Olomu, Osarenoma

Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.

AbstractObjectivePrevious studies have suggested that patients with aspirin‐exacerbated respiratory disease (AERD) have a high likelihood of alcohol intolerance. The purpose of this systematic review is to identify if there is sufficient evidence to confirm this correlation and the impact of medical therapy on subsequent alcohol tolerance.Data SourcesPubMed, EMBASE, SCOPUS, EBSCO, Google Scholar, Cochrane Library, and Grey literature. We also performed snowballing on the identified observational studies (OS) for additional data.Review MethodsA systematic review was conducted from 1968 to 2022 to identify those studies describing AERD symptomatology triggered by alcohol intake. The primary outcome was to analyze the current literature for the association between alcohol intolerance and AERD symptoms. The secondary outcome looked for improvement in alcohol tolerance after aspirin desensitization or biological therapy.ResultsA total of 775 studies were identified and 40 abstracts were evaluated. From these, 5 studies met the inclusion criteria. Of the 5 manuscripts, there was 1 case‐control, 2 cohort, and 2 cross‐sectional studies. A total of 522 participants with AERD and a history of alcohol consumption were included, with 52.8% reporting at least 1 sinopulmonary exacerbation after alcohol intake. One of 3 studies noted improvement in alcohol tolerance after medical therapy with aspirin desensitization.ConclusionThe current literature suggests that patients with AERD have a high risk of alcohol intolerance. Additionally, aspirin desensitization may improve alcohol tolerance in this patient population.