Shifting paradigms: protocol implementation to reduce length of stay for bariatric surgery following the pandemic at a high volume bariatric center.

BACKGROUND: With the Covid-19 pandemic reducing the capacity to perform elective bariatric surgical cases, a multidisciplinary approach to reducing length of stay has been essential to continue providing this service. In conjunction with the use of our local ERAS protocols, same day discharge (SDD) and early next day discharge (NDD) for bariatric surgery is becoming more of a reality. OBJECTIVES: To evaluate the effectiveness of our new protocols targeted at reducing length of stay (LOS) for our bariatric surgery patients during the pandemic. Secondary outcomes included comparisons of readmission and complications compared to baseline data. METHODS: The MBSAQIP data set was analyzed identifying patients who underwent laparoscopic roux-en- Y gastric bypass (LRYGB) and laparoscopic sleeve gastrectomy (SG) from April to November 2021. Mean LOS and complication rates including re-admission in this baseline group were documented. This was compared to a cohort who underwent the surgeries between December 2021 and February 2022 under our new protocols for early discharge. RESULTS: 195 patients underwent bariatric surgery in the baseline group and 87 patients in the early discharge cohort were included. There was a statistically significant decrease in mean LOS comparing baseline group (34.5 h) and next day PACU discharges (25 h) with P = 0.004. No increase in complication rate from the early discharge cohort against the baseline group. (P = 0.014). CONCLUSION: SDD and NDD in carefully selected bariatric surgery patients is feasible with good outcomes. With ERAS protocols as a foundation and a multidisciplinary approach, this can be achieved in spite of pressures placed on bariatric units by the pandemic.

Attention-deficit hyperactivity disorder medication use is associated with testosterone hypofunction-results from a national claims database analysis.

Male hypogonadism is not a risk associated with attention-deficit hyperactivity disorder (ADHD) stimulant medications, but recent studies have explored this connection. Though the pathophysiologic connection remains unclear, we predicted that long-term use of ADHD stimulant medications could increase the risk of hypogonadism in post-pubertal males. Utilizing TriNetX, LLC Research Network data from January 2000 through December 2019, men older than 18 with ADHD receiving long-term stimulant medication (>36 monthly prescriptions) were selected for the study population. Two control groups were constructed: individuals with ADHD but no stimulant medication use, and individuals without ADHD or stimulant medication use. A diagnosis of testicular hypofunction (ICD-10: E29.1) within five years of long-term ADHD stimulant medication use was the chosen primary outcome. After propensity score matching, 17,224 men were analyzed in each group. Of the men with long-term ADHD stimulant medication use, 1.20% were subsequently diagnosed with testicular hypofunction compared to 0.67% of individuals with ADHD without stimulant medication use (RR: 1.78, 95% CI: 1.42-2.23) and 0.68% in men without ADHD or stimulant medication use (RR: 1.75, 95% CI: 1.39-2.19). Therefore, chronic ADHD stimulant medication use was found to be significantly associated with a subsequent diagnosis of testicular hypofunction.

Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy.

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.

Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction.

Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different phase images of the same patient using the coefficient of variation (COV). The relationship between individual COVs and tumor motion magnitude was inspected. Population COVs, the mean COVs of all 20 patients, were used to evaluate feature motion stability and categorize the radiomic features into 4 different groups. The two extremes, the Very Small group (COV≤5%) and the Large group (COV>20%), each accounted for about a quarter of the features. Shape features were the most stable, with COV≤10% for all features. A clinical study was subsequently conducted using 140 early-stage NSCLC patients. Radiomic features were employed to predict the overall survival with a 500-round bootstrapping. Identical multiple regression model development process was applied, and the model performance was compared between models with and without a feature pre-selection step based on 4D COV to pre-exclude unstable features. Among the systematically tested cutoff values, feature pre-selection with 4D COV≤5% achieved the optimal model performance. The resulting 3-feature radiomic model significantly outperformed its counterpart with no 4D COV pre-selection, with P = 2.16x10-27 in the one-tailed t-test comparing the prediction performances of the two models.