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Association of chronic heart failure with mortality in old intensive care patients suffering from Covid-19.

ABSTRACT: Chronic heart failure (CHF) is a major risk factor for mortality in coronavirus disease 2019 (COVID-19). This prospective international multicentre study investigates the role of pre-existing CHF on clinical outcomes of critically ill old (≥70 years) intensive care patients with COVID-19. Patients with pre-existing CHF were subclassified as having ischaemic or non-ischaemic cardiac disease; patients with a documented ejection fraction (EF) were subclassified according to heart failure EF: reduced (HFrEF, n = 132), mild (HFmrEF, n = 91), or preserved (HFpEF, n = 103). Associations of heart failure characteristics with the 30 day mortality were analysed in univariate and multivariate logistic regression analyses. Pre-existing CHF was reported in 566 of 3917 patients (14%). Patients with CHF were older, frailer, and had significantly higher SOFA scores on admission. CHF patients showed significantly higher crude 30 day mortality [60% vs. 48%, P < 0.001; odds ratio 1.87, 95% confidence interval (CI) 1.5-2.3] and 3 month mortality (69% vs. 56%, P < 0.001). After multivariate adjustment for confounders (SOFA, age, sex, and frailty), no independent association of CHF with mortality remained [adjusted odds ratio (aOR) 1.2, 95% CI 0.5-1.5; P = 0.137]. More patients suffered from pre-existing ischaemic than from non-ischaemic disease [233 vs. 328 patients (n = 5 unknown aetiology)]. There were no differences in baseline characteristics between ischaemic and non-ischaemic disease or between HFrEF, HFmrEF, and HFpEF. Crude 30 day mortality was significantly higher in HFrEF compared with HFpEF (64% vs. 48%, P = 0.042). EF as a continuous variable was not independently associated with 30 day mortality (aOR 0.98, 95% CI 0.9-1.0; P = 0.128). In critically ill older COVID-19 patients, pre-existing CHF was not independently associated with 30 day mortality. NCT04321265.

SUBMITTER: Bruno RR

PROVIDER: S-EPMC9065875 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Association of chronic heart failure with mortality in old intensive care patients suffering from Covid-19.

Bruno Raphael Romano RR Wernly Bernhard B Wolff Georg G Fjølner Jesper J Artigas Antonio A Bollen Pinto Bernardo B Schefold Joerg C JC Kindgen-Milles Detlef D Baldia Philipp Heinrich PH Kelm Malte M Beil Michael M Sviri Sigal S van Heerden Peter Vernon PV Szczeklik Wojciech W Topeli Arzu A Elhadi Muhammed M Joannidis Michael M Oeyen Sandra S Kondili Eumorfia E Marsh Brian B Andersen Finn H FH Moreno Rui R Leaver Susannah S Boumendil Ariane A De Lange Dylan W DW Guidet Bertrand B Flaatten Hans H Jung Christian C

ESC heart failure 20220310 3

<h4>Aims</h4>Chronic heart failure (CHF) is a major risk factor for mortality in coronavirus disease 2019 (COVID-19). This prospective international multicentre study investigates the role of pre-existing CHF on clinical outcomes of critically ill old (≥70 years) intensive care patients with COVID-19.<h4>Methods and results</h4>Patients with pre-existing CHF were subclassified as having ischaemic or non-ischaemic cardiac disease; patients with a documented ejection fraction (EF) were subclassifi ...[more]

PMID: 35274490

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Project description:PurposeLactate is an established prognosticator in critical care. However, there still is insufficient evidence about its role in predicting outcome in COVID-19. This is of particular concern in older patients who have been mostly affected during the initial surge in 2020.MethodsThis prospective international observation study (The COVIP study) recruited patients aged 70 years or older (ClinicalTrials.gov ID: NCT04321265) admitted to an intensive care unit (ICU) with COVID-19 disease from March 2020 to February 2021. In addition to serial lactate values (arterial blood gas analysis), we recorded several parameters, including SOFA score, ICU procedures, limitation of care, ICU- and 3-month mortality. A lactate concentration ≥ 2.0 mmol/L on the day of ICU admission (baseline) was defined as abnormal. The primary outcome was ICU-mortality. The secondary outcomes 30-day and 3-month mortality.ResultsIn total, data from 2860 patients were analyzed. In most patients (68%), serum lactate was lower than 2 mmol/L. Elevated baseline serum lactate was associated with significantly higher ICU- and 3-month mortality (53% vs. 43%, and 71% vs. 57%, respectively, p < 0.001). In the multivariable analysis, the maximum lactate concentration on day 1 was independently associated with ICU mortality (aOR 1.06 95% CI 1.02-1.11; p = 0.007), 30-day mortality (aOR 1.07 95% CI 1.02-1.13; p = 0.005) and 3-month mortality (aOR 1.15 95% CI 1.08-1.24; p < 0.001) after adjustment for age, gender, SOFA score, and frailty. In 826 patients with baseline lactate ≥ 2 mmol/L sufficient data to calculate the difference between maximal levels on days 1 and 2 (∆ serum lactate) were available. A decreasing lactate concentration over time was inversely associated with ICU mortality after multivariate adjustment for SOFA score, age, Clinical Frailty Scale, and gender (aOR 0.60 95% CI 0.42-0.85; p = 0.004).ConclusionIn critically ill old intensive care patients suffering from COVID-19, lactate and its kinetics are valuable tools for outcome prediction.Trial registration numberNCT04321265.

Project description:BackgroundThe serum albumin creatinine ratio (sACR) has been established as a potential indicator for heart disease, however, its relationship with prognosis in intensive care unit (ICU) patients with heart failure remains uncertain. This study aimed to investigate the association between sACR levels and all-cause mortality ICU patients with heart failure.MethodsClinical data from MIMIC-Ⅳ database was utilized for the analysis of ICU patients with heart failure. Patients were categorized into quartiles (Q1-Q4) based on sACR levels. Kaplan-Meier survival analysis and multivariate adjusted Cox regression models were employed to assess the association between sACR levels and mortality outcomes within 365 days. Subgroup analysis was used to evaluate the prognostic impact of sACR across diverse populations. Restricted cubic spline curves and threshold effect analysis were utilized to quantify the dose-response relationship between sACR levels and risk of all-cause mortality. Mediating effects analysis was conducted to present the involvement of albumin and creatinine in the association between sACR and outcomes.ResultsThe analysis encompassed a cohort of 4,506 patients, with Kaplan-Meier curves indicating that individuals with lower sACR levels exhibited an elevated risk of all-cause mortality (log-rank p < 0.001). Multivariate adjusted Cox regression and subgroup analysis demonstrated that individuals in Q2 [hazard ratio (HR) 0.82, 95%CI 0.71∼0.96], Q3 (HR 0.76, 95%CI 0.64∼0.91) and Q4 (HR 0.62, 95%CI 0.50∼0.76) had a decreased risk of mortality compared to individuals in Q1 (lower levels of sACR) (p for trend < 0.001), and this inverse relationship was consistently observed across various subgroups. Subsequent restricted cubic spline analysis revealed a negative yet nonlinear relationship between sACR and all-cause mortality (p for nonlinear < 0.001), and threshold effect analysis indicated an effect threshold of 3.75. Additionally, mediating effects analysis emphasized that sACR influenced the outcome not only through serum albumin and creatinine pathways, but also through direct mechanisms.ConclusionThe study found that low levels of sACR were independently associated with an increased risk of one-year all-cause mortality in ICU patients with heart failure, with a threshold effect, which could potentially serve as an early warning indicator for high-risk populations.

Project description:BackgroundThe albumin-bilirubin (ALBI) score has demonstrated prognostic value in a range for liver and heart diseases. However, its association with all-cause mortality in intensive care unit (ICU) patients with heart failure remains uncertain.ObjectiveThis study sought to investigate the relationship between the ALBI score and the risk of all-cause mortality in ICU patients with heart failure.Methods and resultsThe ICU patients diagnosed with heart failure were selected from the Medical Information Mart for Intensive Care IV database (MIMIC-IV, version 2.2) and stratified into tertiles according to their ALBI scores. The primary outcome of interest was the occurrence of all-cause mortality within 365 days post-discharge. The analysis encompassed a cohort of 4,239 patients, with Kaplan-Meier curves indicating that individuals with higher ALBI levels exhibited an elevated risk of all-cause mortality (log-rank p < 0.001). Multivariate adjusted Cox regression and subgroup analysis demonstrated that individuals in T2 (hazard ratio (HR) 1.09, 95% CI 0.99-1.21) and T3 (HR 1.17, 95% CI 1.02-1.34) had an increased risk of mortality compared to individuals in T1 (p for trend < 0.001), and each incremental tertile in ALBI was linked to a 10% rise in mortality risk, while each individual unit increase in ALBI was associated with a 36% increase in mortality risk. This relationship was consistently observed across most subgroups, except for using or not using inotropes or vasopressors, different ages, different creatinine levels. The restricted cubic spline (RCS) analysis indicated a linear relationship between ALBI levels and the risk of all-cause mortality.ConclusionThe ALBI scores are independently associated with the risk of all-cause mortality in ICU patients with heart failure, particularly in those not using inotropes or vasopressors, younger patients, and with lower levels of creatinine. ALBI may help identify high-risk patients and optimize risk stratification in this population.

Project description:AimThe association between red cell distribution width-albumin ratio (RAR) and the risk of all-cause mortality in intensive care unit (ICU) patients with heart failure remains uncertain. This study aimed to investigate this association.MethodsClinical data from MIMIC-Ⅳ (version 2.2) database was utilized for the analysis of ICU patients with heart failure. Patients were categorized into quartiles (Q1-Q4) based on RAR levels. Kaplan-Meier survival analysis and multivariate adjusted Cox regression models were employed to assess the association between RAR levels and mortality outcomes within 1 year. Subgroup analysis was used to evaluate the prognostic impact of RAR across diverse populations. Restricted cubic spline curves and threshold effect analysis were utilized to quantify the dose-response relationship between RAR levels and mortality. The time-concordance index curve was carried out to explore the additional prognostic value of RAR on mortality over the existing scoring systems, Serial Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ).ResultsThe analysis encompassed a cohort of 4,506 patients, with Kaplan-Meier curves indicating that individuals with higher RAR levels exhibited an elevated risk of all-cause mortality (p < 0.001). Multivariate adjusted Cox regression and subgroup analysis demonstrated that individuals in Q2 [hazard ratio (HR) 1.15, 95%CI 0.98-1.34], Q3 (HR 1.65, 95%CI 1.39-1.96) and Q4 (HR 2.16, 95%CI 1.74-2.68) had an increased risk of mortality compared to individuals in Q1 (p for trend < 0.001), and this relationship was consistently observed across most subgroups, except for different ages. Subsequent analysis revealed that the inclusion of RAR significantly improved the prognostic value on the basis of SOFA and APACHE Ⅱ, and the concordance index increased from 0.636 to 0.658 for SOFA, from 0.682 to 0.695 for APACHE Ⅱ (p < 0.001 for both).ConclusionThe study found that high level of RAR was independently associated with an increased risk of 1-year all-cause mortality in ICU patients with heart failure, with a stronger effect in young and middle-aged patients and a threshold effect, which could potentially serve as an early warning indicator for high-risk populations.

Project description:AimsThis study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF).MethodsPatients diagnosed with HF who experienced their first ICU stay lasting between 24 h and 28 days were selected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary outcome was all-cause mortality within 28 days. Data analysis was performed using Python and R, with feature selection conducted via least absolute shrinkage and selection operator (LASSO) regression. Fifteen models were evaluated, and the most effective model was rendered explainable through the Shapley additive explanations (SHAP) approach. A nomogram was developed based on logistic regression to facilitate interpretation. For external validation, the eICU database was utilized.ResultsAfter selection, the study included 2343 records, with 1808 surviving and 535 deceased patients. The median age of the study population was 70.00, with ~3/5 males (60.31%). The median length of stay in the ICU was 6.00 days. The median age of the survival group was younger than the non-survival group (69.00 vs. 73.00), and non-survival patients spent longer time in the ICU. Seventy-five features were initially selected, including basic information, vital signs, laboratory tests, haemodynamics and oxygen status. LASSO regression determined the shrinkage parameter α = 0.020, and 44 features were chosen for model construction. The linear discriminant analysis (LDA) model showed the best performance, and the accuracy reached 0.8354 in the training cohort and 0.8563 in the testing cohort. It showed satisfying area under the curve (AUC), recall, precision, F1 score, Cohen's kappa score and Matthew's correlation coefficient. The concordance index (c-index) reached 0.7972 in the training cohort and 0.8125 in the testing cohort. In external validation, the LDA model achieved approximately 0.9 in accuracy, precision, recall and F1 score, with an AUC of 0.79. Univariable analysis was performed in the training cohort. Features that differed significantly between the survival and non-survival groups were subjected to multiple logistic regression. The nomogram built on multiple logistic regression included 14 features and demonstrated excellent performance. The AUC of the nomogram is 0.852 in the training cohort, 0.855 in the internal validation cohort and 0.770 in the external validation cohort. The calibration curve showed good consistency.ConclusionsThe study developed an LDA and a nomogram model for predicting mortality in HF patients in the ICU. The SHAP approach was employed to elucidate the LDA model, enhancing its utility for clinicians. These models were made accessible online for clinical application.

Dataset Information

Association of chronic heart failure with mortality in old intensive care patients suffering from Covid-19.

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Association of chronic heart failure with mortality in old intensive care patients suffering from Covid-19.

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