Predicting Depression Among Chinese Patients with Narcolepsy Type 1: A Machine-Learning Approach

IF 3 2区医学 Q2 CLINICAL NEUROLOGY

Nature and Science of Sleep Pub Date : 2024-09-19 DOI:10.2147/nss.s468748

Mengmeng Wang, Huanhuan Wang, Zhaoyan Feng, Shuai Wu, Bei Li, Fang Han, Fulong Xiao

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引用次数: 0

Abstract

Objective: Depression is a common psychiatric issue among patients with narcolepsy type 1 (NT1). Effective management requires accurate screening and prediction of depression in NT1 patients. This study aims to identify relevant factors for predicting depression in Chinese NT1 patients using machine learning (ML) approaches.
Methods: A total of 203 drug-free NT1 patients (aged 5– 61), diagnosed based on the ICSD-3 criteria, were consecutively recruited from the Sleep Medicine Center at Peking University People’s Hospital between September 2019 and April 2023. Depression, daytime sleepiness, and impulsivity were assessed using the Center for Epidemiologic Studies Depression Scale for Children (CES-DC) or the Self-Rating Depression Scale (SDS), the Epworth Sleepiness Scale for adult or children and adolescents (ESS or ESS-CHAD), and the Barratt Impulse Scale (BIS-11). Demographic characteristics and objective sleep parameters were also analyzed. Three ML models-Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)-were used to predict depression. Model performance was evaluated using receiver operating curve (AUC), accuracy, precision, recall, F1 score, and decision curve analysis (DCA).
Results: The LR model identified hallucinations (OR 2.21, 95% CI 1.01– 4.90, p = 0.048) and motor impulsivity (OR 1.10, 95% CI 1.02– 1.18, p = 0.015) as predictors of depression. Among the ML models, SVM showed the best performance with an AUC of 0.653, accuracy of 0.659, sensitivity of 0.727, and F1 score of 0.696, reflecting its effectiveness in integrating sleep-related and psychosocial factors.
Conclusion: This study highlights the potential of ML models for predicting depression in NT1 patients. The SVM model shows promise in identifying patients at high risk of depression, offering a foundation for developing a data-driven, personalized decision-making tool. Further research should validate these findings in diverse populations and include additional psychological variables to enhance model accuracy.

Keywords: narcolepsy type 1, depression, machine learning, support vector machine

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预测中国 1 型嗜睡症患者的抑郁状况：一种机器学习方法

目的：抑郁症是 1 型嗜睡症（NT1）患者中常见的精神问题。有效的治疗需要对 NT1 患者进行准确的抑郁筛查和预测。本研究旨在利用机器学习（ML）方法确定预测中国 NT1 患者抑郁的相关因素：在2019年9月至2023年4月期间，从北京大学人民医院睡眠医学中心连续招募了203名根据ICSD-3标准确诊的无药NT1患者（5-61岁）。采用流行病学研究中心儿童抑郁量表（CES-DC）或抑郁自评量表（SDS）、成人或儿童青少年埃普沃思嗜睡量表（ESS或ESS-CHAD）和巴拉特冲动量表（BIS-11）评估抑郁、白天嗜睡和冲动。此外，还分析了人口统计学特征和客观睡眠参数。三种 ML 模型--逻辑回归（LR）、随机森林（RF）和支持向量机（SVM）--用于预测抑郁症。使用接收器工作曲线（AUC）、准确度、精确度、召回率、F1得分和决策曲线分析（DCA）对模型性能进行了评估：LR模型发现幻觉（OR 2.21，95% CI 1.01-4.90，p = 0.048）和运动冲动（OR 1.10，95% CI 1.02-1.18，p = 0.015）是预测抑郁的因素。在ML模型中，SVM表现最佳，其AUC为0.653，准确度为0.659，灵敏度为0.727，F1得分为0.696，反映了其在整合睡眠相关因素和心理社会因素方面的有效性：本研究强调了 ML 模型在预测 NT1 患者抑郁方面的潜力。SVM 模型在识别抑郁症高风险患者方面显示出了前景，为开发数据驱动的个性化决策工具奠定了基础。进一步的研究应在不同人群中验证这些发现，并纳入更多心理变量以提高模型的准确性。关键词：1型嗜睡症；抑郁症；机器学习；支持向量机

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来源期刊

Nature and Science of Sleep Neuroscience-Behavioral Neuroscience

CiteScore

5.70

自引率

5.90%

发文量

245

审稿时长

16 weeks

期刊介绍： Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine, including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders and therapy, and strategies to optimize healthy sleep. Specific topics covered in the journal include: The functions of sleep in humans and other animals Physiological and neurophysiological changes with sleep The genetics of sleep and sleep differences The neurotransmitters, receptors and pathways involved in controlling both sleep and wakefulness Behavioral and pharmacological interventions aimed at improving sleep, and improving wakefulness Sleep changes with development and with age Sleep and reproduction (e.g., changes across the menstrual cycle, with pregnancy and menopause) The science and nature of dreams Sleep disorders Impact of sleep and sleep disorders on health, daytime function and quality of life Sleep problems secondary to clinical disorders Interaction of society with sleep (e.g., consequences of shift work, occupational health, public health) The microbiome and sleep Chronotherapy Impact of circadian rhythms on sleep, physiology, cognition and health Mechanisms controlling circadian rhythms, centrally and peripherally Impact of circadian rhythm disruptions (including night shift work, jet lag and social jet lag) on sleep, physiology, cognition and health Behavioral and pharmacological interventions aimed at reducing adverse effects of circadian-related sleep disruption Assessment of technologies and biomarkers for measuring sleep and/or circadian rhythms Epigenetic markers of sleep or circadian disruption.