Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis

Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of “black box” models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes

Contacts With the Health Care System Before Out-of-Hospital Cardiac Arrest

BACKGROUND: It remains challenging to identify patients at risk of out‐of‐hospital cardiac arrest (OHCA). We aimed to examine health care contacts in patients before OHCA compared with the general population that did not experience an OHCA. METHODS AND RESULTS: Patients with OHCA with a presumed cardiac cause were identified from the Danish Cardiac Arrest Registry (2001–2014) and their health care contacts (general practitioner [GP]/hospital) were examined up to 1 year before OHCA. In a case‐control study (1:9), OHCA contacts were compared with an age‐ and sex‐matched background population. Separately, patients with OHCA were examined by the contact type (GP/hospital/both/no contact) within 2 weeks before OHCA. We included 28 955 patients with OHCA. The weekly percentages of patient contacts with GP the year before OHCA were constant (25%) until 1 week before OHCA when they markedly increased (42%). Weekly percentages of patient contacts with hospitals the year before OHCA gradually increased during the last 6 months (3.5%–6.6%), peaking at the second week (6.8%) before OHCA; mostly attributable to cardiovascular diseases (21%). In comparison, there were fewer weekly contacts among controls with 13% for GP and 2% for hospital contacts (P<0.001). Within 2 weeks before OHCA, 57.8% of patients with OHCA had a health care contact, and these patients had more contacts with GP (odds ratio [OR], 3.17; 95% CI, 3.09–3.26) and hospital (OR, 2.32; 95% CI, 2.21–2.43) compared with controls. CONCLUSIONS: The health care contacts of patients with OHCA nearly doubled leading up to the OHCA event, with more than half of patients having health care contacts within 2 weeks before arrest. This could have implications for future preventive strategies

Immune cell populations and induced immune responses at admission in patients hospitalized with vaccine breakthrough SARS-CoV-2 infections

BackgroundVaccine breakthrough SARS-CoV-2 infections are common and of clinical and public health concern. However, little is known about the immunological characteristics of patients hospitalized due to these infections. We aimed to investigate and compare immune cell subpopulations and induced immune responses in vaccinated and non-vaccinated patients hospitalized with severe COVID-19.MethodsA nested case-control study on adults (≥ 18 years) who received at least two doses of a mRNA-COVID-19 vaccine and were hospitalized with SARS-CoV-2 breakthrough infections and severe COVID-19 between January 7, 2021, and February 1, 2022, were eligible for inclusion. Age- and sex-matched non-vaccinated controls were identified. Immunophenotyping was performed using a custom-designed 10-color flow cytometry prefabricated freeze-dried antibody panel (DuraClone, Beckman Coulter (BC), Brea, Calif). TruCulture (Myriad RBM, Austin, USA) was used to assess induced immune response in whole blood, revealing different critical signaling pathways as a proxy for immune function. All samples were obtained within 48 hours of admission.ResultsIn total, 20 hospitalized patients with severe COVID-19 and a breakthrough SARS-CoV-2 infection were included, ten vaccinated and ten non-vaccinated patients. Vaccinated patients had lower concentrations of CD19 B cells (p = 0.035), naïve CD4 T cells (p = 0.015), a higher proportion of γδ1 T cells (p = 0.019), and higher unstimulated immune cell release of IL-10 (p = 0.015).ConclusionWe observed immunological differences between vaccinated and non-vaccinated patients hospitalized due to severe COVID-19 that indicate that vaccinated patients had lower B cell concentrations, lower concentrations of CD4 naïve T cells, a skewed gamma-delta V1/V2 ratio, and an exaggerated IL-10 response at admission. These results could indicate a suboptimal immune response involved in SARS-CoV-2 breakthrough infections that cause severe COVID-19 in vaccinated adults. However, the sample size was small, and further research is needed to confirm these results

Prodromal complaints and 30-day survival after emergency medical services-witnessed out-of-hospital cardiac arrest

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a frequent and lethal condition with a yearly incidence of approximately 5000 in Denmark. Thirty-day survival is associated with the patient's prodromal complaints prior to cardiac arrest. This paper examines the odds of 30-day survival dependent on the reported prodromal complaints among OHCAs witnessed by the emergency medical services (EMS).METHODS: EMS-witnessed OHCAs in the Capital Region of Denmark from 2016-2018 were included. Calls to the emergency number 1-1-2 and the medical helpline for out-of-hours were analyzed according to the Danish Index; data regarding the OHCA was collected from the Danish Cardiac Arrest Registry. We performed multiple logistic regression to calculate the odds ratio (OR) of 30-day survival with adjustment for sex and age.RESULTS: We identified 311 eligible OHCAs of which 79 (25.4%) survived. The most commonly reported complaints were dyspnea (n = 209, OR 0.79 [95% CI 0.46: 1.36]) and 'feeling generally unwell' (n = 185, OR 1.07 [95% CI 0.63: 1.81]). Chest pain (OR 9.16 [95% CI 5.09:16.9]) and heart palpitations (OR 3.15 [95% CI 1.07:9.46]) had the highest ORs, indicating favorable odds for 30-day survival, while unresponsiveness (OR 0.22 [95% CI 0.11:0.43]) and blue skin or lips (OR 0.30, 95% CI 0.09, 0.81) had the lowest, indicating lesser odds of 30-day survival.CONCLUSION: Experiencing chest pain or heart palpitations prior to EMS-witnessed OHCA was associated with higher 30-day survival. Conversely, complaints of unresponsiveness or having blue skin or lips implied reduced odds of 30-day survival.</p

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.

Peer reviewed: TrueDeep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of "black box" models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes