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Zeng Z, Huang R, Lin H, Peng H, Luo J, Ding N. Serum Lactate Is an Indicator for Short-Term and Long-Term Mortality in Patients with Acute Pancreatitis. Dig Dis Sci 2024; 69:2223-2234. [PMID: 38594436 DOI: 10.1007/s10620-024-08419-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 03/26/2024] [Indexed: 04/11/2024]
Abstract
BACKGROUND Serum lactate, as a single and an easily available biomarker, has been applied in various diseases. AIMS In this study, we aimed to explore the predictive value of serum lactate for short-term and long-term prognosis in acute pancreatitis (AP) admitted in intensive care unit (ICU) based on a large-scale database. METHODS AP patients admitted in ICU in the MIMIC-IV database were included. We constructed three different models to investigate the relationships between serum lactate and clinical outcomes, including 30-day, 180-day and 1-year mortality in AP. Smooth fitting curves were performed for intuitively demonstrating the relationship between serum lactate and different outcomes in AP by the generalized additive model. RESULTS A total of 895 AP patients admitted in ICU were included. The mortalities of 30 days, 180 days, and 1 year were 12.63% (n = 113), 16.87% (n = 151), and 17.54% (n = 157). In model B, with 1-mmol/L increment in serum lactate, the values of OR in 30-day, 180-day and 1-year mortality were 1.20 (95%CI 1.04-1.37, P = 0.0094), 1.21 (95%CI 1.06-1.37, P = 0.0039), and 1.21 (95%CI 1.07-1.38, P = 0.0035). The AUCs of serum lactate for predicting 30-day, 180-day, and 1-year mortality in AP were 0.688 (95%CI 0.633-0.743), 0.655 (95%CI 0.605-0.705), and 0.653 (95%CI 0.603-0.701), respectively. The cut-off value of serum lactate predicting 30-day, 180-day and 1-year mortality in AP was 2.4 mmol/L. CONCLUSION Serum lactate could be an indicator for short-term and long-term mortality in patients with AP admitted in ICU.
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Affiliation(s)
- Zhao Zeng
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, 410004, Hunan, China
| | - Rong Huang
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, 410004, Hunan, China
| | - Hang Lin
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, 410004, Hunan, China
| | - Hongchun Peng
- Department of Orthopedics, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
| | - Ju Luo
- Department of Geriatrics, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
| | - Ning Ding
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, 410004, Hunan, China.
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Lin W, Huang Y, Zhu J, Sun H, Su N, Pan J, Xu J, Chen L. Machine learning improves early prediction of organ failure in hyperlipidemia acute pancreatitis using clinical and abdominal CT features. Pancreatology 2024; 24:350-356. [PMID: 38342660 DOI: 10.1016/j.pan.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 01/24/2024] [Accepted: 02/05/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND This study aimed to investigate and validate machine-learning predictive models combining computed tomography and clinical data to early predict organ failure (OF) in Hyperlipidemic acute pancreatitis (HLAP). METHODS Demographics, laboratory parameters and computed tomography imaging data of 314 patients with HLAP from the First Affiliated Hospital of Wenzhou Medical University between 2017 and 2021, were retrospectively analyzed. Sixty-five percent of patients (n = 204) were assigned to the training group and categorized as patients with and without OF. Parameters were compared by univariate analysis. Machine-learning methods including random forest (RF) were used to establish model to predict OF of HLAP. Areas under the curves (AUCs) of receiver operating characteristic were calculated. The remaining 35% patients (n = 110) were assigned to the validation group to evaluate the performance of models to predict OF. RESULTS Ninety-three (45.59%) and fifty (45.45%) patients from the training and the validation cohort, respectively, developed OF. The RF model showed the best performance to predict OF, with the highest AUC value of 0.915. The sensitivity (0.828) and accuracy (0.814) of RF model were both the highest among the five models in the study cohort. In the validation cohort, RF model continued to show the highest AUC (0.820), accuracy (0.773) and sensitivity (0.800) to predict OF in HLAP, while the positive and negative likelihood ratios and post-test probability were 3.22, 0.267 and 72.85%, respectively. CONCLUSIONS Machine-learning models can be used to predict OF occurrence in HLAP in our pilot study. RF model showed the best predictive performance, which may be a promising candidate for further clinical validation.
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Affiliation(s)
- Weihang Lin
- Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, China
| | - Yingbao Huang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, China
| | - Jiale Zhu
- School of the First Clinical Medical Sciences, Wenzhou Medical University, China
| | - Houzhang Sun
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, China
| | - Na Su
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, China
| | - Jingye Pan
- Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, China; Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, China; Collaborative Innovation Center for Intelligence Medical Education, China; Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, China
| | - Junkang Xu
- Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, China; Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, China; Collaborative Innovation Center for Intelligence Medical Education, China; Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, China
| | - Lifang Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, China.
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Juneja D. Artificial intelligence: Applications in critical care gastroenterology. Artif Intell Gastrointest Endosc 2024; 5:89138. [DOI: 10.37126/aige.v5.i1.89138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/07/2023] [Accepted: 12/26/2023] [Indexed: 02/20/2024] Open
Abstract
Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
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Affiliation(s)
- Deven Juneja
- Department of Critical Care Medicine, Max Super Speciality Hospital, New Delhi 110017, India
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Ren W, Zou K, Huang S, Xu H, Zhang W, Shi X, Shi L, Zhong X, Peng Y, Tang X, Lü M. Prediction of in-hospital Mortality of Intensive Care Unit Patients with Acute Pancreatitis Based on an Explainable Machine Learning Algorithm. J Clin Gastroenterol 2023:00004836-990000000-00212. [PMID: 37712768 DOI: 10.1097/mcg.0000000000001910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/16/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND AND AIM Acute pancreatitis (AP) is potentially fatal. Therefore, early identification of patients at a high mortality risk and timely intervention are essential. This study aimed to establish an explainable machine-learning model for predicting in-hospital mortality of intensive care unit (ICU) patients with AP. METHODS Data on patients with AP, including demographics, vital signs, laboratory tests, comorbidities, treatment, complication, and severity scores, were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU collaborative research database (eICU-CRD). Based on the data from MIMIC-IV, we used the least absolute shrinkage and selection operator algorithm to select variables and then established 9 machine-learning models and screened the optimal model. Data from the eICU-CRD were used for external validation. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, decision curve, and calibration plots were used to assess the models' efficacy. Shapley's additive explanation values were used to explain the model. RESULTS Gaussian naive Bayes (GNB) model had the best performance on the data from MIMIC-IV, with an AUC, accuracy, sensitivity, and specificity of 0.840, 0.787, 0.839, and 0.792, respectively. The GNB model also performed well on the data from the eICU-CRD, with an AUC, accuracy, sensitivity, and specificity of 0.862, 0.833, 0.848, and 0.763, respectively. According to Shapley's additive explanation values, the top 4 predictive factors were maximum red cell distribution width, minimum saturation of blood oxygen, maximum blood urea nitrogen, and the Sequential Organ Failure Assessment score. CONCLUSION The GNB model demonstrated excellent performance and generalizability in predicting mortality in ICU patients with AP. Therefore, it can identify patients at a high mortality risk.
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Affiliation(s)
- Wensen Ren
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Kang Zou
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Shu Huang
- Department of Gastroenterology, Lianshui County People's Hospital
- Department of Gastroenterology, Lianshui People's Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian, China
| | - Huan Xu
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Wei Zhang
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Xiaomin Shi
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Lei Shi
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Xiaolin Zhong
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Yan Peng
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Xiaowei Tang
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Muhan Lü
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
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Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence. Pancreatology 2023; 23:176-186. [PMID: 36610872 DOI: 10.1016/j.pan.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/20/2022] [Accepted: 12/12/2022] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To evaluate the success of artificial intelligence for early prediction of severe course, survival, and intensive care unit(ICU) requirement in patients with acute pancreatitis(AP). METHODS Retrospectively, 1334 patients were included the study. Severity is determined according to the Revised Atlanta Classification(RAC). The success of machine learning(ML) method was evaluated by 13 simple demographic, clinical, etiologic, and laboratory features obtained on ER admission. Additionally, it was evaluated whether Balthazar-computerized tomography severity index(CTSI) at 48-h contributed to success. The dataset was split into two parts, 90% for ML(of which 70% for learning and 30% for testing) and 10% for validation and 5-fold stratified sampling has been utilized. Variable Importance was used in the selection of features during training phase of machine. The Gradient Boost Algorithm trained the machine by KNIME analytics platform. SMOTE has been applied to increase the minority classes for training. The combined effects of the measured features were examined by multivariate logistic regression analysis and reciever operating curve curves of the prediction and confidence of the target variables were obtained. RESULTS Accuracy values for the early estimation of Atlanta severity score, ICU requirement, and survival were found as 88.20%, 98.25%, and 92.77% respectively. When Balthazar-CTSI score is used, results were found as 91.02%, 92.25%, and 98% respectively. CONCLUSIONS The ML method we used successfully predicted the severe course, ICU requirement and survival, with promising accuracy values of over 88%. If 48-h Balthazar-CTSI is included in the calculation, the severity score and survival rates increase even more.
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Khope SR, Elias S. Strategies of Predictive Schemes and Clinical Diagnosis for Prognosis Using MIMIC-III: A Systematic Review. Healthcare (Basel) 2023; 11:healthcare11050710. [PMID: 36900715 PMCID: PMC10001415 DOI: 10.3390/healthcare11050710] [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: 12/11/2022] [Revised: 02/18/2023] [Accepted: 02/21/2023] [Indexed: 03/05/2023] Open
Abstract
The prime purpose of the proposed study is to construct a novel predictive scheme for assisting in the prognosis of criticality using the MIMIC-III dataset. With the adoption of various analytics and advanced computing in the healthcare system, there is an increasing trend toward developing an effective prognostication mechanism. Predictive-based modeling is the best alternative to work in this direction. This paper discusses various scientific contributions using desk research methodology towards the Medical Information Mart for Intensive Care (MIMIC-III). This open-access dataset is meant to help predict patient trajectories for various purposes ranging from mortality forecasting to treatment planning. With a dominant machine learning approach in this perspective, there is a need to discover the effectiveness of existing predictive methods. The resultant outcome of this paper offers an inclusive discussion about various available predictive schemes and clinical diagnoses using MIMIC-III in order to contribute toward better information associated with its strengths and weaknesses. Therefore, the paper provides a clear visualization of existing schemes for clinical diagnosis using a systematic review approach.
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Su Y, Guo C, Zhou S, Li C, Ding N. Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model. Eur J Med Res 2022; 27:294. [PMID: 36528689 PMCID: PMC9758460 DOI: 10.1186/s40001-022-00925-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. METHODS This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. RESULTS A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. CONCLUSION An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.
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Affiliation(s)
- Yingjie Su
- grid.412017.10000 0001 0266 8918Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, 410004 Hunan China
| | - Cuirong Guo
- grid.412017.10000 0001 0266 8918Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, 410004 Hunan China
| | - Shifang Zhou
- grid.412017.10000 0001 0266 8918Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, 410004 Hunan China
| | - Changluo Li
- grid.412017.10000 0001 0266 8918Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, 410004 Hunan China
| | - Ning Ding
- grid.412017.10000 0001 0266 8918Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, NO. 161 Shaoshan South Road, Changsha, 410004 Hunan China
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9
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Hameed MAB, Alamgir Z. Improving mortality prediction in Acute Pancreatitis by machine learning and data augmentation. Comput Biol Med 2022; 150:106077. [PMID: 36137318 DOI: 10.1016/j.compbiomed.2022.106077] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/28/2022] [Accepted: 09/03/2022] [Indexed: 11/22/2022]
Abstract
Acute Pancreatitis (AP) is the inflammation of the pancreas that can be fatal or lead to further complications based on the severity of the attack. Early detection of AP disease can help save lives by providing utmost care, rigorous treatment, and better resources. In this era of data and technology, instead of relying on manual scoring systems, scientists are employing advanced machine learning and data mining models for the early detection of patients with high chances of mortality. The current work on AP mortality prediction is negligible, and the few studies that exist have many shortcomings and are impractical for clinical deployment. In this research work, we tried to overcome the existing issues. One main issue is the lack of high-quality public datasets for AP, which are crucial for effectively training ML models. The available datasets are small in size, have many missing values, and suffer from high class imbalance. We augmented three public datasets, MIMIC-III, MIMIC-IV, and eICU, to obtain a larger dataset, and experiments proved that augmented data trained classifiers better than original small datasets. Moreover, we employed emerging advanced techniques to handle underlying issues in data. The results showed that iterative imputer is best for filling missing values in AP data. It beats not only the basic techniques but also the Knn-based imputation. Class imbalance is first addressed using data downsampling; apparently, it gave decent results on small test sets. However, we conducted numerous experiments on large test sets to prove that downsampling in the case of AP produced misleading and poor results. Next, we applied various techniques to upsample data in two different class splits, a 50 to 50 and a 70 to 30 majority-minority class split. Four different tabular generative adversarial networks, CTGAN, TGAN, CopulaGAN, and CTAB, and a variational autoencoder, TVAE, were deployed for synthetic data generation. SMOTE was also utilized for data upsampling. The computational results showed that the Random Forest (RF) classifier outperformed all other classifiers on a 50 to 50 class split data generated by CTGAN, with 0.702 Fβ and 0.833 recall. Results produced by RF on the TVAE dataset were also comparable, with 0.698 Fβ. In the case of SMOTE-based upsampling, DNN performed best with a 0.671 Fβ score.
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Affiliation(s)
- M Asad Bin Hameed
- Department of Computer Science, National University of Computer and Emerging sciences (NUCES), Lahore, Pakistan.
| | - Zareen Alamgir
- Department of Computer Science, National University of Computer and Emerging sciences (NUCES), Lahore, Pakistan.
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10
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Zou M, Yang Z, Fan Y, Gong L, Han Z, Ji L, Hu X, Wu D. Gut microbiota on admission as predictive biomarker for acute necrotizing pancreatitis. Front Immunol 2022; 13:988326. [PMID: 36105818 PMCID: PMC9466706 DOI: 10.3389/fimmu.2022.988326] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 08/11/2022] [Indexed: 12/12/2022] Open
Abstract
Background Acute necrotizing pancreatitis (NP), a severe form of acute pancreatitis (AP), has higher mortality and worse outcome than non-necrotizing pancreatitis (non-NP). Infected NP is a devastating subgroup of NP. To date neither NP nor infected NP has robust prediction strategies, which may delay early recognition and timely intervention. Recent studies revealed correlations between disturbed gut microbiota and AP severity. Some features of intestinal microbiota have the potential to become biomarkers for NP prediction. Methods We performed 16S rRNA sequencing to analyze gut microbiota features in 20 healthy controls (HC), and 58 AP patients on hospital admission. The AP patients were later classified into NP and non-NP groups based on subsequent diagnostic imaging features. Random forest regression model and ROC curve were applied for NP and infected NP prediction. PIRCUSt2 was used for bacterial functional pathway prediction analysis. Results We found that the three groups (HC, NP, and non-NP) had distinct microorganism composition. NP patients had reduced microbial diversity, higher abundance of Enterobacteriales, but lower abundance of Clostridiales and Bacteroidales compared with the non-NP group. Correlation analyses displayed that intestine bacterial taxonomic alterations were related to severity, ICU admission, and prognosis. By pathway prediction, species more abundant in NP patients had positive correlation with synthesis and degradation of ketone bodies, and benzoate degradation. Enterococcus faecium (ASV2) performed best in discriminating NP and non-NP patients. Finegoldia magna (ASV3) showed the maximal prediction capacity among all ASVs and had comparable accuracy with Balthazar CT to detect patients with infected NP. Conclusions Our study suggests that NP patients have distinct intestinal microbiota on admission compared to non-NP patients. Dysbiosis of intestinal microbiota might influence NP progression through ketone body or benzoate metabolism. Enterococcus faecium and Finegoldia magna are potential predictors for NP and infected NP. Our findings explore biomarkers which may inform clinical decision-making in AP and shed light on further studies on NP pathophysiology and management.
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Affiliation(s)
- Menglian Zou
- Department of Gastroenterology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zihan Yang
- Department of Gastroenterology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yue Fan
- Department of Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
| | - Liang Gong
- Department of Gastroenterology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ziying Han
- Department of Gastroenterology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Li Ji
- Department of Gastroenterology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiaomin Hu
- Department of Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
- *Correspondence: Dong Wu, ; Xiaomin Hu,
| | - Dong Wu
- Department of Gastroenterology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- *Correspondence: Dong Wu, ; Xiaomin Hu,
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11
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Kui B, Pintér J, Molontay R, Nagy M, Farkas N, Gede N, Vincze Á, Bajor J, Gódi S, Czimmer J, Szabó I, Illés A, Sarlós P, Hágendorn R, Pár G, Papp M, Vitális Z, Kovács G, Fehér E, Földi I, Izbéki F, Gajdán L, Fejes R, Németh BC, Török I, Farkas H, Mickevicius A, Sallinen V, Galeev S, Ramírez-Maldonado E, Párniczky A, Erőss B, Hegyi PJ, Márta K, Váncsa S, Sutton R, Szatmary P, Latawiec D, Halloran C, de-Madaria E, Pando E, Alberti P, Gómez-Jurado MJ, Tantau A, Szentesi A, Hegyi P. EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis. Clin Transl Med 2022; 12:e842. [PMID: 35653504 PMCID: PMC9162438 DOI: 10.1002/ctm2.842] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/06/2022] [Accepted: 04/11/2022] [Indexed: 12/17/2022] Open
Abstract
Background Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. Methods The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). Results The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/). Conclusions The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.
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Affiliation(s)
- Balázs Kui
- Department of Medicine, University of Szeged, Szeged, Hungary.,Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - József Pintér
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Roland Molontay
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary.,MTA-BME Stochastics Research Group, Budapest, Hungary
| | - Marcell Nagy
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Nelli Farkas
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Institute of Bioanalysis, Medical School, University of Pécs, Pécs, Hungary
| | - Noémi Gede
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary
| | - Áron Vincze
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Judit Bajor
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Szilárd Gódi
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - József Czimmer
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Imre Szabó
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Anita Illés
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Patrícia Sarlós
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Roland Hágendorn
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Gabriella Pár
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Mária Papp
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Zsuzsanna Vitális
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - György Kovács
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Eszter Fehér
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Ildikó Földi
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Ferenc Izbéki
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - László Gajdán
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - Roland Fejes
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - Balázs Csaba Németh
- Department of Medicine, University of Szeged, Szeged, Hungary.,Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - Imola Török
- County Emergency Clinical Hospital of Târgu Mures-Gastroenterology Clinic and University of Medicine, Pharmacy, Sciences and Technology 'George Emil Palade', Targu Mures, Romania
| | - Hunor Farkas
- County Emergency Clinical Hospital of Târgu Mures-Gastroenterology Clinic and University of Medicine, Pharmacy, Sciences and Technology 'George Emil Palade', Targu Mures, Romania
| | | | - Ville Sallinen
- Department of Transplantation and Liver Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Shamil Galeev
- Saint Luke Clinical Hospital, St. Petersburg, Russia
| | | | - Andrea Párniczky
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Heim Pál National Pediatric Institute, Budapest, Hungary
| | - Bálint Erőss
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Péter Jenő Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary
| | - Katalin Márta
- Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Szilárd Váncsa
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Robert Sutton
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Peter Szatmary
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Diane Latawiec
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Chris Halloran
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Enrique de-Madaria
- Gastroenterology Department, Alicante University General Hospital, ISABIAL, Alicante, Spain
| | - Elizabeth Pando
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Piero Alberti
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Maria José Gómez-Jurado
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alina Tantau
- The 4th Medical Clinic, Iuliu Hatieganu' University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Gastroenterology and Hepatology Medical Center, Cluj-Napoca, Romania
| | - Andrea Szentesi
- Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary.,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary
| | - Péter Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
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12
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Liu Z, Yang Y, Song H, Luo J. A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:676. [PMID: 35845515 PMCID: PMC9279801 DOI: 10.21037/atm-22-1613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/27/2022] [Indexed: 11/27/2022]
Abstract
Background Accurate and prompt clinical assessment of the severity and prognosis of patients with acute pancreatitis (AP) is critical, particularly during hospitalization. Natural language processing algorithms gain an opportunity from the growing number of free-text notes in electronic health records to mine this unstructured data, e.g., nursing notes, to detect and predict adverse outcomes. However, the predictive value of nursing notes for AP prognosis is unclear. In this study, a predictive model for in-hospital mortality in AP was developed using measured sentiment scores in nursing notes. Methods The data of AP patients in the retrospective cohort study were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sentiments in nursing notes were assessed by sentiment analysis. For each individual clinical note, sentiment polarity and sentiment subjectivity scores were assigned. The in-hospital mortality of AP patients was the outcome. A predictive model was built based on clinical information and sentiment scores, and its performance and clinical value were evaluated using the area under curves (AUCs) and decision-making curves, respectively. Results Of the 631 AP patients included, 88 cases (13.9%) cases were dead in hospital. When various confounding factors were adjusted, the mean sentiment polarity was associated with a reduced risk of in-hospital mortality in AP [odds ratio (OR): 0.448; 95% confidence interval (CI): 0.233–0.833; P=0.014]. A predictive model was established in the training group via multivariate logistic regression analysis, including 12 independent variables. In the testing group, the model showed an AUC of 0.812, which was significantly greater than the sequential organ failure assessment (SOFA) of 0.732 and the simplified acute physiology score-II (SAPS-II) of 0.792 (P<0.05). When the same level of risk was considered, the clinical benefits of the predictive model were found to be the highest compared with SOFA and SAPS-II scores. Conclusions The model combined sentiment scores in nursing notes showed well predictive performance and clinical value in in-hospital mortality of AP patients.
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Affiliation(s)
- Zhanxiao Liu
- Emergency Department, Aerospace Center Hospital, Beijing, China
| | - Ya Yang
- Emergency Department, Aerospace Center Hospital, Beijing, China
| | - Huanhuan Song
- Emergency Department, Aerospace Center Hospital, Beijing, China
| | - Ji Luo
- Traditional Chinese Medicine Rheumatic Immunology Department, People's Hospital of Chongqing Banan District, Chongqing, China
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13
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Kiss S, Pintér J, Molontay R, Nagy M, Farkas N, Sipos Z, Fehérvári P, Pecze L, Földi M, Vincze Á, Takács T, Czakó L, Izbéki F, Halász A, Boros E, Hamvas J, Varga M, Mickevicius A, Faluhelyi N, Farkas O, Váncsa S, Nagy R, Bunduc S, Hegyi PJ, Márta K, Borka K, Doros A, Hosszúfalusi N, Zubek L, Erőss B, Molnár Z, Párniczky A, Hegyi P, Szentesi A. Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases. Sci Rep 2022; 12:7827. [PMID: 35552440 PMCID: PMC9098474 DOI: 10.1038/s41598-022-11517-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/07/2022] [Indexed: 12/21/2022] Open
Abstract
Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them.
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Affiliation(s)
- Szabolcs Kiss
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary.,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary
| | - József Pintér
- Human and Social Data Science Lab, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1111, Hungary
| | - Roland Molontay
- Human and Social Data Science Lab, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1111, Hungary.,Stochastics Research Group, Hungarian Academy of Sciences, Budapest University of Technology and Economics, Egry József u. 1, Budapest, 1111, Hungary
| | - Marcell Nagy
- Human and Social Data Science Lab, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1111, Hungary
| | - Nelli Farkas
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Institute of Bioanalysis, Medical School, University of Pécs, Honvéd u. 1, Pécs, 7624, Hungary
| | - Zoltán Sipos
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary
| | - Péter Fehérvári
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Department of Biomathematics and Informatics, University of Veterinary Medicine, István u. 2, Budapest, 1078, Hungary
| | - László Pecze
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary
| | - Mária Földi
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary.,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary
| | - Áron Vincze
- Division of Gastroenterology, First Department of Medicine, Medical School, University of Pécs, Ifjúság út 13, Pécs, 7624, Hungary
| | - Tamás Takács
- Department of Medicine, University of Szeged, Kálvária sgt. 57, Szeged, 6725, Hungary
| | - László Czakó
- Department of Medicine, University of Szeged, Kálvária sgt. 57, Szeged, 6725, Hungary
| | - Ferenc Izbéki
- Department of Internal Medicine, Szent György Teaching Hospital of County Fejér, Seregélyesi út 3, Székesfehérvár, 8000, Hungary
| | - Adrienn Halász
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary.,Department of Internal Medicine, Szent György Teaching Hospital of County Fejér, Seregélyesi út 3, Székesfehérvár, 8000, Hungary
| | - Eszter Boros
- Department of Internal Medicine, Szent György Teaching Hospital of County Fejér, Seregélyesi út 3, Székesfehérvár, 8000, Hungary
| | - József Hamvas
- Bajcsy-Zsilinszky Hospital, Maglódi út 89-91, Budapest, 1106, Hungary
| | - Márta Varga
- Department of Gastroenterology, BMKK Dr Rethy Pal Hospital, Gyulai út 18, Békéscsaba, 5600, Hungary
| | - Artautas Mickevicius
- Vilnius University Hospital Santaros Clinics, Clinics of Abdominal Surgery, Nephrourology and Gastroenterology, Faculty of Medicine, Vilnius University, Santariškių g. 2, 08410, Vilnius, Lithuania
| | - Nándor Faluhelyi
- Department of Medical Imaging, Medical School, University of Pécs, Ifjúság út 13, Pécs, 7624, Hungary
| | - Orsolya Farkas
- Department of Medical Imaging, Medical School, University of Pécs, Ifjúság út 13, Pécs, 7624, Hungary
| | - Szilárd Váncsa
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary
| | - Rita Nagy
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary.,Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary
| | - Stefania Bunduc
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Doctoral School, Carol Davila University of Medicine and Pharmacy, Bulevardul Eroii Sanitari 8, 050474, Bucharest, Romania
| | - Péter Jenő Hegyi
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Katalin Márta
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Katalin Borka
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,2nd Department of Pathology, Semmelweis University, Üllői út 93, Budapest, 1091, Hungary
| | - Attila Doros
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Transplantation and Surgery, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Nóra Hosszúfalusi
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Internal Medicine and Hematology, Semmelweis University, Szentkirályi u. 46, Budapest, 1088, Hungary
| | - László Zubek
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Anaesthesiology and Intensive Therapy, Semmelweis University, Üllői út 78, Budapest, 1082, Hungary
| | - Bálint Erőss
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Zsolt Molnár
- Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Department of Anaesthesiology and Intensive Therapy, Semmelweis University, Üllői út 78, Budapest, 1082, Hungary.,Department of Anaesthesiology and Intensive Therapy, Poznan University of Medical Sciences, ul. św. Marii Magdaleny 14, 61861, Poznan, Wielkopolska, Poland
| | - Andrea Párniczky
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Heim Pál National Pediatric Institute, Üllői út 86, Budapest, 1089, Hungary
| | - Péter Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.,Centre for Translational Medicine, Semmelweis University, Üllői út 26, Budapest, 1085, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Baross u. 23, Budapest, 1082, Hungary
| | - Andrea Szentesi
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary. .,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Szigeti út 12., II. Emelet, Pécs, 7624, Hungary.
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Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database. Diagnostics (Basel) 2022; 12:diagnostics12051068. [PMID: 35626224 PMCID: PMC9139972 DOI: 10.3390/diagnostics12051068] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/10/2022] Open
Abstract
Objective: The mortality rate of critically ill patients in ICUs is relatively high. In order to evaluate patients’ mortality risk, different scoring systems are used to help clinicians assess prognosis in ICUs, such as the Acute Physiology and Chronic Health Evaluation III (APACHE III) and the Logistic Organ Dysfunction Score (LODS). In this research, we aimed to establish and compare multiple machine learning models with physiology subscores of APACHE III—namely, the Acute Physiology Score III (APS III)—and LODS scoring systems in order to obtain better performance for ICU mortality prediction. Methods: A total number of 67,748 patients from the Medical Information Database for Intensive Care (MIMIC-IV) were enrolled, including 7055 deceased patients, and the same number of surviving patients were selected by the random downsampling technique, for a total of 14,110 patients included in the study. The enrolled patients were randomly divided into a training dataset (n = 9877) and a validation dataset (n = 4233). Fivefold cross-validation and grid search procedures were used to find and evaluate the best hyperparameters in different machine learning models. Taking the subscores of LODS and the physiology subscores that are part of the APACHE III scoring systems as input variables, four machine learning methods of XGBoost, logistic regression, support vector machine, and decision tree were used to establish ICU mortality prediction models, with AUCs as metrics. AUCs, specificity, sensitivity, positive predictive value, negative predictive value, and calibration curves were used to find the best model. Results: For the prediction of mortality risk in ICU patients, the AUC of the XGBoost model was 0.918 (95%CI, 0.915–0.922), and the AUCs of logistic regression, SVM, and decision tree were 0.872 (95%CI, 0.867–0.877), 0.872 (95%CI, 0.867–0.877), and 0.852 (95%CI, 0.847–0.857), respectively. The calibration curves of logistic regression and support vector machine performed better than the other two models in the ranges 0–40% and 70%–100%, respectively, while XGBoost performed better in the range of 40–70%. Conclusions: The mortality risk of ICU patients can be better predicted by the characteristics of the Acute Physiology Score III and the Logistic Organ Dysfunction Score with XGBoost in terms of ROC curve, sensitivity, and specificity. The XGBoost model could assist clinicians in judging in-hospital outcome of critically ill patients, especially in patients with a more uncertain survival outcome.
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Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning. J Pers Med 2022; 12:jpm12040616. [PMID: 35455733 PMCID: PMC9031087 DOI: 10.3390/jpm12040616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 11/29/2022] Open
Abstract
Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML). Methods: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model’s performance. Results: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores. Conclusions: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.
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Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients. PLoS One 2022; 17:e0262182. [PMID: 34990485 PMCID: PMC8735614 DOI: 10.1371/journal.pone.0262182] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/17/2021] [Indexed: 01/04/2023] Open
Abstract
Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients' length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30-day period after admission. We found that by only using clinical notes within the 24 hours of admission, our framework can achieve a high area under the receiver operating characteristics (AU-ROC) score for short, mid and long-term mortality prediction tasks. The test AU-ROC scores are 0.87, 0.83, 0.83, 0.82, 0.82, and 0.82 for 48-hour, 4-day, 7-day, 10-day, 15-day, and 30-day period mortality prediction, respectively. We also provide a comparative study among three types of feature extraction techniques from NLP: frequency-based technique, fixed embedding-based technique, and dynamic embedding-based technique. Lastly, we provide an interpretation of the NLP-based predictive models using feature-importance scores.
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Thapa R, Iqbal Z, Garikipati A, Siefkas A, Hoffman J, Mao Q, Das R. Early prediction of severe acute pancreatitis using machine learning. Pancreatology 2022; 22:43-50. [PMID: 34690046 DOI: 10.1016/j.pan.2021.10.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/27/2021] [Accepted: 10/12/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality rate of nearly 30% and is distinguished from milder forms of AP. Risk stratification to identify SAP cases needing inpatient treatment is an important aspect of AP diagnosis. METHODS We developed machine learning algorithms to predict which patients presenting with AP would require treatment for SAP. Three models were developed using logistic regression, neural networks, and XGBoost. Models were assessed in terms of area under the receiver operating characteristic (AUROC) and compared to the Harmless Acute Pancreatitis Score (HAPS) and Bedside Index for Severity in Acute Pancreatitis (BISAP) scores for AP risk stratification. RESULTS 61,894 patients were used to train and test the machine learning models. With an AUROC value of 0.921, the model developed using XGBoost outperformed the logistic regression and neural network-based models. The XGBoost model also achieved a higher AUROC than both HAPS and BISAP for identifying patients who would be diagnosed with SAP. CONCLUSIONS Machine learning may be able to improve the accuracy of AP risk stratification methods and allow for more timely treatment and initiation of interventions.
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Machine learning predictive models for acute pancreatitis: A systematic review. Int J Med Inform 2021; 157:104641. [PMID: 34785488 DOI: 10.1016/j.ijmedinf.2021.104641] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Acute pancreatitis (AP) is a common clinical pancreatic disease. Patients with different severity levels have different clinical outcomes. With the advantages of algorithms, machine learning (ML) has gradually emerged in the field of disease prediction, assisting doctors in decision-making. METHODS A systematic review was conducted using the PubMed, Web of Science, Scopus, and Embase databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Publication time was limited from inception to 29 May 2021. Studies that have used ML to establish predictive tools for AP were eligible for inclusion. Quality assessment of the included studies was conducted in accordance with the IJMEDI checklist. RESULTS In this systematic review, 24 of 2,913 articles, with a total of 8,327 patients and 47 models, were included. The studies could be divided into five categories: 10 studies (42%) reported severity prediction; 10 studies (42%), complication prediction; 3 studies (13%), mortality prediction; 2 studies (8%), recurrence prediction; and 2 studies (8%), surgery timing prediction. ML showed great accuracy in several prediction tasks. However, most of the included studies were retrospective in nature, conducted at a single centre, based on database data, and lacked external validation. According to the IJMEDI checklist and our scoring criteria, two studies were considered to be of high quality. Most studies had an obvious bias in the quality of data preparation, validation, and deployment dimensions. CONCLUSION In the prediction tasks for AP, ML has shown great potential in assisting decision-making. However, the existing studies still have some deficiencies in the process of model construction. Future studies need to optimize the deficiencies and further evaluate the comparability of the ML systems and model performance, so as to consequently develop high-quality ML-based models that can be used in clinical practice.
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