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无监督机器学习显示特发性颅内高压治疗试验中视野损失的变化。

Unsupervised Machine Learning Shows Change in Visual Field Loss in the Idiopathic Intracranial Hypertension Treatment Trial.

机构信息

Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York.

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York.

出版信息

Ophthalmology. 2022 Aug;129(8):903-911. doi: 10.1016/j.ophtha.2022.03.027. Epub 2022 Apr 1.

DOI:10.1016/j.ophtha.2022.03.027
PMID:35378137
Abstract

PURPOSE

We previously reported that archetypal analysis (AA), a type of unsupervised machine learning, identified and quantified patterns of visual field (VF) loss in idiopathic intracranial hypertension (IIH), referred to as archetypes (ATs). We assessed whether AT weight changes over time are consistent with changes in conventional global indices, whether visual outcome or treatment effects are associated with select AT, and whether AA reveals residual VF defects in eyes deemed normal after treatment.

DESIGN

Analysis of data collected from a randomized controlled trial.

PARTICIPANTS

Two thousand eight hundred sixty-two VFs obtained from 165 participants during the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT).

METHODS

We applied a 14-AT model derived from IIHTT VFs. We examined changes in individual AT weights over time for all study eyes and evaluated differences between treatment groups. We created an AT change score to assess overall VF change from baseline. We tested threshold baseline AT weights for association with VF outcome and treatment effect at 6 months. We determined the abnormal ATs with meaningful weight at outcome for VFs with a mean deviation (MD) of -2.00 dB or more.

MAIN OUTCOME MEASURES

Individual AT weighting coefficients and MD.

RESULTS

Archetype 1 (a normal VF pattern) showed the greatest weight change for all study eyes, increasing from 11.9% (interquartile range [IQR], 0.44%-24.1%) at baseline to 31.2% (IQR, 16.0%-45.5%) at outcome (P < 0.001). Archetype 1 weight change (r = 0.795; P < 0.001) and a global score of AT change (r = 0.988; P < 0.001) correlated strongly with MD change. Study eyes with baseline AT2 (a mild diffuse VF loss pattern) weight of 44% or more (≥ 1 standard deviation more than the mean) showed higher AT2 weights at outcome than those with AT2 weight of < 44% at baseline (P < 0.001). Only the latter group showed a significant acetazolamide treatment effect. Archetypal analysis revealed residual VF loss patterns, most frequently representing mild diffuse loss and an enlarged blind spot in 64 of 66 study eyes with MD of -2.00 dB or more at outcome.

CONCLUSIONS

Archetypal analysis provides a quantitative approach to monitoring VF changes in IIH. Baseline AT features may be associated with treatment response and VF outcome. Archetypal analysis uncovers residual VF defects not otherwise revealed by MD.

摘要

目的

我们之前报道过,一种无监督机器学习方法——原型分析(AA)可以识别并量化特发性颅内高压(IIH)患者视野(VF)缺失的模式,这些模式被称为原型(AT)。本研究旨在评估 AT 权重随时间的变化是否与传统的全局指数变化一致,VF 结局或治疗效果是否与特定 AT 有关,以及 AA 是否能揭示治疗后被认为正常的眼睛中残留的 VF 缺陷。

设计

对一项随机对照试验(IIHTT)中收集的数据进行分析。

参与者

165 名参与者的 2862 个 VF。

方法

我们应用了一种从 IIHTT VF 中得出的 14-AT 模型。我们对所有研究眼的个体 AT 权重随时间的变化进行了评估,并比较了治疗组之间的差异。我们创建了一个 AT 变化评分来评估从基线开始的整体 VF 变化。我们检测了阈值基线 AT 权重与 6 个月时的 VF 结局和治疗效果的相关性。我们确定了对于平均偏差(MD)为-2.00 dB 或更大的 VF,具有有意义权重的异常 AT,作为结局的预测因子。

主要观察指标

个体 AT 加权系数和 MD。

结果

所有研究眼的原型 1(正常 VF 模式)的 AT 权重变化最大,从基线时的 11.9%(四分位距 [IQR],0.44%-24.1%)增加到结局时的 31.2%(IQR,16.0%-45.5%)(P < 0.001)。原型 1 的 AT 权重变化(r=0.795;P<0.001)和 AT 变化的全局评分(r=0.988;P<0.001)与 MD 变化密切相关。基线时 AT2 权重为 44%或更高(比平均值高 1 个标准差以上)的研究眼(≥1 个标准差)在结局时的 AT2 权重高于基线时 AT2 权重<44%的研究眼(P<0.001)。只有后者组显示出显著的乙酰唑胺治疗效果。原型分析揭示了残留的 VF 损失模式,在 66 只 MD 为-2.00 dB 或更大的研究眼中,最常见的是代表轻度弥漫性损失和扩大的盲点,占 64 只。

结论

原型分析为监测 IIH 中的 VF 变化提供了一种定量方法。基线 AT 特征可能与治疗反应和 VF 结局有关。原型分析揭示了 MD 无法揭示的残留 VF 缺陷。

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