Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry

Purpose: To evaluate the performance of different probabilistic classifiers to predict posterior capsule rupture (PCR) prior to cataract surgery. Methods: Three probabilistic classifiers were constructed to estimate the probability of PCR: a Bayesian network (BN), logistic regression (LR) model, and multi‐layer perceptron (MLP) network. The classifiers were trained on a sample of 2 853 376 surgeries reported to the European Registry of Quality Outcomes for Cataract and Refractive Surgery (EUREQUO) between 2008 and 2018. The performance of the classifiers was evaluated based on the area under the precision‐recall curve (AUPRC) and compared to existing scoring models in the literature. Furthermore, direct risk factors for PCR were identified by analysing the independence structure of the BN. Results: The MLP network predicted PCR overall the best (AUPRC 13.1 ± 0.41%), followed by the BN (AUPRC 8.05 ± 0.39%) and the LR model (AUPRC 7.31 ± 0.15%). Direct risk factors for PCR include preoperative best‐corrected visual acuity (BCVA), year of surgery, operation type, anaesthesia, target refraction, other ocular comorbidities, white cataract, and corneal opacities. Conclusions: Our results suggest that the MLP network performs better than existing scoring models in the literature, despite a relatively low precision at high recall. Consequently, implementing the MLP network in clinical practice can potentially decrease the PCR rate

Outcomes of cataract surgery complicated by posterior capsule rupture in the European Registry of Quality Outcomes for Cataract and Refractive Surgery

To analyze the outcomes of cataract surgery complicated by posterior capsule rupture (PCR). European clinics affiliated to the European Registry of Quality Outcomes for Cataract and Refractive Surgery (EUREQUO). Retrospective cross-sectional register-based study. Data was retrieved from the EUREQUO between January 1, 2008, and December 31, 2018. The database consists of demographics, intraoperative complications, including PCR, type of intraocular lens (IOL) material, postoperative refraction, corrected distance visual acuity (CDVA), and postoperative complications. 1,371,743 cataract extractions with complete postoperative data were reported in the EUREQUO. In 12,196 cases (0.9%), a PCR was reported. Following PCR, patients were more likely to receive a PMMA IOL (5.2% vs. 0.4%, respectively) or no IOL (1.1% vs. 0.02%, respectively) compared to patients without PCR. The refractive and visual outcomes following PCR were significantly worse than without PCR (mean CDVA 0.13+/-0.21 versus 0.05+/-0.16 logMAR,

Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry

Purpose: To evaluate the performance of different probabilistic classifiers to predict posterior capsule rupture (PCR) prior to cataract surgery. Methods: Three probabilistic classifiers were constructed to estimate the probability of PCR: a Bayesian network (BN), logistic regression (LR) model, and multi-layer perceptron (MLP) network. The classifiers were trained on a sample of 2 853 376 surgeries reported to the European Registry of Quality Outcomes for Cataract and Refractive Surgery (EUREQUO) between 2008 and 2018. The performance of the classifiers was evaluated based on the area under the precision-recall curve (AUPRC) and compared to existing scoring models in the literature. Furthermore, direct risk factors for PCR were identified by analysing the independence structure of the BN. Results: The MLP network predicted PCR overall the best (AUPRC 13.1 ± 0.41%), followed by the BN (AUPRC 8.05 ± 0.39%) and the LR model (AUPRC 7.31 ± 0.15%). Direct risk factors for PCR include preoperative best-corrected visual acuity (BCVA), year of surgery, operation type, anaesthesia, target refraction, other ocular comorbidities, white cataract, and corneal opacities. Conclusions: Our results suggest that the MLP network performs better than existing scoring models in the literature, despite a relatively low precision at high recall. Consequently, implementing the MLP network in clinical practice can potentially decrease the PCR rate

Accuracy of toric intraocular lens calculations using estimated versus measured posterior corneal astigmatism

PURPOSE: To compare the prediction accuracy of toric intraocular lens calculations using estimated versus measured posterior corneal astigmatism (PCA). DESIGN: Retrospective case series. METHODS: 110 eyes of 110 patients with uncomplicated toric IOL implantation were included in this study. Predicted postoperative refractive astigmatism was calculated with the Barrett Toric calculator using estimated PCA (E-PCA), measured IOLMaster 700 PCA (I-PCA), and measured Pentacam PCA (P-PCA). Refractive astigmatism prediction errors (RA-PE), including their trimmed (tr-) centroid (mean vector), spread (precision), tr-mean absolute RA-PE (accuracy), and percentage within a certain threshold, were determined using vector analysis and compared between groups. SETTING: University Eye Clinic, Maastricht University Medical Center+, the Netherlands. RESULTS: The tr-centroid RA-PEs of the E-PCA (0.02D @ 82.2°), the I-PCA (0.08D @ 35.5°), and the P-PCA (0.09D @ 69.1°) were significantly different from each other (P<0.01), but not significantly different from zero (P=0.75, P=0.05, and P=0.05, respectively). The E-PCA had the best precision (tr-mean 0.40D), which was not significantly lower than the I-PCA (0.42D, P=0.53) and P-PCA (0.43D, P=0.06). The E-PCA also had the best accuracy (0.40D), which was not significantly different from the I-PCA (0.42D, P=0.26) and significantly better than the P-PCA (0.44, P<0.01). The precision and accuracy of the I-PCA did not significantly differ from those of the P-PCA. There were no statistically significant differences in the percentage of eyes within a certain absolute RA-PE threshold. CONCLUSIONS: The Barrett Toric calculator using the E-PCA, I-PCA, or P-PCA showed a comparable prediction of postoperative refractive astigmatism in standard clinical practice