背景：识别高危患者并将其从初级保健医生（PCP）转诊给眼保健专业人员仍然是一个挑战。大约190万美国人由于未诊断或未治疗的眼科疾病而患有视力丧失。在眼科，人工智能(AI)用于预测青光眼进展，识别糖尿病视网膜病变（DR），并对眼部肿瘤进行分类；然而，AI尚未用于分类眼科转诊的初级保健患者。
目的：本研究旨在构建和比较机器学习（ML）方法，适用于PCP的电子健康记录(EHR)，能够将患者转诊给眼部护理专家。
方法：访问Optum取消识别的EHR数据集，743,039例患者有5种主要视力状况（年龄相关性黄斑变性[AMD]，视觉上显著的白内障，DR,青光眼,或眼表疾病[OSD]）在年龄和性别上与无眼部疾病的743,039名对照完全匹配。每个患者的非眼科参数在142和182之间输入到5ML方法中：广义线性模型，L1正则化逻辑回归，随机森林,极端梯度提升（XGBoost），和J48决策树。比较每种病理的模型性能以选择最具预测性的算法。对每个结果的所有算法评估曲线下面积(AUC)。
结果：XGBoost表现出最佳性能，显示,分别,对于视觉上有意义的白内障，预测准确性和AUC为78.6%（95%CI78.3%-78.9%）和0.878，77.4%（95%CI76.7%-78.1%）和0.858为渗出性AMD，非渗出性AMD为79.2%（95%CI78.8%-79.6%）和0.879，72.2%（95%CI69.9%-74.5%）和需要药物的OSD0.803，青光眼为70.8%（95%CI70.5%-71.1%）和0.785，85.0%(95%CI84.2%-85.8%),1型非增生性糖尿病视网膜病变(NPDR)为0.924,82.2%（95%CI80.4%-84.0%），1型增殖性糖尿病视网膜病变（PDR）为0.911，2型NPDR为81.3%（95%CI81.0%-81.6%）和0.891，2型PDR为82.1%（95%CI81.3%-82.9%）和0.900。
结论：部署的5ML方法能够成功识别比值比（ORs）升高的患者，因此能够对患者进行分诊，对于眼病，从青光眼的2.4（95％CI2.4-2.5）到1型NPDR的5.7（95％CI5.0-6.4），平均OR为3.9。这些模型的应用可以使PCP更好地识别和分诊有可治疗眼科病理风险的患者。早期识别患有未识别的视力威胁疾病的患者可能会导致更早的治疗和减轻的经济负担。更重要的是,这样的分诊可以改善患者的生活。
BACKGROUND: Identification and referral of at-risk patients from primary care practitioners (PCPs) to eye care professionals remain a challenge. Approximately 1.9 million Americans suffer from vision loss as a result of undiagnosed or untreated ophthalmic conditions. In ophthalmology, artificial intelligence (AI) is used to predict glaucoma progression, recognize diabetic retinopathy (DR), and classify ocular tumors; however, AI has not yet been used to triage primary care patients for ophthalmology referral.
OBJECTIVE: This study aimed to build and compare machine learning (ML) methods, applicable to electronic health records (EHRs) of PCPs, capable of triaging patients for referral to eye care specialists.
METHODS: Accessing the Optum deidentified EHR data set, 743,039 patients with 5 leading vision conditions (age-related macular degeneration [AMD], visually significant cataract, DR, glaucoma, or ocular surface disease [OSD]) were exact-matched on age and gender to 743,039 controls without eye conditions. Between 142 and 182 non-ophthalmic parameters per patient were input into 5 ML methods: generalized linear model, L1-regularized logistic regression, random forest, Extreme Gradient Boosting (XGBoost), and J48 decision tree. Model performance was compared for each pathology to select the most predictive algorithm. The area under the curve (AUC) was assessed for all algorithms for each outcome.
RESULTS: XGBoost demonstrated the best performance, showing, respectively, a prediction accuracy and an AUC of 78.6% (95% CI 78.3%-78.9%) and 0.878 for visually significant cataract, 77.4% (95% CI 76.7%-78.1%) and 0.858 for exudative AMD, 79.2% (95% CI 78.8%-79.6%) and 0.879 for nonexudative AMD, 72.2% (95% CI 69.9%-74.5%) and 0.803 for OSD requiring medication, 70.8% (95% CI 70.5%-71.1%) and 0.785 for glaucoma, 85.0% (95% CI 84.2%-85.8%) and 0.924 for type 1 nonproliferative diabetic retinopathy (NPDR), 82.2% (95% CI 80.4%-84.0%) and 0.911 for type 1 proliferative diabetic retinopathy (PDR), 81.3% (95% CI 81.0%-81.6%) and 0.891 for type 2 NPDR, and 82.1% (95% CI 81.3%-82.9%) and 0.900 for type 2 PDR.
CONCLUSIONS: The 5 ML methods deployed were able to successfully identify patients with elevated odds ratios (ORs), thus capable of patient triage, for ocular pathology ranging from 2.4 (95% CI 2.4-2.5) for glaucoma to 5.7 (95% CI 5.0-6.4) for type 1 NPDR, with an average OR of 3.9. The application of these models could enable PCPs to better identify and triage patients at risk for treatable ophthalmic pathology. Early identification of patients with unrecognized sight-threatening conditions may lead to earlier treatment and a reduced economic burden. More importantly, such triage may improve patients\' lives.
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