Predictors of Improvement in Quality of Life When Treating Hypothyroidism

Background. Primary hypothyroidism is characterized by reduced quality of life (QoL). Although thyrotropin (TSH) is utilized as the primary indicator of thyroid disease and treatment adequacy, no simple correlation between QoL and TSH has been shown. This study aimed to investigate changes in clinically relevant predictors during initiation of levothyroxine (L-T4) therapy and their ability to predict improvement in QoL. Method. Quality of life was measured in patients with newly diagnosed hypothyroidism, during the initial 12 months of L-T4 therapy, by the thyroid-related patient-reported outcome questionnaire, ThyPRO-39. The main outcome measures were the Composite QoL scale and the Tiredness and Emotional Susceptibility subscales (0–100, higher scores worse). Clinical variables (resting energy expenditure (REE), body composition, thyroid function, L-T4 dose, and cognitive function tests) were evaluated as predictors of improvement in QoL by univariate and multiple regression analysis. Results. Thirty-seven hypothyroid patients with a baseline median TSH of 30 mU/l and a median QoL score of 29 were included. After twelve months of L-T4 treatment, the ThyPRO-39 QoL score had significantly improved to a median score of 14, while REE per kg fat-free mass (FFM) increased significantly from a mean of 26.5 to 28.7 kcal/day/kg ( p < 0.001 ). Change in ThyPRO-39 was not associated with a change in REE/FFM (unstandardized coefficient (USC): 0.09 with confidence interval (CI): −1.93 to 2.11, p = 0.93 ) but was positively predicted by baseline body mass index (BMI) (USC: 1.54 with CI: 0.59 to 2.49, ( p = 0.002 ), without association with weight loss (USC: 0.33 with CI: −1.21 to 1.27, p = 0.96 ). Conclusion. Improvement in QoL as measured by ThyPRO-39 after initiation of L-T4 therapy for hypothyroidism was not associated with changes in REE. High baseline BMI, but not weight loss during therapy, was associated with improvement in QoL. This trail is registered with www.Clinicaltrials.gov (registration no. https://clinicaltrials.gov/ct2/show/NCT02891668).

Inspecting Management Strategies of Hepatocellular Carcinoma in a Tertiary Centre in Western Rajasthan

BACKGROUND Hepatocellular carcinoma (HCC) is a lethal malignancy which mostly develops in patients with cirrhosis. It is usually diagnosed late in the course of the illness and the median survival following diagnosis ranges between 6 - 20 months. India lacks data on management strategies and their efficacy. In the absence of data on treatment protocols and its adequacy; we evaluated our own centre data for a period of 1 year to get the estimate of incidence, aetiology, treatment adequacy and response to treatment. Barcelona Clinic Liver Cancer (BCLC) prognostic staging classification comprising five stages is used for prognostication, which is based on the extent of the primary lesion, performance status, vascular invasion and extrahepatic spread. Surgical therapies including resection and transplantation are feasible in early stages (BCLC stage 0 and stage A). Trans arterial chemoembolisation is recommended in intermediate stage (BCLC stage B) while systemic therapies are recommended in advanced stage (BCLC stage C). Best supportive care is recommended in terminal stage (BCLC stage D). This study has included BCLC staging for staging classification and patients were assessed for adequacy of management. METHODS This study was done as a retrospective hospital based observational study. All HCC patients presenting to Mahatma Gandhi Hospital attached to Dr. Sampurnanand Medical College, Jodhpur, Western Rajasthan from January to December 2014 were included. HCC was diagnosed based on European Association for the study of the Liver–European Organisation for Research and Treatment of Cancer (EASL–EORTC) clinical practice guidelines 2011. Patients were classified according to Barcelona Clinic Liver Cancer staging and management given was recorded. RESULTS Thirty-two patients who were diagnosed with HCC between January to December 2014 were included in the study. In three fourths of the patients (24) HCC was diagnosed based on typical findings on dynamic imaging studies (triple phase contrast enhanced CT-computed tomography abdomen and / or MRI- magnetic resonance imaging abdomen). Liver biopsy was needed in one fourth of the patients. Majority of the patients (87.5 %) had cirrhosis of the liver at the time of diagnosis of HCC. Some of these patients [5 (17.8 %)] were known cirrhotic patients. CONCLUSIONS Hepatitis B was the most common aetiology of HCC as mentioned previously in other studies, which is vaccine preventable. HCC is rarely diagnosed at an early stage in developing countries. Various treatment modalities are available which depend on the stage, local expertise and affordability. If the surveillance recommendations are strictly adhered,HCC can be diagnosed at an early stage. Affordability and compliance will remain issues in HCC management in our country increasing the socio-economic burden on the society. KEY WORDS Hepatocellular Carcinoma (HCC), BCLC Staging, Survival

Valganciclovir—Ganciclovir Use and Systematic Therapeutic Drug Monitoring. An Invitation to Antiviral Stewardship

Valganciclovir (VGCV) and ganciclovir (GCV) doses must be adjusted according to indication, renal function and weight. No specific therapeutic exposure values have been established. We aimed to evaluate the adequacy of VGCV/GCV doses, to assess the interpatient variability in GCV serum levels, to identify predictive factors for this variability and to assess the clinical impact. This is a prospective study at a tertiary institution including hospitalized patients receiving VGCV/GCV prophylaxis or treatment. Adequacy of the antiviral dose was defined according to cytomegalovirus guidelines. Serum levels were determined using High-Performance Liquid Chromatography. Blood samples were drawn at least 3 days after antiviral initiation. Outcome was considered favorable if there was no evidence of cytomegalovirus infection during prophylaxis or when a clinical and microbiological resolution was attained within 21 days of treatment and no need for drug discontinuation due to toxicity. Seventy consecutive patients [74.3% male/median age: 59.2 years] were included. VGCV was used in 25 patients (35.7%) and GCV in 45 (64.3%). VGCV/GCV initial dosage was deemed adequate in 47/70 cases (67.1%), lower than recommended in 7/70 (10%) and higher in 16/70 (22.9%). Large inter-individual variability of serum levels was observed, with median trough levels of 2.3 mg/L and median peak levels of 7.8 mg/L. Inadequate dosing of VGCV/GCV and peak levels lower than 8.37 or greater than 11.86 mg/L were related to poor outcome. Further studies must be performed to confirm these results and to conclusively establish if VGCV/GCV therapeutic drug monitoring could be useful to improve outcomes in specific clinical situations.

Pattern analysis: predicting COVID-19 pandemic in India using AutoML

Purpose Since December 2019, global attention has been drawn to the rapid spread of COVID-19. Corona was discovered in India on 30 January 2020. To date, in India, 178,014 disease cases were reported with 14,011 deaths by the Indian Government. In the meantime, with an increasing spread speed, the COVID-19 epidemic occurred in other countries. The survival rate for COVID-19 patients who suffer from a critical illness is efficiently and precisely predicted as more fatal cases can be affected in advanced cases. However, over 400 laboratories and clinically relevant survival rates of all present critically ill COVID-19 patients are estimated manually. The manual diagnosis inevitably results in high misdiagnosis and missed diagnosis owing to a lack of experience and prior knowledge. The chapter presents an option for developing a machine-based prognostic model that exactly predicts the survival of individual severe patients with clinical data from different sources such as Kaggle data.gov and World Health Organization with greater than 95% accuracy. The data set and attributes are shown in detail. The reasonableness of such a mere three elements may depend, respectively, on their representativeness in the indices of tissue injury, immunity and inflammation. The purpose of this paper is to provide detailed study from the diagnostic aspect of COVID-19, the work updates the cost-effective and prompt criticality classification and prediction of survival before the targeted intervention and diagnosis, in particular the triage of the vast COVID-19 explosive epidemic. Design/methodology/approach Automated machine learning (ML) provides resources and platforms to render ML available to non-ML experts, to boost efficiency in ML and to accelerate research in machine learning. H2O AutoML is used to generate the results (Dulhare et al., 2020). ML has achieved major milestones in recent years, and it is on which an increasing range of disciplines depend. But this performance is crucially dependent on specialists in human ML to perform the following tasks: preprocess the info and clean it; choose and create the appropriate apps; choose a family that fits the pattern; optimize hyperparameters for layout; and models of computer learning post processes. Review of the findings collected is important. Findings These days, the concept of automated ML techniques is being used in every field and domain, for example, in the stock market, education institutions, medical field, etc. ML tools play an important role in harnessing the massive amount of data. In this paper, the data set relatively holds a huge amount of data, and appropriate analysis and prediction are necessary to track as the numbers of COVID cases are increasing day by day. This prediction of COVID-19 will be able to track the cases particularly in India and might help researchers in the future to develop vaccines. Researchers across the world are testing different medications to cure COVID; however, it is still being tested in various labs. This paper highlights and deploys the concept of AutoML to analyze the data and to find the best algorithm to predict the disease. Appropriate tables, figures and explanations are provided. Originality/value As the difficulty of such activities frequently goes beyond non-ML-experts, the exponential growth of ML implementations has generated a market for off-the-shelf ML solutions that can be used quickly and without experience. We name the resulting work field which is oriented toward the radical automation of AutoML machine learning. The third class is that of the individuals who have illnesses such as diabetes, high BP, asthma, malignant growth, cardiovascular sickness and so forth. As their safe frameworks have been undermined effectively because of a common ailment, these individuals become obvious objectives. Diseases experienced by the third classification of individuals can be lethal (Shinde et al., 2020). Examining information is fundamental in having the option to comprehend the spread and treatment adequacy. The world needs a lot more individuals investigating the information. The understanding from worldwide data on the spread of the infection and its conduct will be key in limiting the harm. The main contributions of this study are as follows: predicting COVID-19 pandemic in India using AutoML; analyzing the data set predicting the patterns of the virus; and comparative analysis of predictive algorithms. The organization of the paper is as follows, Sections I and II describe the introduction and the related work in the field of analyzing the COVID pandemic. Section III describes the workflow/framework for AutoML using the components with respect to the data set used to analyze the patterns of COVID-19 patients.

441 - Treatment adequacy for depression and anxiety disorders affects quality of life in older adults consulting in primary care

BackgroundPrevious studies show that less than 50% of adults in Canada receive guideline- concordant care for depression and anxiety disorders. Studies on the long-term effects of receiving adequate care for depression and anxiety disorders are scarce, particularly in older adults. This study aimed to assess the 3-year change in health-related quality of life (HRQOL) and satisfaction with life associated with receipt of adequate care for depression and anxiety disorders.MethodThis study was conducted among a sample of 219 older adults recruited in primary care with a common mental disorder (depression or an anxiety disorder) who participated in Quebec’s longitudinal ESA-Services (2011-2016) study. The definition of adequacy of care was based on Canadian guidelines and relevant literature. Administrative and self-reported data were used to measure treatment adequacy at baseline. HRQOL was measured using a Visual Analog Scale (VAS) and satisfaction with life was assessed with the Satisfaction With Life Scale (SWLS). HRQOL and satisfaction with life were measured at baseline and follow-up, 3 years later. Multivariate fixed-effects models were carried out to assess the association between adequacy of care and change in quality of life controlling for individual and healthcare system factors in the overall sample as well as separately for depression and anxiety.ResultsThe results showed that 56%, 37% and 40% of participants received adequate pharmacological or psychological treatment for depression, anxiety, and overall. Receipt of adequate treatment was associated with on average 4 more points on the VAS (0-100) and 1.7 points on the SWLS (5-25). After controlling for potential confounders, patients receiving adequate care for depression had on average 11 more points on the VAS. Treatment adequacy for anxiety disorders and depression or anxiety disorders overall were not associated with change in HRQOL or satisfaction with life.ConclusionOlder adults receiving adequate mental health care had better HRQOL and satisfaction with life. Treatment adequacy for depression was associated with change in quality of life; but not for individuals with anxiety. Future studies should focus on different patient indicators of quality of care which may better predict long-term effects of treatment for people with anxiety.

Factors impacting prescription practice in primary healthcare setting in India: A case study in Rajasthan

A.AbstractBackgroundWe present a case of prescription practices in the Indian state of Rajasthan to demonstrate the effect of provider and system level factors, and their interactions on good prescription practices. We have presented two major dimensions of good prescription practice; first, completeness of prescription, a measure of adequacy; and second, appropriateness of prescription, a measure of quality of care (QoC).MethodWe used mixed method to audit 2801 prescriptions, selected from 24 rural and 7 urban government Primary Health Centres (PHCs) of Rajasthan, India. The findings represent analysis of 97% of the ‘selected prescriptions’ that were considered ‘legible’. The ‘selected prescriptions’ ensure represent variance in patient categories, seasonality and number of OPD foot fall across days in a week. Semi-structured in-depth interviews, followed by Focused Group Discussion (FGD) with providers was undertaken to obtain insights about facilitators and barriers to good prescription practices. We compared descriptive statistics across quintiles on adequacy indicators to understand variations around provider and system level factors; multilevel logistic regression model was used to obtain the adjusted effect. To assess appropriateness of quality of care (QoC), we evaluated 783 prescriptions that had adequate information to compare factors impacting QoC across quintiles. Finally, findings from the FGD was used to substantiate findings from the quantitative analysis.ResultWe found that prescription practices are outcomes of both provider and system level factors, and their interactions. The documentation of patient complaint, examination findings largely depends on system level factors; 59% and 38%, respectively. The treatment adequacy is largely associated with patient category. Ownership compliance of the doctors, measured in terms of their signature in the prescriptions, emerged as an important factor determining both adequacy and accuracy of prescriptions. We also found that treatment appropriateness, measured in terms of QoC, depends on both provider and system level factors. A conducive environment and trained provider are more likely to provide adequate and appropriate treatment. It is also observed that higher patient load is not a counter-productive to treatment appropriateness. Out of 783 legible prescriptions that were assessed for its appropriateness, only 36% were found inappropriate in terms of their documented justification for the treatment advised.ConclusionThere is a need to focus on provider and system level factors to improve prescription practices in primary health care (PHC). We recommend that institutional strengthening at systemic and provider level using innovative ways; such as task shifting to nurses as ‘physician assistants’, and reducing administrative activities of physicians to enhance focus on clinical work can propel better prescription practice.