Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models

Hyponatremia, characterized by a serum sodium concentration below 135 mEq/L, is a prevalent electrolyte imbalance associated with increased morbidity and mortality across various clinical conditions. This study employs the Holt-Winters seasonal method, a robust time series forecasting model, to predict mortality rates attributed to hyponatremia. Leveraging retrospective mortality data from a cohort of hospitals in the United States, our analysis aims to elucidate temporal patterns and trends in hyponatremia-related deaths. The findings underscore the critical role of statistical forecasting in healthcare, facilitating proactive resource allocation and targeted interventions to mitigate mortality risks associated with electrolyte imbalances. Integrating predictive analytics into clinical practice holds promise for enhancing patient care and optimizing health outcomes in populations vulnerable to hyponatremia-related complications.

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Publication Details

Journal
The International Journal of Biostatistics
Published
2025-09-02
DOI
https://doi.org/10.1515/ijb-2024-0075
Primary Topic
Electrolyte and hormonal disorders
Type
article
Field-Weighted Citation Impact
0.00
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article

Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models

Sadik Khuder, Mohamed Abd Allah El‐Hadidy, Qin Shao, Rawiyah Muneer Alraddadi et al.
The International Journal of Biostatistics
Electrolyte and hormonal disorders
article

Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models

Sadik Khuder, Mohamed Abd Allah El‐Hadidy, Qin Shao, Rawiyah Muneer Alraddadi, Qu Xianggui
article en

Abstract

Hyponatremia, characterized by a serum sodium concentration below 135 mEq/L, is a prevalent electrolyte imbalance associated with increased morbidity and mortality across various clinical conditions. This study employs the Holt-Winters seasonal method, a robust time series forecasting model, to predict mortality rates attributed to hyponatremia. Leveraging retrospective mortality data from a cohort of hospitals in the United States, our analysis aims to elucidate temporal patterns and trends in hyponatremia-related deaths. The findings underscore the critical role of statistical forecasting in healthcare, facilitating proactive resource allocation and targeted interventions to mitigate mortality risks associated with electrolyte imbalances. Integrating predictive analytics into clinical practice holds promise for enhancing patient care and optimizing health outcomes in populations vulnerable to hyponatremia-related complications.

The International Journal of BiostatisticsVol. 21(2)
University of Toledo Medical Center (US), Oakland University (US), Tanta University (EG), Taibah University (SA), University of Toledo (US)
Openalex Percentile: Top 66%
Electrolyte and hormonal disorders
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