Citations

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup’s findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence–focused research and applications in anesthesiology. Anesthesiology’s progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup’s recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence–based tools for perioperative care.

Abstract Objective: To better understand clinicians’ rationale for ordering testing for C. difficile infection (CDI) for patients receiving laxatives and the impact of the implementation of a clinical decision support (CDS) intervention. Design: A mixed-methods, case series was performed from March 2, 2017 to December 31, 2018. Setting: Yale New Haven Hospital, a 1,541 bed tertiary academic medical center. Participants: Hospitalized patients ≥ 18 years old, and clinicians who were alerted by the CDS. Intervention: CDS was triggered in real-time when a clinician sought to order testing for CDI for a patient who received one or more doses of laxatives within the preceding 24 hours. Results: A total of 3,376 CDS alerts were triggered during the 21-month study period from 2,567 unique clinician interactions. Clinicians bypassed the CDS alert 74.5% of the time, more frequent among residents (48.3% bypass vs. 39.9% accept) and advanced practice providers (APPs) (34.9% bypass vs. 30.6% accept) than attendings (11.3% bypass vs. 22.5% accept). Ordering clinicians noted increased stool frequency/output (48%), current antibiotic exposure (34%), and instructions by an attending physician to test (28%) were among the most common reasons for overriding the alert and proceeding with testing for CDI. Conclusions: Testing for CDI despite patient laxative use was associated with an increased clinician concern for CDI, patient risk for CDI, and attending physician instruction for testing. Attendings frequently accepted CDS guidance while residents and APPs often reinstated CDI test orders, suggesting a need for greater empowerment and discretion when ordering tests.

The NCCN Guidelines for Myelodysplastic Syndromes (MDS) provide recommendations for the evaluation, diagnosis, and comprehensive care of patients with MDS based on a review of recent clinical evidence that has led to important advances in treatment or has yielded new information on biologic factors that may have prognostic significance in MDS. The multidisciplinary panel of MDS experts is convened at least on an annual basis. During the annual meeting, the panel evaluates new and emerging data to inform their recommendations. These NCCN Guidelines Insights review the recent updates, including treatment recommendations both for lower-risk and higher-risk MDS, preference stratification of therapeutic agents, and emerging data on novel therapeutics.

ABSTRACTIntroductionStudies demonstrate an overlap of constipation with gastroparesis and functional dyspepsia, but the impact of treatments that target constipation on improving upper gastrointestinal (UGI) symptoms is unexplored. We quantified the effects of constipation medication therapies on UGI and constipation symptom severity in subjects presenting with symptoms of gastroparesis.MethodsFifty‐six subjects with symptoms of gastroparesis underwent concurrent wireless motility capsule and gastric emptying scintigraphy and were recommended to receive either a new medication therapy for constipation or a change in constipation therapy based on investigator interpretation of test results. Gastroparesis Cardinal Symptom Index (GCSI), upper abdominal pain, and constipation scores were compared between baseline and 6 months. Data were compared between delayed or non‐delayed gastric emptying and the presence or absence of slow colonic transit.Key ResultsSubjects with slow colonic transit had improvements in GCSI (p = 0.007) and constipation scores (p = 0.004) after treatment with a new or changed constipation medication, with the delayed emptying subgroup driving GCSI improvements (p = 0.004). Reductions in nausea/vomiting (p = 0.02) and early satiety/fullness subscores (p = 0.002) with trends to improved bloating/distention subscores (p = 0.06) were observed in this subgroup, but upper abdominal pain was unchanged. Subjects with normal colonic transit showed no improvement in GCSI scores regardless of gastric emptying status (p > 0.05).Conclusions and InferencesIdentifying and treating delayed colonic transit in gastroparetic (delayed gastric emptying) subjects improves global UGI symptoms as well as selected individual symptoms. Evaluation of whole gut motility as well as recognizing and managing extragastric delay may promote improved outcomes in these patients.Trial RegistrationClinicalTrials.gov: NCT02022826

Abstract Purpose To evaluate the test–retest repeatability of a rapid, free‐breathing two‐dimensional (2D) MR elastography (MRE) technique and to assess the reliability of the liver‐stiffness measurements compared with conventional breath‐hold MREs. Methods Fifteen and 115 participants were enrolled in the technical repeatability and measurement equivalence assessment cohorts, respectively. All participants underwent rapid free‐breathing and conventional breath‐hold 2D MRE (twice in repeatability cohort) on 1.5T scanners. Both methods have four phase offsets over one harmonic motion cycle at 60 Hz; one and 10 cycles were collected and processed in breath‐hold and free‐breathing MREs, respectively. The liver stiffness measurements of free‐breathing (LS F ) and breath‐hold MRE (LS B ) were calculated from manually drawn regions of interest. The repeatability coefficients and Spearman correlation were used to assess technical repeatability and measurement agreement between LS F and LS B . Univariable and multivariable linear regressions were performed to identify potential influencing factors, including age, sex, body mass index, and fat fraction, in the measurement agreement between LS B and LS F . Results The repeatability coefficient of free‐breathing 2D MRE is comparable to breath‐hold MRE (LS F : 20.8%, LS B : 20.4%). LS F showed a strong agreement and significant correlation with LS B (LS F = 1.01 × LS B , ρ = 0.94, p < 0.001). The measurement agreement between LS F and LS B was only significantly affected by sex ( p = 0.047) after adjusting for confounding factors of age, body mass index, and fat fraction. Conclusions The nongated, free‐breathing, multislice 2D MRE technique can provide reliable liver stiffness measurements compared with conventional breath‐hold 2D MRE. It could provide a comfortable alternative method with reliable liver stiffness measurements for patients who have difficulty in suspending respiration.

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup’s findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence–focused research and applications in anesthesiology. Anesthesiology’s progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup’s recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence–based tools for perioperative care.

BACKGROUND Tumor-to-tumor metastasis (TTM) is an exceptionally rare phenomenon. To date, only 12 cases of the metastasis of clear cell renal cell carcinoma (ccRCC) to a meningioma have been reported in the literature. The authors present a unique case of TTM involving ccRCC metastasizing to a meningioma in a patient with a history of both primary cancers. OBSERVATIONS A 58-year-old male with a history of 2 primary cancers developed a biphasic tumor diagnosed as TTM of ccRCC to meningioma. The patient initially presented with left lower-extremity (LLE) weakness and underwent subtotal resection of his meningioma. Postsurgical imaging revealed rapid tumor progression, for which the patient completed adjuvant radiotherapy. Concurrently, the patient developed hematuria and was diagnosed with ccRCC. Subsequent recurrent LLE weakness and intracranial tumor progression prompted re-resection, revealing a TTM. LESSONS In patients with a history of multiple cancers who present with recurrent intracranial disease progression radiographically and/or clinically, it is worth considering TTM in the differential diagnosis to facilitate multidisciplinary workup, management, and treatment. https://thejns.org/doi/10.3171/CASE24670