1
|
Tamura H, Yasuda H, Oishi T, Shinzato Y, Amagasa S, Kashiura M, Moriya T. Association between sub-phenotypes identified using latent class analysis and neurological outcomes in patients with out-of-hospital cardiac arrest in Japan. BMC Cardiovasc Disord 2024; 24:303. [PMID: 38877462 PMCID: PMC11177357 DOI: 10.1186/s12872-024-03975-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 06/10/2024] [Indexed: 06/16/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Hiroyuki Tamura
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Hideto Yasuda
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan.
| | - Takatoshi Oishi
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Yutaro Shinzato
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Shunsuke Amagasa
- Division of Emergency and Transport Services, National Center for Child Health and Development, Tokyo, Japan
| | - Masahiro Kashiura
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| | - Takashi Moriya
- Department of Emergency and Critical Care Medicine, Saitama Medical Center, Jichi Medical University, 1-847 Amanuma-Cho, Omiya-Ku, Saitama-Shi, Saitama, 330-8503, Japan
| |
Collapse
|
2
|
Okada Y, Nakagawa K, Tanaka H, Takahashi H, Kitamura T, Kiguchi T, Nishioka N, Kitamura N, Tagami T, Inoue A, Hifumi T, Sakamoto T, Kuroda Y, Iwami T. Overview and future prospects of out-of-hospital cardiac arrest registries in Japan. Resusc Plus 2024; 17:100578. [PMID: 38362506 PMCID: PMC10867571 DOI: 10.1016/j.resplu.2024.100578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Yohei Okada
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koshi Nakagawa
- Graduate School of Emergency Medical System, Kokushikan University, Japan
| | - Hideharu Tanaka
- Graduate School of Emergency Medical System, Kokushikan University, Japan
| | | | - Tetsuhisa Kitamura
- Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Takeyuki Kiguchi
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Emergency and Critical Care, Osaka General Medical Center, Osaka, Japan
| | - Norihiro Nishioka
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobuya Kitamura
- Department of Emergency and Critical Care Medicine, Kimitsu Chuo Hospital, Kisarazu-shi, Chiba, Japan
| | - Takashi Tagami
- Department of Emergency and Critical Care Medicine, Nippon Medical School Musashikosugi Hospital
| | - Akihiko Inoue
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Hyogo, Japan
| | - Toru Hifumi
- Department of Emergency and Critical Care Medicine, St. Luke’s International Hospital, Tokyo, Japan
| | - Tetsuya Sakamoto
- Department of Emergency Medicine, Teikyo University School of Medicine, Tokyo, Japan
| | - Yasuhiro Kuroda
- Department of Emergency, Disaster and Critical Care Medicine, Kagawa University Hospital, Kagawa, Japan
| | - Taku Iwami
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| |
Collapse
|
3
|
Fujiwara G, Okada Y, Shiomi N, Sakakibara T, Yamaki T, Hashimoto N. Derivation of Coagulation Phenotypes and the Association with Prognosis in Traumatic Brain Injury: A Cluster Analysis of Nationwide Multicenter Study. Neurocrit Care 2024; 40:292-302. [PMID: 36977962 DOI: 10.1007/s12028-023-01712-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/01/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND The pathogenesis and pathophysiology of traumatic coagulopathy during traumatic brain injury is not well understood, and the appropriate treatment strategy for this condition has not been established. This study aimed to evaluate the coagulation phenotypes and their effect on prognosis in patients with isolated traumatic brain injury. METHODS In this multicenter cohort study, we retrospectively analyzed data from the Japan Neurotrauma Data Bank. Adults with isolated traumatic brain injury (head abbreviated injury scale > 2; abbreviated injury scale of any other trauma < 3) who were registered in the Japan Neurotrauma Data Bank were included in this study. The primary outcome was the association of coagulation phenotypes with in-hospital mortality. Coagulation phenotypes were derived using k-means clustering with coagulation markers, including prothrombin time international normalized ratio (PT-INR), activated partial thromboplastin time (APTT), fibrinogen (FBG), and D-dimer (DD) on arrival at the hospital. Multivariable logistic regression analyses were conducted to calculate the adjusted odds ratios of coagulation phenotypes with their 95% confidence intervals (CIs) for in-hospital mortality. RESULTS In total, 556 patients were enrolled and five coagulation phenotypes were identified. The median (interquartile range) score for the Glasgow Coma Scale was 6 (4-9). Cluster A (n = 129) had the closest to normal coagulation values; cluster B (n = 323) had a mild high DD phenotype; cluster C (n = 30) had a prolonged PT-INR phenotype with a higher frequency of antithrombotic medication in elderly patients than in younger patients; cluster D (n = 45) had a low amount of FBG, high DD, and prolonged APTT phenotype with a high incidence of skull fracture; and cluster E (n = 29) had a low amount of FBG and extremely high DD phenotype with high energy trauma and a high incidence of skull fracture. In the multivariable logistic regression analysis, the association of clusters B, C, D, and E with in-hospital mortality yielded the corresponding adjusted odds ratios of 2.17 (95% CI 1.22-3.86), 2.61 (95% CI 1.01-6.72), 10.0 (95% CI 4.00-25.2), and 24.1 (95% CI 7.12-81.3), respectively, relative to cluster A. CONCLUSIONS This multicenter, observational study identified five different coagulation phenotypes of traumatic brain injury and showed associations of these phenotypes with in-hospital mortality.
Collapse
Affiliation(s)
- Gaku Fujiwara
- Department of Neurosurgery, Saiseikai Shiga Hospital, Imperial Gift Foundation Inc, 2-4-1, Ohashi, Ritto, Shiga, Japan.
| | - Yohei Okada
- Department of Preventive Services, School of Public Health, Kyoto University, Kyoto, Japan
| | - Naoto Shiomi
- Department of Critical and Intensive Care Medicine, Shiga University of Medical Science, Ritto, Shiga, Japan
| | | | - Tarumi Yamaki
- Department of Neurosurgery, Kyoto Kujo Hospital, Kyoto, Japan
| | - Naoya Hashimoto
- Department of Neurosurgery, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| |
Collapse
|
4
|
Abbott EE, Oh W, Dai Y, Feuer C, Chan L, Carr BG, Nadkarni GN. Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis. JMIR Aging 2023; 6:e51844. [PMID: 38059569 PMCID: PMC10721134 DOI: 10.2196/51844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/28/2023] [Accepted: 10/29/2023] [Indexed: 12/08/2023] Open
Abstract
Background Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.
Collapse
Affiliation(s)
- Ethan E Abbott
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Wonsuk Oh
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Yang Dai
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Cole Feuer
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Lili Chan
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Brendan G Carr
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Girish N Nadkarni
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| |
Collapse
|
5
|
Okada Y, Shahidah N, Ng YY, Chia MYC, Gan HN, Leong BSH, Mao DR, Ng WM, Edwin N, Kiguchi T, Nishioka N, Kitamura T, Iwami T, Ong MEH. Comparing outcomes of out-of-hospital cardiac arrest patients with initial shockable rhythm in Singapore and Osaka using population-based databases. Crit Care 2023; 27:479. [PMID: 38057881 PMCID: PMC10699037 DOI: 10.1186/s13054-023-04771-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 11/30/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Previous research indicated outcomes among refractory out-of-hospital cardiac arrest (OHCA) patients with initial shockable rhythm were different in Singapore and Osaka, Japan, possibly due to the differences in access to extracorporeal cardiopulmonary resuscitation. However, this previous study had a risk of selection bias. To address this concern, this study aimed to evaluate the outcomes between Singapore and Osaka for OHCA patients with initial shockable rhythm using only population-based databases. METHODS This was a secondary analysis of two OHCA population-based databases in Osaka and Singapore, including adult OHCA patients with initial shockable rhythm. A machine-learning-based prediction model was derived from the Osaka data (n = 3088) and applied to the PAROS-SG data (n = 2905). We calculated the observed-expected ratio (OE ratio) for good neurological outcomes observed in Singapore and the expected derived from the data in Osaka by dividing subgroups with or without prehospital ROSC. RESULTS The one-month good neurological outcomes in Osaka and Singapore among patients with prehospital ROSC were 70% (791/1,125) and 57% (440/773), and among patients without prehospital ROSC were 10% (196/1963) and 2.8% (60/2,132). After adjusting patient characteristics, the outcome in Singapore was slightly better than expected from Osaka in patients with ROSC (OE ratio, 1.067 [95%CI 1.012 to 1.125]), conversely, it was worse than expected in patients without prehospital ROSC (OE ratio, 0.238 [95%CI 0.173 to 0.294]). CONCLUSION This study showed the outcomes of OHCA patients without prehospital ROSC in Singapore were worse than expected derived from Osaka data even using population-based databases. (249/250 words).
Collapse
Affiliation(s)
- Yohei Okada
- Duke-NUS Medical School, Health Services and Systems Research, Singapore, Singapore.
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Nur Shahidah
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Yih Yng Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - Michael Y C Chia
- Emergency Department, Tan Tock Seng Hospital, Singapore, Singapore
| | - Han Nee Gan
- Accident and Emergency, Changi General Hospital, Singapore, Singapore
| | - Benjamin S H Leong
- Emergency Medicine Department, National University Hospital, Singapore, Singapore
| | - Desmond R Mao
- Department of Acute and Emergency Care, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Wei Ming Ng
- Emergency Medicine Department, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Nausheen Edwin
- Department of Emergency Medicine, Sengkang General Hospital, Singapore, Singapore
| | - Takeyuki Kiguchi
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Emergency and Critical Care, Osaka General Medical Center, Osaka, Japan
| | - Norihiro Nishioka
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tetsuhisa Kitamura
- Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Taku Iwami
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, Health Services and Systems Research, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| |
Collapse
|
6
|
Okada Y, Ning Y, Ong MEH. Explainable artificial intelligence in emergency medicine: an overview. Clin Exp Emerg Med 2023; 10:354-362. [PMID: 38012816 PMCID: PMC10790070 DOI: 10.15441/ceem.23.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/06/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Yohei Okada
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yilin Ning
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Marcus Eng Hock Ong
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore
| |
Collapse
|
7
|
Okada Y, Shahidah N, Ng YY, Chia MYC, Gan HN, Leong BSH, Mao DR, Ng WM, Irisawa T, Yamada T, Nishimura T, Kiguchi T, Kishimoto M, Matsuyama T, Nishioka N, Kiyohara K, Kitamura T, Iwami T, Ong MEH. Outcome assessment for out-of-hospital cardiac arrest patients in Singapore and Japan with initial shockable rhythm. Crit Care 2023; 27:351. [PMID: 37700335 PMCID: PMC10496207 DOI: 10.1186/s13054-023-04636-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Singapore and Osaka in Japan have comparable population sizes and prehospital management; however, the frequency of ECPR differs greatly for out-of-hospital cardiac arrest (OHCA) patients with initial shockable rhythm. Given this disparity, we hypothesized that the outcomes among the OHCA patients with initial shockable rhythm in Singapore were different from those in Osaka. The aim of this study was to evaluate the outcomes of OHCA patients with initial shockable rhythm in Singapore compared to the expected outcomes derived from Osaka data using machine learning-based prediction models. METHODS This was a secondary analysis of two OHCA databases: the Singapore PAROS database (SG-PAROS) and the Osaka-CRITICAL database from Osaka, Japan. This study included adult (18-74 years) OHCA patients with initial shockable rhythm. A machine learning-based prediction model was derived and validated using data from the Osaka-CRITICAL database (derivation data 2012-2017, validation data 2018-2019), and applied to the SG-PAROS database (2010-2016 data), to predict the risk-adjusted probability of favorable neurological outcomes. The observed and expected outcomes were compared using the observed-expected ratio (OE ratio) with 95% confidence intervals (CI). RESULTS From the SG-PAROS database, 1,789 patients were included in the analysis. For OHCA patients who achieved return of spontaneous circulation (ROSC) on hospital arrival, the observed favorable neurological outcome was at the same level as expected (OE ratio: 0.905 [95%CI: 0.784-1.036]). On the other hand, for those who had continued cardiac arrest on hospital arrival, the outcomes were lower than expected (shockable rhythm on hospital arrival, OE ratio: 0.369 [95%CI: 0.258-0.499], and nonshockable rhythm, OE ratio: 0.137 [95%CI: 0.065-0.235]). CONCLUSION This observational study found that the outcomes for patients with initial shockable rhythm but who did not obtain ROSC on hospital arrival in Singapore were lower than expected from Osaka. We hypothesize this is mainly due to differences in the use of ECPR.
Collapse
Affiliation(s)
- Yohei Okada
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Nur Shahidah
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Yih Yng Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - Michael Y C Chia
- Emergency Department, Tan Tock Seng Hospital, Singapore, Singapore
| | - Han Nee Gan
- Accident & Emergency, Changi General Hospital, Singapore, Singapore
| | - Benjamin S H Leong
- Emergency Medicine Department, National University Hospital, Singapore, Singapore
| | - Desmond R Mao
- Department of Acute and Emergency Care, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Wei Ming Ng
- Emergency Medicine Department, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Taro Irisawa
- Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Tomoki Yamada
- Emergency and Critical Care Medical Center, Osaka Police Hospital, Osaka, Japan
| | - Tetsuro Nishimura
- Department of Critical Care Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Takeyuki Kiguchi
- Critical Care and Trauma Center, Osaka General Medical Center, Osaka, Japan
| | - Masafumi Kishimoto
- Osaka Prefectural Nakakawachi Medical Center of Acute Medicine, Higashi-Osaka, Japan
| | - Tasuku Matsuyama
- Department of Emergency Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Norihiro Nishioka
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kosuke Kiyohara
- Department of Food Science Faculty of Home Economics, Otsuma Women's University, Tokyo, Japan
| | - Tetsuhisa Kitamura
- Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Taku Iwami
- Department of Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Marcus Eng Hock Ong
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| |
Collapse
|
8
|
Okada Y, Mertens M, Liu N, Lam SSW, Ong MEH. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resusc Plus 2023; 15:100435. [PMID: 37547540 PMCID: PMC10400904 DOI: 10.1016/j.resplu.2023.100435] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Yohei Okada
- Duke-NUS Medical School, National University of Singapore, Singapore
- Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mayli Mertens
- Antwerp Center for Responsible AI, Antwerp University, Belgium
- Centre for Ethics, Department of Philosophy, Antwerp University, Belgium
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Sean Shao Wei Lam
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital
| |
Collapse
|
9
|
Nakashima T. Should We Focus on the "Who" When Identifying Candidates for Extracorporeal Cardiopulmonary Resuscitation? Circ J 2021; 86:677-678. [PMID: 34866123 DOI: 10.1253/circj.cj-21-0910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Takahiro Nakashima
- Department of Emergency Medicine and Michigan Center for Integrative Research in Critical Care, University of Michigan
| |
Collapse
|