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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., Competing Interests: DI received lecture fees from Ono Pharmaceutical Co., Ltd. and Kyowa Hakko Kirin Co. YY received research support grants from Otsuka Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., and Chugai Pharmaceutical Co., Ltd. IBM Research provided support for this study in the form of salaries for AK, TI and MK. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Erythropoiesis-stimulating agents (ESAs) and iron supplements may be prescribed appropriately under nephrology care. However, there are few reports detailing the differences in prescription rates of these therapies among clinical departments. Methods: A total of 39,585 patients with renal impairment were enrolled from a database of 914,280 patients. Patients were selected based on an estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 m2. There were eight clinical departments from internal medicine, including nephrology. We defined a hemoglobin level less than 11.0 g/dL as anemia and set 20% of transferrin saturation and 100 ng/mL of serum ferritin as cutoff points. We compared the prescription rates of ESAs and iron supplementation based on the hemoglobin level and iron status among the patients seen across the eight clinical departments. Results: The lower the eGFR, the more the number of patients seen under nephrology care. The rates of patients with no prescription were 52.3, 39.9, 45.9, and 54.3% among those with hemoglobin levels of < 8, 8 ≤ < 9, 9 ≤ < 10, and 10 ≤ < 11 g/dL, respectively. Of the patients with less than 11.0 g/dL of hemoglobin, 77.3% were prescribed ESAs under nephrology care. Meanwhile, only 18.5 and 8.2% of patients were prescribed ESAs in clinical departments of internal medicine, other than nephrology, and non-internal medicine care, respectively. Conclusion: Treatment for anemia has not been sufficiently performed in patients with renal impairment under non-nephrology care in a real-world clinical setting. [ABSTRACT FROM AUTHOR]