1
|
Ramaswamy T, Sparling JL, Chang MG, Bittner EA. Ten misconceptions regarding decision-making in critical care. World J Crit Care Med 2024; 13:89644. [PMID: 38855268 PMCID: PMC11155500 DOI: 10.5492/wjccm.v13.i2.89644] [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: 11/07/2023] [Revised: 01/25/2024] [Accepted: 03/01/2024] [Indexed: 06/03/2024] Open
Abstract
Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.
Collapse
Affiliation(s)
- Tara Ramaswamy
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Jamie L Sparling
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Marvin G Chang
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| | - Edward A Bittner
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States
| |
Collapse
|
2
|
Ghazi L, Farhat K, Hoenig MP, Durant TJS, El-Khoury JM. Biomarkers vs Machines: The Race to Predict Acute Kidney Injury. Clin Chem 2024; 70:805-819. [PMID: 38299927 DOI: 10.1093/clinchem/hvad217] [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: 07/06/2023] [Accepted: 10/20/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Acute kidney injury (AKI) is a serious complication affecting up to 15% of hospitalized patients. Early diagnosis is critical to prevent irreversible kidney damage that could otherwise lead to significant morbidity and mortality. However, AKI is a clinically silent syndrome, and current detection primarily relies on measuring a rise in serum creatinine, an imperfect marker that can be slow to react to developing AKI. Over the past decade, new innovations have emerged in the form of biomarkers and artificial intelligence tools to aid in the early diagnosis and prediction of imminent AKI. CONTENT This review summarizes and critically evaluates the latest developments in AKI detection and prediction by emerging biomarkers and artificial intelligence. Main guidelines and studies discussed herein include those evaluating clinical utilitiy of alternate filtration markers such as cystatin C and structural injury markers such as neutrophil gelatinase-associated lipocalin and tissue inhibitor of metalloprotease 2 with insulin-like growth factor binding protein 7 and machine learning algorithms for the detection and prediction of AKI in adult and pediatric populations. Recommendations for clinical practices considering the adoption of these new tools are also provided. SUMMARY The race to detect AKI is heating up. Regulatory approval of select biomarkers for clinical use and the emergence of machine learning algorithms that can predict imminent AKI with high accuracy are all promising developments. But the race is far from being won. Future research focusing on clinical outcome studies that demonstrate the utility and validity of implementing these new tools into clinical practice is needed.
Collapse
Affiliation(s)
- Lama Ghazi
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Kassem Farhat
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Melanie P Hoenig
- Renal Division, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA 02215, United States
| | - Thomas J S Durant
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, United States
- Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
| | - Joe M El-Khoury
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510, United States
| |
Collapse
|
3
|
Perschinka F, Peer A, Joannidis M. [Artificial intelligence and acute kidney injury]. Med Klin Intensivmed Notfmed 2024; 119:199-207. [PMID: 38396124 PMCID: PMC10995052 DOI: 10.1007/s00063-024-01111-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 02/25/2024]
Abstract
Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primarily focused on the prediction of AKI, but further approaches are also being used to classify existing AKI into different phenotypes. Different AI models are used for prediction. The area under the receiver operating characteristic curve values (AUROC) achieved with these models vary and are influenced by several factors, such as the prediction time and the definition of AKI. Most models have an AUROC between 0.650 and 0.900, with lower values for predictions further into the future and when applying Acute Kidney Injury Network (AKIN) instead of KDIGO criteria. Classification into phenotypes already makes it possible to categorize patients into groups with different risks of mortality or requirement of renal replacement therapy (RRT), but the etiologies or therapeutic consequences derived from this are still lacking. However, all the models suffer from AI-specific shortcomings. The use of large databases does not make it possible to promptly include recent changes in therapy and the implementation of new biomarkers in a relevant proportion. For this reason, serum creatinine and urinary output, with their known limitations, dominate current AI models for prediction impairing the performance of the current models. On the other hand, the increasingly complex models no longer allow physicians to understand the basis on which the warning of a threatening AKI is calculated and subsequent initiation of therapy should take place. The successful use of AIs in routine clinical practice will be highly determined by the trust of the physicians in the systems and overcoming the aforementioned weaknesses. However, the clinician will remain irreplaceable as the decisive authority for critically ill patients by combining measurable and nonmeasurable parameters.
Collapse
Affiliation(s)
| | | | - Michael Joannidis
- Gemeinsame Einrichtung für Internistische Notfall- und Intensivmedizin, Department Innere Medizin, Medizinische Universität Innsbruck, Anichstraße 35, 6020, Innsbruck, Österreich.
| |
Collapse
|
4
|
Lundy DJ, Szomolay B, Liao CT. Systems Approaches to Cell Culture-Derived Extracellular Vesicles for Acute Kidney Injury Therapy: Prospects and Challenges. FUNCTION 2024; 5:zqae012. [PMID: 38706963 PMCID: PMC11065115 DOI: 10.1093/function/zqae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/02/2024] [Accepted: 03/05/2024] [Indexed: 05/07/2024] Open
Abstract
Acute kidney injury (AKI) is a heterogeneous syndrome, comprising diverse etiologies of kidney insults that result in high mortality and morbidity if not well managed. Although great efforts have been made to investigate underlying pathogenic mechanisms of AKI, there are limited therapeutic strategies available. Extracellular vesicles (EV) are membrane-bound vesicles secreted by various cell types, which can serve as cell-free therapy through transfer of bioactive molecules. In this review, we first overview the AKI syndrome and EV biology, with a particular focus on the technical aspects and therapeutic application of cell culture-derived EVs. Second, we illustrate how multi-omic approaches to EV miRNA, protein, and genomic cargo analysis can yield new insights into their mechanisms of action and address unresolved questions in the field. We then summarize major experimental evidence regarding the therapeutic potential of EVs in AKI, which we subdivide into stem cell and non-stem cell-derived EVs. Finally, we highlight the challenges and opportunities related to the clinical translation of animal studies into human patients.
Collapse
Affiliation(s)
- David J Lundy
- Graduate Institute of Biomedical Materials & Tissue Engineering, Taipei Medical University, Taipei 235603, Taiwan
- International PhD Program in Biomedical Engineering, Taipei Medical University, Taipei 235603, Taiwan
- Center for Cell Therapy, Taipei Medical University Hospital, Taipei 110301, Taiwan
| | - Barbara Szomolay
- Systems Immunity Research Institute, Cardiff University School of Medicine, Cardiff CF14 4XN, UK
- Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff CF14 4XN, UK
| | - Chia-Te Liao
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Research Center of Urology and Kidney, Taipei Medical University, Taipei 110, Taiwan
| |
Collapse
|
5
|
Persson I, Grünwald A, Morvan L, Becedas D, Arlbrandt M. A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study. JMIR Form Res 2023; 7:e45979. [PMID: 38096015 PMCID: PMC10755657 DOI: 10.2196/45979] [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: 02/02/2023] [Revised: 10/08/2023] [Accepted: 10/26/2023] [Indexed: 12/31/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes. OBJECTIVE The primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes. METHODS We developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older. RESULTS The developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy. CONCLUSIONS NAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.
Collapse
Affiliation(s)
- Inger Persson
- Department of Statistics, Uppsala University, Uppsala, Sweden
- AlgoDx AB, Stockholm, Sweden
| | | | | | | | - Martin Arlbrandt
- Department of Anaesthesiology and Intensive Care, Södersjukhuset (Stockholm South General Hospital), Stockholm, Sweden
| |
Collapse
|
6
|
Yousif ZK, Koola JD, Macedo E, Cerda J, Goldstein SL, Chakravarthi R, Lewington A, Selewski D, Zappitelli M, Cruz D, Tolwani A, Joy MS, Jha V, Ramachandran R, Ostermann M, Pandya B, Acharya A, Brophy P, Ponce D, Steinke J, Bouchard J, Irarrazabal CE, Irarrazabal R, Boltansky A, Askenazi D, Kolhe N, Claure-Del Granado R, Benador N, Castledine C, Davenport A, Barratt J, Bhandari S, Riley AA, Davis T, Farmer C, Hogarth M, Thomas M, Murray PT, Robinson-Cohen C, Nicoletti P, Vaingankar S, Mehta R, Awdishu L. Clinical Characteristics and Outcomes of Drug-Induced Acute Kidney Injury Cases. Kidney Int Rep 2023; 8:2333-2344. [PMID: 38025217 PMCID: PMC10658426 DOI: 10.1016/j.ekir.2023.07.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 06/22/2023] [Accepted: 07/31/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Drug-induced acute kidney injury (DI-AKI) is a frequent adverse event. The identification of DI-AKI is challenged by competing etiologies, clinical heterogeneity among patients, and a lack of accurate diagnostic tools. Our research aims to describe the clinical characteristics and predictive variables of DI-AKI. Methods We analyzed data from the Drug-Induced Renal Injury Consortium (DIRECT) study (NCT02159209), an international, multicenter, observational cohort study of enriched clinically adjudicated DI-AKI cases. Cases met the primary inclusion criteria if the patient was exposed to at least 1 nephrotoxic drug for a minimum of 24 hours prior to AKI onset. Cases were clinically adjudicated, and inter-rater reliability (IRR) was measured using Krippendorff's alpha. Variables associated with DI-AKI were identified using L1 regularized multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC). Results A total of 314 AKI cases met the eligibility criteria for this analysis, and 271 (86%) cases were adjudicated as DI-AKI. The majority of the AKI cases were recruited from the United States (68%). The most frequent causal nephrotoxic drugs were vancomycin (48.7%), nonsteroidal antiinflammatory drugs (18.2%), and piperacillin/tazobactam (17.8%). The IRR for DI-AKI adjudication was 0.309. The multivariable model identified age, vascular capacity, hyperglycemia, infections, pyuria, serum creatinine (SCr) trends, and contrast media as significant predictors of DI-AKI with good performance (ROC AUC 0.86). Conclusion The identification of DI-AKI is challenging even with comprehensive adjudication by experienced nephrologists. Our analysis identified key clinical characteristics and outcomes of DI-AKI compared to other AKI etiologies.
Collapse
Affiliation(s)
- Zaid K. Yousif
- Division of Clinical Pharmacy, University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical, La Jolla, California, USA
| | - Jejo D. Koola
- Division of Biomedical Informatics, Department of Medicine, University of California, La Jolla, California, USA
- Division of Hospital Medicine, Department of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Etienne Macedo
- Division of Nephrology, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Jorge Cerda
- Albany Medical College, Albany, New York, USA
- St. Peter's Hospital Partners, Albany, New York, USA
| | - Stuart L. Goldstein
- Center for Acute Care Nephrology, Cincinnati Children's Hospital, Cincinnati, Ohio, USA
- University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | | | - David Selewski
- Department of Pediatrics, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Michael Zappitelli
- Department of Pediatrics, Division of Nephrology, Hospital for Sick Children, University of Toronto, Ontario, Canada
| | - Dinna Cruz
- Division of Nephrology, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | | | - Melanie S. Joy
- University of Colorado School of Pharmacy and Pharmaceutical Sciences and School of Medicine in Aurora, Colorado, USA
| | - Vivekanand Jha
- George Institute for Global Health, UNSW, New Delhi, India
- School of Public Health, Imperial College, London, UK
- Prasanna School of Public Health, MManipal Academy of Higher Education, Manipal, India
| | - Raja Ramachandran
- Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Marlies Ostermann
- Department of Critical Care and Nephrology, King’s College London, Guy’s and St Thomas’ Hospital, London, UK
| | - Bhavna Pandya
- Medical and Dental Staff Governor, Liverpool University Hospitals NHS Foundation Trust/Aintree University Hospital, Liverpool, UK
| | - Anjali Acharya
- Jacobi Medical Center, Albert Einstein College of Medicine, The Bronx, New York, New York, USA
| | - Patrick Brophy
- Department of Pediatrics at the University of Rochester School of Medicine and Dentistry, New York, USA
| | | | - Julia Steinke
- Helen DeVos Children's Hospital, Grand Rapids, Michigan, USA
| | - Josee Bouchard
- Hopital du Sacre-Coeur de Montreal, Montreal, Quebec, Canada
| | - Carlos E. Irarrazabal
- Programa de Fisiología, Centro de Investigación e Innovación Biomédica, Universidad de los Andes, Santiago, Chile
| | | | | | - David Askenazi
- Children's of Alabama (UAB-Pediatrics), Birmingham, Alabama, USA
| | - Nitin Kolhe
- Consultant Nephrologist, Royal Derby Hospital, Derby, UK
| | - Rolando Claure-Del Granado
- Division of Nephrology Hospital Obrero No 2 – CNS Cochabamba, Bolivia/Universidad Mayor de San Simón School of Medicine Cochabamba, Bolivia
| | - Nadine Benador
- University of California San Diego, San Diego, California, USA / Rady Children's Hospital, San Diego, USA
| | | | - Andrew Davenport
- University College London, Department of Renal Medicine, Royal Free London NHS Trust London, UK
| | - Jonathan Barratt
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | | | - Alyssa A. Riley
- Department of Pediatrics, Section of Nephrology, Baylor College of Medicine, Houston, Texas, USA
| | - T.K. Davis
- St. Louis Children's Hospital, St. Louis, Missouri, USA
| | - Christopher Farmer
- Centre for Health Services Studies, George Allen Wing, Cornwallis Building, University of Kent, Canterbury, Kent, UK
| | - Michael Hogarth
- Division of Biomedical Informatics, Department of Medicine, University of California, La Jolla, California, USA
| | - Mark Thomas
- Birmingham Heartlands Hospital, Birmingham, Alabama, USA
| | | | - Cassianne Robinson-Cohen
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Tennessee, USA
| | - Paola Nicoletti
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Ravindra Mehta
- Division of Nephrology, Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Linda Awdishu
- Division of Clinical Pharmacy, University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical, La Jolla, California, USA
| |
Collapse
|
7
|
Sharma S, Rawal R, Shah D. Addressing the challenges of AI-based telemedicine: Best practices and lessons learned. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:338. [PMID: 38023098 PMCID: PMC10671014 DOI: 10.4103/jehp.jehp_402_23] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/02/2023] [Indexed: 12/01/2023]
Abstract
Telemedicine is the use of technology to provide healthcare services and information remotely, without requiring physical proximity between patients and healthcare providers. The coronavirus disease 2019 (COVID-19) pandemic has accelerated the rapid growth of telemedicine worldwide. Integrating artificial intelligence (AI) into telemedicine has the potential to enhance and expand its capabilities in addressing various healthcare needs, such as patient monitoring, healthcare information technology (IT), intelligent diagnosis, and assistance. Despite the potential benefits, implementing AI in telemedicine presents challenges that can be overcome with physician-guided implementation. AI can assist physicians in decision-making, improve healthcare delivery, and automate administrative tasks. To ensure optimal effectiveness, AI-powered telemedicine should comply with existing clinical practices and adhere to a framework adaptable to various technologies. It should also consider technical and scientific factors, including trustworthiness, reproducibility, usability, availability, and cost. Education and training are crucial for the appropriate use of new healthcare technologies such as AI-enabled telemedicine. This article examines the benefits and limitations of AI-based telemedicine in various medical domains and underscores the importance of physician-guided implementation, compliance with existing clinical practices, and appropriate education and training for healthcare providers.
Collapse
Affiliation(s)
- Sachin Sharma
- Department of Computer Science and Engineering, Indrashil University, Mehsana, Gujarat, India
| | - Raj Rawal
- Department of Critical Care, Gujarat Pulmonary and Critical Care Medicine, Ahmedabad, Gujarat, India
| | - Dharmesh Shah
- Department of ICT, Indrashil University, Mehsana, Gujarat, India
| |
Collapse
|
8
|
Cheung HC, De Louche C, Komorowski M. Artificial Intelligence Applications in Space Medicine. Aerosp Med Hum Perform 2023; 94:610-622. [PMID: 37501303 DOI: 10.3357/amhp.6178.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.
Collapse
|
9
|
Zhang H, Wang AY, Wu S, Ngo J, Feng Y, He X, Zhang Y, Wu X, Hong D. Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy. BMC Nephrol 2022; 23:405. [PMID: 36536317 PMCID: PMC9761969 DOI: 10.1186/s12882-022-03025-w] [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: 01/11/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods. We conducted this systematic review and meta-analysis to estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period. METHODS Pubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. True positives, false positives, true negatives and false negatives were pooled to collate specificity and sensitivity with 95% CIs and results were portrayed in forest plots. The risk of bias of eligible studies was assessed using the PROBAST tool. RESULTS Nineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias. CONCLUSIONS Our review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. The limitations of lacking external validation performance and being conducted only at a single center should be overcome. TRIAL REGISTRATION This study was not registered with PROSPERO.
Collapse
Affiliation(s)
- Hanfei Zhang
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Amanda Y. Wang
- grid.1004.50000 0001 2158 5405The faculty of medicine and health sciences, Macquarie University, Sydney, NSW Australia
| | - Shukun Wu
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Johnathan Ngo
- grid.1013.30000 0004 1936 834XConcord Clinical School, University of Sydney, Sydney, Australia
| | - Yunlin Feng
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin He
- grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China ,grid.488387.8Department of Nephrology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yingfeng Zhang
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xingwei Wu
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Pharmacy, Sichuan Provincial Peoples Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Daqing Hong
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Renal Department and Nephrology Institute, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
10
|
Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach. Diagnostics (Basel) 2022; 12:diagnostics12123157. [PMID: 36553164 PMCID: PMC9776989 DOI: 10.3390/diagnostics12123157] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Background: The benefits of immune checkpoint inhibitors (ICPis) in the treatment of patients with malignancies emerged recently, but immune-related adverse events (IRAEs), including acute kidney injury (AKI), cannot be ignored. The present study established and validated an ICPi-AKI prediction model based on machine learning algorithms to achieve early prediction of AKI events and timely intervention adjustment. Methods: We performed a retrospective study based on data from the First Medical Center of the PLA General Hospital. Patients with malignancy who received at least one dose of ICPi between January 2014 and December 2019 were included in the study. The characteristics of available variables were included after case review, and the baseline characteristics and clinical data of ICPi AKI and non-AKI patients were compared. After variable preprocessing, eight machine learning algorithms were used to construct a full variable availability model. Variable simplification models were constructed after screening important variables using the random forest recursive feature elimination method, and the performance of different machine learning methods and two types of modeling strategies were evaluated using multiple indicators. Results: Among the 1616 patients receiving checkpoint inhibitors, the overall incidence of AKI was 6.9% during the total follow-up time. Sixty-eight patients were associated with ICPi treatment after chart review, primarily in AKI stage 1 (70.5%), with a median time from first ICPi administration to AKI of 12.7 (IQR 2 to 56) weeks. The demographic characteristics, comorbidities, and proportions of malignancy types were similar between the ICPi-AKI and non-AKI groups, but there were significant differences in multiple characteristics, such as concomitant medications and laboratory test indicators. For model performance evaluation and comparison, the AUC values of all 38 variable availability models ranged from 0.7204−0.8241, and the AUC values of the simplicity model constructed using 16 significant variables ranged from 0.7528−0.8315. The neural networks model (NNs) and support vector machine (SVM) model had the best performance in the two types of modeling strategies, respectively; however, there was no significant difference in model performance comparison (p > 0.05). In addition, compared with the full variable availability model, the performance of the variable simplicity model was slightly improved. We also found that concomitant medications contributed more to the model prediction performance by screening the optimal feature combination. Conclusion: We successfully developed a machine learning-based ICPi-AKI prediction model and validated the best prediction performance of each machine model. It is reasonable to believe that clinical decision models driven by artificial intelligence can improve AKI prediction in patients with malignancies treated with ICPi. These models can be used to assist clinicians in the early identification of patients at high risk of AKI, support effective prevention and intervention, and ultimately improve the overall benefit of antitumor therapy in the target population.
Collapse
|
11
|
Vagliano I, Chesnaye NC, Leopold JH, Jager KJ, Abu-Hanna A, Schut MC. Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal. Clin Kidney J 2022; 15:2266-2280. [PMID: 36381375 PMCID: PMC9664575 DOI: 10.1093/ckj/sfac181] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND The number of studies applying machine learning (ML) to predict acute kidney injury (AKI) has grown steadily over the past decade. We assess and critically appraise the state of the art in ML models for AKI prediction, considering performance, methodological soundness, and applicability. METHODS We searched PubMed and ArXiv, extracted data, and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. RESULTS Forty-six studies from 3166 titles were included. Thirty-eight studies developed a model, five developed and externally validated one, and three studies externally validated one. Flexible ML methods were used more often than deep learning, although the latter was common with temporal variables and text as predictors. Predictive performance showed an area under receiver operating curves ranging from 0.49 to 0.99. Our critical appraisal identified a high risk of bias in 39 studies. Some studies lacked internal validation, whereas external validation and interpretability of results were rarely considered. Fifteen studies focused on AKI prediction in the intensive care setting, and the US-derived Medical Information Mart for Intensive Care (MIMIC) data set was commonly used. Reproducibility was limited as data and code were usually unavailable. CONCLUSIONS Flexible ML methods are popular for the prediction of AKI, although more complex models based on deep learning are emerging. Our critical appraisal identified a high risk of bias in most models: Studies should use calibration measures and external validation more often, improve model interpretability, and share data and code to improve reproducibility.
Collapse
Affiliation(s)
- Iacopo Vagliano
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Nicholas C Chesnaye
- ERA Registry, Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jan Hendrik Leopold
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Kitty J Jager
- ERA Registry, Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Martijn C Schut
- Deptartment of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| |
Collapse
|
12
|
Hu Y, Liu K, Ho K, Riviello D, Brown J, Chang AR, Singh G, Kirchner HL. A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients. J Clin Med 2022; 11:jcm11195688. [PMID: 36233556 PMCID: PMC9573390 DOI: 10.3390/jcm11195688] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/19/2022] [Accepted: 09/23/2022] [Indexed: 11/29/2022] Open
Abstract
Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of >0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes.
Collapse
Affiliation(s)
- Yirui Hu
- Department of Population Health Sciences, Geisinger Health, Danville, PA 17822, USA
| | - Kunpeng Liu
- Department of Computer Science, Portland State University, Portland, OR 97201, USA
| | - Kevin Ho
- Fresenius Medical Care North America, Waltham, MA 02451, USA
| | - David Riviello
- Anticipatory Management Program, Steele Institute, Geisinger Health, Danville, PA 17822, USA
| | - Jason Brown
- Phenomics Analytics and Clinical Data Core, Geisinger Health, Danville, PA 17822, USA
| | - Alex R. Chang
- Department of Nephrology, Geisinger Health, Danville, PA 17822, USA
| | - Gurmukteshwar Singh
- Department of Nephrology, Geisinger Health, Danville, PA 17822, USA
- Correspondence: (G.S.); (H.L.K.); Tel.: +1-(570)-214-8688 (G.S. & H.L.K.)
| | - H. Lester Kirchner
- Department of Population Health Sciences, Geisinger Health, Danville, PA 17822, USA
- Correspondence: (G.S.); (H.L.K.); Tel.: +1-(570)-214-8688 (G.S. & H.L.K.)
| |
Collapse
|
13
|
Giovanni RD, Cochrane A, Parker J, Lewis DJ. Adverse events in the digital age and where to find them. Pharmacoepidemiol Drug Saf 2022; 31:1131-1139. [PMID: 35996833 DOI: 10.1002/pds.5532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 07/02/2022] [Accepted: 08/19/2022] [Indexed: 11/12/2022]
Abstract
Exponential growth of health-related data collected by digital tools is a reality within pharmaceutical and medical device research and development. Data generated through digital tools may be categorized as relevant to efficacy and/or safety. The enormity of these data requires the adoption of new approaches for processing and evaluation. Recognition of patterns within the safety data is vital for sponsors seeking regulatory approval for their new products. Non-traditional data sources may contain relevant safety information; early evaluation of these data will help to determine the product safety profile. Advanced technologies have allowed the development of digital tools to screen these data, which in some situations are classified as software as a medical devices and subject to clinical evaluation and post-marketing surveillance. Artificial intelligence may help to reduce or even eliminate noise from within these data, allowing safety experts to focus on the most pertinent evidence. We propose a data typology and provide considerations on how to define adverse events within different types of data, even where no human reporter exists. Proposals are made for the automation of screening processes. We consider validation aspects to support solutions that are proven to produce reliable results, and to deliver trusted outputs to stakeholders.
Collapse
Affiliation(s)
- Robert Di Giovanni
- Chief Medical Office and Patient Safety, Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| | - Andrew Cochrane
- Chief Medical Office and Patient Safety, Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| | - Jeremy Parker
- Enterprise Risk Management, Research and Development, ERC, Novartis Pharma AG, Basel, Switzerland
| | - David J Lewis
- Chief Medical Office and Patient Safety, Global Drug Development, Novartis Pharma GmbH, Oeflinger Strasse 44, Wehr, Germany.,Department of Pharmacy, Pharmacology and Postgraduate Medicine, University of Hertfordshire, Hatfield, Hertfordshire, UK
| |
Collapse
|
14
|
Magherini R, Mussi E, Volpe Y, Furferi R, Buonamici F, Servi M. Machine Learning for Renal Pathologies: An Updated Survey. SENSORS 2022; 22:s22134989. [PMID: 35808481 PMCID: PMC9269842 DOI: 10.3390/s22134989] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 12/04/2022]
Abstract
Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.
Collapse
|
15
|
Lazebnik T, Bahouth Z, Bunimovich-Mendrazitsky S, Halachmi S. Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model. BMC Med Inform Decis Mak 2022; 22:133. [PMID: 35578278 PMCID: PMC9112450 DOI: 10.1186/s12911-022-01877-8] [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: 07/13/2021] [Accepted: 05/10/2022] [Indexed: 11/22/2022] Open
Abstract
Background One of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms. Methods We included all adult patients (n = 723) who underwent open PN in our department since 1995 and on whom we have data on the pre-operative renal function. We developed a random forest (RF) model with Boolean satisfaction-based pruned decision trees for binary classification (AKI or non-AKI). Hyper-parameter grid search was performed to optimize the model's performance. Fivefold cross-validation was applied to evaluate the model. We implemented a RF model with greedy feature selection to binary classify AKI and non-AKI cases based on pre-operative data. Results The best model obtained a 0.69 precision and 0.69 recall in classifying the AKI and non-AKI groups on average (k = 5). In addition, the model's probability to correctly classify a new prediction is 0.75. The proposed model is available as an online calculator. Conclusions Our model predicts the occurrence of AKI following open PN with (75%) accuracy. We plan to externally validate this model and modify it to minimally-invasive PN. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01877-8.
Collapse
Affiliation(s)
- Teddy Lazebnik
- Department of Cancer Biology, Cancer Institute, University College London, London, UK.
| | - Zaher Bahouth
- Department of Urology, Bnai Zion Medical Center, Haifa, Israel
| | | | - Sarel Halachmi
- Department of Urology, Bnai Zion Medical Center, Haifa, Israel
| |
Collapse
|
16
|
Ang Y, Li S, Ong MEH, Xie F, Teo SH, Choong L, Koniman R, Chakraborty B, Ho AFW, Liu N. Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Sci Rep 2022; 12:7111. [PMID: 35501411 PMCID: PMC9061747 DOI: 10.1038/s41598-022-11129-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/12/2022] [Indexed: 12/24/2022] Open
Abstract
Acute kidney injury (AKI) in hospitalised patients is a common syndrome associated with poorer patient outcomes. Clinical risk scores can be used for the early identification of patients at risk of AKI. We conducted a retrospective study using electronic health records of Singapore General Hospital emergency department patients who were admitted from 2008 to 2016. The primary outcome was inpatient AKI of any stage within 7 days of admission based on the Kidney Disease Improving Global Outcome (KDIGO) 2012 guidelines. A machine learning-based framework AutoScore was used to generate clinical scores from the study sample which was randomly divided into training, validation and testing cohorts. Model performance was evaluated using area under the curve (AUC). Among the 119,468 admissions, 10,693 (9.0%) developed AKI. 8491 were stage 1 (79.4%), 906 stage 2 (8.5%) and 1296 stage 3 (12.1%). The AKI Risk Score (AKI-RiSc) was a summation of the integer scores of 6 variables: serum creatinine, serum bicarbonate, pulse, systolic blood pressure, diastolic blood pressure, and age. AUC of AKI-RiSc was 0.730 (95% CI 0.714–0.747), outperforming an existing AKI Prediction Score model which achieved AUC of 0.665 (95% CI 0.646–0.679) on the testing cohort. At a cut-off of 4 points, AKI-RiSc had a sensitivity of 82.6% and specificity of 46.7%. AKI-RiSc is a simple clinical score that can be easily implemented on the ground for early identification of AKI and potentially be applied in international settings.
Collapse
|
17
|
Dang TK, Lan X, Weng J, Feng M. Federated Learning for Electronic Health Records. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3514500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.
Collapse
Affiliation(s)
| | - Xiang Lan
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | | | - Mengling Feng
- Institute of Data Science & Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| |
Collapse
|
18
|
Nikkinen O, Kolehmainen T, Aaltonen T, Jämsä E, Alahuhta S, Vakkala M. Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients. Comput Biol Med 2022; 144:105351. [DOI: 10.1016/j.compbiomed.2022.105351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/29/2022]
|
19
|
A Novel Radiomics-Based Machine Learning Framework for Prediction of Acute Kidney Injury-Related Delirium in Patients Who Underwent Cardiovascular Surgery. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4242069. [PMID: 35341014 PMCID: PMC8956431 DOI: 10.1155/2022/4242069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/02/2022] [Accepted: 02/11/2022] [Indexed: 11/18/2022]
Abstract
Acute kidney injury (AKI) can be caused by multiple etiologies and is characterized by a sudden and severe decrease in kidney function. Understanding the independent risk factors associated with the development of AKI and its early detection can refine the risk management and clinical decision-making of high-risk patients after cardiovascular surgery. A retrospective analysis was performed in a single teaching hospital between December 1, 2019, and December 31, 2020. The diagnostic performance of novel biomarkers was assessed using random forest, support vector machine, and multivariate logistic regression. The nomogram from multivariate analysis of risk factors associated with AKI indicated that only LVEF, red blood cell input, and ICUmvat contribute to AKI differentiation and that the difference is statistically significant (P < 0.05). Seven radiomics biomarkers were found among 65 patients to be highly correlated with AKI-associated delirium. The importance of the variables was determined using the multilayer perceptron model; fivefold cross-validation was applied to determine the most important delirium risk factors in radiomics of the hippocampus. Finally, we established a radiomics-based machine learning framework to predict AKI-induced delirium in patients who underwent cardiovascular surgery.
Collapse
|
20
|
Yang L, Gabriel N, Hernandez I, Vouri SM, Kimmel SE, Bian J, Guo J. Identifying Patients at Risk of Acute Kidney Injury Among Medicare Beneficiaries With Type 2 Diabetes Initiating SGLT2 Inhibitors: A Machine Learning Approach. Front Pharmacol 2022; 13:834743. [PMID: 35359843 PMCID: PMC8961669 DOI: 10.3389/fphar.2022.834743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction: To predict acute kidney injury (AKI) risk in patients with type 2 diabetes (T2D) prescribed sodium-glucose cotransporter two inhibitors (SGLT2i). Methods: Using a 5% random sample of Medicare claims data, we identified 17,694 patients who filled ≥1 prescriptions for canagliflozin, dapagliflozin and empagliflozin in 2013–2016. The cohort was split randomly and equally into training and testing sets. We measured 65 predictor candidates using claims data from the year prior to SGLT2i initiation. We then applied three machine learning models, including random forests (RF), elastic net and least absolute shrinkage and selection operator (LASSO) for risk prediction. Results: The incidence rate of AKI was 1.1% over a median 1.5 year follow up. Among three machine learning methods, RF produced the best prediction (C-statistic = 0.72), followed by LASSO and elastic net (both C-statistics = 0.69). Among individuals classified in the top 10% of the RF risk score (i.e., high risk group), the actual incidence rate of AKI was as high as 3.7%. In the logistic regression model including 14 important risk factors selected by LASSO, use of loop diuretics [adjusted odds ratio (95% confidence interval): 3.72 (2.44–5.76)] had the strongest association with AKI incidence. Disscusion: Our machine learning model efficiently identified patients at risk of AKI among Medicare beneficiaries with T2D undergoing SGLT2i treatment.
Collapse
Affiliation(s)
- Lanting Yang
- Department of Pharmacy and Therapeutics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Nico Gabriel
- Division of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, United States
| | - Inmaculada Hernandez
- Division of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, United States
| | - Scott M. Vouri
- Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, United States
| | - Stephen E. Kimmel
- Department of Epidemiology, University of Florida, Gainesville, FL, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, United States
- *Correspondence: Jingchuan Guo,
| |
Collapse
|
21
|
Alfieri F, Ancona A, Tripepi G, Crosetto D, Randazzo V, Paviglianiti A, Pasero E, Vecchi L, Cauda V, Fagugli RM. A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients. J Nephrol 2021; 34:1875-1886. [PMID: 33900581 PMCID: PMC8610952 DOI: 10.1007/s40620-021-01046-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/02/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions. METHODS The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model. RESULTS The deep learning model defined an area under the curve (AUC) of 0.89 (± 0.01), sensitivity = 0.8 and specificity = 0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12 h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI. CONCLUSION In conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12 h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated in the ICU setting to better manage, and potentially prevent, AKI episodes.
Collapse
Affiliation(s)
- Francesca Alfieri
- Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Andrea Ancona
- Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Giovanni Tripepi
- Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, CNR-IFC, Nefrologia-Ospedali Riuniti, 89100 Reggio Calabria, Italy
| | - Dario Crosetto
- Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Vincenzo Randazzo
- Department of Electronics and Telecomunications, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Annunziata Paviglianiti
- Department of Electronics and Telecomunications, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Eros Pasero
- Department of Electronics and Telecomunications, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Luigi Vecchi
- S.C. Nefrologia e Dialisi, Azienda Ospedaliera Di Terni, Viale Tristano Di Joannuccio, 05100 Terni, Italy
| | - Valentina Cauda
- Department of Applied Science and Technology, Politecnico Di Torino, C.so Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Riccardo Maria Fagugli
- S.C. Nefrologia e Dialisi, Azienda Ospedaliera Di Perugia, Piazzale Giorgio Menghini 1, 06129 Perugia, Italy
| |
Collapse
|
22
|
Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction. Healthcare (Basel) 2021; 9:healthcare9121662. [PMID: 34946388 PMCID: PMC8701097 DOI: 10.3390/healthcare9121662] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/19/2021] [Accepted: 11/26/2021] [Indexed: 02/06/2023] Open
Abstract
Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction.
Collapse
|
23
|
Wong XY, Ang YK, Li K, Chin YH, Lam SSW, Tan KBK, Chua MCH, Ong MEH, Liu N, Pourghaderi AR, Ho AFW. Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework. Resuscitation 2021; 170:126-133. [PMID: 34843878 DOI: 10.1016/j.resuscitation.2021.11.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/17/2023]
Abstract
BACKGROUND Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC. METHODS We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses. RESULTS 5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort. CONCLUSION We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.
Collapse
Affiliation(s)
- Xiang Yi Wong
- Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore; Singapore Civil Defence Force, Ministry of Home Affairs, Singapore.
| | - Yu Kai Ang
- Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore
| | - Keqi Li
- Institute of System Science, National University of Singapore, Singapore
| | - Yip Han Chin
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | | | | | - Marcus Eng Hock Ong
- Department of Emergency Medicine, Singapore General Hospital, Singapore; Health Services & Systems Research, Duke-NUS Medical School, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Ahmad Reza Pourghaderi
- Health Services Research Centre, SingHealth, Singapore; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Andrew Fu Wah Ho
- Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore.
| | | |
Collapse
|
24
|
Herman DS, Rhoads DD, Schulz WL, Durant TJS. Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review. Clin Chem 2021; 67:1466-1482. [PMID: 34557917 DOI: 10.1093/clinchem/hvab165] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 07/26/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.
Collapse
Affiliation(s)
- Daniel S Herman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel D Rhoads
- Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH, USA.,Department of Pathology, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Wade L Schulz
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Thomas J S Durant
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| |
Collapse
|
25
|
Scanlon LA, O’Hara C, Garbett A, Barker-Hewitt M, Barriuso J. Developing an Agnostic Risk Prediction Model for Early AKI Detection in Cancer Patients. Cancers (Basel) 2021; 13:cancers13164182. [PMID: 34439336 PMCID: PMC8393922 DOI: 10.3390/cancers13164182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 12/23/2022] Open
Abstract
Acute kidney injury (AKI) is a common complication among oncology patients associated with lower remission rates and higher mortality. To reduce the impact of this condition, we aimed to predict AKI earlier than existing tools, to allow clinical intervention before occurrence. We trained a random forest model on 597,403 routinely collected blood test results from 48,865 patients undergoing cancer treatment at The Christie NHS Foundation Trust between January 2017 and May 2020, to identify AKI events upcoming in the next 30 days. AKI risk levels were assigned to upcoming AKI events and tested through a prospective analysis between June and August 2020. The trained model gave an AUROC of 0.881 (95% CI 0.878-0.883), when assessing predictions per blood test for AKI occurrences within 30 days. Assigning risk levels and testing the model through prospective validation from the 1st June to the 31st August identified 73.8% of patients with an AKI event before at least one AKI occurrence, 61.2% of AKI occurrences. Our results suggest that around 60% of AKI occurrences experienced by patients undergoing cancer treatment could be identified using routinely collected blood results, allowing clinical remedial action to be taken and disruption to treatment by AKI to be minimised.
Collapse
Affiliation(s)
- Lauren A. Scanlon
- The Christie NHS Foundation Trust, Manchester M20 4BX, UK; (C.O.); (A.G.); (M.B.-H.)
- Correspondence: (L.A.S.); (J.B.)
| | - Catherine O’Hara
- The Christie NHS Foundation Trust, Manchester M20 4BX, UK; (C.O.); (A.G.); (M.B.-H.)
| | - Alexander Garbett
- The Christie NHS Foundation Trust, Manchester M20 4BX, UK; (C.O.); (A.G.); (M.B.-H.)
| | - Matthew Barker-Hewitt
- The Christie NHS Foundation Trust, Manchester M20 4BX, UK; (C.O.); (A.G.); (M.B.-H.)
| | - Jorge Barriuso
- Division of Cancer Sciences, Manchester Cancer Research Centre, The University of Manchester, Manchester M13 9PL, UK
- Correspondence: (L.A.S.); (J.B.)
| |
Collapse
|
26
|
Zhang Y, Yang D, Liu Z, Chen C, Ge M, Li X, Luo T, Wu Z, Shi C, Wang B, Huang X, Zhang X, Zhou S, Hei Z. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation. J Transl Med 2021; 19:321. [PMID: 34321016 PMCID: PMC8317304 DOI: 10.1186/s12967-021-02990-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/14/2021] [Indexed: 02/06/2023] Open
Abstract
Background Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making. Methods Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms. Results 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model. Conclusions Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT. Graphic abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02990-4.
Collapse
Affiliation(s)
- Yihan Zhang
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Dong Yang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Zifeng Liu
- Department of Clinical Data Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Chaojin Chen
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Mian Ge
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Xiang Li
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Tongsen Luo
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Zhengdong Wu
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Chenguang Shi
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Bohan Wang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Xiaoshuai Huang
- Guangzhou AID Cloud Technology Co., LTD, Guangzhou, Guangdong, China
| | - Xiaodong Zhang
- Department of Information, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shaoli Zhou
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China
| | - Ziqing Hei
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, Guangdong, China. .,Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, Yuedong Hospital, Meizhou, Guangdong, China.
| |
Collapse
|
27
|
Acute kidney injury in the critically ill: an updated review on pathophysiology and management. Intensive Care Med 2021; 47:835-850. [PMID: 34213593 PMCID: PMC8249842 DOI: 10.1007/s00134-021-06454-7] [Citation(s) in RCA: 163] [Impact Index Per Article: 54.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/04/2021] [Indexed: 01/10/2023]
Abstract
Acute kidney injury (AKI) is now recognized as a heterogeneous syndrome that not only affects acute morbidity and mortality, but also a patient’s long-term prognosis. In this narrative review, an update on various aspects of AKI in critically ill patients will be provided. Focus will be on prediction and early detection of AKI (e.g., the role of biomarkers to identify high-risk patients and the use of machine learning to predict AKI), aspects of pathophysiology and progress in the recognition of different phenotypes of AKI, as well as an update on nephrotoxicity and organ cross-talk. In addition, prevention of AKI (focusing on fluid management, kidney perfusion pressure, and the choice of vasopressor) and supportive treatment of AKI is discussed. Finally, post-AKI risk of long-term sequelae including incident or progression of chronic kidney disease, cardiovascular events and mortality, will be addressed.
Collapse
|
28
|
Tripathy S. Acute Kidney Injury in the Intensive Care Unit: The Most Reliable Way to Predict the Future is to Create It. Indian J Crit Care Med 2021; 25:747-749. [PMID: 34316165 PMCID: PMC8286389 DOI: 10.5005/jp-journals-10071-23911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
How to cite this article: Tripathy S. Acute Kidney Injury in the Intensive Care Unit: The Most Reliable Way to Predict the Future is to Create It. Indian J Crit Care Med 2021;25(7):747-749.
Collapse
Affiliation(s)
- Swagata Tripathy
- Department of Anesthesia and Intensive Care, AIIMS, Bhubaneswar, Odisha, India
| |
Collapse
|
29
|
The importance of the urinary output criterion for the detection and prognostic meaning of AKI. Sci Rep 2021; 11:11089. [PMID: 34045582 PMCID: PMC8159993 DOI: 10.1038/s41598-021-90646-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 05/13/2021] [Indexed: 12/23/2022] Open
Abstract
Most reports on AKI claim to use KDIGO guidelines but fail to include the urinary output (UO) criterion in their definition of AKI. We postulated that ignoring UO alters the incidence of AKI, may delay diagnosis of AKI, and leads to underestimation of the association between AKI and ICU mortality. Using routinely collected data of adult patients admitted to an intensive care unit (ICU), we retrospectively classified patients according to whether and when they would be diagnosed with KDIGO AKI stage ≥ 2 based on baseline serum creatinine (Screa) and/or urinary output (UO) criterion. As outcomes, we assessed incidence of AKI and association with ICU mortality. In 13,403 ICU admissions (62.2% male, 60.8 ± 16.8 years, SOFA 7.0 ± 4.1), incidence of KDIGO AKI stage ≥ 2 was 13.2% when based only the SCrea criterion, 34.3% when based only the UO criterion, and 38.7% when based on both criteria. By ignoring the UO criterion, 66% of AKI cases were missed and 13% had a delayed diagnosis. The cause-specific hazard ratios of ICU mortality associated with KDIGO AKI stage ≥ 2 diagnosis based on only the SCrea criterion, only the UO criterion and based on both criteria were 2.11 (95% CI 1.85–2.42), 3.21 (2.79–3.69) and 2.85 (95% CI 2.43–3.34), respectively. Ignoring UO in the diagnosis of KDIGO AKI stage ≥ 2 decreases sensitivity, may lead to delayed diagnosis and results in underestimation of KDIGO AKI stage ≥ 2 associated mortality.
Collapse
|
30
|
Huang C, Chen Y, Lai B, Chen YX, Xu CY, Liu YF. Overexpression of SP1 restores autophagy to alleviate acute renal injury induced by ischemia-reperfusion through the miR-205/PTEN/Akt pathway. JOURNAL OF INFLAMMATION-LONDON 2021; 18:7. [PMID: 33546692 PMCID: PMC7863508 DOI: 10.1186/s12950-021-00270-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/21/2021] [Indexed: 12/30/2022]
Abstract
Background Acute kidney injury (AKI) is a major kidney disease with poor clinical outcome. SP1, a well-known transcription factor, plays a critical role in AKI and subsequent kidney repair through the regulation of various cell biologic processes. However, the underlying mechanism of SP1 in these pathological processes remain largely unknown. Methods An in vitro HK-2 cells with anoxia-reoxygenation injury model (In vitro simulated ischemic injury disease) and an in vivo rat renal ischemia-reperfusion injury model were used in this study. The expression levels of SP1, miR-205 and PTEN were detected by RT-qPCR, and the protein expression levels of SP1, p62, PTEN, AKT, p-AKT, LC3II, LC3I and Beclin-1 were assayed by western blot. Cell proliferation was assessed by MTT assay, and the cell apoptosis was detected by flow cytometry. The secretions of IL-6 and TNF-α were detected by ELISA. The targeted relationship between miR-205 and PTEN was confirmed by dual luciferase report assay. The expression and positioning of LC-3 were observed by immunofluorescence staining. TUNEL staining was used to detect cell apoptosis and immunohistochemical analysis was used to evaluate the expression of SP1 in renal tissue after ischemia-reperfusion injury in rats. Results The expression of PTEN was upregulated while SP1 and miR-205 were downregulated in renal ischemia-reperfusion injury. Overexpression of SP1 protected renal tubule cell against injury induced by ischemia-reperfusion via miR-205/PTEN/Akt pathway mediated autophagy. Overexpression of SP1 attenuated renal ischemia-reperfusion injury in rats. Conclusions SP1 overexpression restored autophagy to alleviate acute renal injury induced by ischemia-reperfusion through the miR-205/PTEN/Akt pathway.
Collapse
Affiliation(s)
- Chong Huang
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, 330006, Nanchang, Jiangxi Province, People's Republic of China
| | - Yan Chen
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, 330006, Nanchang, Jiangxi Province, People's Republic of China
| | - Bin Lai
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, 330006, Nanchang, Jiangxi Province, People's Republic of China
| | - Yan-Xia Chen
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, 330006, Nanchang, Jiangxi Province, People's Republic of China
| | - Cheng-Yun Xu
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, 330006, Nanchang, Jiangxi Province, People's Republic of China
| | - Yuan-Fei Liu
- Department of Emergency, The Second Affiliated Hospital of Nanchang University, No.1, Minde Road, 330006, Nanchang, Jiangxi Province, People's Republic of China.
| |
Collapse
|
31
|
Gong K, Lee HK, Yu K, Xie X, Li J. A prediction and interpretation framework of acute kidney injury in critical care. J Biomed Inform 2020; 113:103653. [PMID: 33338667 DOI: 10.1016/j.jbi.2020.103653] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/27/2020] [Accepted: 12/01/2020] [Indexed: 12/28/2022]
Abstract
Acute kidney injury (AKI) is a common clinical condition with high mortality and resource consumption. Early identification of high-risk patients to achieve an appropriate allocation of limited clinical resources and timely interventions is of significant importance, which has attracted substantial research to develop prediction models for AKI risk stratification. However, most available AKI prediction models have moderate performance and lack of interpretability, which limits their applicability in supporting care intervention. In this paper, a machine learning-based framework for AKI prediction and interpretation in critical care is presented. First, an ensemble model is developed to predict a patient's risk of AKI within 72 h of admission to the intensive care units. Next, the model is interpreted both globally and locally. For the global interpretation, the important predictors are pinpointed and the detailed relationships between AKI risk and these predictors are illustrated. For the local interpretation, patient-specific analysis is presented to provide a visualized explanation for each individual prediction. Experimental results show that such a prediction and interpretation framework can lead to good prediction and interpretation performance, which has the potential to provide effective clinical decision support.
Collapse
Affiliation(s)
- Kaidi Gong
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
| | - Hyo Kyung Lee
- Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Kaiye Yu
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
| | - Xiaolei Xie
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
| | - Jingshan Li
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
| |
Collapse
|
32
|
De Bruyne S, Speeckaert MM, Van Biesen W, Delanghe JR. Recent evolutions of machine learning applications in clinical laboratory medicine. Crit Rev Clin Lab Sci 2020; 58:131-152. [PMID: 33045173 DOI: 10.1080/10408363.2020.1828811] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.
Collapse
Affiliation(s)
- Sander De Bruyne
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | | | - Wim Van Biesen
- Department of Nephrology, Ghent University Hospital, Ghent, Belgium
| | - Joris R Delanghe
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| |
Collapse
|
33
|
Gameiro J, Fonseca JA, Marques F, Lopes JA. Management of Acute Kidney Injury Following Major Abdominal Surgery: A Contemporary Review. J Clin Med 2020; 9:E2679. [PMID: 32824854 PMCID: PMC7463962 DOI: 10.3390/jcm9082679] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/05/2020] [Accepted: 08/14/2020] [Indexed: 12/12/2022] Open
Abstract
Acute kidney injury (AKI) is a frequent occurrence following major abdominal surgery and is independently associated with both in-hospital and long-term mortality, as well as with a higher risk of progressing to chronic kidney disease (CKD) and cardiovascular events. Postoperative AKI can account for up to 40% of in-hospital AKI cases. Given the differences in patient characteristics and the pathophysiology of postoperative AKI, it is inappropriate to assume that the management after noncardiac and nonvascular surgery are the same as those after cardiac and vascular surgery. This article provides a comprehensive review on the available evidence on the management of postoperative AKI in the setting of major abdominal surgery.
Collapse
Affiliation(s)
- Joana Gameiro
- Division of Nephrology and Renal Transplantation, Department of Medicine, Centro Hospitalar Lisboa Norte, EPE. Av. Prof. Egas Moniz, 1649-035 Lisboa, Portugal; (J.A.F.); (F.M.); (J.A.L.)
| | | | | | | |
Collapse
|
34
|
Predicting Acute Kidney Injury: A Machine Learning Approach Using Electronic Health Records. INFORMATION 2020. [DOI: 10.3390/info11080386] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients’ records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI.
Collapse
|
35
|
New Markers of Renal Failure in Multiple Myeloma and Monoclonal Gammopathies. J Clin Med 2020; 9:jcm9061652. [PMID: 32486490 PMCID: PMC7355449 DOI: 10.3390/jcm9061652] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 01/18/2023] Open
Abstract
* Correspondence: kasiajanda@op [...].
Collapse
|
36
|
Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020; 9:jcm9041107. [PMID: 32294906 PMCID: PMC7230205 DOI: 10.3390/jcm9041107] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023] Open
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.
Collapse
Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, USA;
| | - Api Chewcharat
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Maria L. Gonzalez-Suarez
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
| |
Collapse
|