Tag: statistics

Mycoses. 2021 Mar 2. doi: 10.1111/myc.13262. Online ahead of print.

ABSTRACT

BACKGROUND: Onychomycosis is the most common nail disease seen in clinical practice. Inclusion of diverse groups in onychomycosis clinical trials subjects is necessary to generalize efficacy data.

OBJECTIVES: We aimed to systematically review race and ethnicity reporting and representation, as well as, treatment outcomes in onychomycosis clinical trials.

METHODS: A PubMed search for onychomycosis clinical trials was performed in August 2020. Primary clinical trial data were included and post-hoc analyses were excluded. Categorical variables were compared using chi-squared and Fisher’s exact tests. Statistical significance was set at P<0.05. Photos in articles were categorized by Fitzpatrick skin type.

RESULTS: Only 32/182 (17.5%) trials reported on race and/or ethnicity and only one trial compared treatment efficacy in different subgroups. Darker skin colors were infrequently depicted in articles. Topical treatment, location with ≥ 1 US-based site, industry funding type, and publication date after 2000 were significantly associated with reporting of racial/ethnic data (P<0.05 for all comparisons).

LIMITATIONS: Demographics on excluded subjects and methods of recruitment were not available. Assigning Fitzpatrick skin type is inherently subjective.

CONCLUSIONS: This study highlights a need for consistent reporting of races and ethnicities of onychomycosis clinical trial participants with subgroup analyses of treatment efficacies.

PMID:33655595 | DOI:10.1111/myc.13262

Mol Reprod Dev. 2021 Mar 3. doi: 10.1002/mrd.23459. Online ahead of print.

ABSTRACT

MicroRNA (miRNA) is a posttranscriptional downregulator that plays a vital role in a wide variety of biological processes. In this study, we constructed five ovarian and testicular small RNA libraries using two somatic libraries as reference controls for the identification of sex-biased miRNAs and gonadal differentially expressed miRNAs (DEMs) of the Chinese mitten crab, Eriocheir sinensis. A total of 535 known and 243 novel miRNAs were identified, including 312 sex-biased miRNAs and 402 gonadal DEMs. KEGG pathway analysis showed that DEM target genes were statistically enriched in MAPK, Wnt, and GnRH signaling pathway, and so on. A number of the sex-biased miRNAs target genes associated with sex determination/differentiation, such as IAG, Dsx, Dmrt1, and Fem1, while others target the genes related to gonadal development, such as P450s, Wnt, Ef1, and Tra-2c. Dual-luciferase reporter assay in vitro further confirmed that miR-34 and let-7b can downregulate IAG expression, miR-9-5p, let-7d, let-7b, and miR-8915 can downregulate Dsx. Taken together, these data strongly suggest a potential role for the sex-biased miRNAs in sex determination/differentiation and gonadal development in the crab.

PMID:33655621 | DOI:10.1002/mrd.23459

Acta Ophthalmol. 2021 Mar 2. doi: 10.1111/aos.14808. Online ahead of print.

ABSTRACT

PURPOSE: High myopic patients may develop strabismus due to globe dislocation out of the normal extraocular muscle cone. Surgical correction of this strabismus type is possible by joining the superior and lateral rectus muscles without the need for a scleral suture called the Yokoyama procedure. Data from large patient samples and the evaluation of a potential effect of an additional medial rectus recession (MRR) have been lacking so far.

METHODS: We pooled retrospective patient data of 14 departments of ophthalmology in Germany and Switzerland and analysed determinants of postoperative results using multivariable regression models.

RESULTS: We included 133 patients (mean age: 59.7 ± 13.4 years, surgery between 2008 and 2017) with a mean preoperative esotropia (both Yokoyama with and without MRR) of 23.8°±4.6°. The angle of preoperative esotropia increased with age. The postoperative esotropia was 8.7° ± 9.9°, and six patients were overcorrected. While preoperative esotropia was highly associated with postoperative results, we found no association of additional MRR with any of our postoperative outcome measures. The Yokoyama procedure had a higher absolute effect in patients with higher preoperative esotropia.

CONCLUSION: Our study confirms the positive effect of the Yokoyama procedure on strabismus due to high myopia in large-scale real-world data. In some cases, MRR may be needed because of muscle contracture, although additional MRR statistically did not affect the postoperative outcome. In patients with bilateral high myopic strabismus, correction of both eyes seems beneficial. The effect size of the Yokoyama procedure appears to be mainly driven by preoperative esotropia.

PMID:33655633 | DOI:10.1111/aos.14808

Eur J Haematol. 2021 Mar 2. doi: 10.1111/ejh.13609. Online ahead of print.

ABSTRACT

INTRODUCTION: Hypoalbuminemia is a known adverse prognostic factor in lymphomas. Yet, it is unknown if Axicabtagene Ciloleucel (axi-cel) overcomes the adverse prognostic impact of hypoalbuminemia in relapsed/refractory large B-cell lymphoma.

METHODS: We conducted a retrospective analysis across three Mayo Clinic centers to assess the relationship of hypoalbuminemia (defined as a serum albumin (SA) levels ≤ 3.5 g/dL) on outcomes of patients treated with axi-cel.

RESULTS: This analysis included 81 patients. Two patients had no available SA levels preceding axi-cel infusion. Eighteen patients (22.8%) had hypoalbuminemia with a median SA of 3.3 g/dL. Patients with normal SA had a statistically higher ORR than those without hypoalbuminemia (p=0.018). There was no difference in 1-year PFS and OS between the group with hypoalbuminemia and the group with normal SA levels (48% vs 49%, p=0.81) and (74% vs 73%, p=0.97), respectively. There was no difference in the severity or median duration of cytokine release syndrome or neurotoxicity between the two groups.

CONCLUSION: Notwithstanding the limitations related to the relatively small sample size, axi-cel therapy appears to overcome the adverse effect of hypoalbuminemia on OS and PFS. Large multicenter clinical studies are certainly needed to validate these findings.

PMID:33655560 | DOI:10.1111/ejh.13609

J Clin Nurs. 2021 Mar 3. doi: 10.1111/jocn.15740. Online ahead of print.

ABSTRACT

AIMS AND OBJECTIVES: To explore the relationships among emotional distress, cognitive function, and life satisfaction in people living with type 2 diabetes mellitus (T2DM), and to verify the mediating role of cognitive function.

BACKGROUND: People with T2DM face cognitive decline caused by age and disease complications. Emotional distress will reduce their life satisfaction, and cognitive function will also affect the life satisfaction, but whether cognitive function mediates the effect of emotional distress on life satisfaction has not been verified.

DESIGN: A cross-sectional study.

METHODS: A total of 200 people living with T2DM in the community by convenience sampling were enrolled from November – December 2018. Data collection involved a demographic and disease characteristics questionnaire, Problem Areas in Diabetes Scale, Subjective and Objective Cognitive Function Evaluation, and Life Satisfaction Questionnaire. Data analysis included descriptive statistics and structural equation modeling. This report followed the STROBE guideline.

RESULTS: The emotional distress and subjective memory complaints of cognitive function had a significant positive correlation, while both emotional distress and cognitive function showed significant negative correlations with life satisfaction. In addition, cognitive function completely mediated the relationship between emotional distress and life satisfaction.

CONCLUSION: The cognitive function played a mediating role in life satisfaction and explain how emotional distress affects life satisfaction of people with T2DM. Therefore, it is suggested that diabetes nurses should early identify the decline of cognitive function, and to intervene at early stage.

RELEVANCE TO CLINICAL PRACTICE: This study provides opinions on the mediating factors of cognitive function. Coping strategies and supporting resources to help the T2DM people to improve their life satisfaction is suggested.

PMID:33655571 | DOI:10.1111/jocn.15740

Int J Clin Pract. 2021 Mar 2:e14120. doi: 10.1111/ijcp.14120. Online ahead of print.

ABSTRACT

INTRODUCTION: Non-adherence to therapy is very common in patients with type II diabetes, leading to an increase in morbidity and mortality. The development of new oral therapies following metformin has increased the possibilities of treatment but little has been done in terms of improving medication adherence. The goal of the following study is to evaluate adherence and persistence over a period of three years in real-world diabetic patients and describe the comorbidities found in the group of patients studied.

MATERIAL AND METHODOLOGY: A non-interventional pharmacological observational study was conducted by examining all therapies from 2011 to 2019 at a local health centre in Pescara (ASL). The medication adherence and persistence over a three-year period were calculated using the pharmacy-refill method. The identification of the comorbidities was carried out in accordance with the Anatomical Therapeutic Chemical (ATC) Classification system at the fourth level.

RESULTS: 19600 patients undergoing treatment for type II diabetes from January 2011 to December 2019 were analysed. The absolute adherence value at three years was 0.68 ± 0.23. The three-year persistence curves showed a statistically significant difference (p < 0.0001). The ATCs with highest figures in the entire study group were: A02BC, B01AC and C10AA with 14220, 13934 and 10858 patients, respectively.

CONCLUSIONS: Adherence to therapy was suboptimal, while persistence curves showed a statistically significant difference, with patients treated with Metformin showing better results. Comorbidities analysed showed a greater relevance of heart disease.

PMID:33655579 | DOI:10.1111/ijcp.14120

Indoor Air. 2021 Mar 2. doi: 10.1111/ina.12790. Online ahead of print.

ABSTRACT

This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM_2.5 , black carbon, CO); household air pollution (PM_2.5 , CO); personal exposure (PM_2.5 , CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM_2.5 (R² = 0.45) and CO (R² = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R² = 0.76) and a root mean-squared error of 85 µg/m³ , and the survey-only-based regression model was able to predict PM2.5 exposures with an R² of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R² of 0.57 and RMSE of 98 µg/m³ .

PMID:33655590 | DOI:10.1111/ina.12790

Br J Dermatol. 2021 Mar 2. doi: 10.1111/bjd.19885. Online ahead of print.

ABSTRACT

BACKGROUND: Atopic dermatitis (AD) disease activity and severity is highly variable during childhood. Early attempts to identify subtypes based on disease trajectory have assessed AD presence over time without incorporating severity.

OBJECTIVE: To identify childhood AD subtypes from symptom severity and trajectories, and determine associations with genetic risk factors, comorbidities and demographic and environmental variables.

METHODS: We split data from children in the Avon Longitudinal Study of Parents and Children birth cohort into development and validation sets. To identify subtypes, we ran latent class analyses in the development set on AD symptom reports up to age 14. We regressed identified subtypes on non-genetic variables in mutually adjusted, multiply imputed (genetic: unadjusted, complete-case) multinomial regression analyses. We repeated analyses in the validation set and report confirmed results.

RESULTS: 11,866 children contributed to analyses. We identified one Unaffected/Rare class (66% of children) and four AD subtypes: Severe-Frequent (4%); Moderate-Frequent (7%); Moderate-Declining (11%); and Mild-Intermittent (12%). Symptom patterns within the first two subtypes appeared more homogeneous than the last two. Filaggrin null mutations (FLG), an AD polygenic risk score (PRS), being female, parental AD and comorbid asthma were associated with higher risk for some or all subtypes; FLG, AD-PRS and asthma associations were stronger along a subtype gradient arranged by increasing severity and frequency; FLG and AD-PRS further differentiated some phenotypes from each other.

CONCLUSIONS: Considering severity and AD trajectories leads to four well-defined and recognisable subtypes. The differential associations of risk factors among and between subtypes is novel and requires further research.

PMID:33655501 | DOI:10.1111/bjd.19885

Pharmacotherapy. 2021 Mar 2. doi: 10.1002/phar.2516. Online ahead of print.

ABSTRACT

INTRODUCTION: Two case reports describe a possible interaction between levetiracetam and high-dose methotrexate (HDMTX). Only two small retrospective studies have evaluated the interaction, and it remains unclear if concomitant use should be avoided.

METHODS: This single-center, retrospective study evaluated adult lymphoma patients who received HDMTX as a 4-hour infusion with or without concomitant levetiracetam for a difference in incidence of delayed MTX elimination (MTX level > 1µmol/L at 48 hours). Secondary outcomes included incidence of acute kidney injury (AKI) and hospital length of stay (LOS). Generalized estimating equations clustered on patient were used to assess each outcome.

RESULTS: The 430 included patients receiving 1993 doses of HDMTX had a median (IQR) age of 66 (57.5, 72.6) years, 88 (20.5%) received concomitant levetiracetam with at least one dose of MTX, 267 (62.1%) were male, and 397 (92.3%) were Caucasian. HDMTX doses ranged from 1-8 g/m² . The most common lymphoma diagnoses were systemic diffuse large B-cell lymphoma (DLBCL) (58.5%) and systemic DLBCL with central nervous system (CNS) involvement (32.8%). Rates of delayed elimination with and without levetiracetam were 13.4% and 16.3%, respectively (OR = 0.80, 95% CI 0.47-1.34, p=0.39). AKI occurred in 15.6% and 17.0% of patients with and without concomitant levetiracetam, respectively (OR=0.83, 95% CI 0.52-1.33, p=0.28). The median LOS with and without levetiracetam was 4.2 and 4.1 days, respectively (p=0.039). On multivariable analyses, only age, body surface area, diagnosis of systemic DLBCL with CNS involvement, serum creatinine, hemoglobin, total bilirubin, and dose of HDMTX were associated with delayed elimination.

CONCLUSIONS: HDMTX administered with concomitant levetiracetam was not associated with increased risk for delayed MTX elimination or AKI. These results support that levetiracetam and HDMTX are safe for coadministration.

PMID:33655525 | DOI:10.1002/phar.2516

Med Phys. 2021 Mar 2. doi: 10.1002/mp.14810. Online ahead of print.

ABSTRACT

PURPOSE: Radiation dose to specific cardiac substructures, such as the atria and ventricles, has been linked to post-treatment toxicity and has shown to be more predictive of these toxicities than dose to the whole heart. A deep learning-based algorithm for automatic generation of these contours is proposed to aid in either retrospective or prospective dosimetric studies to better understand the relationship between radiation dose and toxicities.

METHODS: The proposed method uses a mask-scoring regional convolutional neural network (R-CNN) which consists of five major subnetworks: backbone, regional proposal network (RPN), regional convolutional neural network (RCNN) head, mask head, and mask-scoring head. Multi-scale feature maps are learned from CT via the backbone network. The RPN utilizes these feature maps to detect the location and region-of-interest (ROI) of all substructures, and the final three subnetworks work in series to extract structural information from these ROIs. The network is trained using 55 patient CT datasets, with 22 patients having contrast scans. Three-fold cross validation (CV) is used for evaluation on 45 datasets, and a separate cohort of 10 patients are used for holdout evaluation. The proposed method is compared to a 3D UNet.

RESULTS: The proposed method produces contours that are qualitatively similar to the ground truth contours. Quantitatively, the proposed method achieved average Dice score coefficients (DSC) for the whole heart, chambers, great vessels, coronary arteries, the valves of the heart of 0.96, 0.94, 0.93, 0.66, and 0.77 respectively, outperforming the 3D UNet, which achieved DSCs of 0.92, 0.87, 0.88, 0.48, 0.59 for the corresponding substructure groups. Mean surface distances (MSD) between substructures segmented by the proposed method and the ground truth were less than 2 mm except for the left anterior descending coronary artery and the mitral and tricuspid valves, and less than 5 mm for all substructures. When dividing results into non-contrast and contrast datasets, the model performed statistically significantly better in terms of DSC, MSD, centroid mean distance (CMD), and volume difference for the chambers and whole heart with contrast. Notably, the presence of contrast did not statistically significantly affect coronary artery segmentation DSC or MSD. After network training, all substructures and the whole heart can be segmented on new datasets in less than five seconds.

CONCLUSIONS: A deep-learning network was trained for automatic delineation of cardiac substructures based on CT alone. The proposed method can be used as a tool to investigate the relationship between cardiac substructure dose and treatment toxicities.

PMID:33655548 | DOI:10.1002/mp.14810