OBSERVATIONAL STUDY ON POISON CASES IN A TERTIARY CARE HOSPITAL

Objective: To evaluate the prevalence, pattern, and cause of poisoning. To characterize the poisoning cases admitted in a tertiary care hospital; followed by the outcome and to observe the antidote given for the poison cases. Methods: This observational study was undertaken in emergency departments (EMD) and Medical Record Department (MRD). Totally 557 poison cases was recruited in this study. Grade of poison was assessed by using poison severity score. Statistical analysis was done by using Statistical Package for Social Sciences (SPSS). Results: A total of 557 poison cases were identified in 2, 39, 828 patients out of which 360(64%) were suicidal and 189(34) cases admitted were accidental. The patients who were admitted between 2-5 h after exposed to poison were found to be more followed by 0-1 hr,>1-2 h,>6-24 h,>24 h and>5-6 h. More number of cases were seen in the others (Synthetic cow dung powder and medicine) type of poison 296(53%) followed by household poisoning 93(17%), bites 86 (15%), insecticide poisoning 64(12%) and food poisoning 16(3%). Activated charcoal was the maximum used antidote. Conclusion: Through this study, it was found that suicidal poisoning was the most common type

Gluteal rhabdomyosarcoma in a newborn – A case report

Rhabdomyosarcoma is the most common soft tissue malignancy of childhood; however, can be seen very rarelyin the neonatal period also. It may arise anywhere in the body; head and neck, and genitourinary regions beingthe most frequent sites. Truncal and gluteal rhabdomyosarcoma is relatively rare occurrence. We report aneonate with embryonal rhabdomyosarcoma arising from the gluteal muscles at birth. Ultrasonography andMagnetic resonance imaging raised the possibility of hemangioma lymhangioma. Total excision was done andchemotherapy given. The child had a recurrence after 6 months where the nodule along with the scar wasexcised. A chemoport was introduced and the child underwent further 4 cycles of chemotherapy afterrecurrence. He is well on 2 years follow up without any disability

COST ANALYSIS OF EMERGENCY VISITS DUE TO DRUG RELATED PROBLEMS

Objective: To identify patients coming to Emergency Medicine Department (EMD) with drug related problems, classify the DRPs and calculate the direct cost spent for treating them. Methods: This was a prospective observational study conducted in emergency medicine department. The patients coming to EMD with DRPs were classified according to Cipolle’s classification and the direct medical and non-medical costs were calculated. Results: A total of around 107 patients identified with DRPs of which 99 patients were included in the study. In this study, 51% of the cases were due to ADR and 35% due to non-adherence and rest of the cases were due to overdose (10%), drug interaction (3%) and sub therapeutic dose (1%). Major portion for treatment was spent for direct medical cost in which cost for laboratory investigations have contributed the most, INR 10,93,992 (42%) followed by Health care professional cost INR 55,6814 (21%), Pharmacy cost INR 4,00,524.6 (15%), Admission cost INR 3,80,400 (15%). The direct non-medical cost includes cost for diet and travel which was found to be INR 1,68,443 and INR 71,947 respectively. Conclusion: The drug related problems adds a significant economic burden on the patients which can be reduced by imparting knowledge about the proper use of medicines and by improving collaborative efforts of the patients, physicians, pharmacists and caregivers

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Orthodontics in the era of big data analytics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/1/ocr12279_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/2/ocr12279.pd

Placebo-controlled phase 3 study of oral BG-12 or glatiramer in multiple sclerosis.

BACKGROUND: BG-12 (dimethyl fumarate) is in development as an oral treatment for relapsing-remitting multiple sclerosis, which is commonly treated with parenteral agents (interferon or glatiramer acetate). METHODS: In this phase 3, randomized study, we investigated the efficacy and safety of oral BG-12, at a dose of 240 mg two or three times daily, as compared with placebo in patients with relapsing-remitting multiple sclerosis. An active agent, glatiramer acetate, was also included as a reference comparator. The primary end point was the annualized relapse rate over a period of 2 years. The study was not designed to test the superiority or noninferiority of BG-12 versus glatiramer acetate. RESULTS: At 2 years, the annualized relapse rate was significantly lower with twice-daily BG-12 (0.22), thrice-daily BG-12 (0.20), and glatiramer acetate (0.29) than with placebo (0.40) (relative reductions: twice-daily BG-12, 44%, P<0.001; thrice-daily BG-12, 51%, P<0.001; glatiramer acetate, 29%, P=0.01). Reductions in disability progression with twice-daily BG-12, thrice-daily BG-12, and glatiramer acetate versus placebo (21%, 24%, and 7%, respectively) were not significant. As compared with placebo, twice-daily BG-12, thrice-daily BG-12, and glatiramer acetate significantly reduced the numbers of new or enlarging T(2)-weighted hyperintense lesions (all P<0.001) and new T(1)-weighted hypointense lesions (P<0.001, P<0.001, and P=0.002, respectively). In post hoc comparisons of BG-12 versus glatiramer acetate, differences were not significant except for the annualized relapse rate (thrice-daily BG-12), new or enlarging T(2)-weighted hyperintense lesions (both BG-12 doses), and new T(1)-weighted hypointense lesions (thrice-daily BG-12) (nominal P<0.05 for each comparison). Adverse events occurring at a higher incidence with an active treatment than with placebo included flushing and gastrointestinal events (with BG-12) and injection-related events (with glatiramer acetate). There were no malignant neoplasms or opportunistic infections reported with BG-12. Lymphocyte counts decreased with BG-12. CONCLUSIONS: In patients with relapsing-remitting multiple sclerosis, BG-12 (at both doses) and glatiramer acetate significantly reduced relapse rates and improved neuroradiologic outcomes relative to placebo. (Funded by Biogen Idec; CONFIRM ClinicalTrials.gov number, NCT00451451.).clinical trial, phase iiicomparative studyjournal articlemulticenter studyrandomized controlled trialresearch support, non-u.s. gov't2012 Sep 20importedErratum in : N Engl J Med. 2012 Oct 25;367(17):1673

Multimodal surface-based morphometry reveals diffuse cortical atrophy in traumatic brain injury.

<p>Abstract</p> <p>Background</p> <p>Patients with traumatic brain injury (TBI) often present with significant cognitive deficits without corresponding evidence of cortical damage on neuroradiological examinations. One explanation for this puzzling observation is that the diffuse cortical abnormalities that characterize TBI are difficult to detect with standard imaging procedures. Here we investigated a patient with severe TBI-related cognitive impairments whose scan was interpreted as normal by a board-certified radiologist in order to determine if quantitative neuroimaging could detect cortical abnormalities not evident with standard neuroimaging procedures.</p> <p>Methods</p> <p>Cortical abnormalities were quantified using multimodal surfaced-based morphometry (MSBM) that statistically combined information from high-resolution structural MRI and diffusion tensor imaging (DTI). Normal values of cortical anatomy and cortical and pericortical DTI properties were quantified in a population of 43 healthy control subjects. Corresponding measures from the patient were obtained in two independent imaging sessions. These data were quantified using both the average values for each lobe and the measurements from each point on the cortical surface. The results were statistically analyzed as z-scores from the mean with a p < 0.05 criterion, corrected for multiple comparisons. False positive rates were verified by comparing the data from each control subject with the data from the remaining control population using identical statistical procedures.</p> <p>Results</p> <p>The TBI patient showed significant regional abnormalities in cortical thickness, gray matter diffusivity and pericortical white matter integrity that replicated across imaging sessions. Consistent with the patient's impaired performance on neuropsychological tests of executive function, cortical abnormalities were most pronounced in the frontal lobes.</p> <p>Conclusions</p> <p>MSBM is a promising tool for detecting subtle cortical abnormalities with high sensitivity and selectivity. MSBM may be particularly useful in evaluating cortical structure in TBI and other neurological conditions that produce diffuse abnormalities in both cortical structure and tissue properties.</p

Big Data for the Greater Good: An Introduction

Big Data, perceived as one of the breakthrough technological developments of our times, has the potential to revolutionize essentially any area of knowledge and impact on any aspect of our life. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, analysts, researchers, and business users can analyze previously inaccessible or unusable data to gain new insights resulting in better and faster decisions, and producing both economic and social value; it can have an impact on employment growth, productivity, the development of new products and services, traffic management, spread of viral outbreaks, and so on. But great opportunities also bring great challenges, such as the loss of individual privacy. In this chapter, we aim to provide an introduction into what Big Data is and an overview of the social value that can be extracted from it; to this aim, we explore some of the key literature on the subject. We also call attention to the potential ‘dark’ side of Big Data, but argue that more studies are needed to fully understand the downside of it. We conclude this chapter with some final reflections