Citations

Objective: To assess the effect of a practice-level preoperative frailty screening and optimization toolkit (OPTI-Surg) on postoperative functional recovery and complications in elderly cancer patients undergoing major surgery. Background: Frailty is common in older adults. It increases the risk of poor postoperative functional recovery and complications. The potential for a practice-level screening/optimization intervention to improve outcomes is unknown. Methods: Thoracic, gastrointestinal, and urologic oncological surgery practices within the National Cancer Institute Community Oncology Research Program (NCORP) were randomized 1:1:1 to usual care (UC), OPTI-Surg, or OPTI-Surg with an implementation coach. OPTI-Surg consisted of the Edmonton Frail Scale and guided recommendations for referral interventions. Patients 70 years old or above undergoing curative intent surgery were eligible. The primary outcome was 8 weeks postoperative function (kcal/wk). The key secondary outcome was complications within 90 days. Mixed models were used to compare UC to the 2 OPTI-Surg arms combined. Results: From July 2019 to September 2022, 325 patients were enrolled in 29 practices. One hundred ninety-nine (64 UC, 135 OPTI-Surg) and 279 (78 UC, 201 OPTI-Surg) were evaluable for primary and secondary analysis, respectively. UC and OPTI-Surg patients did not significantly differ in total caloric expenditure (2.2 UC, 2.0 OPTI-Surg) after adjusting for baseline function (P=0.53). UC and OPTI-Surg patients did not significantly differ in postoperative complications (25.6% UC, 35.3% OPTI-Surg, P=0.5). Conclusions: Frailty assessment was successfully performed, but the OPTI-Surg intervention did not improve postoperative function nor reduce postoperative complications compared with UC. Future analysis will explore practice-level factors associated with toolkit implementation and the differences between the coaching and noncoaching arms.

Abstract The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient’s demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.

Abstract Transition services—programs that support adolescents and young adults (AYAs) as they move from a child‐centered to a more autonomous, adult‐orientated healthcare system—have been associated with improved short‐ and long‐term healthcare outcomes. Unfortunately, there is a paucity of evidence exploring transition services within the neurogastroenterology and motility (NGM) field. The overall aim of this article, endorsed by the American Neurogastroenterology and Motility Society and European Society of Neurogastroenterology and Motility, is to promote a discussion about the role of transition services for patients with NGM disorders. The AYAs addressed herein are those who have: (a) a ROME positive disorder of gut–brain interaction (DGBI), (b) a primary or secondary motility disorder (including those with motility disorders that have been surgically managed), or (c) an artificial feeding requirement (parenteral or enteral tube feeding) to manage malnutrition secondary to categories (a) or (b). The issues explored in this position paper include the specific physical and psychological healthcare needs of patients with NGM disorders; key healthcare professionals who should form part of a secondary care NGM transition service; the triadic relationship between healthcare professionals, caregivers, and patients; approaches to selecting patients who may benefit most from transition care; methods to assess transition readiness; and strategies with which to facilitate transfer of care between healthcare professionals. Key areas for future research are also addressed, including the construction of NGM‐specific transition readiness questionnaires, tools to assess post‐transfer healthcare outcomes, and educational programs to train healthcare professionals about transition care in NGM.

Importance The clinical effects of risankizumab (a monoclonal antibody that selectively targets the p19 subunit of IL-23) for the treatment of ulcerative colitis are unknown. Objective To evaluate the efficacy and safety of risankizumab when administered as an induction and a maintenance therapy for patients with ulcerative colitis. Design, Setting, and Participants Two phase 3 randomized clinical trials were conducted. The induction trial was conducted at 261 clinical centers (in 41 countries) and enrolled 977 patients from November 5, 2020, to August 4, 2022 (final follow-up on May 16, 2023). The maintenance trial was conducted at 238 clinical centers (in 37 countries) and enrolled 754 patients from August 28, 2018, to March 30, 2022 (final follow-up on April 11, 2023). Eligible patients had moderately to severely active ulcerative colitis; a history of intolerance or inadequate response to 1 or more conventional therapies, advanced therapies, or both types of therapies; and no prior exposure to risankizumab. Interventions For the induction trial, patients were randomized 2:1 to receive 1200 mg of risankizumab or placebo administered intravenously at weeks 0, 4, and 8. For the maintenance trial, patients with a clinical response (determined using the adapted Mayo score) after intravenous treatment with risankizumab were randomized 1:1:1 to receive subcutaneous treatment with 180 mg or 360 mg of risankizumab or placebo (no longer receiving risankizumab) every 8 weeks for 52 weeks. Main Outcomes and Measures The primary outcome was clinical remission (stool frequency score ≤1 and not greater than baseline, rectal bleeding score of 0, and endoscopic subscore ≤1 without friability) at week 12 for the induction trial and at week 52 for the maintenance trial. Results Among the 975 patients analyzed in the induction trial (aged 42.1 [SD, 13.8] years; 586/973 [60.1%] were male; and 677 [69.6%] were White), the clinical remission rates at week 12 were 132/650 (20.3%) for 1200 mg of risankizumab and 20/325 (6.2%) for placebo (adjusted between-group difference, 14.0% [95% CI, 10.0%-18.0%], P < .001). Among the 548 patients analyzed in the maintenance trial (aged 40.9 [SD, 14.0] years; 313 [57.1%] were male; and 407 [74.3%] were White), the clinical remission rates at week 52 were 72/179 (40.2%) for 180 mg of risankizumab, 70/186 (37.6%) for 360 mg of risankizumab, and 46/183 (25.1%) for placebo (adjusted between-group difference for 180 mg of risankizumab vs placebo, 16.3% [97.5% CI, 6.1%-26.6%], P < .001; adjusted between-group difference for 360 mg of risankizumab vs placebo, 14.2% [97.5% CI, 4.0%-24.5%], P = .002). No new safety risks were detected in the treatment groups. Conclusion and Relevance Compared with placebo, risankizumab improved clinical remission rates in an induction trial and in a maintenance trial for patients with moderately to severely active ulcerative colitis. Further study is needed to identify benefits beyond the 52-week follow-up. Trial Registration ClinicalTrials.gov Identifiers: NCT03398148 and NCT03398135

AbstractBackgroundA phase 2 cemiplimab study (NCT03132636) demonstrated a 24.1% objective response rate in patients diagnosed with metastatic basal cell carcinoma (mBCC) who were not candidates for continued hedgehog inhibitor (HHI) therapy due to intolerance to previous HHI therapy, disease progression while receiving HHI therapy, or having not better than stable disease on HHI therapy after 9 months. Here, health‐related quality of life (QoL) for this patient population is reported.MethodsAdult patients with mBCC were treated with intravenous cemiplimab at a dose of 350 mg every 3 weeks for 5 treatment cycles of 9 weeks/cycle then 4 treatment cycles of 12 weeks/cycle. Patients completed the European Organisation for Research and Treatment of Cancer Quality of Life‐Core 30 (QLQ‐C30) and Skindex‐16 questionnaires at baseline and Day 1 of each cycle. Across Cycles 2 to 9, the overall change from baseline was analyzed using a mixed model with repeated measures. Responder analyses determined clinically meaningful improvement or deterioration (changes ≥10 points) or maintenance across all scales.ResultsPatients reported low symptom burden and moderate‐to‐high functioning at baseline. Maintenance for QLQ‐C30 global health status (GHS)/QoL and across all functioning and symptom scales was indicated by overall mean changes from baseline. Clinically meaningful improvement or maintenance was reported at Cycle 2 for GHS/QoL (77%), functioning scales (77% to 86%), and symptom scales (70% to 93%), with similar proportions of improvement or maintenance at Cycles 6 and 9, excluding fatigue. On the Skindex‐16, clinically meaningful improvement or maintenance was reported across the emotional, symptom, and functional subscales, in 76%–88% of patients at Cycle 2, which were generally maintained at Cycles 6 and 9. Overall mean changes from baseline showed maintenance across these subscales.ConclusionsThe majority of patients treated with cemiplimab reported improvement or maintenance in GHS/QoL and functioning while maintaining a low symptom burden.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

We report the case of a term neonate who was somnolent at birth with ventilatory distress and experienced 2 seizures shortly after delivery. Laboratory tests revealed the neonate had a serum sodium of 113 mmol/L. The seizures stopped after treatment with midazolam, and the sodium was corrected slowly with 3% hypertonic saline without further sequelae. The severe neonatal hyponatremia and seizures were attributed to maternal consumption of excessive amounts of coconut water during labor. This case demonstrates the importance of careful consideration of both fluid volume and fluid electrolyte composition during labor to prevent adverse maternal and neonatal outcomes.

Insulin is a key regulator of amino acid metabolism. Many plasma amino acids, including lysine and its metabolite, α-aminoadipic acid (α-AA), a predictor for developing diabetes, are elevated in insulin resistance (IR). In 18 overweight women with IR and polycystic ovary syndrome compared with 12 lean control women, high physiological insulin during a euglycemic clamp failed to normalize many elevated amino acid metabolites, including branched-chain and aromatic amino acids, α-aminobutyric acid, and lysine, but normalized α-AA. To understand the underpinnings of differential responses of lysine and its metabolic product α-AA to high physiological insulin in IR compared with control participants, we developed a kinetic model using [α-15N1]-lysine and [13C1]-α-AA as tracers and measured the two tracers simultaneously in α-AA by innovative mass spectrometry. High insulin increased lysine conversion to α-AA in the IR and control groups but failed to normalize plasma lysine concentrations in IR due to a decrease in lysine metabolic clearance rate (MCR). In contrast, despite higher conversion rates of lysine to α-AA by high insulin, α-AA concentration decreased in IR because of the sustained greater MCR of α-AA. The abnormal amino acids and metabolites, even while on high physiological insulin, could potentially explain many functional derangements in IR. Article Highlights

Lay Summary In this prospective, multicenter observational study of highly refractory patients with Crohn’s disease of the pouch, risankizumab achieved the primary outcome of clinical remission in 50% and the more stringent secondary outcome of antibiotic- and steroid-free remission in 30.8% at 12 weeks.