Modelo predictivo y análisis discriminante del desarrollo de demencia en pacientes con delirium en urgencias

Pub Date : 2024-06-26 DOI:10.1016/j.semerg.2024.102283
P. Lupiáñez Seoane , J.E. Muñoz Negro , U. Torres Parejo , F.J. Gómez Jiménez
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Abstract

Objective

The main aim of our study is to know the sociodemographic, clinical, analytical, and functional variables that predict the probability of developing dementia in patients with delirium who attend the emergency room.

Method

All patients with delirium (n = 45) from the emergency room who were admitted to the Geriatrics service of the General University Hospital of Ciudad Real (HGUCR) in 2016-2018 and met the inclusion and exclusion criteria were included. Subsequently, we ran a bivariate and multivariate analysis of the variables that predicted a diagnosis of dementia at six months and a discriminant analysis.

Results

15.6% of patients presented dementia at six months of follow-up, 22.2% had developed cognitive impairment. We conducted a multivariate model (R2 Nagelkerke 0.459) for the probability of developing dementia, with elevated heart rate being the most crucial variable (OR = 11.5). The model could excluded dementia with 100% accuracy. Finally, we achieved a discriminant function capable of correctly classifying 95.6% of the cases. It included the following variables of influence: pH, Lawton Brody index, calcium, urea, and heart rate.

Conclusions

A few clinical and analytical variables that are easily detectable in the emergency room, especially tachycardia, could help us better identify those patients with delirium at higher risk of developing dementia, as well as formulate hypotheses about the variables involved in the development of dementia in patients with delirium.

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[急诊科谵妄患者痴呆发展的预测模型和判别分析]。
研究目的我们研究的主要目的是了解可预测急诊室谵妄患者患痴呆症概率的社会人口学、临床、分析和功能变量:纳入2016-2018年期间在雷阿尔城综合大学医院(HGUCR)老年病科住院的所有急诊室谵妄患者(n=45),这些患者均符合纳入和排除标准。随后,我们对预测六个月后痴呆诊断的变量进行了双变量和多变量分析,并进行了判别分析:结果:15.6%的患者在随访六个月时出现痴呆,22.2%的患者出现认知障碍。我们针对痴呆症的发病概率建立了一个多变量模型(R2 Nagelkerke 0.459),其中心率升高是最关键的变量(OR=11.5)。该模型排除痴呆症的准确率为 100%。最后,我们得到了一个判别函数,能够对 95.6% 的病例进行正确分类。它包括以下影响变量:pH 值、劳顿-布罗迪指数、钙、尿素和心率:结论:在急诊室中很容易检测到的一些临床和分析变量,尤其是心动过速,可以帮助我们更好地识别谵妄患者中罹患痴呆症风险较高的人群,并对谵妄患者痴呆症发生的相关变量提出假设。
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