Prescription-based cost analysis of medicines for cardiovascular risk factors at Indian Council of Medical Research-Rational Use of Medicine Centre Hospitals of India.

OBJECTIVES: India has taken several initiatives to provide health care to its population while keeping the related expenditure minimum. Since cardiovascular diseases are the most prevalent chronic conditions, in the present study, we aimed to analyze the difference in prices of medicines prescribed for three cardiovascular risk factors, based on (a) listed and not listed in the National List of Essential Medicines (NLEM) and (b) generic and branded drugs. MATERIALS AND METHODS: Outpatient prescriptions for diabetes mellitus, hypertension, and dyslipidemia were retrospectively analyzed from 12 tertiary centers. The prices of medicines prescribed were compared based on presence or absence in NLEM India-2015 and prescribing by generic versus brand name. The price was standardized and presented as average price per medicine per year for a given medicine. The results are presented in Indian rupee (INR) and as median (range). RESULTS: Of the 4,736 prescriptions collected, 843 contained oral antidiabetic, antihypertensive, and/or hypolipidemic medicines. The price per medicine per year for NLEM oral antidiabetics was INR 2849 (2593-3104) and for non-NLEM was INR 5343 (2964-14364). It was INR 806 (243-2132) for generic and INR 3809 (1968-14364) for branded antidiabetics. Antihypertensives and hypolipidemics followed the trend. The price of branded non-NLEM medicines was 5-22 times higher compared to generic NLEM which, for a population of 1.37 billion, would translate to a potential saving of 346.8 billion INR for statins. The variability was significant for sulfonylureas, angiotensin receptor blockers, beta-blockers, diuretics, and statins (P < 0.0001). CONCLUSION: The study highlights an urgent need for intervention to actualize the maximum benefit of government policies and minimize the out-of-pocket expenditure on medicines.

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient.

The Covid-19 pandemic is one of the most significant global health concerns that have emerged in this decade. Intelligent healthcare technology and techniques based on speech signal and artificial intelligence make it feasible to provide a faster and more efficient timely detection of Covid-19. The main objective of our study is to design speech signal-based noninvasive, low-cost, remote diagnosis of Covid-19. In this study, we have developed system to detect Covid-19 from speech signal using Mel frequency magnitude coefficients (MFMC) and machine learning techniques. In order to capture higher-order spectral features, the spectrum is divided into a larger number of subbands with narrower bandwidths as MFMC, which leads to better frequency resolution and less overall noise. As a consequence of an improvement in frequency resolution as well as a decrease in the quantity of noise that is included with the extraction of MFMC, the higher-order MFMCs are able to identify Covid-19 from speech signals with an increased level of accuracy. The procedures for machine learning are often less complicated than those for deep learning, and they may commonly be carried out on regular computers. However, deep learning systems need extensive computing power and data storage. Twelve, twenty-four, thirty, and forty spectral coefficients are obtained using MFMC in our study, and from these coefficients, performance is accessed using machine learning classifiers, such as random forests and K-nearest neighbor (KNN); however, KNN has performed better than the other model with having AUC score of 0.80.

An observational multi-centric COVID-19 sequelae study among health care workers.

Background: India has seen more than 43 million confirmed cases of COVID-19 as of April 2022, with a recovery rate of 98.8%, resulting in a large section of the population including the healthcare workers (HCWs), susceptible to develop post COVID sequelae. This study was carried out to assess the nature and prevalence of medical sequelae following COVID-19 infection, and risk factors, if any. Methods: This was an observational, multicenter cross-sectional study conducted at eight tertiary care centers. The consenting participants were HCWs between 12 and 52 weeks post discharge after COVID-19 infection. Data on demographics, medical history, clinical features of COVID-19 and various symptoms of COVID sequelae was collected through specific questionnaire. Finding: Mean age of the 679 eligible participants was 31.49 ± 9.54 years. The overall prevalence of COVID sequelae was 30.34%, with fatigue (11.5%) being the most common followed by insomnia (8.5%), difficulty in breathing during activity (6%) and pain in joints (5%). The odds of having any sequelae were significantly higher among participants who had moderate to severe COVID-19 (OR 6.51; 95% CI 3.46-12.23) and lower among males (OR 0.55; 95% CI 0.39-0.76). Besides these, other predictors for having sequelae were age (≥45 years), presence of any comorbidity (especially hypertension and asthma), category of HCW (non-doctors vs doctors) and hospitalisation due to COVID-19. Interpretation: Approximately one-third of the participants experienced COVID sequelae. Severity of COVID illness, female gender, advanced age, co-morbidity were significant risk factors for COVID sequelae. Funding: This work is a part of Indian Council for Medical Research (ICMR)- Rational Use of Medicines network. No additional financial support was received from ICMR to carry out the work, for study materials, medical writing, and APC.

Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments.

Brain extraction is an indispensable step in neuro-imaging with a direct impact on downstream analyses. Most such methods have been developed for non-pathologically affected brains, and hence tend to suffer in performance when applied on brains with pathologies, e.g., gliomas, multiple sclerosis, traumatic brain injuries. Deep Learning (DL) methodologies for healthcare have shown promising results, but their clinical translation has been limited, primarily due to these methods suffering from i) high computational cost, and ii) specific hardware requirements, e.g., DL acceleration cards. In this study, we explore the potential of mathematical optimizations, towards making DL methods amenable to application in low resource environments. We focus on both the qualitative and quantitative evaluation of such optimizations on an existing DL brain extraction method, designed for pathologically-affected brains and agnostic to the input modality. We conduct direct optimizations and quantization of the trained model (i.e., prior to inference on new data). Our results yield substantial gains, in terms of speedup, latency, through-put, and reduction in memory usage, while the segmentation performance of the initial and the optimized models remains stable, i.e., as quantified by both the Dice Similarity Coefficient and the Hausdorff Distance. These findings support post-training optimizations as a promising approach for enabling the execution of advanced DL methodologies on plain commercial-grade CPUs, and hence contributing to their translation in limited- and low- resource clinical environments.

OpenFL: the open federated learning library.

Objective.Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets.Approach.Open federated learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective of the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks.Main results.In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases.Significance.The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL's initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open-sourced atgithub.com/intel/openfl.

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research.

Objective.De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria.Approach.Towards this end, this manuscript describes theFederatedTumorSegmentation (FeTS) tool, in terms of software architecture and functionality.Main results.The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data.Significance.Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced athttps://github.com/FETS-AI/Front-End.

Bedaquiline, Delamanid, Linezolid and Clofazimine for Treatment of Pre-extensively Drug-Resistant Tuberculosis.

BACKGROUND: Treatment success rates for multidrug-resistant tuberculosis (MDR-TB) remain low globally. Availability of newer drugs has given scope to develop regimens that can be patient-friendly, less toxic, with improved outcomes. We proposed to determine the effectiveness of an entirely oral, short-course regimen with Bedaquiline and Delamanid in treating MDR-TB with additional resistance to fluoroquinolones (MDR-TBFQ+) or second-line injectable (MDR-TBSLI+). METHODS: We prospectively determined the effectiveness and safety of combining two new drugs with two repurposed drugs - Bedaquiline, Delamanid, Linezolid, and Clofazimine for 24-36 weeks in adults with pulmonary MDR-TBFQ+ or/and MDR-TBSLI+. The primary outcome was a favorable response at end of treatment, defined as two consecutive negative cultures taken four weeks apart. The unfavorable outcomes included bacteriologic or clinical failure during treatment period. RESULTS: Of the 165 participants enrolled, 158 had MDR-TBFQ+. At the end of treatment, after excluding 12 patients due to baseline drug susceptibility and culture negatives, 139 of 153 patients (91%) had a favorable outcome. Fourteen patients (9%) had unfavorable outcomes: four deaths, seven treatment changes, two bacteriological failures, and one withdrawal. During treatment, 85 patients (52%) developed myelosuppression, 69 (42%) reported peripheral neuropathy, and none had QTc(F) prolongation >500msec. At 48 weeks of follow-up, 131 patients showed sustained treatment success with the resolution of adverse events in the majority. CONCLUSION: After 24-36 weeks of treatment, this regimen resulted in a satisfactory favorable outcome in pulmonary MDR-TB patients with additional drug resistance. Cardiotoxicity was minimal, and myelosuppression, while common, was detected early and treated successfully.

Disintegration testing augmented by computer Vision technology.

Oral solid dosage forms, specifically immediate release tablets, are prevalent in the pharmaceutical industry. Disintegration testing is often the first step of commercialization and large-scale production of these dosage forms. Current disintegration testing in the pharmaceutical industry, according to United States Pharmacopeia (USP) chapter 〈701〉, only gives information about the duration of the tablet disintegration process. This information is subjective, variable, and prone to human error due to manual or physical data collection methods via the human eye or contact disks. To lessen the data integrity risk associated with this process, efforts have been made to automate the analysis of the disintegration process using digital lens and other imaging technologies. This would provide a non-invasive method to quantitatively determine disintegration time through computer algorithms. The main challenges associated with developing such a system involve visualization of tablet pieces through cloudy and turbid liquid. The Computer Vision for Disintegration (CVD) system has been developed to be used along with traditional pharmaceutical disintegration testing devices to monitor tablet pieces and distinguish them from the surrounding liquid. The software written for CVD utilizes data captured by cameras or other lenses then uses mobile SSD and CNN, with an OpenCV and FRCNN machine learning model, to analyze and interpret the data. This technology is capable of consistently identifying tablets with ≥ 99.6% accuracy. Not only is the data produced by CVD more reliable, but it opens the possibility of a deeper understanding of disintegration rates and mechanisms in addition to duration.

Prevalence of latent structural heart disease in Nepali schoolchildren.

BACKGROUND: The present study aimed to quantify the burden of structural heart disease in Nepali children. METHODS: We performed a school-based cross-sectional echocardiographic screening study with cluster random sampling among children 5-16 years of age. RESULTS: Between December 2012 and January 2019, 6573 children (mean age 10.6 ± 2.9 years) from 41 randomly selected schools underwent echocardiographic screening. Structural heart disease was detected in 14.0 per 1000 children (95% CI 11.3-17.1) and was congenital in 3.3 per 1000 (95% CI 2.1-5.1) and rheumatic in 10.6 per 1000 (95% CI 8.3-13.4). Rates of rheumatic heart disease were higher among children attending public as compared to private schools (OR 2.8, 95% CI 1.6-5.2, p = 0.0001). CONCLUSION: Rheumatic heart disease accounted for three out of four cases of structural heart disease and was more common among children attending public as compared to private schools.

Impact of COVID-19 pandemic on pediatric cardiac services in India.

BACKGROUND: COVID-19 pandemic has disrupted pediatric cardiac services across the globe. Limited data are available on the impact of COVID.19 on pediatric cardiac care in India. AIMS: The aims are to study the impact of COVID-19 pandemic on the care of children with heart disease in India in terms of number of outpatient visits, hospitalizations, catheter-based interventions, and cardiac surgeries. SETTINGS AND DESIGN: This is a retrospective, multicentric, observational study. METHODS: We collected monthly data on the number and characteristics of outpatient visits, hospitalizations, catheter-based interventions, and cardiac surgeries and major hospital statistics, over a period of 5 months (April to August 2020), which coincided with the first wave of COVID-19 pandemic in India and compared it with data from the corresponding months in 2019. RESULTS: The outpatient visits across the 24 participating pediatric cardiac centers decreased by 74.5% in 2020 (n = 13,878) as compared to the corresponding period in 2019 (n = 54,213). The reduction in the number of hospitalizations, cardiac surgeries, and catheterization procedures was 66.8%, 73.0%, and 74.3%, respectively. The reduction in hospitalization was relatively less pronounced among neonates as compared to infants/children (47.6% vs. 70.1% reduction) and for emergency surgeries as compared to elective indications (27.8% vs. 79.2%). The overall in-hospital mortality was higher in 2020 (8.1%) as compared to 2019 (4.8%), with a higher postoperative mortality (9.1% vs. 4.3%). CONCLUSIONS: The current COVID-19 pandemic significantly impacted the delivery of pediatric cardiac care across India with two-third reduction in hospitalizations and cardiac surgeries. In an already resource-constrained environment, the impact of such a massive reduction in the number of surgeries could be significant over the coming years. These findings may prove useful in formulating strategy to manage subsequent waves of ongoing COVID-19 pandemic.

Effectiveness of Systematic Echocardiographic Screening for Rheumatic Heart Disease in Nepalese Schoolchildren: A Cluster Randomized Clinical Trial.

Importance: Echocardiographic screening allows for early detection of subclinical stages of rheumatic heart disease among children in endemic regions. Objective: To investigate the effectiveness of systematic echocardiographic screening in combination with secondary antibiotic prophylaxis on the prevalence of rheumatic heart disease. Design, Setting, and Participants: This cluster randomized clinical trial included students 9 to 16 years of age attending public and private schools in urban and rural areas of the Sunsari district in Nepal that had been randomly selected on November 17, 2012. Echocardiographic follow-up was performed between January 7, 2016, and January 3, 2019. Interventions: In the experimental group, children underwent systematic echocardiographic screening followed by secondary antibiotic prophylaxis in case they had echocardiographic evidence of latent rheumatic heart disease. In the control group, children underwent no echocardiographic screening. Main Outcomes and Measures: Prevalence of the composite of definite or borderline rheumatic heart disease according to the World Heart Federation criteria in experimental and control schools as assessed 4 years after intervention. Results: A total of 35 schools were randomized to the experimental group (n = 19) or the control group (n = 16). After a median of 4.3 years (interquartile range [IQR], 4.0-4.5 years), 17 of 19 schools in the experimental group (2648 children; median age at follow-up, 12.1 years; IQR, 10.3-12.5 years; 1308 [49.4%] male) and 15 of 16 schools in the control group (1325 children; median age at follow-up, 10.6 years; IQR, 10.0-12.5 years; 682 [51.5%] male) underwent echocardiographic follow-up. The prevalence of definite or borderline rheumatic heart disease was 10.8 per 1000 children (95% CI, 4.7-24.7) in the control group and 3.8 per 1000 children (95% CI, 1.5-9.8) in the experimental group (odds ratio, 0.34; 95% CI, 0.11-1.07; P = .06). The prevalence in the experimental group at baseline had been 12.9 per 1000 children (95% CI, 9.2-18.1). In the experimental group, the odds ratio of definite or borderline rheumatic heart disease at follow-up vs baseline was 0.29 (95% CI, 0.13-0.65; P = .008). Conclusions and Relevance: School-based echocardiographic screening in combination with secondary antibiotic prophylaxis in children with evidence of latent rheumatic heart disease may be an effective strategy to reduce the prevalence of definite or borderline rheumatic heart disease in endemic regions. Trial Registration: ClinicalTrials.gov Identifier: NCT01550068.

Anaemia among Patients of Heart Failure in a Tertiary Care Centre of Nepal: A Descriptive Cross-sectional Study.

INTRODUCTION: Anaemia is an important comorbidity common in patients with heart failure and is associated with poor clinical status and worse outcomes. In Nepal few studies have evaluated anaemia amongst patients suffering from heart failure. We intended to find out the prevalence of anaemia in patients with heart failure in a tertiary care centre. METHODS: This is a descriptive cross-sectional study conducted among patients of heart failure presenting to tertiary care hospital in eastern Nepal from April 2017 to January 2018. Ethical approval was taken from the Institutional Review Committee of a tertiary care centre (reference number: IRC/0842/016). Using the convenience sampling method, 100 patients were enrolled in the study. Blood samples from the patients were taken for haemoglobin and serum iron studies. Data was analysed using Statistical Package for Social Sciences version 11. Point estimate at 95% Confidence Interval was calculated, with frequency and percentage. RESULTS: Among 100 patients with heart failure, 82 (82%) (74.47-89.53 at 95% Confidence Interval) had anaemia. Mean haemoglobin level of the study population was 10.40±2.73 g/dl. Fifty four (54%) of patients had iron deficiency status irrespective of presence or absence of anaemia. CONCLUSIONS: Prevalence of anaemia among patients of heart failure in our study was found to be higher than various other homologous international studies.

Case Report: Using basic liver function tests as a guide to suspected Wilson's disease.

We present a case of a 36-year-old female patient who presented with subacute liver disease with a history of alcohol abuse. On basic liver function tests (LFT), she had aspartate transaminase / alanine transaminase > 2.2 and alkaline phosphatase / total bilirubin < 4. This pattern in acute liver failure patients signifies Wilson's disease. Its presence in our patient with subacute liver disease also prompted us to suspect Wilson's disease and we extended the liver disease screen to include slit lamp eye examination for Kayser-Fleischer rings, serum ceruloplasmin and 24-hour urinary copper level, which led to the diagnosis. She improved clinically and biochemically with zinc acetate therapy.  As screening for rare diseases is not always possible in low-income countries, this case demonstrates the usefulness of the basic LFT as a guide for suspecting Wilson's disease in patients with liver disease.

Unroofed coronary sinus: An unusual interatrial communication and a rare childhood entity.

Unroofed coronary sinus, an unusual form of interatrial communication, is a rare cardiac anomaly. It is not a true defect of the atrial septum. It is described as a partial (focal or fenestrated) or complete absence of the roof of the coronary sinus, resulting in a communication between the coronary sinus and left atrium. It is presumably the least common variety of defects associated with interatrial shunting. Such defects are often difficult to diagnose and may even be overlooked during surgery for complex congenital heart disease. In most cases, they are associated with a persistent left superior vena cava, pulmonary or tricuspid atresia, and hearts with isomeric right atrial appendages. We report a case of this unusual form of interatrial communication without any associated anomalies in a 7-year-old girl child who presented to us with exertional dyspnea.

Establishing Cardiac Surgery in Eastern Nepal: Early Results.

BACKGROUND: Only few dedicated cardiac centres provide cardiac surgery service in Nepal. We are the only government affiliated centre outside the capital providing this service. In this study, we aim to present our early results of cardiac surgery. METHODS: This retrospective study was conducted at B P Koirala Institute of Health Sciences with objective of analysing the early results of cardiac surgery in the patients operated from July 2016 to March 2017.The data were analysed for patient demographics, type of surgery and cardiac disease, mortality, hospital and intensive care unit stay, valve related complications. RESULTS: Total 51 major cardiac surgeries (42 on pump and nine off pump) were performed. There were 27 (53%) males and 24 (47%) females with median age of 36 years (range: 1 to 70 years).The cardiac diseases consisted of 28 rheumatic heart disease, 12 congenital heart diseases, five coronary artery disease, five chronic constrictive pericarditis and one left atrial myxoma. The mean cardiopulmonary bypass and cross clamp times were 106 ±35 and 80±26 minutes respectively. The mean intensive care unit and hospital stay was 4±2 and 8±3 days respectively. Two (4%) patients required re-exploration for mediastinal bleeding. There was no prosthetic valve thrombosis or infection.Two patients (4%) had superficial wound infections.There were four (7.8%) in hospital mortalities. Remaining 47 patients (91.8%) are in NYHA class I aftermean follow up duration of five months. CONCLUSIONS: Our early result of cardiac surgery is encouraging and has established the safety and feasibility of starting open heart surgery in other parts of Nepal.

Mixed Reality Meets Pharmaceutical Development.

As science evolves, the need for more efficient and innovative knowledge transfer capabilities becomes evident. Advances in drug discovery and delivery sciences have directly impacted the pharmaceutical industry, though the added complexities have not shortened the development process. These added complexities also make it difficult for scientists to rapidly and effectively transfer knowledge to offset the lengthened drug development timelines. While webcams, camera phones, and iPads have been explored as potential new methods of real-time information sharing, the non-"hands-free" nature and lack of viewer and observer point-of-view render them unsuitable for the R&D laboratory or manufacturing setting. As an alternative solution, the Microsoft HoloLens mixed-reality headset was evaluated as a more efficient, hands-free method of knowledge transfer and information sharing. After completing a traditional method transfer between 3 R&D sites (Rahway, NJ; West Point, PA and Schnachen, Switzerland), a retrospective analysis of efficiency gain was performed through the comparison of a mock method transfer between NJ and PA sites using the HoloLens. The results demonstrated a minimum 10-fold gain in efficiency, weighing in from a savings in time, cost, and the ability to have real-time data analysis and discussion. In addition, other use cases were evaluated involving vendor and contract research/manufacturing organizations.

Primary mucoepidermoid carcinoma of the lung with prominent clear cells.

Mucoepidermoid carcinoma of the lung is a rare malignancy of salivary gland-type origin. We report a case of a 21-year-old man with a right mainstem bronchus mass composed predominantly of clear cells. This case represents a rare primary pulmonary low-grade mucoepidermoid carcinoma positive for MAML2 rearrangement by fluorescence in situ hybridization with a prominent clear cell component.

microRNA-10b is a prognostic biomarker for melanoma.

Malignant melanoma is an aggressive form of skin cancer. Recently, drug therapy of advanced disease has been revolutionized by new agents. More therapeutic options, coupled with the desire to extend treatment to the adjuvant setting mean that prognostic biomarkers that can be assayed from formalin-fixed paraffin-embedded clinical would be valuable. microRNAs have potential to fill this need. We analyzed 377 microRNAs in 79 primary melanomas and 32 metastases using a split sample discovery strategy. From a discovery analysis using 40 thick primary melanomas (20 cases with metastasis and 20 controls without metastasis at 5 years), microRNA expression was measured by quantitative RT-PCR (QRT-PCR). MiR-10b emerged as a candidate prognostic microRNA. This was confirmed in an independent validation set of thick primary melanomas (20 cases with metastasis and 19 controls without metastasis at 5 years). In the combined discovery and validation cohorts (n=79), miR-10b expression showed a 3.7-fold increase in expression between cases and controls (P=0.005) and showed a trend of increasing expression between primary melanomas and their matched metastases (P<0.001). In situ hybridization showed expression was in melanoma cells and correlated with expression measured by QRT-PCR (P=0.0005). We used the combined discovery and validation samples to verify the prognostic value of additional candidate microRNAs identified from other studies, and proceeded to analyze miR-200b. We demonstrated that miR-10b and miR-200b showed independent prognostic value (P=0.002 and 0.047, respectively) in multivariable analysis alongside known clinico-pathological prognostic features (eg, Breslow thickness) using a Cox proportional hazards regression model. Furthermore, the addition of these microRNAs to the clinico-pathological features led to an improved regression model with better identification of aggressive thick melanomas. Taken together, these data suggest that miR-10b is a new prognostic microRNA for melanoma and that there could be a place for microRNA analysis in stratifying melanoma for therapy.

Unruptured Sinus of Valsalva Aneurysm Obstructing the Left Ventricular Outflow Tract: An Uncommon Presentation in Childhood.

Congenital aneurysms of the sinus of Valsalva are uncommon abnormalities that are usually silent and slowly progressive without symptoms of cardiac dysfunction unless catastrophic rupture occurs. However, in rare cases, unruptured aneurysms can produce symptoms resulting from compression of adjacent structures, ventricular outflow tract obstruction, heart block, and coronary and valvular insufficiency. We report a case of a single unruptured sinus of Valsalva aneurysm producing left ventricular outflow tract obstruction in an 8-year-old boy who presented with chest pain on exertion.

Survivorship in Non-Small Cell Lung Cancer: Challenges Faced and Steps Forward.

Improvements in curative therapies and the advent of screening have led to increased numbers of non-small cell lung cancer (NSCLC) survivors. Most survivors have undergone invasive treatment (surgery, radiation therapy, and/or chemotherapy) and carry a higher comorbidity burden than survivors of other cancers. Overall quality of life (QOL) and health-related quality of life (HRQOL) suffer during the treatment phase, with the potential for long-term decline, and both clinical characteristics and treatment impact these measures. Physical and mental components of HRQOL seem to be most at risk for decline. The issues faced by survivors include physical symptoms such as respiratory issues, fatigue, hearing loss, neuropathy, and postsurgical pain; psychological distress leading to depression, financial issues, and poor compliance with recommended guidelines; and fear or risk of recurrence and secondary malignancies. This article summarizes the major issues faced by NSCLC survivors and suggests appropriate management. Future collaborative efforts are needed to further elucidate the complex issues that affect overall QOL and HRQOL in NSCLC survivors and to develop appropriate interventions in this large and diverse survivor population.