Associations of residential greenspace exposure and fetal growth across four areas in Spain

An accumulating body of evidence has associated exposure to greenspace with improved birth outcomes, including higher birth weight and lower risk of low birth weight; however, evidence on such association with in-utero fetal growth is scarce. We explored the influence of maternal exposure to residential greenspace and fetal growth in four INMA (Infancia y Medio Ambiente) Spanish birth cohorts (2003–2008), with 2,465 participants. Residential greenspace was characterised by the Normalised Difference Vegetation Index (NDVI) average across 100 m, 300 m, and 500 m buffers around the residence. Repeated ultrasound measurements of the abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), and estimated fetal weight (EFW) were used. We created customised-generalised least squares models to evaluate associations of residential greenspace exposure on each fetal growth parameter, controlled for the relevant confounders. There were associations between the 500 m buffer and BPD, FL, and AC. We also found associations in the 300 m buffer and FL and AC. The associations in the 100 m buffer were null. Estimates were higher among participants with lower socioeconomic status. Mediation analyses found that air pollution might explain 15–37% of our associations. Mediation by physical activity was not observed. Greenspace exposure may be beneficial for fetal growth.     

Fourteen years of the Pregnancy Registry on maternal immunisation with a reduced-antigen-content tetanus-diphtheria-acellular pertussis (Tdap) vaccine

BackgroundGSK initiated a Pregnancy Registry in the United States (US) for the reduced-antigen-content tetanus-diphtheria-acellular pertussis (Tdap; Boostrix, GSK) vaccine with the aim to detect and describe pregnancy outcomes in women vaccinated with Boostrix 28 days before estimated conception or during pregnancy.MethodsVoluntary reports of pregnancy exposure to Boostrix received from spontaneous and post-marketing surveillance sources in the US were assessed. Reports were classified as prospective or retrospective based on the knowledge of pregnancy outcomes at the time of reporting. For completeness, reports of exposure to Boostrix or to the Tdap-inactivated poliovirus vaccine (Boostrix-IPV, GSK) reported to the global safety database from countries outside the US were also evaluated.ResultsFrom May 2005 to August 2019, 1517 (1455 prospective and 62 retrospective) pregnancy reports were received in the Boostrix US Pregnancy Registry. Of the prospective reports, 250 had known outcomes: 244 live infants with no apparent birth defects (BDs), three live infants with BDs, and three spontaneous abortions with no apparent BDs. Of the retrospective reports, 55 had known outcomes: 33 live infants with no apparent BDs, 16 live infants with BDs, one spontaneous abortion with no apparent BDs, four stillbirths with no apparent BDs, and one stillbirth with BDs. Cumulatively, 1321 pregnancy reports (1006 for Boostrix; 315 for Boostrix-IPV) were received from countries outside the US. Of these, 163 prospective reports and 551 retrospective reports had known outcomes. Results were in line with those from the Boostrix US Pregnancy Registry.ConclusionsData currently available from the Boostrix US Pregnancy Registry and from countries outside the US suggested that exposure to Boostrix or Boostrix-IPV during pregnancy does not raise safety concerns related to adverse pregnancy outcomes or BDs.     

Assessment of a novel deep learning-based marker-less motion capture system for gait study

BackgroundMarker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint center position but has not been yet evaluated in terms of gait outcomes.Research questionHow does this novel marker-less system compare to a marker-based reference system in terms of clinically relevant gait parameters?MethodsThe deep learning method behind the developed marker-less system was trained on a dedicated dataset consisting of forty-one asymptomatic and pathological subjects each performing ten walking trials. The system could estimate the three-dimensional position of seventeen joint centers or keypoints (e.g., neck, shoulders, hip, knee, and ankles). We evaluated the marker-less system against a marker-based system in terms of differences in joint position (Euclidean distance), detection of gait events (e.g., heel strike and toe-off), spatiotemporal parameters (e.g., step length, time), kinematic parameters (e.g., hip and knee extension-flexion), and inter-trial reliability for kinematic parameters.ResultsThe marker-less system was able to estimate the three-dimensional position of joint centers with a mean difference of 13.1 mm (SD = 10.2 mm). 99% of the estimated gait events were estimated within 10 ms of the corresponding reference values. Estimated spatiotemporal parameters showed zero bias. The mean and standard deviation of the differences of the estimated kinematic parameters varied by parameter (for example, the mean and standard deviation for knee extension flexion angle were −3.0° and 2.7°). Inter-trial reliability of the measured parameters was similar to that of the marker-based references.SignificanceThe developed marker-less system can measure the spatiotemporal parameters within the range of the minimum detectable changes obtained using the marker-based reference system. Moreover, except for hip extension flexion, the system showed promising results in terms of several kinematic parameters.     

基于正交试验的肾造瘘管固定方案研究

目的 探讨基于正交试验的肾造瘘管不同固定方案的比较效果。方法 将14Fr硅胶肾造瘘管固定在聚乙烯展板和拉力显示器上，以固定材料、固定方法、面积为3个影响因素，每个因素3个水平，每组进行3次试验再求平均值作为最后拉力值F，共进行9个固定方案共27次试验。利用L9（33）正交试验矩阵研究不同材料（医用橡皮膏、医用透气胶带、医用无纺布胶带）、固定方法（交叉固定法、“工”字固定法和改良“工”字固定法）及面积(16 cm2、24 cm2、32 cm2）对肾造瘘管固定强度的影响。结果 正交试验所选的3种影响因素中，对拉力值影响显著性排序为：材料＞方法＞面积；3种固定材料中，医用橡皮膏固定强度最大。结论 肾造瘘管固定方案中，最佳固定组合为以医用橡皮膏结合改良“工”字法固定，可为临床管道固定方案的选择提供参考。     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.     

Racial and geographic disparities with gastrostomy tube placement in dementia and parkinsonian disorders

IntroductionMany patients with advanced dementia and Parkinson's disease and related disorders (PDRD) are receiving gastrostomy tube (GT) placement annually, despite its lack of proven benefit for preventing aspiration, enhancing nutrition, or prolonging survival. Given clinical practice variability in the care of people with neurodegenerative disorders, we sought to examine racial and geographic disparities in GT placement for these populations in the United States.MethodData were extracted from a publicly-available national database using diagnostic and procedural codes from 2006 to 2010. GT placement rates and odds ratios were calculated for two groups: PDRD and non-parkinsonian dementia (NPD).ResultsIn the PDRD group, odds of GT placement were higher among patients coded as Black (OR 1.69, CI 0.80–3.56, p = 0.17) and Asian (OR 2.17, CI 0.70–6.78, p = 0.18) than Whites; although these tendencies did not reach statistical significance. In the NPD group, GT placement among Black patients was significantly more likely (OR 2.88, CI 1.90–4.36, p < 0.001) than their white counterparts, while Asian patients were significantly less likely (OR 0.12, CI 0.02–0.91, p = 0.04). Compared to the Northeast region, there were significantly lower odds of GT placement in the Midwest region (OR 0.37, CI 0.24–0.58, p < 0.001) in the NPD group only. No difference in odds was observed between the sexes in both groups.ConclusionThis study showed geographic and racial disparities in GT placement among PDRD and NPD patients. Further studies should aim to clarify best practices for GT placement in PDRD and causes of practice differences within and between PDRD and NPD groups.     

微视频在ICU气管套管带管出院患者家属健康教育中的应用

目的探讨应用微视频对ICU气管套管带管出院患者的家属实施健康教育的效果。方法选取2018年9月—2020年9月的100例气管套管带管出院患者的家属,根据随机数字表法将其随机分为对照组和试验组各50例,对照组实施常规健康教育和随访;试验组在此基础上将患者出院后面临的共性问题进行归纳总结,制定并应用微视频对其实施健康教育。分别比较两组患者家属在气管套管相关知识知晓率、气管套管护理操作的准确性以及患者气管切开并发症发生率之间的差异。结果试验组家属在气管套管相关知识的知晓率和护理技能的得分均高于对照组家属(P<0.05),但患者气管切开相关并发症发生率低于对照组(P<0.05)。结论应用微视频实施健康教育可有效提高ICU气管套管带管出院患者家属的管路护理水平,提高患者带管期间的安全性。     

A Predictive Model for Reactive Tube Feeding in Head and Neck Cancer Patients Undergoing Definitive (Chemo) Radiotherapy

AimsCareful management of a patient's nutritional status during and after treatment for head and neck squamous cell cancers (HNSCC) is crucial for optimal outcomes. The aim of this study was to develop a model for stratifying a patient's risk of requiring reactive enteral feeding through a nasogastric tube during radiotherapy for HNSCC, based on clinical and treatment-related factors.Materials and methodsA cohort of consecutive patients treated with definitive (chemo)radiotherapy for HNSCC between January 2016 and January 2018 was identified in the institutional electronic database for retrospective analysis. Patients requiring enteral feeding pretreatment were excluded. Clinical and treatment data were obtained from prospectively recorded electronic clinical notes and planning software.ResultsBaseline patient characteristics and tumour-related parameters were captured for 225 patients. Based on the results of the univariate analysis and using a stepwise backwards selection process, clinical and dosimetric variables were selected to optimise a clinically predictive multivariate model, fitted using logistic regression. The parameters found to affect the probability, P, of requiring a nasogastric feeding tube for >4 weeks in our clinical multivariate model were: tumour site, tumour stage (early T0/1/2 stage versus advanced T3/T4 stage), chemotherapy drug (none versus any drug) and mean dose to the contralateral parotid gland. A scoring model using the regression coefficients of the selected variables in the clinical multivariate model achieved an area under the curve (AUC) of 0.745 (95% confidence interval 0.678–0.812), indicating good discriminative performance. Internal validation of the model involved splitting the dataset 80:20 into training and test datasets 10 times and assessing differences in AUC of the model fitted to these.ConclusionsWe developed an easy-to-use prediction model based on both clinical and dosimetric parameters, which, once externally validated, can lead to more personalised treatment planning and inform clinical decision-making on the appropriateness of prophylactic versus reactive enteral feeding.     

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

ObjectivesDespite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD).MethodsIn this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the “pROC” package of R (version 1.16.2).ResultsThe area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912–1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment.ConclusionsOur deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.     

Radiation-Shielding Devices: The Best Combination for Spine Interventional Procedures

PurposeAlthough many studies have examined the efficiency of various protective devices for reducing the dose of radiation exposure to physicians during interventional pain procedures, no study has compared the protective effect of these devices when they are used in combination. The purpose of this prospective experimental study was to determine the best combination of radiation-shielding devices.Materials and MethodsUsing anthropomorphic phantoms of a physician and patient, we measured the radiation protection efficiency (RPE) of each of the following protection methods and in combination during C-arm–guided simulated lumbar epidural injection: (a) personal protective equipment (PPE), (b) bedside curtain shield (Curtain), (c) x-ray tube filter (Filter), and (d) fluoroscopic collimation method (Collimation). We measured exposure doses using personal electronic dosimeters at the eye, thyroid, and gonad levels for 1 minute. Each experiment was repeated 15 times.ResultsThe radiation exposure dose and RPE with the best single-, double-, and triple-protection methods were as follows: PPE for the single-protection method (11.82 μSv/min, 80.04%), PPE + Collimation for the double-combination method (4.68 μSv/min, 92.09%), and PPE + Collimation + Curtain for the triple-combination method (3.08 μSv/min, 93.39%). Additionally, PPE + Collimation + Curtain + Filter for the quadruple-combination method resulted in a radiation exposure and RPE of 2.91 μSv/min and 93.61%, respectively, compared with nonprotection.ConclusionsThe best single-, double-, and triple-protection method was PPE, PPE + Collimation, and PPE + Collimation + Curtain, respectively. While preparing protective equipment, we recommend prioritizing equipment in this order.