypertension (HR: 3.11 [CI: 1.26-3.81]), coronary artery disease [CAD] (HR: 2.82 [CI: 1.51-4.8]), and diabetes mellitus (HR: 2.45 [CI: 1.36-2.99]). The area under ROC (AUROC) for mean SOFA-score (0.77) and highest SOFA-score (0.71) were larger than other SOFA intervals. Calculating the first 96 h of SOFA trends, it was obtained that fatality rate was <12.3% if the score dropped, between 28.8% and 46.29% if the score remained unchanged, and >50.45% if the score increased.ConclusionTo predict the 28-day mortality among ICU-admitted COVID-19 patients, mean SOFA upon first 96 h of ICU stay is reliable; while having inadequate accuracy comparing with well-acknowledged COVID-19 mortality predictors (age, diabetes mellitus, hypertension, CAD). Notably, increased SOFA levels in the course of first 96 h of ICU-admission, prognosticate at least 50% fatality regardless of initial SOFA score.

Project description:BackgroundAnemia is one of the most common complications of sepsis. Sepsis-related anemia is associated mainly with inflammation. We aimed to observe the changes in the inflammatory anemia-associated parameters of patients with sepsis in the early stage of intensive care unit (ICU) admission and to evaluate their association with 28-day mortality.MethodsA total of 198 patients with sepsis were divided into survivor (n = 110) and non-survivor (n = 88) groups on the basis of 28-day survival. Healthy volunteers (n = 20) were enrolled as a control group. Plasma levels of iron, ferritin, erythropoietin (EPO), soluble transferrin receptor (sTfR), hepcidin, interleukin-6 (IL-6), hemoglobin and the red blood cell distribution width (RDW) were measured on days 1, 3 and 7 of ICU admission. Clinical data and laboratory findings were collected, and the Sequential Organ Failure Assessment (SOFA) score was calculated.ResultsPatients with sepsis showed significant decreases in hemoglobin, plasma iron and sTfR/log ferritin and significant increases in plasma EPO, sTfR, hepcidin, ferritin and IL-6 on days 1, 3 and 7 of ICU admission compared with healthy volunteers. Hemoglobin was correlated negatively with plasma IL-6 and hepcidin. In patients with sepsis, non-survivors had significantly lower plasma iron, EPO and sTfR/log ferritin, but higher plasma hepcidin, ferritin and IL-6 than survivors on days 1, 3 and 7 of ICU admission. Plasma EPO, hepcidin, ferritin, IL-6, sTfR/log ferritin, the RDW and SOFA score were associated significantly with 28-day mortality but to a varying extent. In particular, in predicting 28-day mortality, plasma hepcidin had an area under the receiver operating curve of 0.808 and 87.3% specificity, which was the highest among the inflammatory anemia-associated parameters tested.ConclusionsInflammatory anemia-associated parameters changed significantly in patients with sepsis in the first week of ICU admission. Plasma EPO, hepcidin, ferritin, IL-6, sTfR/log ferritin, the RDW and SOFA score were associated significantly with 28-day mortality. Plasma hepcidin might have a superior predictive value, with high specificity, compared with other inflammatory anemia-associated parameters for 28-day mortality of sepsis patients in the ICU.

Project description:Limited information is available about clinical predictors of in-hospital mortality in acute unselected medical admissions. Such information could assist medical decision-making.To develop a clinical model for predicting in-hospital mortality in unselected acute medical admissions and to test the impact of secondary conditions on hospital mortality.This is an analysis of the medical records of patients admitted to internal medicine wards at one university-affiliated hospital. Data obtained from the years 2013 to 2014 were used as a derivation dataset for creating a prediction model, while data from 2015 was used as a validation dataset to test the performance of the model. For each admission, a set of clinical and epidemiological variables was obtained. The main diagnosis at hospitalization was recorded, and all additional or secondary conditions that coexisted at hospital admission or that developed during hospital stay were considered secondary conditions.The derivation and validation datasets included 7268 and 7843 patients, respectively. The in-hospital mortality rate averaged 7.2%. The following variables entered the final model; age, body mass index, mean arterial pressure on admission, prior admission within 3 months, background morbidity of heart failure and active malignancy, and chronic use of statins and antiplatelet agents. The c-statistic (ROC-AUC) of the prediction model was 80.5% without adjustment for main or secondary conditions, 84.5%, with adjustment for the main diagnosis, and 89.5% with adjustment for the main diagnosis and secondary conditions. The accuracy of the predictive model reached 81% on the validation dataset.A prediction model based on clinical data with adjustment for secondary conditions exhibited a high degree of prediction accuracy. We provide a proof of concept that there is an added value for incorporating secondary conditions while predicting probabilities of in-hospital mortality. Further improvement of the model performance and validation in other cohorts are needed to aid hospitalists in predicting health outcomes.

Project description:ObjectiveSepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients with SIC.MethodsIn this retrospective cohort study, we extracted SIC patients from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU-CRD database according to Toshiaki Iba's scale. And the overlapping in the MIMIC-IV was excluded for this study. Afterward, only the MIMIC-III cohort was randomly divided into the training set, and the internal validation set according to the ratio of 7:3, while the MIMIC-IV and eICU-CRD databases were considered the external validation sets. The predictive factors for 28-day mortality of SIC patients were determined using recursive feature elimination combined with tenfold cross-validation (RFECV). Then, we constructed models using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity, negative predictive value, positive predictive value, recall, and F1 score. Finally, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide a reasonable interpretation for the prediction results.ResultsA total of 3280, 2798, and 1668 SIC patients were screened from MIMIC-III, MIMIC-IV, and eICU-CRD databases, respectively. Seventeen features were selected to construct ML prediction models. XGBoost had the best performance in predicting the 28-day mortality of SIC patients, with AUC of 0.828, 0.913 and 0.923, the AUPRC of 0.807, 0.796 and 0.921, the accuracy of 0.785, 0.885 and 0.891, the F1 scores were 0.63, 0.69 and 0.70 in MIMIC-III (internal validation set), MIMIC-IV, and eICU-CRD databases. The importance ranking and SHAP analyses showed that initial SOFA score, red blood cell distribution width (RDW), and age were the top three critical features in the XGBoost model.ConclusionsWe developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.

Dataset Information

Inflammatory biomarkers as independent prognosticators of 28-day mortality for COVID-19 patients admitted to general medicine or ICU wards: a retrospective cohort study.

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Inflammatory biomarkers as independent prognosticators of 28-day mortality for COVID-19 patients admitted to general medicine or ICU wards: a retrospective cohort study.

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