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

Autor: Triepels RJMA; Department of Data Analytics and Digitalisation, Maastricht University, Maastricht, the Netherlands., Segers MHM; University Eye Clinic, Maastricht University Medical Center+, Maastricht, the Netherlands., Rosen P; Department of Ophthalmology, Oxford Eye Hospital, Oxford, UK., Nuijts RMMA; University Eye Clinic, Maastricht University Medical Center+, Maastricht, the Netherlands., van den Biggelaar FJHM; University Eye Clinic, Maastricht University Medical Center+, Maastricht, the Netherlands., Henry YP; Department of Ophthalmology, Amsterdam UMC, Amsterdam, the Netherlands., Stenevi U; Department of Ophthalmology, Sahlgrenska University Hospital, Göteborg, Sweden., Tassignon MJ; Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium., Young D; Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK., Behndig A; Department of Clinical Sciences, Ophthalmology, Umeå University, Umeå, Sweden., Lundström M; Department of Clinical Sciences, Ophthalmology, Lund University, Lund, Sweden., Dickman MM; University Eye Clinic, Maastricht University Medical Center+, Maastricht, the Netherlands.
Jazyk: angličtina
Zdroj: Acta ophthalmologica [Acta Ophthalmol] 2023 Sep; Vol. 101 (6), pp. 644-650. Date of Electronic Publication: 2023 Feb 15.
DOI: 10.1111/aos.15648
Abstrakt: 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.
(© 2023 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.)
Databáze: MEDLINE
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