Machine Learning Based Algorithm as an Innovative Approach for the Differentiation Between Diabetes Insipidus and Primary Polydipsia in Clinical Practice.

Objective Differentiation between central diabetes insipidus (cDI) and primary polydipsia (PP) remains challenging in clinical practice. Although the hypertonic saline infusion test led to a high diagnostic accuracy, it is a laborious test requiring close monitoring of plasma sodium levels. As such we leverage Machine Learning (ML) to facilitate differential diagnosis of cDI. Design We analyzed data of 59 patients with cDI and 81 patients with PP from a prospective multi-center study evaluating the hypertonic saline test as new test approach to diagnose cDI. Our primary outcome was the diagnostic accuracy of the ML based algorithm in differentiating cDI from PP patients. Methods The analysis dataset included 56 clinical, biochemical, and radiological covariates. We identified a set of 5 covariates which were crucial for differentiating cDI from PP patients utilizing standard ML methods. We developed ML based algorithms on the data and validated them with an unseen test-dataset. Results Urine osmolality, plasma sodium and glucose, known transphenoidal surgery or anterior pituitary deficiencies were selected as input parameters for the basic ML based algorithm. Testing it on an unseen test-data set resulted in a high AUC score of 0.87. A further improvement of the ML based algorithm was reached with the addition of MRI characteristics and the results of the hypertonic saline infusion test (AUC 0.93 and 0.98 respectively). Conclusion The developed ML based algorithm facilitated differentiation between cDI and PP patients with a high accuracy even if only clinical information and laboratory data were available, thereby possibly avoiding cumbersome clinical tests in the future.