Designing a new sustainable Test Kit supply chain network utilizing Internet of Things.

The advent of COVID-19 put much economic pressure on countries worldwide, especially low-income countries. Providing test kits for Covid-19 posed a huge challenge at the beginning of the pandemic. Especially the low-income and less developed countries that did not have the technology to produce this kit and had to import it into the country, which itself cost a lot to buy and distribute these kits. This paper proposes a sustainable COVID-19 test kits supply chain network (STKSCN) for the first time to fill this gap. Distribution and transportation of test kits, location of distribution centers, and management of used test kits are considered in this network. A mixed integer linear programming Multi-Objective (MO), multi-period, multi-resource mathematical model is extended for the proposed supply chain. Another contribution is designing a platform based on the Internet of Things (IoT) to increase the speed, accuracy and security of the network. In this way, patients set their appointment online by registering their personal details and clinical symptoms. An augmented ɛ-constraint2 (AUGMECON2) is proposed for solving small and medium size of problem. Also, two meta-heuristic algorithms, namely NSGA-II and PESA-II are presented to solve the small, medium and large size of the problem. Taguchi method is utilized to control the parameters, and for comparison between meta-heuristic, five performance metrics are suggested. In addition, a case study in Iran is presented to validate the proposed model. Finally, the results show that PESA-II is more efficient and has better performance than the others based on assessment metrics and computational time.

Lorlatinib with or without chemotherapy in ALK-driven refractory/relapsed neuroblastoma: phase 1 trial results.

Neuroblastomas harbor ALK aberrations clinically resistant to crizotinib yet sensitive pre-clinically to the third-generation ALK inhibitor lorlatinib. We conducted a first-in-child study evaluating lorlatinib with and without chemotherapy in children and adults with relapsed or refractory ALK-driven neuroblastoma. The trial is ongoing, and we report here on three cohorts that have met pre-specified primary endpoints: lorlatinib as a single agent in children (12 months to <18 years); lorlatinib as a single agent in adults (≥18 years); and lorlatinib in combination with topotecan/cyclophosphamide in children (<18 years). Primary endpoints were safety, pharmacokinetics and recommended phase 2 dose (RP2D). Secondary endpoints were response rate and 123I-metaiodobenzylguanidine (MIBG) response. Lorlatinib was evaluated at 45-115 mg/m2/dose in children and 100-150 mg in adults. Common adverse events (AEs) were hypertriglyceridemia (90%), hypercholesterolemia (79%) and weight gain (87%). Neurobehavioral AEs occurred mainly in adults and resolved with dose hold/reduction. The RP2D of lorlatinib with and without chemotherapy in children was 115 mg/m2. The single-agent adult RP2D was 150 mg. The single-agent response rate (complete/partial/minor) for <18 years was 30%; for ≥18 years, 67%; and for chemotherapy combination in <18 years, 63%; and 13 of 27 (48%) responders achieved MIBG complete responses, supporting lorlatinib's rapid translation into active phase 3 trials for patients with newly diagnosed high-risk, ALK-driven neuroblastoma. ClinicalTrials.gov registration: NCT03107988 .

Evaluating the efficiency of relief centers in disaster and epidemic conditions using multi-criteria decision-making methods and GIS: A case study.

Disaster response refers to any action taken and performed by disaster team managers after and during a disaster. According to the prevalence of the coronavirus and the unpredictability of the behavior of this virus, the capacities of hospitals and medical centers have been overshadowed by this epidemic. Governments have set up temporary rehabilitation centers to control the epidemic, make better use of resources, and quarantine COVID-19 patients. The Tehran (Iran) Disaster Management Organization has designated centers to house the injured and displaced during natural disasters such as floods and earthquakes. In this study, the efficiency and sustainability of the evaluation criteria of selected disaster management centers were evaluated in three scenarios: disaster conditions (natural disasters), epidemic conditions, and disaster-epidemic situations. Firstly, the research criteria were classified by experts using the fuzzy Delphi method and weighted using the triangular fuzzy aggregation method. In addition, the criteria are evaluated as information layers in the Geographic Information System (GIS) and the relief locations determined by the disaster management are evaluated against the research criteria. By forming a decision matrix, the alternatives in all three scenarios were prioritized using the PROMETHEE Method and evaluated in terms of efficiency. As a results, the main ways criterion shown with an impact factor of 13% among the evaluation criteria of centers in disaster situations. Additionally, the security criterion with an impact factor of 22% among the evaluation criteria of centers in epidemic conditions achieved the most important criteria in the PROMETHEE ranking.

A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms.

Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ε-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses.

A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms.

With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.

A possibilistic-robust-fuzzy programming model for designing a game theory based blood supply chain network.

This paper presents a bi-level blood supply chain network under uncertainty during the COVID-19 pandemic outbreak using a Stackelberg game theory technique. A new two-phase bi-level mixed-integer linear programming model is developed in which the total costs are minimized and the utility of donors is maximized. To cope with the uncertain nature of some of the input parameters, a novel mixed possibilistic-robust-fuzzy programming approach is developed. The data from a real case study is utilized to show the applicability and efficiency of the proposed model. Finally, some sensitivity analyses are performed on the important parameters and some managerial insights are suggested.

A new humanitarian relief logistic network for multi-objective optimization under stochastic programming.

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i) minimizing the expected value of the total costs of relief supply chain, ii) minimizing the maximum number of unsatisfied demands for relief staff and iii) minimizing the total probability of unsuccessful evacuation in routes. In this paper, a scenario based stochastic multi-objective location-allocation-routing model is proposed for a real humanitarian relief logistics problem which focused on both pre- and post-disaster situations in presence of uncertainty. To cope with demand uncertainty, a simulation approach is used. The proposed model integrates these two phases simultaneously. Then, both strategic and operational decisions (pre-disaster and post-disaster), fairness in the evacuation, and relief item distribution including commodities and relief workers, victim evacuation including injured people, corpses and homeless people are also considered simultaneously in this paper. The presented model is solved utilizing the Epsilon-constraint method for small- and medium-scale problems and using three metaheuristic algorithms for the large-scale problem (case study). Empirical results illustrate that the model can be used to locate the shelters and relief distribution centers, determine appropriate routes and allocate resources in uncertain and real-life disaster situations.

Investigating a Dual-Channel Network in a Sustainable Closed-Loop Supply Chain Considering Energy Sources and Consumption Tax.

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved.

Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions.

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence.

Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm.

Demand plays a vital role in designing every closed-loop supply chain network in today's world. The flow of materials and commodities in the opposite direction of the standard supply chain is inevitable. In this way, this study addresses a new multi-echelon multi-period closed-loop supply chain network to minimize the total costs of the network. The echelons include suppliers, manufacturers, distribution centers, customers, and recycling and recovery units of components in the proposed network. Also, a Mixed Integer Linear Programming (MILP) model considering factories' vehicles and rental cars of transportation companies is formulated for the proposed problem. Moreover, for the first time, the demand for the products is estimated using an Auto-Regressive Integrated Moving Average (ARIMA) time series model to decrease the shortage that may happen in the whole supply chain network. Conversely, for solving the proposed model, the GAMS software is utilized in small and medium-size problems, and also, genetic algorithm is applied for large-size problems to obtain initial results of the model. Numerical results show that the proposed model is closer to the actual situation and could reach a reasonable solution in terms of service level, shortage, etc. Accordingly, sensitivity analysis is performed on essential parameters to show the performance of the proposed model. Finally, some potential topics are discussed for future study.

Imaging of congenital genitourinary anomalies.

Congenital genitourinary anomalies are among the most frequent types of birth defects in neonates. Some anomalies can be a significant cause of morbidity in infancy, while others remain asymptomatic even until adulthood and can be at times the only manifestation of a complex systemic disease. The spectrum of these anomalies results from the developmental insults that can occur at various embryologic stages, and an understanding of the formation of the genitourinary system is helpful in the evaluation and treatment of a child with a congenital genitourinary anomaly. Imaging plays an essential role in the diagnosis of congenital genitourinary anomalies and treatment planning. In this article, we highlight the embryologic and characteristic imaging features of various congenital genitourinary anomalies, demonstrate the utility of different imaging modalities in management, and review specific imaging modalities and protocols for image optimization.

A sustainable-resilience healthcare network for handling COVID-19 pandemic.

In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching-learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between - 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-021-04238-2.

A discrete event simulation method for performance analysis of an additive manufacturing in the dental clinic.

Three-dimensional printing (3DP) is an evolutionary solution for making customize items for all sectors, but it has become more prominent in the healthcare sector. In this field, some solutions have to be adapted to patients. This is especially true for dentistry, where all the patients have their own unique mouth and tooth structure. It is now possible to provide an accurate model of the patient's mouth and teeth with solutions that are perfectly compatible with them, leading to the provision of a dental service with a high success rate. Even if there is a problem, it is enough to change the three-dimensional design. Therefore, it is a time-saving method, too. The purpose of this study is to investigate the role of 3DP in dentistry and to identify the processes and procedures resulting from the use of this technology. To do so, with the help of a case study, a 3DP-based dental clinic that provides implant, orthodontics, restoration and dentures services is simulated in Arena software. The current state of the system is assessed by defining appropriate evaluation criteria including net profit, utilization, waiting time, patients makespan and laboratory makespan. The simulation model is then developed with innovations such as adding an inventory control policy, creating rest time for resources and controlling the policy of sending products from laboratory to the clinic. After an extensive sensitivity analysis, improving the performance of the system is on the agenda of this paper by examining various scenarios. Results show that scenarios such as reducing some resources of the system or considering rest time in exchange for increasing the duration of the work shift can have a significant impact on clinic performance. Supplementary Information: The online version contains supplementary material available at 10.1007/s00170-021-08135-7.

Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms.

This study develops a novel mathematical model to design a sustainable mask Closed-Loop Supply Chain Network (CLSCN) during the COVID-19 outbreak for the first time. A multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to address the locational, supply, production, distribution, collection, quarantine, recycling, reuse, and disposal decisions within a multi-period multi-echelon multi-product supply chain. Additionally, sustainable development is studied in terms of minimizing the total cost, total pollution and total human risk at the same time. Since the CLSCN design is an NP-hard problem, Multi-Objective Grey Wolf Optimization (MOGWO) algorithm and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented to solve the proposed model and to find Pareto optimal solutions. Since Meta-heuristic algorithms are sensitive to their input parameters, the Taguchi design method is applied to tune and control the parameters. Then, a comparison is performed using four assessment metrics including Max-Spread, Spread of Non-Dominance Solution (SNS), Number of Pareto Solutions (NPS), and Mean Ideal Distance (MID). Additionally, a statistical test is employed to evaluate the quality of the obtained Pareto frontier by the presented algorithms. The obtained results reveal that the MOGWO algorithm is more reliable to tackle the problem such that it is about 25% superior to NSGA-II in terms of the dispersion of Pareto solutions and about 2% superior in terms of the solution quality. To validate the proposed mathematical model and testing its applicability, a real case study in Tehran/Iran is investigated as well as a set of sensitivity analyses on important parameters. Finally, the practical implications are discussed and useful managerial insights are given.

Randomized Phase II Trial of MIBG Versus MIBG, Vincristine, and Irinotecan Versus MIBG and Vorinostat for Patients With Relapsed or Refractory Neuroblastoma: A Report From NANT Consortium.

PURPOSE: 131I-metaiodobenzylguanidine (MIBG) is an active radiotherapeutic for neuroblastoma. The primary aim of this trial was to identify which of three MIBG regimens was likely associated with the highest true response rate. PATIENTS AND METHODS: Patients 1-30 years were eligible if they had relapsed or refractory neuroblastoma, at least one MIBG-avid site, and adequate autologous stem cells. Patients received MIBG 18 mCi/kg on day 1 and autologous stem cell on day 15. Patients randomly assigned to arm A received only MIBG; patients randomly assigned to arm B received intravenous vincristine on day 0 and irinotecan daily on days 0-4; patients randomly assigned to arm C received vorinostat (180 mg/m2/dose) orally once daily on days 1 to 12. The primary end point was response after one course by New Approaches to Neuroblastoma Therapy criteria. The trial was designed with 105 patients to ensure an 80% chance that the arm with highest response rate was selected. RESULTS: One hundred fourteen patients were enrolled, with three ineligible and six unevaluable, leaving 105 eligible and evaluable patients (36 in arm A, 35 in arm B, and 34 in arm C; 55 boys; and median age 6.5 years). After one course, the response rates (partial response or better) on arms A, B, and C were 14% (95% CI, 5 to 30), 14% (5 to 31), and 32% (18 to 51). An additional five, five, and four patients met New Approaches to Neuroblastoma Therapy Minor Response criteria on arms A, B, and C, respectively. On arms A, B, and C, rates of any grade 3+ nonhematologic toxicity after first course were 19%, 49%, and 35%. CONCLUSION: Vorinostat and MIBG is likely the arm with the highest true response rate, with manageable toxicity. Vincristine and irinotecan do not appear to improve the response rate to MIBG and are associated with increased toxicity.

An integrated sustainable medical supply chain network during COVID-19.

Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production-distribution-inventory-allocation-location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.

A bi-objective home healthcare routing and scheduling problem considering patients' satisfaction in a fuzzy environment.

Home care services are an alternative answer to hospitalization, and play an important role in reducing the healthcare costs for governments and healthcare practitioners. To find a valid plan for these services, an optimization problem called the home healthcare routing and scheduling problem is motivated to perform the logistics of the home care services. Although most studies mainly focus on minimizing the total cost of logistics activities, no study, as far as we know, has treated the patients' satisfaction as an objective function under uncertainty. To make this problem more practical, this study proposes a bi-objective optimization methodology to model a multi-period and multi-depot home healthcare routing and scheduling problem in a fuzzy environment. With regards to a group of uncertain parameters such as the time of travel and services as well as patients' satisfaction, a fuzzy approach named as the Jimenez's method, is also utilized. To address the proposed home healthcare problem, new and well-established metaheuristics are obtained. Although the social engineering optimizer (SEO) has been applied to several optimization problems, it has not yet been applied in the healthcare routing and scheduling area. Another innovation is to develop a new modified multi-objective version of SEO by using an adaptive memory strategy, so-called AMSEO. Finally, a comprehensive discussion is provided by comparing the algorithms based on multi-objective metrics and sensitivity analyses. The practicality and efficiency of the AMSEO in this context lends weight to the development and application of the approach more broadly.

Premature epiphyseal growth plate arrest after isotretinoin therapy for high-risk neuroblastoma: A case series and review of the literature.

BACKGROUND: Vitamin A-derived retinoids have been reported to cause skeletal abnormalities ranging from hypercalcemia to premature epiphyseal closure. Isotretinoin is a retinoid used as standard therapy for high-risk neuroblastoma and has been reported to cause premature epiphyseal growth plate arrest. PROCEDURE: We identified patients from the Children's Hospital Los Angeles (CHLA) database with high-risk neuroblastoma diagnosed from 1991 to 2018 who experienced premature epiphyseal growth plate arrest and compared their characteristics to other patients with high-risk neuroblastoma. We then performed a literature review of this complication. Data collection included diagnosis age of neuroblastoma, presentation age, agent of exposure, dose, exposure range, and skeletal deformity. RESULTS: Among 216 patients, high-risk neuroblastoma was diagnosed before age of five years (n = 165), between ages of 5 and 10 years (n = 41), and after 10 years of age (n = 13). Three out of 216 patients developed premature epiphyseal growth arrest after isotretinoin exposure (overall incidence = 1.38%). The incidence of bony abnormalities was significantly higher in patients diagnosed in 5- to 10-year age group than in other two groups (P = 0.014). Literature review identified eight additional patients with neuroblastoma who presented with retinoid associated skeletal abnormalities. The median range of isotretinoin exposure for these 11 patients was between 6.5 and 7.625 years (range, 2-14) with no cases of isotretinoin therapy completion before age 5 years. CONCLUSION: Bone toxicity associated with isotretinoin exposure is a concern. Growth plate arrest is a serious adverse effect that is attributable to isotretinoin therapy. Our findings suggest the prepubescent growth plate may be most at risk, and we recommend special attention to this population.

Ensuring Appropriateness of Pediatric Second Opinion Consultations.

PURPOSE: We sought to evaluate discrepancy rates between outside interpretations, radiology trainee preliminary reports, and subspecialist attending final interpretations for pediatric second opinion consultations on plain film and computed tomography imaging and to evaluate the impact of a process improvement for second opinion consultations. METHODS: Of a total of 572 requests for second opinion consultations during 1-year preintervention period, we utilized RADPEER to score concurrence of 158 requests which occurred overnight and included outside radiologist interpretations and resident preliminary reports. In consultation with clinician committees, we developed new guidelines for requesting second opinion consultations. We evaluated the impact on the number of consultations for the 1-year period following implementation of this process improvement. RESULTS: There was concurrence between the outside interpretation and pediatric subspecialist second opinion in 146 of 158 cases (92%). There was concurrence between the radiology resident and pediatric subspecialist second opinion in 145 of 158 cases (92%). During the 1-year period following our process improvement implementation, the total number of second opinion consultations decreased to 185 (from 572, a decrease of 68%) and the number of overnight requests for resident preliminary reports decreased to 11 (from 158, a decrease of 93%). CONCLUSIONS: There was a high degree of concurrence between interpretations provided by outside radiologists, overnight radiology residents, and attending pediatric radiologists at our institution. Analyzing institutional-specific discrepancy rates is a valuable first step in partnering with clinicians to develop appropriate guidelines for second opinion consultations.

Absence of Testicular Adrenal Rest Tumors in Newborns, Infants, and Toddlers with Classical Congenital Adrenal Hyperplasia.

INTRODUCTION: Testicular adrenal rest tumors (TART) are a known consequence for males with classical congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency. TART are associated with potential infertility in adults. However, little is known about TART in very young males with CAH. OBJECTIVE: We assessed the presence of TART in newborn, infant, and toddler males with classical CAH via scrotal ultrasound. METHODS: Males with CAH had scrotal ultrasounds during the first 4 years of life, evaluating testes for morphology, blood flow, and presence of TART. Newborn screen 17-hydroxyprogesterone (17-OHP) and serum 17-OHP at the time of ultrasound were recorded. Bone ages were considered very advanced if ≥2 SD above chronological age. RESULTS: Thirty-one ultrasounds in 16 males were performed. An initial ultrasound was obtained in four newborns at diagnosis (6.8 ± 2.1 days), six infants (2.2 ± 0.9 months), and six toddlers (2.4 ± 0.9 years). Eleven males had at least one repeat ultrasound. A large proportion (11/16) were in poor hormonal control with an elevated 17-OHP (325 ± 298 nmol/L). One infant was in very poor hormonal control (17-OHP 447 nmol/L) at initial ultrasound, and two toddlers had advanced bone ages (+3.2 and +4.5 SD) representing exposure to postnatal androgens. However, no TART were detected in any subjects. CONCLUSIONS: TART were not found by scrotal ultrasound in males up to 4 years of age with classical CAH despite settings with expected high ACTH drive. Further research into the occurrence of TART in CAH may elucidate factors that contribute to the detection and individual predisposition to TART.