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Nikravangolsefid N, Reddy S, Truong HH, Charkviani M, Ninan J, Prokop LJ, Suppadungsuk S, Singh W, Kashani KB, Garces JPD. Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review. J Crit Care 2024; 84:154889. [PMID: 39059094 DOI: 10.1016/j.jcrc.2024.154889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/10/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
INTRODUCTION Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis. METHODS Following a pre-established protocol registered at the International Prospective Register of Systematic Reviews, we performed a comprehensive search of databases from inception to February 2024. We included peer-reviewed articles reporting predicting mortality in critically ill adult patients with sepsis. RESULTS Among the 1822 articles, 31 were included, involving 1,477,200 adult patients with sepsis. Nineteen studies had a high risk of bias. Among the diverse ML models, Logistic regression and eXtreme Gradient Boosting were the most frequently used, in 22 and 16 studies, respectively. Nine studies performed internal and external validation. Compared with conventional scoring systems such as SOFA, the ML models showed slightly higher performance in predicting mortality (AUROC ranges: 0.62-0.90 vs. 0.47-0.86). CONCLUSIONS ML models demonstrate a modest improvement in predicting sepsis-associated mortality. The certainty of these findings remains low due to the high risk of bias and significant heterogeneity. Studies should include comprehensive methodological details on calibration and hyperparameter selection, adopt a standardized definition of sepsis, and conduct multicenter prospective designs along with external validations.
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Affiliation(s)
- Nasrin Nikravangolsefid
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Swetha Reddy
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Hong Hieu Truong
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Saint Francis Hospital, Department of Medicine, Evanston, IL, USA
| | - Mariam Charkviani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jacob Ninan
- Department of Nephrology and Critical Care, MultiCare Capital Medical Center, Olympia, WA, USA
| | | | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Waryaam Singh
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Juan Pablo Domecq Garces
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA; Department of Critical Care Medicine, Mayo Clinic Health System, Mankato, MN, USA.
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Wang L, Ma X, Zhou G, Gao S, Pan W, Chen J, Su L, He H, Long Y, Yin Z, Shu T, Zhou X. SOFA in sepsis: with or without GCS. Eur J Med Res 2024; 29:296. [PMID: 38790024 PMCID: PMC11127461 DOI: 10.1186/s40001-024-01849-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/18/2024] [Indexed: 05/26/2024] Open
Abstract
PURPOSE Sepsis is a global public health burden. The sequential organ failure assessment (SOFA) is the most commonly used scoring system for diagnosing sepsis and assessing severity. Due to the widespread use of endotracheal intubation and sedative medications in sepsis, the accuracy of the Glasgow Coma Score (GCS) is the lowest in SOFA. We designed this multicenter, cross-sectional study to investigate the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis. METHODS First, 3048 patients with sepsis admitted to Peking Union Medical College Hospital (PUMCH) were enrolled in this survey. The data were collected from June 8, 2013 to October 12, 2022. Second, 18,108 patients with sepsis in the eICU database were enrolled. Third, 2397 septic patients with respiratory system ≥ 3 points in SOFA in the eICU database were included. We investigated the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis in various ICUs of PUMCH, and then we validated the results in the eICU database. MAIN RESULTS In data of ICUs in PUMCH, the predictive efficiency of SOFA without GCS (AUROC [95% CI], 24 h, 0.724 [0.688, 0.760], 48 h, 0.734 [0.699, 0.769], 72 h, 0.748 [0.713, 0.783], 168 h, 0.781 [0.747, 0.815]) was higher than that of SOFA with GCS (AUROC [95% CI], 24 h, 0.708 [0.672, 0.744], 48 h, 0.721 [0.685, 0.757], 72 h, 0.735 [0.700, 0.757], 168 h, 0.770 [0.736, 0.804]) on ICU mortality in patients with sepsis, and the difference was statistically significant (P value, 24 h, 0.001, 48 h, 0.003, 72 h, 0.004, 168 h, 0.005). In septic patients with respiratory system ≥ 3 points in SOFA in the eICU database, although the difference was not statistically significant (P value, 24 h, 0.148, 48 h, 0.178, 72 h, 0.132, 168 h, 0.790), SOFA without GCS (AUROC [95% CI], 24 h, 0.601 [0.576, 0.626], 48 h, 0.625 [0.601, 0.649], 72 h, 0.639 [0.615, 0.663], 168 h, 0.653 [0.629, 0.677]) had a higher predictive efficiency on ICU mortality than SOFA with GCS (AUROC [95% CI], 24 h, 0.591 [0.566, 0.616], 48 h, 0.616 [0.592, 0.640], 72 h, 0.628 [0.604, 0.652], 168 h, 0.651 [0.627, 0.675]). CONCLUSIONS In severe sepsis, it is realistic and feasible to discontinue the routine GCS for SOFA in patients with a respiratory system ≥ 3 points, and even better predict ICU mortality.
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Affiliation(s)
- Lu Wang
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College and Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Xudong Ma
- Department of Medical Administration, National Health Commission of the People's Republic of China, Beijing, 100044, China
| | - Guanghua Zhou
- Department of Information Technology, Center of Statistics and Health Informatics, National Health Commission of the People's Republic of China, Beijing, 100044, China
| | - Sifa Gao
- Department of Medical Administration, National Health Commission of the People's Republic of China, Beijing, 100044, China
| | - Wei Pan
- Information Center Department/Department of Information Management, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jieqing Chen
- Information Center Department/Department of Information Management, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College and Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Huaiwu He
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College and Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College and Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Zhi Yin
- Department of Intensive Care Unit, The People's Hospital of Zizhong, Neijiang, 641000, Sichuang, China.
| | - Ting Shu
- National Institute of Hospital Administration, Beijing, 100730, China.
| | - Xiang Zhou
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College and Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, 100730, China.
- Information Center Department/Department of Information Management, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
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Palmowski L, Nowak H, Witowski A, Koos B, Wolf A, Weber M, Kleefisch D, Unterberg M, Haberl H, von Busch A, Ertmer C, Zarbock A, Bode C, Putensen C, Limper U, Wappler F, Köhler T, Henzler D, Oswald D, Ellger B, Ehrentraut SF, Bergmann L, Rump K, Ziehe D, Babel N, Sitek B, Marcus K, Frey UH, Thoral PJ, Adamzik M, Eisenacher M, Rahmel T. Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction. PLoS One 2024; 19:e0300739. [PMID: 38547245 PMCID: PMC10977876 DOI: 10.1371/journal.pone.0300739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/04/2024] [Indexed: 04/01/2024] Open
Abstract
INTRODUCTION An increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease's trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction. METHODS We used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation. RESULTS Both SVM (AUC 0.84; 95% CI: 0.71-0.96) and aNN (AUC 0.82; 95% CI: 0.69-0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65-0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58-0.74; p < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort. CONCLUSIONS The ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units.
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Affiliation(s)
- Lars Palmowski
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Hartmuth Nowak
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
- Zentrum für Künstliche Intelligenz, Medizininformatik und Datenwissenschaften, Universitätsklinikum Knappschaftskrankenhaus Bochum, Bochum, Germany
| | - Andrea Witowski
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Björn Koos
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Alexander Wolf
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Maike Weber
- Medizinische Fakultät, Medizinisches Proteom-Center, Ruhr Universität Bochum, Bochum, Germany
- Zentrum für Proteindiagnostik (PRODI), Ruhr Universität Bochum, Bochum, Germany
| | - Daniel Kleefisch
- Medizinische Fakultät, Medizinisches Proteom-Center, Ruhr Universität Bochum, Bochum, Germany
- Zentrum für Proteindiagnostik (PRODI), Ruhr Universität Bochum, Bochum, Germany
| | - Matthias Unterberg
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Helge Haberl
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Alexander von Busch
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Christian Ertmer
- Klinik für Anästhesiologie, Operative Intensivmedizin und Schmerztherapie, Universitätsklinikum Münster, Münster, Germany
| | - Alexander Zarbock
- Klinik für Anästhesiologie, Operative Intensivmedizin und Schmerztherapie, Universitätsklinikum Münster, Münster, Germany
| | - Christian Bode
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Bonn, Bonn, Germany
| | - Christian Putensen
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Bonn, Bonn, Germany
| | - Ulrich Limper
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universität Witten/Herdecke, Krankenhaus Köln-Merheim, Köln, Germany
| | - Frank Wappler
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universität Witten/Herdecke, Krankenhaus Köln-Merheim, Köln, Germany
| | - Thomas Köhler
- Klinik für Anästhesiologie und Operative Intensiv-, Rettungsmedizin und Schmerztherapie, Klinikum Herford, Herford, Germany
- Klinik für Anästhesiologie und Intensivmedizin, AMEOS-Klinikum Halberstadt, Halberstadt, Germany
| | - Dietrich Henzler
- Klinik für Anästhesiologie und Operative Intensiv-, Rettungsmedizin und Schmerztherapie, Klinikum Herford, Herford, Germany
| | - Daniel Oswald
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Klinikum Westfalen, Dortmund, Germany
| | - Björn Ellger
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Klinikum Westfalen, Dortmund, Germany
| | - Stefan F. Ehrentraut
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Bonn, Bonn, Germany
| | - Lars Bergmann
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Katharina Rump
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Dominik Ziehe
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Nina Babel
- Centrum für Translationale Medizin, Medizinische Klinik I, Marien Hospital Herne, Universitätsklinikum der Ruhr-Universität Bochum, Herne, Germany
| | - Barbara Sitek
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
- Medizinische Fakultät, Medizinisches Proteom-Center, Ruhr Universität Bochum, Bochum, Germany
| | - Katrin Marcus
- Medizinische Fakultät, Medizinisches Proteom-Center, Ruhr Universität Bochum, Bochum, Germany
| | - Ulrich H. Frey
- Klinik für Anästhesiologie, Operative Intensivmedizin, Schmerz- und Palliativmedizin, Marien Hospital Herne, Universitätsklinikum der Ruhr-Universität Bochum, Bochum, Germany
| | - Patrick J. Thoral
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Cardiovascular Science (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Michael Adamzik
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
| | - Martin Eisenacher
- Medizinische Fakultät, Medizinisches Proteom-Center, Ruhr Universität Bochum, Bochum, Germany
- Zentrum für Proteindiagnostik (PRODI), Ruhr Universität Bochum, Bochum, Germany
| | - Tim Rahmel
- Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum, Ruhr Universität Bochum, Bochum, Germany
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Park SW, Yeo NY, Kang S, Ha T, Kim TH, Lee D, Kim D, Choi S, Kim M, Lee D, Kim D, Kim WJ, Lee SJ, Heo YJ, Moon DH, Han SS, Kim Y, Choi HS, Oh DK, Lee SY, Park M, Lim CM, Heo J. Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study. J Korean Med Sci 2024; 39:e53. [PMID: 38317451 PMCID: PMC10843974 DOI: 10.3346/jkms.2024.39.e53] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/05/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.
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Affiliation(s)
- Sang Won Park
- Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea
- Institute of Medical Science, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Na Young Yeo
- Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Korea
| | - Seonguk Kang
- Department of Convergence Security, Kangwon National University, Chuncheon, Korea
| | - Taejun Ha
- Department of Biomedical Research Institute, Kangwon National University Hospital, Chuncheon, Korea
| | - Tae-Hoon Kim
- University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea
| | - DooHee Lee
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Dowon Kim
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Seheon Choi
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Minkyu Kim
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - DongHoon Lee
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - DoHyeon Kim
- Department of Research and Development, ZIOVISION Co. Ltd., Chuncheon, Korea
| | - Woo Jin Kim
- Department of Medical Informatics, School of Medicine, Kangwon National University, Chuncheon, Korea
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Seung-Joon Lee
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Yeon-Jeong Heo
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Da Hye Moon
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Seon-Sook Han
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea
| | - Yoon Kim
- University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Korea
| | - Hyun-Soo Choi
- University-Industry Cooperation Foundation, Kangwon National University, Chuncheon, Korea
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea
| | - Dong Kyu Oh
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Su Yeon Lee
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - MiHyeon Park
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Chae-Man Lim
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeongwon Heo
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea
- Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.
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Liu D, Langston JC, Prabhakarpandian B, Kiani MF, Kilpatrick LE. The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics. Front Cell Infect Microbiol 2024; 13:1274842. [PMID: 38259971 PMCID: PMC10800980 DOI: 10.3389/fcimb.2023.1274842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.
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Affiliation(s)
- Dan Liu
- Department of Bioengineering, Temple University, Philadelphia, PA, United States
| | - Jordan C. Langston
- Department of Bioengineering, Temple University, Philadelphia, PA, United States
| | | | - Mohammad F. Kiani
- Department of Bioengineering, Temple University, Philadelphia, PA, United States
- Department of Mechanical Engineering, Temple University, Philadelphia, PA, United States
- Department of Radiation Oncology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States
| | - Laurie E. Kilpatrick
- Center for Inflammation and Lung Research, Department of Microbiology, Immunology and Inflammation, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States
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Covino M, Sandroni C, Della Polla D, De Matteis G, Piccioni A, De Vita A, Russo A, Salini S, Carbone L, Petrucci M, Pennisi M, Gasbarrini A, Franceschi F. Predicting ICU admission and death in the Emergency Department: A comparison of six early warning scores. Resuscitation 2023; 190:109876. [PMID: 37331563 DOI: 10.1016/j.resuscitation.2023.109876] [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: 03/16/2023] [Revised: 05/30/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023]
Abstract
AIM To compare the ability of the most used Early Warning Scores (EWS) to identify adult patients at risk of poor outcomes in the emergency department (ED). METHODS Single-center, retrospective observational study. We evaluated the digital records of consecutive ED admissions in patients ≥ 18 years from 2010 to 2019 and calculated NEWS, NEWS2, MEWS, RAPS, REMS, and SEWS based on parameters measured on ED arrival. We assessed the discrimination and calibration performance of each EWS in predicting death/ICU admission within 24 hours using ROC analysis and visual calibration. We also measured the relative weight of clinical and physiological derangements that identified patients missed by EWS risk stratification using neural network analysis. RESULTS Among 225,369 patients assessed in the ED during the study period, 1941 (0.9%) were admitted to ICU or died within 24 hours. NEWS was the most accurate predictor (area under the receiver operating characteristic [AUROC] curve 0.904 [95% CI 0.805-0.913]), followed by NEWS2 (AUROC 0.901). NEWS was also well calibrated. In patients judged at low risk (NEWS < 2), 359 events occurred (18.5% of the total). Neural network analysis revealed that age, systolic BP, and temperature had the highest relative weight for these NEWS-unpredicted events. CONCLUSIONS NEWS is the most accurate EWS for predicting the risk of death/ICU admission within 24 h from ED arrival. The score also had a fair calibration with few events occurring in patients classified at low risk. Neural network analysis suggests the need for further improvements by focusing on the prompt diagnosis of sepsis and the development of practical tools for the measurement of the respiratory rate.
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Affiliation(s)
- Marcello Covino
- Emergency Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy; Università Cattolica del Sacro Cuore, Roma, Italy.
| | - Claudio Sandroni
- Università Cattolica del Sacro Cuore, Roma, Italy; Department of Anaesthesiology and Intensive Care Medicine, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Davide Della Polla
- Emergency Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Giuseppe De Matteis
- Department of Internal Medicina and Gastroenterology, Fondazione Policlinico Universitario A. Gemelli, Rome, Italy
| | - Andrea Piccioni
- Emergency Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Antonio De Vita
- Department of Cardiovascular Medicine, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Andrea Russo
- Department of Geriatrics, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Sara Salini
- Department of Geriatrics, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Luigi Carbone
- Emergency Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy; Department of Emergency Medicine, Ospedale Fatebenefratelli Isola Tiberina, Gemelli, Isola, Roma, Italy
| | - Martina Petrucci
- Emergency Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Mariano Pennisi
- Università Cattolica del Sacro Cuore, Roma, Italy; Department of Anaesthesiology and Intensive Care Medicine, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy
| | - Antonio Gasbarrini
- Università Cattolica del Sacro Cuore, Roma, Italy; Department of Internal Medicina and Gastroenterology, Fondazione Policlinico Universitario A. Gemelli, Rome, Italy
| | - Francesco Franceschi
- Emergency Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Roma, Italy; Università Cattolica del Sacro Cuore, Roma, Italy
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