体表测量计算漏斗胸大鼠胸腔容积的研究

目的：通过体表测量计算胸腔容积评价肺发育、胸廓缩小程度及漏斗胸畸形程度。方法将90只健康4周龄SD大鼠分成实验组和对照组,实验组70只,对照组20只。实验组从胸骨旁切断下位3对肋软骨制作漏斗胸大鼠模型。分别于术前,术后2、4、8、12周测量胸部多条径线,并进行组间比较。胸腔容积大小用数学公式计算,利用阿基米德原理测量及C T三维重建所得,采用SPSS17．0进行 t检验及直线回归分析。结果实验采用多条径线测量结果,经过数学计算,阿基米德原理测量,以及CT三维重建所得结果实验组与对照组比较,差异有统计学意义（P＜0．05）。结论通过体表测量计算胸腔容积的方法是可行的,该方法可替代CT检查对于评估漏斗胸形成过程中的胸腔容积的改变。     

Hydrophobic forces and the length limit of foldable protein domains

To find the native conformation (fold), proteins sample a subspace that is typically hundreds of orders of magnitude smaller than their full conformational space. Whether such fast folding is intrinsic or the result of natural selection, and what is the longest foldable protein, are open questions. Here, we derive the average conformational degeneracy of a lattice polypeptide chain in water and quantitatively show that the constraints associated with hydrophobic forces are themselves sufficient to reduce the effective conformational space to a size compatible with the folding of proteins up to approximately 200 amino acids long within a biologically reasonable amount of time. This size range is in general agreement with the experimental protein domain length distribution obtained from approximately 1,200 proteins. Molecular dynamics simulations of the Trp-cage protein confirm this picture on the free energy landscape. Our analytical and computational results are consistent with a model in which the length and time scales of protein folding, as well as the modular nature of large proteins, are dictated primarily by inherent physical forces, whereas natural selection determines the native state.     

Level‐adjusted funnel plots based on predicted marginal expectations: an application to prophylactic antibiotics in gallstone surgery

Funnel plots are widely used to visualize grouped data, for example, in institutional comparison. This paper extends the concept to a multi‐level setting, displaying one level at a time, adjusted for the other levels, as well as for covariates at all levels. These level‐adjusted funnel plots are based on a Markov chain Monte Carlo fit of a random effects model, translating the estimated model parameters to predicted marginal expectations. Working within the estimation framework, we accommodate outlying institutions using heavy‐tailed random effects distributions. We also develop computer‐efficient methods to compute predicted probabilities in the case of dichotomous outcome data and various random effect distributions. We apply the method to a data set on prophylactic antibiotics in gallstone surgery. Copyright © 2014 John Wiley & Sons, Ltd.     

Funnel plots for population‐based cancer survival: principles,methods and applications

Funnel plots are graphical tools designed to detect excessive variation in performance indicators by simple visual inspection of the data. Their main use in the biomedical domain so far has been to detect publication bias in meta‐analyses, but they have also been recommended as the most appropriate way to display performance indicators for a vast range of health‐related outcomes. Here, we extend the use of funnel plots to population‐based cancer survival and several related measures. We present three applications to familiarise the reader with their interpretation. We propose funnel plots for various cancer survival measures, as well as age‐standardised survival, trends in survival and excess hazard ratios. We describe the components of a funnel plot and the formulae for the construction of the control limits for each of these survival measures. We include three transformations to construct the control limits for the survival function: complementary log–log, logit and logarithmic transformations. We present applications of funnel plots to explore the following: (i) small‐area and temporal variation in cancer survival; (ii) racial and geographical variation in cancer survival; and (iii) geographical variation in the excess hazard of death. Funnel plots provide a simple and informative graphical tool to display geographical variation and trend in a range of cancer survival measures. We recommend their use as a routine instrument for cancer survival comparisons, to inform health policy makers in planning and assessing cancer policies. We advocate the use of the complementary log–log or logit transformation to construct the control limits for the survival function. Copyright © 2013 John Wiley & Sons, Ltd.     

附加轮廓线漏斗图的绘制在R软件中的实现

目的介绍附加轮廓线漏斗图在R软件中的实现及应用。方法对实例中的数据采用R软件“meta”扩展包的funnel（ ）和trimfill（ ）函数绘制附加轮廓线漏斗图,并判断缺失研究分布的区域。结果通过观察附加轮廓线漏斗图,可以发现存在不对称,缺失数据分布在无统计学显著性区域,表明存在发表偏倚。结论附加轮廓线漏斗图可以用来帮助判断漏斗图不对称是发表偏倚还是其他原因导致。     

The trouble with clinical indicators: Intact lower genital tract following childbirth in NSW Hospitals, 2003–2005

Background:  The federal government wants outcomes of hospital care to be made publicly available. League tables based on single clinical indicators are misleading, largely because of their inability to take case-complexity into account.
Aim:  To demonstrate the application of a graphical tool (the risk-adjusted funnel plot) to the comparison of clinical outcomes across hospitals; and its advantages over league tables.
Methods:  We looked at publicly available data on intact lower genital tract (ILGT), for all hospitals in New South Wales at which more than 200 births occurred in 2005. The 'excess' percentage of women at each hospital with an ILGT following a vaginal birth, was calculated after adjustment for instrumental assistance, the use of epidural analgesia/anaesthesia, the use of induction/augmentation, and the number of births per annum.
Results:  In 2005, ILGT ranged from 13.1 to 55.8%. A plot of ILGT against vaginal births per annum (a funnel plot) revealed huge heterogeneity among hospitals, and an inverse association with the number of births per annum. A residual funnel plot, constructed from the differences between observed and expected ILGT identified four hospitals (three public and one private) with consistently better ILGT than expected – and four public hospitals with ILGT consistently worse than expected. Some of these hospitals were not located at the extremes of the league table.
Conclusion:  The risk-adjusted funnel plot is a useful graphical tool which may overcome the shortcomings of league tables. We need to become more sophisticated in our use of clinical indicators for comparing hospital performances.     

Meta分析中漏斗图的绘制

在本杂志我们曾讨论过“Meta分析中失安全系数的估计问题”．它是用来识别和控制Meta分析中最常见的偏倚“发表偏倚（publication bias）”的一种方法。现我们继续讨论识别和控制Meta分析中发表偏倚的另一方法，漏斗图（funnel plots）分析法。     

Monitoring Clinical Performance: The Role of Software Architecture

Methods of assessing and monitoring the performance of clinicians have received a lot of publicity in recent years. We review the main methodologies concentrating on the distinction between monitoring individual performance and monitoring aggregated performance. We also highlight the importance and difficulties associated with incorporating and assessing risk factors into the process. We discuss how software architecture can be developed to implement these methodologies. We illustrate this development by a case study involving the creation of a software tool to produce funnel plots to analyse surgeon performance. We discuss how such tools are currently evaluated and propose that in future assessments of usability would benefit from an experimental study.     

Effect of an acute medical admission unit on key quality indicators assessed by funnel plots

Objective

To determine the effect of the introduction of an acute medical admissions unit (AMAU) on key quality efficiency and outcome indicator comparisons between medical teams as assessed by funnel plots.

Methods

A retrospective analysis was performed of data relating to emergency medical patients admitted to St James'' Hospital, Dublin between 1 January 2002 and 31 December 2004, using data on discharges from hospital recorded in the hospital in‐patient enquiry system. The base year was 2002 during which patients were admitted to a variety of wards under the care of a named consultant physician. In 2003, two centrally located wards were reconfigured to function as an AMAU, and all emergency patients were admitted directly to this unit. The quality indicators examined between teams were length of stay (LOS) <30 days, LOS >30 days, and readmission rates.

Results

The impact of the AMAU reduced overall hospital LOS from 7 days in 2002 to 5 days in 2003/04 (p<0.0001). There was no change in readmission rates between teams over the 3 year period, with all teams displaying expected variability within control (95%) limits. Overall, the performance in LOS, both short term and long term, was significantly improved (p<0.0001), and was less varied between medical teams between 2002 and 2003/04.

Conclusions

Introduction of the AMAU improved performance among medical teams in LOS, both short term and long term, with no change in readmissions. Funnel plots are a powerful graphical technique for presenting quality performance indicator variation between teams over time.