Tag: statistics

BMC Pediatr. 2022 May 10;22(1):260. doi: 10.1186/s12887-022-03326-x.

ABSTRACT

BACKGROUND: Monitoring body composition and changes in energy expenditure during maturation and growth is significant, as many components can influence body structure in adulthood. In the case of young players, when these changes can influence their strength and power, it seems to be equally important. Our aim was to examine whether resting energy expenditure (REE) and body composition would change after 10 months from baseline in physically active children and adolescents.

METHODS: We obtained data from 80 children and adolescents aged 9 to 17 years at two measurement points: the baseline in September 2018 and after 10 months in July 2019. The study was carried out using a calorimeter (Fitmate MED, Cosmed, Rome, Italy), a device used to assess body composition using by the electrical bioimpedance method by means of a segment analyzer (TANITA MC-980). The Student’s t-test and linear regression analysis were used. Using the stepwise forward regression procedure, the selection of factors in a statistically significant way that describes the level of REE was made.

RESULTS: We noticed that REE was not significantly different between baseline (1596.94 ± 273.01 kcal) and after 10 months (1625.38 ± 253.26 kcal). When analyzing the difference in REE between studies girls, we found body height as a significant predictor. The results of our study show a negative relationship between growth and REE. Differences between sexes and age in REE between baseline and after 10 months were not significant.

CONCLUSIONS: Our study involving physically active children and adolescents, which used repeated objective measures and longitudinal statistical modeling to analyze them, was unable to demonstrate any interaction between body weight change, body composition measurements, and REE.

PMID:35538456 | DOI:10.1186/s12887-022-03326-x

JMIR Med Inform. 2022 May 2. doi: 10.2196/37239. Online ahead of print.

ABSTRACT

BACKGROUND: A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes, with the aim of designing targeted interventions. However, while several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups, and their modeling and interpretation for clinical applications.

OBJECTIVE: To develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a three-step modeling approach. (1) Visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities, and determine their statistical significance and clinical interpretability. (2) Classification modeling to classify patients into subgroups and measure its accuracy. (3) Prediction modeling to predict a patient’s risk for an adverse outcome, and compare its accuracy with and without patient subgroup information.

METHODS: The MIPS framework was developed using (1) bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities; (2) multinomial logistic regression to classify patients into subgroups; and (3) hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership, compared to standard logistic regression without subgroup membership. The MIPS framework was evaluated on three hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip/knee arthroplasty (THA/TKA). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge, and controls defined as patients not readmitted within 90 days of discharge, matched by age, gender, race, and Medicaid eligibility (n[COPD]=29,016, n[CHF]=51,550, n[THA/TKA]=16,498).

RESULTS: In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), were significantly replicated (RI=0.92, 0.94, 0.89; P<.001, <.001, <.01), and were clinically meaningful to clinicians. (2) In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.60%, 99.34%, 99.86%). (3) In two conditions (COPD, THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between the readmitted and not readmitted patients as measured by net reclassification improvement (NRI=.059, .11), but not as measured by the C-statistic or integrated discrimination improvement (IDI).

CONCLUSIONS: While the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and inter-cluster associations. The high accuracy of the classification models reflects the strong separation of the patient subgroups despite the size and density of the datasets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors for hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission and beyond.

PMID:35537203 | DOI:10.2196/37239

J Occup Environ Hyg. 2022 May 10:1-15. doi: 10.1080/15459624.2022.2076860. Online ahead of print.

ABSTRACT

Recently, the National Institute for Occupational Safety and Health (NIOSH) released an updated version of the NIOSH Industry and Occupation Computerized Coding System (NIOCCS), which uses supervised machine learning to assign industry and occupational codes based on provided free-text information. However, no efforts have been made to externally verify the quality of assigned industry and job titles when the algorithm is provided with inputs of varying quality. This study sought to evaluate whether the NIOCCS algorithm was sufficiently robust with low quality inputs and how variable quality could impact subsequent job estimated exposures in a large job exposure matrix for noise (NoiseJEM).Using free-text industry and job descriptions from >700,000 noise measurements in the NoiseJEM, three files were created and input into NIOCCS: (1) N1, “raw” industries and job titles; (2) N2, “refined” industries and “raw” job titles; and (3) N3, “refined” industries and job titles. Standardized industry and occupation codes were output by NIOCCS. Descriptive statistics of performance metrics (e.g., misclassification/discordance of occupation codes) were evaluated for each input relative to the original NoiseJEM dataset (N0).Across major Standardized Occupational Classifications (SOC), total discordance rates for N1, N2, and N3 compared to N0 were 53.6%, 42.3%, and 5.0%, respectively. The impact of discordance on major SOC group varied and included both over- and under-estimates of average noise exposure compared to N0. N2 had the most accurate noise exposure estimates (i.e., smallest bias) across major SOC groups compared to N1 and N3. Further refinement of job titles in N3 showed little improvement. Some variation in classification efficacy was seen over time, particularly prior to 1985.Machine learning algorithms can systematically and consistently classify data but are highly dependent on the quality and amount of input data. The greatest benefit for an end-user may come from cleaning industry information before applying this method for job classification. Our results highlight the need for standardized classification methods that remain constant over time.

PMID:35537195 | DOI:10.1080/15459624.2022.2076860

Sleep. 2022 May 10:zsac109. doi: 10.1093/sleep/zsac109. Online ahead of print.

ABSTRACT

We investigated the potential role of sleep-trait associated genetic loci in conferring a degree of their effect via pancreatic α- and β- cells, given that both sleep disturbances and metabolic disorders, including type 2 diabetes and obesity, involve polygenic contributions and complex interactions. We determined genetic commonalities between sleep and metabolic disorders, conducting linkage disequilibrium genetic correlation analyses with publicly available GWAS summary statistics. Then we investigated possible enrichment of sleep-trait associated SNPs in promoter-interacting open chromatin regions within α- and β- cells, intersecting public GWAS reports with our own ATAC-seq and high-resolution promoter-focused Capture C data generated from both sorted human α-cells and an established human beta-cell line (EndoC-βH1). Finally, we identified putative effector genes physically interacting with sleep-trait associated variants in α- and EndoC-βH1cells running variant-to-gene mapping and establish pathways in which these genes are significantly involved. We observed that insomnia, short and long sleep – but not morningness – were significantly correlated with type 2 diabetes, obesity and other metabolic traits. Both the EndoC-βH1 and α-cells were enriched for insomnia loci (P=0.01; P=0.0076), short sleep loci (P=0.017; P=0.022) and morningness loci (P=2.2×10 -7; P=0.0016), while the α-cells were also enriched for long sleep loci (P=0.034). Utilizing our promoter contact data, we identified 63 putative effector genes in EndoC-βH1 and 76 putative effector genes in α-cells, with these genes showing significant enrichment for organonitrogen and organophosphate biosynthesis, phosphatidylinositol and phosphorylation, intracellular transport and signaling, stress responses and cell differentiation. Our data suggest that a subset of sleep-related loci confer their effects via cells in pancreatic islets.

PMID:35537191 | DOI:10.1093/sleep/zsac109

Am J Audiol. 2022 May 10:1-8. doi: 10.1044/2022_AJA-21-00181. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study was to evaluate the impact of file compression on clinically measured word recognition scores obtained using the Northwestern University Test Number Six (NU-6; Auditec recording) materials.

METHOD: Participants were 86 adults (N = 170 ears; M _age = 65.5). The 25 most difficult words from each of four NU-6 test lists were used to measure word recognition. Two lists were compressed using a freely available Advanced Audio Coding compression algorithm and two were not. Word recognition was measured in each ear using one compressed file and one uncompressed file. Percent correct scores were calculated in each test condition and log transformed for analyses. Clinically meaningful differences between uncompressed and compressed scores were examined using 95% critical difference ranges. The effects of file compression on word recognition scores were examined in the context of multiple potential confounding effects, including age and degree of hearing loss, using linear mixed-effects models (LMMs).

RESULTS: Differences between compressed and uncompressed scores in a given ear exceeded the 95% critical difference range in about 7% of cases, approximating the 5% of expected cases occurring due to chance. Likewise, LMM results revealed no significant effect of file compression on clinically measured NU-6 word recognition scores and no significant interactions between compression effects and age or degree of hearing loss.

CONCLUSIONS: While the original uncompressed audio files are clearly the most appropriate stimuli for clinical purposes, our study results suggest that file compression, even at an aggressive 64 kilobits per second, does not have a statistically significant, or clinically meaningful, effect on word recognition scores when measured using these Auditec materials.

PMID:35537124 | DOI:10.1044/2022_AJA-21-00181

J ECT. 2022 Feb 3. doi: 10.1097/YCT.0000000000000820. Online ahead of print.

ABSTRACT

OBJECTIVE: Our study aimed to (1) examine the effect of adjunctive high-definition transcranial direct current stimulation (HD-tDCS) in craving and withdrawal among patients with opioid use disorder on buprenorphine-naloxone, and (2) examine effect of HD-tDCS changes in glutamate-glutamine and [gamma]-aminobutyric acid (GABA) at the left dorsolateral prefrontal cortex (DLPFC) among patients with opioid use disorder on buprenorphine-naloxone.

METHODS: This was a pilot randomized double-blind, sham-controlled parallel-group study. A total of 28 patients on buprenorphine-naloxone (6/1.5 mg/d) were randomly allocated into 2 groups for active and sham HD-tDCS stimulation. High-definition transcranial direct current stimulation was administered twice daily for consecutive 5 days, from days 2 to 6. The Clinical Opiate Withdrawal Scale (COWS), the Desire for Drug Questionnaire (DDQ), the Obsessive-Compulsive Drug Use Scale (OCDUS), and glutamate-glutamine and GABA at DLPFC via proton magnetic resonance spectroscopy were measured at baseline and on day 7.

RESULTS: Both active and sham groups had comparable changes in DDQ, OCDUS (except 2 subcomponents), COWS, and glutamate-glutamine and GABA at DLPFC. In the active HD-tDCS group, statistically significant reductions were observed in DDQ, OCDUS, and COWS but not in glutamate-glutamine and GABA.

CONCLUSIONS: The adjunctive active HD-tDCS group showed comparable changes in craving and withdrawal, and glutamate-glutamine and GABA at DLPFC compared with sham HD-tDCS. Craving and withdrawal but not glutamate-glutamine and GABA at DLPFC decreased significantly with adjunctive HD-tDCS. Future studies with larger sample size and online assessment of glutamate-glutamine and GABA would enhance our knowledge.

PMID:35537121 | DOI:10.1097/YCT.0000000000000820

JCO Glob Oncol. 2022 May;8:e2100425. doi: 10.1200/GO.21.00425.

ABSTRACT

PURPOSE: International comparisons of patient demographics, tumor characteristics, and survival can shed light on areas for health care system improvement. The International Society of Pediatric Oncology Wilms Tumor 2001 trial/study registered patients through national clinical study groups in Western Europe and Brazil. This retrospective post hoc analysis of the International Society of Pediatric Oncology Wilms Tumor 2001 database aims to make visible and suggest reasons for any variations in outcomes.

METHODS: All patients with unilateral Wilms tumor (WT), age > 6 months, treated with preoperative chemotherapy as per protocol, and registered between 2001 and 2011 were eligible. Countries were grouped to give comparable case numbers and geographical representation. Cox univariable and multivariable (MVA) statistics were applied, with the German collaborative group (Gesellschaft für Pädiatrische Onkologie und Hämatologie-Austria, Germany, and Switzerland) as reference for hazard ratios for event-free survival (EFS) and overall survival (OS).

RESULTS: A total of 3,176 eligible patients were registered from 24 countries assigned into six groups. Age and histologic risk group distribution were similar across all groupings. The distribution of WT stage varied by country grouping, with 14.9% (range, 11.1%-18.2%) metastatic at diagnosis. Median follow-up was 78.9 months. For localized WT, 5-year EFS varied from 80% (Brazilian group) to 91% (French group; P < .0001), retaining significance only for Brazil in MVA (P = .001). Five-year OS varied from 89% (Brazilian group) to 98% (French group; P < .0001). In MVA, only superior OS in France was significant (P = .001). Five-year EFS/OS for stage IV did not vary significantly. High-risk histology and tumor volume at surgery were significantly associated with increased risk of death in MVA for metastatic disease.

CONCLUSION: International benchmarking of survival rates from WT within a large trial/study database has demonstrated statistically significant differences. Clinical interpretation should take account of variation in tumor stage but also treatment factors.

PMID:35537105 | DOI:10.1200/GO.21.00425

Ned Tijdschr Tandheelkd. 2022 May;129(5):219-222. doi: 10.5177/ntvt.2022.05.21120.

ABSTRACT

Stannous fluoride is one of the first fluoride compounds that were added to dentifrices. Besides the well-known effect of fluoride, the presence of tin could also have an effect on dental health by its anti-microbial activity and the ability to form insoluble metal salts. The functioning of stannous fluoride has been studied extensively in many scientific publications. On the basis of the available literature, the use of stannous fluoride instead of sodium fluoride could be advantageous in case of gingivitis, halitosis, dentine hypersensitivity, or erosion. The effects that were found are statistically significant, albeit rather small, which makes it harder to predict the actual gain in dental health or the clinical relevance for an individual patient.

PMID:35537088 | DOI:10.5177/ntvt.2022.05.21120

J Clin Oncol. 2022 May 10:JCO2102285. doi: 10.1200/JCO.21.02285. Online ahead of print.

ABSTRACT

Advances in biology and immunology have elucidated genetic and immunologic origins of cancer. Innovations in sequencing technologies revealed that distinct cancer histologies shared common genetic and immune phenotypic traits. Pharmacologic developments made it possible to target these alterations, yielding novel classes of targeted agents whose therapeutic potential span multiple tumor types. Basket trials, one type of master protocol, emerged as a tool for evaluating biomarker-targeted therapies among multiple tumor histologies. Conventionally conducted within the phase II setting and designed to estimate high and durable objective responses, basket trials pose challenges to statistical design and interpretation of results. This article reviews basket trials implemented in oncology studies and discusses issues related to their statistical design and analysis.

PMID:35537102 | DOI:10.1200/JCO.21.02285

Vet Med Sci. 2022 May 10. doi: 10.1002/vms3.835. Online ahead of print.

ABSTRACT

Bats are the natural reservoir host for many pathogenic and non-pathogenic viruses, potentially spilling over to humans and domestic animals directly or via an intermediate host. The ongoing COVID-19 pandemic is the continuation of virus spillover events that have taken place over the last few decades, particularly in Asia and Africa. Therefore, these bat-associated epidemics provide a significant number of hints, including respiratory cellular tropism, more intense susceptibility to these cell types, and overall likely to become a pandemic for the next spillover. In this systematic review, we analysed data to insight, through bat-originated spillover in Asia and Africa. We used STATA/IC-13 software for descriptive statistics and meta-analysis. The random effect of meta-analysis showed that the pooled estimates of case fatality rates of bat-originated viral zoonotic diseases were higher in Africa (61.06%, 95%CI: 50.26 to 71.85, l² % = 97.3, p < 0.001). Moreover, estimates of case fatality rates were higher in Ebola (61.06%; 95%CI: 50.26 to 71.85, l² % = 97.3, p < 0.001) followed by Nipah (55.19%; 95%CI: 39.29 to 71.09, l² % = 94.2, p < 0.001), MERS (18.49%; 95%CI: 8.19 to 28.76, l² % = 95.4, p < 0.001) and SARS (10.86%; 95%CI: 6.02 to 15.71, l² % = 85.7, p < 0.001) with the overall case fatality rates of 29.86 (95%CI: 29.97 to 48.58, l² % = 99.0, p < 0.001). Bat-originated viruses have caused several outbreaks of deadly diseases, including Nipah, Ebola, SARS and MERS in Asia and Africa in a sequential fashion. Nipah virus emerged first in Malaysia, but later, periodic outbreaks were noticed in Bangladesh and India. Similarly, the Ebola virus was detected in the African continent with neurological disorders in humans, like Nipah, seen in the Asian region. Two important coronaviruses, MERS and SARS, were introduced, both with the potential to infect respiratory passages. This paper explores the dimension of spillover events within and/or between bat-human and the epidemiological risk factors, which may lead to another pandemic occurring. Further, these processes enhance the bat-originated virus, which utilises an intermediate host to jump into human species.

PMID:35537080 | DOI:10.1002/vms3.835