Samson, Susan L.

Background Recruitment for clinical studies is challenging. To overcome barriers, investigators have previously established call-to-entry rates to assist in planning. However, rates specific to low-income minority populations are needed to account for additional barriers to enrolment these individuals face. Objective To obtain a call-to-entry rate in a low-income uninsured Hispanic population with chronic disease. Methods We used data from four of our randomised clinical studies to determine the call-to-entry rate for individuals (n=1075) with or at risk for type 2 diabetes: participants needed/potential participants contacted=recruitment rate (yield). Research staff contacted potential participants to enrol in a study that evaluated 6 month diabetes programmes at community clinics from 2015 to 2020. We recorded call-to-entry rates, reasons for declining the study, show rates, and attrition. Results The call-to-entry rate was 14.5%. Forty per cent of potential participants could not be contacted, and 30.6%, 19.1%, and 5.4% responded yes , no , and maybe , respectively. No show percentages were 54% for yes and 91.4% for maybe responders. The majority (61.6%) declined due to inability to attend; reasons to decline included work (43%), eligibility (18%), transportation (10%), out of town (9%), did not think they needed the programme (7%) and other/unknown (14%). Being a physician predicted inability to reach participants (adjusted OR 2.91, 95% CI 1.73 to 4.90). Attrition was 6.8%. Conclusions We described a call-to-entry rate and detailed recruitment data, including reasons to decline the study. This valuable information can assist investigators in study planning and overcoming enrolment barriers in low-income populations. Telehealth-based or strategies that limit transportation needs may increase participant involvement. Trial registration number NCT03394456 .

Abstract Disclosure: S.L. Samson: Consulting Fee; Self; Novartis Pharmaceuticals, Chiasma. M. Bolanowski: Grant Recipient; Self; Amryt, Recordati, Pfizer, Inc., Novartis Pharmaceuticals, Ipsen, IBSA, Teva Pharmaceutical Industries Ltd., Berlin Chemie. Speaker; Self; Amryt, Recordati, Pfizer, Inc., Novartis Pharmaceuticals, Ipsen, IBSA, Teva Pharmaceutical Industries Ltd., Berlin Chemie. S. Zhang: None. Y. Yu: None. P. Witek: Grant Recipient; Self; Novartis Pharmaceuticals, Ipsen, Recordati, Novo Nordisk, Xeris Pharmaceuticals (Strongbridge), Lilly USA, LLC. Speaker; Self; Novartis Pharmaceuticals, Ipsen, Recordati, Novo Nordisk, Xeris Pharmaceuticals (Strongbridge), Lilly USA, LLC. N. Kietsiriroje: None. A. Piacentini: Employee; Self; Recordati. A.M. Pedroncelli: Employee; Self; Recordati. U. Feldt-Rasmussen: Advisory Board Member; Self; Novartis Pharmaceuticals, Recordati, Novo Nordisk, Xeris Pharmaceuticals (Strongbridge). Grant Recipient; Self; Ipsen, Recordati, Novo Nordisk. Speaker; Self; Ipsen, Recordati, Novo Nordisk. Introduction: Pasireotide efficacy and safety in persons with acromegaly or Cushing’s disease (CD) is confirmed in a comprehensive clinical trial program. Pasireotide-induced hyperglycemia events, expected based on the drug’s mechanism of action, are manageable with low rates of treatment discontinuation. To advance knowledge on pasireotide-induced hyperglycemia management, a prospective randomized Phase IV study (B2219, NCT02060383) was conducted. This secondary analysis assessed factors that might predict development of pasireotide-induced hyperglycemia. Methods: In B2219, participants (Pts) with acromegaly or CD who did not need therapy for hyperglycemia or were managed with metformin alone entered the non-randomized observational arm. Pts who developed hyperglycemia not managed with metformin alone were randomized to incretin-based therapy or insulin. This analysis evaluated Pts according to whether therapy for hyperglycemia during pasireotide treatment was needed. Logistic regression analyses evaluated quantitative and qualitative predictive factors associated with pasireotide-induced hyperglycemia. Results: Of 190 Pts with acromegaly and 59 with CD, 88 and 15, respectively, did not need antihyperglycemic medication during pasireotide treatment; Pt characteristics were generally balanced across subgroups at study baseline (BL). Most Pts who did not develop hyperglycemia had a mean age of <40 years (acromegaly 62.5%, CD 86.7%), BL glycated hemoglobin (HbA1c) <6.5% (acromegaly 98.9%, CD 100.0%) and BL fasting plasma glucose (FPG) <100 mg/dL (acromegaly 76.1%, CD 100.0%). Age (odds ratio [OR] 1.0, 95% CI 1.0−1.1), BL HbA1c (OR 3.5, 95% CI 1.3−9.9) and prediabetes or diabetes conditions (OR 3.0, 95% CI 1.2−7.6) were identified as risk factors associated with pasireotide-induced hyperglycemia in Pts with acromegaly. BL FPG (OR 1.2, 95% CI 1.0−1.3) was associated with increased risk of pasireotide-induced hyperglycemia in Pts with CD. Overall, adverse events (AEs) of hyperglycemia and diabetes mellitus (investigator reported) occurred in 21.1% and 14.2% of Pts with acromegaly and 28.8% and 11.9% of Pts with CD, respectively. More Pts who had diabetes or prediabetes conditions at BL had hyperglycemia-related AEs than Pts with normal glucose levels. Hyperglycemia-related AEs were managed with antihyperglycemic medication (acromegaly 26.3%, CD 30.5%); few Pts temporarily interrupted treatment (acromegaly 3.7%, CD 10.2%). Conclusions: These data suggest that age, HbA1c, FPG and having prediabetes or diabetes conditions at BL are associated with increased risk of developing pasireotide-induced hyperglycemia. AEs were manageable with antihyperglycemic medication. Potential risk factors associated with pasireotide-induced hyperglycemia may be used to identify Pts who need vigilant monitoring to ensure optimal outcomes during pasireotide treatment. Presentation: Thursday, June 15, 2023

Abstract Objective Sellar repair techniques with varying complexity and effectiveness have been employed to reduce the incidence of postoperative cerebrospinal fluid (CSF) leaks following endoscopic transsphenoidal pituitary surgery. This study describes our institutional experience with a minimalistic, highly effective approach to sellar repair. Methods The authors performed a retrospective review of consecutive cases of transsphenoidal pituitary surgery performed at our institution. All included subjects underwent sellar repair with an epidural interlay graft. Relationships between patient and tumor characteristics, incidence of CSF leak, adjunct repair techniques, and endocrine outcomes were examined. Results Three hundred and thirty-four (334) cases were included. Intraoperative CSF leak was encountered in 116 cases (34.7%), and postoperative CSF leak occurred only once (0.3%) early in the series. Seventy intraoperative CSF leaks were successfully repaired with an epidural interlay alone, including 13 high-flow leaks. Our rate of fat graft use (1.2%), nasoseptal flap (3.0%), and lumbar drain placement (6.6%) in the second half of the series showed a significant decline compared with the first half, indicating increased reliance on the interlay graft over time. Our 65% functional macroadenoma remission rate compares favorably with large historical series. Conclusion Simple sellar repair using an epidural interlay graft is highly effective in preventing postoperative CSF leaks, including cases of high-flow intraoperative leaks. More complex repair techniques are reserved for select high-risk cases or those with inadequate sellar bony ledges. With this technique, there has been no postoperative CSF leak at our institution in over 300 consecutive transsphenoidal pituitary surgeries.

In recent years, the endoscopic endonasal approach (EEA) for craniopharyngiomas has proven to be a safe option for extensive tumor resection, with minimal or no manipulation of the optic nerves and excellent visualization of the superior hypophyseal branches when compared to the Transcranial Approach (TCA). However, there is an ongoing debate regarding the criteria for selecting different approaches. To explore the current results of EEA and discuss its role in the management of craniopharyngiomas, we performed MEDLINE, Embase, and LILACS searches from 2012 to 2022. Baseline characteristics, the extent of resection, and clinical outcomes were evaluated. Statistical analysis was performed through an X2 and Fisher exact test, and a comparison between quantitative variables through a Kruskal–Wallis and verified with post hoc Bonferroni. The tumor volume was similar in both groups (EEA 11.92 cm3, -TCA 13.23 cm3). The mean follow-up in months was 39.9 for EEA and 43.94 for TCA, p = 0.76). The EEA group presented a higher visual improvement rate (41.96% vs. 25% for TCA, p < 0.0001, OR 7.7). Permanent DI was less frequent with EEA (29.20% vs. 67.40% for TCA, p < 0.0001, OR 0.2). CSF Leaks occurred more frequently with EEA (9.94% vs. 0.70% for TCA, p < 0.0001, OR 15.8). Recurrence rates were lower in the EEA group (EEA 15.50% vs. for TCA 21.20%, p = 0.04, OR 0.7). Our results demonstrate that, in selected cases, EEA for resection of craniopharyngiomas is associated with better results regarding visual preservation and extent of tumor resection. Postoperative CSF leak rates associated with EEA have improved compared to the historical series. The decision-making process should consider each person’s characteristics; however, it is noticeable that recent data regarding EEA justify its widespread application as a first-line approach in centers of excellence for skull base surgery.

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.