Knowledge-based planning for Gamma Knife.

BACKGROUND: Current methods for Gamma Knife (GK) treatment planning utilizes either manual forward planning, where planners manually place shots in a tumor to achieve a desired dose distribution, or inverse planning, whereby the dose delivered to a tumor is optimized for multiple objectives based on established metrics. For other treatment modalities like IMRT and VMAT, there has been a recent push to develop knowledge-based planning (KBP) pipelines to address the limitations presented by forward and inverse planning. However, no complete KBP pipeline has been created for GK. PURPOSE: To develop a novel (KBP) pipeline, using inverse optimization (IO) with 3D dose predictions for GK. METHODS: Data were obtained for 349 patients from Sunnybrook Health Sciences Centre. A 3D dose prediction model was trained using 322 patients, based on a previously published deep learning methodology, and dose predictions were generated for the remaining 27 out-of-sample patients. A generalized IO model was developed to learn objective function weights from dose predictions. These weights were then used in an inverse planning model to generate deliverable treatment plans. A dose mimicking (DM) model was also implemented for comparison. The quality of the resulting plans was compared to their clinical counterparts using standard GK quality metrics. The performance of the models was also characterized with respect to the dose predictions. RESULTS: Across all quality metrics, plans generated using the IO pipeline performed at least as well as or better than the respective clinical plans. The average conformity and gradient indices of IO plans was 0.737 ± $\pm$ 0.158 and 3.356 ± $\pm$ 1.030 respectively, compared to 0.713 ± $\pm$ 0.124 and 3.452 ± $\pm$ 1.123 for the clinical plans. IO plans also performed better than DM plans for five of the six quality metrics. Plans generated using IO also have average treatment times comparable to that of clinical plans. With regards to the dose predictions, predictions with higher conformity tend to result in higher quality KBP plans. CONCLUSIONS: Plans resulting from an IO KBP pipeline are, on average, of equal or superior quality compared to those obtained through manual planning. The results demonstrate the potential for the use of KBP to generate GK treatment with minimal human intervention.

Lung- and diaphragm-protective strategies in acute respiratory failure: an in silico trial.

BACKGROUND: Lung- and diaphragm-protective (LDP) ventilation may prevent diaphragm atrophy and patient self-inflicted lung injury in acute respiratory failure, but feasibility is uncertain. The objectives of this study were to estimate the proportion of patients achieving LDP targets in different modes of ventilation, and to identify predictors of need for extracorporeal carbon dioxide removal (ECCO2R) to achieve LDP targets. METHODS: An in silico clinical trial was conducted using a previously published mathematical model of patient-ventilator interaction in a simulated patient population (n = 5000) with clinically relevant physiological characteristics. Ventilation and sedation were titrated according to a pre-defined algorithm in pressure support ventilation (PSV) and proportional assist ventilation (PAV+) modes, with or without adjunctive ECCO2R, and using ECCO2R alone (without ventilation or sedation). Random forest modelling was employed to identify patient-level factors associated with achieving targets. RESULTS: After titration, the proportion of patients achieving targets was lower in PAV+ vs. PSV (37% vs. 43%, odds ratio 0.78, 95% CI 0.73-0.85). Adjunctive ECCO2R substantially increased the probability of achieving targets in both PSV and PAV+ (85% vs. 84%). ECCO2R alone without ventilation or sedation achieved LDP targets in 9%. The main determinants of success without ECCO2R were lung compliance, ventilatory ratio, and strong ion difference. In silico trial results corresponded closely with the results obtained in a clinical trial of the LDP titration algorithm (n = 30). CONCLUSIONS: In this in silico trial, many patients required ECCO2R in combination with mechanical ventilation and sedation to achieve LDP targets. ECCO2R increased the probability of achieving LDP targets in patients with intermediate degrees of derangement in elastance and ventilatory ratio.

Structural insights into ligand recognition and selectivity of the human hydroxycarboxylic acid receptor HCAR2.

Hydroxycarboxylic acid receptor 2 (HCAR2) belongs to the family of class A G protein-coupled receptors with key roles in regulating lipolysis and free fatty acid formation in humans. It is deeply involved in many pathophysiological processes and serves as an attractive target for the treatment of cardiovascular, neoplastic, autoimmune, neurodegenerative, inflammatory, and metabolic diseases. Here, we report four cryo-EM structures of human HCAR2-Gi1 complexes with or without agonists, including the drugs niacin (2.69 Å) and acipimox (3.23 Å), the highly subtype-specific agonist MK-6892 (3.25 Å), and apo form (3.28 Å). Combined with molecular dynamics simulation and functional analysis, we have revealed the recognition mechanism of HCAR2 for different agonists and summarized the general pharmacophore features of HCAR2 agonists, which are based on three key residues R1113.36, S17945.52, and Y2847.43. Notably, the MK-6892-HCAR2 structure shows an extended binding pocket relative to other agonist-bound HCAR2 complexes. In addition, the key residues that determine the ligand selectivity between the HCAR2 and HCAR3 are also illuminated. Our findings provide structural insights into the ligand recognition, selectivity, activation, and G protein coupling mechanism of HCAR2, which shed light on the design of new HCAR2-targeting drugs for greater efficacy, higher selectivity, and fewer or no side effects.

3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification.

PURPOSE: To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of any number, size, and shape. METHODS: Data from 322 GK treatment plans was modified by isolating and cropping the contoured MRI and clinical dose distributions based on tumor location, then scaling the resulting tumor spaces to a standard size. An accompanying 3D tensor was created for each instance to account for tumor size. The modified dataset for 272 patients was used to train both a generative adversarial network (GAN-GK) and a 3D U-Net model (U-Net-GK). Unmodified data was used to train equivalent baseline models. All models were used to predict the dose distribution of 50 out-of-sample patients. Prediction accuracy was evaluated using gamma, with criteria of 4 %/2mm, 3 %/3mm, 3 %/1mm and 1 %/1mm. Prediction quality was assessed using coverage, selectivity, and conformity indices. RESULTS: The predictions resulting from GAN-GK and U-Net-GK were similar to their clinical counterparts, with average gamma (4 %/2mm) passing rates of 84.9 ± 15.3 % and 83.1 ± 17.2 %, respectively. In contrast, the gamma passing rate of baseline models were significantly worse than their respective GK-specific models (p < 0.001) at all criterion levels. The quality of GK-specific predictions was also similar to that of clinical plans. CONCLUSION: Deep learning models can use GK-specific data modification to predict 3D dose distributions for GKRS plans with a large range in size, shape, or number of targets. Standard deep learning models applied to unmodified GK data generated poorer predictions.

An Innovative Insight into Performance Degradation of NCM111 Cathode Induced by Suspension of Operation.

The lifespan of lithium-ion batteries varies enormously from fundamental study to practical applications. This big difference has been typically ascribed to the high degree of uncertainty in unpredictable and complicated operation conditions in real-life applications. Here, we report that the pause of the charging-discharging process, which is frequently operated in practice but rarely studied in academics, is an important reason for the performance degradation of the NCM111 cathode. It is found that the pause during cycling could trigger a remarkable drop in capacity, giving rise to ∼30% more capacity decay compared with the continuously cycled sample. In situ synchrotron X-ray diffraction analysis reveals that the harmful H1-H2 phase transition, which typically appears in the initial cycle but disappears in subsequent cycles, is reactivated by the pausing process. The anisotropic lattice strains that occur during the H1-H2 transition result in mechanical fractures that terminate with an inert NiO-type rock-salt phase on the surface of particles. The present study indicates that the discontinuous usage of rechargeable batteries is also a key factor for cycle life, which might provide a distinct perspective on the performance decay in practical applications.

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.

Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

Cryo-EM structure of G-protein-coupled receptor GPR17 in complex with inhibitory G protein.

GPR17 is a class A orphan G protein-coupled receptor (GPCR) expressed in neurons and oligodendrocyte progenitors of the central nervous system (CNS). The signalling of GPR17 occurs through the heterotrimeric Gi, but its activation mechanism is unclear. Here, we employed cryo-electron microscopy (cryo-EM) technology to elucidate the structure of activated GPR17-Gi complex. The 3.02 Å resolution structure, together with mutagenesis studies, revealed that the extracellular loop2 of GPR17 occupied the orthosteric binding pocket to promote its self-activation. The active GPR17 carried several typical microswitches like other class A GPCRs. Moreover, the Gi interacted with the key residues of transmembrane helix 3 (TM3), the amphipathic helix 8 (Helix8), and intracellular loops 3 (ICL3) in GPR17 to engage in the receptor core. In summary, our results highlight the activation mechanism of GPR17 from the structural basis. Elucidating the structural and activation mechanism of GPR17 may facilitate the pharmacological intervention for acute/chronic CNS injury.

Strategies for lung- and diaphragm-protective ventilation in acute hypoxemic respiratory failure: a physiological trial.

BACKGROUND: Insufficient or excessive respiratory effort during acute hypoxemic respiratory failure (AHRF) increases the risk of lung and diaphragm injury. We sought to establish whether respiratory effort can be optimized to achieve lung- and diaphragm-protective (LDP) targets (esophageal pressure swing - 3 to - 8 cm H2O; dynamic transpulmonary driving pressure ≤ 15 cm H2O) during AHRF. METHODS: In patients with early AHRF, spontaneous breathing was initiated as soon as passive ventilation was not deemed mandatory. Inspiratory pressure, sedation, positive end-expiratory pressure (PEEP), and sweep gas flow (in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO)) were systematically titrated to achieve LDP targets. Additionally, partial neuromuscular blockade (pNMBA) was administered in patients with refractory excessive respiratory effort. RESULTS: Of 30 patients enrolled, most had severe AHRF; 16 required VV-ECMO. Respiratory effort was absent in all at enrolment. After initiating spontaneous breathing, most exhibited high respiratory effort and only 6/30 met LDP targets. After titrating ventilation, sedation, and sweep gas flow, LDP targets were achieved in 20/30. LDP targets were more likely to be achieved in patients on VV-ECMO (median OR 10, 95% CrI 2, 81) and at the PEEP level associated with improved dynamic compliance (median OR 33, 95% CrI 5, 898). Administration of pNMBA to patients with refractory excessive effort was well-tolerated and effectively achieved LDP targets. CONCLUSION: Respiratory effort is frequently absent  under deep sedation but becomes excessive when spontaneous breathing is permitted in patients with moderate or severe AHRF. Systematically titrating ventilation and sedation can optimize respiratory effort for lung and diaphragm protection in most patients. VV-ECMO can greatly facilitate the delivery of a LDP strategy. TRIAL REGISTRATION: This trial was registered in Clinicaltrials.gov in August 2018 (NCT03612583).

Anomalous Self-Optimizing Microporous Graphene-Based Lithium-ion Battery Anode from Laser Activation of Small Organic Molecules.

Laser scribing technology is a straightforward technique to fabricate porous graphene, yet only conducted with polymeric precursors. Compared to polymers, molecular engineering of small organic molecules is much easier, which can be used to modify the graphene with tailored performance. Here we report the first employment of a laser to respectively transform small organic molecules, pentacene quinone and tetraazapentacene quinone (TAPQ), into graphene (P-LIG and N-LIG) as high-performance lithium-ion battery anodes. The TAPQ, as the N-fused molecular precursor, produces nitrogen-doped graphene. Both N-LIG and P-LIG exhibit significant self-enhancement of capacity upon cycling; the N-LIG anode delivers reversible capacities of 5863 mAh g-1 at 0.2 A g-1 and retains 1970 mAh g-1 at 2 A g-1 after another 500 cycles, which is the best performance for the graphene-type anode. Kinetics studies and structural characterizations verify that the surface- and diffusion-controlled processes are both progressively optimized, providing extra lithium storage upon cycling. It is also supported by small-angle X-ray scattering that the disordering level of micropores is increased upon cycling for N-LIG, corresponding to the enhancement of microporous level. Our work successfully develops a novel facile approach to fabricating heteroatom-doped microporous graphene exhibiting high performance and provides new insight into the lithium storage mechanism.

Cuffless and Touchless Measurement of Blood Pressure from Ballistocardiogram Based on a Body Weight Scale.

Currently, in terms of reducing the infection risk of the COVID-19 virus spreading all over the world, the development of touchless blood pressure (BP) measurement has potential benefits. The pulse transit time (PTT) has a high relation with BP, which can be measured by electrocardiogram (ECG) and photoplethysmogram (PPG). The ballistocardiogram (BCG) reflects the mechanical vibration (or displacement) caused by the heart contraction/relaxation (or heart beating), which can be measured from multiple degrees of the body. The goal of this study is to develop a cuffless and touchless BP-measurement method based on a commercial weight scale combined with a PPG sensor when measuring body weight. The proposed method was that the PTTBCG-PPGT was extracted from the BCG signal measured by a weight scale, and the PPG signal was measured from the PPG probe placed at the toe. Four PTT models were used to estimate BP. The reference method was the PTTECG-PPGF extracted from the ECG signal and PPG signal measured from the PPG probe placed at the finger. The standard BP was measured by an electronic blood pressure monitor. Twenty subjects were recruited in this study. By the proposed method, the root-mean-square error (ERMS) of estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 6.7 ± 1.60 mmHg and 4.8 ± 1.47 mmHg, respectively. The correlation coefficients, r2, of the proposed model for the SBP and DBP are 0.606 ± 0.142 and 0.284 ± 0.166, respectively. The results show that the proposed method can serve for cuffless and touchless BP measurement.

OpenKBP: The open-access knowledge-based planning grand challenge and dataset.

PURPOSE: To advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research. METHODS: We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three-dimensional (3D) dose distributions, and (b) dose-volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out-of-sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head-and-neck cancer with radiation therapy. The data were partitioned into training ( n = 200 ), validation ( n = 40 ), and testing ( n = 100 ) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the testing data to quantify the out-of-sample performance, which was hidden from participants and used to determine the final competition ranking. Participants also responded to a survey to summarize their models. RESULTS: The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 of those teams, which represents 28 unique prediction methods. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved the best dose score (2.429) and DVH score (1.478), which were both significantly better than the dose score (2.564) and the DVH score (1.529) that was achieved by the runner-up models. Lastly, many of the top performing teams reported that they used generalizable techniques (e.g., ensembles) to achieve higher performance than their competition. CONCLUSION: OpenKBP is the first competition for knowledge-based planning research. The Challenge helped launch the first platform that enables researchers to compare KBP prediction methods fairly and consistently using a large open-source dataset and standardized metrics. OpenKBP has also democratized KBP research by making it accessible to everyone, which should help accelerate the progress of KBP research. The OpenKBP datasets are available publicly to help benchmark future KBP research.

Bridging Structural Inhomogeneity to Functionality: Pair Distribution Function Methods for Functional Materials Development.

The correlation between structure and function lies at the heart of materials science and engineering. Especially, modern functional materials usually contain inhomogeneities at an atomic level, endowing them with interesting properties regarding electrons, phonons, and magnetic moments. Over the past few decades, many of the key developments in functional materials have been driven by the rapid advances in short-range crystallographic techniques. Among them, pair distribution function (PDF) technique, capable of utilizing the entire Bragg and diffuse scattering signals, stands out as a powerful tool for detecting local structure away from average. With the advent of synchrotron X-rays, spallation neutrons, and advanced computing power, the PDF can quantitatively encode a local structure and in turn guide atomic-scale engineering in the functional materials. Here, the PDF investigations in a range of functional materials are reviewed, including ferroelectrics/thermoelectrics, colossal magnetoresistance (CMR) magnets, high-temperature superconductors (HTSC), quantum dots (QDs), nano-catalysts, and energy storage materials, where the links between functions and structural inhomogeneities are prominent. For each application, a brief description of the structure-function coupling will be given, followed by selected cases of PDF investigations. Before that, an overview of the theory, methodology, and unique power of the PDF method will be also presented.

A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection.

Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid-base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient's transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO2 respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

Tuning the Kinetics of Zinc-Ion Insertion/Extraction in V2 O5 by In Situ Polyaniline Intercalation Enables Improved Aqueous Zinc-Ion Storage Performance.

Rechargeable zinc-ion batteries (ZIBs) are emerging as a promising alternative for Li-ion batteries. However, the developed cathodes suffer from sluggish Zn2+ diffusion kinetics, leading to poor rate capability and inadequate cycle life. Herein, an in situ polyaniline (PANI) intercalation strategy is developed to facilitate the Zn2+ (de)intercalation kinetics in V2 O5 . In this way, a remarkably enlarged interlayer distance (13.90 Å) can be constructed alternatively between the VO layers, offering expediting channels for facile Zn2+ diffusion. Importantly, the electrostatic interactions between the Zn2+ and the host O2- , which is another key factor in hindering the Zn2+ diffusion kinetics, can be effectively blocked by the unique π-conjugated structure of PANI. As a result, the PANI-intercalated V2 O5 exhibits a stable and highly reversible electrochemical reaction during repetitive Zn2+ insertion and extraction, as demonstrated by in situ synchrotron X-ray diffraction and Raman studies. Further first-principles calculations clearly reveal a remarkably lowered binding energy between Zn2+ and host O2- , which explains the favorable kinetics in PANI-intercalated V2 O5 . Benefitting from the above, the overall electrochemical performance of PANI-intercalated V2 O5 electrode is remarkable improved, exhibiting excellent high rate capability of 197.1 mAh g-1 at current density of 20 A g-1 with capacity retention of 97.6% over 2000 cycles.

Revisiting Indolo[3,2-b]carbazole: Synthesis, Structures, Properties, and Applications.

Indolo[3,2-b]carbazole presents a π-skeleton with a remarkable electronic structure and interesting potential applications. It is, however, also associated with ambiguity and controversy. Herein, new derivatives of indolo[3,2-b]carbazole are reported and they have enabled a comprehensive study on the electronic structure of indolo[3,2-b]carbazole and the development of a new n-type organic semiconductor. Experimental and computational studies show that indolo[3,2-b]carbazole has a largely localized p-benzoquinonediimine moiety and significant antiaromaticity. When substituted with (4-silylethynyl)phenyl groups, the indolo[3,2-b]carbazole exhibits one-dimensional π-π stacking and functions as an n-type organic semiconductor in solution-processed field effect transistors.

What Do Adult Surgical Patients Speaking Primarily Cantonese, Italian, or Portuguese Really Want to Know About Pain and Pain Management?

BACKGROUND: Surgical patients consider information about pain and pain management to be highly important (Apfelbaum, 2003). At the same time, evidence indicates that members of racial and ethnic minorities are more likely to experience inadequate pain management (Green, Anderson, Baker, Campbell, Decker, Fillingim, & Todd, 2003; Mossey, 2011). AIMS: This study investigated the needs of general day surgery patients who spoke primarily Cantonese, Italian, or Portuguese at home for information about postoperative pain. DESIGN: This was a mixed methods, descriptive study. SETTING: The day surgery unit of a large, quaternary care hospital in downtown Toronto. PARTIPANTS/SUBJECTS: Inclusion criteria were day patients who were at least 18 years of age or older and spoke primariy Cantonese, Italian or Portugues at home. and were able to read and write in their primary language. METHODS: Participants who had undergone a day surgery procedure completed a telephone information needs survey in their primary language (Cantonese, Italian, Portuguese) within 72 hours after discharge. Composite mean scores were calculated for each item. Chi-squared analyses were used to probe for intergroup differences and compare with English-fluent participants from phase 1 of this study (Kastanias, Denny, Robinson, Sabo, & Snaith, 2009). RESULTS: Sixty-three participants in total completed the survey: 21% Cantonese, 41% Italian, and 38% Portuguese. The mean age of the sample was 70 years old; 89% were born outside of Canada, and 52% were male. For the combined group, the average importance rating score range for the information items was 6.2-8.9 out of a possible score of 10. All items were rated as moderate (5-6 out of 10) to high (≥7out of 10) importance. Surgical subtype, health status, and age had no effect on the importance of any information item. There were no significant differences between the three language groups on any of the information items. This lack of difference may have been a result of a lack of power due to the small sample size of the individual language groupings. Overall, the top-ranked information items were "the plan for which drugs to take and when," "what I can do if I still have pain or side effects," and "side effects I was most likely to get." CONCLUSIONS: Similar to English-fluent participants (Kastanias et al., 2009), participants who primarily spoke either Cantonese, Italian, or Portuguese at home placed moderate to high importance on all of the information items. and neither surgical subtype, health status nor age had any effect on the importance of any item. The multilingual sample in this study placed more importance than English-fluent participants on information regarding help with paying for pain medication (p = .001) and the side effects they were most likely to experience (p < .05). Due to a paucity of literature in this area, further research is warranted. Results may assist with evaluating and improving current approaches to surgical patient pain management education.

Factors Leading to Self-Removal from the Bariatric Surgery Program After Attending the Orientation Session.

BACKGROUND: Bariatric surgery orientation sessions are often the first point of contact and a recommended component of pre-bariatric surgery assessment. Self-removal rates after bariatric program orientation are as high as 25 % despite the proven efficacy of this procedure. The objective of this study was to identify factors contributing to patient self-removal after orientation using a mixed method approach. METHODS: Patients who attended the Toronto Western Hospital Bariatric Surgery Program orientation between 2012 and 2013 and then self-removed from the program (N = 216) were included in the study. Subjects were interviewed via telephone using a semi-structured interview guide, generating both quantitative and qualitative data. Factors leading to discontinuation were rated on a five-point Likert scale. Qualitative data was analyzed using constant comparative methodology. RESULTS: The response rate was 59 % with a 40.7 % completion rate (N = 88). Concerns about potential surgical risks and complications and the ability to adapt to changes in eating and drinking post-operatively were identified as the top two factors for patients' self-removal from the program. Thematic analysis uncovered 11 major themes related to patient self-removal. Unexpected themes include perceived personal suitability for the surgery, family impact of surgery, miscommunication with the family physician, and fears related to the orientation information. CONCLUSIONS: This is one of the first studies examining barriers to bariatric surgery in the pre-operative setting and offers new insights into the reasons patients self-remove from bariatric surgery programs. This study may inform bariatric orientation program changes resulting in improved access to this effective surgical intervention.