Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM

Study design and setting

This population-based cross-sectional study was done in the Eye and ENT Hospital of Fudan University, Shanghai, China, from June 2015 to June 2022. This study was approved by the Eye and ENT Hospital of Fudan University’s Ethics Committee (EENT2015011), which followed the Declaration of Helsinki. In addition, we obtained informed consent from all subjects.

A total of 20,870 unrelated subjects (HM = 19,284, HMRD = 1586) were enrolled in the study after quality control. We used a nested case-control design. First, HMRD cases and HM controls were matched based on age and sex. Then, the HMRD cases and HM controls were randomly assigned to the discovery cohort, validation cohort 1, and validation cohort 2 with a ratio of about 6:2:2. The data split was stratified randomly to ensure that the discovery cohort and the validation cohorts had a similar distribution of data. Other subjects were assigned to the HM validation cohort.

Inclusion and exclusion criteria

Emmetropia was defined as a mean spherical equivalent (SE) ranging from −0.25 to +0.25 diopters (D). High myopia was defined as a SE of ≤ −6.00 D.

Inclusion criteria of HM: (1) age ≧ 18 years; (2) SE of − 6.00 D or higher. Exclusion criteria of HM: (1) missing refraction data; (2) history of fundus oculi surgery and self-reported refractive surgery; (3) retinal detachment; (4) other types of fundus oculi diseases, such as macular degeneration, diabetic retinopathy, glaucoma and so on; (5) ocular trauma; (6) coagulation disorders; (7) hematologic diseases; (8) received drugs that can affect blood components; (9) systemic diseases, such as infectious diseases, metabolic syndrome, autoimmune disorders, and cancer.

Inclusion criteria of HMRD: (1) age ≧ 18 years; (2) SE of − 6.00 D or higher; (3) retinal detachment. Exclusion criteria of HMRD: (1) missing refraction data; (2) history of fundus oculi surgery and self-reported refractive surgery; (3) other types of fundus oculi diseases, such as macular degeneration, diabetic retinopathy, glaucoma and so on; (4) ocular trauma; (5) coagulation disorders; (6) hematologic diseases; (7) received drugs that can affect blood components; (8) systemic diseases, including acute infectious diseases, metabolic syndrome, autoimmune disease, and cancer.

All patients underwent a comprehensive ophthalmologic and medical examination. In this study, patients with a missing value (such as age and sex) were excluded.

Ophthalmic and medical examinations

All patients underwent a comprehensive ophthalmologic examination as described previously [23,24,25]. As described previously, all subjects were examined by their respective specialty physicians at Fudan University’s Eye and ENT Hospital [26,27,28]. The examinations included slit-lamp examination, uncorrected distance visual acuity, corrected distance visual acuity, autorefraction, manifest refraction, intraocular pressure (IOP), and funduscopic examinations. Digital retinal camera analysis of their fundi was performed (TRC-NW200, Topcon). The central corneal thickness, axial length, and anterior chamber depth were all measured using an A-scan ultrasonography (A-Scan Pachymeter, Ultrasonic, Exton, PA, USA).

Collection and analysis of blood sample

During the morning, blood samples were obtained by venipuncture from the antecubital fossae (anterior elbow veins). Blood was drawn from the participants after 8 h of fasting. Laboratory parameters were measured within 0.5 h after blood samples were collected in ethylenediaminetetraacetic acid tubes. Laboratory tests were performed at the Department of Clinical Laboratory of Eye and ENT Hospital of Fudan University [29].

Quantification of common blood indicators, such as platelet count (PLT), thrombocytocrit (PCT), eosinophil count (EOA), monocyte count (MONOA), white blood cell count (WBC), mean corpuscular hemoglobin concentration (MCHC), red blood cell (RBC), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), hematocrit (HCT), hemoglobin (HGB), neutrophil (NEUTA) (Kobe, Japan). Platelet count divided by lymphocyte count is known as the platelet-to-lymphocyte ratio (PLR). The ratio of neutrophils to lymphocytes (NLR) was established as neutrophil count divided by lymphocyte count. Lymphocyte count divided by monocyte count is known as the lymphocyte-to-monocyte ratio (LMR). The systemic immune-inflammation index (SII) was defined as the neutrophil count × platelet count/lymphocyte count. Internal controls were also analyzed daily for 10 years, and no significant changes were found in their coefficient of variations (CVs).

Selection of the feature indexes

Twenty-two routine blood indexes, including eighteen quantitative feature indexes and four transformed indexes, were included. The quantitative feature indexes included PLT, PCT, EOA, MONOA, WBC, MCHC, RBC, MCV, MCH, HCT, HGB, NEUTA, RDWCV, RDWSD, LYMPHA, BASOA, MPV, and PDW. The four transformed indexes included: PLR, NLR, LMR, and SII.

To select a subset of variables for model development, all candidate variables were ranked by the area under the curve (AUC) value. Only variables with high predictability (AUC > 0.6) were included in the final model to achieve better model performance and offset complexity. In addition, logistic analysis was performed to validate the association between selected feature indexes and HMRD. The significant change in the mean of selected feature indexes between the HM and HMRD group were also estimated.

Model development and selection

The algorithm of conditional probability (ACP) model, logistic regression model, and classification models are applied to predict the event. The accuracy and AUC values are set as the output of the model. The classification models include decision tree, random forest, C5.0, CHAID, and neural networks. All selected feature indexes were arranged and combined and input into the model. Logistic regression model and classification model analyses were performed using IBM SPSS Modeler 18.0.

All selected feature indexes were arranged, combined, and input into the ACP model. In this study, the Bayesian networks model was used. Use Bayes’ theorem to calculate the conditional probability of the event given the condition. This is done by multiplying the probability of the event given the condition by the probability of the condition and then dividing by the probability of the event. This is given by P (X| pa (X)), where P is the conditional probability, a represents each index, and pa (X) represents the parents of index X (mathematical formula: 1−(1-P)/(1−P+P*X)). A similar approach has been recently applied to clinical research [30, 31].

Sample size

To calculate the minimum total sample size, we used an open-source calculator which is based on the methods described by Obuchowski et al. [32] and Li, et al. [33]. The input parameters were specificity = 0.8 (allowable error = 0.05), sensitivity = 0.8 (allowable error = 0.05), and α = 0.025 (2-tailed). Based on this calculation, the minimum sample size required for the new model development was 247 per group. The total sample size in all our cohorts was at least four times higher than this minimum.

Statistical analysis

We performed descriptive statistical analyses for all variables, and normality was assessed using the Shapiro–Wilk test. The statistical difference between cases and controls was analyzed using multiple tests. For instance, an independent Student’s t-test was performed for normally distributed continuous variables, the Kruskal-Wallis test was done for non-normally distributed continuous variables, and the chi-squared test was used for categorical variables when necessary. In addition, one-way ANOVA analysis was used for normally distributed continuous variables among the three groups. Data of continuous variables were expressed as mean ± SD. Data of categorical variables were summarized as frequency and percentage.

Receiver operating characteristic (ROC) curve analysis was performed to calculate the AUC value, and the Youden index maximizing sensitivity plus specificity was applied to determine the best cutoff value. AUC stands for the area under the curve and is a measure used to compare the performance of different models/biomarkers. AUC is calculated by plotting the true positive rate against the false positive rate and measuring the AUC value. A model with a higher AUC accurately distinguishes between the two classes. It measures how well a model can distinguish between two classes (in this case, positive and negative outcomes). In this study, the ability to distinguish between RD and HM patients was determined using the AUC of the ROC curve. When the AUC surpasses 0.8, the diagnostic ability is deemed strong. It is regarded as reasonable when the AUC is higher than 0.7 [34].

The models used to achieve prediction are the ACP model, logistic regression model, decision tree, random forest, C5.0, CHAID, and neural networks. The performance of the diagnostic model was evaluated by metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and AUC with 95% confidence intervals (CIs). The Hosmer-Lemeshow goodness of fit test analyzed calibration.

Univariate and multivariate logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). P-values less than 5% were considered statistically significant. All statistical analyses were performed using MedCalc statistical software and SPSS (version 19.0; SPSS Inc., Chicago, IL, USA).

留言 (0)

沒有登入
gif