Background Pharmacovigilance studies have reported a higher risk of problematic gambling (PG) in people receiving aripiprazole (ARI), a partial dopamine agonist. This association needs to be specifically assessed in schizophrenia (SZ) given the high prevalence of risk factors for PG in this population (eg, comorbid substance use) and given the nature of the dopamine dysfunction in this disorder. At the present stage, case studies may shed light on such an association. Methods All published cases involving SZ patients with PG while on ARI were systematically identified. Two instruments were used to assess causality. Results We identified 16 published SZ cases exposed to ARI experiencing PG. Half of whom had a gambling history before ARI exposition. Naranjo scores led to the estimation of a possible link between ARI exposition and PG in 15 of 16 cases (average score of 3) and probable (score of 5) in 1 case. More than 50% of items were left unknown owing to the lack of information or scale limitations. Using the Liverpool algorithm, causality estimation was raised to probable in 13 of 16 cases, definite in 1 case, and nonassessable in 2 cases. Conclusions The present review confirms that ARI may be involved in the occurrence of PG in some SZ patients. However, important information to assess causality was frequently missing, and the 2 scales used did not yield the same degree of certainty. The current article calls for including more details in future case reports and for well-powered studies carefully assessing factors such as comorbid diagnoses.

Purpose The purpose of this study was to assess the feasibility of deep learning (DL) methods to enhance the prediction of visual acuity (VA) improvement after macular hole (MH) surgery from a combined model using DL on high-definition optical coherence tomography (HD-OCT) B-scans and clinical features. Methods We trained a DL convolutional neural network (CNN) using pre-operative HD-OCT B-scans of the macula and combined with a logistic regression model of pre-operative clinical features to predict VA increase ≥15 Early Treatment Diabetic Retinopathy Study (ETDRS) letters at 6 months post-vitrectomy in closed MHs. A total of 121 MHs with 242 HD-OCT B-scans and 484 clinical data points were used to train, validate, and test the model. Prediction of VA increase was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 scores. We also extracted the weight of each input feature in the hybrid model. Results All performances are reported on the held-out test set, matching results obtained with cross-validation. Using a regression on clinical features, the AUROC was 80.6, with an F1 score of 79.7. For the CNN, relying solely on the HD-OCT B-scans, the AUROC was 72.8 ± 14.6, with a F1 score of 61.5 ± 23.7. For our hybrid regression model using clinical features and CNN prediction, the AUROC was 81.9 ± 5.2, with an F1 score of 80.4 ± 7.7. In the hybrid model, the baseline VA was the most important feature (weight = 59.1 ± 6.9%), while the weight of HD-OCT prediction was 9.6 ± 4.2%. Conclusions Both the clinical data and HD-OCT models can predict postoperative VA improvement in patients undergoing vitrectomy for a MH with good discriminative performances. Combining them into a hybrid model did not significantly improve performance. Translational Relevance OCT-based DL models can predict postoperative VA improvement following vitrectomy for MH but fusing those models with clinical data might not provide improved predictive performance.

Purpose. To investigate the anatomical and functional outcomes of revision surgery after failed primary surgery for idiopathic macular hole (MH). Methods. All consecutive patients with MH were identified from a cohort of patients operated between 2014 and 2018 at the CHU de Québec-Université Laval (Québec). The clinical and anatomical features of patients with unclosed MH after primary surgery were retrospectively collected. Our primary outcome was MH nonclosure rate after revision surgery. Our secondary outcomes were best-corrected visual acuity (BCVA) with ETDRS scale and MH size of eyes with revision surgery preoperatively and at 3 and 12 months after revision surgery. Results. In our cohort of 1085 eyes, 926 eyes met inclusion criteria and were analyzed in the study. We identified 22 eyes with failed primary surgery (2.4%), of which 20 underwent revision surgery. We had no bilateral MH in these 22 eyes. The nonclosure rate of MH after revision surgery was 15%. The mean final BCVA for closed MH after revision surgery was 55 ± 19 letters. Compared to the initial presentation, the mean change in visual acuity (VA) for closed MH was +4 ± 31 letters and +16 ± 17 letters at 3 and 12 months after the revision surgery, respectively. At initial presentation, patients with failed primary surgery had a baseline MH size of 665 ± 226 μm. The mean MH size after failed primary surgery was 607 ± 162 μm and 546 ± 156 μm for the three unclosed MHs one month after revision surgery. Conclusion. The success rate of revision surgery in eyes with unclosed MH is 85%. After successful revision surgery, eyes demonstrated an improvement in VA and closure of the MH.