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9. Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study

Author

Participating Nation Investigators, Ramakrishnan, T.V., Jamaluddin, Sabariah Faizah, Tanaka, Hideharu, Velasco, Bernadett, Hong, Ki Jeong, Sun, Jen Tang, Khruekarnchana, Pairoj, Fares, Saleh, Son, Do Ngoc, Participating Site Investigators, Rao, Ramana, Abraham, George P., Bin Mohidin, Mohd Amin, Saim, Al-Hilmi, Kean, Lim Chee, Anthonysamy, Cecilia, Din Mohd Yssof, Shah Jahan, Ji, Kang Wen, Kheng, Cheah Phee, Ali, Shamila bt Mohamad, Ramanathan, Periyanayaki, Yang, Chia Boon, Chia, Hon Woei, Hamad, Hafidahwati Binti, Ismail, Samsu Ambia, Wan Abdullah, Wan Rasydan B., Kimura, Akio, Gundran, Carlos D., Convocar, Pauline, Sabarre, Nerissa G., Tiglao, Patrick Joseph, Song, Kyoung Jun, Jeong, Joo, Moon, Sung Woo, Kim, Joo-yeong, Cha, Won Chul, Lee, Seung Chul, Ahn, Jae Yun, Lee, Kang Hyeon, Yeom, Seok Ran, Ryu, Hyeon Ho, Kim, Su Jin, Kim, Sang Chul, Hu, Ray-Heng, Wang, Ruei-Fang, Hsieh, Shang-Lin, Kao, Wei-Fong, Riyapan, Sattha, Tianwibool, Parinya, Buaprasert, Phudit, Akaraborworn, Osaree, Al Sakaf, Omer Ahmed, Huy, Le Bao, Van Dai, Nguyen, Wang, Shao-An, Chang, Chih-Jung, Do Shin, Shan, Chu, Sheng-En, Huang, Chun-Yen, Hsu, Li-Min, Lin, Hao-Yang, Chiang, Wen-Chu, Sun, Jen-Tang, and Huei-Ming Ma, Matthew

Published
2024
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37. Association between Case Volumes of Extracorporeal Life Support and Clinical Outcome in Out-of-Hospital Cardiac Arrest.

Author

Choi, Seulki, Hong, Ki Jeong, Lee, Stephen Gyung Won, Kim, Tae Han, Shin, Sang Do, Song, Kyoung Jun, Ro, Young Sun, Jeong, Joo, Park, Jeong Ho, and Lee, Gyeong Min

Subjects
CARDIOPULMONARY resuscitation, RESEARCH, SCIENTIFIC observation, CONFIDENCE intervals, CROSS-sectional method, CONVALESCENCE, MULTIPLE regression analysis, EXTRACORPOREAL membrane oxygenation, TREATMENT effectiveness, COMPARATIVE studies, CARDIAC arrest, EMERGENCY medical services, DESCRIPTIVE statistics, ODDS ratio, STATISTICAL correlation, EMERGENCY medicine, EVALUATION
Abstract

Extracorporeal life support (ECLS) for out-of-hospital cardiac arrest (OHCA) is increasing. There is little evidence identifying the association between hospital ECLS case volumes and outcomes in different populations receiving ECLS or conventional cardiopulmonary resuscitation (CPR). The goal of this investigation was to identify the association between ECLS case volumes and clinical outcomes of OHCA patients. This cross-sectional observational study used the National OHCA Registry for adult OHCA cases in Seoul, Korea between January 2015 and December 2019. If the ECLS volume during the study period was >20, the institution was defined as a high-volume ECLS center. Others were defined as low-volume ECLS centers. Outcomes were good neurologic recovery (cerebral performance category 1 or 2) and survival to discharge. We performed multivariate logistic regression and interaction analyses to assess the association between case volume and clinical outcome. Of the 17,248 OHCA cases, 3,731 were transported to high-volume centers. Among the patients who underwent ECLS, those at high-volume centers had a higher neurologic recovery rate than those at low-volume centers (17.0% vs. 12.0%), and the adjusted OR for good neurologic recovery was 2.22 (95% confidence interval (CI): 1.15–4.28) in high-volume centers compared to low-volume centers. For patients who received conventional CPR, high-volume centers also showed higher survival-to-discharge rates (adjusted OR of 1.16, 95%CI: 1.01–1.34). High-volume ECLS centers showed better neurological recovery in patients who underwent ECLS. High-volume centers also had better survival-to-discharge rates than low-volume centers for patients not receiving ECLS. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Machine Learning Analysis to Identify Data Entry Errors in Prehospital Patient Care Reports: A Case Study of a National Out-of-Hospital Cardiac Arrest Registry.

Author

Choi, Dong Hyun, Park, Jeong Ho, Choi, Young Ho, Song, Kyoung Jun, Kim, Sungwan, and Shin, Sang Do

Subjects
PREVENTION of medical errors, DATA quality, STATISTICS, CONFIDENCE intervals, CROSS-sectional method, MACHINE learning, MANN Whitney U Test, DOCUMENTATION, HUMAN services programs, INTER-observer reliability, CARDIAC arrest, HUMAN error, QUALITY assurance, EMERGENCY medical services, CHI-squared test, DESCRIPTIVE statistics, RESEARCH funding, PREDICTION models, LOGISTIC regression analysis, RECEIVER operating characteristic curves, STATISTICAL sampling, EMERGENCY medicine, ALGORITHMS
Abstract

Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable. Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71–0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models. [ABSTRACT FROM AUTHOR]

Published
2024
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