Current medical research and opinion
-
Meta Analysis
Conducting and critically appraising a high-quality systematic review and meta-analysis pertaining to COVID-19.
With constantly emerging new information regarding the epidemiology, pathogenesis, diagnosis and management of Coronavirus Disease 2019 (COVID-19), reviewing literature related to it has become increasingly complicated and resource-intensive. In the setting of this global pandemic, clinical decisions are being guided by the results of multiple pertinent studies; however, it has been observed that these studies are often heterogenous in design and population characteristics and results of initial trials may not be replicated in subsequent studies. The resulting clinical conundrum can be resolved by high-quality systematic review and meta-analysis with a robust and reliable methodology, encapsulating and critically appraising all the available literature relevant to the clinical scenario under scrutiny. ⋯ It can identify optimal diagnostic algorithms, assess efficacy of treatment strategies, and analyze inherent factors influencing the efficacy of treatment for COVID-19. The current review aims to provide a basic guide to plan and conduct a high-quality systematic review and meta-analysis pertaining to COVID-19, describing the main steps and addressing the pitfalls commonly encountered at each step. Knowledge of the basic steps would also allow the reader to critically appraise published systematic review and meta-analysis and the quality of evidence provided therein.
-
Economic evaluations conducted to inform healthcare resource allocation often rely on quality-adjusted life years (QALYs) to measure therapeutic benefit. However, QALYs, with underlying health utilities estimated using the EQ-5D or SF-36, may fail to capture the impact of disease for all patients. How well-being and heath utility differ across several common conditions was explored. ⋯ Differences in rankings of disease severity by metric indicate that the results of cost-utility analyses might be biased against treatments for certain diseases. As patient preferences for clinical outcomes vary, the full burden of disease should be considered in evaluations. Restricting access to treatments based on an incomplete estimate of burden could lead to misallocation of resources and a withholding of therapies that patients find valuable.
-
This study evaluated body mass index (BMI) and weight changes in people living with human immunodeficiency virus (HIV-1; PLWH) initiated on single-tablet darunavir/cobicistat/emtricitabine/tenofovir alafenamide (DRV/c/FTC/TAF) or bictegravir/FTC/TAF (BIC/FTC/TAF). ⋯ BIC/FTC/TAF was associated with greater BMI and weight increases compared to DRV/c/FTC/TAF. Weight gain and its sequelae may add to the clinical burden of PLWH and should be considered among other factors when selecting antiretroviral single-tablet regimens.
-
Human Immunodeficiency Virus (HIV) prevalence has substantially increased over the years, leading to increased direct medical costs. The aim of the present study was to assess the long-term cost of HIV care in Greece incurred over the last decade. ⋯ The paper presents some evidence towards the direction that HIV management in Greece can be considered efficient in both clinical and financial terms, as it offers measurable clinical outcomes at well-controlled, almost inelastic spending.
-
The prevalence of hypothyroidism in systemic lupus erythematosus (SLE) is significantly higher than that in the common public. While SLE itself can affect multiple organs, abnormal thyroid function may exacerbate organ damage in patients with SLE. We aimed to predict abnormal thyroid function and to examine the associated factors with multiple machine learning approaches. ⋯ Random Forest model performed best and is recommended for selecting vital indices and assessing clinical complications of SLE, it indicated that anti-SSB and anti-dsDNA antibodies may play principal roles in the development of hypothyroidism in SLE patients. It's feasible to build an accurate machine learning model for early diagnosis or risk factors assessment in SLE using clinical parameters, which would provide a reference for the research work of SLE in China.