Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients

Anemia is commonly present in hemodialysis (HD) patients and significantly affects their survival and quality of life. NIR spectroscopy and machine learning were used as a method to detect anemia in hemodialysis patients. The aim of this investigation has been to evaluate the near-infrared spectroscopy (NIRS) as a method for non-invasive on-line detection of anemia parameters from HD effluent by assessing the correlation between the spectrum of spent dialysate in the wavelength range of 700-1700 nm and the levels of hemoglobin (Hb), red blood cells (RBC), hematocrit (Hct), iron (Fe), total iron binding capacity (TIBC), ferritin (FER), mean corpuscular volume (MCV) and mean corpuscular hemoglobin concentration (MCHC) in patient blood. The obtained correlation coefficient (R) for RBC was 0.93, for Hb 0.92, for Fe 0.94, for TIBC 0.96, for FER 0.91, for Hct 0.94, for MCV 0.92, for MCHC 0.92 and for MCH 0.93. The observed high correlations between the NIR spectrum of the dialysate fluid and the levels of the studied variables support the use of NIRS as a promising method for on-line monitoring of anemia and iron saturation parameters in HD patients

Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients

Anemia is commonly present in hemodialysis (HD) patients and significantly affects their survival and quality of life. NIR spectroscopy and machine learning were used as a method to detect anemia in hemodialysis patients. The aim of this investigation has been to evaluate the near-infrared spectroscopy (NIRS) as a method for non-invasive on-line detection of anemia parameters from HD effluent by assessing the correlation between the spectrum of spent dialysate in the wavelength range of 700-1700 nm and the levels of hemoglobin (Hb), red blood cells (RBC), hematocrit (Hct), iron (Fe), total iron binding capacity (TIBC), ferritin (FER), mean corpuscular volume (MCV) and mean corpuscular hemoglobin concentration (MCHC) in patient blood. The obtained correlation coefficient (R) for RBC was 0.93, for Hb 0.92, for Fe 0.94, for TIBC 0.96, for FER 0.91, for Hct 0.94, for MCV 0.92, for MCHC 0.92 and for MCH 0.93. The observed high correlations between the NIR spectrum of the dialysate fluid and the levels of the studied variables support the use of NIRS as a promising method for on-line monitoring of anemia and iron saturation parameters in HD patients

Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of Big Data and will require real-time predictive models. These may come from the fields of Machine Learning and Computational Intelligence, both included in Artificial Intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of Artificial Intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in Artificial Intelligence and Machine Learning, a scientific meeting was organized in the Hospital of Bellvitge (Barcelona, Spain). As an outcome of that meeting, the aim of this review is to investigate Artificial Intelligence experiences on dialysis, with a focus on potential barriers, challenges and prospects for future applications of these technologies.Postprint (author's final draft

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

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. The review was registered on PROSPERO.ResultsFrom 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

Application of machine learning and NIR spectroscopy for monitoring patients on hemodyalisis

Koncept adekvatnosti dijalize ne podrazumeva samo uklanjanje uremijskih simptoma, već i potpunu rehabilitaciju i stabilizaciju stanja pacijenta sa terminalnom insuficijencijom bubrega. Posledice neadekvatnog sistema monitoringa počivaju na činjenici da se provere adekvantosti dijalize u praksi ne izvode dovoljno često, a da u međuvremenu bolesnici mogu biti subdijalizirani. Ova pojava može imati mnoštvo negativnih efekata na pacijenta. Dosadašnja istraživanja usmerena su pretežno na detekciju uremijskih toksina, i to uglavnom uree i kreatinina, i njihovu eliminaciju iz organizma, obično na osnovu skeniranja otpadnog dijalizata uz pomoć neke od optičkih metoda (prevashodno pomoću UV izvora svetlosti). Za sada nema podataka o praćenju nivoa glukoze u krvi pacijenta, stepena anemije, uremije i nivoa elektrolita tokom hemodijalize, na osnovu NIR spektralne informacije otpadnog dijalizata. Praćenje većeg broja parametara koji odslikavaju stanje pacijenta dalo bi kompletniju sliku i približilo se konceptu optimalne dijalize. Istraživanje je obuhvatilo metodu UV-VIS-NIR spektrometrije (Lambda 950, Perkin Elmer), pri čemu je merenje spektara otpadnog dijalizata bilo izvršeno na Mašinskom fakultetu Univerziteta u Beogradu u okviru laboratorije NanoLab. Pored metode UV-VIS-NIR spektrometrije, korišćene su konvencionalne laboratorijske metode koje se koriste u kliničkoj praksi i koje su predstavljale referentne vrednosti za upoređivanje sa eksperimentalnim rezultatima. Softversko rešenje sistema za praćenje parametara otpadnog dijalizata podrazumevalo je implementaciju metoda mašinskog učenja, koje služi za utvrđivanje relacije između spektralnih karakteristika otpadnog dijalizata i parametara izmerenih u krvi. Akvizicija spektara i algoritmi mašinskog učenja napisani su u programskom paketu Python i Matlab®. Izvršena je klasifikacija i regresija parametara koji definišu hiperglikemiju, anemiju i uremiju. Hiperglikemija je praćena kroz koncentraciju glukoze u krvi i tom prilikom AUC zabeležena je vrednost od 0.91 i korelacioni koeficijent od 0.93. Anemija je ogledana kroz koncentraciju parametara eritrocita, hematrokrita, hemoglobina, MCV, MCHC, MCV, nivoa gvožđa u serumu, i tom prilikom prosečna AUC vrednost bila je viša od 0.9, dok je korelacioni koeficijent regresije imao vrednost takođe višu od 0.9. Uremija se ogledala kroz koncentraciju kreatinina, uree i mokraćne kiseline i tom prilikom prosečna AUC vrednost svih parametara bila je viša od 0.85 dok je korelacioni koeficijent imao prosečnu vrednost za sve parametre od 0.91. Metode mašinskog učenja na mnogobrojne načine, bile su primenjene za modeliranje matematičkog modela i sistema za neinvazivnu predikciju koncentracije određenih elemenata. Metode mašinskog učenja su u mogućnosti da, zahvaljujući optičkim, hemijskim, električnim i mikrosenzornim tehnikama, znatno poboljšaju dosadašnje rezultate postignute u neinvazivnim metodama monitoringa hemodijalize.The concept of dialysis adequacy is focused on achieving the complete rehabilitation and stabilization of a patient’s condition, and not merely the elimination of uremic symptoms, . In standard clinical practice the delivered dose of hemodialysis is determined from the urea concentration in patients’ blood samples collected before and after a hemodialysis treatment once a month or even less frequently. Previous research has focused mainly on the detection and elimination of uremic toxins, mainly urea and creatinine, and their elimination from the body, usually based on scanning of waste dialysate using some of the optical methods (primarily using UV light sources). There are currently no data on monitoring the patient's blood glucose levels, degree of anemia or uremia, or electrolyte levels during dialysis, based on the NIR spectral information of the waste dialysate. Monitoring several parameters that reflect the patient's condition would provide a more complete picture and approach the concept of optimal dialysis monitoring. The research was conducted using UV-VIS-NIR spectrometry (Lambda 950, Perkin Elmer). The measurements of waste dialysate spectra were performed at the Faculty of Mechanical Engineering, University of Belgrade within the NanoLab laboratory. In addition to the UV-VIS-NIR spectrometry, conventional clinical practice laboratory methods were used to obtain reference red blood analysis values. The software solution devised for monitoring waste dialysate parameters involved the implementation of machine learning methods, which serve to determine the relationship between its spectral characteristics and the parameters measured in the blood. Spectrum acquisition and machine learning algorithms are written in Python and Matlab®. Classification and regression of parameters defining hyperglycemia, anemia and uremia were performed. Hyperglycemia was monitored through blood glucose concentration and on that occasion AUC value of 0.91 and correlation coefficient of 0.93. Anemia was reflected through the concentration of parameters of erythrocytes, hematocrit, hemoglobin, MCV, MCHC, MCV, serum iron levels, and on that occasion the average AUC value was higher than 0.9, while the correlation regression coefficient was also higher than 0.9. Uremia was reflected through the concentration of creatinine, urea and uric acid and on that occasion the average AUC value of all parameters was higher than 0.85 while the correlation coefficient had an average value for all parameters of 0.91. NIR data contains a huge amount of information, usually of very high dimension, which lends itself to the successful implementation of machine learning methods. Machine learning is a set of methods that can automatically detect patterns in data, and then use the detected patterns to make predictions on future data relevant to the hemodialysis process