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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. [ABSTRACT FROM AUTHOR]

Background: EMRs have enabled the application of machine learning (ML) to characterize disease progression. ML requires large data to produce meaningful results, thus penalizing health systems with small EMRs. Applying models pre-trained on different EMRs is useful but may also suffer from discrepancies between medical sites using different disease codes, procedures, etc. We study the models trained on a large EMR and applied on a smaller one to estimate T2DM complication time using survival models. Method: We trained survival models on a large US insurance claim dataset to create a right-censored cohort (C_1) that includes onset time of T2DM complications, patient profiles, and disease histories. We constructed features using age, sex, and past occurrences of 250 most frequent Clinical Classification Software codes. We trained Random Survival Forest (RSF) models on this cohort. We then created another cohort (C_2) from a smaller EMR from a large Japanese hospital, on which we trained RSF models and also applied models trained on C_1. Results: Table 1 shows a feature importance ranking of trained RSF models and C-indices results. When applying C_1 pre-trained models to C_2, C-indices are high in Nephrology, Hyperosmolar, and Neuropathy. In these cases, common features are highly ranked by both models. Conclusions: This study illustrates the applicability of survival models with disease code covariates on cohorts from different geos. Disclosure: A. Koseki: Employee; Self; IBM, Research Support; Self; Astellas Pharma Inc. M. Makino: None. A. Suzuki: Research Support; Self; Chugai Pharmaceutical Co., Ltd., Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Speaker's Bureau; Self; Eli Lilly Japan K. K. R. Tokumasu: None. P. Chakraborty: None. M. Ghalwash: None. T. Iwamori: None. M. Kudo: Employee; Self; IBM. D. Sow: None. H. Yanagisawa: None. R. Yanagiya: None. [ABSTRACT FROM AUTHOR]

In Chronic Kidney Disease (CKD), kidneys are damaged and lose their ability to filter blood, leading to a plethora of health consequences that end up in dialysis. Despite its prevalence, CKD goes often undetected at early stages. In order to better understand disease progression, we stratified patients with CKD by considering the time to dialysis from diagnosis of early CKD (stages 1 or 2). To achieve this, we first reduced the number of clinical features in a predictive time-to-dialysis model and identified the top important features on a cohort of ∼ 40, 000 CKD patients. The extracted features were used to stratify a subpopulation of 3 , 522 patients that showed anemia and were prescribed for cardiovascular-related drugs and progressed faster to dialysis. On the other side, clustering patients using conventional clustering methods based on their clinical features did not allow such clear interpretation to identify the main factors for leading fast progression to dialysis. To our knowledge this is the first study extracting interpretable features for stratifying a cohort of early CKD patients using time-to-event analysis which could help prevention and the development of new treatments., (©2023 AMIA - All rights reserved.)

Objectives: Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach., Design: Retrospective single-centre cohort study., Settings: Tertiary referral university hospital in Toyoake city, Japan., Participants: A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m 2 and eGFR decline of ≥30% within 2 years., Primary Outcome: Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters., Results: Our clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics., Conclusions: The random forest model could be useful in identifying patients with extremely rapid eGFR decline., Trial Registration: UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry., Competing Interests: Competing interests: Yes, there are competing interests for one or more authors and I have provided a Competing Interests statement in my manuscript and in the box below., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)