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BACKGROUND: There are few comparative data on the analgesic options used to manage patients undergoing minimally invasive repair of pectus excavatum (MIRPE). The Society for Pediatric Anesthesia Improvement Network was established to investigate outcomes for procedures where there is significant management variability. For our first study, we established a multicenter observational database to characterize the analgesic strategies used to manage pediatric patients undergoing MIRPE. Outcome data from the participating centers were used to assess the association between analgesic strategy and pain outcomes. METHODS: Fourteen institutions enrolled patients from June 2014 through August 2015. Network members agreed to an observational methodology where each institution managed patients based on their institutional standards and protocols. There was no requirement to standardize care. Patients were categorized based on analgesic strategy: epidural catheter (EC), paravertebral catheter (PVC), wound catheter (WC), no regional (NR) analgesia, and intrathecal morphine techniques. Primary outcomes, pain score and opioid consumption by postoperative day (POD), for each technique were compared while adjusting for confounders using multivariable modeling that included 5 covariates: age, sex, number of bars, Haller index, and use of preoperative pain medication. Pain scores were analyzed using repeated-measures analysis of variance with Bonferroni correction. Opioid consumption was analyzed using a multivariable quantile regression. RESULTS: Data were collected on 348 patients and categorized based on primary analgesic strategy: EC (122), PVC (57), WC (41), NR (120), and intrathecal morphine (8). Compared to EC, daily median pain scores were higher in patients managed with PVC (POD 0), WC (POD 0, 1, 2, 3), and NR (POD 0, 1, 2), respectively ( P < .001–.024 depending on group). Daily opioid requirements were higher in patients managed with PVC (POD 0, 1), WC (POD 0, 1, 2), and NR (POD 0, 1, 2) when compared to patients managed with EC ( P < .001). CONCLUSIONS: Our data indicate variation in pain management strategies for patients undergoing MIRPE within our network. The results indicate that most patients have mild-to-moderate pain postoperatively regardless of analgesic management. Patients managed with EC had lower pain scores and opioid consumption in the early recovery period compared to other treatment strategies.

PurposeProvide an adaptive multicriteria optimization (MCO) method for intensity‐modulated proton therapy (IMPT) utilizing GPU technology. Previously described limitations of MCO such as Pareto approximation and limitation on the number of objectives were addressed.MethodsThe treatment planning process for IMPT must account for multiple objectives, which requires extensive treatment planning resources. Often a large number of objectives (>10) are required. Hence the need for an MCO algorithm that can handle large number of objectives. The novelty of the MCO method presented here lies on the introduction of the adaptive weighting scheme that can generate a well‐distributed and dense representation of the Pareto surface for a large number of objectives in an efficient manner. In our approach the generated Pareto surface is constructed for a set of DVH objectives. The MCO algorithm is based on the augmented weighted Chebychev metric (AWCM) method with an adaptive weighting scheme. This scheme uses the differential evolution (DE) method to generate a set of well‐distributed Pareto points. The quality of the Pareto points’ distribution in the objective space was assessed quantitatively using the Pareto sampling metric. The MCO algorithm was developed to perform multiple parallel searches to achieve a rapid mapping of the Pareto surface, produce clinically deliverable plans, and was implemented on a GPU cluster. The MCO algorithm was tested on two clinical cases with 10 and 18 objectives. For each case one of the MCO‐generated plans was selected for comparison with the clinically generated plan. The MCO plan was randomly selected out of the set of MCO plans that had target coverage similar to the clinically generated plan and the same or better sparing of the organs at risk (OAR). Additionally, a validation study of the AWCM method vs the weighted sum method was performed.ResultsThe adaptive MCO algorithm generated Pareto points on the Pareto hypersurface in a fast (2–3 hr) and efficient manner for 2 cases with 10 and 18 objectives. The MCO algorithm generated a dense and well‐distributed set of Pareto points on the objective space, and was able to achieve minimization of the Pareto sampling metric. The selected MCO plan showed an improvement of the DVH objectives in comparison to the clinically optimized plan in both cases. For case one, the MCO plan showed a 48% reduction of the 50% dose to OARs and a 16% reduction of the 1% dose to OARs. For case 2, the MCO plan showed a 72% reduction of the 50% dose to OARs and a 42% reduction of the 1% dose to OARs. The comparison of AWCM to WS showed that the AWCM method has a dosimetric advantage over WS for both patient cases.ConclusionWe introduced an adaptive MCO algorithm for IMPT accelerated using GPUs. The algorithm is based on an adaptive method for generating Pareto plans in the objective space. We have shown that the algorithm can provide rapid and efficient mapping of the multicriteria Pareto surface with clinically deliverable plans.

Abstract Rationale Improved therapeutic options are needed for patients with treatment-refractory nontuberculous mycobacterial lung disease caused by Mycobacterium avium complex (MAC). Objectives To evaluate the efficacy and safety of daily amikacin liposome inhalation suspension (ALIS) added to standard guideline-based therapy (GBT) in patients with refractory MAC lung disease. Methods Adults with amikacin-susceptible MAC lung disease and MAC-positive sputum cultures despite at least 6 months of stable GBT were randomly assigned (2:1) to receive ALIS with GBT (ALIS + GBT) or GBT alone. Once-daily ALIS was supplied in single-use vials delivering 590 mg amikacin to the nebulizer. The primary endpoint was culture conversion, defined as three consecutive monthly MAC-negative sputum cultures by Month 6. Measurements and Main Results Enrolled patients (ALIS + GBT, n = 224; GBT-alone, n = 112) were a mean 64.7 years old and 69.3% female. Most had underlying bronchiectasis (62.5%), chronic obstructive pulmonary disease (14.3%), or both (11.9%). Culture conversion was achieved by 65 of 224 patients (29.0%) with ALIS + GBT and 10 of 112 (8.9%) with GBT alone (odds ratio, 4.22; 95% confidence interval, 2.08–8.57; P &lt; 0.001). Patients in the ALIS + GBT arm versus GBT alone were more likely to achieve conversion (hazard ratio, 3.90; 95% confidence interval, 2.00–7.60). Respiratory adverse events (primarily dysphonia, cough, and dyspnea) were reported in 87.4% of patients receiving ALIS + GBT and 50.0% receiving GBT alone; serious treatment-emergent adverse events occurred in 20.2% and 17.9% of patients, respectively. Conclusions Addition of ALIS to GBT for treatment-refractory MAC lung disease achieved significantly greater culture conversion by Month 6 than GBT alone, with comparable rates of serious adverse events. Clinical trial registered with www.clinicaltrials.gov (NCT02344004).

PurposeThe presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy tissue sparing, particularly for techniques like pencil beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four‐dimensional (4D) dose, they typically have one or more deficiencies such as not including the particular effects from a dynamic delivery, using analytical dose calculations, and/or using nonphysical dose‐accumulation methods. This work presents a clinically useful 4D‐dose calculator that addresses each of these shortcomings.MethodsTo quickly compute the 4D dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were (a) simulating the delivery of the plan using measured delivery parameters to distribute the plan amongst 4DCT phases characterizing the patient breathing, (b) using an in‐house Monte Carlo simulation (MC) dose calculator to determine the dose delivered to each breathing phase, and (c) accumulating the doses from the various breathing phases onto a single phase for evaluation. The accumulation was performed by individually transferring the energy and mass of dose‐grid subvoxels, a technique that models the transfer of dose in a more physically realistic manner. The calculator was run on three test cases, with lung, esophagus, and liver targets, respectively, to assess the various uncertainties in the beam delivery simulation as well as to characterize the dose‐accumulation technique.ResultsFour‐dimensional doses were successfully computed for the three test cases with computation times ranging from 4–6 min on a server with eight NVIDIA Titan X graphics cards; the most time‐consuming component was the MC dose engine. The subvoxel‐based dose‐accumulation technique produced stable 4D‐dose distributions at subvoxel scales of 0.5–1.0 mm without impairing the total computation time. The uncertainties in the beam delivery simulation led to moderate variations of the dose–volume histograms for these cases; the variations were reduced by implementing repainting or phase‐gating motion mitigation techniques in the calculator.ConclusionsA MC‐based and GPU‐accelerated 4D‐dose calculator was developed to estimate the effects of respiratory motion on pencil beam scanning proton therapy treatments. After future validation, the calculator could be used to assess treatment plans and its quick runtime would make it easily usable in a future 4D‐robust optimization system.