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Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65% were male. Overall in-hospital mortality was 45.5%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95% CI 0.79-0.82] and 0.81 [95% CI 0.8-0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95% CI 0.81-0.83], DeLong test, p < 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95% CI 0.86-0.88], DeLong test, p < 0.001), with an improvement in performance (Brier score reduction of 22%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient's survival. Key limitations include the absence of pre-hospital data and absence of external validation. ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.

Hypertension is the leading risk factor for heart disease and stroke, and is estimated to cause 9.4 million deaths globally every year. The pathogenesis of hypertension is complex, but lifestyle factors such as diet are important contributors to the disease. High dietary intake of fruit and vegetables is associated with reduced blood pressure and lower cardiovascular mortality. A critical relationship between dietary intake and the composition of the gut microbiota has been described in the literature, and a growing body of evidence supports the role of the gut microbiota in the regulation of blood pressure. In this Review, we describe the mechanisms by which the gut microbiota and its metabolites, including short-chain fatty acids, trimethylamine N-oxide, and lipopolysaccharides, act on downstream cellular targets to prevent or contribute to the pathogenesis of hypertension. These effects have a direct influence on tissues such as the kidney, the endothelium, and the heart. Finally, we consider the role of the gut microbiota in resistant hypertension, the possible intergenerational effect of the gut microbiota on blood pressure regulation, and the promising therapeutic potential of gut microbiota modification to improve health and prevent disease.

This is a complete teaching and learning package for the 2011 specifications helping both students and tutors to get the best results.

PurposeVenoarterial extracorporeal membrane oxygenation (VA-ECMO) is a complex and high-risk life support modality used in severe cardiorespiratory failure. ECMO survival scores are used clinically for patient prognostication and outcomes risk adjustment. This study aims to create the first artificial intelligence (AI)-driven ECMO survival score to predict in-hospital mortality based on a large international patient cohort.MethodsA deep neural network, ECMO Predictive Algorithm (ECMO PAL) was trained on a retrospective cohort of 18,167 patients from the international Extracorporeal Life Support Organisation (ELSO) registry (2017–2020), and performance was measured using fivefold cross-validation. External validation was performed on all adult registry patients from 2021 (N = 5015) and compared against existing prognostication scores: SAVE, Modified SAVE, and ECMO ACCEPTS for predicting in-hospital mortality.ResultsMean age was 56.8 ± 15.1 years, with 66.7% of patients being male and 50.2% having a pre-ECMO cardiac arrest. Cross-validation demonstrated an inhospital mortality sensitivity and precision of 82.1 ± 0.2% and 77.6 ± 0.2%, respectively. Validation accuracy was only 2.8% lower than training accuracy, reducing from 75.5% to 72.7% [99% confidence interval (CI) 71.1–74.3%]. ECMO PAL accuracy outperformed the ECMO ACCEPTS (54.7%), SAVE (61.1%), and Modified SAVE (62%) scores.ConclusionsECMO PAL is the first AI-powered ECMO survival score trained and validated on large international patient cohorts. ECMO PAL demonstrated high generalisability across ECMO regions and outperformed existing, widely used scores. Beyond ECMO, this study highlights how large international registry data can be leveraged for AI prognostication for complex critical care therapies.

Heart failure with preserved ejection fraction is responsible for half of all heart failure and confers substantial morbidity and mortality, and yet to date, there have been no effective pharmacologic interventions. Although the pathophysiology is complex, the primary aetiology of exercise intolerance is due to an elevated left atrial pressure, particularly with exercise. In this context, device-based therapy has become a focus. Several companies have developed techniques to percutaneously create an iatrogenic left to right shunt at the atrial level, thereby reducing left atrial pressure and reducing transmitted pressures to the pulmonary circulation and reducing pulmonary congestion. In this review, we explore the pathophysiology, evidence base, benefits, and considerations of these devices and their place in the therapeutic landscape of heart failure with preserved ejection fraction.

Heart failure is a common syndrome associated with substantial morbidity and mortality. The management of symptoms and the strategies for improving prognosis have largely been based on pharmacological treatments. The pathophysiology of heart failure is complex because of the multiple causes responsible for this syndrome. This Series paper presents some examples of advances in heart failure management, in which the treatment specifically targets the underlying pathophysiological mechanisms responsible for the symptoms. These treatments include treatment of electromechanical dyssynchrony and dysrhythmia by cardiac resynchronisation and implantable cardioverter-defibrillators; neurohumoral modification by baroreflex and vagal stimulation; prevention of adverse cardiac remodelling by interatrial shunts; and finally targeting the myocardium directly by cell therapy in an attempt to regenerate new myocardial cells.

Heart failure with preserved ejection fraction (HFPEF) is a common, globally recognised, form of heart failure for which no treatment has yet been shown to improve symptoms or prognosis. The pathophysiology of HFPEF is complex but characterised by increased left atrial pressure, especially during exertion, which might be a key therapeutic target. The rationale for the present study was that a mechanical approach to reducing left atrial pressure might be effective in HFPEF. The REDUCe Elevated Left Atrial Pressure in Patients with Heart Failure (REDUCE LAP-HF) study was an open-label, single-arm, phase 1 study designed to assess the performance and safety of a transcatheter interatrial shunt device (IASD, Corvia Medical, Tewkesbury, MA, USA) in patients older than 40 years of age with symptoms of HFPEF despite pharmacological therapy, left ventricular ejection fraction higher than 40%, and a raised pulmonary capillary wedge pressure at rest (>15 mm Hg) or during exercise (>25 mm Hg). The study was done at 21 centres (all departments of cardiology in the UK, Netherlands, Belgium, France, Germany, Austria, Denmark, Australia, and New Zealand). The co-primary endpoints were the safety and performance of the IASD at 6 months, together with measures of clinical efficacy, including functional capacity and clinical status, analysed per protocol. This study is registered with ClinicalTrials.gov, number NCT01913613. Between Feb 8, 2014, and June 10, 2015, 68 eligible patients were entered into the study. IASD placement was successful in 64 patients and seemed to be safe and well tolerated; no patient had a peri-procedural or major adverse cardiac or cerebrovascular event or need for cardiac surgical intervention for device-related complications during 6 months of follow-up. At 6 months, 31 (52%) of 60 patients had a reduction in pulmonary capillary wedge pressure at rest, 34 (58%) of 59 had a lower pulmonary capillary wedge pressure during exertion, and 23 (39%) of 59 fulfilled both these criteria. Mean exercise pulmonary capillary wedge pressure was lower at 6 months than at baseline, both at 20 watts workload (mean 32 mm Hg [SD 8] at baseline vs 29 mm Hg [9] at 6 months, p=0·0124) and at peak exercise (34 mm Hg [8] vs 32 [8], p=0·0255), despite increased mean exercise duration (baseline vs 6 months: 7·3 min [SD 3·1] vs 8·2 min [3·4], p=0·03). Sustained device patency at 6 months was confirmed by left-to-right shunting (pulmonary/systemic flow ratio: 1·06 [SD 0·32] at baseline vs 1·27 [0·20] at 6 months, p=0·0004). Implantation of an interatrial shunt device is feasible, seems to be safe, reduces left atrial pressure during exercise, and could be a new strategy for the management of HFPEF. The effectiveness of IASD compared with existing treatment for patients with HFPEF requires validation in a randomised controlled trial. Corvia Medical Inc.

Heart failure (HF) is an increasingly recognized complication of diabetes. Cardiac fibrosis is an important causative mechanism of HF associated with diabetes. Recent data indicate that inflammation may be particularly important in the pathogenesis of cardiovascular fibrosis. We sought to determine the mechanism by which cardiac fibrosis develops and to specifically investigate the role of the CXCR4 axis in this process. Animals with type I diabetes (streptozotocin treated mice) or type II diabetes (Israeli Sand-rats) and controls were randomized to treatment with a CXCR4 antagonist, candesartan or vehicle control. Additional groups of mice also underwent bone marrow transplantation (GFP+ donor marrow) to investigate the potential role of bone marrow derived cell mobilization in the pathogenesis of cardiac fibrosis. Both type I and II models of diabetes were accompanied by the development of significant cardiac fibrosis. CXCR4 antagonism markedly reduced cardiac fibrosis in both models of diabetes, similar in magnitude to that seen with candesartan. In contrast to candesartan, the anti-fibrotic actions of CXCR4 antagonism occurred in a blood pressure independent manner. Whilst the induction of diabetes did not increase the overall myocardial burden of GFP+ cells, it was accompanied by an increase in GFP+ cells expressing the fibroblast marker alpha-smooth muscle actin and this was attenuated by CXCR4 antagonism. CXCR4 antagonism was also accompanied by increased levels of circulating regulatory T cells. Taken together the current data indicate that pharmacological inhibition of CXCR4 significantly reduces diabetes induced cardiac fibrosis, providing a potentially important therapeutic approach.