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Khan AA, Khabbaz KR. Commentary: Welcome to the machine. J Thorac Cardiovasc Surg 2023; 166:e565-e566. [PMID: 36411141 DOI: 10.1016/j.jtcvs.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 10/01/2022] [Indexed: 11/19/2022]
Affiliation(s)
- Adnan A Khan
- Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Mass
| | - Kamal R Khabbaz
- Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Mass.
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Suppadungsuk S, Thongprayoon C, Miao J, Krisanapan P, Qureshi F, Kashani K, Cheungpasitporn W. Exploring the Potential of Chatbots in Critical Care Nephrology. MEDICINES (BASEL, SWITZERLAND) 2023; 10:58. [PMID: 37887265 PMCID: PMC10608511 DOI: 10.3390/medicines10100058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023]
Abstract
The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.
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Affiliation(s)
- Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Nephrology and Hypertension, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Kianoush Kashani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Garcia Valencia OA, Suppadungsuk S, Thongprayoon C, Miao J, Tangpanithandee S, Craici IM, Cheungpasitporn W. Ethical Implications of Chatbot Utilization in Nephrology. J Pers Med 2023; 13:1363. [PMID: 37763131 PMCID: PMC10532744 DOI: 10.3390/jpm13091363] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
This comprehensive critical review critically examines the ethical implications associated with integrating chatbots into nephrology, aiming to identify concerns, propose policies, and offer potential solutions. Acknowledging the transformative potential of chatbots in healthcare, responsible implementation guided by ethical considerations is of the utmost importance. The review underscores the significance of establishing robust guidelines for data collection, storage, and sharing to safeguard privacy and ensure data security. Future research should prioritize defining appropriate levels of data access, exploring anonymization techniques, and implementing encryption methods. Transparent data usage practices and obtaining informed consent are fundamental ethical considerations. Effective security measures, including encryption technologies and secure data transmission protocols, are indispensable for maintaining the confidentiality and integrity of patient data. To address potential biases and discrimination, the review suggests regular algorithm reviews, diversity strategies, and ongoing monitoring. Enhancing the clarity of chatbot capabilities, developing user-friendly interfaces, and establishing explicit consent procedures are essential for informed consent. Striking a balance between automation and human intervention is vital to preserve the doctor-patient relationship. Cultural sensitivity and multilingual support should be considered through chatbot training. To ensure ethical chatbot utilization in nephrology, it is imperative to prioritize the development of comprehensive ethical frameworks encompassing data handling, security, bias mitigation, informed consent, and collaboration. Continuous research and innovation in this field are crucial for maximizing the potential of chatbot technology and ultimately improving patient outcomes.
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Affiliation(s)
- Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (S.S.); (C.T.); (J.M.); (S.T.); (I.M.C.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (S.S.); (C.T.); (J.M.); (S.T.); (I.M.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (S.S.); (C.T.); (J.M.); (S.T.); (I.M.C.)
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (S.S.); (C.T.); (J.M.); (S.T.); (I.M.C.)
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (S.S.); (C.T.); (J.M.); (S.T.); (I.M.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Iasmina M. Craici
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (S.S.); (C.T.); (J.M.); (S.T.); (I.M.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (S.S.); (C.T.); (J.M.); (S.T.); (I.M.C.)
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Thongprayoon C, Tangpanithandee S, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, Acharya PC, Leeaphorn N, Kaewput W, Pattharanitima P, Suppadungsuk S, Krisanapan P, Nissaisorakarn P, Cooper M, Craici IM, Cheungpasitporn W. Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering: Pathway to Personalized Care. J Pers Med 2023; 13:1273. [PMID: 37623523 PMCID: PMC10455164 DOI: 10.3390/jpm13081273] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023] Open
Abstract
Longer pre-transplant dialysis duration is known to be associated with worse post-transplant outcomes. Our study aimed to cluster kidney transplant recipients with prolonged dialysis duration before transplant using an unsupervised machine learning approach to better assess heterogeneity within this cohort. We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 5092 kidney transplant recipients who had been on dialysis ≥ 10 years prior to transplant in the OPTN/UNOS database from 2010 to 2019. We characterized each assigned cluster and compared the posttransplant outcomes. Overall, the majority of patients with ≥10 years of dialysis duration were black (52%) or Hispanic (25%), with only a small number (17.6%) being moderately sensitized. Within this cohort, three clinically distinct clusters were identified. Cluster 1 patients were younger, non-diabetic and non-sensitized, had a lower body mass index (BMI) and received a kidney transplant from younger donors. Cluster 2 recipients were older, unsensitized and had a higher BMI; they received kidney transplant from older donors. Cluster 3 recipients were more likely to be female with a higher PRA. Compared to cluster 1, cluster 2 had lower 5-year death-censored graft (HR 1.40; 95% CI 1.16-1.71) and patient survival (HR 2.98; 95% CI 2.43-3.68). Clusters 1 and 3 had comparable death-censored graft and patient survival. Unsupervised machine learning was used to characterize kidney transplant recipients with prolonged pre-transplant dialysis into three clinically distinct clusters with variable but good post-transplant outcomes. Despite a dialysis duration ≥ 10 years, excellent outcomes were observed in most recipients, including those with moderate sensitization. A disproportionate number of minority recipients were observed within this cohort, suggesting multifactorial delays in accessing kidney transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Caroline C. Jadlowiec
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;
| | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Michael A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Pradeep Vaitla
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Prakrati C. Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA;
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke’s Health System, Kansas City, MO 64108, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand;
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand;
- Division of Nephrology, Department of Internal Medicine, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Pitchaphon Nissaisorakarn
- Deparment of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Matthew Cooper
- Department of Surgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Iasmina M. Craici
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
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Xiao S, Liu F, Yu L, Li X, Ye X, Gong X. Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis. BMC Med Inform Decis Mak 2023; 23:71. [PMID: 37076865 PMCID: PMC10114399 DOI: 10.1186/s12911-023-02157-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/17/2023] [Indexed: 04/21/2023] Open
Abstract
PURPOSE Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm surgery. METHODS Patients, who underwent intracranial aneurysm surgery in our hospital between January 2019 and December 2021 were enrolled. Four machine learning models were benchmarked and the best learning model was used to establish the nomogram, before conducting a discriminative assessment. RESULTS A total of 375 patients were included for analysis in this model, among whom 108 received an intraoperative blood transfusion during the intracranial aneurysm surgery. The least absolute shrinkage selection operator identified six preoperative relative factors: hemoglobin, platelet, D-dimer, sex, white blood cell, and aneurysm rupture before surgery. Performance evaluation of the classification error demonstrated the following: K-nearest neighbor, 0.2903; logistic regression, 0.2290; ranger, 0.2518; and extremely gradient boosting model, 0.2632. A nomogram based on a logistic regression algorithm was established using the above six parameters. The AUC values of the nomogram were 0.828 (0.775, 0.881) and 0.796 (0.710, 0.882) in the development and validation groups, respectively. CONCLUSIONS Machine learning algorithms present a good performance evaluation of intraoperative blood transfusion. The nomogram established using a logistic regression algorithm showed a good discriminative ability to predict intraoperative blood transfusion during aneurysm surgery.
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Affiliation(s)
- Shugen Xiao
- Institute of Brain Disease and Neuroscience, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Fan Liu
- Institute of Brain Disease and Neuroscience, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Liyuan Yu
- Institute of Brain Disease and Neuroscience, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Xiaopei Li
- Institute of Brain Disease and Neuroscience, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Xihong Ye
- Institute of Brain Disease and Neuroscience, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
| | - Xingrui Gong
- Institute of Brain Disease and Neuroscience, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
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Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury. J Clin Med 2023; 12:jcm12031166. [PMID: 36769813 PMCID: PMC9917969 DOI: 10.3390/jcm12031166] [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: 12/25/2022] [Revised: 01/16/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE We aimed to develop and validate a predictive machine learning (ML) model for cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter randomized control trial (RCT) and a Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset. METHODS This was a subanalysis from a completed RCT approved by the Ethics Committee of Fuwai Hospital in Beijing, China (NCT03782350). Data from Fuwai Hospital were randomly assigned, with 80% for the training dataset and 20% for the testing dataset. The data from three other centers were used for the external validation dataset. Furthermore, the MIMIC-IV dataset was also utilized to validate the performance of the predictive model. The area under the receiver operating characteristic curve (ROC-AUC), the precision-recall curve (PR-AUC), and the calibration brier score were applied to evaluate the performance of the traditional logistic regression (LR) and eleven ML algorithms. Additionally, the Shapley Additive Explanations (SHAP) interpreter was used to explain the potential risk factors for CSA-AKI. RESULT A total of 6495 eligible patients undergoing cardiopulmonary bypass (CPB) were eventually included in this study, 2416 of whom were from Fuwai Hospital (Beijing), for model development, 562 from three other cardiac centers in China, and 3517 from the MIMICIV dataset, were used, respectively, for external validation. The CatBoostClassifier algorithms outperformed other models, with excellent discrimination and calibration performance for the development, as well as the MIMIC-IV, datasets. In addition, the CatBoostClassifier achieved ROC-AUCs of 0.85, 0.67, and 0.77 and brier scores of 0.14, 0.19, and 0.16 in the testing, external, and MIMIC-IV datasets, respectively. Moreover, the utmost important risk factor, the N-terminal brain sodium peptide (NT-proBNP), was confirmed by the LASSO method in the feature section process. Notably, the SHAP explainer identified that the preoperative blood urea nitrogen level, prothrombin time, serum creatinine level, total bilirubin level, and age were positively correlated with CSA-AKI; preoperative platelets level, systolic and diastolic blood pressure, albumin level, and body weight were negatively associated with CSA-AKI. CONCLUSIONS The CatBoostClassifier algorithms outperformed other ML models in the discrimination and calibration of CSA-AKI prediction cardiac surgery with CPB, based on a multicenter RCT and MIMIC-IV dataset. Moreover, the preoperative NT-proBNP level was confirmed to be strongly related to CSA-AKI.
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Eysenbach G, Kang YX, Duan SB, Yan P, Song GB, Zhang NY, Yang SK, Li JX, Zhang H. Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study. J Med Internet Res 2023; 25:e41142. [PMID: 36603200 PMCID: PMC9893730 DOI: 10.2196/41142] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication following pediatric cardiac surgery, which is associated with increased morbidity and mortality. The early prediction of CSA-AKI before and immediately after surgery could significantly improve the implementation of preventive and therapeutic strategies during the perioperative periods. However, there is limited clinical information on how to identify pediatric patients at high risk of CSA-AKI. OBJECTIVE The study aims to develop and validate machine learning models to predict the development of CSA-AKI in the pediatric population. METHODS This retrospective cohort study enrolled patients aged 1 month to 18 years who underwent cardiac surgery with cardiopulmonary bypass at 3 medical centers of Central South University in China. CSA-AKI was defined according to the 2012 Kidney Disease: Improving Global Outcomes criteria. Feature selection was applied separately to 2 data sets: the preoperative data set and the combined preoperative and intraoperative data set. Multiple machine learning algorithms were tested, including K-nearest neighbor, naive Bayes, support vector machines, random forest, extreme gradient boosting (XGBoost), and neural networks. The best performing model was identified in cross-validation by using the area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using the Shapley additive explanations (SHAP) method. RESULTS A total of 3278 patients from one of the centers were used for model derivation, while 585 patients from another 2 centers served as the external validation cohort. CSA-AKI occurred in 564 (17.2%) patients in the derivation cohort and 51 (8.7%) patients in the external validation cohort. Among the considered machine learning models, the XGBoost models achieved the best predictive performance in cross-validation. The AUROC of the XGBoost model using only the preoperative variables was 0.890 (95% CI 0.876-0.906) in the derivation cohort and 0.857 (95% CI 0.800-0.903) in the external validation cohort. When the intraoperative variables were included, the AUROC increased to 0.912 (95% CI 0.899-0.924) and 0.889 (95% CI 0.844-0.920) in the 2 cohorts, respectively. The SHAP method revealed that baseline serum creatinine level, perfusion time, body length, operation time, and intraoperative blood loss were the top 5 predictors of CSA-AKI. CONCLUSIONS The interpretable XGBoost models provide practical tools for the early prediction of CSA-AKI, which are valuable for risk stratification and perioperative management of pediatric patients undergoing cardiac surgery.
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Affiliation(s)
| | - Yi-Xin Kang
- Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shao-Bin Duan
- Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ping Yan
- Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Guo-Bao Song
- Department of Cardiovascular Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ning-Ya Zhang
- Information Center, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shi-Kun Yang
- Department of Nephrology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Jing-Xin Li
- Department of Cardiovascular Surgery, Xiangya Hospital of Central South University, Changsha, China
| | - Hui Zhang
- Department of Pediatrics, Xiangya Hospital of Central South University, Changsha, China
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Fan T, Wang J, Li L, Kang J, Wang W, Zhang C. Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost. Front Public Health 2023; 11:1087297. [PMID: 37089510 PMCID: PMC10117643 DOI: 10.3389/fpubh.2023.1087297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/17/2023] [Indexed: 04/25/2023] Open
Abstract
Objective The purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU). Methods Patients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient's medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model. Results The final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT). Conclusion An ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent.
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Affiliation(s)
- Tingting Fan
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, China
| | - Jiaxin Wang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, China
| | - Luyao Li
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, China
| | - Jing Kang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, China
| | - Wenrui Wang
- Digestive Diseases Center, Department of Hepatopancreatobiliary Medicine, Second Affiliated Hospital of Jilin University, Changchun, China
| | - Chuan Zhang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Changchun, China
- *Correspondence: Chuan Zhang,
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Milne B, Gilbey T, Kunst G. Perioperative Management of the Patient at High-Risk for Cardiac Surgery-Associated Acute Kidney Injury. J Cardiothorac Vasc Anesth 2022; 36:4460-4482. [PMID: 36241503 DOI: 10.1053/j.jvca.2022.08.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/27/2022] [Accepted: 08/22/2022] [Indexed: 11/11/2022]
Abstract
Acute kidney injury (AKI) is one of the most common major complications of cardiac surgery, and is associated with increased morbidity and mortality. Cardiac surgery-associated AKI has a complex, multifactorial etiology, including numerous factors such as primary cardiac dysfunction, hemodynamic derangements of cardiac surgery and cardiopulmonary bypass, and the possibility of a large volume of blood transfusion. There are no truly effective pharmacologic therapies for the management of AKI, and, therefore, anesthesiologists, intensivists, and cardiac surgeons must remain vigilant and attempt to minimize the risk of developing renal dysfunction. This narrative review describes the current state of the scientific literature concerning the specific aspects of cardiac surgery-associated AKI, and presents it in a chronological fashion to aid the perioperative clinician in their approach to this high-risk patient group. The evidence was considered for risk prediction models, preoperative optimization, and the intraoperative and postoperative management of cardiac surgery patients to improve renal outcomes.
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Affiliation(s)
- Benjamin Milne
- Department of Anaesthetics and Pain Medicine, King's College Hospital NHS Foundation Trust, London, United Kingdom; National Institute of Health Research Academic Clinical Fellow, King's College London, London, United Kingdom
| | - Tom Gilbey
- Department of Anaesthetics and Pain Medicine, King's College Hospital NHS Foundation Trust, London, United Kingdom; National Institute of Health Research Academic Clinical Fellow, King's College London, London, United Kingdom
| | - Gudrun Kunst
- Department of Anaesthetics and Pain Medicine, King's College Hospital NHS Foundation Trust, London, United Kingdom; School of Cardiovascular Medicine and Metabolic Medicine and Sciences, King's College London, British Heart Foundation Centre of Excellence, Faculty of Life Sciences and Medicine, London, United Kingdom.
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Thongprayoon C, Pattharanitima P, Kattah AG, Mao MA, Keddis MT, Dillon JJ, Kaewput W, Tangpanithandee S, Krisanapan P, Qureshi F, Cheungpasitporn W. Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury. J Clin Med 2022; 11:6264. [PMID: 36362493 PMCID: PMC9656700 DOI: 10.3390/jcm11216264] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/15/2022] [Accepted: 10/21/2022] [Indexed: 08/30/2023] Open
Abstract
BACKGROUND We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). METHODS Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. RESULTS The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. CONCLUSION We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Andrea G. Kattah
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Mira T. Keddis
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - John J. Dillon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Faculty of Medicine, Chakri Naruebodindra Medical Institute, Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Xue X, Liu Z, Xue T, Chen W, Chen X. Machine learning for the prediction of acute kidney injury in patients after cardiac surgery. Front Surg 2022; 9:946610. [PMID: 36157418 PMCID: PMC9490319 DOI: 10.3389/fsurg.2022.946610] [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: 05/17/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792–0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.
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Affiliation(s)
- Xin Xue
- Department of Cardiothoracic Surgery, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
- Department of Cardiothoracic Surgery, School of Medicine, Southeast University, Nanjing, China
| | - Zhiyong Liu
- Department of Cardiothoracic Surgery, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tao Xue
- Department of Cardiothoracic Surgery, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Wen Chen
- Department of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xin Chen
- Department of Cardiothoracic Surgery, School of Medicine, Southeast University, Nanjing, China
- Department of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Correspondence: Xin Chen
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Biernawska J, Kotfis K, Szymańska-Pasternak J, Bogacka A, Bober J. Long-Term Consequences of Increased Activity of Urine Enzymes After Cardiac Surgery - A Prospective Observational Study. Ther Clin Risk Manag 2022; 18:867-877. [PMID: 36051850 PMCID: PMC9427009 DOI: 10.2147/tcrm.s371288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction Cardiac surgery associated AKI (CSA-AKI) complicates recovery and may be associated with a greater risk of developing chronic kidney disease and mortality. The aim of this study was to assess long-term clinical consequences of transient increased activity of urinary enzymes after cardiac surgery (CS). Methods An observational study was conducted in a group of 88 adult patients undergoing planned coronary artery bypass grafting (CABG), but all samples were obtained from 79 patients. The activity of urinary enzymes: N-acetyl-beta-glucosaminidase (NAG), arylsulfatase A (ASA) and beta-glucuronidase was evaluated in sequential urine samples. A comparative analysis of biochemical parameters was performed regarding the occurrence of acute kidney injury (AKI) defined by KIDGO at 24 hours, at day 30 and 5-years after the operation. Results During the first 24 hours after CS AKI was diagnosed in 13 patients. A comparison of the activity of urinary enzymes in pre-defined time-points showed significant differences for ASA and NAG (post OP-sample p < 0.028 and p < 0.022; POD 1 sample p < 0.004 and p < 0.001 respectively). No patient had any biochemical or clinical features of kidney failure at day 30. In the AKI group kidney failure was diagnosed in 36% of patients within 5 years of follow-up as opposed to 5% in the no AKI group. The activities of tubular enzymes in urine reflect a general injury of kidney tubules during and after the operation. However, they are not ideal biomarkers for prediction of the degree of kidney injury and further poor prognosis of CS-AKI.
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Affiliation(s)
- Jowita Biernawska
- Department of Anesthesiology and Intensive Therapy, Pomeranian Medical University, Szczecin, Poland
| | - Katarzyna Kotfis
- Department of Anesthesiology, Intensive Therapy and Acute Intoxications, Pomeranian Medical University, Szczecin, Poland
| | | | - Anna Bogacka
- Department of Commodity Science, Quality Assessment, Process Engineering and Human Nutrition, West Pomeranian University of Technology, Szczecin, Poland
| | - Joanna Bober
- Department of Medical Chemistry, Pomeranian Medical University, Szczecin, Poland
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Biomarkers of acute kidney injury after pediatric cardiac surgery: a meta-analysis of diagnostic test accuracy. Eur J Pediatr 2022; 181:1909-1921. [PMID: 35039910 DOI: 10.1007/s00431-022-04380-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/05/2022] [Accepted: 01/09/2022] [Indexed: 12/29/2022]
Abstract
UNLABELLED Acute kidney injury (AKI) occurs frequently after cardiac surgery in children. Although current diagnostic criteria rely on serum creatinine and urine output, changes occur only after considerable loss of kidney function. This meta-analysis aimed to synthesize the knowledge on novel biomarkers and compare their ability to predict AKI. PubMed/MEDLINE, Embase, Scopus, and reference lists were searched for relevant studies published by March 2021. Diagnostic accuracy parameters were extracted and analyzed using hierarchical summary receiver operating characteristic (HSROC) method. Pooled estimates of the area under the curve (AUC) were calculated using conventional random-effects meta-analysis. Fifty-six articles investigating 49 biomarkers in 8617 participants fulfilled our eligibility criteria. Data from 37 studies were available for meta-analysis. Of the 10 biomarkers suitable for HSROC analysis, urinary neutrophil gelatinase-associated lipocalin (uNGAL) to creatinine (Cr) ratio yielded the highest diagnostic odds ratio (91.0, 95% CI 90.1-91.9), with a sensitivity of 91.3% (95% CI 91.2-91.3%) and a specificity of 89.7% (95% CI 89.6-89.7%). These results were confirmed in pooled AUC analysis, as uNGAL-to-Cr ratio and uNGAL were the only elaborately studied biomarkers (> 5 observations) with pooled AUCs ≥ 0.800. Liver fatty acid-binding protein (L-FABP), serum cystatin C (sCysC), serum NGAL (sNGAL), and interleukin-18 (IL-18) all had AUCs ≥ 0.700. CONCLUSION A variety of biomarkers have been proposed as predictors of cardiac surgery-associated AKI in children, of which uNGAL was the most prominent with excellent diagnostic qualities. However, more consolidatory evidence will be required before these novel biomarkers may eventually help realize precision medicine in AKI management. WHAT IS KNOWN • Acute kidney injury (AKI) occurs in about 30-60% of children undergoing cardiac surgery and is associated with increased in-hospital mortality and adverse short-term outcomes. However, in current clinical practice, AKI definitions and detection often rely on changes in serum creatinine and urine output, which are late and insensitive markers of kidney injury. • Although various novel biomarkers have been studied for the diagnosis of AKI in children after cardiac surgery, it remains unclear how these compare to one another in terms of diagnostic accuracy. WHAT IS NEW • Pooled analyses suggest that for the diagnosis of AKI in children who underwent cardiac surgery, NGAL is the most accurate among the most frequently studied biomarkers. • A number of other promising biomarkers have been reported, although they will require further research into their diagnostic accuracy and clinical applicability.
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Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering. Med Sci (Basel) 2021; 9:medsci9040060. [PMID: 34698185 PMCID: PMC8544570 DOI: 10.3390/medsci9040060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/19/2021] [Accepted: 09/21/2021] [Indexed: 12/22/2022] Open
Abstract
Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.
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Abstract
PURPOSE OF REVIEW Artificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. RECENT FINDINGS Artificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery. SUMMARY Artificial intelligence has many potential exciting applications in cardiac surgery. It can streamline physician workload and help make medicine more human again by placing the physician back at the bedside. Here, we offer cardiac surgeons an introduction to this transformative tool so that they may actively participate in creating clinically relevant implementations to improve our practice.
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Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering. MEDICINA-LITHUANIA 2021; 57:medicina57090903. [PMID: 34577826 PMCID: PMC8465989 DOI: 10.3390/medicina57090903] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/26/2021] [Accepted: 08/26/2021] [Indexed: 01/20/2023]
Abstract
Background and Objectives: Despite the association between hyperchloremia and adverse outcomes, mortality risks among patients with hyperchloremia have not consistently been observed among all studies with different patient populations with hyperchloremia. The objective of this study was to characterize hyperchloremic patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. Materials and Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,394 hospitalized adult patients with admission serum chloride of >108 mEq/L. We calculated the standardized mean difference of each variable to identify each cluster's key features. We assessed the association of each hyperchloremia cluster with hospital and one-year mortality. Results: There were three distinct clusters of patients with admission hyperchloremia: 3237 (28%), 4059 (36%), and 4098 (36%) patients in clusters 1 through 3, respectively. Cluster 1 was characterized by higher serum chloride but lower serum sodium, bicarbonate, hemoglobin, and albumin. Cluster 2 was characterized by younger age, lower comorbidity score, lower serum chloride, and higher estimated glomerular filtration (eGFR), hemoglobin, and albumin. Cluster 3 was characterized by older age, higher comorbidity score, higher serum sodium, potassium, and lower eGFR. Compared with cluster 2, odds ratios for hospital mortality were 3.60 (95% CI 2.33-5.56) for cluster 1, and 4.83 (95% CI 3.21-7.28) for cluster 3, whereas hazard ratios for one-year mortality were 4.49 (95% CI 3.53-5.70) for cluster 1 and 6.96 (95% CI 5.56-8.72) for cluster 3. Conclusions: Our cluster analysis identified three clinically distinct phenotypes with differing mortality risks in hospitalized patients with admission hyperchloremia.
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Tanabe S. How can artificial intelligence and humans work together to fight against cancer? Artif Intell Cancer 2020; 1:45-50. [DOI: 10.35713/aic.v1.i3.45] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/18/2020] [Accepted: 09/22/2020] [Indexed: 02/06/2023] Open
Abstract
This editorial will focus on and discuss growing artificial intelligence (AI) and the utilization of AI in human cancer therapy. The databases and big data related to genomes, genes, proteins and molecular networks are rapidly increasing all worldwide where information on human diseases, including cancer and infection resides. To overcome diseases, prevention and therapeutics are being developed with the abundant data analyzed by AI. AI has so much potential for handling considerable data, which requires some orientation and ambition. Appropriate interpretation of AI is essential for understanding disease mechanisms and finding targets for prevention and therapeutics. Collaboration with AI to extract the essence of cancer data and model intelligent networks will be explored. The utilization of AI can provide humans with a predictive future in disease mechanisms and treatment as well as prevention.
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Affiliation(s)
- Shihori Tanabe
- Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Kanagawa, Japan
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Mahmood T, Owais M, Noh KJ, Yoon HS, Haider A, Sultan H, Park KR. Artificial Intelligence-based Segmentation of Nuclei in Multi-organ Histopathology Images: Model Development and Validation (Preprint). JMIR Med Inform 2020. [DOI: 10.2196/24394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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