Glaucoma Services, Royal Surrey County Hospital NHS Foundation Trust, Guildford, UK.
Department of Optometry and Visual Sciences, School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, England.
Eye (Lond). 2024 Sep;38(13):2568-2574. doi: 10.1038/s41433-024-03081-6. Epub 2024 May 27.
Cataract waiting lists are growing globally. Pragmatic, cost-effective methods are required to prioritise the most urgent cases. Here we investigate the feasibility of using a third-party pen-and-paper contrast sensitivity, CS, test (SpotChecks), delivered by mail, and performed by patients at home unsupervised, to flag eyes requiring surgery.
Pen-and-paper CS tests were mailed to 233 people waiting for a cataract assessment, along with a prepaid return envelope (cross-sectional study). Response rates were tabulated (stratified by age, sex and socioeconomic status), and test scores analysed to see how well the home tests predicted which eyes were listed subsequently for surgery. A subset of patients (N = 39) also underwent in-person follow-up testing, to confirm the accuracy of the home data.
Forty-six percent of patients responded (216 eyes). No gross differences were observed between respondents and non-respondents, either in terms of age, sex, socioeconomic status, or geographic location (all P > 0.05). The home-test CS scores predicted which eyes were subsequently listed for surgery, with an AUROC {±CI} of 0.69 {0.61-0.76}. Predictive performance was further-improved when machine learning was used to combine CS scores with letter acuity, extracted from patients' medical records (AUROC {±CI} = 0.77 {0.70-0.83}). Among 39 patients who underwent follow-up testing, home CS scores were correlated with various measures made in clinic: biometry signal-to-noise (P = 0.032), LogMAR acuity, Pelli-Robson CS and SpotChecks CS (all P < 0.001).
Mailing patients pen-and-paper CS tests may be a feasible, 'low-tech' way of prioritising patients on cataract waiting lists.
白内障等候名单在全球范围内不断增加。需要采用实用且具有成本效益的方法对最紧急的病例进行优先排序。在此，我们研究了使用第三方纸笔对比敏感度（CS）测试（SpotChecks）的可行性，该测试通过邮件发送给等候白内障评估的 233 人，并由患者在家中无人监督的情况下进行，以标记需要手术的眼睛。
向 233 名等待白内障评估的患者邮寄纸笔 CS 测试，并随附预付回邮信封（横断面研究）。记录回复率（按年龄、性别和社会经济状况分层），并分析测试分数，以了解家庭测试对随后列出手术的眼睛的预测效果如何。患者的一个子集（N=39）也接受了面对面的随访测试，以确认家庭数据的准确性。
46%的患者（216 只眼）做出了回应。无论是在年龄、性别、社会经济地位还是地理位置方面，受访者与未受访者之间均未观察到明显差异（均 P>0.05）。家庭测试 CS 分数可以预测哪些眼睛随后被列入手术名单，AUROC（±CI）为 0.69（0.61-0.76）。当使用机器学习将 CS 分数与从患者病历中提取的字母视力相结合时，预测性能得到进一步提高（AUROC（±CI）=0.77（0.70-0.83））。在接受随访测试的 39 名患者中，家庭 CS 分数与诊所中的各种测量值相关：生物测量信号噪声（P=0.032）、LogMAR 视力、Pelli-Robson CS 和 SpotChecks CS（均 P<0.001）。
向患者邮寄纸笔 CS 测试可能是一种可行的、“低技术”的白内障等候名单优先排序方法。
