Category: Statistics

J Appl Clin Med Phys. 2022 Apr 23:e13609. doi: 10.1002/acm2.13609. Online ahead of print.

ABSTRACT

OBJECTIVE: To quantify the clinical performance of a machine learning (ML) algorithm for organ-at-risk (OAR) dose prediction for lung stereotactic body radiation therapy (SBRT) and estimate the treatment planning benefit from having upfront access to these dose predictions.

METHODS: ML models were trained using multi-center data consisting of 209 patients previously treated with lung SBRT. Two prescription levels were investigated, 50 Gy in five fractions and 54 Gy in three fractions. Models were generated using a gradient-boosted regression tree algorithm using grid searching with fivefold cross-validation. Twenty patients not included in the training set were used to test OAR dose prediction performance, ten for each prescription. We also performed blinded re-planning based on OAR dose predictions but without access to clinically delivered plans. Differences between predicted and delivered doses were assessed by root-mean square deviation (RMSD), and statistical differences between predicted, delivered, and re-planned doses were evaluated with one-way analysis of variance (ANOVA) tests.

RESULTS: ANOVA tests showed no significant differences between predicted, delivered, and replanned OAR doses (all p ≥ 0.36). The RMSD was 2.9, 3.9, 4.3, and 1.7Gy for max dose to the spinal cord, great vessels, heart, and trachea, respectively, for 50 Gy in five fractions. Average improvements of 1.0, 1.4, and 2.0 Gy were seen for spinal cord, esophagus, and trachea max doses in blinded replans compared to clinically delivered plans with 54 Gy in three fractions, and 1.8, 0.7, and 1.5 Gy, respectively, for the esophagus, heart and bronchus max doses with 50 Gy in five fractions. Target coverage was similar with an average PTV V100% of 94.7% for delivered plans compared to 97.3% for blinded re-plans for 50 Gy in five fractions, and respectively 98.4% versus 99.2% for 54 Gy in three fractions.

CONCLUSION: This study validated ML-based OAR dose prediction for lung SBRT, showing potential for improved OAR dose sparing and more consistent plan quality using dose predictions for patient-specific planning guidance.

PMID:35460150 | DOI:10.1002/acm2.13609

J Sleep Res. 2022 Apr 22. doi: 10.1111/jsr.13604. Online ahead of print.

ABSTRACT

Insomnia disorder comprises symptoms during night and day that strongly affect quality of life and wellbeing. Prolonged sleep latency, difficulties to maintain sleep and early morning wakening characterize sleep complaints, whereas fatigue, reduced attention, impaired cognitive functioning, irritability, anxiety and low mood are key daytime impairments. Insomnia disorder is well acknowledged in all relevant diagnostic systems: Diagnostic and Statistical Manual of the American Psychiatric Association, 5th revision, International Classification of Sleep Disorders, 3rd version, and International Classification of Diseases, 11th revision. Insomnia disorder as a chronic condition is frequent (up to 10% of the adult population, with a preponderance of females), and signifies an important and independent risk factor for physical and, especially, mental health. Insomnia disorder diagnosis primarily rests on self-report. Objective measures like actigraphy or polysomnography are not (yet) part of the routine diagnostic canon, but play an important role in research. Disease concepts of insomnia range from cognitive-behavioural models to (epi-) genetics and psychoneurobiological approaches. The latter is derived from knowledge about basic sleep-wake regulation and encompass theories like rapid eye movement sleep instability/restless rapid eye movement sleep. Cognitive-behavioural models of insomnia led to the conceptualization of cognitive-behavioural therapy for insomnia, which is now considered as first-line treatment for insomnia worldwide. Future research strategies will include the combination of experimental paradigms with neuroimaging and may benefit from more attention to dysfunctional overnight alleviation of distress in insomnia. With respect to therapy, cognitive-behavioural therapy for insomnia merits widespread implementation, and digital cognitive-behavioural therapy may assist delivery along treatment guidelines. However, given the still considerable proportion of patients responding insufficiently to cognitive-behavioural therapy for insomnia, fundamental studies are highly necessary to better understand the brain and behavioural mechanisms underlying insomnia. Mediators and moderators of treatment response/non-response and the associated development of tailored and novel interventions also require investigation. Recent studies suggest that treatment of insomnia may prove to add significantly as a preventive strategy to combat the global burden of mental disorders.

PMID:35460140 | DOI:10.1111/jsr.13604

J Magn Reson Imaging. 2022 Apr 22. doi: 10.1002/jmri.28199. Online ahead of print.

ABSTRACT

BACKGROUND: In vivo cardiac diffusion tensor imaging (cDTI) characterizes myocardial microstructure. Despite its potential clinical impact, considerable technical challenges exist due to the inherent low signal-to-noise ratio.

PURPOSE: To reduce scan time toward one breath-hold by reconstructing diffusion tensors for in vivo cDTI with a fitting-free deep learning approach.

STUDY TYPE: Retrospective.

POPULATION: A total of 197 healthy controls, 547 cardiac patients.

FIELD STRENGTH/SEQUENCE: A 3 T, diffusion-weighted stimulated echo acquisition mode single-shot echo-planar imaging sequence.

ASSESSMENT: A U-Net was trained to reconstruct the diffusion tensor elements of the reference results from reduced datasets that could be acquired in 5, 3 or 1 breath-hold(s) (BH) per slice. Fractional anisotropy (FA), mean diffusivity (MD), helix angle (HA), and sheetlet angle (E2A) were calculated and compared to the same measures when using a conventional linear-least-square (LLS) tensor fit with the same reduced datasets. A conventional LLS tensor fit with all available data (12 ± 2.0 [mean ± sd] breath-holds) was used as the reference baseline.

STATISTICAL TESTS: Wilcoxon signed rank/rank sum and Kruskal-Wallis tests. Statistical significance threshold was set at P = 0.05. Intersubject measures are quoted as median [interquartile range].

RESULTS: For global mean or median results, both the LLS and U-Net methods with reduced datasets present a bias for some of the results. For both LLS and U-Net, there is a small but significant difference from the reference results except for LLS: MD 5BH (P = 0.38) and MD 3BH (P = 0.09). When considering direct pixel-wise errors the U-Net model outperformed significantly the LLS tensor fit for reduced datasets that can be acquired in three or just one breath-hold for all parameters.

DATA CONCLUSION: Diffusion tensor prediction with a trained U-Net is a promising approach to minimize the number of breath-holds needed in clinical cDTI studies.

EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

PMID:35460138 | DOI:10.1002/jmri.28199

Am J Hum Biol. 2022 Apr 23:e23753. doi: 10.1002/ajhb.23753. Online ahead of print.

ABSTRACT

OBJECTIVES: Imaging methods to measure the human pelvis in vivo provide opportunities to better understand pelvic variation and adaptation. Magnetic resonance imaging (MRI) provides high-resolution images, but is more expensive than dual-energy X-ray absorptiometry (DXA). We sought to compare pelvic breadth measurements collected from the same individuals using both methods, to investigate if there are systematic differences in pelvic measurement between these imaging methods.

METHODS: Three pelvic breadth dimensions (bi-iliac breadth, bi-acetabular breadth, medio-lateral inlet breadth) were collected from MRI and DXA scans of a cross-sectional sample of healthy, nulliparous adult women of South Asian ancestry (n = 63). Measurements of MRI and DXA pelvic dimensions were collected four times in total, with one baseline data collection session and three replications. Data collected from these sessions were averaged, used to calculate technical error of measurement and entered into a Bland-Altman analysis. Linear regression models were fitted with a given MRI pelvic measurement regressed on the same measurement collected from DXA scans, as well as MRI mean bias regressed on DXA mean bias.

RESULTS: Technical error of measurement was higher in DXA measurements of bi-iliac breadth and medio-lateral pelvic inlet breadth and higher for MRI measurements of bi-acetabular breadth. Bland Altman analyses showed no statistically significant relationship between the mean bias of MRI and DXA, and the differences between MRI and DXA pelvic measurements.

CONCLUSIONS: DXA measurements of pelvic breadth are comparable to MRI measurements of pelvic breadth. DXA is a less costly imaging technique than MRI and can be used to collect measurements of skeletal elements in living people.

PMID:35460113 | DOI:10.1002/ajhb.23753

Birth. 2022 Apr 22. doi: 10.1111/birt.12633. Online ahead of print.

ABSTRACT

INTRODUCTION: Despite calls for increased vaginal birth after cesarean (VBAC), <14% of candidates have VBAC. Requirements for documentation of scar type, and prohibitions on induction or augmentation of labor are not supported by evidence but may be widespread. The purpose of this study was to document midwives’ perceptions of barriers to labor after cesarean (LAC) and their effects on midwives’ ability to accommodate patient desires for LAC.

METHODS: Midwives certified by the American Midwifery Certification Board (AMCB) were surveyed in 2019. Multiple option and open-ended text responses were analyzed using quantitative statistics and thematic content analysis. Select barriers to LAC, ability to accommodate LAC, and supportiveness of collaborators among midwives offering LAC were explored.

RESULTS: Responses from 1398 midwives were analyzed. Eighty-four percent felt able to accommodate LAC “most of the time,” and 39% reported one or more barriers to LAC. Barriers decreased ability to accommodate LAC by as much as 80%. Analysis of text responses revealed specific themes.

CONCLUSIONS: Thirty-nine percent of midwives reported their practice was limited by one or more barriers that were inconsistent with professional guidelines. Imposition of barriers was driven primarily by collaborating physicians, and superceded supportive practices of midwives, nurses, and system administrators. Affected midwives were significantly less able to accommodate patient requests for LAC than those not affected. Midwives also reported pride in providing VBAC care, restrictions specific to midwifery scope of practice, and variation in physician support for LAC within practices affecting their ability to provide care.

PMID:35460106 | DOI:10.1111/birt.12633

Risk Anal. 2022 Apr 22. doi: 10.1111/risa.13934. Online ahead of print.

ABSTRACT

During public emergencies, the level of public safety will be resilient and follow a process from decline to rise. Regarding the concept and influencing factors of public safety resilience, a three-level public safety resilience framework that includes personal, community, and government levels was proposed in this study. It provided the overall metrics that used the resistance and recovery ability to describe the dynamic characteristics of public safety resilience as well as the resilience assessment indexes on three levels. In the context of the Coronavirus Disease 2019 (COVID-19) pandemic, this study applied the proposed framework in a case study on public safety resilience at the Beihang community, Beijing, China through descriptive statistics, structural equation model, and principal component regression analysis of questionnaire data. The data analysis results showed that community resilience was the most important of the three levels of public safety resilience. In addition, community resilience could improve personal resilience, and government resilience had a positive effect on community and personal resilience. Compared with the resistance ability, the recovery ability was influenced more by the operation and improvement of the community. This study is conducive to understanding and improving public safety resilience on the personal, community, and government levels and can help relevant parties improve their ability to respond to the COVID-19 pandemic. Furthermore, the methods used in this study can be extended to other studies on public emergencies.

PMID:35460097 | DOI:10.1111/risa.13934

Risk Anal. 2022 Apr 22. doi: 10.1111/risa.13931. Online ahead of print.

ABSTRACT

A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.

PMID:35460101 | DOI:10.1111/risa.13931

Gerodontology. 2022 Apr 22. doi: 10.1111/ger.12632. Online ahead of print.

ABSTRACT

OBJECTIVES: The main objectives of the study were to review patient characteristics, recommendations made and treatment outcomes of frail/older patients referred to a specialist multidisciplinary geriatric assessment and optimisation-based preoperative clinic (PROKARE), prior to patients receiving dental treatment under general anaesthesia (GA) or conscious sedation (CS).

BACKGROUND: Although the use of preoperative comprehensive geriatric assessment to improve pre/peri and postoperative mortality has been reported for many surgical domains, its use prior to dental surgery has not been reported previously.

METHODS: The data were collected retrospectively from the dental notes of 52 patients referred from the Special Care Dental (SCD) Department to the PROKARE service for optimisation prior to dental treatment under GA/CS using a case note study approach. The data extracted included patient demographic characteristics, medical history, clinical management and the treatment outcomes for each patient. The data extracted was analysed with descriptive statistics.

RESULTS: Key reasons for referral were caries management, retained roots and poor co-operation. Multiple co-morbidities were noted among the patients referred, with 14 (27%) having four or more co-morbidities. The PROKARE assessment identified issues such as treatment could be carried out under CS instead of GA; consent; and the need for medication change and/or further medical investigations. As per recommendations from PROKARE, 39 patients (75%) received dental treatment while five (10%) did not receive treatment, and a further eight (15%) died prior to treatment.

CONCLUSION: Geriatric assessment and optimisation-based preoperative clinics in the dental management of frail, elderly patients having treatment under GA or CS techniques is valuable, but further research and assessment of current service provision are needed to increase the evidence base.

PMID:35460087 | DOI:10.1111/ger.12632

J Sci Food Agric. 2022 Apr 22. doi: 10.1002/jsfa.11955. Online ahead of print.

ABSTRACT

BACKGROUND AND AIM: To date, no study has investigated the effects of probiotic yogurt as a functional food in patients with chronic heart failure (CHF). So, the aim of this study was to compare the impact of probiotic yogurt versus ordinary yogurt on inflammatory, endothelial, lipid and renal indices in CHF patients.

MATERIAL AND METHODS: In this randomized, triple-blind clinical trial, 90 patients with CHF were randomly allocated into two groups to take either probiotic or ordinary yogurt for 10 weeks. Serum levels of Soluble Tumor Necrosis Factor-Like Weak Inducer of Apoptosis) sTWEAK(, Soluble Cluster of Differentiation 163 )sCD163(, Asymmetric dimethylarginine (ADMA) , lecithin cholesterol acyltransferase (LCAT) were measured by using ELISA kits, and Blood urea nitrogen (BUN) was measured by calorimetry method at baseline and at the end of trial. P-value < 0.05 was defined as statistically significant.

RESULTS: 78 patients completed the study. At the end of the intervention, the levels of sTWEAK in both groups increased significantly, and this increase was greater in the probiotic yogurt group (691.84 (335.60, 866.95)) compared to control group (581.96 (444.99, 929.40)), and the difference between the groups was statistically significant after adjusting for confounders (P-value: 0.257, adjusted P-value: 0.038). However, no significant differences were found between the groups in the cases of other study indices.

CONCLUSION: Probiotic yogurt may be useful for improving the inflammatory status in patients with CHF through increasing sTWEAK levels, however, further studies are needed in this area. This article is protected by copyright. All rights reserved.

PMID:35460085 | DOI:10.1002/jsfa.11955

Int J Cancer. 2022 Apr 22. doi: 10.1002/ijc.34043. Online ahead of print.

NO ABSTRACT

PMID:35460074 | DOI:10.1002/ijc.34043