Hemodynamic management of cardiogenic shock in the intensive care unit

Hemodynamic derangements are defining features of cardiogenic shock. Randomized clinical trials have examined the efficacy of various therapeutic interventions, from percutaneous coronary intervention to inotropes and mechanical circulatory support (MCS). However, hemodynamic management in cardiogenic shock has not been well-studied. This State-of-the-Art review will provide a framework for hemodynamic management in cardiogenic shock, including a description of the 4 therapeutic phases from initial 'Rescue' to 'Optimization', 'Stabilization' and 'de-Escalation or Exit therapy' (RO-S-E), phenotyping and phenotype-guided tailoring of pharmacological and MCS support, to achieve hemodynamic and therapeutic goals. Finally, the premises that form the basis for clinical management and the hypotheses for randomized controlled trials will be discussed, with a view to the future direction of cardiogenic shock. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of International Society for Heart and Lung Transplantation. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/)

Complete Hemodynamic Profiling With Pulmonary Artery Catheters in Cardiogenic Shock Is Associated With Lower In-Hospital Mortality

OBJECTIVES: The purpose of this study was to investigate the association between obtaining hemodynamic data from early pulmonary artery catheter (PAC) placement and outcomes in cardiogenic shock (CS). BACKGROUND: Although PACs are used to guide CS management decisions, evidence supporting their optimal use in CS is lacking. METHODS: The Cardiogenic Shock Working Group (CSWG) collected retrospective data in CS patients from 8 tertiary care institutions from 2016 to 2019. Patients were divided by Society for Cardiovascular Angiography and Interventions (SCAI) stages and outcomes analyzed by the PAC-use group (no PAC data, incomplete PAC data, complete PAC data) prior to initiating mechanical circulatory support (MCS). RESULTS: Of 1,414 patients with CS analyzed, 1,025 (72.5%) were male, and 494 (34.9%) presented with myocardial infarction; 758 (53.6%) were in SCAI Stage D shock, and 263 (18.6%) were in Stage C shock. Temporary MCS devices were used in 1,190 (84%) of those in advanced CS stages. PAC data were not obtained in 216 patients (18%) prior to MCS, whereas 598 patients (42%) had complete hemodynamic data. Mortality differed significantly between PAC-use groups within the overall cohort (p \u3c 0.001), and each SCAI Stage subcohort (Stage C: p = 0.03; Stage D: p = 0.05; Stage E: p = 0.02). The complete PAC assessment group had the lowest in-hospital mortality than the other groups across all SCAI stages. Having no PAC assessment was associated with higher in-hospital mortality than complete PAC assessment in the overall cohort (adjusted odds ratio: 1.57; 95% confidence interval: 1.06 to 2.33). CONCLUSIONS: The CSWG is a large multicenter registry representing real-world patients with CS in the contemporary MCS era. Use of complete PAC-derived hemodynamic data prior to MCS initiation is associated with improved survival from CS

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Hemodynamic management of cardiogenic shock in the intensive care unit

Hemodynamic derangements are defining features of cardiogenic shock. Randomized clinical trials have examined the efficacy of various therapeutic interventions, from percutaneous coronary intervention to inotropes and mechanical circulatory support (MCS). However, hemodynamic management in cardiogenic shock has not been well-studied. This State-of-the-Art review will provide a framework for hemodynamic management in cardiogenic shock, including a description of the 4 therapeutic phases from initial 'Rescue' to 'Optimization', 'Stabilization' and 'de-Escalation or Exit therapy' (R-O-S-E), phenotyping and phenotype-guided tailoring of pharmacological and MCS support, to achieve hemodynamic and therapeutic goals. Finally, the premises that form the basis for clinical management and the hypotheses for randomized controlled trials will be discussed, with a view to the future direction of cardiogenic shock.</p

Clinical Course of Patients in Cardiogenic Shock Stratified by Phenotype

BACKGROUND: Cardiogenic shock (CS) patients remain at 30% to 60% in-hospital mortality despite therapeutic innovations. Heterogeneity of CS has complicated clinical trial design. Recently, 3 distinct CS phenotypes were identified in the CSWG (Cardiogenic Shock Working Group) registry version 1 (V1) and external cohorts: I, noncongested; II, cardiorenal; and III, cardiometabolic shock. OBJECTIVES: The aim was to confirm the external reproducibility of machine learning-based CS phenotypes and to define their clinical course. METHODS: The authors included 1,890 all-cause CS patients from the CSWG registry version 2. CS phenotypes were identified using the nearest centroids of the initially reported clusters. RESULTS: Phenotypes were retrospectively identified in 796 patients in version 2. In-hospital mortality rates in phenotypes I, II, III were 23%, 41%, 52%, respectively, comparable to the initially reported 21%, 45%, and 55% in V1. Phenotype-related demographic, hemodynamic, and metabolic features resembled those in V1. In addition, 58.8%, 45.7%, and 51.9% of patients in phenotypes I, II, and III received mechanical circulatory support, respectively (P = 0.013). Receiving mechanical circulatory support was associated with increased mortality in cardiorenal (odds ratio [OR]: 1.82 [95% CI: 1.16-2.84]; P = 0.008) but not in noncongested or cardiometabolic CS (OR: 1.26 [95% CI: 0.64-2.47]; P = 0.51 and OR: 1.39 [95% CI: 0.86-2.25]; P = 0.18, respectively). Admission phenotypes II and III and admission Society for Cardiovascular Angiography and Interventions stage E were independently associated with increased mortality in multivariable logistic regression compared to noncongested stage C CS (P \u3c 0.001). CONCLUSIONS: The findings support the universal applicability of these phenotypes using supervised machine learning. CS phenotypes may inform the design of future clinical trials and enable management algorithms tailored to a specific CS phenotype

Impact of Female Sex on Cardiogenic Shock Outcomes: A Cardiogenic Shock Working Group Report

BACKGROUND: Studies reporting cardiogenic shock (CS) outcomes in women are scarce. OBJECTIVES: The authors compared survival at discharge among women vs men with CS complicating acute myocardial infarction (AMI-CS) and heart failure (HF-CS). METHODS: The authors analyzed 5,083 CS patients in the Cardiogenic Shock Working Group. Propensity score matching (PSM) was performed with the use of baseline characteristics. Logistic regression was performed for log odds of survival. RESULTS: Among 5,083 patients, 1,522 were women (30%), whose mean age was 61.8 ± 15.8 years. There were 30% women and 29.1% men with AMI-CS (P = 0.03). More women presented with de novo HF-CS compared with men (26.2% vs 19.3%; P \u3c 0.001). Before PSM, differences in baseline characteristics and sex-specific outcomes were seen in the HF-CS cohort, with worse survival at discharge (69.9% vs 74.4%; P = 0.009) and a higher rate of maximum Society for Cardiac Angiography and Interventions stage E (26% vs 21%; P = 0.04) in women than in men. Women were less likely to receive pulmonary artery catheterization (52.9% vs 54.6%; P \u3c 0.001), heart transplantation (6.5% vs 10.3%; P \u3c 0.001), or left ventricular assist device implantation (7.8% vs 10%; P = 0.01). Regardless of CS etiology, women had more vascular complications (8.8% vs 5.7%; P \u3c 0.001), bleeding (7.1% vs 5.2%; P = 0.01), and limb ischemia (6.8% vs 4.5%; P = 0.001). More vascular complications persisted in women after PSM (10.4% women vs 7.4% men; P = 0.06). CONCLUSIONS: Women with HF-CS had worse outcomes and more vascular complications than men with HF-CS. More studies are needed to identify barriers to advanced therapies, decrease complications, and improve outcomes of women with CS

Machine Learning Identifies Clinical Parameters to Predict Mortality in Patients Undergoing Transcatheter Mitral Valve Repair

OBJECTIVES The aim of this study was to develop a machine learning (ML)-based risk stratification tool for 1-year mortality in transcatheter mitral valve repair (TMVR) patients incorporating metabolic and hemodynamic parameters. BACKGROUND The lack of appropriate, well-validated, and specific means to risk-stratify patients with mitral regurgitation complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. METHODS A total of 1,009 TMVR patients from 3 university hospitals within the Heart Failure Network Rhineland were included; 1 hospital (n = 317) served as external validation. The primary endpoint was all-cause 1-year mortality. Model performance was assessed using receiver-operating characteristic curve analysis. In the derivation cohort, different ML algorithms were tested using 5-fold cross-validation. The final model, called MITRALITY (transcatheter mitral valve repair mortality prediction system) was tested in the validation cohort with respect to existing clinical scores. RESULTS Extreme gradient boosting was selected for the MITRALITY score, using only 6 baseline clinical features for prediction (in order of predictive importance): urea, hemoglobin, N-terminal pro-brain natriuretic peptide, mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY score's area under the curve was 0.783 (95% CI: 0.716-0.849), while existing scores yielded areas under the curve of 0.721 (95% CI: 0.63-0.811) and 0.657 (95% CI: 0.536-0.778) at best. CONCLUSIONS The MITRALITY score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR, potentially serving future clinical trials and daily clinical practice. (C) 2021 by the American College of Cardiology Foundation

Transvalvular Ventricular Unloading Before Reperfusion in Acute Myocardial Infarction

BACKGROUND: Myocardial damage due to acute ST-segment elevation myocardial infarction (STEMI) remains a significant global health problem. New approaches to limit myocardial infarct size and reduce progression to heart failure after STEMI are needed. Mechanically reducing left ventricular (LV) workload (LV unloading) before coronary reperfusion is emerging as a potential approach to reduce infarct size. OBJECTIVES: Given the central importance of mitochondria in reperfusion injury, we hypothesized that compared with immediate reperfusion (IR), LV unloading before reperfusion improves myocardial energy substrate use and preserves mitochondrial structure and function. METHODS: To explore the effect of LV unloading duration on infarct size, we analyzed data from the STEMI-Door to Unload (STEMI-DTU) trial and then tested the effect of LV unloading on ischemia and reperfusion injury, cardiac metabolism, and mitochondrial function in swine models of acute myocardial infarction. RESULTS: The duration of LV unloading before reperfusion was inversely associated with infarct size in patients with large anterior STEMI. In preclinical models, LV unloading reduced the expression of hypoxia-sensitive proteins and myocardial damage due to ischemia alone. LV unloading with a transvalvular pump (TV-P) but not with venoarterial extracorporeal membrane oxygenation (ECMO) reduced infarct size. Using unbiased and blinded metabolic profiling, TV-P improved myocardial energy substrate use and preserved mitochondrial structure including cardiolipin content after reperfusion compared with IR or ECMO. Functional testing in mitochondria isolated from the infarct zone showed an intact mitochondrial structure including cardiolipin content, preserved activity of the electron transport chain including mitochondrial complex I, and reduced oxidative stress with TV-P-supported reperfusion but not with IR or ECMO. CONCLUSIONS: These novel findings identify that transvalvular unloading limits ischemic injury before reperfusion, improves myocardial energy substrate use, and preserves mitochondrial structure and function after reperfusion

Impact of age on outcomes in patients with cardiogenic shock

Background: Advanced age is associated with poor outcomes in cardiovascular emergencies. We sought to determine the association of age, use of support devices and shock severity on mortality in cardiogenic shock (CS).Methods: Characteristics and outcomes in CS patients included in the Cardiogenic Shock Work Group (CSWG) registry from 8 US sites between 2016 and 2019 were retrospectively reviewed. Patients were subdivided by age into quintiles and Society for Cardiovascular Angiography &amp; Interventions (SCAI) shock severity.Results: We reviewed 1,412 CS patients with a mean age of 59.9 ± 14.8 years, including 273 patients &gt; 73 years of age. Older patients had significantly higher comorbidity burden including diabetes, hypertension and coronary artery disease. Veno-arterial extracorporeal membrane oxygenation was used in 332 (23%) patients, Impella in 410 (29%) and intra-aortic balloon pump in 770 (54%) patients. Overall in-hospital survival was 69%, which incrementally decreased with advancing age (p &lt; 0.001). Higher age was associated with higher mortality across all SCAI stages (p = 0.003 for SCAI stage C; p &lt; 0.001 for SCAI stage D; p = 0.005 for SCAI stage E), regardless of etiology (p &lt; 0.001).Conclusion: Increasing age is associated with higher in-hospital mortality in CS across all stages of shock severity. Hence, in addition to other comorbidities, increasing age should be prioritized during patient selection for device support in CS