Predicting chronic kidney disease in cats using laboratory tests and machine learning

2020 ◽  
Vol 33 (11) ◽  
pp. 5
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Laboratory Tests

scholarly journals Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning

2019 ◽  
Vol 33 (6) ◽  
pp. 2644-2656 ◽  
Author(s):  
Richard Bradley ◽  
Ilias Tagkopoulos ◽  
Minseung Kim ◽  
Yiannis Kokkinos ◽  
Theodoros Panagiotakos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Laboratory Tests ◽  
Clinical Laboratory ◽  
Early Risk ◽  
Routine Clinical Laboratory

Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat

2021 ◽  
pp. 1098612X2110012
Author(s):  
Jade Renard ◽  
Mathieu R Faucher ◽  
Anaïs Combes ◽  
Didier Concordet ◽  
Brice S Reynolds
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Diagnostic Performance ◽  
Clinical Signs ◽  
Learning System ◽  
Clinical Severity ◽  
Medium Term ◽  
Term Survival

Objectives The aim of this study was to develop an algorithm capable of predicting short- and medium-term survival in cases of intrinsic acute-on-chronic kidney disease (ACKD) in cats. Methods The medical record database was searched to identify cats hospitalised for acute clinical signs and azotaemia of at least 48 h duration and diagnosed to have underlying chronic kidney disease based on ultrasonographic renal abnormalities or previously documented azotaemia. Cases with postrenal azotaemia, exposure to nephrotoxicants, feline infectious peritonitis or neoplasia were excluded. Clinical variables were combined in a clinical severity score (CSS). Clinicopathological and ultrasonographic variables were also collected. The following variables were tested as inputs in a machine learning system: age, body weight (BW), CSS, identification of small kidneys or nephroliths by ultrasonography, serum creatinine at 48 h (Crea48), spontaneous feeding at 48 h (SpF48) and aetiology. Outputs were outcomes at 7, 30, 90 and 180 days. The machine-learning system was trained to develop decision tree algorithms capable of predicting outputs from inputs. Finally, the diagnostic performance of the algorithms was calculated. Results Crea48 was the best predictor of survival at 7 days (threshold 1043 µmol/l, sensitivity 0.96, specificity 0.53), 30 days (threshold 566 µmol/l, sensitivity 0.70, specificity 0.89) and 90 days (threshold 566 µmol/l, sensitivity 0.76, specificity 0.80), with fewer cats still alive when their Crea48 was above these thresholds. A short decision tree, including age and Crea48, predicted the 180-day outcome best. When Crea48 was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF48 and identification of small kidneys with an overall diagnostic performance similar to that using Crea48. Conclusions and relevance Crea48 helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF48 and identification of small kidneys.

scholarly journals Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0233976 ◽  
Author(s):  
Erik Dovgan ◽  
Anton Gradišek ◽  
Mitja Luštrek ◽  
Mohy Uddin ◽  
Aldilas Achmad Nursetyo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Renal Replacement Therapy ◽  
Kidney Disease ◽  
Replacement Therapy ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Vitamin D (25 ( OH) D ) in patients with chronic kidney disease stages 2-5

2016 ◽  
pp. 160-166 ◽  
Author(s):  
César Augusto Restrepo Valencia ◽  
Jose Vicente Aguirre Arango
Keyword(s):  
Chronic Kidney Disease ◽  
Vitamin D ◽  
Kidney Disease ◽  
Statistical Methods ◽  
Laboratory Tests ◽  
Chronic Kidney Disease Stage ◽  
Sun Exposure ◽  
Disease Stage ◽  
Disease Etiology ◽  
Vitamin D Levels

Objective: To determine whether patients with chronic kidney disease (CKD) without dialysis their stage impacts the native vitamin D levels. Methods: Patients over 18 years with chronic kidney disease stage 2-5 without dialysis treatment. They demographic, anthropometric variables, degree of sun exposure, disease etiology and laboratory variables related to bone and mineral disorders were evaluated. Study analytical cross-sectional prospective. Descriptive statistical methods for quantitative and qualitative are characterized, and analytical correlation between levels of vitamin D statistical laboratory tests related to bone and mineral disorders, sun exposure and ethnicity variables for each stage were characterized. By descriptive statistical methods, quantitative and qualitative variables were characterized, and analytical statistical correlation between levels of vitamin D with laboratory tests related to bone and mineral disorders, sun exposure and ethnicity for each stage were practiced. Results: 331 patients were evaluated, with a mean age of 71 years, the mestizo majority (71%), 173 women, main etiology of CKD hypertensive nephropathy (33.2%). 21.1% of patients had normal levels of vitamin D, 70.1% insufficient, and 8.8% in deficit. Negative correlation was detected between the levels of vitamin 25(OH)D and serum creatinine, phosphorus, calcium x phosphorus product, PTH, proteins in urine 24 hours and BMI. Positive correlation for calcium and albumin. Positive statistical significance between the levels of vitamin 25(OH)D and sun exposure for 3b and 4 stages was found. Conclusions: In patients with CKD is common to detect low levels of vitamin 25(OH)D, which can contribute to the generation of secondary hyperparathyroidism.

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

2022 ◽  
pp. ASN.2021040538
Author(s):  
Arthur M. Lee ◽  
Jian Hu ◽  
Yunwen Xu ◽  
Alison G. Abraham ◽  
Rui Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Support Vector ◽  
Pathway Enrichment Analysis ◽  
Disease Etiology ◽  
Content Type ◽  
Extreme Gradient Boosting ◽  
Pediatric Ckd

BackgroundUntargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).MethodsUntargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.ResultsML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome–derived histidine metabolites.ConclusionML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome–derived histidine metabolites are associated with OU.

scholarly journals Machine learning approaches revealed metabolic signatures of incident chronic kidney disease in persons with pre-and type 2 diabetes

2020 ◽  
Author(s):  
Ada Admin ◽  
Jialing Huang ◽  
Cornelia Huth ◽  
Marcela Covic ◽  
Martina Troll ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Population Based ◽  
Learning Approaches ◽  
Clinical Variables

Early and precise identification of individuals with pre-diabetes and type 2 diabetes (T2D) at risk of progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in persons with pre- and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

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