Quality of Heart Failure Care in the Intensive Care Unit

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period

BACKGROUND

Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period.

METHODS

We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values.

RESULTS

A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality.

CONCLUSIONS

The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Patient-centered care for mental health in patients with heart failure in the intensive care unit: A systematic review

AIM

To assess basic data for developing appropriate interventions by examining the effects of patient-centered care (PCC) on the mental health of patients with heart failure in the intensive care unit (ICU).

BACKGROUND

Patients with heart failure are frequently admitted to ICUs, and ICU stays are associated with prolonged mental health problems.

METHODS

We conducted a systematic review using the CINAHL, Cochrane Library, Embase, MEDLINE, PsycINFO, and gray literature databases. Inclusion criteria were studies with participants aged ≥18 years with heart failure in the ICU who received a PCC intervention, and studies that described the outcomes for mental health problems. Data were extracted from five selected studies published after 2020 and analyzed.

RESULTS

PCC is classified into three areas: comprehensive nursing, multidisciplinary disease management, and targeted motivational interviewing with conventional nursing. The two specific areas of focus for PCC regarding mental health were integrated mental healthcare and specific psychological nursing. Specific psychological nursing comprised relationship building, therapeutic communication, relaxation and motivational techniques, active therapeutic cooperation, psychological status evaluation, music therapy, and environmental management.

CONCLUSIONS

This review provides a distinctive understanding of multidisciplinary and multicomponent PCC interventions for patients with heart failure in the ICU as an effective approach for improving their mental health. Future PCC intervention strategies aimed at patients with heart failure in the ICU should consider their preferences and family participation.

Development and internal validation of a clinical prediction model to predict independence in daily living at discharge for patients with heart failure: analysis using a Japanese national inpatient database real-world dataset

PURPOSE

To develop a clinical prediction model (CPM) to predict independence in activities of daily living (ADLs) in patients with heart failure.

SUBJECTS AND METHODS

We collected the data of the individuals who were admitted and rehabilitated for heart failure from January 2017 to June 2022 from Japan's Diagnosis Procedure Combination database. We assessed the subjects' ADLs at discharge using the Barthel Index and classified them into independence, partial-independence, and total-dependence groups based on their ADLs at discharge. Two CPMs (an independence model and a partial-independence model) were developed by a binomial logistic regression analysis. The predictors included subject characteristics, treatment, and post-hospitalization disease onset. The CPMs' accuracy was validated by the area under the curve (AUC). Internal validation was performed using the bootstrap method. The final CPM is presented in a nomogram.

RESULTS

We included 96,753 patients whose ADLs could be traced at discharge. The independence model had a 0.73 mean AUC and a 1.0 slope at bootstrapping. We thus developed a simplified model using nomograms, which also showed adequate predictive accuracy in the independence model. The partial-independence model had a 0.65 AUC and inadequate predictive accuracy.

CONCLUSIONS

The independence model of ADLs in patients with heart failure is a useful CPM.

Clinical guidance for unfractionated heparin dosing and monitoring in critically ill patients

INTRODUCTION

Unfractionated heparin is a widely used anticoagulant in critically ill patients. It has a well-established safety profile and remains an attractive option for clinicians due to its short half-life and reversibility. Heparin has a unique pharmacokinetic profile, which contributes to significant inter-patient and intra-patient variability in effect. The variability in anticoagulant effect combined with heparin's short half-life mean close monitoring is required for clinical efficacy and preventing adverse effects. To optimize heparin use in critically ill patients, effective monitoring assays and dose adjustment strategies are needed.

AREAS COVERED

This paper explores the use of heparin as an anticoagulant and optimal approaches to monitoring in critically ill patients.

EXPERT OPINION

Conventional monitoring assays for heparin dosing have significant limitations. Emerging data appear to favor using anti-Xa assay monitoring for heparin anticoagulation, which many centers have successfully adopted as the standard. The anti-Xa assay appears have important benefits relative to the aPTT for heparin monitoring in critically ill patients, and should be considered for broader use.

Association Between Early Initiation of Cardiac Rehabilitation and Short-Term Outcomes of Patients With Acute Heart Failure Admitted to the Intensive Care Unit

Cardiac rehabilitation (CR) is a promising therapeutic option for chronic heart failure (HF). However, the extent to which early rehabilitation is beneficial for patients receiving critical care remains controversial. This study examined the association between the early initiation of CR and the short-term clinical outcomes of patients admitted to the intensive care unit (ICU) with acute HF. We used the Diagnosis Procedure Combination database, a nationwide inpatient database in Japan, and included patients with acute HF admitted to the ICU within 2 days after hospital admission. We defined the early initiation of CR as its initiation within 2 days of hospital admission. We performed an overlap weighting based on the propensity scores and inverse probability of treatment weighting analysis to compare the clinical outcomes between patients with and without early initiation of CR. Among 25,362 eligible patients, 3,582 (14.1%) received an early initiation of CR. Overlap weighting created well-balanced cohorts, which showed that the early initiation of CR was related to lower in-hospital mortality (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.68 to 0.96) and shorter hospital stay. The inverse probability of treatment weighting analysis also showed that in-hospital mortality was lower in the patients with the early initiation of CR (OR 0.80, 95% CI 0.67 to 0.96). The instrumental variable analysis also demonstrated the association of the early initiation of CR with lower in-hospital mortality (OR 0.64, 95% CI 0.44 to 0.93). In conclusion, early initiation of CR after hospital admission was associated with better short-term outcomes in patients with acute HF admitted to the ICU, suggesting the potential of the early administration of CR for acute HF requiring intensive care.

Training Pathways in Critical Care Cardiology: Competencies and Considerations for Cardiologists

PURPOSE OF REVIEW

Critical care cardiology (CCC) is a rapidly developing field undergoing a renaissance of interest and growth to meet the well-documented population shift in the cardiac intensive care unit (CICU). With this has come the emergence of novel training paradigms that seek to combine specialties with meaningful overlap.

RECENT FINDINGS

The benefit of having critical care expertise in the CICU has been clearly established; however, there is no formal or uniform CCC training pathway. Contemporary approaches seek to provide appropriate clinical and procedural experience while minimizing opportunity cost. The combination of additional cardiology subspecialties, specifically advanced heart failure or interventional cardiology, has been demonstrated. Educational tracks that integrate critical care training have generated interest but have not yet manifested. CCC training strives to meet the needs of an increasingly sick and diverse patient population while preparing trainees for fulfilling and meaningful careers. The hope is for ongoing development of novel training pathways to satisfy evolving needs.

Critical care for multimorbid patients

Multimorbidity - the simultaneous presence of several chronic diseases - is very common in the critically ill patients. Its prevalence is roughly 40-85 % and continues to increase further. Certain chronic diseases such as diabetes, obesity, chronic heart, pulmonary, liver or kidney disease and malignancy are associated with higher risk of developing serious acute complications and therefore the possible need for intensive care. This review summarizes and discusses selected specifics of critical care for multimorbid patients.

Critical Care Cardiology Trials Network (CCCTN): a cohort profile

AIMS

The aims of the Critical Care Cardiology Trials Network (CCCTN) are to develop a registry to investigate the epidemiology of cardiac critical illness and to establish a multicentre research network to conduct randomised clinical trials (RCTs) in patients with cardiac critical illness.

METHODS AND RESULTS

The CCCTN was founded in 2017 with 16 centres and has grown to a research network of over 40 academic and clinical centres in the United States and Canada. Each centre enters data for consecutive cardiac intensive care unit (CICU) admissions for at least 2 months of each calendar year. More than 20 000 unique CICU admissions are now included in the CCCTN Registry. To date, scientific observations from the CCCTN Registry include description of variations in care, the epidemiology and outcomes of all CICU patients, as well as subsets of patients with specific disease states, such as shock, heart failure, renal dysfunction, and respiratory failure. The CCCTN has also characterised utilization patterns, including use of mechanical circulatory support in response to changes in the heart transplantation allocation system, and the use and impact of multidisciplinary shock teams. Over years of multicentre collaboration, the CCCTN has established a robust research network to facilitate multicentre registry-based randomised trials in patients with cardiac critical illness.

CONCLUSION

The CCCTN is a large, prospective registry dedicated to describing processes-of-care and expanding clinical knowledge in cardiac critical illness. The CCCTN will serve as an investigational platform from which to conduct randomised controlled trials in this important patient population.

A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure

BACKGROUND

Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.

METHODS

Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation.

RESULTS

The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820-0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805-0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk.

CONCLUSION

Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.

Predictive Value of Arterial Blood Lactic Acid Concentration on the Risk of in-Hospital All-Cause Death in Patients with Acute Heart Failure

The study aims to examine the predictive value of arterial blood lactic acid concentration for in-hospital all-cause mortality in the intensive care unit (ICU) for patients with acute heart failure (AHF). We retrospectively analyzed the clinical data of 7558 AHF patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The exposure variable of the present study was arterial blood lactic acid concentration and the outcome variable was in-hospital all-cause death. The patients were divided into those who survived (n = 6792) and those who died (n = 766). The multivariate logistic regression model, restricted cubic spline (RCS) plot, and subgroup analysis were used to evaluate the association between lactic acid and in-hospital all-cause mortality. In addition, receiver operating curve (ROC) analysis also was performed. Finally, we further explore the association between NT-proBNP and lactic acid and in-hospital all-cause mortality. Compared with the lowest quartiles, the odds ratios with 95% confidence intervals for in-hospital all-cause mortality across the quartiles were 1.46 (1.07-2.00), 1.48 (1.09-2.00), and 2.36 (1.73-3.22) for lactic acid, and in-hospital all-cause mortality was gradually increased with lactic acid levels increasing (P for trend <0.05). The RCS plot revealed a positive and linear connection between lactic acid and in-hospital all-cause mortality. A combination of lactic acid concentration and the Simplified Acute Physiology Score (SAPS) II may improve the predictive value of in-hospital all-cause mortality in patients with AHF (AUC = 0.696). Among subgroups, respiratory failure interacted with an association between lactic acid and in-hospital all-cause mortality (P for interaction <0.05). The correlation heatmap revealed that NT-proBNP was positively correlated with lactic acid (r = 0.07) and positively correlated with in-hospital all-cause mortality (r = 0.18). There was an inverse L-shaped curve relationship between NT-proBNP and in-hospital all-cause mortality, respectively. Mediation analysis suggested that a positive relationship between lactic acid and in-hospital all-cause death was mediated by NT-proBNP. For AHF patients in the ICU, the arterial blood lactic acid concentration during hospitalization was a significant independent predictor of in-hospital all-cause mortality. The combination of lactic acid and SAPS II can improve the predictive value of the risk of in-hospital all-cause mortality in patients with AHF.

The usefulness of repeated CMR and FDG PET/CT in the diagnosis of patients with initial possible cardiac sarcoidosis

BACKGROUND

Cardiac sarcoidosis (CS) diagnosis is usually based on advanced imaging techniques and multidisciplinary evaluation. Diagnosis is classified as definite, probable, possible or unlikely. If diagnostic confidence remains uncertain, cardiac imaging can be repeated. The objective is to evaluate the usefulness of repeated cardiac magnetic resonance imaging (CMR) and fluorodeoxyglucose positron emission tomography (FDG PET/CT) for CS diagnosis in patients with an initial "possible" CS diagnosis.

METHODS

We performed a retrospective cohort study in 35 patients diagnosed with possible CS by our multidisciplinary team (MDT), who received repeated CMR and FDG PET/CT within 12 months after diagnosis. Imaging modalities were scored on abnormalities suggestive for CS and classified as CMR+/PET+, CMR+/PET-, CMR-/PET+ and CMR-/PET-. Primary endpoint was final MDT diagnosis of CS.

RESULTS

After re-evaluation, nine patients (25.7%) were reclassified as probable CS and 16 patients (45.7%) as unlikely CS. Two patients started immunosuppressive treatment after re-evaluation. At baseline, eleven patients (31.4%) showed late gadolinium enhancement (LGE) on CMR (CMR+) and 26 (74.3%) patients showed myocardial FDG-uptake (PET+). At re-evaluation, nine patients (25.7%) showed LGE (CMR+), while 16 patients (45.7%) showed myocardial FDG-uptake (PET+). When considering both imaging modalities together, 82.6% of patients with CMR-/PET+ at baseline were reclassified as possible or unlikely CS, while 36.4% of patients with CMR+ at baseline were reclassified as probable CS. Three patients with initial CMR-/PET+ showed LGE at re-evaluation.

CONCLUSION

Repeated CMR and FDG PET/CT may be useful in establishing or rejecting CS diagnosis, when initial diagnosis is uncertain. However, clinical relevance has to be further determined.