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

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-09-02 DOI:10.1515/ijb-2024-0075

Rawiyah Muneer Alraddadi, Mohamed Abd Allah El-Hadidy, Qin Shao, Qu Xianggui, Sadik Khuder

引用次数: 0

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.

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预测低钠血症死亡率：使用霍尔特-温特斯模型的统计方法

低钠血症的特征是血清钠浓度低于135 mEq/L，是一种普遍的电解质失衡，与各种临床条件下发病率和死亡率增加有关。本研究采用霍尔特-温特斯季节性方法，一种稳健的时间序列预测模型，来预测低钠血症的死亡率。利用来自美国医院队列的回顾性死亡率数据，我们的分析旨在阐明低钠血症相关死亡的时间模式和趋势。研究结果强调了统计预测在医疗保健中的关键作用，促进了积极的资源分配和有针对性的干预，以减轻与电解质失衡相关的死亡风险。将预测分析整合到临床实践中，有望加强患者护理，并优化易受低钠血症相关并发症影响的人群的健康结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.