Sex-related differences in patients presenting with heart failure-related cardiogenic shock

BACKGROUND

Heart failure-related cardiogenic shock (HF-CS) accounts for a significant proportion of all CS cases. Nevertheless, there is a lack of evidence on sex-related differences in HF-CS, especially regarding use of treatment and mortality risk in women vs. men. This study aimed to investigate potential differences in clinical presentation, use of treatments, and mortality between women and men with HF-CS.

METHODS

In this international observational study, patients with HF-CS (without acute myocardial infarction) from 16 tertiary-care centers in five countries were enrolled between 2010 and 2021. Logistic and Cox regression models were used to assess differences in clinical presentation, use of treatments, and 30-day mortality in women vs. men with HF-CS.

RESULTS

N = 1030 patients with HF-CS were analyzed, of whom 290 (28.2%) were women. Compared to men, women were more likely to be older, less likely to have a known history of heart failure or cardiovascular risk factors, and lower rates of highly depressed left ventricular ejection fraction and renal dysfunction. Nevertheless, CS severity as well as use of treatments were comparable, and female sex was not independently associated with 30-day mortality (53.0% vs. 50.8%; adjusted HR 0.94, 95% CI 0.75-1.19).

CONCLUSIONS

In this large HF-CS registry, sex disparities in risk factors and clinical presentation were observed. Despite these differences, the use of treatments was comparable, and both sexes exhibited similarly high mortality rates. Further research is necessary to evaluate if sex-tailored treatment, accounting for the differences in cardiovascular risk factors and clinical presentation, might improve outcomes in HF-CS.

Association between left ventricular ejection fraction, mortality and use of mechanical circulatory support in patients with non-ischaemic cardiogenic shock

BACKGROUND

Currently, use of mechanical circulatory support (MCS) in non-ischaemic cardiogenic shock (CS) is predominantly guided by shock-specific markers, and not by markers of cardiac function. We hypothesise that left ventricular ejection fraction (LVEF) can identify patients with a higher likelihood to benefit from MCS and thus help to optimise their expected benefit.

METHODS

Patients with non-ischaemic CS and available data on LVEF from 16 tertiary-care centres in five countries were analysed. Cox regression models were fitted to evaluate the association between LVEF and mortality, as well as the interaction between LVEF, MCS use and mortality.

RESULTS

N = 807 patients were analysed: mean age 63 [interquartile range (IQR) 51.5-72.0] years, 601 (74.5%) male, lactate 4.9 (IQR 2.6-8.5) mmol/l, LVEF 20 (IQR 15-30) %. Lower LVEF was more frequent amongst patients with more severe CS, and MCS was more likely used in patients with lower LVEF. There was no association between LVEF and 30-day mortality risk in the overall study cohort. However, there was a significant interaction between MCS use and LVEF, indicating a lower 30-day mortality risk with MCS use in patients with LVEF ≤ 20% (hazard ratio 0.72, 95% confidence interval 0.51-1.02 for LVEF ≤ 20% vs. hazard ratio 1.31, 95% confidence interval 0.85-2.01 for LVEF > 20%, interaction-p = 0.017).

CONCLUSION

This retrospective study may indicate a lower mortality risk with MCS use only in patients with severely reduced LVEF. This may propose the inclusion of LVEF as an adjunctive parameter for MCS decision-making in non-ischaemic CS, aiming to optimise the benefit-risk ratio.

Clinical presentation, shock severity and mortality in patients with de novo versus acute-on-chronic heart failure-related cardiogenic shock

AIMS

Heart failure-related cardiogenic shock (HF-CS) accounts for a significant proportion of CS cases. Whether patients with de novo HF and those with acute-on-chronic HF in CS differ in clinical characteristics and outcome remains unclear. The aim of this study was to evaluate differences in clinical presentation and mortality between patients with de novo and acute-on-chronic HF-CS.

METHODS AND RESULTS

In this international observational study, patients with HF-CS from 16 tertiary care centres in five countries were enrolled between 2010 and 2021. To investigate differences in clinical presentation and 30-day mortality, adjusted logistic/Cox regression models were fitted. Patients (n = 1030) with HF-CS were analysed, of whom 486 (47.2%) presented with de novo HF-CS and 544 (52.8%) with acute-on-chronic HF-CS. Traditional markers of CS severity (e.g. blood pressure, heart rate and lactate) as well as use of treatments were comparable between groups. However, patients with acute-on-chronic HF-CS were more likely to have a higher CS severity and also a higher mortality risk, after adjusting for relevant confounders (de novo HF 45.5%, acute-on-chronic HF 55.9%, adjusted hazard ratio 1.38, 95% confidence interval 1.10-1.72, p = 0.005).

CONCLUSION

In this large HF-CS cohort, acute-on-chronic HF-CS was associated with more severe CS and higher mortality risk compared to de novo HF-CS, although traditional markers of CS severity and use of treatments were comparable. These findings highlight the vast heterogeneity of patients with HF-CS, emphasize that HF chronicity is a relevant disease modifier in CS, and indicate that future clinical trials should account for this.

Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging

BACKGROUND

Left ventricular ejection fraction (LVEF) measurement in echocardiography (Echo) using the recommended modified biplane Simpson (MBS) method is operator-dependent and exhibits variability. We aimed to assess the accuracy of a novel fully automated (Auto) artificial intelligence (AI) in view selection and biplane LVEF calculation compared to MBS-Echo, with cardiac magnetic resonance imaging (CMR) as reference.

METHODS

Each of the 301 consecutive patients underwent CMR and Echo on the same day. LVEF was measured independently by Auto-Echo, MBS-Echo and CMR. Interobserver (n = 40) and test-retest (n = 14) analysis followed.

RESULTS

A total of 229 patients (76%) underwent complete analysis. Auto-Echo and MBS-Echo showed high correlations with CMR (R = 0.89 and 0.89) and with each other (R = 0.93). Auto underestimated LVEF (bias: 2.2%; limits of agreement [LOA]: -13.5 to 17.9%), while MBS overestimated it (bias: -2.2%; LOA: 18.6 to 14.1%). Despite comparable areas under the curves of Auto- and MBS-Echo (0.93 and 0.92), 46% (n = 70) of MBS-Echo misclassified LVEF by ≥5% units in patients with a reduced CMR-LVEF <51%. Although LVEF bias variability across different LV function ranges was significant (p < 0.001), Auto-Echo was closer to CMR for patients with reduced LVEF, wall motion abnormalities, and poor image quality than MBS-Echo. The interobserver correlation coefficient of Auto-Echo was excellent compared to MBS-Echo (1.00 vs. <0.91) for different readers. True test-retest variability was higher for MBS-Echo than for Auto-Echo (7.9% vs. 2.5%).

CONCLUSION

The tested AI has the potential to improve the clinical utility of Echo by reducing user-related variability, providing more accurate and reliable results than MBS.

Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography

Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, p < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (n = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.

Use of mechanical circulatory support in patients with non-ischaemic cardiogenic shock

AIMS

Despite its high incidence and mortality risk, there is no evidence-based treatment for non-ischaemic cardiogenic shock (CS). The aim of this study was to evaluate the use of mechanical circulatory support (MCS) for non-ischaemic CS treatment.

METHODS AND RESULTS

In this multicentre, international, retrospective study, data from 890 patients with non-ischaemic CS, defined as CS due to severe de-novo or acute-on-chronic heart failure with no need for urgent revascularization, treated with or without active MCS, were collected. The association between active MCS use and the primary endpoint of 30-day mortality was assessed in a 1:1 propensity-matched cohort. MCS was used in 386 (43%) patients. Patients treated with MCS presented with more severe CS (37% vs. 23% deteriorating CS, 30% vs. 25% in extremis CS) and had a lower left ventricular ejection fraction at baseline (21% vs. 25%). After matching, 267 patients treated with MCS were compared with 267 patients treated without MCS. In the matched cohort, MCS use was associated with a lower 30-day mortality (hazard ratio 0.76, 95% confidence interval 0.59-0.97). This finding was consistent through all tested subgroups except when CS severity was considered, indicating risk reduction especially in patients with deteriorating CS. However, complications occurred more frequently in patients with MCS; e.g. severe bleeding (16.5% vs. 6.4%) and access-site related ischaemia (6.7% vs. 0%).

CONCLUSION

In patients with non-ischaemic CS, MCS use was associated with lower 30-day mortality as compared to medical therapy only, but also with more complications. Randomized trials are needed to validate these findings.

The association of prior paracetamol intake with outcome of very old intensive care patients with COVID-19: results from an international prospective multicentre trial

BACKGROUND

In the early COVID-19 pandemic concerns about the correct choice of analgesics in patients with COVID-19 were raised. Little data was available on potential usefulness or harmfulness of prescription free analgesics, such as paracetamol. This international multicentre study addresses that lack of evidence regarding the usefulness or potential harm of paracetamol intake prior to ICU admission in a setting of COVID-19 disease within a large, prospectively enrolled cohort of critically ill and frail intensive care unit (ICU) patients.

METHODS

This prospective international observation study (The COVIP study) recruited ICU patients ≥ 70 years admitted with COVID-19. Data on Sequential Organ Failure Assessment (SOFA) score, prior paracetamol intake within 10 days before admission, ICU therapy, limitations of care and survival during the ICU stay, at 30 days, and 3 months. Paracetamol intake was analysed for associations with ICU-, 30-day- and 3-month-mortality using Kaplan Meier analysis. Furthermore, sensitivity analyses were used to stratify 30-day-mortality in subgroups for patient-specific characteristics using logistic regression.

RESULTS

44% of the 2,646 patients with data recorded regarding paracetamol intake within 10 days prior to ICU admission took paracetamol. There was no difference in age between patients with and without paracetamol intake. Patients taking paracetamol suffered from more co-morbidities, namely diabetes mellitus (43% versus 34%, p < 0.001), arterial hypertension (70% versus 65%, p = 0.006) and had a higher score on Clinical Frailty Scale (CFS; IQR 2-5 versus IQR 2-4, p < 0.001). Patients under prior paracetamol treatment were less often subjected to intubation and vasopressor use, compared to patients without paracetamol intake (65 versus 71%, p < 0.001; 63 versus 69%, p = 0.007). Paracetamol intake was not associated with ICU-, 30-day- and 3-month-mortality, remaining true after multivariate adjusted analysis.

CONCLUSION

Paracetamol intake prior to ICU admission was not associated with short-term and 3-month mortality in old, critically ill intensive care patients suffering from COVID-19.

TRIAL REGISTRATION

This prospective international multicentre study was registered on ClinicalTrials.gov with the identifier "NCT04321265" on March 25, 2020.

The automatic identification of left ventricular chambers and quantification of ejection fraction using a novel artificial intelligence-based system – a validation against cardiac magnetic resonance

Background/Introduction

Cardiac magnetic resonance imaging (CMR) is regarded as the reference method in assessing left ventricular (LV) ejection fraction (EF). However, 2-dimensional echocardiography (2D-Echo) is the most frequently used technique due to availability and practicability. The interpretation of 2D-Echo examinations depends on the user's expertise and may vary between different operators. A novel vendor-independent software based on artificial intelligence (AI) performs both, automated evaluation of 2D-Echo exams and calculations of LV EF in one workflow.

Purpose

We sought to assess the ability of the AI to automatically identify appropriate LV 4- and 2-chamber views (4CV) (2CV) from 2D-Echo exams and validate the resulting EF with CMR.

Methods

We consecutively enrolled 128 patients who underwent clinically indicated CMR examinations and performed a standard 2D-Echo at the same day. The server-based AI solution recognized the optimal LV 4CV and 2CV from 2D-Echo according to quality and depth criteria and automatically performed calculation of biplane EF by endocardial borderline detection. LV EF from CMR and AI were supervised by independent cardiologists blinded to the mutual results. Pearson's correlation (R) and Bland-Altman analysis with limits of agreement (LOA) were performed in order to assess bias between the two methods. Significance was defined as a 2-tailed P value &lt;0.05.

Results

CMR was performed and LV EF was measured in all 128 patients. The median age was 60 years [20–86], 65% were males and CMR was performed due to coronary artery diseases (33%), suspected/florid myocarditis (20%) or further diagnosis of non-ischemic heart failure (47%). Eleven cases (9%) did not pass AI's criteria due to impaired acoustic window or poor 2D-Echo images. The AI system detected either 4CV or 2CV (ratio 1.2) in 13 patients (10%), and both 4CV and 2CV in 104 patients (81% overall feasibility) with a correct classification of 100%. For these 104 patients, excellent correlation was found for AI's biplane LV EF and LV EF from CMR with r=0.91 (p&lt;0.001) (Figure 1, left). However, the absolute mean bias between AI and CMR was 3.5% (p&lt;0.001) and LOAs were −10.6 and +17.5% (Figure 1, right).

Conclusion

The results provided by the AI-based software showed good capabilities and a perfect classification rate to identify 4CV and 2CV. In addition, the LV EF results were excellent compared to CMR, especially since our study did not include “echocardiographically” pre-selected patients. However, differences between AI and CMR measurements are not negligible and warrant further investigation.

Funding Acknowledgement

Type of funding sources: None.

Left ventricular ejection fraction – human vs artificial intelligence: quo vadis?

Background/Introduction

Two-dimensional echocardiography (Echo) is a feasible method for assessing left ventricular (LV) ejection fraction (EF) in daily practice. However, the interpretation of Echo exams depends on the user's expertise and may vary between different operators. A novel, vendor-neutral artificial intelligence (AI) performs both, automated evaluation of Echo exams and calculations of biplane LV EF in one workflow.

Purpose

We sought to assess the ability of the AI to automatically identify appropriate LV 4- and 2-chamber views (4CV) (2CV) from routine Echo examinations and compare the resulting biplane EF with conventional hand-tracing biplane Simpson method (Human).

Methods

We prospectively enrolled 311 patients who underwent clinically indicated Echo exams. Biplane LV EF was manually traced online on 4CV and 2CV by cardiologists (Human). After completion of the exam, the AI-based solution recognized the optimal LV 4CV and 2CV according to quality and depth criteria and automatically performed the calculation of biplane EF by endocardial borderline detection without Human's interaction. Spearman's correlation (R) and Bland-Altman analysis with limits of agreement (LOA) were assessed for bias between the two methods. In a subgroup of 20 patients, Echo exams were automatically reanalyzed by the AI, and conventional biplane Simpson of LV EF was performed by two cardiologists blinded to the previous results to determine intraclass correlation (ICC). Significance was defined as a 2-tailed p value &lt;0.05.

Results

311 patients (median age 72 years [19–97]; 40% female) received an Echo for valvular heart disease, ischemic and non-ischemic heart failure or other indications (39, 31, 19 and 11%). 16 cases (5%) did not pass AI's criteria due to poor Echo imaging or impaired acoustic window of patients. In 53 patients (17%) either 4CV or 2CV were recognized, but the AI system successfully identified both 4CV and 2CV in 242 patients (overall feasibility 78%). For these 242 patients, correlation between AI and Human biplane LV EF was r=0.83 (p&lt;0.001) (Figure 1 left). The absolute mean bias between methods was 5.2% (p&lt;0.001) and absolute LOA ranged from −9.0 to +19.4% (Figure 1 right). ICC of LV EF by Human was 0.77 (p&lt;0.001). The AI's ability to correctly re-/classify 4CV and 2CV was 100% with an ICC of 1 for fully automated LV EF measurements.

Conclusion

The results provided by the AI-based software showed very good capability to identify 4CV and 2CV and good LV EF result compared to Human manual tracings, especially since patients were not pre-selected. However, differences between AI and Human measurements are not negligible and warrant further investigations.

Funding Acknowledgement

Type of funding sources: None.

Noninvasive ventilation in COVID-19 patients aged ≥ 70 years-a prospective multicentre cohort study

BACKGROUND

Noninvasive ventilation (NIV) is a promising alternative to invasive mechanical ventilation (IMV) with a particular importance amidst the shortage of intensive care unit (ICU) beds during the COVID-19 pandemic. We aimed to evaluate the use of NIV in Europe and factors associated with outcomes of patients treated with NIV.

METHODS

This is a substudy of COVIP study-an international prospective observational study enrolling patients aged ≥ 70 years with confirmed COVID-19 treated in ICU. We enrolled patients in 156 ICUs across 15 European countries between March 2020 and April 2021.The primary endpoint was 30-day mortality.

RESULTS

Cohort included 3074 patients, most of whom were male (2197/3074, 71.4%) at the mean age of 75.7 years (SD 4.6). NIV frequency was 25.7% and varied from 1.1 to 62.0% between participating countries. Primary NIV failure, defined as need for endotracheal intubation or death within 30 days since ICU admission, occurred in 470/629 (74.7%) of patients. Factors associated with increased NIV failure risk were higher Sequential Organ Failure Assessment (SOFA) score (OR 3.73, 95% CI 2.36-5.90) and Clinical Frailty Scale (CFS) on admission (OR 1.46, 95% CI 1.06-2.00). Patients initially treated with NIV (n = 630) lived for 1.36 fewer days (95% CI - 2.27 to - 0.46 days) compared to primary IMV group (n = 1876).

CONCLUSIONS

Frequency of NIV use varies across European countries. Higher severity of illness and more severe frailty were associated with a risk of NIV failure among critically ill older adults with COVID-19. Primary IMV was associated with better outcomes than primary NIV. Clinical Trial Registration NCT04321265 , registered 19 March 2020, https://clinicaltrials.gov .