Rumana Z. Omar

Generic, Simple Risk Stratification Model for Heart Valve Surgery

Background—Heart valve surgery has an associated in-hospital mortality rate of 4% to 8%. This study aims to develop a simple risk model to predict the risk of in-hospital mortality for patients undergoing heart valve surgery to provide information to patients and clinicians and to facilitate institutional comparisons. Methods and Results—Data on 32 839 patients were obtained from the Society of Cardiothoracic Surgeons of Great Britain and Ireland on patients who underwent heart valve surgery between April 1995 and March 2003. Data from the first 5 years (n=16 679) were used to develop the model; its performance was evaluated on the remaining data (n=16 160). The risk model presented here is based on the combined data. The overall in-hospital mortality was 6.4%. The risk model included, in order of importance (all P<0.01), operative priority, age, renal failure, operation sequence, ejection fraction, concomitant tricuspid valve surgery, type of valve operation, concomitant CABG surgery, body mass index, preoperative arrhythmias, diabetes, gender, and hypertension. The risk model exhibited good predictive ability (Hosmer-Lemeshow test, P=0.78) and discriminated between high- and low-risk patients reasonably well (receiver-operating characteristics curve area, 0.77). Conclusions—This is the first risk model that predicts in-hospital mortality for aortic and/or mitral heart valve patients with or without concomitant CABG. Based on a large national database of heart valve patients, this model has been evaluated successfully on patients who had valve surgery during a subsequent time period. It is simple to use, includes routinely collected variables, and provides a useful tool for patient advice and institutional comparisons.

A multilevel model framework for meta-analysis of clinical trials with binary outcomes.

In this paper we explore the potential of multilevel models for meta-analysis of trials with binary outcomes for both summary data, such as log-odds ratios, and individual patient data. Conventional fixed effect and random effects models are put into a multilevel model framework, which provides maximum likelihood or restricted maximum likelihood estimation. To exemplify the methods, we use the results from 22 trials to prevent respiratory tract infections; we also make comparisons with a second example data set comprising fewer trials. Within summary data methods, confidence intervals for the overall treatment effect and for the between-trial variance may be derived from likelihood based methods or a parametric bootstrap as well as from Wald methods; the bootstrap intervals are preferred because they relax the assumptions required by the other two methods. When modelling individual patient data, a bias corrected bootstrap may be used to provide unbiased estimation and correctly located confidence intervals; this method is particularly valuable for the between-trial variance. The trial effects may be modelled as either fixed or random within individual data models, and we discuss the corresponding assumptions and implications. If random trial effects are used, the covariance between these and the random treatment effects should be included; the resulting model is equivalent to a bivariate approach to meta-analysis. Having implemented these techniques, the flexibility of multilevel modelling may be exploited in facilitating extensions to standard meta-analysis methods.

A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome

Risk models that aim to predict the future course and outcome of disease processes are increasingly used in health research, and it is important that they are accurate and reliable. Most of these risk models are fitted using routinely collected data in hospitals or general practices. Clinical outcomes such as short-term mortality will be near-complete, but many of the predictors may have missing values. A common approach to dealing with this is to perform a complete-case analysis. However, this may lead to overfitted models and biased estimates if entire patient subgroups are excluded. The aim of this paper is to investigate a number of methods for imputing missing data to evaluate their effect on risk model estimation and the reliability of the predictions. Multiple imputation methods, including hotdecking and multiple imputation by chained equations (MICE), were investigated along with several single imputation methods. A large national cardiac surgery database was used to create simulated yet realistic datasets. The results suggest that complete case analysis may produce unreliable risk predictions and should be avoided. Conditional mean imputation performed well in our scenario, but may not be appropriate if using variable selection methods. MICE was amongst the best performing multiple imputation methods with regards to the quality of the predictions. Additionally, it produced the least biased estimates, with good coverage, and hence is recommended for use in practice.

Meta-analysis using multilevel models with an application to the study of class size effects

Meta-analysis is formulated as a special case of a multilevel (hierarchical data) model in which the highest level is that of the study and the lowest level that of an observation on an individual respondent. Studies can be combined within a single model where the responses occur at different levels of the data hierarchy and efficient estimates are obtained. An example is given from studies of class sizes and achievement in schools, where study data are available at the aggregate level in terms of overall mean values for classes of different sizes, and also at the student level.

Non-pharmacological interventions for agitation in dementia: systematic review of randomised controlled trials

BACKGROUND Agitation in dementia is common, persistent and distressing and can lead to care breakdown. Medication is often ineffective and harmful. AIMS To systematically review randomised controlled trial evidence regarding non-pharmacological interventions. Method We reviewed 33 studies fitting predetermined criteria, assessed their validity and calculated standardised effect sizes (SES). RESULTS Person-centred care, communication skills training and adapted dementia care mapping decreased symptomatic and severe agitation in care homes immediately (SES range 0.3-1.8) and for up to 6 months afterwards (SES range 0.2-2.2). Activities and music therapy by protocol (SES range 0.5-0.6) decreased overall agitation and sensory intervention decreased clinically significant agitation immediately. Aromatherapy and light therapy did not demonstrate efficacy. CONCLUSIONS There are evidence-based strategies for care homes. Future interventions should focus on consistent and long-term implementation through staff training. Further research is needed for people living in their own homes.

Off-pump Coronary Artery Bypass (OPCAB) surgery reduces risk-stratified morbidity and mortality: A United Kingdom multi-center comparative analysis of early clinical outcome

Objective—Off-Pump Coronary Artery Bypass (OPCAB) surgery is gaining more popularity worldwide. The aim of this United Kingdom (UK) multi-center study was to assess the early clinical outcome of the OPCAB technique and perform a risk-stratified comparison with the conventional Coronary Artery Bypass Grafting (CABG) using the Cardio-Pulmonary Bypass (CPB) technique. Methods—Data were collected on 5,163 CPB patients from the database of the National Heart and Lung institute, Imperial College, University of London, and on 2,223 OPCAB patients from eight UK cardiac surgical centers, which run established OPCAB surgery programs. All patients had undergone primary isolated CABG for multi-vessel disease through a midline sternotomy approach, between January 1997 and April 2001. Postoperative morbidity and mortality were compared between the CPB and OPCAB patients after adjusting for case-mix. The mortality of the OPCAB patients was also compared, using risk stratification, to the mortality figures reported by the Society of Cardiothoracic Surgeons of Great Britain and Ireland (SCTS) based on 28,018 patients in the national database who were operated on between January 1996 and December 1999. Results—Morbidity and mortality were significantly lower in the OPCAB patients compared with the CPB patients and the UK national database of CABG patients, over the same period of time, after adjusting for case-mix. Conclusion—This study demonstrates that risk stratified morbidity and mortality are significantly lower in OPCAB patients than CPB patients and patients in the UK national database.

Bayesian methods of analysis for cluster randomized trials with binary outcome data

We explore the potential of Bayesian hierarchical modelling for the analysis of cluster randomized trials with binary outcome data, and apply the methods to a trial randomized by general practice. An approximate relationship is derived between the intracluster correlation coefficient (ICC) and the between-cluster variance used in a hierarchical logistic regression model. By constructing an informative prior for the ICC on the basis of available information, we are thus able implicitly to specify an informative prior for the between-cluster variance. The approach also provides us with a credible interval for the ICC for binary outcome data. Several approaches to constructing informative priors from empirical ICC values are described. We investigate the sensitivity of results to the prior specified and find that the estimate of intervention effect changes very little in this data set, while its interval estimate is more sensitive. The Bayesian approach allows us to assume distributions other than normality for the random effects used to model the clustering. This enables us to gain insight into the robustness of our parameter estimates to the classical normality assumption. In a model with a more complex variance structure, Bayesian methods can provide credible intervals for a difference between two variance components, in order for example to investigate whether the effect of intervention varies across clusters. We compare our results with those obtained from classical estimation, discuss the relative merits of the Bayesian framework, and conclude that the flexibility of the Bayesian approach offers some substantial advantages, although selection of prior distributions is not straightforward.

How to develop a more accurate risk prediction model when there are few events.

When the number of events is low relative to the number of predictors, standard regression could produce overfitted risk models that make inaccurate predictions. Use of penalised regression may improve the accuracy of risk prediction

Analysing repeated measurements data: a practical comparison of methods.

A variety of methods are available for analysing repeated measurements data where the outcome is continuous. However, there is little information on how established methods, such as summary statistics and repeated measures analysis of variance (RMAOV), compare in practice with methods that have become available to applied statisticians more recently, such as marginal models (based on generalized estimating equation methodology) and multilevel models (that is, hierarchical random effects models). The aim of this paper is to exemplify the use of these methods, and directly compare their results by application to a clinical trial data set. The focus is on practical aspects rather than technical issues. The data considered were taken from a clinical trial of treatments for asthma in 240 children, in which a baseline and four post-randomization measurements of outcomes were taken. The simplicity of the method of summary statistics using the post-randomization mean of observations provided a useful initial analysis. However, fixed time effects or treatment-time interactions cannot be included in such an analysis, and choice of appropriate weighting when there is substantial missing data is problematic. RMAOV, marginal models and multilevel models generally provided similar estimates and standard errors for the treatment effects, although in one example with a relatively complex variance structure the marginal model produced less efficient estimates. Two advantages of multilevel models are that they provide direct estimates of variance components which are often of interest in their own right, and that they can be naturally extended to handle multivariate outcomes.

An evaluation of existing risk stratification models as a tool for comparison of surgical performances for coronary artery bypass grafting between institutions

OBJECTIVE Risk stratification systems are used in cardiac surgery to estimate mortality risk for individual patients and to compare surgical performance between institutions or surgeons. This study investigates the suitability of six existing risk stratification systems for these purposes. METHODS Data on 5471 patients who underwent isolated coronary artery bypass grafting at two UK cardiac centres between 1993 and 1999 were extracted from a prospective computerised clinical data base. Of these patients, 184 (3.3%) died in hospital. In-hospital mortality risk scores were calculated for each patient using the Parsonnet score, the EuroSCORE, the ACC/AHA score and three UK Bayes models (old, new complex and new simple). The accuracy for predicting mortality at an institutional level was assessed by comparing total observed and predicted mortality. The accuracy of the risk scores for predicting mortality for a patient was assessed by the Hosmer-Lemeshow test. The receiver operating characteristic (ROC) curve was used to evaluate how well a system ranks the patient with respect to their risk of mortality and can be useful for patient management. RESULTS Both EuroSCORE and the simple Bayes model were reasonably accurate at predicting overall mortality. However predictive accuracy at the patient level was poor for all systems, although EuroSCORE was accurate for low to medium risk patients. Discrimination was fair with the following ROC areas: Parsonnet 0.73, EuroSCORE 0.76, ACC/AHA system 0.76, old Bayes 0.77, complex Bayes 0.76, simple Bayes 0.76. CONCLUSIONS This study suggests that two of the scores may be useful in comparing institutions. None of the risk scores provide accurate risk estimates for individual patients in the two hospitals studied although EuroSCORE may have some utility for certain patients. All six systems perform moderately at ranking the patients and so may be useful for patient management. More results are needed from other institutions to confirm that the EuroSCORE and the simple Bayes model are suitable for institutional risk-adjusted comparisons.