Objectives We implement a novel enhanced recovery after surgery (ERAS) protocol with pre‐operative non‐opioid loading, total intravenous anesthesia, multimodal peri‐operative analgesia, and restricted red blood cell (pRBC) transfusions. 1) Compare differences in mean postoperative peak pain scores, opioid usage, and pRBC transfusions. 2) Examine changes in overall length of stay (LOS), intensive care unit LOS, complications, and 30‐day readmissions. Methods Retrospective cohort study comparing 132 ERAS vs. 66 non‐ERAS patients after HNC tissue transfer reconstruction. Data was collected in a double‐blind fashion by two teams. Results Mean postoperative peak pain scores were lower in the ERAS group up to postoperative day (POD) 2. POD0: 4.6 ± 3.6 vs. 6.5 ± 3.5; P = .004) (POD1: 5.2 ± 3.5 vs. 7.3 ± 2.3; P = .002) (POD2: 4.1 ± 3.5 vs. 6.6 ± 2.8; P = .000). Opioid utilization, converted into morphine milligram equivalents, was decreased in the ERAS group (POD0: 6.0 ± 9.8 vs. 10.3 ± 10.8; P = .010) (POD1: 14.1 ± 22.1 vs. 34.2 ± 23.2; P = .000) (POD2: 11.4 ± 19.7 vs. 37.6 ± 31.7; P = .000) (POD3: 13.7 ± 20.5 vs. 37.9 ± 42.3; P = .000) (POD4: 11.7 ± 17.9 vs. 36.2 ± 39.2; P = .000) (POD5: 10.3 ± 17.9 vs. 35.4 ± 45.6; P = .000). Mean pRBC transfusion rate was lower in ERAS patients (2.1 vs. 3.1 units, P = .017). There were no differences between ERAS and non‐ERAS patients in hospital LOS, ICU LOS, complication rates, and 30‐day readmissions. Conclusion Our ERAS pathway reduced postoperative pain, opioid usage, and pRBC transfusions after HNC reconstruction. These benefits were obtained without an increase in hospital or ICU LOS, complications, or readmission rates. Level of Evidence 3 Laryngoscope, 131:E792–E799, 2021

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Objectives/Hypothesis: To formally document online support community (OSC) use among patients with vestibular symptoms and gain an appreciation for the perceived influence of participation on psychosocial outcomes and the impact on medical decision-making.Study Design: Self reported internet-based questionnaire.Methods: The Facebook search function was paired with a comprehensive list of vestibular diagnoses to systematically collect publicly available information on vestibular OSCs. Next, a survey was designed to gather clinicodemographic information, OSC characteristics, participation measures, perceived outcomes, and influence on medical decision-making. The anonymous instrument was posted to two OSCs that provide support for patients with general vestibular symptoms.Results: Seventy-three OSCs were identified with >250,000 cumulative members and >10,000 posts per month. The survey was completed by 549 participants, a cohort of primarily educated middle-aged (median = 50, interquartile range 40-60), non-Hispanic white (84%), and female (89%) participants. The participants' most cited initial motivation and achieved goal of participants was to hear from others with the same diagnosis (89% and 88%, respectively). Daily users and those who reported seeing ≥5 providers before receiving a diagnosis indicated that OSC utilization significantly influenced their requested medical treatments (72% daily vs. 61% nondaily, P = .012; 61% <5 providers vs. 71% ≥5 providers P = .019, respectively). Most participants agreed that OSC engagement provides emotional support (74%) and helps to develop coping strategies (68%). Membership of ≥1 year was associated with a higher rate of learned coping skills (61% membership <1-year vs. 71% ≥1-year P = .016).Conclusions: The use of OSCs is widespread among vestibular diagnoses. A survey of two OSCs suggests these groups provide a significant source of peer support and can influence users' ability to interface with the medical system.

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Objective This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). Data Sources Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. Review Methods A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. Conclusions AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. Implications for Practice AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.