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Susianti H, Asmoro AA, Sujarwoto, Jaya W, Sutanto H, Kusdijanto AY, Kuwoyo KP, Hananto K, Khrisna MB. Acute Kidney Injury Prediction Model Using Cystatin-C, Beta-2 Microglobulin, and Neutrophil Gelatinase-Associated Lipocalin Biomarker in Sepsis Patients. Int J Nephrol Renovasc Dis 2024; 17:105-112. [PMID: 38562530 PMCID: PMC10984190 DOI: 10.2147/ijnrd.s450901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/12/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction AKI is a frequent complication in sepsis patients and is estimated to occur in almost half of patients with severe sepsis. However, there is currently no effective therapy for AKI in sepsis. Therefore, the therapeutic approach is focused on prevention. Based on this, there is an opportunity to examine a panel of biomarker models for predicting AKI. Material and Methods This prospective cohort study analysed the differences in Cystatin C, Beta-2 Microglobulin, and NGAL levels in sepsis patients with AKI and sepsis patients without AKI. The biomarker modelling of AKI prediction was done using machine learning, namely Orange Data Mining. In this study, 130 samples were analysed by machine learning. The parameters used to obtain the biomarker panel were 23 laboratory examination parameters. Results This study used SVM and the Naïve Bayes model of machine learning. The SVM model's sensitivity, specificity, NPV, and PPV were 50%, 94.4%, 71.4%, and 87.5%, respectively. For the Naïve Bayes model, the sensitivity, specificity, NPV, and PPV were 83.3%, 77.8%, 87.5%, and 71.4%, respectively. Discussion This study's SVM machine learning model has higher AUC and specificity but lower sensitivity. The Naïve Bayes model had better sensitivity; it can be used to predict AKI in sepsis patients. Conclusion The Naïve Bayes machine learning model in this study is useful for predicting AKI in sepsis patients.
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Affiliation(s)
- Hani Susianti
- Clinical Pathology Department, Faculty of Medicine Brawijaya University/Saiful Anwar General Hospital, Malang, Indonesia
| | - Aswoco Andyk Asmoro
- Anesthesiology and Intensive Therapy Department, Faculty of Medicine Brawijaya University/Saiful Anwar General Hospital, Malang, Indonesia
| | - Sujarwoto
- Faculty of Public Administration, Brawijaya University, Malang, Indonesia
| | - Wiwi Jaya
- Anesthesiology and Intensive Therapy Department, Faculty of Medicine Brawijaya University/Saiful Anwar General Hospital, Malang, Indonesia
| | - Heri Sutanto
- Internal Medicine Department, Faculty of Medicine Brawijaya University/Saiful Anwar General Hospital, Malang, Indonesia
| | - Amanda Yuanita Kusdijanto
- Clinical Pathology Department, Faculty of Medicine Brawijaya University/Saiful Anwar General Hospital, Malang, Indonesia
| | - Kevin Putro Kuwoyo
- Clinical Pathology Department, Faculty of Medicine Brawijaya University/Saiful Anwar General Hospital, Malang, Indonesia
| | | | - Matthew Brian Khrisna
- Clinical Pathology Department, Faculty of Medicine Brawijaya University/Saiful Anwar General Hospital, Malang, Indonesia
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Zhou H, Liu L, Zhao Q, Jin X, Peng Z, Wang W, Huang L, Xie Y, Xu H, Tao L, Xiao X, Nie W, Liu F, Li L, Yuan Q. Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization. Front Immunol 2023; 14:1140755. [PMID: 37077912 PMCID: PMC10106833 DOI: 10.3389/fimmu.2023.1140755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/17/2023] [Indexed: 04/05/2023] Open
Abstract
BackgroundSepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).MethodsA total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation.ResultsIn this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75).ConclusionsAfter selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.
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Affiliation(s)
- Hongshan Zhou
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Leping Liu
- Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Qinyu Zhao
- College of Engineering and Computer Science, Australian National University, Canberra, ACT, Australia
| | - Xin Jin
- Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhangzhe Peng
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Wang
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ling Huang
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yanyun Xie
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hui Xu
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Lijian Tao
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiangcheng Xiao
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Wannian Nie
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Fang Liu
- Health Management Center, Xiangya Hospital of Central South University, Changsha, Hunan, China
- *Correspondence: Fang Liu, ; Li Li, ; Qiongjing Yuan,
| | - Li Li
- Critical Care Medicine, Xiangya Hospital of Central South University, Changsha, Hunan, China
- *Correspondence: Fang Liu, ; Li Li, ; Qiongjing Yuan,
| | - Qiongjing Yuan
- Department of Nephrology, Xiangya Hospital of Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Medical Research Center for Geriatric Diseases, Xiangya Hospital of Central South University, Changsha, Hunan, China
- *Correspondence: Fang Liu, ; Li Li, ; Qiongjing Yuan,
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Douthit BJ, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic TA, Lee MA, Pruinelli L, Schultz MA, Wieben A, Jeffery AD. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Appl Clin Inform 2022; 13:161-179. [PMID: 35139564 PMCID: PMC8828453 DOI: 10.1055/s-0041-1742218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. OBJECTIVES This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. METHODS We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. RESULTS Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. CONCLUSION This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.
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Affiliation(s)
- Brian J. Douthit
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Rachel L. Walden
- Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
| | - Kenrick Cato
- Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
| | - Cynthia P. Coviak
- Professor Emerita of Nursing, Grand Valley State University, Allendale, Michigan, United States
| | - Christopher Cruz
- Global Health Technology and Informatics, Chevron, San Ramon, California, United States
| | - Fabio D'Agostino
- Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Thompson Forbes
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Grace Gao
- Department of Nursing, St Catherine University, Saint Paul, Minnesota, United States
| | - Theresa A. Kapetanovic
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Mikyoung A. Lee
- College of Nursing, Texas Woman's University, Denton, Texas, United States
| | - Lisiane Pruinelli
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
| | - Mary A. Schultz
- Department of Nursing, California State University, San Bernardino, California, United States
| | - Ann Wieben
- School of Nursing, University of Wisconsin-Madison, Wisconsin, United States
| | - Alvin D. Jeffery
- School of Nursing, Vanderbilt University; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, Tennessee, United States,Address for correspondence Alvin D. Jeffery, PhD, RN-BC, CCRN-K, FNP-BC 461 21st Avenue South, Nashville, TN 37240United States
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