Machine learning-based prediction of impaired arginine vasopressin secretion during hypertonic saline test
Abstract
Hypertonic saline infusion and water deprivation tests are commonly used to stimulate arginine vasopressin (AVP) for the diagnosis of AVP deficiency (AVP-D); however, these procedures impose a considerable burden on patients. We aimed to develop machine learning models to predict impaired AVP secretion and thereby reduce the need for AVP stimulation testing. This retrospective cohort study included 64 patients who underwent the hypertonic saline test (HST) at Nagoya University Hospital, Japan, between 2018 and 2024. Impaired AVP secretion was defined as a predicted plasma AVP level <1.0 pg/mL at a serum sodium level of 149 mEq/L during the HST. Feature selection was performed using univariate screening and systematic selection procedures. Logistic regression and support vector machine models were developed using baseline clinical and laboratory parameters obtained before the HST and validated using nested cross-validation. The primary outcome was the area under the receiver operating characteristic curve (AUC); secondary outcomes included the sensitivity, specificity, and positive and negative predictive values. Four variables (urinary osmolality, serum sodium, plasma AVP, and blood urea nitrogen) were selected. The logistic regression model achieved an AUC of 0.862 for predicting impaired AVP secretion. Among 35 patients who underwent the HST for suspected AVP-D, at the optimized threshold, the positive and negative predictive values were 100% (14/14 patients) and 33.3% (7/21 patients), respectively. The developed machine learning model can identify a subset of patients with impaired AVP secretion without requiring the HST, potentially reducing the number of patients who need AVP stimulation testing for AVP-D diagnosis.
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BibTeX
@article{naito2026machine,
title = {{Machine learning-based prediction of impaired arginine vasopressin secretion during hypertonic saline test}},
author = {Satoshi Naito and Daisuke Hagiwara and Masaki Kitamura and Ryosei Ashida and Yohei Kawaguchi and Takashi Miyata and Tomoko Handa and Tomoko Kobayashi and Mariko Sugiyama and Takeshi Onoue and Shintaro Iwama and Hidetaka Suga and Ryoichi Banno and Hiroshi Arima},
journal = {Endocrine Journal},
year = {2026},
month = aug,
doi = {10.1507/endocrj.ej26-0094},
url = {https://www.jstage.jst.go.jp/article/endocrj/73/8/73_EJ26-0094/_article},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Machine learning-based prediction of impaired arginine vasopressin secretion during hypertonic saline test
AU - Satoshi Naito
AU - Daisuke Hagiwara
AU - Masaki Kitamura
AU - Ryosei Ashida
AU - Yohei Kawaguchi
AU - Takashi Miyata
AU - Tomoko Handa
AU - Tomoko Kobayashi
AU - Mariko Sugiyama
AU - Takeshi Onoue
AU - Shintaro Iwama
AU - Hidetaka Suga
AU - Ryoichi Banno
AU - Hiroshi Arima
PY - 2026
DA - 2026/08/03
JO - Endocrine Journal
DO - 10.1507/endocrj.ej26-0094
UR - https://www.jstage.jst.go.jp/article/endocrj/73/8/73_EJ26-0094/_article
AB - Hypertonic saline infusion and water deprivation tests are commonly used to stimulate arginine vasopressin (AVP) for the diagnosis of AVP deficiency (AVP-D); however, these procedures impose a considerable burden on patients. We aimed to develop machine learning models to predict impaired AVP secretion and thereby reduce the need for AVP stimulation testing. This retrospective cohort study included 64 patients who underwent the hypertonic saline test (HST) at Nagoya University Hospital, Japan, between 2018 and 2024. Impaired AVP secretion was defined as a predicted plasma AVP level <1.0 pg/mL at a serum sodium level of 149 mEq/L during the HST. Feature selection was performed using univariate screening and systematic selection procedures. Logistic regression and support vector machine models were developed using baseline clinical and laboratory parameters obtained before the HST and validated using nested cross-validation. The primary outcome was the area under the receiver operating characteristic curve (AUC); secondary outcomes included the sensitivity, specificity, and positive and negative predictive values. Four variables (urinary osmolality, serum sodium, plasma AVP, and blood urea nitrogen) were selected. The logistic regression model achieved an AUC of 0.862 for predicting impaired AVP secretion. Among 35 patients who underwent the HST for suspected AVP-D, at the optimized threshold, the positive and negative predictive values were 100% (14/14 patients) and 33.3% (7/21 patients), respectively. The developed machine learning model can identify a subset of patients with impaired AVP secretion without requiring the HST, potentially reducing the number of patients who need AVP stimulation testing for AVP-D diagnosis.
SN - 1348-4540
SN - 0918-8959
ER -