Category: Statistics

Ann Palliat Med. 2021 Jul 5:apm-21-956. doi: 10.21037/apm-21-956. Online ahead of print.

ABSTRACT

BACKGROUND: Palliative radiation therapy is essential to the care of patients with advanced cancer. Unfortunately, despite their benefits, the principles of palliative radiation therapy and palliative and supportive care are underrepresented in radiation oncology residency curricula. In this study, we attempted to identify areas of emphasis for future palliative radiation therapy curricula by examining the relevant questions posted to theMednet.

METHODS: Questions tagged with both “Palliation” and “Radiation Oncology” or “General Radiation Oncology” that were posted to theMednet on or before January 7, 2020 were included in this analysis. The questions were grouped thematically, and subthemes within each broader thematic group were identified. Among the thematic groups, variations in social engagement metrics were assessed using the Kruskal-Wallis Test and non-parametric analysis of variance.

RESULTS: A total of 4,188 questions tagged with the terms “Radiation Oncology,” “General Radiation Oncology,” or “Palliation” and posed between 2012 and 2020 were identified. Of these, 161 questions satisfied our inclusion criteria. Upon examination of the identified questions, eight thematic groups and several subthemes were identified, representing areas of possible emphasis for future palliative radiation therapy curricula. Among questions in different thematic groups, however, there were no statistically significant differences in any of the examined social engagement metrics.

CONCLUSIONS: We found many common question themes and subthemes in our examination of the palliative radiation oncology questions posted to theMednet. Our findings suggest that several opportunities for education exist for radiation oncology residents in regards to palliative and supportive care and palliative radiation therapy.

PMID:34263626 | DOI:10.21037/apm-21-956

Ann Palliat Med. 2021 Jul 13:apm-21-338. doi: 10.21037/apm-21-338. Online ahead of print.

ABSTRACT

BACKGROUND: Cases of carbapenem-resistant Klebsiella pneumoniae infection have been increasing. Patients with carbapenem-resistant Klebsiella pneumoniae infection have a poor prognosis and a high mortality rate. Identification of potential risk factors associated with carbapenem-resistant Klebsiella pneumoniae infection-related mortality may help improve patient outcomes.

METHODS: Embase, PubMed, and the Cochrane Library databases were searched to identify articles describing predictors of mortality in patients with carbapenem-resistant Klebsiella pneumoniae infection. The quality of articles was assessed with the Newcastle-Ottawa Scale score (NOS). Review Manager was used for statistical analyses.

RESULTS: Twenty-seven observational studies were included in the analysis. Factors associated with higher mortality were septic shock [odds ratio (OR): 4.41, 95% CI: 3.17-6.15], congestive heart failure (OR: 2.65, 95% CI: 1.71-4.13), chronic obstructive pulmonary disease (COPD; OR: 2.43, 95% CI: 1.87-3.15), chronic kidney disease (CKD; OR: 1.78, 95% CI: 1.43-2.22), diabetes mellitus (OR: 1.41, 95% CI: 1.16-1.72), mechanical ventilation (OR: 1.65, 95% CI: 1.25-2.18), and inappropriate empirical antimicrobial treatment (OR: 1.25, 95% CI: 1.03-1.52). The average Acute Physiology and Chronic Health Evaluation (APACHE) II score at the time of diagnosis of carbapenem-resistant Klebsiella pneumoniae infection was considerably higher in patients who did not survive than in those who survived (weighted mean difference: 5.86, 95% CI: 2.46-9.26).

DISCUSSION: Patient condition, timing appropriate antimicrobial treatment, and disease severity according to the APACHE II score are the most important risk factors for death in patients with carbapenem-resistant Klebsiella pneumoniae infection. Our finding may help predict patients’ outcomes and improve management for them.

REGISTRATION NUMBER: 20210417EuEGX/INPLASY2020100037.

PMID:34263631 | DOI:10.21037/apm-21-338

Ann Palliat Med. 2021 Jun 26:apm-21-675. doi: 10.21037/apm-21-675. Online ahead of print.

ABSTRACT

BACKGROUND: Severe adenovirus pneumonia (SAP) of children is prone to multi-system complications, has the high mortality rate and high incidence of sequelae. Severity prediction can facilitate an adequate individualized treatment plan. Our study try to develop and evaluate a predictive nomogram for children with SAP.

METHODS: An observational study was designed and performed retrospectively. The data were categorized as training and validation datasets using the method of credible random split-sample (split ratio =0.7:0.3). The predictors were selected using Lasso (least absolute shrinkage and selection operator) logistic regression and the nomogram was developed. Nomogram discrimination was assessed using the receiver operating characteristic (ROC) curve, and the prediction accuracy was evaluated using a calibration curve. The nomogram was also evaluated for clinical effectiveness by the decision curve analysis (DCA). A P value of <0.05 was deemed statistically significant.

RESULTS: The identified predictors were fever duration, and interleukin-6 and CD4+ T cells and were assembled into the nomogram. The nomogram exhibited good discrimination with area under ROC curve in training dataset (0.79, 95% CI: 0.60-0.92) and test dataset (0.76, 95% CI: 0.63-0.87). The nomogram seems to be useful clinically as per DCA.

CONCLUSIONS: A nomogram with a potentially effective application was developed to facilitate individualized prediction for SAP in children.

PMID:34263632 | DOI:10.21037/apm-21-675

Sci Rep. 2021 Jul 14;11(1):14451. doi: 10.1038/s41598-021-93865-7.

ABSTRACT

A mismatch exists between people’s mental representations of their own body and their real body measurements, which may impact general well-being and health. We investigated whether this mismatch is reduced when contextualizing body size estimation in a real-life scenario. Using a reverse correlation paradigm, we constructed unbiased, data-driven visual depictions of participants’ implicit body representations. Across three conditions-own abstract, ideal, and own concrete body-participants selected the body that looked most like their own, like the body they would like to have, or like the body they would use for online shopping. In the own concrete condition only, we found a significant correlation between perceived and real hip width, suggesting that the perceived/real body match only exists when body size estimation takes place in a practical context, although the negative correlation indicated inaccurate estimation. Further, participants who underestimated their body size or who had more negative attitudes towards their body weight showed a positive correlation between perceived and real body size in the own abstract condition. Finally, our results indicated that different body areas were implicated in the different conditions. These findings suggest that implicit body representations depend on situational and individual differences, which has clinical and practical implications.

PMID:34262115 | DOI:10.1038/s41598-021-93865-7

NPJ Precis Oncol. 2021 Jul 14;5(1):64. doi: 10.1038/s41698-021-00178-z.

ABSTRACT

In children with cancer, the heterogeneity in ototoxicity occurrence after similar treatment suggests a role for genetic susceptibility. Using a genome-wide association study (GWAS) approach, we identified a genetic variant in TCERG1L (rs893507) to be associated with hearing loss in 390 non-cranial irradiated, cisplatin-treated children with cancer. These results were replicated in two independent, similarly treated cohorts (n = 192 and 188, respectively) (combined cohort: P = 5.3 × 10^-10, OR 3.11, 95% CI 2.2-4.5). Modulating TCERG1L expression in cultured human cells revealed significantly altered cellular responses to cisplatin-induced cytokine secretion and toxicity. These results contribute to insights into the genetic and pathophysiological basis of cisplatin-induced ototoxicity.

PMID:34262104 | DOI:10.1038/s41698-021-00178-z

Sci Rep. 2021 Jul 14;11(1):14435. doi: 10.1038/s41598-021-93864-8.

ABSTRACT

Visna/maedi (VM) is a multisystemic lentivirus infection of sheep that affecting sheep industry across the globe. TMEM154 gene has been identified to be a major VM-associated host gene, nevertheless, a recent study showed that the frequency of the VM-resistant TMEM154 haplotypes was very low or absent in indigenous sheep. Thus, the present study was designed to determine other possible co-receptors associated with VM. For this purpose, DRB1 gene, which is renowned for its role in host immune response against various diseases was targeted. A total number of 151 case-control matched pairs were constructed from 2266 serologically tested sheep. A broad range of DRB1 haplotype diversity was detected by sequence-based genotyping. Moreover, a novel 2 bp deletion (del) in the DRB1 intron 1 was identified. For the final statistic, the sheep carrying VM-resistant TMEM154 diplotypes were removed and a McNemar’s test with a matched pairs experimental design was conducted. Consequently, it was identified for the first time that the 2 bp del variant is a genetic risk factor for VM (p value 0.002; chi-square 8.31; odds ratio 2.9; statistical power 0.90) in the dominant model. Thus, negative selection for 2 bp del variant could decrease VM infection risk in Turkish sheep.

PMID:34262107 | DOI:10.1038/s41598-021-93864-8

Sci Rep. 2021 Jul 14;11(1):14460. doi: 10.1038/s41598-021-93713-8.

ABSTRACT

The aim of this study was to explore cardiac autonomic changes assessed by linear and nonlinear indexes of heart rate variability (HRV) and body composition modifications in breast cancer survivors and cancer-free control women. Women who were breast cancer survivors (BCS, n = 27) and without cancer with similar characteristics (Control, n = 31) were recruited for this study. We calculated some relevant linear and nonlinear parameters of 5 min of RR interval time series such as mean RR interval (RR_ave), the corrected Poincaré index (cSD1/SD2), the sample entropy (SampEn), the long-term fractal scaling exponent (α₂) and 2UV from symbolic dynamics. Additionally, we indirectly assessed body composition measures such as body weight, fat mass, visceral fat rating (VFR), normalized VRF (nVFR), muscle mass, metabolic age, and total body water. We found that diverse HRV indexes and only one body composition measure showed statistical differences (p < 0.05) between the BCS and Control groups. RR_ave: 729 (648-802) vs. 795 (713-852) ms; cSD2/SD1: 3.4 (2.7-5.0) vs. 2.9 (2.3-3.5); SampEn: 1.5 (1.3-1.8) vs. 1.7 (1.5-1.8); α₂: 0.6 (0.3-0.6) vs. 0.5 (0.4-0.5); 2UV: 7.1 (4.3-11.5) vs. 10.8 (6.4-15.7) and nVFR 0.12 (0.11-0.13) vs. 0.10 (0.08-0.12) points/kg, respectively. The nVFR was strongly significantly correlated with several indexes of HRV only in the BCS group.Our findings suggest that BCS exhibit lower parasympathetic cardiac activity and changes in HRV patterns compared to Controls. A concomitant increase of visceral fat, among other factors, may contribute to cardiac autonomic disturbances and changes in HRV patterns in BCS.

PMID:34262078 | DOI:10.1038/s41598-021-93713-8

Sci Rep. 2021 Jul 14;11(1):14490. doi: 10.1038/s41598-021-93651-5.

ABSTRACT

As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare different algorithms. While the machine learning community has generally accepted methods such as k-fold stratified cross-validation (CV) to be more rigorous than single split validation, the standard research practice in medical fields is the use of single split validation techniques. This is especially concerning given the relatively small sample sizes of datasets used for cardiovascular imaging. We aim to examine how train-test split variation impacts the stability of machine learning (ML) model performance estimates in several validation techniques on two real-world cardiovascular imaging datasets: stratified split-sample validation (70/30 and 50/50 train-test splits), tenfold stratified CV, 10 × repeated tenfold stratified CV, bootstrapping (500 × repeated), and leave one out (LOO) validation. We demonstrate that split validation methods lead to the highest range in AUC and statistically significant differences in ROC curves, unlike the other aforementioned approaches. When building predictive models on relatively small data sets as is often the case in medical imaging, split-sample validation techniques can produce instability in performance estimates with variations in range over 0.15 in the AUC values, and thus any of the alternate validation methods are recommended.

PMID:34262098 | DOI:10.1038/s41598-021-93651-5

Sci Rep. 2021 Jul 14;11(1):14478. doi: 10.1038/s41598-021-91644-y.

ABSTRACT

The prevalence of smokers is a major driver of lung cancer incidence in a population, though the “exposure-lag” effects are ill-defined. Here we present a multi-country ecological modelling study using a 30-year smoking prevalence history to quantify the exposure-lag response. To model the temporal dependency between smoking prevalence and lung cancer incidence, we used a distributed lag non-linear model (DLNM), controlling for gender, age group, country, outcome year, and population at risk, and presented the effects as the incidence rate ratio (IRR) and cumulative incidence rate ratio (IRR_cum). The exposure-response varied by lag period, whilst the lag-response varied according to the magnitude and direction of changes in smoking prevalence in the population. For the cumulative lag-response, increments above and below the reference level was associated with an increased and decreased IRR_cum respectively, with the magnitude of the effect varying across the lag period. Though caution should be exercised in interpretation of the IRR and IRR_cum estimates reported herein, we hope our work constitutes a preliminary step towards providing policy makers with meaningful indicators to inform national screening programme developments. To that end, we have implemented our statistical model a shiny app and provide an example of its use.

PMID:34262067 | DOI:10.1038/s41598-021-91644-y

Sci Rep. 2021 Jul 14;11(1):14488. doi: 10.1038/s41598-021-93579-w.

ABSTRACT

Since its emergence, the phenomenon of SARS-CoV-2 transmission by seemingly healthy individuals has become a major challenge in the effort to achieve control of the pandemic. Identifying the modes of transmission that drive this phenomenon is a perquisite in devising effective control measures, but to date it is still under debate. To address this problem, we have formulated a detailed mathematical model of discrete human actions (such as coughs, sneezes, and touching) and the continuous decay of the virus in the environment. To take into account those discrete and continuous events we have extended the common modelling approach and employed a hybrid stochastic mathematical framework. This allowed us to calculate higher order statistics which are crucial for the reconstruction of the observed distributions. We focused on transmission within a household, the venue with the highest risk of infection and validated the model results against the observed secondary attack rate and the serial interval distribution. Detailed analysis of the model results identified the dominant driver of pre-symptomatic transmission as the contact route via hand-face transfer and showed that wearing masks and avoiding physical contact are an effective prevention strategy. These results provide a sound scientific basis to the present recommendations of the WHO and the CDC.

PMID:34262069 | DOI:10.1038/s41598-021-93579-w