Outcomes of cardiac arrest complicating acute myocardial infarction in patients with current and historical cancer: An 18-year United States cohort study

Sudden cardiac arrest in patients with cancer in the general population: insights from the Paris-SDEC registry

BACKGROUND

Data on the management of patients with cancer presenting with sudden cardiac arrest (SCA) are scarce. We aimed to assess the characteristics and outcomes of SCA according to cancer history.

METHODS

Prospective, population-based registry including every out-of-hospital SCA in adults in Paris and its suburbs, between 2011 and 2019, with a specific focus on patients with cancer.

RESULTS

Out of 4069 patients who had SCA admitted alive in hospital, 207 (5.1%) had current or past medical history of cancer. Patients with cancer were older (69.2 vs 59.3 years old, p<0.001), more often women (37.2% vs 28.0%, p=0.006) with more frequent underlying cardiovascular disease (41.1% vs 32.5%, p=0.01). SCA happened more often with a non-shockable rhythm (62.6% vs 43.1%, p<0.001) with no significant difference regarding witness presence and cardiopulmonary resuscitation (CPR) performed. Cardiac causes were less frequent among patients with cancer (mostly acute coronary syndromes, 25.5% vs 46.8%, p<0.001) and had more respiratory causes (pulmonary embolism and hypoxaemia in 34.2% vs 10.8%, p<0.001). Still, no difference regarding in-hospital survival was found after SCA in patients with cancer versus other patients (26.2% vs 29.8%, respectively, p=0.27). Public location, CPR by witness and shockable rhythm were independent predictors of in-hospital survival after SCA in the cancer group.

CONCLUSIONS

One in 20 SCA occurs in patients with a history of cancer, yet with fewer cardiac causes than in patients who are cancer-free. Still, in-hospital outcomes remain similar even in patients with known cancer. Cancer history should therefore not compromise the initiation of resuscitation in the context of SCA.

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score

Background and objectives: Little is known about outcome improvements and disparities in cardiac arrest and active cancer. We performed the first known AI and propensity score (PS)-augmented clinical, cost-effectiveness, and computational ethical analysis of cardio-oncology cardiac arrests including left heart catheterization (LHC)-related mortality reduction and related disparities. Materials and methods: A nationally representative cohort analysis was performed for mortality and cost by active cancer using the largest United States all-payer inpatient dataset, the National Inpatient Sample, from 2016 to 2018, using deep learning and machine learning augmented propensity score-adjusted (ML-PS) multivariable regression which informed cost-effectiveness and ethical analyses. The Cardiac Arrest Cardio-Oncology Score (CACOS) was then created for the above population and validated. The results informed the computational ethical analysis to determine ethical and related policy recommendations. Results: Of the 101,521,656 hospitalizations, 6,656,883 (6.56%) suffered cardiac arrest of whom 61,300 (0.92%) had active cancer. Patients with versus without active cancer were significantly less likely to receive an inpatient LHC (7.42% versus 20.79%, p < 0.001). In ML-PS regression in active cancer, post-arrest LHC significantly reduced mortality (OR 0.18, 95%CI 0.14−0.24, p < 0.001) which PS matching confirmed by up to 42.87% (95%CI 35.56−50.18, p < 0.001). The CACOS model included the predictors of no inpatient LHC, PEA initial rhythm, metastatic malignancy, and high-risk malignancy (leukemia, pancreas, liver, biliary, and lung). Cost-benefit analysis indicated 292 racial minorities and $2.16 billion could be saved annually by reducing racial disparities in LHC. Ethical analysis indicated the convergent consensus across diverse belief systems that such disparities should be eliminated to optimize just and equitable outcomes. Conclusions: This AI-guided empirical and ethical analysis provides a novel demonstration of LHC mortality reductions in cardio-oncology cardiac arrest and related disparities, along with an innovative predictive model that can be integrated within the digital ecosystem of modern healthcare systems to improve equitable clinical and public health outcomes.