Citation:Hu L,Li DH,Wang SY.A algorithm for prediction of exudative retinal detachment risk of patients with pregnancy-induced hypertension.Int J Ophthalmol 2022;15(8):1310-1315,doi:10.18240/ijo.2022.08.13
A algorithm for prediction of exudative retinal detachment risk of patients with pregnancy-induced hypertension
Received:May 21, 2021  Revised:March 28, 2022
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DOI:10.18240/ijo.2022.08.13
Key Words:pregnancy-induced hypertension  exudative retinal detachment  plasma total protein  blood pressure
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Li Hu The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou , Guangdong Province, China
Dong-Hao Li The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou , Guangdong Province, China
Shuang-Yong Wang The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou , Guangdong Province, China
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Abstract:
      AIM: To investigate the risk of exudative retinal detachment (ERD) morbidity in patients with pregnancy-induced hypertension (PIH) by using the logistic regression combined with the receiver operating characteristic (ROC) curve.

    METHODS: A total of 46 patients with ERD and 142 patients with non-ERD were diagnosed as PIH from January 2017 to February 2020. A retrospective comparison of the clinical manifestations and laboratory tests were conducted. The risk of ERD morbidity with PIH was predicted by using logistic regression combined with an ROC curve model.

    RESULTS: There was no significant difference in age and body mass index between the two groups before pregnancy (P>0.05). However, significant differences were found in gestational weeks, duration of hypertension, maximum and minimum systolic and diastolic blood pressure (BP), and plasma total protein (PTP) concentration between the two groups (P<0.05). Binary logistic regression analysis showed that the maximum systolic BP (OR=1.050, 95%CI: 1.016-1.085) and PTP concentration (OR=0.764, 95%CI: 0.702-0.832) were independent prediction risks of ERD in PIH. The sensitivities of maximum systolic BP, PTP concentration and combined diagnosis were 0.717, 0.870, and 0.870, respectively; the specificities were 0.617, 0.837, and 0.908, respectively; the area under the curve (AUC) was 0.707 (95%CI: 0.622-0.792), 0.917 (95%CI: 0.868-0.967), and 0.933 (95%CI: 0.890-0.975), respectively; the AUC of combined diagnosis was higher than that of single diagnosis (P<0.01).

    CONCLUSION: Logistic regression and ROC curve model combined with maximum systolic BP and PTP can improve the early identification of high-risk PIH patients in the hospital.

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