COVID-19 and venous thromboembolism: Known and unknown for imaging decisions

As we continue to fight against the current coronavirus disease-2019 (COVID-19) pandemic, healthcare professionals across the globe are trying to answer questions surrounding how to best help patients with the up-to-date available science while awaiting the development of new therapies and mass vaccination. Since early in the pandemic, studies indicated a heightened risk of venous thromboembolism (VTE) in COVID-19 infected patients. There have been differing expert opinions about how to assess pretest probability of VTE in this patient population. This has been partly due to the high prevalence of respiratory failure in this patient population and the use of D-dimer as a prognostic test which is also frequently elevated in patients with COVID-19 in absence of VTE. Some experts have argued for an approach similar to usual care with testing if clinical suspicion is high enough. Some have argued for more routine screening at different points of care. Others have even suggested empiric therapeutic anti-coagulation in moderate to severely ill COVID-19 patients. In the following article, we review and summarize the most current literature in hopes of assisting clinicians in decision making and guidance for when to be concerned for VTE in COVID-19 patients. We also discuss research gaps and share pathways currently being used within our institution.     

《针灸大成》关于气海穴临床应用的文献研究

目的:通过检索《针灸大成》中与气海穴治疗作用相关的文献条文,总结气海穴在治疗各系统疾病中运用频次较高的疾病及其配穴规律,为临床针灸对气海穴的使用提供理论支持。方法:以《中华医典》(第五版)中《针灸大成》作为文献检索来源,将气海穴及气海穴的别称“脖胦”“下肓”“丹田”“肓之原”“肓原”“下言”和“气泽”为检索词,用计算机检索工具及人工检索相结合的方法检索符合要求的文献条文,通过建立本研究的数据库,频次分析、条形统计图比较分析等方法,总结出气海穴在治疗各系统疾病中的运用频次及其配穴规律。结果:在《针灸大成》所涉及的条文中,气海穴尤善治疗内科疾病,在治疗内科疾病中排名前3位的是脾胃系病症、气血津液疾病、肾系病症和妇科疾病,气海穴配穴习惯为上下配穴法,同名经配穴法,以及前后配穴法,其中主要为前后配穴法和同名经配穴法。结论:气海穴《针灸大成》中单穴应用占比最高,而在气海穴众多配穴中,运用了本经配穴法、上下配穴法、前后配穴法,配穴归经主要来自任脉和足太阳膀胱经。同名经配穴法,同气相求,可增加疗效;与气海穴配伍较多的足太阳膀胱经以背腧穴为主,此为前后配穴法,亦称腹背阴阳配穴法,腹部为阴,腰背为阳,前后配穴法可起到“从阳引阴”亦可“从阴引阳”的作用,以达到调节阴阳,调和脏法,调畅经络的目的。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Selecting patients for early interdisciplinary rehabilitation during neurointensive care after moderate to severe traumatic brain injury

Background

Early interdisciplinary rehabilitation (EIR) in neurointensive care is a limited resource reserved for patients with moderate to severe traumatic brain injury (TBI) believed to profit from treatment. We evaluated how key parameters related to injury severity and patient characteristics were predictive of receiving EIR, and whether these parameters changed over time.

Methods

Among 1003 adult patients with moderate to severe TBI admitted over 72 h to neurointensive care unit during four time periods between 2005 and 2020, EIR was given to 578 and standard care to 425 patients. Ten selection criteria thought to best represent injury severity and patient benefit were evaluated (Glasgow Coma Scale, Head Abbreviated Injury Scale, New-Injury-Severity-Scale, intracranial pressure monitoring, neurosurgery, age, employment, Charlson Comorbidity Index, severe psychiatric disease, and chronic substance abuse).

Results

In multivariate regression analysis, patients who were employed (adjOR 1.99 [95% CI 1.41, 2.80]), had no/mild comorbidity (adjOR 3.15 [95% CI 1.72, 5.79]), needed neurosurgery, had increasing injury severity and were admitted by increasing time period were more likely to receive EIR, whereas receiving EIR was less likely with increasing age (adjOR 0.97 [95% CI 0.96, 0.98]) and chronic substance abuse. Overall predictive ability of the model was 71%. Median age and comorbidity increased while employment decreased from 2005 to 2020, indicating patient selection became less restrictive with time.

Conclusion

Injury severity and need for neurosurgery remain important predictors for receiving EIR, but the importance of age, employment, and comorbidity have changed over time. Moderate prediction accuracy using current clinical criteria suggest unrecognized factors are important for patient selection.     

Penalized variable selection for accelerated failure time models with random effects

Accelerated failure time (AFT) models allowing for random effects are linear mixed models under the log-transformation of survival time with censoring and describe dependence in correlated survival data. It is well known that the AFT models are useful alternatives to frailty models. To the best of our knowledge, however, there is no literature on variable selection methods for such AFT models. In this paper, we propose a simple but unified variable-selection procedure of fixed effects in the AFT random-effect models using penalized h-likelihood (HL). We consider four penalty functions (ie, least absolute shrinkage and selection operator (LASSO), adaptive LASSO, smoothly clipped absolute deviation (SCAD), and HL). We show that the proposed method can be easily implemented via a slight modification to existing h-likelihood estimation procedures. We thus demonstrate that the proposed method can also be easily extended to AFT models with multilevel (or nested) structures. Simulation studies also show that the procedure using the adaptive LASSO, SCAD, or HL penalty performs well. In particular, we find via the simulation results that the variable selection method with HL penalty provides a higher probability of choosing the true model than other three methods. The usefulness of the new method is illustrated using two actual datasets from multicenter clinical trials.     

Selection in employer sponsored health insurance

This paper examines the extensive margin of selection into employer-sponsored health insurance (ESHI) using data from the Medical Expenditures Panel Survey 2001–2010 and 2014–2016 and the National Longitudinal Survey of Youth’97 in 2010. Controlling for a large set of firm and job characteristics, I find that before the implementation of the Affordable Care Act (ACA) in 2014, workers aged 25–40 who declined ESHI and remained privately uninsured had significantly higher health risk than those who enrolled. No correlation between health and insurance take-up is found in the 41–64 age group. These results are partly explained by differences in income and Medicaid crowding out ESHI for high risk workers. The paper sheds light on the characteristics of uninsured workers, their incentives for declining insurance and the interaction between private and public health insurance. The allocation of ESHI remained unchanged after the ACA was introduced due to the provisions’ counteracting effects.     

运用彭静山教授“一点二穴三线四面取穴法”针刺治疗难治性面瘫

彭静山教授提出“一点二穴三线四面”八字取穴法。笔者运用此八字取穴法指导针灸临床取穴治疗难治性面瘫,临床效果显著。本文列举临床典型病例,详细阐述取穴和针刺治疗方法,并对治疗机理进行论述。对于难治性面瘫,单纯毫针针刺治疗效果有一定局限性,以“一点二穴三线四面”八字取穴法为基础,取穴点以透刺连接构成线、面,以芒针联合透刺法为主要治疗方法,可强化治疗效果、提高临床效应。