Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects

ABSTRACT Purpose: To use machine learning to predict the risk of intraocular pressure peaks at 6 a.m. in primary open-angle glaucoma patients and suspects. Methods: This cross-sectional observational study included 98 eyes of 98 patients who underwent a 24-hour intraocular pressure curve (including the intraocular pressure measurements at 6 a.m.). The diurnal intraocular pressure curve was defined as a series of three measurements at 8 a.m., 9 a.m., and 11 a.m. from the 24-hour intraocular pressure curve. Two new variables were introduced: slope and concavity. The slope of the curve was calculated as the difference between intraocular pressure measurements at 9 a.m. and 8 a.m. and reflected the intraocular pressure change in the first hour. The concavity of the curve was calculated as the difference between the slopes at 9 a.m. and 8 a.m. and indicated if the curve was bent upward or downward. A classification tree was used to determine a multivariate algorithm from the measurements of the diurnal intraocular pressure curve to predict the risk of elevated intraocular pressure at 6 a.m. Results: Forty-nine (50%) eyes had intraocular pressure measurements at 6 a.m. >21 mmHg, and the median intraocular pressure peak in these eyes at 6 a.m. was 26 mmHg. The best predictors of intraocular pressure measurements >21 mmHg at 6 a.m. were the intraocular pressure measurements at 8 a.m. and concavity. The proposed model achieved a sensitivity of 100% and a specificity of 86%, resulting in an accuracy of 93%. Conclusions: The machine learning approach was able to predict the risk of intraocular pressure peaks at 6 a.m. with good accuracy. This new approach to the diurnal intraocular pressure curve may become a widely used tool in daily practice and the indication of a 24-hour intraocular pressure curve could be rationalized according to risk stratification.

【摘要】目的：本研究旨在利用机器学习方法，预测原发性开角型青光眼患者及疑似患者于晨间6时出现眼压（intraocular pressure）峰值的风险。方法：本项横断面观察性研究共纳入98例患者的98只患眼，所有受试者均完成24小时眼压曲线检测（包含晨间6时的眼压测量值）。研究从24小时眼压曲线中选取8时、9时、11时三次测量值，以此构建日间眼压曲线。引入两项全新变量：斜率与凹度。其中，曲线斜率通过9时与8时的眼压测量值差值计算得出，用于反映8时至9时这一小时内的眼压变化情况；曲线凹度通过9时与8时的斜率差值计算得出，用于表征曲线的弯曲方向（向上或向下）。本研究采用分类树算法，基于日间眼压曲线的测量数据构建多变量预测模型，以预测晨间6时眼压升高的风险。结果：共49只（占比50%）患眼的晨间6时眼压测量值＞21mmHg，该类患眼晨间6时的眼压峰值中位数为26mmHg。预测晨间6时眼压测量值＞21mmHg的最优指标为8时眼压测量值与曲线凹度。所构建的模型灵敏度达100%，特异度为86%，整体准确率为93%。结论：本研究所采用的机器学习方法可较为精准地预测晨间6时眼压峰值的发生风险。这种针对日间眼压曲线的全新分析方法，有望在临床日常实践中得到广泛应用；而24小时眼压曲线的适应证也可根据风险分层进行合理化调整。