Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis

Association between sub-phenotypes identified using latent class analysis and neurological outcomes in patients with out-of-hospital cardiac arrest in Japan

BACKGROUND

In patients who experience out-of-hospital cardiac arrest (OHCA), it is important to assess the association of sub-phenotypes identified by latent class analysis (LCA) using pre-hospital prognostic factors and factors measurable immediately after hospital arrival with neurological outcomes at 30 days, which would aid in making treatment decisions.

METHODS

This study retrospectively analyzed data obtained from the Japanese OHCA registry between June 2014 and December 2019. The registry included a complete set of data on adult patients with OHCA, which was used in the LCA. The association between the sub-phenotypes and 30-day survival with favorable neurological outcomes was investigated. Furthermore, adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by multivariate logistic regression analysis using in-hospital data as covariates.

RESULTS

A total of, 22,261 adult patients who experienced OHCA were classified into three sub-phenotypes. The factor with the highest discriminative power upon patient's arrival was Glasgow Coma Scale followed by partial pressure of oxygen. Thirty-day survival with favorable neurological outcome as the primary outcome was evident in 66.0% participants in Group 1, 5.2% in Group 2, and 0.5% in Group 3. The 30-day survival rates were 80.6%, 11.8%, and 1.3% in groups 1, 2, and 3, respectively. Logistic regression analysis revealed that the ORs (95% CI) for 30-day survival with favorable neurological outcomes were 137.1 (99.4-192.2) for Group 1 and 4.59 (3.46-6.23) for Group 2 in comparison to Group 3. For 30-day survival, the ORs (95%CI) were 161.7 (124.2-212.1) for Group 1 and 5.78 (4.78-7.04) for Group 2, compared to Group 3.

CONCLUSIONS

This study identified three sub-phenotypes based on the prognostic factors available immediately after hospital arrival that could predict neurological outcomes and be useful in determining the treatment strategy of patients experiencing OHCA upon their arrival at the hospital.

Overview and future prospects of out-of-hospital cardiac arrest registries in Japan

Aim

Out-of-hospital cardiac arrest (OHCA) is a life-threatening emergency with high mortality. The "chain of survival" is critical to improving patient outcomes. To develop and enhance this chain of survival, measuring and monitoring the resuscitation processes and outcomes are essential for quality assurance. In Japan, several OHCA registries have successfully been implemented at both local and national levels. We aimed to review and summarise the conception, strengths, and challenges of OHCA registries in Japan.

Method and results

The following representing registries in Japan were reviewed: the All-Japan Utstein registry, the Utstein Osaka Project/the Osaka-CRITICAL study, the SOS-KANTO study, the JAAM-OHCA study, and the SAVE-J II study. The All-Japan Utstein registry, operated by the Fire and Disaster Management Agency of Japan and one of the largest nationwide population-based registries in the world, collects data concerning all patients with OHCA in Japan, excluding in-hospital data. Other research- and hospital-based registries collect detailed out-of-hospital and in-hospital data. The Osaka-CRITICAL study and the SOS-KANTO study are organized at regional levels, and hospitals in the Osaka prefecture and in the Kanto area participate in these registries. The JAAM-OHCA study is managed by the Japanese Association of Acute Medicine and includes 107 hospitals throughout Japan. The Save-J II study focuses on patients with OHCA treated with extracorporeal cardiopulmonary resuscitation.

Conclusion

Each OHCA registry has its own philosophy, strengths, perspectives, and challenges; however, all have been successful in contributing to the improvement of emergency medical service (EMS) systems through the quality improvement process. These registries are expected to be further utilized to enhance EMS systems and improve outcomes for patients with OHCA, while also contributing to the field of resuscitation science.

Application of the TiPS65 score for out-of-hospital cardiac arrest patients with initial non-shockable rhythm treated with ECPR

Background

The TiPS65 score is a validated scoring system used to predict neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients with shockable rhythm treated with extracorporeal cardiopulmonary resuscitation (ECPR). This study aimed to assess the predictive performance of the TiPS65 score in OHCA patients with initial non-shockable rhythm treated with ECPR.

Methods

This was a secondary analysis using the JAAM-OHCA registry, a multicenter prospective cohort study. The study included adult OHCA patients with initial non-shockable rhythm who underwent ECPR. The TiPS65 score assigned one point to each of four variables: time to hospital ≤25 minutes, pH value ≥7.0 on initial blood gas assessment, shockable on hospital arrival, and age younger than 65 years. Based on the sum score, the predictive performance for 1-month survival and favorable neurological outcomes, defined as the Cerebral Performance Category 1 or 2, was evaluated.

Results

Among 57,754 patients in the registry, 370 were included in the analysis. The overall one-month survival and favorable neurological outcome were 11.1% (41/370) and 4.2% (15/370), respectively. The 1-month survival rates based on the TiPS65 score were as follows: 11.2% (12/107) for 0 points, 9.3% (14/150) for 1 point, 10.0% (9/90) for 2 points, and 26.1% (6/23) for ≥3 points. Similarly, the 1-month favorable neurological outcomes were: 5.6% (6/107) for 0 points, 2.7% (4/150) for 1 point, 4.4% (4/90) for 2 points, and 4.3% (1/23) for ≥3 points. The area under the curve was 0.535 (95% CI: 0.437-0.630) for 1-month survival and 0.530 (95% CI: 0.372-0.683) for 1-month neurological outcome.

Conclusion

This study demonstrates that the TiPS65 score has limited prognostic performance among OHCA patients with initial non-shockable rhythm treated with ECPR. Further research is warranted to develop a predictive tool specifically focused on OHCA with initial non-shockable rhythm to aid in determining candidates for ECPR.

Explainable artificial intelligence in emergency medicine: an overview

Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a "black box," leading to trust issues. To address this, "explainable AI," which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of "explainable AI" for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.

AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

Aim

Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.

Main text

We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.

Conclusion

In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.