Machine learning algorithm for early detection of end-stage renal disease

Abstract

Background End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database. Methods This study analyzed 10,000,000 medical insurance claims from 550,000 patient records using a commercial health insurance database. Inclusion criteria were patients over the age of 18 diagnosed with CKD Stages 1–4. We compiled 240 predictor candidates, divided into six feature groups: demographics, chronic conditions, diagnosis and procedure features, medication features, medical costs, and episode counts. We used a feature embedding method based on implementation of the Word2Vec algorithm to further capture temporal information for the three main components of the data: diagnosis, procedures, and medications. For the analysis, we used the gradient boosting tree algorithm (XGBoost implementation). Results The C-statistic for the model was 0.93 [(0.916–0.943) 95% confidence interval], with a sensitivity of 0.715 and specificity of 0.958. Positive Predictive Value (PPV) was 0.517, and Negative Predictive Value (NPV) was 0.981. For the top 1 percentile of patients identified by our model, the PPV was 1.0. In addition, for the top 5 percentile of patients identified by our model, the PPV was 0.71. All the results above were tested on the test data only, and the threshold used to obtain these results was 0.1. Notable features contributing to the model were chronic heart and ischemic heart disease as a comorbidity, patient age, and number of hypertensive crisis events. Conclusions When a patient is approaching the threshold of ESRD risk, a warning message can be sent electronically to the physician, who will initiate a referral for a nephrology consultation to ensure an investigation to hasten the establishment of a diagnosis and initiate management and therapy when appropriate.

Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients

“…In total, we included 19 parameters: age, sex, BMI, smoking and drinking habit, hypertension, use of ACE inhibitors and antihypertensive medicine, daily insulin dose, hypercholesterolemia, duration of insulin-dependent diabetes mellitus (IDDM), glycated hemoglobin (HbA1c) levels, total cholesterol, triglycerides, high-density lipoproteins (HDL), low-density lipoproteins (LDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure. These parameters were considered essential for CKD detection in other studies [ 13 , 19 , 20 , 38 , 39 , 40 ]. To avoid overfitting problems, we did not consider parameters such as albumin excretion rate (AER), serum creatinine, and current GFR because serum creatinine is used to calculate eGFR, and AER is a CKD identifier [ 10 , 24 , 35 ].…”

“…Although CKD diagnosis and classification have changed over time, according to KDIGO 2012 and current international standards, a person with an eGFR less than 60 mL/min/1.73 m 2 for more than 3 months is considered a CKD patient [ 12 ]. Weariness, fluid retention, abnormalities in the urine, limb edema, nausea, vomiting, and neurological and cognitive impairment are the symptoms of CKD, although it can be asymptomatic in many cases [ 13 ]. Thus, there is typically a chance of a delay in recognizing, diagnosing, and treating the many etiologies of CKD, since people can be asymptomatic and need a specific laboratory-based test to identify CKD.…”

“…The use of machine learning (ML) algorithms in addressing various disease classification problems has recently expanded due to remarkable advancements in related technologies [ 16 , 17 , 18 ]. Although there are some examples of using ML tools in kidney disease prediction [ 13 , 19 , 20 , 21 , 22 ], their use in developing CKD prediction models for type 1 diabetes mellitus patients is scarce. For example, Segal et al [ 13 ] employed a gradient boosting tree algorithm (extreme gradient boosting implementation) to construct a model for predicting ESRD.…”

“…Although there are some examples of using ML tools in kidney disease prediction [ 13 , 19 , 20 , 21 , 22 ], their use in developing CKD prediction models for type 1 diabetes mellitus patients is scarce. For example, Segal et al [ 13 ] employed a gradient boosting tree algorithm (extreme gradient boosting implementation) to construct a model for predicting ESRD. Another study [ 20 ] established and compared nine ML models to estimate the 24 h urinary protein result response to detect CKD.…”

Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting the Risk of Surgical Site Infection Following Minimally Invasive Transforaminal Lumbar Interbody Fusion Under Quadrant Channel

“…Machine learning (ML) is a form of arti cial intelligence (AI) in which algorithms automatically learn and improve by identifying patterns and complex relationships, with the ultimate goal of making decisions using minimal human intervention [9][10][11][12][13]. Through the good performance exhibited by ML algorithms in medical big data, we have the potential to obtain more superior prediction tools than traditional statistical modeling modalities under certain conditions for better prediction of the risk of SSI.…”