True and false positive rates for different criteria of evaluating statistical evidence from clinical trials

Waist-to-Height Ratio Cut-Off Points for Central Obesity in Individuals with Overweight Across Different Ethnic Groups in NHANES 2011-2018

BACKGROUND

The identification of surrogate measures of central obesity is of clinical importance, and the waist-to-height ratio (WtHR) has recently attracted great interest as an alternative method.

OBJECTIVE

For this reason, we aimed to establish specific WtHR cut-off points for adiposity (i.e., central obesity) in four different ethnicity groups across both sexes based on data from the National Health and Nutrition Examination Survey (NHANES) population.

METHODS

Of the total 23,037 participants who completed four cycles of the survey between the years 2011 and 2018, anthropometric measures (i.e., body weight, waist circumference, and height) and dual X-ray absorptiometry-derived visceral adipose tissue (DXA-derived VAT) results were available for 3566 individuals who were assessed in this cross-sectional study. Participants with an overweight status defined according to the World Health Organization (WHO) body mass index (BMI) cut-off points (25-29.9 kg/m2) were included. The sample was then categorized by adiposity according to the DXA-derived VAT tertiles (highest), and based on the receiver operating characteristic (ROC) curve analysis, the best sensitivity and specificity were attained for predicting central obesity using the WtHR.

RESULTS

The following WtHR cut-offs were identified as having the best discriminating ability for central obesity: 0.57 for White males and 0.58 for White females; 0.55 for Black males and 0.57 for Black females; 0.56 for Asian males and 0.59 for Asian females; and 0.57 for Hispanic males and 0.59 for Hispanic females.

CONCLUSIONS

These new WtHR cut-off points should be utilized in adults with overweight to screen for central adiposity based on their sex and ethnicity, and obesity guidelines therefore need to be revised accordingly.

The Hemodialysis Distress Thermometer for Caregivers (HD-DT-C): development and testing of the psychometric properties of a new tool for screening psychological distress among family caregivers of adults on hemodialysis

PURPOSE

To develop and test the measurement properties of the HD-DT-C, a new tool designed to facilitate the screening of psychological distress and its sources in family caregivers of adults on hemodialysis.

METHODS

The present investigation was carried out in three phases: Phase 1 focused on the process of developing and exploring the content validity and clinical utility of the HD-DT-C using a mixed-methods approach and feedback panels; Phase 2, where the psychometric properties of this new tool were tested in a cross-sectional study (n = 106 caregivers); and Phase 3, where the European Portuguese version of the HD-DT-C was translated and culturally adapted into American English using a forward-backward translation procedure, followed by an expert panel review.

RESULTS

Findings suggested that the HD-DT-C was perceived by feedback panels as practical, appropriate, and useful for increasing dialysis provider/family caregiver communication in nephrology centers. The European Portuguese version of the HD-DT-C showed good test-retest reliability (ICC = 0.991 for the barometer and κ ≥ 0.80 in 77% of the checklist items), high diagnostic accuracy (AUC = 0.956), and strong convergent validity (all r ≥ 0.50) with reference measures that assess quality of life, caregiver burden, and symptoms of anxiety and depression. Cutoff scores with good clinical utility (CUI +  ≥ 0.70) were recommended for screening distress in research (≥ 6) and clinical practice (≥ 5).

CONCLUSION

The HD-DT-C is a brief, reliable, valid, and acceptable measure for identifying self-reported psychological distress and its sources among people caring for a family member or friend on hemodialysis. Future research is needed to explore the measurement properties of the American English version of this new tool.

A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+

OBJECTIVE

Radiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations.

METHODS

105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time.

RESULTS

The average DSC of the liver in the validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively.

CONCLUSION

UNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.

Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning

Cancer is a broad term that refers to a wide range of diseases that can affect any part of the human body. To minimize the number of cancer deaths and to prepare an appropriate health policy on cancer spread mitigation, scientifically supported knowledge of cancer causes is critical. As a result, in this study, we analyzed lung cancer risk factors that lead to a highly severe cancer case using a decision tree-based ranking algorithm. This feature relevance ranking algorithm computes the weight of each feature of the dataset by using split points to improve detection accuracy, and each risk factor is weighted based on the number of observations that occur for it on the decision tree. Coughing of blood, air pollution, and obesity are the most severe lung cancer risk factors out of nine, with a weight of 39%, 21%, and 14%, respectively. We also proposed a machine learning model that uses Extreme Gradient Boosting (XGBoost) to detect lung cancer severity levels in lung cancer patients. We used a dataset of 1000 lung cancer patients and 465 individuals free from lung cancer from Tikur Ambesa (Black Lion) Hospital in Addis Ababa, Ethiopia, to assess the performance of the proposed model. The proposed cancer severity level detection model achieved 98.9%, 99%, and 98.9% accuracy, precision, and recall, respectively, for the testing dataset. The findings can assist governments and non-governmental organizations in making lung cancer-related policy decisions.

The Functional Erythropoetin rs1617640 Gene Polymorphism does not Affect Life Expectancy of Patients with Peripheral Arterial Disease

Background

A common functional variant (c.-1306A > C, rs1617640) in the gene encoding erythropoietin (EPO) has been linked to expression of erythropoietin and markers of erythropoiesis. Aim of the current study was the analysis of the role of this polymorphism for long term survival of patients with peripheral arterial disease (PAD).

Methods

EPO genotypes as well as biomarkers for erythropoiesis were analyzed in a cohort of 946 patients with PAD. Survival follow-up was performed 20 years af-ter recruitment of patients.

Results

Twenty years after recruitment, 752 (79.5%) patients were dead, 103 (10.9%) were still alive, and 91 (9.6%) were lost-to-follow up. In a Cox regression analysis including smoking habit, sex, type-2 diabetes, hypercholesterolemia and arterial hypertension, EPO genotypes were not associated with overall survival (Hazard ratio 0.63; 95% confidence interval 0.88-1.08, p = 0.63).

Conclusions

The functional EPO rs1617640 gene polymorphism, irrespective of its association with markers of erythropoiesis, does not affect survival of PAD patients.

The failure of drug repurposing for COVID-19 as an effect of excessive hypothesis testing and weak mechanistic evidence

The current strategy of searching for an effective treatment for COVID-19 relies mainly on repurposing existing therapies developed to target other diseases. Conflicting results have emerged in regard to the efficacy of several tested compounds but later results were negative. The number of conducted and ongoing trials and the urgent need for a treatment pose the risk that false-positive results will be incorrectly interpreted as evidence for treatments' efficacy and a ground for drug approval. Our purpose is twofold. First, we show that the number of drug-repurposing trials can explain the false-positive results. Second, we assess the evidence for treatments' efficacy from the perspective of evidential pluralism and argue that considering mechanistic evidence is particularly needed in cases when the evidence from clinical trials is conflicting or of low quality. Our analysis is an application of the program of Evidence Based Medicine Plus (EBM+) to the drug repurposing trials for COVID. Our study shows that if decision-makers applied EBM+, authorizing the use of ineffective treatments would be less likely. We analyze the example of trials assessing the efficacy of hydroxychloroquine as a treatment for COVID-19 and mechanistic evidence in favor of and against its therapeutic power to draw a lesson for decision-makers and drug agencies on how excessive hypothesis testing can lead to spurious findings and how studying negative mechanistic evidence can be helpful in discriminating genuine from spurious results.

Eating and Control Styles Axis in Mentalisation-Based Psychotherapy in Eating Disorders: A Randomised Clinical Trial

Background

Clinicians need an instrument that helps their patients with eating disorders (ED) to explore their agent's inner intentions and confront negative behaviour and control styles.

Objectives

To assess the feasibility and impact of an eating and control styles axis (ECOSA) during the first 8 months of mentalisation-based psychotherapy with a community-based sample of ED patients.

Methods

Six experienced therapists and their consecutively admitted patients were randomly allocated to the intervention and control groups. A total of 94 women, M age = 24 were recruited between June 2020 and October 2021. Ninety completed it. Both groups received mentalisation-based psychotherapy, but only the intervention group used the ECOSA repeatedly. Therapists and participants were blinded to the study aims and hypothesis. Fidelity assessment was applied to ensure that the two groups differed mainly in terms of ECOSA usage.

Results

The use of ECOSA, although less than advised, was reported as feasible. The effect size of the improvement in reflective functioning was larger than that of the control group and correlated significantly only in the intervention group with EDE-Q score (r = 0.46; p = 0.001).

Conclusion

Although the study limitations: selective population, relatively small sample size and the lack of controlled confounder, the combined quantitative and qualitative results lend preliminary evidence for the validity and contribution of ECOSA as a possible instrument that may upgrade the clinician's toolbox in the treatment of ED. A more rigorous study design is needed to explore the potential usage of ECOSA as a clinical tool to enhance mentalisation among people with ED.

Comparing the evidential strength for psychotropic drugs: a Bayesian meta-analysis

Approval and prescription of psychotropic drugs should be informed by the strength of evidence for efficacy. Using a Bayesian framework, we examined (1) whether psychotropic drugs are supported by substantial evidence (at the time of approval by the Food and Drug Administration), and (2) whether there are systematic differences across drug groups. Data from short-term, placebo-controlled phase II/III clinical trials for 15 antipsychotics, 16 antidepressants for depression, nine antidepressants for anxiety, and 20 drugs for attention deficit hyperactivity disorder (ADHD) were extracted from FDA reviews. Bayesian model-averaged meta-analysis was performed and strength of evidence was quantified (i.e. BFBMA). Strength of evidence and trialling varied between drugs. Median evidential strength was extreme for ADHD medication (BFBMA = 1820.4), moderate for antipsychotics (BFBMA = 365.4), and considerably lower and more frequently classified as weak or moderate for antidepressants for depression (BFBMA = 94.2) and anxiety (BFBMA = 49.8). Varying median effect sizes (ESschizophrenia = 0.45, ESdepression = 0.30, ESanxiety = 0.37, ESADHD = 0.72), sample sizes (Nschizophrenia = 324, Ndepression = 218, Nanxiety = 254, NADHD = 189.5), and numbers of trials (kschizophrenia = 3, kdepression = 5.5, kanxiety = 3, kADHD = 2) might account for differences. Although most drugs were supported by strong evidence at the time of approval, some only had moderate or ambiguous evidence. These results show the need for more systematic quantification and classification of statistical evidence for psychotropic drugs. Evidential strength should be communicated transparently and clearly towards clinical decision makers.

Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder

Working memory (WM) impairments are common features of psychiatric disorders. A systematic meta-analysis was performed to determine common and disorder-specific brain fMRI response during performance of WM tasks in patients with SZ and patients with MDD relative to healthy controls (HC). Thirty-four published fMRI studies of WM in patients with SZ and 18 published fMRI studies of WM in patients with MDD, including relevant HC, were included in the meta-analysis. In both SZ and MDD there was common stronger fMRI response in right medial prefrontal cortex (MPFC) and bilateral anterior cingulate cortex (ACC), which are part of the default mode network (DMN). The effects were of greater magnitude in SZ than MDD, especially in prefrontal-temporal-cingulate-striatal-cerebellar regions. In addition, a disorder-specific weaker fMRI response was observed in right middle frontal gyrus (MFG) in MDD, relative to HC. For both SZ and MDD a significant correlation was observed between the severity of clinical symptoms and lateralized fMRI response relative to HC. These findings indicate that there may be common and distinct anomalies in brain function underlying deficits in WM in SZ and MDD, which may serve as a potential functional neuroimaging-based diagnostic biomarker with value in supporting clinical diagnosis, measuring illness severity and assessing the efficacy of treatments for SZ and MDD at the brain level.

The Erythropoetin rs1617640 Gene Polymorphism Associates with Hemoglobin Levels, Hematocrit and Red Blood Cell Count in Patients with Peripheral Arterial Disease

Background: Erythropoietin has a pivotal role in erythropoiesis and angiogenesis. A common polymorphism (rs1617640, A > C) in the promoter of the erythropoietin gene (EPO) has been associated with erythropoietin expression and microvascular complications of diabetes. We aimed to analyze the potential role of this polymorphism in the pathogenesis of peripheral arterial disease (PAD). Methods: EPO genotypes and laboratory markers for erythropoiesis were determined in 945 patients with PAD. Results: The minor EPO rs1617640 C-allele was associated in an allele-dose-dependent manner with hemoglobin levels (p = 0.006), hematocrit (p = 0.029), and red blood cell count (p = 0.003). In a multivariate linear regression analysis including conventional risk factors diabetes, sex, and smoking, EPO genotypes were furthermore associated with age at onset of PAD symptoms (p = 0.009). Conclusions: The EPO rs1617640 gene polymorphism affects erythropoiesis, leads to an earlier onset of PAD, and is a potential biomarker for the pathogenesis of this disease.