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. 2023 Mar 28:16:479-488.
doi: 10.2147/RMHP.S403136. eCollection 2023.

A Prediction Nomogram for Recurrent Retinal Detachment

Yongying Zhou  1   2 , Qianyi Lu  1 , Zhigang Chen  1 , Peirong Lu  1
Affiliations

A Prediction Nomogram for Recurrent Retinal Detachment

Yongying Zhou et al. Risk Manag Healthc Policy. .

Abstract

Purpose: Recurrent retinal detachment (re-RD) is one of the complications in rhegmatogenous retinal detachment patients who underwent surgical treatment. We investigated the risk factors for re-RD and developed a nomogram for estimating clinical risk.

Methods: Univariate and multivariable logistic regression models were performed to determine the association between variables and re-RD, and a nomogram was then developed for re-RD. The nomogram performance was assessed based on its discrimination, calibration, and clinical usefulness.

Results: This study analyzed 15 potential variables of re-RD in 403 rhegmatogenous retinal detachment patients who underwent initial surgical treatment. Axial length, inferior breaks, retinal break diameter, and surgical methods were independent risk factors for re-RD. A clinical nomogram incorporating these four independent risk factors was constructed. The diagnostic performance of the nomogram was excellent (area under the curve = 0.892, 95% CI: 0.831-0.953). Our study further validated this nomogram by bootstrapping for 500 repetitions. The area under the curve of the bootstrap model was 0.797 (95% CI: 0.712-0.881). This model showed good calibration curve fitting and a positive net benefit in decision curve analysis.

Conclusion: Axial length, inferior breaks, retinal break diameter, and surgical methods could be risk factors for re-RD. We have developed a prediction nomogram of re-RD for rhegmatogenous retinal detachment following initial surgical treatment.

Keywords: nomogram; recurrent retinal detachment; risk factors.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Clinical nomogram predicting the re-RD probability in patients with RRD. Interpretation: The nomogram represents the regression equation visually. It develops scoring criteria based on the magnitude of the regression coefficients of all independent variables in the model. Determine the value of the variable on the corresponding axis, draw a vertical line to the total points axis to determine the points, add the points of each variable, and draw a line from the total point axis to determine the re-RD probabilities at the lower line of the nomogram.
Figure 2
Figure 2
Receiver operating characteristic curve.
Figure 3
Figure 3
Internal validation of the nomogram using bootstrap sampling. The ROC curve was measured by bootstrapping for 500 repetitions, and the AUC of the bootstrap stepwise model was shown.
Figure 4
Figure 4
Internal validation of the nomogram using bootstrap sampling. A calibration curve was measured by bootstrapping for 500 repetitions. The X axis is the predicted probability of the nomogram, and the Y axis is the observed probability. The red line shows the ideal calibration line, while the yellow area shows the 95% confidence interval of the prediction model.
Figure 5
Figure 5
Decision curve analyses depict the prediction nomogram’s clinical net benefit. The graph indicates the expected net benefit per patient relative to the nomogram prediction of re-RD. Red solid line: a prediction model. Tin slash line: Assume all patients have re-RD. Solid horizontal line: Assume no patients have re-RD.
See this image and copyright information in PMC

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