Prognostic performance of the IABP-SHOCK II Risk Score among cardiogenic shock subtypes in the critical care cardiology trials network registry.

BACKGROUND: Risk stratification has potential to guide triage and decision-making in cardiogenic shock (CS). We assessed the prognostic performance of the IABP-SHOCK II score, derived in Europe for acute myocardial infarct-related CS (AMI-CS), in a contemporary North American cohort, including different CS phenotypes. METHODS: The critical care cardiology trials network (CCCTN) coordinated by the TIMI study group is a multicenter network of cardiac intensive care units (CICU). Participating centers annually contribute ≥2 months of consecutive medical CICU admissions. The IABP-SHOCK II risk score includes age > 73 years, prior stroke, admission glucose > 191 mg/dl, creatinine > 1.5 mg/dl, lactate > 5 mmol/l, and post-PCI TIMI flow grade < 3. We assessed the risk score across various CS etiologies. RESULTS: Of 17,852 medical CICU admissions 5,340 patients across 35 sites were admitted with CS. In patients with AMI-CS (n = 912), the IABP-SHOCK II score predicted a >3-fold gradient in in-hospital mortality (low risk = 26.5%, intermediate risk = 52.2%, high risk = 77.5%, P < .0001; c-statistic = 0.67; Hosmer-Lemeshow P = .79). The score showed a similar gradient of in-hospital mortality in patients with non-AMI-related CS (n = 2,517, P < .0001) and mixed shock (n = 923, P < .001), as well as in left ventricular (<0.0001), right ventricular (P = .0163) or biventricular (<0.0001) CS. The correlation between the IABP-SHOCK II score and SOFA was moderate (r2 = 0.17) and the IABP-SHOCK II score revealed a significant risk gradient within each SCAI stage. CONCLUSIONS: In an unselected international multicenter registry of patients admitted with CS, the IABP- SHOCK II score only moderately predicted in-hospital mortality in a broad population of CS regardless of etiology or irrespective of right, left, or bi-ventricular involvement.

Modes of Death in Patients with Cardiogenic Shock in the Cardiac Intensive Care Unit: A Report from the Critical Care Cardiology Trials Network.

BACKGROUND: There are limited data on how patients with cardiogenic shock (CS) die. METHODS: The Critical Care Cardiology Trials Network is a research network of cardiac intensive care units coordinated by the Thrombolysis In Myocardial Infarction (TIMI) Study Group (Boston, MA). Using standardized definitions, site investigators classified direct modes of in-hospital death for CS admissions (October 2021 to September 2022). Mutually exclusive categories included 4 modes of cardiovascular death and 4 modes of noncardiovascular death. Subgroups defined by CS type, preceding cardiac arrest (CA), use of temporary mechanical circulatory support (tMCS), and transition to comfort measures were evaluated. RESULTS: Among 1068 CS cases, 337 (31.6%) died during the index hospitalization. Overall, the mode of death was cardiovascular in 82.2%. Persistent CS was the dominant specific mode of death (66.5%), followed by arrhythmia (12.8%), anoxic brain injury (6.2%), and respiratory failure (4.5%). Patients with preceding CA were more likely to die from anoxic brain injury (17.1% vs 0.9%; P < .001) or arrhythmia (21.6% vs 8.4%; P < .001). Patients managed with tMCS were more likely to die from persistent shock (P < .01), both cardiogenic (73.5% vs 62.0%) and noncardiogenic (6.1% vs 2.9%). CONCLUSIONS: Most deaths in CS are related to direct cardiovascular causes, particularly persistent CS. However, there is important heterogeneity across subgroups defined by preceding CA and the use of tMCS.

Comparison of Bayesian and frequentist monitoring boundaries motivated by the Multiplatform Randomized Clinical Trial.

BACKGROUND: The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary. METHODS: The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline. RESULTS: A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary. CONCLUSIONS: In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.

A comparison of computational algorithms for the Bayesian analysis of clinical trials.

BACKGROUND: Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design. METHODS: Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded. RESULTS: The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS. CONCLUSION: Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.

Preexisting Diabetes Mellitus and All-Cause Mortality in Adult Patients With Sepsis: A Population-Based Cohort Study.

OBJECTIVES: We assessed the association of preexisting diabetes mellitus with all-cause mortality and organ support receipt in adult patients with sepsis. DESIGN: Population-based cohort study. SETTING: Ontario, Canada (2008-2019). POPULATION: Adult patients (18 yr old or older) with a first sepsis-related hospitalization episode. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main exposure of interest was preexisting diabetes (either type 1 or 2). The primary outcome was all-cause mortality by 90 days; secondary outcomes included receipt of invasive mechanical ventilation and new renal replacement therapy. We report adjusted (for baseline characteristics using standardization) risk ratios (RRs) alongside 95% CIs. A main secondary analysis evaluated the potential mediation by prior metformin use of the association between preexisting diabetes and all-cause mortality following sepsis. Overall, 503,455 adults with a first sepsis-related hospitalization episode were included; 36% had preexisting diabetes. Mean age was 73 years, and 54% of the cohort were females. Preexisting diabetes was associated with a lower adjusted risk of all-cause mortality at 90 days (RR, 0.81; 95% CI, 0.80-0.82). Preexisting diabetes was associated with an increased risk of new renal replacement therapy (RR, 1.53; 95% CI, 1.46-1.60) but not invasive mechanical ventilation (RR, 1.03; 95% CI, 1.00-1.05). Overall, 21% (95% CI, 19-28) of the association between preexisting diabetes and reduced risk of all-cause mortality was mediated by prior metformin use. CONCLUSIONS: Preexisting diabetes is associated with a lower risk of all-cause mortality and higher risk of new renal replacement therapy among adult patients with sepsis. Future studies should evaluate the underlying mechanisms of these associations.

Admission Total Leukocyte Count as a Predictor of Mortality in Cardiac Intensive Care Unit Patients.

Background: Inflammation is a sequela of cardiovascular critical illness and a risk factor for mortality. Objectives: This study aimed to evaluate the association between white blood cell count (WBC) and mortality in a broad population of patients admitted to the cardiac intensive care unit (CICU). Methods: This retrospective cohort study included patients admitted to the Mayo Clinic CICU between 2007 and 2018. We analyzed WBC as a continuous variable and then categorized WBC as low (<4.0 × 103/mL), normal (≥4.0 to <11.0 × 103/mL), high (≥11.0 to <22.0 × 103/mL), or very high (≥22.0 × 103/mL). The association between WBC and in-hospital mortality was evaluated using multivariable logistic regression and random forest models. Results: We included 11,699 patients with a median age of 69.3 years (37.6% females). Median WBC was 9.6 (IQR: 7.4-12.7). Mortality was higher in the low (10.5%), high (12.0%), and very high (33.3%) WBC groups relative to the normal WBC group (5.3%). A rising WBC was incrementally associated with higher in-hospital mortality after adjustment (AICc adjusted OR: 1.03 [95% CI: 1.02-1.04] per 1 × 103 increase in WBC). After adjustment, only the high (AICc adjusted OR: 1.37 [95% CI: 1.15-1.64]) and very high (AICc adjusted OR: 1.99 [1.47-2.71]) WBC groups remained associated with increased risk of in-hospital mortality. Conclusions: Leukocytosis is associated with an increased mortality risk in a diverse cohort of CICU patients. This readily available marker of systemic inflammation may be useful for risk stratification within the increasingly complex CICU patient population.

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

AIMS: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU. METHODS: We screened 6008 Mayo Clinic CICU patients with an admission diagnosis of HF from 2007 to 2018 and included those without missing values for common laboratory tests. Consensus k-means clustering was performed based on 10 common admission laboratory values (potassium, chloride, anion gap, blood urea nitrogen, haemoglobin, red blood cell distribution width, mean corpuscular volume, platelet count, white blood cell count and neutrophil-to-lymphocyte ratio). In-hospital mortality was evaluated using logistic regression, and 1 year mortality was evaluated using Cox proportional hazard models after multivariable adjustment. RESULTS: Among 4877 CICU patients with HF who had complete admission laboratory data (mean age 69.4 years, 38.4% females), we identified five clusters with divergent demographics, comorbidities, laboratory values, admission diagnoses and use of critical care therapies. We labelled these clusters based on the characteristic laboratory profile of each group: uncomplicated (25.7%), iron-deficient (14.5%), cardiorenal (18.4%), inflamed (22.3%) and hypoperfused (19.2%). In-hospital mortality occurred in 10.7% and differed between the phenotypes: uncomplicated, 2.7% (reference); iron-deficient, 8.1% [adjusted odds ratio (OR) 2.18 (1.38-3.48), P < 0.001]; cardiorenal, 10.3% [adjusted OR 2.11 (1.37-3.32), P < 0.001]; inflamed, 12.5% [adjusted OR 1.79 (1.18-2.76), P = 0.007]; and hypoperfused, 21.9% [adjusted OR 4.32 (2.89-6.62), P < 0.001]. These differences in mortality between phenotypes were consistent when patients were stratified based on demographics, aetiology, admission diagnoses, mortality risk scores, shock severity and systolic function. One-year mortality occurred in 31.5% and differed between the phenotypes: uncomplicated, 11.9% (reference); inflamed, 26.8% [adjusted hazard ratio (HR) 1.56 (1.27-1.92), P < 0.001]; iron-deficient, 33.8% [adjusted HR 2.47 (2.00-3.04), P < 0.001]; cardiorenal, 41.2% [adjusted HR 2.41 (1.97-2.95), P < 0.001]; and hypoperfused, 52.3% [adjusted HR 3.43 (2.82-4.18), P < 0.001]. Similar findings were observed for post-discharge 1 year mortality. CONCLUSIONS: Unsupervised machine learning clustering can identify multiple distinct clinical HF phenotypes within the CICU population that display differing mortality profiles both in-hospital and at 1 year. Mortality was lowest for the uncomplicated HF phenotype and highest for the hypoperfused phenotype. The inflamed phenotype had comparatively higher in-hospital mortality yet lower post-discharge mortality, suggesting divergent short-term and long-term prognosis.

Premature Acute Myocardial Infarction Treated With Invasive Revascularization: Comparing STEMI With NSTEMI in a Population-Based Study of Young Patients.

BACKGROUND: Acute myocardial infarction (AMI) usually presents in older populations, in which there are established demographic and outcome differences for ST-elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI). No similar comparisons for AMI in the young population exist. METHODS: We compared all index NSTEMI and STEMI hospitalizations in young (18-45 years) patients who required revascularization in Alberta, Canada. Outcomes were survival to discharge, and a composite of heart failure hospitalization, cardiac arrest hospitalization, and all-cause mortality at 1 and 5 years. RESULTS: There were 1679 patients included with an index AMI who required revascularization: 655 (39.0%) NSTEMI and 1024 (61.0%) STEMI. The population was disproportionately male (86%), particularly in STEMI patients (87.3%). Marked dyslipidemia (35%) and active smoking (42%) were common, with similar rates among groups. Percutaneous coronary intervention was used in 98.7% of STEMI and 91.5% of NSTEMI patients (P < 0.001), with the remainder who underwent surgical revascularization. The in-hospital mortality rate during index AMI was higher in STEMI compared with NSTEMI patients (1.7% vs 0%; P < 0.001). The rates of the composite outcome were similar for both groups at 1 and 5 years of follow-up in patients who survived to index hospital discharge. After adjusting for sex, age, heart failure and/or cardiac arrest at index AMI, outcomes remained similar among groups at 1 and 5 years. CONCLUSIONS: In young patients with AMI, STEMI was a disproportionately male phenomenon and associated with higher mortality at index hospitalization. One-year and 5-year outcomes were similar among STEMI and NSTEMI patients in those discharged alive at index AMI. Smoking and dyslipidemia appear to be major risk factors in the young.

Sex Differences in Characteristics, Resource Utilization, and Outcomes of Cardiogenic Shock: Data From the Critical Care Cardiology Trials Network (CCCTN) Registry.

BACKGROUND: Sex disparities exist in the management and outcomes of various cardiovascular diseases. However, little is known about sex differences in cardiogenic shock (CS). We sought to assess sex-related differences in the characteristics, resource utilization, and outcomes of patients with CS. METHODS: The Critical Care Cardiology Trials Network is a multicenter registry of advanced cardiac intensive care units (CICUs) in North America. Between 2018 and 2022, each center (N=35) contributed annual 2-month snapshots of consecutive CICU admissions. Patients with CS were stratified as either CS after acute myocardial infarction or heart failure-related CS (HF-CS). Multivariable logistic regression was used for analyses. RESULTS: Of the 22 869 admissions in the overall population, 4505 (20%) had CS. Among 3923 patients with CS due to ventricular failure (32% female), 1235 (31%) had CS after acute myocardial infarction and 2688 (69%) had HF-CS. Median sequential organ failure assessment scores did not differ by sex. Women with HF-CS had shorter CICU lengths of stay (4.5 versus 5.4 days; P<0.0001) and shorter overall lengths of hospital stay (10.9 versus 12.8 days; P<0.0001) than men. Women with HF-CS were less likely to receive pulmonary artery catheters (50% versus 55%; P<0.01) and mechanical circulatory support (26% versus 34%; P<0.0001) compared with men. Women with HF-CS had higher in-hospital mortality than men, even after adjusting for age, illness severity, and comorbidities (34% versus 23%; odds ratio, 1.76 [95% CI, 1.42-2.17]). In contrast, there were no significant sex differences in utilization of advanced CICU monitoring and interventions, or mortality, among patients with CS after acute myocardial infarction. CONCLUSIONS: Women with HF-CS had lower use of pulmonary artery catheters and mechanical circulatory support, shorter CICU lengths of stay, and higher in-hospital mortality than men, even after accounting for age, illness severity, and comorbidities. These data highlight the need to identify underlying reasons driving the differences in treatment decisions, so outcomes gaps in HF-CS can be understood and eliminated.

Heparin Dose Intensity and Organ Support-Free Days in Patients Hospitalized for COVID-19.

Background: Clinical trials suggest that therapeutic-dose heparin may prevent critical illness and vascular complications due to COVID-19, but knowledge gaps exist regarding the efficacy of therapeutic heparin including its comparative effect relative to intermediate-dose anticoagulation. Objectives: The authors performed 2 complementary secondary analyses of a completed randomized clinical trial: 1) a prespecified per-protocol analysis; and 2) an exploratory dose-based analysis to compare the effect of therapeutic-dose heparin with low- and intermediate-dose heparin. Methods: Patients who received initial anticoagulation dosed consistently with randomization were included. The primary outcome was organ support-free days (OSFDs), a combination of in-hospital death and days free of organ support through day 21. Results: Among 2,860 participants, 1,761 (92.8%) noncritically ill and 857 (89.1%) critically ill patients were treated per-protocol. Among noncritically ill per-protocol patients, the posterior probability that therapeutic-dose heparin improved OSFDs as compared with usual care was 99.3% (median adjusted OR: 1.36; 95% credible interval [CrI]: 1.07-1.74). Therapeutic heparin had a high posterior probability of efficacy relative to both low- (94.6%; adjusted OR: 1.26; 95% CrI: 0.95-1.64) and intermediate- (99.8%; adjusted OR: 1.80; 95% CrI: 1.22-2.62) dose thromboprophylaxis. Among critically ill per-protocol patients, the posterior probability that therapeutic heparin improved outcomes was low. Conclusions: Among noncritically ill patients hospitalized for COVID-19 who were randomized to and initially received therapeutic-dose anticoagulation, heparin, compared with usual care, was associated with improved OSFDs, a combination of in-hospital death and days free of organ support. Therapeutic heparin appeared superior to both low- and intermediate-dose thromboprophylaxis.

Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness: Part 2 of 2.

Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.

Advances in the Staging and Phenotyping of Cardiogenic Shock: Part 1 of 2.

Cardiogenic shock (CS) is a heterogeneous syndrome reflecting a broad spectrum of shock severity, diverse etiologies, variable cardiac function, different hemodynamic trajectories, and concomitant organ dysfunction. These factors influence the clinical presentation, management, response to therapy, and outcomes of CS patients, necessitating a tailored approach to care. To better understand the variability inherent to CS populations, recent algorithms for staging the severity of CS have been described and validated. This paper is part 1 of a 2-part state-of-the-art review. In this first article, we consider the context for clinical staging and stratification in CS with a focus on established severity staging systems for CS and their use for risk stratification and clinical care. We describe the use of staging for predicting outcomes in populations with or at risk for CS, including risk modifiers that provide more nuanced risk stratification, and highlight how these approaches may allow individualized care.