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Abstract Background Post-operative complications present a challenge to the healthcare system due to the high unpredictability of their incidence. Socioeconomic conditions have been established as social determinants of health. However, their contribution relating to postoperative complications is still unclear as it can be heterogeneous based on community, type of surgical services, and sex and gender. Uncovering these relations can enable improved public health policy to reduce such complications. Methods In this study, we conducted a large population cross-sectional analysis of social vulnerability and the odds of various post-surgical complications. We collected electronic health records data from over 50,000 surgeries that happened between 2012 and 2018 at a quaternary health center in St. Louis, Missouri, United States and the corresponding zip code of the patients. We built statistical logistic regression models of postsurgical complications with the social vulnerability index of the tract consisting of the zip codes of the patient as the independent variable along with sex and race interaction. Results Our sample from the St. Louis area exhibited high variance in social vulnerability with notable rapid increase in vulnerability from the south west to the north of the Mississippi river indicating high levels of inequality. Our sample had more females than males, and females had slightly higher social vulnerability index. Postoperative complication incidence ranged from 0.75% to 41% with lower incidence rate among females. We found that social vulnerability was associated with abnormal heart rhythm with socioeconomic status and housing status being the main association factors. We also found associations of the interaction of social vulnerability and female sex with an increase in odds of heart attack and surgical wound infection. Those associations disappeared when controlling for general health and comorbidities. Conclusions Our results indicate that social vulnerability measures such as socioeconomic status and housing conditions could affect postsurgical outcomes through preoperative health. This suggests that the domains of preventive medicine and public health should place social vulnerability as a priority to achieve better health outcomes of surgical interventions.

Abstract Introduction Reducing brain levels of both soluble and insoluble forms of amyloid beta (Aβ) remains the primary goal of most therapies that target Alzheimer's disease (AD). However, no treatment has so far resulted in patient benefit, and clinical trials of the most promising drug candidates have generally failed due to significant adverse effects. This highlights the need for safer and more selective ways to target and modulate Aβ biogenesis. Methods Peptide technology has advanced to allow reliable synthesis, purification, and delivery of once‐challenging hydrophobic sequences. This is opening up new routes to target membrane processes associated with disease. Here we deploy a combination of atomic detail molecular dynamics (MD) simulations, living‐cell Förster resonance energy transfer (FRET), and in vitro assays to elucidate the atomic‐detail dynamics, molecular mechanisms, and cellular activity and selectivity of a membrane‐active peptide that targets the Aβ precursor protein (APP). Results We demonstrate that Aβ biogenesis can be downregulated selectively using an APP occlusion peptide (APPOP). APPOP inhibits Aβ production in a dose‐dependent manner, with a mean inhibitory concentration (IC50) of 450 nM toward exogenous APP and 50 nM toward endogenous APP in primary rat cortical neuronal cultures. APPOP does not impact the γ‐secretase cleavage of Notch‐1, or exhibit toxicity toward cultured primary rat neurons, suggesting that it selectively shields APP from proteolysis. Discussion Drugs targeting AD need to be given early and for very long periods to prevent the onset of clinical symptoms. This necessitates being able to target Aβ production precisely and without affecting the activity of key cellular enzymes such as γ‐secretase for other substrates. Peptides offer a powerful way for targeting key pathways precisely, thereby reducing the risk of adverse effects. Here we show that protecting APP from proteolytic processing offers a promising route to safely and specifically lower Aβ burden. In particular, we show that the amyloid pathway can be targeted directly and specificically. This reduces the risk of off‐target effects and paves the way for a safe prophylactic treatment.

Purpose: Second malignancies (SMs) after radiation therapy are rare but serious sequelae of treatment. This study investigates whether radiation therapy use is associated with changes in baseline SM risk. Methods and Materials: We extracted all patients with cancer, with or without SM, in the Surveillance, Epidemiology, and End Results database from 1973 to 2014. Cumulative incidence of SM for patients stratified by radiation therapy status was calculated using a competing risk model, both for the entire cohort and for subgroups based on the primary tumor's anatomic location. Results: We identified 2,872,063 patients with cancer, including 761,289 patients who received radiation therapy and 2,110,774 who did not. The SM rate at 20 years for patients receiving radiation therapy versus no radiation therapy was 21.4% versus 18.8%. The relative risk for SM associated with radiation therapy for the overall group was 1.138 at 20 years. The relative risks for SM associated with radiation therapy to malignancies arising from central nervous system and orbits, head and neck, thorax, abdomen, and pelvis at 20 years were 0.704, 1.011, 0.559, 0.646, and 1.106 for men and 0.792, 1.298, 1.265, 0.780, and 0.988 for women, respectively. Conclusions: The association between SM and radiation therapy varies with both sex and disease anatomic location, with the largest increase in SM seen in females irradiated to the head and neck region. Overall, the absolute change in SM rates associated with radiation therapy remains small, with differences in various clinical contexts.

Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials.Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network.Results: Median follow-up time was 32.3 months (range 3–98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses.Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

Purpose: To assess the optimal planning target volume (PTV) margins for stereotactic body radiotherapy (SBRT) of prostate cancer based on inter- and intra-fractional prostate motion determined from daily image guidance.Methods and Materials: Two hundred and five patients who were enrolled on two prospective studies of SBRT (8 Gy × 5 fractions) for localized prostate cancer treated at a single institution between 2012 and 2017 had complete inter- and intra-fractional shift data available. All patients had scheduled kilovoltage planar imaging during SBRT with rigid registration to intraprostatic fiducials prior to each of four half-arcs delivered per fraction, as well as cone beam CT verification of anatomy prior to each fraction. Inter- and intra- fractional shift data were obtained to estimate the required PTV margins based on the classic van Herk formula. Inter- and intra-fractional motion were compared between patients with and without severe toxicities using the independent two-sample Wilcoxon test.Results: The margins required to account for inter-fractional motion were estimated to be 0.99, 1.52, and 1.45 cm in lateral (LR), longitudinal (SI), and vertical (AP) directions, respectively. The margins required to account for intra-fractional motion were estimated to be 0.19, 0.27, and 0.31 cm in LR, SI and AP directions, respectively. Large intra-fractional shifts were mostly observed in the SI and AP directions, with 2.0 and 5.4% of patients experiencing average intra-fractional motion >3 mm in the SI and AP directions, respectively, compared with none experiencing mean shifts >3 mm in the LR direction. Six patients experienced grade 3 gastrointestinal or genitourinary toxicity. There were no significant differences in mean inter- or intra-fractional motion in any of the cardinal directions compared to patients without severe toxicity (inter-fractional p = 0.46–0.99, intra-fractional p = 0.10–0.84).Conclusion: The inter- and intra-fractional margins estimated from this study are in line with prior reported values. Intra-fractional prostate motion was generally small with larger margins required for the SI and AP directions, notably just slightly exceeding the commonly used 3 mm posterior PTV margin even with realignment between half-arcs. Development of severe toxicity was not significantly associated with the degree of inter- or intra-fractional motion.

Introduction: Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. With advances in technology, there is an opportunity to research the utility of a telemedicine-based control center for anesthesia clinicians that assess risk, diagnoses negative patient trajectories, and implements evidence-based practices. Objectives: The primary objective of this trial is to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Secondary objectives are to determine whether the ACT improves perioperative quality of care metrics including management of temperature, mean arterial pressure, mean airway pressure with mechanical ventilation, blood glucose, anesthetic concentration, antibiotic redosing, and efficient fresh gas flow. Methods and analysis: We are conducting a single center, randomized, controlled, phase 3 pragmatic clinical trial. A total of 58 operating rooms are randomized daily to receive support from the ACT or not. All adults (eighteen years and older) undergoing surgical procedures in these operating rooms are included and followed until 30 days after their surgery. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses. Differences between groups will be presented with 99% confidence intervals; p-values

Introduction Obstructive sleep apnoea (OSA) is common among older surgical patients, and delirium is a frequent and serious postoperative complication. Emerging evidence suggests that OSA increases the risk for postoperative delirium. We hypothesise that OSA is an independent risk factor for postoperative delirium, and that in patients with OSA, perioperative adherence to positive airway pressure (PAP) therapy decreases the incidence of postoperative delirium and its sequelae. The proposed retrospective cohort analysis study will use existing datasets to: (i) describe and compare the incidence of postoperative delirium in surgical patients based on OSA diagnosis and treatment with PAP; (ii) assess whether preoperatively untreated OSA is independently associated with postoperative delirium; and (iii) explore whether preoperatively untreated OSA is independently associated with worse postoperative quality of life (QoL). The findings of this study will inform on the potential utility and approach of an interventional trial aimed at preventing postoperative delirium in patients with diagnosed and undiagnosed OSA.Methods and analysis Observational data from existing electronic databases will be used, including over 100 000 surgical patients and ~10 000 intensive care unit (ICU) admissions. We will obtain the incidence of postoperative delirium in adults admitted postoperatively to the ICU who underwent structured preoperative assessment, including OSA diagnosis and screening. We will use doubly robust propensity score methods to assess whether untreated OSA independently predicts postoperative delirium. Using similar methodology, we will assess if untreated OSA independently predicts worse postoperative QoL.Ethics and dissemination This study has been approved by the Human Research Protection Office at Washington University School of Medicine. We will publish the results in a peer-reviewed venue. Because the data are secondary and high risk for reidentification, we will not publicly share the data. Data will be destroyed after 1 year of completion of active Institutional Review Board (IRB) approved projects.