Derivation and validation of a novel comorbidity‐based delirium risk index to predict postoperative delirium using national administrative healthcare database

ObjectiveTo derive and validate a comorbidity‐based delirium risk index (DRI) to predict postoperative delirium.Data Source/Study SettingData of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20‐25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation.Study DesignThirty‐six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson‐Deyo Index for goodness‐of‐fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness‐of‐fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries.Data CollectionHip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD‐9 codes. Postoperative delirium was defined by using ICD‐9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium.Principal FindingsThe derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson‐Deyo Comorbidity Index with better goodness‐of‐fit and predictive performance.ConclusionsDelirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.     

Interaction of treatment with a continuous variable: simulation study of power for several methods of analysis

In a large simulation study reported in a companion paper, we investigated the significance levels of 21 methods for investigating interactions between binary treatment and a continuous covariate in a randomised controlled trial. Several of the methods were shown to have inflated type 1 errors. In the present paper, we report the second part of the simulation study in which we investigated the power of the interaction procedures for two sample sizes and with two distributions of the covariate (well and badly behaved). We studied several methods involving categorisation and others in which the covariate was kept continuous, including fractional polynomials and splines. We believe that the results provide sufficient evidence to recommend the multivariable fractional polynomial interaction procedure as a suitable approach to investigate interactions of treatment with a continuous variable. If subject‐matter knowledge gives good arguments for a non‐monotone treatment effect function, we propose to use a second‐degree fractional polynomial approach, but otherwise a first‐degree fractional polynomial (FP1) function with added flexibility (FLEX3) is the method of choice. The FP1 class includes the linear function, and the selected functions are simple, understandable, and transferable. Furthermore, software is available. We caution that investigation of interactions in one dataset can only be interpreted in a hypothesis‐generating sense and needs validation in new data. Copyright © 2014 John Wiley & Sons, Ltd.     

The impact of baseline covariates on the efficiency of statistical analyses of crossover designs

We investigate the impact of baseline covariates on the efficiency of statistical analyses of crossover designs. For practical considerations, we contemplate two different baseline methods: study baselines and period‐dependent baselines. For each baseline method, we establish analytical upper bounds for the relative efficiency of a large class of crossover designs, the totally balanced designs, under a model with the baseline covariates as compared with the model without the baseline covariates. We present numerical details based on these bounds for assorted scenarios and reveal implications of these results. Copyright © 2012 John Wiley & Sons, Ltd.     

Pericardial Mesothelioma Presented as Constrictive Pericardial Disease

The aim of this report is to present a case of malignant mesothelioma of the pericardium and to illustrate points in diagnosis and treatment of this extremely rare disease. On admission to hospital there were signs of cardiac tamponade, which responded to peri-cardial drainage. The patient subsequently deteriorated and signs of constrictive pericarditis were demonstrated by right heart catheterization and angiography. Surgery was undertaken, but the patient died three hours after pericardectomy due to cardiac failure and multiple thromboembolism.

Pericardial mesotheliomas are rare, as are all primary tumours of the heart. The disease is seldom diagnosed antemortem. The aim of this report is to present one further case and discuss points in diagnosis and treatment.     

Semimechanistically Based Modeling of Pembrolizumab Time-Varying Clearance Using 4 Longitudinal Covariates in Patients With Non–Small Cell Lung Cancer

Time-varying clearance (CL) has been recently recognized in U.S. Food and Drug Administration drug labels for oncology monoclonal antibodies. Pembrolizumab population CL at steady state decreased about 20% from the first dose, and individual CL changes varied from 75% decrease to 25% increase, which were correlating with disease conditions. From mechanism of action perspective, this research explored the longitudinal covariate effect on pembrolizumab CL based on data from a phase II/III clinical trial in patients with non–small cell lung cancer. Time courses of sum of the longest tumor dimensions, lymphocyte count, albumin, and lactate dehydrogenase were first characterized separately, and the post hoc parameters of each individual patient were fixed in the subsequent semimechanistically based modeling analysis. Pembrolizumab time-varying CL was assumed to be associated with the patient's sum of the longest tumor dimensions, lymphocyte count, albumin, and lactate dehydrogenase, and tumor-related pembrolizumab CL was assumed to be a fraction of total pembrolizumab CL in the semimechanistically based modeling.     

Inverse probability weighting for covariate adjustment in randomized studies

Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a ‘favorable’ model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two‐stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a ‘favorable’ model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented. Copyright © 2013 John Wiley & Sons, Ltd.     

Age‐dependent biochemical quantities: An approach for calculating reference intervals

Objective. A parametric method is often preferred when calculating reference intervals for biochemical quantities, as non‐parametric methods are less efficient and require more observations/study subjects. Parametric methods are complicated, however, because of three commonly encountered features. First, biochemical quantities seldom display a Gaussian distribution, and there must either be a transformation procedure to obtain such a distribution or a more complex distribution has to be used. Second, biochemical quantities are often dependent on a continuous covariate, exemplified by rising serum concentrations of MUC1 (episialin, CA15.3) with increasing age. Third, outliers often exert substantial influence on parametric estimations and therefore need to be excluded before calculations are made. Material and methods. The International Federation of Clinical Chemistry (IFCC) currently recommends that confidence intervals be calculated for the reference centiles obtained. However, common statistical packages allowing for the adjustment of a continuous covariate do not make this calculation. Results. In the method described in the current study, Tukey's fence is used to eliminate outliers and two‐stage transformations (modulus‐exponential‐normal) in order to render Gaussian distributions. Fractional polynomials are employed to model functions for mean and standard deviations dependent on a covariate, and the model is selected by maximum likelihood. Confidence intervals are calculated for the fitted centiles by combining parameter estimation and sampling uncertainties. Finally, the elimination of outliers was made dependent on covariates by reiteration. Conclusions. Though a good knowledge of statistical theory is needed when performing the analysis, the current method is rewarding because the results are of practical use in patient care.     

Comparison of Likelihood Approaches for Combined Segregation and Linkage Analysis of a Complex Disease and a Candidate Gene Marker Under Different Ascertainment Schemes

We compared two joint likelihood approaches, with complete (L1) or without (L2) linkage disequilibrium, under different ascertainment schemes, for the genetic analysis of the disease trait and marker gene 1 in replicate 42. Joint likelihoods were computed without a correction for the selection scheme. For the different sampling schemes we have explored, our results suggest that L1 is a more powerful approach than L2 to detect major gene and covariatc effects as well as to identify accurately gene×covariate interaction effects in a common and complex disease such as the Genetic Analysis Workshop 12 MG6 simulated trait. © 2001 Wiley‐Liss, Inc.