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Zoccali C, Mallamaci F, Lightstone L, Jha V, Pollock C, Tuttle K, Kotanko P, Wiecek A, Anders HJ, Remuzzi G, Kalantar-Zadeh K, Levin A, Vanholder R. A new era in the science and care of kidney diseases. Nat Rev Nephrol 2024; 20:460-472. [PMID: 38575770 DOI: 10.1038/s41581-024-00828-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2024] [Indexed: 04/06/2024]
Abstract
Notable progress in basic, translational and clinical nephrology research has been made over the past five decades. Nonetheless, many challenges remain, including obstacles to the early detection of kidney disease, disparities in access to care and variability in responses to existing and emerging therapies. Innovations in drug development, research technologies, tissue engineering and regenerative medicine have the potential to improve patient outcomes. Exciting prospects include the availability of new drugs to slow or halt the progression of chronic kidney disease, the development of bioartificial kidneys that mimic healthy kidney functions, and tissue engineering techniques that could enable transplantable kidneys to be created from the cells of the recipient, removing the risk of rejection. Cell and gene therapies have the potential to be applied for kidney tissue regeneration and repair. In addition, about 30% of kidney disease cases are monogenic and could potentially be treated using these genetic medicine approaches. Systemic diseases that involve the kidney, such as diabetes mellitus and hypertension, might also be amenable to these treatments. Continued investment, communication, collaboration and translation of innovations are crucial to realize their full potential. In addition, increasing sophistication in exploring large datasets, implementation science, and qualitative methodologies will improve the ability to deliver transformational kidney health strategies.
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Affiliation(s)
- Carmine Zoccali
- Kidney Research Institute, New York City, NY, USA.
- Institute of Molecular Biology and Genetics (Biogem), Ariano Irpino, Italy.
- Associazione Ipertensione Nefrologia Trapianto Kidney (IPNET), c/o Nefrologia, Grande Ospedale Metropolitano, Reggio Calabria, Italy.
| | - Francesca Mallamaci
- Nephrology, Dialysis and Transplantation Unit Azienda Ospedaliera "Bianchi-Melacrino-Morelli", Reggio Calabria, Italy
- CNR-IFC, Institute of Clinical Physiology, Research Unit of Clinical Epidemiology and Physiopathology of Kidney Diseases and Hypertension of Reggio Calabria, Reggio Calabria, Italy
| | - Liz Lightstone
- Department of Immunology and Inflammation, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, UK
| | - Vivek Jha
- George Institute for Global Health, UNSW, New Delhi, India
- School of Public Health, Imperial College, London, UK
- Prasanna School of Public Health, Manipal Academy of Medical Education, Manipal, India
| | - Carol Pollock
- Kolling Institute, Royal North Shore Hospital University of Sydney, Sydney, NSW, Australia
| | - Katherine Tuttle
- Providence Medical Research Center, Providence Inland Northwest, Spokane, Washington, USA
- Department of Medicine, University of Washington, Seattle, Spokane, Washington, USA
- Kidney Research Institute, Institute of Translational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Peter Kotanko
- Kidney Research Institute, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrzej Wiecek
- Department of Nephrology, Transplantation and Internal Medicine, Medical University of Silesia, 40-027, Katowice, Poland
| | - Hans Joachim Anders
- Division of Nephrology, Department of Medicine IV, Hospital of the Ludwig Maximilians University Munich, Munich, Germany
| | - Giuseppe Remuzzi
- Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Bergamo, Italy
| | - Kamyar Kalantar-Zadeh
- Harold Simmons Center for Kidney Disease Research and Epidemiology, California, USA
- Division of Nephrology and Hypertension, University of California Irvine, School of Medicine, Orange, Irvine, USA
- Veterans Affairs Healthcare System, Division of Nephrology, Long Beach, California, USA
| | - Adeera Levin
- University of British Columbia, Vancouver General Hospital, Division of Nephrology, Vancouver, British Columbia, Canada
- British Columbia, Provincial Kidney Agency, Vancouver, British Columbia, Canada
| | - Raymond Vanholder
- European Kidney Health Alliance, Brussels, Belgium
- Nephrology Section, Department of Internal Medicine and Paediatrics, University Hospital Ghent, Ghent, Belgium
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Fuertinger DH, Wang LC, Jörg DJ, Rivera Fuentes L, Ye X, Casper S, Zhang H, Mermelstein A, Cherif A, Ho K, Raimann JG, Tisdale L, Kotanko P, Thijssen S. Effects of Individualized Anemia Therapy on Hemoglobin Stability: A Randomized Controlled Pilot Trial in Patients on Hemodialysis. Clin J Am Soc Nephrol 2024:01277230-990000000-00401. [PMID: 38861324 DOI: 10.2215/cjn.0000000000000488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 06/05/2024] [Indexed: 06/13/2024]
Abstract
Key Points
We conducted a randomized controlled pilot trial in patients on hemodialysis using a physiology-based individualized anemia therapy assistance software.Patients in the group receiving erythropoiesis-stimulating agent dose recommendations from the novel software showed improvement in hemoglobin stability and erythropoiesis-stimulating agent utilization.
Background
Anemia is common among patients on hemodialysis. Maintaining stable hemoglobin levels within predefined target levels can be challenging, particularly in patients with frequent hemoglobin fluctuations both above and below the desired targets. We conducted a multicenter, randomized controlled trial comparing our anemia therapy assistance software against a standard population-based anemia treatment protocol. We hypothesized that personalized dosing of erythropoiesis-stimulating agents (ESAs) improves hemoglobin target attainment.
Methods
Ninety-six patients undergoing hemodialysis and receiving methoxy polyethylene glycol-epoetin beta were randomized 1:1 to the intervention group (personalized ESA dose recommendations computed by the software) or the standard-of-care group for 26 weeks. The therapy assistance software combined a physiology-based mathematical model and a model predictive controller designed to stabilize hemoglobin levels within a tight target range (10–11 g/dl). The primary outcome measure was the percentage of hemoglobin measurements within the target. Secondary outcome measures included measures of hemoglobin variability and ESA utilization.
Results
The intervention group showed an improved median percentage of hemoglobin measurements within target at 47% (interquartile range, 39–58), with a 10% point median difference between the two groups (95% confidence interval, 3 to 16; P = 0.008). The odds ratio of being within the hemoglobin target in the standard-of-care group compared with the group receiving the personalized ESA recommendations was 0.68 (95% confidence interval, 0.51 to 0.92). The variability of hemoglobin levels decreased in the intervention group, with the percentage of patients experiencing fluctuating hemoglobin levels being 45% versus 82% in the standard-of-care group. ESA usage was reduced by approximately 25% in the intervention group.
Conclusions
Our results demonstrated an improved hemoglobin target attainment and variability by using personalized ESA recommendations using the physiology-based anemia therapy assistance software.
Clinical Trial registration number:
NCT04360902.
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Affiliation(s)
| | | | - David J Jörg
- Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany
| | | | - Xiaoling Ye
- Renal Research Institute, New York, New York
| | - Sabrina Casper
- Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany
| | | | | | | | - Kevin Ho
- Fresenius Medical Care North America, Waltham, Massachusetts
| | | | | | - Peter Kotanko
- Renal Research Institute, New York, New York
- Department of Medicine, Icahn school of Medicine at Mount Sinai, New York, New York
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3
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Canaud B, Davenport A, Leray-Moragues H, Morena-Carrere M, Cristol JP, Kooman J, Kotanko P. Digital Health Support: Current Status and Future Development for Enhancing Dialysis Patient Care and Empowering Patients. Toxins (Basel) 2024; 16:211. [PMID: 38787063 PMCID: PMC11125858 DOI: 10.3390/toxins16050211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/18/2024] [Accepted: 04/27/2024] [Indexed: 05/25/2024] Open
Abstract
Chronic kidney disease poses a growing global health concern, as an increasing number of patients progress to end-stage kidney disease requiring kidney replacement therapy, presenting various challenges including shortage of care givers and cost-related issues. In this narrative essay, we explore innovative strategies based on in-depth literature analysis that may help healthcare systems face these challenges, with a focus on digital health technologies (DHTs), to enhance removal and ensure better control of broader spectrum of uremic toxins, to optimize resources, improve care and outcomes, and empower patients. Therefore, alternative strategies, such as self-care dialysis, home-based dialysis with the support of teledialysis, need to be developed. Managing ESKD requires an improvement in patient management, emphasizing patient education, caregiver knowledge, and robust digital support systems. The solution involves leveraging DHTs to automate HD, implement automated algorithm-driven controlled HD, remotely monitor patients, provide health education, and enable caregivers with data-driven decision-making. These technologies, including artificial intelligence, aim to enhance care quality, reduce practice variations, and improve treatment outcomes whilst supporting personalized kidney replacement therapy. This narrative essay offers an update on currently available digital health technologies used in the management of HD patients and envisions future technologies that, through digital solutions, potentially empower patients and will more effectively support their HD treatments.
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Affiliation(s)
- Bernard Canaud
- School of Medicine, Montpellier University, 9 Rue des Carmelites, 34090 Montpellier, France
- Fondation Charles Mion, AIDER-SANTE, 34000 Montpellier, France; (H.L.-M.)
- MTX Consulting International, 34090 Montpellier, France
| | - Andrew Davenport
- UCL Department of Renal Medicine, University College London, London WC1E 6BT, UK;
| | | | - Marion Morena-Carrere
- PhyMedExp, Department of Biochemistry and Hormonology, INSERM, CNRS, University Hospital Center of Montpellier, University of Montpellier, 34000 Montpellier, France;
| | - Jean Paul Cristol
- Fondation Charles Mion, AIDER-SANTE, 34000 Montpellier, France; (H.L.-M.)
- PhyMedExp, Department of Biochemistry and Hormonology, INSERM, CNRS, University Hospital Center of Montpellier, University of Montpellier, 34000 Montpellier, France;
| | - Jeroen Kooman
- Department of Internal Medicine, Division of Nephrology, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
| | - Peter Kotanko
- Renal Research Institute, Icahn University, New York, NY 10065, USA;
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4
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Jayanti S, Rangan GK. Advances in Human-Centered Care to Address Contemporary Unmet Needs in Chronic Dialysis. Int J Nephrol Renovasc Dis 2024; 17:91-104. [PMID: 38525412 PMCID: PMC10961023 DOI: 10.2147/ijnrd.s387598] [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: 12/23/2023] [Accepted: 03/12/2024] [Indexed: 03/26/2024] Open
Abstract
Advances in the treatment of kidney failure with chronic dialysis have stagnated over the past three decades, with over 50% of patients still managed by conventional in-hospital haemodialysis. In parallel, the demands of chronic dialysis medical care have changed and evolved due to a growing population that has higher frailty and multimorbidity. Thus, the gap between the needs of kidney failure patients and the healthcare capability to provide effective overall management has widened. To address this problem, healthcare policy has increasingly aligned towards a human-centred approach. The paradigm shift of human-centred approach places patients at the forefront of decision-making processes, ensuring that specific needs are understood and prioritised. Integration of human-centred approaches with patient care has been shown to improve satisfaction and quality of life. The aim of this narrative is to evaluate the current clinical challenges for managing kidney failure for dialysis providers; summarise current experiences and unmet needs of chronic dialysis patients; and finally emphasise how human-centred care has advanced chronic dialysis care. Specific incremental advances include implementation of renal supportive care; home-assisted dialysis; hybrid dialysis; refinements to dialysis methods; whereas emerging advances include portable and wearable dialysis devices and the potential for the integration of artificial intelligence in clinical practice.
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Affiliation(s)
- Sumedh Jayanti
- Department of Renal Medicine, Westmead Hospital, Sydney, NSW, Australia
- Michael Stern Laboratory for Polycystic Kidney Disease, Centre for Transplant and Renal Research, Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia
| | - Gopala K Rangan
- Department of Renal Medicine, Westmead Hospital, Sydney, NSW, Australia
- Michael Stern Laboratory for Polycystic Kidney Disease, Centre for Transplant and Renal Research, Westmead Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia
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5
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Ryan DK, Maclean RH, Balston A, Scourfield A, Shah AD, Ross J. Artificial intelligence and machine learning for clinical pharmacology. Br J Clin Pharmacol 2024; 90:629-639. [PMID: 37845024 DOI: 10.1111/bcp.15930] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023] Open
Abstract
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.
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Affiliation(s)
- David K Ryan
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Rory H Maclean
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Alfred Balston
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andrew Scourfield
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anoop D Shah
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Jack Ross
- Department of Clinical Pharmacology, University College London Hospitals NHS Foundation Trust, London, UK
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Garbelli M, Bellocchio F, Baro Salvador ME, Chermisi M, Rincon Bello A, Godoy IB, Perez SO, Shkolenko K, Perez AS, Toro DS, Apel C, Petrovic J, Stuard S, Barbieri C, Mari F, Neri L. The Use of Anemia Control Model Is Associated with Improved Hemoglobin Target Achievement, Lower Rates of Inappropriate Erythropoietin Stimulating Agents, and Severe Anemia among Dialysis Patients. Blood Purif 2024; 53:405-417. [PMID: 38382484 DOI: 10.1159/000536181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 12/29/2023] [Indexed: 02/23/2024]
Abstract
INTRODUCTION The Anemia Control Model (ACM) is a certified medical device suggesting the optimal ESA and iron dosage for patients on hemodialysis. We sought to assess the effectiveness and safety of ACM in a large cohort of hemodialysis patients. METHODS This is a retrospective study of dialysis patients treated in NephroCare centers between June 1, 2013 and December 31, 2019. We compared patients treated according to ACM suggestions and patients treated in clinics where ACM was not activated. We stratified patients belonging to the reference group by historical target achievement rates in their referral centers (tier 1: <70%; tier 2: 70-80%; tier 3: >80%). Groups were matched by propensity score. RESULTS After matching, we obtained four groups with 85,512 patient-months each. ACM had 18% higher target achievement rate, 63% smaller inappropriate ESA administration rate, and 59% smaller severe anemia risk compared to Tier 1 centers (all p < 0.01). The corresponding risk ratios for ACM compared to Tier 2 centers were 1.08 (95% CI: 1.08-1.09), 0.49 (95% CI: 0.47-0.51), and 0.64 (95% CI: 0.61-0.68); for ACM compared to Tier 3 centers, 1.01 (95% CI: 1.01-1.02), 0.66 (95% CI: 0.63-0.69), and 0.94 (95% CI: 0.88-1.00), respectively. ACM was associated with statistically significant reductions in ESA dose administration. CONCLUSION ACM was associated with increased hemoglobin target achievement rate, decreased inappropriate ESA usage and a decreased incidence of severe anemia among patients treated according to ACM suggestion.
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Affiliation(s)
- Mario Garbelli
- Global Medical Office - Clinical Advanced Analytics - Data Science - EMEA, APAC, LATAM region, Fresenius Medical Care Italia spa, Vaiano Cremasco, Italy,
| | - Francesco Bellocchio
- Global Medical Office - Clinical Advanced Analytics - Data Science - EMEA, APAC, LATAM region, Fresenius Medical Care Italia spa, Vaiano Cremasco, Italy
| | | | - Milena Chermisi
- Global Medical Office - Clinical Advanced Analytics - Data Science - EMEA, APAC, LATAM region, Fresenius Medical Care Italia spa, Vaiano Cremasco, Italy
| | - Abraham Rincon Bello
- Country Medical Office - NephroCare Spain, Fresenius Medical Care, Madrid, Spain
| | - Isabel Berdud Godoy
- Country Medical Office - NephroCare Spain, Fresenius Medical Care, Madrid, Spain
| | - Sofia Ortego Perez
- Country Medical Office - NephroCare Spain, Fresenius Medical Care, Madrid, Spain
| | - Kateryna Shkolenko
- Country Medical Office - NephroCare Spain, Fresenius Medical Care, Madrid, Spain
| | - Alicia Sobrino Perez
- Country Medical Office - NephroCare Spain, Fresenius Medical Care, Madrid, Spain
| | - Diana Samaniego Toro
- Country Medical Office - NephroCare Spain, Fresenius Medical Care, Madrid, Spain
| | - Christian Apel
- Health Economics and Market Access, Fresenius Medical Care, Bad Homburg, Germany
| | - Jovana Petrovic
- Health Economics and Market Access, Fresenius Medical Care, Bad Homburg, Germany
| | - Stefano Stuard
- Global Medical Office - Clinical and Therapeutic Governance EMEA, Fresenius Medical Care, Bad Homburg, Germany
| | - Carlo Barbieri
- Global Digital Transformation and Innovation, Clinical Digital Center of Excellence, Fresenius Medical Care, Vaiano Cremasco, Italy
| | - Flavio Mari
- Global Digital Transformation and Innovation, Clinical Digital Center of Excellence, Fresenius Medical Care, Vaiano Cremasco, Italy
| | - Luca Neri
- Global Medical Office - Clinical Advanced Analytics - Data Science - EMEA, APAC, LATAM region, Fresenius Medical Care Italia spa, Vaiano Cremasco, Italy
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7
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Fayos De Arizón L, Viera ER, Pilco M, Perera A, De Maeztu G, Nicolau A, Furlano M, Torra R. Artificial intelligence: a new field of knowledge for nephrologists? Clin Kidney J 2023; 16:2314-2326. [PMID: 38046016 PMCID: PMC10689169 DOI: 10.1093/ckj/sfad182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Indexed: 12/05/2023] Open
Abstract
Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.
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Affiliation(s)
- Leonor Fayos De Arizón
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Elizabeth R Viera
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Melissa Pilco
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alexandre Perera
- Center for Biomedical Engineering Research (CREB), Universitat Politècnica de Barcelona (UPC), Barcelona, Spain; Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain; Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | | | | | - Monica Furlano
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Roser Torra
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
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Portoles J, Serrano Salazar ML, González Peña O, Gallego Domínguez S, Vera Rivera M, Caro Espada J, Herreros García A, Munar Vila MA, José Espigares Huete M, Sosa Barrios H, Paraíso V, Mariscal de Gante L, Bajo MA, Mijaylova AG, Pascual Pajares E, Areste Fosalba N, Espinel L, Tornero Molina F, Pizarro Sánchez S, Ortega Díaz M, Cases A, Quiroga B. Opportunities to improve the management of anemia in peritoneal dialysis patients: lessons from a national study in routine clinical practice. Clin Kidney J 2023; 16:2493-2502. [PMID: 38046036 PMCID: PMC10689165 DOI: 10.1093/ckj/sfad152] [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: 03/13/2023] [Indexed: 12/05/2023] Open
Abstract
Background Current guidelines establish the same hemoglobin (Hb) and iron biomarkers targets for hemodialysis (HD) and peritoneal dialysis (PD) in patients receiving erythropoiesis-stimulating agents (ESAs) even though patients having PD are usually younger, more active and less comorbid. Unfortunately, specific renal anemia [anemia in chronic kidney disease (aCKD)] trials or observational studies on PD are scanty. The aims of this study were to describe current aCKD management, goals and adherence to clinical guidelines, identifying opportunities for healthcare improvement in PD patients. Methods This was a retrospective, nationwide, multicentre study including patients from 19 PD units. The nephrologists collected baseline data, demographics, comorbidities and data related to anemia management (laboratory values, previously prescribed treatments and subsequent adjustments) from electronic medical records. The European adaptation of KDIGO guidelines was the reference for definitions, drug prescriptions and targets. Results A total of 343 patients (mean age 62.9 years, 61.2% male) were included; 72.9% were receiving ESAs and 33.2% iron therapy [20.7% intravenously (IV)]. Eighty-two patients were receiving ESA without iron therapy, despite 53 of them having an indication according to the European Renal Best Practice guidelines. After laboratory results, iron therapy was only started in 15% of patients. Among ESA-treated patients, 51.9% had an optimal control [hemoglobin (Hb) 10-12 g/dL] and 28.3% between 12-12.9 g/dL. Seventeen patients achieved Hb >13 g/dL, and 12 of them remained on ESA after overshooting. Only three patients had Hb <10 g/dL without ESAs. Seven patients (2%) met criteria for ESA resistance (epoetin dose >300 IU/kg/week). The highest tertile of erythropoietin resistance index (>6.3 UI/kg/week/g/dL) was associated with iron deficiency and low albumin corrected by renal replacement therapy vintage and hospital admissions in the previous 3 months. Conclusion Iron therapy continues to be underused (especially IV). Low albumin, iron deficiency and prior events explain most of the ESA hyporesponsiveness. Hb targets are titrated to/above the upper limits. Thus, several missed opportunities for adequate prescriptions and adherence to guidelines were identified.
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Affiliation(s)
- Jose Portoles
- Nephrology Department, Hospital Universitario Puerta de Hierro, Facultad de Medicina, Universidad Autónoma de Madrid, IDIPHISA, Madrid, Spain
- Anemia Working Group of the Spanish Society of Nephrology, Spain
| | | | | | | | | | - Jara Caro Espada
- Nephrology Department, Hospital Universitario Doce de Octubre, Madrid, Spain
| | | | | | | | | | - Vicente Paraíso
- Nephrology Department, Hospital Universitario Henares, Madrid, Spain
| | | | | | | | | | | | - Laura Espinel
- Nephrology Department, Hospital Universitario de Getafe. Madrid, Spain
| | | | | | - Mayra Ortega Díaz
- Nephrology Department, Hospital Universitario Infanta Leonor, Madrid, Spain
| | - Aleix Cases
- Anemia Working Group of the Spanish Society of Nephrology, Spain
- Nephrology Department, Hospital Clinic, Barcelona, Spain
- Medicine Department, Universitat de Barcelona, Barcelona, Spain
| | - Borja Quiroga
- Anemia Working Group of the Spanish Society of Nephrology, Spain
- IIS-La Princesa, Nephrology Department, Hospital Universitario de la Princesa, Madrid, Spain
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9
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Barbieri C, Neri L, Stuard S, Mari F, Martín-Guerrero JD. From electronic health records to clinical management systems: how the digital transformation can support healthcare services. Clin Kidney J 2023; 16:1878-1884. [PMID: 37915897 PMCID: PMC10616428 DOI: 10.1093/ckj/sfad168] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Indexed: 11/03/2023] Open
Abstract
Healthcare systems worldwide are currently undergoing significant transformations in response to increasing costs, a shortage of healthcare professionals and the growing complexity of medical needs among the population. Value-based healthcare reimbursement systems are emerging as an attempt to incentivize patient-centricity and cost containment. From a technological perspective, the transition to digitalized services is intended to support these transformations. A Health Information System (HIS) is a technological solution designed to govern the data flow generated and consumed by healthcare professionals and administrative staff during the delivery of healthcare services. However, the exponential growth of digital capabilities and applied advanced analytics has expanded their traditional functionalities and brought the promise of automating administrative procedures and simple repetitive tasks, while enhancing the efficiency and outcomes of healthcare services by incorporating decision support tools for clinical management. The future of HIS is headed towards modular architectures that can facilitate implementation and adaptation to different environments and systems, as well as the integration of various tools, such as artificial intelligence (AI) models, in a seamless way. As an example, we present the experience and future developments of the European Clinical Database (EuCliD®). EuCliD is a multilingual HIS used by 20 000 nurses and physicians on a daily basis to manage 105 000 patients treated in 1100 clinics in 43 different countries. EuCliD encompasses patients' follow-up, automatic reporting and mobile applications while enabling efficient management of clinical processes. It is also designed to incorporate multiagent systems to automate repetitive tasks, AI modules and advanced dynamic dashboards.
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Affiliation(s)
- Carlo Barbieri
- Global Digital Transformation and Innovation, Clinical Digital Center of Excellence, Fresenius Medical Care, Crema Italy
| | - Luca Neri
- Global Medical Office, Clinical Advanced Analytics, Fresenius Medical Care, Crema Italy
| | - Stefano Stuard
- Global Medical Office, Clinical and Therapeutic Governance, Fresenius Medical Care, Naples, Italy
| | - Flavio Mari
- Global Digital Transformation and Innovation, Clinical Digital Center of Excellence, Fresenius Medical Care, Crema Italy
| | - José D Martín-Guerrero
- Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE -UV, Universitat de València, Valencia, Spain
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Stankevičiūtė K, Woillard JB, Peck RW, Marquet P, van der Schaar M. Bridging the Worlds of Pharmacometrics and Machine Learning. Clin Pharmacokinet 2023; 62:1551-1565. [PMID: 37803104 DOI: 10.1007/s40262-023-01310-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.
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Affiliation(s)
- Kamilė Stankevičiūtė
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK
| | - Jean-Baptiste Woillard
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France.
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research and Development, Roche Innovation Center, Basel, Switzerland
| | - Pierre Marquet
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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Inoue H, Oya M, Aizawa M, Wagatsuma K, Kamimae M, Kashiwagi Y, Ishii M, Wakabayashi H, Fujii T, Suzuki S, Hattori N, Tatsumoto N, Kawakami E, Asanuma K. Predicting dry weight change in Hemodialysis patients using machine learning. BMC Nephrol 2023; 24:196. [PMID: 37386392 PMCID: PMC10308746 DOI: 10.1186/s12882-023-03248-5] [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: 04/17/2023] [Accepted: 06/19/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient's physical conditions. METHODS All medical data and 69,375 dialysis records of 314 Asian patients undergoing hemodialysis at a single dialysis center in Japan between July 2018 and April 2020 were collected from the electronic medical record system. Using the random forest classifier, we developed models to predict the probabilities of adjusting the dry weight at each dialysis session. RESULTS The areas under the receiver-operating-characteristic curves of the models for adjusting the dry weight upward and downward were 0.70 and 0.74, respectively. The average probability of upward adjustment of the dry weight had sharp a peak around the actual change over time, while the average probability of downward adjustment of the dry weight formed a gradual peak. Feature importance analysis revealed that median blood pressure decline was a strong predictor for adjusting the dry weight upward. In contrast, elevated serum levels of C-reactive protein and hypoalbuminemia were important indicators for adjusting the dry weight downward. CONCLUSIONS The random forest classifier should provide a helpful guide to predict the optimal changes to the dry weight with relative accuracy and may be useful in clinical practice.
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Affiliation(s)
- Hiroko Inoue
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Megumi Oya
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan
| | - Masashi Aizawa
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Kyogo Wagatsuma
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan
| | - Masatomo Kamimae
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan
| | - Yusuke Kashiwagi
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Masayoshi Ishii
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan
| | - Hanae Wakabayashi
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan
| | - Takayuki Fujii
- Department of Nephrology, Seirei Sakura Citizen hospital, Sakura, Chiba, Japan
| | - Satoshi Suzuki
- Department of Nephrology, Seirei Sakura Citizen hospital, Sakura, Chiba, Japan
| | - Noriyuki Hattori
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan
- Department of Emergency and Critical Care Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Narihito Tatsumoto
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan.
- Advanced Data Science Project (ADSP), RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Kanagawa, Japan.
| | - Katsuhiko Asanuma
- Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
- Department of Artificial Kidney, Chiba University Hospital, Chuo-ku, Chiba, Japan.
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12
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Kotanko P, Zhang H, Wang Y. Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time? Clin J Am Soc Nephrol 2023; 18:803-805. [PMID: 36795031 PMCID: PMC10278848 DOI: 10.2215/cjn.0000000000000089] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Peter Kotanko
- Renal Research Institute, New York, New York
- Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Yuedong Wang
- Department of Statistics & Applied Probability, University of California at Santa Barbara, Santa Barbara, California
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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [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: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
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Jörg DJ, Fuertinger DH, Kotanko P. Mechanisms of hemoglobin cycling in anemia patients treated with erythropoiesis-stimulating agents. PLoS Comput Biol 2023; 19:e1010850. [PMID: 36693034 PMCID: PMC9873166 DOI: 10.1371/journal.pcbi.1010850] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 01/04/2023] [Indexed: 01/25/2023] Open
Abstract
Patients with renal anemia are frequently treated with erythropoiesis-stimulating agents (ESAs), which are dynamically dosed in order to stabilize blood hemoglobin levels within a specified target range. During typical ESA treatments, a fraction of patients experience hemoglobin 'cycling' periods during which hemoglobin levels periodically over- and undershoot the target range. Here we report a specific mechanism of hemoglobin cycling, whereby cycles emerge from the patient's delayed physiological response to ESAs and concurrent ESA dose adjustments. We introduce a minimal theoretical model that can explain dynamic hallmarks of observed hemoglobin cycling events in clinical time series and elucidates how physiological factors (such as red blood cell lifespan and ESA responsiveness) and treatment-related factors (such as dosing schemes) affect cycling. These results show that in general, hemoglobin cycling cannot be attributed to patient physiology or ESA treatment alone but emerges through an interplay of both, with consequences for the design of ESA treatment strategies.
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Affiliation(s)
- David J. Jörg
- Computational Medicine Group, Global Medical Office, Fresenius Medical Care Germany, Bad Homburg, Germany
- * E-mail:
| | - Doris H. Fuertinger
- Computational Medicine Group, Global Medical Office, Fresenius Medical Care Germany, Bad Homburg, Germany
| | - Peter Kotanko
- Renal Research Institute, New York, New York, United States of America
- Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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Zhou XJ, Zhong XH, Duan LX. Integration of artificial intelligence and multi-omics in kidney diseases. FUNDAMENTAL RESEARCH 2023; 3:126-148. [PMID: 38933564 PMCID: PMC11197676 DOI: 10.1016/j.fmre.2022.01.037] [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: 09/14/2021] [Revised: 12/14/2021] [Accepted: 01/24/2022] [Indexed: 10/18/2022] Open
Abstract
Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.
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Affiliation(s)
- Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
| | - Xu-Hui Zhong
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Li-Xin Duan
- The Big Data Research Center, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
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Yang JY, Shu KH, Peng YS, Hsu SP, Chiu YL, Pai MF, Wu HY, Tsai WC, Tung KT, Kuo RN. Physician Compliance with Computerized Clinical Decision Support System is a Complete Intermediate Factor in the Anemia Management of Patients with End-Stage Kidney Disease on Hemodialysis: A Retrospective Electronic Health Record Observational Study (Preprint). JMIR Form Res 2022; 7:e44373. [PMID: 37133912 DOI: 10.2196/44373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Previous studies on clinical decision support systems (CDSSs) for the management of renal anemia in patients with end-stage kidney disease undergoing hemodialysis have previously focused solely on the effects of the CDSS. However, the role of physician compliance in the efficacy of the CDSS remains ill-defined. OBJECTIVE We aimed to investigate whether physician compliance was an intermediate variable between the CDSS and the management outcomes of renal anemia. METHODS We extracted the electronic health records of patients with end-stage kidney disease on hemodialysis at the Far Eastern Memorial Hospital Hemodialysis Center (FEMHHC) from 2016 to 2020. FEMHHC implemented a rule-based CDSS for the management of renal anemia in 2019. We compared the clinical outcomes of renal anemia between the pre- and post-CDSS periods using random intercept models. Hemoglobin levels of 10 to 12 g/dL were defined as the on-target range. Physician compliance was defined as the concordance of adjustments of the erythropoietin-stimulating agent (ESA) between the CDSS recommendations and the actual physician prescriptions. RESULTS We included 717 eligible patients on hemodialysis (mean age 62.9, SD 11.6 years; male n=430, 59.9%) with a total of 36,091 hemoglobin measurements (average hemoglobin and on-target rate were 11.1, SD 1.4, g/dL and 59.9%, respectively). The on-target rate decreased from 61.3% (pre-CDSS) to 56.2% (post-CDSS) owing to a high hemoglobin percentage of >12 g/dL (pre: 21.5%; post: 29%). The failure rate (hemoglobin <10 g/dL) decreased from 17.2% (pre-CDSS) to 14.8% (post-CDSS). The average weekly ESA use of 5848 (SD 4211) units per week did not differ between phases. The overall concordance between CDSS recommendations and physician prescriptions was 62.3%. The CDSS concordance increased from 56.2% to 78.6%. In the adjusted random intercept model, the post-CDSS phase showed increased hemoglobin by 0.17 (95% CI 0.14-0.21) g/dL, weekly ESA by 264 (95% CI 158-371) units per week, and 3.4-fold (95% CI 3.1-3.6) increased concordance rate. However, the on-target rate (29%; odds ratio 0.71, 95% CI 0.66-0.75) and failure rate (16%; odds ratio 0.84, 95% CI 0.76-0.92) were reduced. After additional adjustments for concordance in the full models, increased hemoglobin and decreased on-target rate tended toward attenuation (from 0.17 to 0.13 g/dL and 0.71 to 0.73 g/dL, respectively). Increased ESA and decreased failure rate were completely mediated by physician compliance (from 264 to 50 units and 0.84 to 0.97, respectively). CONCLUSIONS Our results confirmed that physician compliance was a complete intermediate factor accounting for the efficacy of the CDSS. The CDSS reduced failure rates of anemia management through physician compliance. Our study highlights the importance of optimizing physician compliance in the design and implementation of CDSSs to improve patient outcomes.
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Affiliation(s)
- Ju-Yeh Yang
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Center for General Education, Lee-Ming Institute of Technology, New Taipei City, Taiwan
| | - Kai-Hsiang Shu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Yu-Sen Peng
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Shih-Ping Hsu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Yen-Ling Chiu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan
- Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan, Taiwan
| | - Mei-Fen Pai
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Hon-Yen Wu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Wan-Chuan Tsai
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Kuei-Ting Tung
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Raymond N Kuo
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
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Sandys V, Sexton D, O'Seaghdha C. Artificial intelligence and digital health for volume maintenance in hemodialysis patients. Hemodial Int 2022; 26:480-495. [PMID: 35739632 PMCID: PMC9796027 DOI: 10.1111/hdi.13033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 05/18/2022] [Accepted: 05/30/2022] [Indexed: 12/30/2022]
Abstract
Chronic fluid overload is associated with morbidity and mortality in hemodialysis patients. Optimizing the diagnosis and treatment of fluid overload remains a priority for the nephrology community. Although current methods of assessing fluid status, such as bioimpedance and lung ultrasound, have prognostic and diagnostic value, no single system or technique can be used to maintain euvolemia. The difficulty in maintaining and assessing fluid status led to a publication by the Kidney Health Initiative in 2019 aimed at fostering innovation in fluid management therapies. This review article focuses on the current limitations in our assessment of extracellular volume, and the novel technology and methods that can create a new paradigm for fluid management. The cardiology community has published research on multiparametric wearable devices that can create individualized predictions for heart failure events. In the future, similar wearable technology may be capable of tracking fluid changes during the interdialytic period and enabling behavioral change. Machine learning methods have shown promise in the prediction of volume-related adverse events. Similar methods can be leveraged to create accurate, automated predictions of dry weight that can potentially be used to guide ultrafiltration targets and interdialytic weight gain goals.
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Affiliation(s)
- Vicki Sandys
- Royal College of Surgeons in IrelandDublinIreland
| | - Donal Sexton
- St James's HospitalDublin 8Ireland,Trinity Health Kidney CentreSchool of Medicine, Trinity College DublinDublinIreland,ADAPT: Research Centre for AI‐Driven Digital Content TechnologyIreland
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Biomechanical behaviour of tension-band-reconstruction titanium plate in open-door laminoplasty: a study based on finite element analysis. BMC Musculoskelet Disord 2022; 23:851. [PMID: 36076212 PMCID: PMC9454233 DOI: 10.1186/s12891-022-05804-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 09/05/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE To investigate and evaluate the biomechanical behaviour of tension-band-reconstruction (TBR) and ordinary titanium plates in open-door laminoplasty by finite element (FE) analysis. METHODS TBR titanium plate and ordinary titanium plate were implanted into a validated finite element model of healthy adult cervical vertebrae. Among them, 5 ordinary titanium plate were used in model A, 2 TBR titanium plates and 3 ordinary titanium plates were used in model B, and 5 TBR titanium plates were used in model C. The same loading conditions was applied identically to all models. Range of motion (ROM) of the vertebral body, stress distribution of the titanium plate and intradiscal pressure (IDP) were compared in flexion, extension, lateral bending and rotation. RESULTS The ROM of model B and C was similar in flexion and extension, and both were smaller than that of model A. The highest von Mises stress in the titanium plate appears is in model C. The IDP in C2/3 was significantly higher than that in other segments in flexion. There was no significant difference in IDP among three models in left lateral bending and left axial rotation. CONCLUSION Application of TBR titanium plate in open-door laminoplasty can reduced ROM in flexion, extension and axial rotation of the cervical vertebrae. But the increase of stress in TBR titanium plate could lead to higher risk of adverse events such as titanium plate deformation. Moreover, compared with complete TBR titanium plate, the combination of TBR titanium plate for C3 and C7 with ordinary titanium plate for the other vertebrae largely reduce the stress of the titanium plates by ensuring stability. The proposed FE model (C2-T1) exhibits a great potential in evaluating biomechanical behaviour of TBR titanium plate for open-door laminoplasty.
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Liu WT, Liu XQ, Jiang TT, Wang MY, Huang Y, Huang YL, Jin FY, Zhao Q, Wu QY, Liu BC, Ruan XZ, Ma KL. Using a machine learning model to predict the development of acute kidney injury in patients with heart failure. Front Cardiovasc Med 2022; 9:911987. [PMID: 36176988 PMCID: PMC9512707 DOI: 10.3389/fcvm.2022.911987] [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: 04/03/2022] [Accepted: 08/15/2022] [Indexed: 11/19/2022] Open
Abstract
Background Heart failure (HF) is a life-threatening complication of cardiovascular disease. HF patients are more likely to progress to acute kidney injury (AKI) with a poor prognosis. However, it is difficult for doctors to distinguish which patients will develop AKI accurately. This study aimed to construct a machine learning (ML) model to predict AKI occurrence in HF patients. Materials and methods The data of HF patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was retrospectively analyzed. A ML model was established to predict AKI development using decision tree, random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR) algorithms. Thirty-nine demographic, clinical, and treatment features were used for model establishment. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the ML algorithms. Results A total of 2,678 HF patients were engaged in this study, of whom 919 developed AKI. Among 5 ML algorithms, the RF algorithm exhibited the highest performance with the AUROC of 0.96. In addition, the Gini index showed that the sequential organ function assessment (SOFA) score, partial pressure of oxygen (PaO2), and estimated glomerular filtration rate (eGFR) were highly relevant to AKI development. Finally, to facilitate clinical application, a simple model was constructed using the 10 features screened by the Gini index. The RF algorithm also exhibited the highest performance with the AUROC of 0.95. Conclusion Using the ML model could accurately predict the development of AKI in HF patients.
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Affiliation(s)
- Wen Tao Liu
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Xiao Qi Liu
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Ting Ting Jiang
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Meng Ying Wang
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Yang Huang
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Yu Lin Huang
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Feng Yong Jin
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Qing Zhao
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Qin Yi Wu
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Bi Cheng Liu
- School of Medicine, Institute of Nephrology, Zhongda Hospital, Southeast University, Nanjing, China
| | - Xiong Zhong Ruan
- John Moorhead Research Laboratory, Department of Renal Medicine, University College London (UCL) Medical School, London, United Kingdom
| | - Kun Ling Ma
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Kun Ling Ma,
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Chen W, Howard K, Gorham G, O'Bryan CM, Coffey P, Balasubramanya B, Abeyaratne A, Cass A. Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. J Am Med Inform Assoc 2022; 29:1757-1772. [PMID: 35818299 PMCID: PMC9471723 DOI: 10.1093/jamia/ocac110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 01/10/2023] Open
Abstract
Objectives Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases. Material and Methods We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted. Results The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY. Conclusion We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies. Registration PROSPERO (CRD42020203716)
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Affiliation(s)
- Winnie Chen
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Kirsten Howard
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Claire Maree O'Bryan
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Patrick Coffey
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Bhavya Balasubramanya
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Asanga Abeyaratne
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Alan Cass
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
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Jiang J, Chan L, Nadkarni GN. The promise of artificial intelligence for kidney pathophysiology. Curr Opin Nephrol Hypertens 2022; 31:380-386. [PMID: 35703218 PMCID: PMC10309072 DOI: 10.1097/mnh.0000000000000808] [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] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions. RECENT FINDINGS We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions. SUMMARY The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.
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Affiliation(s)
- Joy Jiang
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lili Chan
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N. Nadkarni
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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22
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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23
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Chu H, Moon S, Park J, Bak S, Ko Y, Youn BY. The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Front Pharmacol 2022; 13:826044. [PMID: 35431917 PMCID: PMC9011141 DOI: 10.3389/fphar.2022.826044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 01/04/2023] Open
Abstract
Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status. Objective: To categorize and seek the current usage of AI in CAM. Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies. Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.
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Affiliation(s)
- Hongmin Chu
- Daecheong Public Health Subcenter, Incheon, South Korea
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Jeongsu Park
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Seongjun Bak
- Department of College of Korean Medicine, Wonkwang University, Iksan, South Korea
| | - Youme Ko
- National Institute for Korean Medicine Development (NIKOM), Seoul, South Korea
| | - Bo-Young Youn
- Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Bo-Young Youn,
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24
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Effets biologiques et cliniques, et résultats au long cours du traitement par ol-HDF des patients adultes insuffisants rénaux chroniques. Nephrol Ther 2022. [DOI: 10.1016/s1769-7255(22)00035-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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25
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Kotzerke D, Costa MW, Voigt J, Kleinhempel A, Schmidt M, Söhnlein T, Kaiser T, Henschler R. Novelle QLL 2020 – welche Auswirkungen haben die neu empfohlenen Hämoglobin-Transfusionstrigger auf die klinische Versorgung? TRANSFUSIONSMEDIZIN 2022. [DOI: 10.1055/a-1669-3918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
ZusammenfassungIn der Gesamtnovelle der Querschnittsleitlinie (QLL) Hämotherapie der Bundesärztekammer (BÄK) 2020 wurde der Hämoglobin-Transfusionstrigger (Hb-Transfusionstrigger) bei akutem Blutverlust
ohne zusätzliche Risikofaktoren aufgrund einer Neubewertung der internationalen Evidenz von 3,7 mmol/l (6 g/dl) auf 4,3 mmol/l (7 g/dl) angepasst. Ziel der vorliegenden Studie ist die
retrospektive Analyse des Transfusionsverhaltens von EK bezüglich der Maßgaben der QLL. Zu diesem Zweck analysierten wir individuelle Prä- und Posttransfusions-Hb-Werte von
Erythrozytenkonzentraten (EK), die im 4. Quartal 2019 (4946 EKs, 129 560 Hb-Werte) und 2020 (5502 EKs, 134 404 Hb-Werte) am Universitätsklinikum Leipzig (UKL) transfundiert wurden. Der
mediane Hb-Wert vor der Transfusion betrug 4,3 mmol/l (7 g/dl) (680 medizinische Fälle, die 2724 EK in 1801 Transfusionen im Jahr 2019 erhielten). Von allen Transfusionen im Jahr 2019
zeigten 899 (49,9%) Transfusionen Hb-Werte < 4,3 mmol/l (7 g/dl) vor der Transfusion, während 152 (8,4%) Hb-Werte < 3,7 mmol/l (6 g/dl) aufwiesen. 2020 wurden jeweils vergleichbare
Ergebnisse ermittelt. Wir zeigen, dass der mediane Hb-Anstieg nach der Transfusion eines EK 0,6 mmol/l (1 g/dl) betrug. 34,7% aller Transfusionen erreichten den erwarteten Anstieg von
0,6 mmol/l (1 g/dl) pro EK. Der absolute Anstieg nahm bei Transfusionen mit mehreren EK im Vergleich zu Transfusionen mit einem EK nicht linear zu. Der Grad der Hb-Erhöhung korrelierte
invers mit dem Hb-Wert vor Transfusion. Der Hb-Wert nach der Transfusion wurde bei 96,3% der Fälle innerhalb von 24 Stunden nach Hämotherapie kontrolliert. Zusammenfassend spiegelt das
Transfusionsverhalten generell die Empfehlungen der Leitlinie. Um ein optimiertes, individualisiertes und dennoch restriktives Transfusionsverhalten bei EK zu erreichen, schlagen wir die
Implementierung eines klinischen Entscheidungsunterstützungssystems (CDSS) bei Verschreibung jeder einzelnen EK-Transfusion vor, welches Ärzte bei der Einhaltung der Transfusionsleitlinie
unterstützt und über Abweichungen informiert.
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Affiliation(s)
- David Kotzerke
- Institut für Laboratoriumsmedizin, Klinische Chemie und Molekulare Diagnostik, Universitätsklinikum Leipzig, Leipzig, Deutschland
- Klinik und Poliklinik für Anästhesiologie und Intensivtherapie, Universitätsklinikum Leipzig, Leipzig, Deutschland
| | - Maria Walter Costa
- Institut für Laboratoriumsmedizin, Klinische Chemie und Molekulare Diagnostik, Universitätsklinikum Leipzig, Leipzig, Deutschland
| | - Jenny Voigt
- Institut für Laboratoriumsmedizin, Klinische Chemie und Molekulare Diagnostik, Universitätsklinikum Leipzig, Leipzig, Deutschland
| | - Alisa Kleinhempel
- Institut für Transfusionsmedizin, Universitätsklinikum Leipzig, Leipzig, Deutschland
| | - Maria Schmidt
- Institut für Laboratoriumsmedizin, Klinische Chemie und Molekulare Diagnostik, Universitätsklinikum Leipzig, Leipzig, Deutschland
| | - Tim Söhnlein
- Institut für Transfusionsmedizin, Universitätsklinikum Leipzig, Leipzig, Deutschland
| | - Thorsten Kaiser
- Institut für Laboratoriumsmedizin, Klinische Chemie und Molekulare Diagnostik, Universitätsklinikum Leipzig, Leipzig, Deutschland
| | - Reinhard Henschler
- Institut für Transfusionsmedizin, Universitätsklinikum Leipzig, Leipzig, Deutschland
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26
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AIM in Hemodialysis. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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27
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Gaweda AE, Brier ME. Artificial Intelligence in Medicine in Anemia. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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28
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Barbieri C, Neri L, Chermisi M, Bolzoni E, Cattinelli I, Decker W, Stuard S, Martín-Guerrero JD, Mari F. How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as medical device. Expert Rev Med Devices 2021; 18:1117-1121. [PMID: 34612120 DOI: 10.1080/17434440.2021.1990037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as a medical device (MD), which is important to assess the associated risks. METHODS An anemia control model (ACM), certified as MD, may face adverse events as a result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard. While severities are typically assessed by clinicians, probabilities are tightly related to the performance of the predictive model. RESULTS A postmarketing data set formed by all adult patients registered in French, Portuguese, and Spanish clinics, belonging to an international network, was considered; 3876 patients and 11,508 suggestions were eventually included. The achieved results show that there are no statistical differences between the probabilities of adverse events that are estimated in the ACM test set (using only Spanish clinics) and those actually observed in the postmarketing cohort. CONCLUSIONS The risks of an ACM-MD can be accurately and robustly estimated, thus enhancing patients' safety. The proposed methodology is applicable to other clinical decisions based on predictive models since our proposal does not depend on the particular predictive model.
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Affiliation(s)
- Carlo Barbieri
- Operation and Digital Strategy, Fresenius Medical Care, Bad Homburg, Germany
| | - Luca Neri
- Operation and Digital Strategy, Fresenius Medical Care, Bad Homburg, Germany
| | - Milena Chermisi
- Operation and Digital Strategy, Fresenius Medical Care, Bad Homburg, Germany
| | - Elena Bolzoni
- Operation and Digital Strategy, Fresenius Medical Care, Bad Homburg, Germany
| | - Isabella Cattinelli
- Operation and Digital Strategy, Fresenius Medical Care, Bad Homburg, Germany
| | - Wolfgang Decker
- QREM (Quality, Regulatory Affairs & Management Systems), Fresenius Medical Care, Bad Homburg, Germany
| | - Stefano Stuard
- Global Medical Office, Fresenius Medical Care, Bad Homburg, Germany
| | - José D Martín-Guerrero
- Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE-UV, Universitat de Valéncia, Burjassot, Valencia, Spain
| | - Flavio Mari
- Operation and Digital Strategy, Fresenius Medical Care, Bad Homburg, Germany
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Abstract
A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.
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30
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Liu YS, Yang CY, Chiu PF, Lin HC, Lo CC, Lai ASH, Chang CC, Lee OKS. Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study. J Med Internet Res 2021; 23:e27098. [PMID: 34491204 PMCID: PMC8456349 DOI: 10.2196/27098] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 07/10/2021] [Accepted: 08/12/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device-integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. OBJECTIVE We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. METHODS Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. RESULTS Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. CONCLUSIONS Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.
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Affiliation(s)
- Yi-Shiuan Liu
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan.,Stem Cell Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Physiology and Pharmacology, Chang Gung University College of Medicine, Taoyuan, Taiwan.,Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chih-Yu Yang
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan.,Stem Cell Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Center for Intelligent Drug Systems and Smart Bio-devices, Hsinchu, Taiwan
| | - Ping-Fang Chiu
- Division of Nephrology, Department of Medicine, Changhua Christian Hospital, Changhua, Taiwan
| | - Hui-Chu Lin
- Stem Cell Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chung-Chuan Lo
- Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, Taiwan
| | - Alan Szu-Han Lai
- Stem Cell Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chia-Chu Chang
- Department of Medicine, Kuang Tien General Hospital, Taichung, Taiwan.,Department of Nutrition, Hungkuang University, Taichung, Taiwan
| | - Oscar Kuang-Sheng Lee
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan.,Stem Cell Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Orthopedics, China Medical University Hospital, Taichung, Taiwan
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31
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Yun HR, Lee G, Jeon MJ, Kim HW, Joo YS, Kim H, Chang TI, Park JT, Han SH, Kang SW, Kim W, Yoo TH. Erythropoiesis stimulating agent recommendation model using recurrent neural networks for patient with kidney failure with replacement therapy. Comput Biol Med 2021; 137:104718. [PMID: 34481182 DOI: 10.1016/j.compbiomed.2021.104718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 12/17/2022]
Abstract
In patients with kidney failure with replacement therapy (KFRT), optimizing anemia management in these patients is a challenging problem because of the complexities of the underlying diseases and heterogeneous responses to erythropoiesis-stimulating agents (ESAs). Therefore, we propose a ESA dose recommendation model based on sequential awareness neural networks. Data from 466 KFRT patients (12,907 dialysis sessions) in seven tertiary-care general hospitals were included in the experiment. First, a Hb prediction model was developed to simulate longitudinal heterogeneous ESA and Hb interactions. Based on the prediction model as a prospective study simulator, we built an ESA dose recommendation model to predict the required amount of ESA dose to reach a target hemoglobin level after 30 days. Each model's performance was evaluated in the mean absolute error (MAE). The MAEs presenting the best results of the prediction and recommendation model were 0.59 (95% confidence interval: 0.56-0.62) g/dL and 43.2 μg (ESAs dose), respectively. Compared to the results in the real-world clinical data, the recommendation model achieved a reduction of ESA dose (Algorithm: 140 vs. Human: 150 μg/month, P < 0.001), a more stable monthly Hb difference (Algorithm: 0.6 vs. Human: 0.8 g/dL, P < 0.001), and an improved target Hb success rate (Algorithm: 79.5% vs. Human: 62.9% for previous month's Hb < 10.0 g/dL; Algorithm: 95.7% vs. Human:73.0% for previous month's Hb 10.0-12.0 g/dL). We developed an ESA dose recommendation model for optimizing anemia management in patients with KFRT and showed its potential effectiveness in a simulated prospective study.
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Affiliation(s)
- Hae-Ryong Yun
- Division of Nephrology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Gyubok Lee
- Graduate School of AI, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Myeong Jun Jeon
- Intelligence Laboratories, Nexon Korea, Seongnam, South Korea
| | - Hyung Woo Kim
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea
| | - Young Su Joo
- Division of Nephrology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyoungnae Kim
- Division of Nephrology, Soonchunhyang University Hospital, Seoul, South Korea
| | - Tae Ik Chang
- Department of Internal Medicine, National Health Insurance Service Medical Center, Ilsan Hospital, Goyang, Gyeonggi-do, South Korea
| | - Jung Tak Park
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea
| | - Seung Hyeok Han
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea
| | - Shin-Wook Kang
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea; Department of Internal Medicine, College of Medicine, Severance Biomedical Science Institute, Brain Korea 21 PLUS, Yonsei University, Seoul, South Korea
| | - Wooju Kim
- Department of Industrial Engineering, College of Engineering, Yonsei University, Seoul, South Korea.
| | - Tae-Hyun Yoo
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea.
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Dialysis adequacy predictions using a machine learning method. Sci Rep 2021; 11:15417. [PMID: 34326393 PMCID: PMC8322325 DOI: 10.1038/s41598-021-94964-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/19/2021] [Indexed: 01/16/2023] Open
Abstract
Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman's rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.
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33
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Chaudhuri S, Han H, Usvyat L, Jiao Y, Sweet D, Vinson A, Johnstone Steinberg S, Maddux D, Belmonte K, Brzozowski J, Bucci B, Kotanko P, Wang Y, Kooman JP, Maddux FW, Larkin J. Machine learning directed interventions associate with decreased hospitalization rates in hemodialysis patients. Int J Med Inform 2021; 153:104541. [PMID: 34343957 DOI: 10.1016/j.ijmedinf.2021.104541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/14/2021] [Accepted: 06/29/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND An integrated kidney disease company uses machine learning (ML) models that predict the 12-month risk of an outpatient hemodialysis (HD) patient having multiple hospitalizations to assist with directing personalized interdisciplinary interventions in a Dialysis Hospitalization Reduction Program (DHRP). We investigated the impact of risk directed interventions in the DHRP on clinic-wide hospitalization rates. METHODS We compared the hospital admission and day rates per-patient-year (ppy) from all hemodialysis patients in 54 DHRP and 54 control clinics identified by propensity score matching at baseline in 2015 and at the end of the pilot in 2018. We also used paired T test to compare the between group difference of annual hospitalization rate and hospitalization days rates at baseline and end of the pilot. RESULTS The between group difference in annual hospital admission and day rates was similar at baseline (2015) with a mean difference between DHRP versus control clinics of -0.008 ± 0.09 ppy and -0.05 ± 0.96 ppy respectively. The between group difference in hospital admission and day rates became more distinct at the end of follow up (2018) favoring DHRP clinics with the mean difference being -0.155 ± 0.38 ppy and -0.97 ± 2.78 ppy respectively. A paired t-test showed the change in the between group difference in hospital admission and day rates from baseline to the end of the follow up was statistically significant (t-value = 2.73, p-value < 0.01) and (t-value = 2.29, p-value = 0.02) respectively. CONCLUSIONS These findings suggest ML model-based risk-directed interdisciplinary team interventions associate with lower hospitalization rates and hospital day rate in HD patients, compared to controls.
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Affiliation(s)
- Sheetal Chaudhuri
- Fresenius Medical Care, Global Medical Office, Waltham, United States; Maastricht University Medical Center, Maastricht, The Netherlands
| | - Hao Han
- Fresenius Medical Care, Global Medical Office, Waltham, United States
| | - Len Usvyat
- Fresenius Medical Care, Global Medical Office, Waltham, United States
| | - Yue Jiao
- Fresenius Medical Care, Global Medical Office, Waltham, United States
| | - David Sweet
- Fresenius Medical Care North America, Waltham, United States
| | - Allison Vinson
- Fresenius Medical Care North America, Waltham, United States
| | | | - Dugan Maddux
- Fresenius Medical Care North America, Waltham, United States
| | | | - Jane Brzozowski
- Fresenius Medical Care, Global Medical Office, Waltham, United States
| | - Brad Bucci
- Fresenius Medical Care North America, Waltham, United States
| | - Peter Kotanko
- Renal Research Institute, NY, United States; Icahn School of Medicine at Mount Sinai, New York, United States
| | - Yuedong Wang
- University of California, Santa Barbara, United States
| | - Jeroen P Kooman
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - Franklin W Maddux
- Fresenius Medical Care, Global Medical Office, Waltham, United States
| | - John Larkin
- Fresenius Medical Care, Global Medical Office, Waltham, United States.
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34
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Hughes JH, Keizer RJ. A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1150-1160. [PMID: 34270885 PMCID: PMC8520755 DOI: 10.1002/psp4.12684] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/18/2021] [Accepted: 07/02/2021] [Indexed: 12/19/2022]
Abstract
Model‐informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowledge about the individual, in the form of drug levels or pharmacodynamic biomarkers, with prior knowledge of the drug PK in the general population. Use of “flattened priors” (FPs), in which the weight of the model priors is reduced relative to observations about the patient, has been previously proposed to estimate individual PK parameters in instances where the patient is poorly described by the PK model. However, little is known about the predictive performance of FPs and when to apply FPs in MIPD. Here, FP is evaluated in a data set of 4679 adult patients treated with vancomycin. Depending on the PK model, prediction error could be reduced by applying FPs in 42–55% of PK parameter estimations. Machine learning (ML) models could identify instances where FPs would outperform MAPs with a specificity of 81–86%, reducing overall root mean squared error (RMSE) of PK model predictions by 12–22% (0.5–1.2 mg/L) relative to MAP alone. The factors most indicative of the use of FPs were past prediction residuals and bias in past PK predictions. A more clinically practical minimal model was developed using only these two features, reducing RMSE by 5–18% (0.20–0.93 mg/L) relative to MAP. This hybrid ML/PK approach advances the precision dosing toolkit by leveraging the power of ML while maintaining the mechanistic insight and interpretability of PK models.
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Luo G. A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support. JMIR Med Inform 2021; 9:e27778. [PMID: 34042600 PMCID: PMC8193496 DOI: 10.2196/27778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/25/2021] [Accepted: 04/14/2021] [Indexed: 01/20/2023] Open
Abstract
Using machine learning predictive models for clinical decision support has great potential in improving patient outcomes and reducing health care costs. However, most machine learning models are black boxes that do not explain their predictions, thereby forming a barrier to clinical adoption. To overcome this barrier, an automated method was recently developed to provide rule-style explanations of any machine learning model’s predictions on tabular data and to suggest customized interventions. Each explanation delineates the association between a feature value pattern and an outcome value. Although the association and intervention information is useful, the user of the automated explaining function often requires more detailed information to better understand the patient’s situation and to aid in decision making. More specifically, consider a feature value in the explanation that is computed by an aggregation function on the raw data, such as the number of emergency department visits related to asthma that the patient had in the prior 12 months. The user often wants to rapidly drill through to see certain parts of the related raw data that produce the feature value. This task is frequently difficult and time-consuming because the few pieces of related raw data are submerged by many pieces of raw data of the patient that are unrelated to the feature value. To address this issue, this paper outlines an automated lineage tracing approach, which adds automated drill-through capability to the automated explaining function, and provides a roadmap for future research.
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Affiliation(s)
- Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
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36
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McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021; 88:1482-1499. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 12/13/2022] Open
Abstract
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.
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Affiliation(s)
- Mason McComb
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Bies
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Institute for Computational Data Science, University at Buffalo, NY, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Department of Neurology, University at Buffalo, State University of New York, Buffalo, NY, USA
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37
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Abstract
Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.
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38
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Chaudhuri S, Long A, Zhang H, Monaghan C, Larkin JW, Kotanko P, Kalaskar S, Kooman JP, van der Sande FM, Maddux FW, Usvyat LA. Artificial intelligence enabled applications in kidney disease. Semin Dial 2021; 34:5-16. [PMID: 32924202 PMCID: PMC7891588 DOI: 10.1111/sdi.12915] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.
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Affiliation(s)
- Sheetal Chaudhuri
- Maastricht University Medical CenterMaastrichtThe Netherlands
- Fresenius Medical CareWalthamMAUSA
| | | | | | | | | | - Peter Kotanko
- Renal Research InstituteNew YorkNYUSA
- Icahn School of Medicine at Mount SinaiNew YorkNYUSA
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39
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Gaweda AE, Brier ME. Artificial Intelligence in Medicine in Anemia. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_183-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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40
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Yao L, Zhang H, Zhang M, Chen X, Zhang J, Huang J, Zhang L. Application of artificial intelligence in renal disease. CLINICAL EHEALTH 2021. [DOI: 10.1016/j.ceh.2021.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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41
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Ohara T, Ikeda H, Sugitani Y, Suito H, Huynh VQH, Kinomura M, Haraguchi S, Sakurama K. Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients. Int J Med Sci 2021; 18:1831-1839. [PMID: 33746600 PMCID: PMC7976591 DOI: 10.7150/ijms.53298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/24/2021] [Indexed: 12/11/2022] Open
Abstract
Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications appropriately is not keeping pace with the rapid increase of hemodialysis patients. Moreover, the high cost of ESAs imposes heavy burdens on medical insurance systems. An artificial-intelligence-supported anemia control system (AISACS) trained using administration direction data from experienced physicians has been developed by the authors. For the system, appropriate data selection and rectification techniques play important roles. Decision making related to ESAs poses a multi-class classification problem for which a two-step classification technique is introduced. Several validations have demonstrated that AISACS exhibits high performance with correct classification rates of 72%-87% and clinically appropriate classification rates of 92%-98%.
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Affiliation(s)
- Toshiaki Ohara
- Department of Pathology & Experimental Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.,Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Hiroshi Ikeda
- Department of Internal Medicine, Shigei Medical Research Hospital, Okayama, Japan
| | - Yoshiki Sugitani
- Advanced Institute for Materials Research, Tohoku University, Miyagi, Japan
| | - Hiroshi Suito
- Advanced Institute for Materials Research, Tohoku University, Miyagi, Japan
| | | | - Masaru Kinomura
- Division of Hemodialysis and Apheresis, Okayama University Hospital, Okayama, Japan
| | | | - Kazufumi Sakurama
- Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.,Department of Dialysis Access Center, Shigei Medical Research Hospital, Okayama, Japan
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42
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AIM in Hemodialysis. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_254-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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43
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Irfan F. Artificial Intelligence: Help or Hindrance for Family Physicians? Pak J Med Sci 2020; 37:288-291. [PMID: 33437293 PMCID: PMC7794111 DOI: 10.12669/pjms.37.1.3351] [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: 11/15/2022] Open
Abstract
The use of Artificial Intelligence (AI) and related technologies is rapidly increasing and its application in clinical practice is a promising area of development. Artificial Intelligence can be a solution in the future as a physician’s new assistant; AI-physician combinations can act like models of ‘peaceful co-existence’. While it has the potential to mold many dimensions of patient care and can augment quality improvement, it cannot replace a family physician’s diagnostic intelligence, empathy and relationships. Physicians need to strike a balance between these combinations for better health outcomes without increasing patients’ frustration.
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Affiliation(s)
- Farhana Irfan
- Farhana Irfan King Saud University Chair for Medical Education Research and Development, Department of Family and Community Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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44
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Pinter J, Chazot C, Stuard S, Moissl U, Canaud B. Sodium, volume and pressure control in haemodialysis patients for improved cardiovascular outcomes. Nephrol Dial Transplant 2020; 35:ii23-ii30. [PMID: 32162668 PMCID: PMC7066545 DOI: 10.1093/ndt/gfaa017] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Indexed: 12/12/2022] Open
Abstract
Chronic volume overload is pervasive in patients on chronic haemodialysis and substantially increases the risk of cardiovascular death. The rediscovery of the three-compartment model in sodium metabolism revolutionizes our understanding of sodium (patho-)physiology and is an effect modifier that still needs to be understood in the context of hypertension and end-stage kidney disease. Assessment of fluid overload in haemodialysis patients is central yet difficult to achieve, because traditional clinical signs of volume overload lack sensitivity and specificity. The highest all-cause mortality risk may be found in haemodialysis patients presenting with high fluid overload but low blood pressure before haemodialysis treatment. The second highest risk may be found in patients with both high blood pressure and fluid overload, while high blood pressure but normal fluid overload may only relate to moderate risk. Optimization of fluid overload in haemodialysis patients should be guided by combining the traditional clinical evaluation with objective measurements such as bioimpedance spectroscopy in assessing the risk of fluid overload. To overcome the tide of extracellular fluid, the concept of time-averaged fluid overload during the interdialytic period has been established and requires possible readjustment of a negative target post-dialysis weight. 23Na-magnetic resonance imaging studies will help to quantitate sodium accumulation and keep prescribed haemodialytic sodium mass balance on the radar. Cluster-randomization trials (e.g. on sodium removal) are underway to improve our therapeutic approach to cardioprotective haemodialysis management.
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Affiliation(s)
- Jule Pinter
- Renal Division, University Hospital of Würzburg, Würzburg, Germany
| | | | - Stefano Stuard
- Global Medical Office, FMC Deutschland, Bad Homburg, Germany
| | - Ulrich Moissl
- Global Medical Office, FMC Deutschland, Bad Homburg, Germany
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45
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Inaguma D, Kitagawa A, Yanagiya R, Koseki A, Iwamori T, Kudo M, Yuzawa Y. Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database. PLoS One 2020; 15:e0239262. [PMID: 32941535 PMCID: PMC7497987 DOI: 10.1371/journal.pone.0239262] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 09/02/2020] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence is increasingly being adopted in medical fields to predict various outcomes. In particular, chronic kidney disease (CKD) is problematic because it often progresses to end-stage kidney disease. However, the trajectories of kidney function depend on individual patients. In this study, we propose a machine learning-based model to predict the rapid decline in kidney function among CKD patients by using a big hospital database constructed from the information of 118,584 patients derived from the electronic medical records system. The database included the estimated glomerular filtration rate (eGFR) of each patient, recorded at least twice over a period of 90 days. The data of 19,894 patients (16.8%) were observed to satisfy the CKD criteria. We characterized the rapid decline of kidney function by a decline of 30% or more in the eGFR within a period of two years and classified the available patients into two groups—those exhibiting rapid eGFR decline and those exhibiting non-rapid eGFR decline. Following this, we constructed predictive models based on two machine learning algorithms. Longitudinal laboratory data including urine protein, blood pressure, and hemoglobin were used as covariates. We used longitudinal statistics with a baseline corresponding to 90-, 180-, and 360-day windows prior to the baseline point. The longitudinal statistics included the exponentially smoothed average (ESA), where the weight was defined to be 0.9*(t/b), where t denotes the number of days prior to the baseline point and b denotes the decay parameter. In this study, b was taken to be 7 (7-day ESA). We used logistic regression (LR) and random forest (RF) algorithms based on Python code with scikit-learn library (https://scikit-learn.org/) for model creation. The areas under the curve for LR and RF were 0.71 and 0.73, respectively. The 7-day ESA of urine protein ranked within the first two places in terms of importance according to both models. Further, other features related to urine protein were likely to rank higher than the rest. The LR and RF models revealed that the degree of urine protein, especially if it exhibited an increasing tendency, served as a prominent risk factor associated with rapid eGFR decline.
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Affiliation(s)
- Daijo Inaguma
- Department of Internal Medicine, Fujita Health University Bantane Hospital–Nagoya, Japan
- * E-mail:
| | - Akimitsu Kitagawa
- Department of Internal Medicine, Fujita Health University Bantane Hospital–Nagoya, Japan
| | - Ryosuke Yanagiya
- Division of Medical Information Systems, Fujita Health University School of Medicine–Toyoake, Japan
| | | | | | | | - Yukio Yuzawa
- Department of Nephrology, Fujita Health University School of Medicine–Toyoake, Japan
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46
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Choi GH, Yun J, Choi J, Lee D, Shim JH, Lee HC, Chung YH, Lee YS, Park B, Kim N, Kim KM. Development of machine learning-based clinical decision support system for hepatocellular carcinoma. Sci Rep 2020; 10:14855. [PMID: 32908183 PMCID: PMC7481788 DOI: 10.1038/s41598-020-71796-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 08/04/2020] [Indexed: 12/29/2022] Open
Abstract
There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision support system (CDSS) for recommending initial treatment option in HCC and predicting overall survival (OS). From hospital records of 1,021 consecutive patients with HCC treated at a single centre in Korea between January 2010 and October 2010, we collected information on 61 pretreatment variables, initial treatment, and survival status. Twenty pretreatment key variables were finally selected. We developed the CDSS from the derivation set (N = 813) using random forest method and validated it in the validation set (N = 208). Among the 1,021 patients (mean age: 56.9 years), 81.8% were male and 77.0% had positive hepatitis B BCLC stages 0, A, B, C, and D were observed in 13.4%, 26.0%, 18.0%, 36.6%, and 6.3% of patients, respectively. The six multi-step classifier model was developed for treatment decision in a hierarchical manner, and showed good performance with 81.0% of accuracy for radiofrequency ablation (RFA) or resection versus not, 88.4% for RFA versus resection, and 76.8% for TACE or not. We also developed seven survival prediction models for each treatment option. Our newly developed HCC-CDSS model showed good performance in terms of treatment recommendation and OS prediction and may be used as a guidance in deciding the initial treatment option for HCC.
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Affiliation(s)
- Gwang Hyeon Choi
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Jihye Yun
- Department of Convergence Medicine and Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jonggi Choi
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Danbi Lee
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Ju Hyun Shim
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Han Chu Lee
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Young-Hwa Chung
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Yung Sang Lee
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Beomhee Park
- Department of Convergence Medicine and Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Convergence Medicine and Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Kang Mo Kim
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
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47
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Pellicer-Valero OJ, Cattinelli I, Neri L, Mari F, Martín-Guerrero JD, Barbieri C. Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks. Artif Intell Med 2020; 107:101898. [PMID: 32828446 DOI: 10.1016/j.artmed.2020.101898] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 12/20/2022]
Abstract
Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to provide suggestions when some strict requirements are not met (such as having a three month history prior to the prediction). Here, recurrent neural networks (RNNs) are used to model whole patient histories, providing predictions at every time step since the very first day. Furthermore, an unprecedented amount of data (∼110,000 patients from many different medical centers in twelve countries, without exclusion criteria) was used to train it, thus allowing it to generalize for every single patient. The resulting model outperforms state-of-the-art Hemoglobin prediction, providing excellent results even when tested on a prospective dataset. Simultaneously, it allows to bring the benefits of algorithmic anemia control to a very large group of patients.
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Affiliation(s)
- Oscar J Pellicer-Valero
- Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València (UV), Av. Universitat, sn, 46100 Bujassot, Valencia, Spain.
| | | | - Luca Neri
- Fresenius Medical Care, Else-Kröner-Straße 1, 61352 Bad Homburg, Germany.
| | - Flavio Mari
- Fresenius Medical Care, Else-Kröner-Straße 1, 61352 Bad Homburg, Germany.
| | - José D Martín-Guerrero
- Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València (UV), Av. Universitat, sn, 46100 Bujassot, Valencia, Spain.
| | - Carlo Barbieri
- Fresenius Medical Care, Else-Kröner-Straße 1, 61352 Bad Homburg, Germany.
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48
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Garcia-Montemayor V, Martin-Malo A, Barbieri C, Bellocchio F, Soriano S, Pendon-Ruiz de Mier V, Molina IR, Aljama P, Rodriguez M. Predicting mortality in hemodialysis patients using machine learning analysis. Clin Kidney J 2020; 14:1388-1395. [PMID: 34221370 PMCID: PMC8247746 DOI: 10.1093/ckj/sfaa126] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 06/10/2020] [Indexed: 12/18/2022] Open
Abstract
Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.
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Affiliation(s)
| | - Alejandro Martin-Malo
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain.,RETICs-REDinREN (National Institute of Health Carlos III), Madrid, Spain
| | - Carlo Barbieri
- Fresenius Medical Care Italia, Vaiano Cremasco, Cremona, Italy
| | | | - Sagrario Soriano
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain
| | - Victoria Pendon-Ruiz de Mier
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain
| | - Ignacio R Molina
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain
| | - Pedro Aljama
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain
| | - Mariano Rodriguez
- Department of Nephrology, Reina Sofia University Hospital, Cordoba, Spain.,Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Spain.,RETICs-REDinREN (National Institute of Health Carlos III), Madrid, Spain
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49
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Komaru Y, Yoshida T, Hamasaki Y, Nangaku M, Doi K. Hierarchical Clustering Analysis for Predicting 1-Year Mortality After Starting Hemodialysis. Kidney Int Rep 2020; 5:1188-1195. [PMID: 32775818 PMCID: PMC7403509 DOI: 10.1016/j.ekir.2020.05.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 05/05/2020] [Accepted: 05/11/2020] [Indexed: 11/17/2022] Open
Abstract
Introduction For patients with end-stage renal disease (ESRD), due to the heterogeneity of the population, appropriate risk assessment approaches and strategies for further follow-up remain scarce. We aimed to conduct a pilot study for better risk stratification, applying machine learning–based classification to patients with ESRD who newly started maintenance hemodialysis. Methods We prospectively studied 101 patients with ESRD, who were new to maintenance hemodialysis therapy, between August 2016 and March 2018. Baseline values of variables such as blood and urine tests were obtained before the initiation of hemodialysis. Agglomerative hierarchical clustering was conducted with the collected continuous data. The resulting clusters were followed up for the primary outcome of 1-year mortality, as analyzed by the Kaplan-Meier survival curve with log-rank test and the Cox proportional hazard model. Results The participants were divided into 3 clusters (cluster 1, n = 62; cluster 2, n = 15; cluster 3, n = 24) by hierarchical clustering, using 46 clinical variables. Patients in cluster 3 showed lower systolic blood pressures, and lower serum creatinine and urinary liver-type fatty acid-binding protein levels, before the initiation of hemodialysis. Consequently, cluster 3 was associated with the highest 1-year mortality in the study cohort (P < 0.001), and the difference was significant after adjustment for age and sex (hazard ratio: 10.2; 95% confidence interval: 2.94–46.8, cluster 1 as reference). Conclusion In this proof-of-concept study, hierarchical clustering discovered a subgroup with a higher 1-year mortality at the initiation of hemodialysis. Applying machine learning–derived classification to patients with ESRD may contribute to better risk stratification.
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Affiliation(s)
- Yohei Komaru
- Division of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Division of Dialysis and Apheresis, The University of Tokyo Hospital, Tokyo, Japan
| | - Teruhiko Yoshida
- Division of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Division of Dialysis and Apheresis, The University of Tokyo Hospital, Tokyo, Japan
| | - Yoshifumi Hamasaki
- Division of Dialysis and Apheresis, The University of Tokyo Hospital, Tokyo, Japan
| | - Masaomi Nangaku
- Division of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Division of Dialysis and Apheresis, The University of Tokyo Hospital, Tokyo, Japan
| | - Kent Doi
- Department of Acute Medicine, The University of Tokyo Hospital, Tokyo, Japan
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50
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Shaikh F, Dehmeshki J, Bisdas S, Roettger-Dupont D, Kubassova O, Aziz M, Awan O. Artificial Intelligence-Based Clinical Decision Support Systems Using Advanced Medical Imaging and Radiomics. Curr Probl Diagn Radiol 2020; 50:262-267. [PMID: 32591104 DOI: 10.1067/j.cpradiol.2020.05.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/04/2020] [Accepted: 05/26/2020] [Indexed: 01/09/2023]
Abstract
Artificial intelligence (AI) is poised to make a veritable impact in medicine. Clinical decision support (CDS) is an important area where AI can augment the clinician's capability to collect, understand and make inferences on an overwhelming volume of patient data to reach the optimal clinical decision. Advancements in medical image analysis, such as Radiomics, and data computation, such as machine learning, have expanded our understanding of disease processes and their management. In this article, we review the most relevant concepts of AI as applicable to advanced imaging-based clinical decision support systems.
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Affiliation(s)
| | | | | | | | | | | | - Omer Awan
- Department of Radiology, University of Maryland, Baltimore, MD
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