Developing and Validating Models to Predict Progression to Proliferative Diabetic Retinopathy.
Developing and Validating Models to Predict Progression to Proliferative Diabetic Retinopathy.
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DOI：
10.1016/j.xops.2023.100276
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发表时间：
2023-06
影响因子：
--
通讯作者：
Sun, Catherine Q.
中科院分区：
文献类型：
--
作者：
Guo, Yian;Yonamine, Sean;Ma, Chu Jian;Stewart, Jay M.;Acharya, Nisha;Arnold, Benjamin F.;McCulloch, Charles;Sun, Catherine Q.
关键词：
开发非增殖性糖尿病视网膜病变（NPDR）进展为增殖性糖尿病视网膜病变（PDR）的模型，并确定纳入更新信息是否改善模型性能。回顾性队列研究。来自三级学术中心加州大学旧金山分校（UCSF）和安全网医院扎克伯格旧金山弗朗西斯科总医院（Zuckerberg San弗朗西斯科General Hospital）的电子健康记录（EHR）数据用于识别诊断为NPDR、年龄≥ 18岁、诊断为1型或2型糖尿病、眼科随访≥ 6个月、并且在索引日期（EHR中首次NPDR诊断的日期）之前没有PDR的先前诊断。开发了四种生存模型：考克斯比例风险，考克斯与向后选择，考克斯与LASSO回归和随机生存森林。对于每个模型，比较了三个变量集，以确定纳入更新的临床信息的影响：静态0（截至索引日期的数据）、静态6 m（索引日期后6个月更新的数据）和动态（静态0中的数据加上6个月期间的数据变化）。UCSF数据分为80%的训练和20%的测试（内部验证）。将CRFG数据用于外部验证。模型性能通过Harrell一致性指数（C-Index）进行评价。时间到PDR。UCSF队列包括1130例患者，92例（8.1%）患者进展为PDR。CRFG队列包括687例患者，30例（4.4%）患者进展为PDR。所有模型在内部验证中表现相似（C指数<0.70）。Static 6 m集的随机生存森林在外部验证中表现最好（C指数0.76）。保险和年龄被所有模型选择或列为高度重要。其他关键的预测因子是NPDR严重程度、糖尿病神经病变、中风次数、平均血红蛋白A1 c和住院次数。我们的NPDR进展到PDR的模型实现了可接受的预测性能，并在外部环境中得到了很好的验证。用新的临床信息更新基线变量并不能持续改善预测性能。在参考文献之后可以找到专有或商业披露。
To develop models for progression of nonproliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR) and determine if incorporating updated information improves model performance. Retrospective cohort study. Electronic health record (EHR) data from a tertiary academic center, University of California San Francisco (UCSF), and a safety-net hospital, Zuckerberg San Francisco General (ZSFG) Hospital were used to identify patients with a diagnosis of NPDR, age ≥ 18 years, a diagnosis of type 1 or 2 diabetes mellitus, ≥ 6 months of ophthalmology follow-up, and no prior diagnosis of PDR before the index date (date of first NPDR diagnosis in the EHR). Four survival models were developed: Cox proportional hazards, Cox with backward selection, Cox with LASSO regression and Random Survival Forest. For each model, three variable sets were compared to determine the impact of including updated clinical information: Static0 (data up to the index date), Static6m (data updated 6 months after the index date), and Dynamic (data in Static0 plus data change during the 6-month period). The UCSF data were split into 80% training and 20% testing (internal validation). The ZSFG data were used for external validation. Model performance was evaluated by the Harrell’s concordance index (C-Index). Time to PDR. The UCSF cohort included 1130 patients and 92 (8.1%) patients progressed to PDR. The ZSFG cohort included 687 patients and 30 (4.4%) patients progressed to PDR. All models performed similarly (C-indices ∼ 0.70) in internal validation. The random survival forest with Static6m set performed best in external validation (C-index 0.76). Insurance and age were selected or ranked as highly important by all models. Other key predictors were NPDR severity, diabetic neuropathy, number of strokes, mean Hemoglobin A1c, and number of hospital admissions. Our models for progression of NPDR to PDR achieved acceptable predictive performance and validated well in an external setting. Updating the baseline variables with new clinical information did not consistently improve the predictive performance. Proprietary or commercial disclosure may be found after the references.
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DOI：
10.1097/iae.0000000000002481
发表时间：
2020-05-01
影响因子：
3.3
作者：
Hinkle, John W.;Flynn, Harry W.;Vanner, Elizabeth A.
通讯作者：
Vanner, Elizabeth A.
影响因子：
13.7
作者：
Kazemian P;Lavieri MS;Van Oyen MP;Andrews C;Stein JD
通讯作者：
Stein JD
影响因子：
3.4
作者：
Hu K;Jiang M;Zhou Q;Zeng W;Lan X;Gao Q;Mei F;Zhao L;Chen F;Wu A;Tao G;Mou C;Ma B
通讯作者：
Ma B
影响因子：
168.9
作者：
Turner, RC;Holman, RR;Ward, JD
通讯作者：
Ward, JD
影响因子：
2.2
作者：
Breheny P;Huang J
通讯作者：
Huang J