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Purpose <p>To develop and validate the OCT-RiSK (Optical Coherence Tomography–based Retinal Integrity and Surgical Knowledge) scoring model for predicting early macular anatomical outcomes following rhegmatogenous retinal detachment (RRD) repair.</p> Methods <p>This retrospective, single-center study included eyes with fovea-off primary RRD that underwent pars plana vitrectomy (PPV), scleral buckle (SB), or pneumatic retinopexy between January 2020 and December 2024 at a tertiary eye hospital in India. Preoperative OCT scans were analyzed for eleven predefined macular biomarkers, and 4-week postoperative OCT images were evaluated for macular reattachment. Suboptimal macular reattachment was defined as persistent macular subretinal fluid on 4-week OCT. An elastic-net logistic regression model incorporating OCT biomarkers and surgical technique was developed and validated to predict early macular attachment. Based on statistically significant predictors, a non-weighted OCT-RiSK score (+ 1 point per variable) was derived, stratifying eyes into low- and high-risk groups for suboptimal reattachment.</p> Results <p>A total of 1,491 eyes (median age 55 years) were analyzed. Suboptimal anatomical recovery occurred in 23.9% (<i>n</i> = 357). Four OCT biomarkers—absence of posterior vitreous dots, intraretinal cysts, and outer retinal corrugations, and presence of photoreceptor loss—along with SB surgery were identified as independent predictors of suboptimal macular reattachment. The OCT-RiSK model achieved an overall accuracy of 84.3%, with higher OCT-RiSK scores (&gt; 2) significantly associated with suboptimal outcomes (<i>p</i> &lt; 0.001).</p> Conclusion <p>The OCT-RiSK scoring system provides a practical, data-driven framework for predicting early postoperative macular outcomes, supporting personalized surgical planning, patient counselling, and risk stratification in RRD management.</p>

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The OCT-RiSK model: predicting early macular reattachment after rhegmatogenous retinal detachment repair

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Ramesh Venkatesh,
  • Pragati Raj,
  • Shubhangi Tripathi,
  • Jay Chhablani,
  • Rupak Roy,
  • Vishma Prabhu,
  • Prathibha Hande,
  • Chaitra Jayadev,
  • Preksha Biradar,
  • Naresh Yadav

摘要

Purpose

To develop and validate the OCT-RiSK (Optical Coherence Tomography–based Retinal Integrity and Surgical Knowledge) scoring model for predicting early macular anatomical outcomes following rhegmatogenous retinal detachment (RRD) repair.

Methods

This retrospective, single-center study included eyes with fovea-off primary RRD that underwent pars plana vitrectomy (PPV), scleral buckle (SB), or pneumatic retinopexy between January 2020 and December 2024 at a tertiary eye hospital in India. Preoperative OCT scans were analyzed for eleven predefined macular biomarkers, and 4-week postoperative OCT images were evaluated for macular reattachment. Suboptimal macular reattachment was defined as persistent macular subretinal fluid on 4-week OCT. An elastic-net logistic regression model incorporating OCT biomarkers and surgical technique was developed and validated to predict early macular attachment. Based on statistically significant predictors, a non-weighted OCT-RiSK score (+ 1 point per variable) was derived, stratifying eyes into low- and high-risk groups for suboptimal reattachment.

Results

A total of 1,491 eyes (median age 55 years) were analyzed. Suboptimal anatomical recovery occurred in 23.9% (n = 357). Four OCT biomarkers—absence of posterior vitreous dots, intraretinal cysts, and outer retinal corrugations, and presence of photoreceptor loss—along with SB surgery were identified as independent predictors of suboptimal macular reattachment. The OCT-RiSK model achieved an overall accuracy of 84.3%, with higher OCT-RiSK scores (> 2) significantly associated with suboptimal outcomes (p < 0.001).

Conclusion

The OCT-RiSK scoring system provides a practical, data-driven framework for predicting early postoperative macular outcomes, supporting personalized surgical planning, patient counselling, and risk stratification in RRD management.