Association between type of bystander cardiopulmonary resuscitation and survival in out-of-hospital cardiac arrest: A machine learning study

Jerkeman, Matilda; Lundgren, Peter; Ömerovic, Elmir; Strömsöe, Anneli; Riva, Gabriel; Hollenberg, Jacob; Nivedahl, Per; Herlitz, Johan; Rawshani, Araz

doi:10.1016/j.resplu.2022.100245

Cited by 5 publications

(2 citation statements)

References 18 publications

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“… 54 , 55 , 56 Three retrospective studies employed ML to analyze OHCA patient care reports to evaluate data integrity and predict outcomes, one of which also used NLP. 57 , 58 , 59 Finally, two studies described the use of ML and DL to augment the lay rescuer and prehospital response to OHCA including chatbots to guide lay rescuer OHCA response 60 and the use of drones to deliver automated external defibrillators 61 …”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

Toy,

Bosson,

Schlesinger

et al. 2023

Resuscitation Plus

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

Toy,

Bosson,

Schlesinger

et al. 2023

Resuscitation Plus

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“…Recent advances in machine learning provide a robust platform to explore complex interrelations among clinical variables [7][8][9] . Cardiology has increasingly used this approach to predict outcomes and guide treatment strategies, showcasing a promising avenue for understanding and managing complex conditions like TTS [10][11][12] .…”

mentioning

confidence: 99%

Importance of hospital and clinical factors for early mortality in Takotsubo syndrome: Insights from the Swedish Coronary Angiography and Angioplasty Registry

Gudmundsson,

Redfors,

Råmunddal

et al. 2024

Preprint

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Background Takotsubo syndrome (TTS) is an acute heart failure syndrome with symptoms similar to acute myocardial infarction. TTS is often triggered by acute emotional or physical stress and is a significant cause of morbidity and mortality among patients with acute coronary syndromes. Predictors of mortality in patients with TS are not well understood, and there is a need to identify high-risk patients and tailor treatment accordingly. This study aimed to assess the importance of various clinical factors in predicting 30-day mortality in TTS patients using a machine learning algorithm. Methods We analyzed data from the nationwide Swedish Coronary Angiography and Angioplasty Registry (SCAAR) for all patients with TTS in Sweden between 2015–2022. Gradient boosting was used to assess the relative importance of variables in predicting 30-day mortality in TTS patients. Results Of 3,180 patients hospitalized with TTS, 76.0% were women. The median age was 71.0 years (interquartile range 62–77). The crude all-cause mortality rate was 3.2% at 30 days. Machine learning algorithms by gradient boosting identified treating hospital as the most important predictor of 30-day mortality. This factor was followed in significance by the clinical indication for angiography, creatinine level, Killip class, and age. Other less important factors included weight, height, and certain medical conditions such as hyperlipidemia and smoking status. Conclusions In an analysis of all Swedish patients diagnosed with TTS over seven years, using machine learning with gradient boosting, we found that the treating hospital was the most significant predictor of 30-day mortality.

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Artificial intelligence in emergency medicine. A systematic literature review

Piliuk,

Tomforde

2023

International Journal of Medical Informatics

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Association between type of bystander cardiopulmonary resuscitation and survival in out-of-hospital cardiac arrest: A machine learning study

Cited by 5 publications

References 18 publications

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

Importance of hospital and clinical factors for early mortality in Takotsubo syndrome: Insights from the Swedish Coronary Angiography and Angioplasty Registry

Artificial intelligence in emergency medicine. A systematic literature review

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