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832 results on '"Ma, Matthew Huei‐Ming"'

1. End-tidal Carbon Dioxide Trajectory-based Prognostication of Out-of-hospital Cardiac Arrest

Author

Wang, Chih-Hung, Lu, Tsung-Chien, Tay, Joyce, Wu, Cheng-Yi, Wu, Meng-Che, Huang, Chun-Yen, Tsai, Chu-Lin, Huang, Chien-Hua, Ma, Matthew Huei-Ming, and Chen, Wen-Jone

Subjects
cardiopulmonary resuscitation, End-tidal Carbon Dioxide, Group-based trajectory modelling, Out-of-hospital Cardiac Arrest, survival, trajectory
Abstract

Background: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO2) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO2 trajectory-based prediction model for prognostication at specific time points during CPR in patients with out-of-hospital cardiac arrest (OHCA).Methods: We screened patients receiving CPR between 2015–2021 from a prospectively collected database of a tertiary-care medical center. The primary outcome was survival to hospital discharge. We used group-based trajectory modeling to identify the EtCO2 trajectories. Multivariable logistic regression analysis was used for model development and internally validated using bootstrapping. We assessed performance of the model using the area under the receiver operating characteristic curve (AUC).Results: The primary analysis included 542 patients with a median age of 68.0 years. Three distinct EtCO2 trajectories were identified in patients resuscitated for 20 minutes (min): low (average EtCO2 10.0 millimeters of mercury [mm Hg]; intermediate (average EtCO2 26.5 mm Hg); and high (average EtCO2: 51.5 mm Hg). Twenty-min EtCO2 trajectory was fitted as an ordinal variable (low, intermediate, and high) and positively associated with survival (odds ratio 2.25, 95% confidence interval [CI] 1.07–4.74). When the 20-min EtCO2 trajectory was combined with other variables, including arrest location and arrest rhythms, the AUC of the 20-min prediction model for survival was 0.89 (95% CI 0.86–0.92). All predictors in the 20-min model remained statistically significant after bootstrapping.Conclusion: Time-specific EtCO2 trajectory was a significant predictor of OHCA outcomes, which could be combined with other baseline variables for intra-arrest prognostication. For this purpose, the 20-min survival model achieved excellent discriminative performance in predicting survival to hospital discharge.

Published
2024

4. Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department

Author

Tay, Joyce, Yen, Yi-Hsuan, Rivera, Kevin, Chou, Eric H, Wang, Chih-Hung, Chou, Fan-Ya, Sun, Jen-Tang, Han, Shih-Tsung, Tsai, Tzu-Ping, Chen, Yen-Chia, Bhakta, Toral, Tsai, Chu-Lin, Lu, Tsung-Chien, and Ma, Matthew Huei-Ming

Subjects
Machine learning, Emergency department, COVID-19, SARS-CoV-2
Abstract

Introduction: Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population.Methods: To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23–May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12–June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort.Results: In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747–0.822) performed better than gradient boosting (0.774, 95% CI 0.739–0.811) and extra trees classifier (0.72, 95% CI 0.677–0.762). There was no significant difference between the constructed models.Conclusion: Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.

Published
2024

12. Mortality Variations of COVID-19 from Different Hospital Settings During Different Pandemic Phases: A Multicenter Retrospective Study

Author

Chou, Eric H., Wang, Chih-Hung, Tsai, Chu-Lin, Garrett, John, Bhakta, Toral, Shedd, Andrew, Hassani, Dahlia, Risch, Robert, d’Etienne, James, Ogola, Gerald O., Ma, Matthew Huei-Ming, Lu, Tsung-Chien, and Wang, Hao

Subjects
COVID-19, SARS-CoV-2, mortality, emergency department
Abstract

Introduction: Diverse coronavirus disease 2019 (COVID-19) mortalities have been reported but focused on identifying susceptible patients at risk of more severe disease or death. This study aims to investigate the mortality variations of COVID-19 from different hospital settings during different pandemic phases. Methods: We retrospectively included adult (≥18 years) patients who visited emergency departments (ED) of five hospitals in the state of Texas and who were diagnosed with COVID-19 between March–November 2020. The included hospitals were dichotomized into urban and suburban based on their geographic location. The primary outcome was mortality that occurred either during hospital admission or within 30 days after the index ED visit. We used multivariable logistic regression to investigate the associations between independent variables and outcome. Generalized additive models were employed to explore the mortality variation during different pandemic phases. Results: A total of 1,788 adult patients who tested positive for COVID-19 were included in the study. The median patient age was 54.6 years, and 897 (50%) patients were male. Urban hospitals saw approximately 59.5% of the total patients. A total of 197 patients died after the index ED visit. The analysis indicated visits to the urban hospitals (odds ratio [OR] 2.14, 95% confidence interval [CI], 1.41, 3.23), from March to April (OR 2.04, 95% CI, 1.08, 3.86), and from August to November (OR 2.15, 95% CI, 1.37, 3.38) were positively associated with mortality. Conclusion: Visits to the urban hospitals were associated with a higher risk of mortality in patients with COVID-19 when compared to visits to the suburban hospitals. The mortality risk rebounded and showed significant difference between urban and suburban hospitals since August 2020. Optimal allocation of medical resources may be necessary to bridge this gap in the foreseeable future.

Published
2021

21. Outcomes of out-of-hospital cardiac arrests after a decade of system-wide initiatives optimising community chain of survival in Taipei city

Author

Lin, Hao-Yang, Chien, Yu-Chun, Lee, Bin-Chou, Wu, Yung-Lung, Liu, Yueh-Ping, Wang, Tzong-Luen, Ko, Patrick Chow-In, Chong, Kah-Meng, Wang, Hui-Chih, Huang, Edward Pei-Chuan, Sun, Jen-Tang, Hsieh, Ming-Ju, Hou, Sheng-Wen, Yang, Wen-Shuo, Wang, Yao-Cheng, Huang, Chien-Hua, Chiang, Wen-Chu, and Ma, Matthew Huei-Ming

Published
2022
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23. International multi-center real world implementation trial to increase out-of-hospital cardiac arrest survival with a dispatcher-assisted cardio-pulmonary resuscitation package (Pan-Asian resuscitation outcomes study phase 2)

Author

Ong, Marcus Eng Hock, Shin, Sang Do, Ko, Patrick Chow-In, Lin, Xinyi, Ma, Matthew Huei-Ming, Ryoo, Hyun Wook, Wong, Kwanhathai Darin, Supasaowapak, Jirapong, Lin, Chih-Hao, Kuo, Chan-Wei, Rao, Ramana, Cai, Wenwei, Gaerlan, Faith Joan, Khursheed, Munawar, Son, Do Ngoc, Sarah, Karim, El Sayed, Mazen, Al Qahtani, Saad, and Tanaka, Hideharu

Published
2022
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26. Development and Evaluation of a Novel Resuscitation Teamwork Model for Out-of-Hospital Cardiac Arrest in the Emergency Department

Author

Chong, Kah Meng, Chou, Eric Hao-Chang, Chiang, Wen-Chu, Wang, Hui-Chih, Liu, Yeh-Ping, Ko, Patrick Chow-In, Huang, Edward Pei-Chuan, Hsieh, Ming-Ju, Lin, Hao-Yang, Lien, Wan-Ching, Huang, Chien-Hua, Fang, Cheng-Chung, Chen, Shyr-Chyr, Bhanji, Farhan, Yang, Chih-Wei, and Ma, Matthew Huei-Ming

Abstract

Cardiopulmonary resuscitation (CPR) is critical for out-of-hospital cardiac arrest patients but is prone to rapid changes and errors. Effective teamwork and leadership are essential for high-quality CPR. We aimed to introduce the Airway-Circulation-Leadership-Support (A-C-L-S) teamwork model in the emergency department (ED) to address these challenges.

Published
2025
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28. Clinical decision rules for termination of resuscitation during in-hospital cardiac arrest: A systematic review of diagnostic test accuracy studies

Author

Bigham, Blair L., Bray, Janet E., Breckwoldt, Jan, Duff, Jonathan P., Gilfoyle, Elaine, Hsieh, Ming-Ju, Iwami, Taku, Lockey, Andrew S., Ma, Matthew Huei-Ming, Monsieurs, Koenraad G., Pellegrino, Jeffrey L., Yeung, Joyce, Finn, Judith, Morley, Peter, Lauridsen, Kasper G., Baldi, Enrico, Smyth, Michael, Perkins, Gavin D., and Greif, Robert

Published
2021
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29. Significant reduction in full‐thickness pressure injuries through the quality improvement project that implements alternating pressure air mattresses.

Author

Chang, Hui‐Hsiu, Chiang, Wen‐Chu, Hsieh, Chia‐Chin, Chang, Chen‐Shu, Liao, Hui‐Chun, Torng, Chau‐Chen, and Ma, Matthew Huei‐Ming

Subjects
STATISTICAL correlation, HUMAN services programs, BEDSORE risk factors, RETROSPECTIVE studies, DESCRIPTIVE statistics, LONGITUDINAL method, SURVEYS, RESEARCH, QUALITY assurance, WOUND care, PRESSURE ulcers, EVALUATION
Abstract

To assess a quality improvement project using alternating pressure air mattresses' impact on reducing full‐thickness pressure injuries by enhancing setting accuracy and device utilization. We retrospectively evaluated adult acute care unit patients with inclusion criteria (Braden scores ≤12 or existing full‐thickness pressure injuries) between May 2020 and August 2023. A wound team attempted to enhance the accuracy, utilization and effectiveness of alternating pressure air mattress implementation. The implementation outcomes were setting accuracy and accurate utilization rates. The clinical outcome was the full‐thickness pressure injury proportion. Utilization and allocation gaps were also calculated. The setting accuracy and accurate utilization rates increased (0.59 to 0.88 and 0.15 to 0.37, respectively). The full‐thickness pressure injury proportion decreased (0.17 to 0.06), with a strong negative correlation coefficient (−0.789) (p < 0.001) with accurate utilization rates. The full‐thickness pressure injury proportion declined faster during the project's complete phase than the partial phase (−0.0046 vs. −0.0016; p < 0.05). The utilization gap narrowed (99 to 60); however, the allocation gap increased (1 to 13), suggesting increased alternating pressure air mattress usage among ineligible patients. Targeting high‐risk patients for alternating pressure air mattress utilization and ensuring correct settings, both performed by a dedicated team, substantially reduces the full‐thickness pressure injury incidence. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Education, Implementation, and Teams: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

Author

Greif, Robert, Bhanji, Farhan, Bigham, Blair L., Bray, Janet, Breckwoldt, Jan, Cheng, Adam, Duff, Jonathan P., Gilfoyle, Elaine, Hsieh, Ming-Ju, Iwami, Taku, Lauridsen, Kasper G., Lockey, Andrew S., Ma, Matthew Huei-Ming, Monsieurs, Koenraad G., Okamoto, Deems, Pellegrino, Jeffrey L., Yeung, Joyce, Finn, Judith C., Baldi, Enrico, Beck, Stefanie, Beckers, Stefan K., Blewer, Audrey L., Boulton, Adam, Cheng-Heng, Liu, Yang, Chi-Wie, Coppola, Alison, Dainty, Katie N., Damjanovic, Domagoj, Djärv, Therese, Donoghue, Aaron, Georgiou, Marios, Gunson, Imogen, Krob, Jamillee L., Kuzovlev, Artem, Ko, Ying-Chih, Leary, Marion, Lin, Yiqun, Mancini, Mary E., Matsuyama, Tasuku, Navarro, Kenneth, Nehme, Ziad, Orkin, Aaron M., Pellis, Tommaso, Pflanzl-Knizacek, Lucas, Pisapia, Luca, Saviani, Michela, Sawyer, Taylor, Scapigliati, Andrea, Schnaubelt, Sebastian, Scholefield, Barnaby, Semeraro, Federico, Shammet, Salma, Smyth, Michael A., Ward, Abigail, and Zace, Drieda

Published
2020
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33. Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients

Author

Hisamuddin, N.A.R., Karim, S., Julina, M.N., Omer, A.S., Yagdir, T., Khunkhlai, N., Monsomboon, A., Piyasuwankul, T., Wong, K.D., Mao, D.R., Tham, L.P., Kajino, K., Nishiuchi, T., Cheah, S.O., Chia, M.Y.C., Tiah, L., Shin, S.D., Koh, P.C.I., Ryoo, H.W., Liu, Nan, Ong, Marcus Eng Hock, Ho, Andrew Fu Wah, Pek, Pin Pin, Lu, Tsung-Chien, Khruekarnchana, Pairoj, Song, Kyoung Jun, Tanaka, Hideharu, Naroo, Ghulam Yasin, Gan, Han Nee, Koh, Zhi Xiong, and Ma, Matthew Huei-Ming

Published
2020
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38. 2019 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

Author

Soar, Jasmeet, Maconochie, Ian, Wyckoff, Myra H., Olasveengen, Theresa M., Singletary, Eunice M., Greif, Robert, Aickin, Richard, Bhanji, Farhan, Donnino, Michael W., Mancini, Mary E., Wyllie, Jonathan P., Zideman, David, Andersen, Lars W., Atkins, Dianne L., Aziz, Khalid, Bendall, Jason, Berg, Katherine M., Berry, David C., Bigham, Blair L., Bingham, Robert, Couto, Thomaz Bittencourt, Böttiger, Bernd W., Borra, Vere, Bray, Janet E., Breckwoldt, Jan, Brooks, Steven C., Buick, Jason, Callaway, Clifton W., Carlson, Jestin N., Cassan, Pascal, Castrén, Maaret, Chang, Wei-Tien, Charlton, Nathan P., Cheng, Adam, Chung, Sung Phil, Considine, Julie, Couper, Keith, Dainty, Katie N., Dawson, Jennifer Anne, de Almeida, Maria Fernanda, de Caen, Allan R., Deakin, Charles D., Drennan, Ian R., Duff, Jonathan P., Epstein, Jonathan L., Escalante, Raffo, Gazmuri, Raúl J., Gilfoyle, Elaine, Granfeldt, Asger, Guerguerian, Anne-Marie, Guinsburg, Ruth, Hatanaka, Tetsuo, Holmberg, Mathias J., Hood, Natalie, Hosono, Shigeharu, Hsieh, Ming-Ju, Isayama, Tetsuya, Iwami, Taku, Jensen, Jan L., Kapadia, Vishal, Kim, Han-Suk, Kleinman, Monica E., Kudenchuk, Peter J., Lang, Eddy, Lavonas, Eric, Liley, Helen, Lim, Swee Han, Lockey, Andrew, Lofgren, Bo, Ma, Matthew Huei-Ming, Markenson, David, Meaney, Peter A., Meyran, Daniel, Mildenhall, Lindsay, Monsieurs, Koenraad G., Montgomery, William, Morley, Peter T., Morrison, Laurie J., Nadkarni, Vinay M., Nation, Kevin, Neumar, Robert W., Ng, Kee-Chong, Nicholson, Tonia, Nikolaou, Nikolaos, Nishiyama, Chika, Nuthall, Gabrielle, Ohshimo, Shinichiro, Okamoto, Deems, O’Neil, Brian, Ong, Gene Yong-Kwang, Paiva, Edison F., Parr, Michael, Pellegrino, Jeffrey L., Perkins, Gavin D., Perlman, Jeffrey, Rabi, Yacov, Reis, Amelia, Reynolds, Joshua C., Ristagno, Giuseppe, Roehr, Charles C., Sakamoto, Tetsuya, Sandroni, Claudio, Schexnayder, Stephen M., Scholefield, Barnaby R., Shimizu, Naoki, Skrifvars, Markus B., Smyth, Michael A., Stanton, David, Swain, Janel, Szyld, Edgardo, Tijssen, Janice, Travers, Andrew, Trevisanuto, Daniele, Vaillancourt, Christian, Van de Voorde, Patrick, Velaphi, Sithembiso, Wang, Tzong-Luen, Weiner, Gary, Welsford, Michelle, Woodin, Jeff A., Yeung, Joyce, Nolan, Jerry P., and Hazinski, Mary Fran

Published
2019
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39. Cardiac Arrest and Cardiopulmonary Resuscitation Outcome Reports: Update of the Utstein Resuscitation Registry Template for In-Hospital Cardiac Arrest: A Consensus Report From a Task Force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa, Resuscitation Council of Asia)

Author

Aickin, Richard, Atkins, Dianne L., Berg, Katherine M., Bingham, Robert, Böttiger, Bernd W., Brooks, Steven C., Callaway, Clifton W., Castrén, Maaret, Chung, Sung Phil, Considine, Julie, Couto, Thomaz Bittencourt, de Caen, Allan R., Deakin, Charles D., Drennan, Ian R., Escalante, Raffo, Gazmuri, Raúl J., Guerguerian, Anne-Marie, Hazinski, Mary Fran, Kudenchuk, Peter J., Løfgren, Bo, Maconochie, Ian, Mancini, Mary E., Meaney, Peter A., Neumar, Robert W., Ng, Kee-Chong, Nicholson, Tonia C., Nishiyama, Chika, Nuthall, Gabrielle A., Olasveengen, Theresa M., Paiva, Edison F., Parr, Michael J., Reis, Amelia G., Reynolds, Joshua C., Ristagno, Giuseppe, Sandroni, Claudio, Schexnayder, Stephen M., Scholefield, Barnaby R., Smyth, Michael A., Stanton, David, Tijssen, Janice A., Vaillancourt, Christian, Van de Voorde, Patrick, Wang, Tzong-Luen, Welsford, Michelle, Nolan, Jerry P., Berg, Robert A., Andersen, Lars W., Bhanji, Farhan, Chan, Paul S., Donnino, Michael W., Lim, Swee Han, Ma, Matthew Huei-Ming, Nadkarni, Vinay M., Starks, Monique A., Perkins, Gavin D., Morley, Peter T., and Soar, Jasmeet

Published
2019
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42. International variation in survival after out-of-hospital cardiac arrest: A validation study of the Utstein template

Author

Dyson, Kylie, Brown, Siobhan P., May, Susanne, Smith, Karen, Koster, Rudolph W., Beesems, Stefanie G., Kuisma, Markku, Salo, Ari, Finn, Judith, Sterz, Fritz, Nürnberger, Alexander, Morrison, Laurie J., Olasveengen, Theresa M., Callaway, Clifton W., Shin, Sang Do, Gräsner, Jan-Thorsten, Daya, Mohamud, Ma, Matthew Huei-Ming, Herlitz, Johan, Strömsöe, Anneli, Aufderheide, Tom P., Masterson, Siobhán, Wang, Henry, Christenson, Jim, Stiell, Ian, Vilke, Gary M., Idris, Ahamed, Nishiyama, Chika, Iwami, Taku, and Nichol, Graham

Published
2019
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