Medication-related harm in older adults following hospital discharge: development and validation of a prediction tool

Objectives: To develop and validate a tool to predict the risk of an older adult experiencing medication-related harm (MRH) requiring healthcare use following hospital discharge. Design, setting, participants: Multicentre, prospective cohort study recruiting older adults (65 years) discharged from five UK teaching hospitals between 2013 and 2015. Primary outcome measure: Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non-adherence, harm from medication error). Three data sources provided MRH and healthcare use information; hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH, and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated. Results: 1116 out of 1280 recruited participants completed follow-up (87%). Uncertain MRH cases (‘possible’ and ‘probable’) were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced ‘definite’ MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge; age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool’s discrimination C-statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches. Conclusions: The PRIME tool could be used to identify older patients at high risk of MRH requiring healthcare use following hospital discharge. Prior to clinical use we recommend the tool’s evaluation in other settings

The SAInT study: a protocol for a randomized controlled trial of steroid injection for subacromial pain syndrome using the anterolateral versus posterior approach

Aims Steroid injections are used for subacromial pain syndrome and can be administered via the anterolateral or posterior approach to the subacromial space. It is not currently known which approach is superior in terms of improving clinical symptoms and function. This is the protocol for a randomized controlled trial (RCT) to compare the clinical effectiveness of a steroid injection given via the anterolateral or the posterior approach to the subacromial space. Methods The Subacromial Approach Injection Trial (SAInT) study is a single-centre, parallel, two-arm RCT. Participants will be allocated on a 1:1 basis to a subacromial steroid injection via either the anterolateral or the posterior approach to the subacromial space. Participants in both trial arms will then receive physiotherapy as standard of care for subacromial pain syndrome. The primary analysis will compare the change in Oxford Shoulder Score (OSS) at three months after injection. Secondary outcomes include the change in OSS at six and 12 months, as well as the Pain Numeric Rating Scale (0 = no pain, 10 = worst pain), Disabilities of Arm, Shoulder and Hand questionnaire (DASH), and 36-Item Short-Form Health Survey (SF-36) (RAND) at three months, six months, and one year after injection. Assessment of pain experienced during the injection will also be determined. A minimum of 86 patients will be recruited to obtain an 80% power to detect a minimally important difference of six points on the OSS change between the groups at three months after injection. Conclusion The results of this trial will demonstrate if there is a difference in shoulder pain and function after a subacromial space steroid injection between the anterolateral versus posterior approach in patients with subacromial pain syndrome. This will help to guide treatment for patients with subacromial pain syndrome

A multivariate version of the Benjamini-Hochberg method

AbstractWe propose a multivariate method for combining results from independent studies about the same ‘large scale’ multiple testing problem. The method works asymptotically in the number of hypotheses and consists of applying the Benjamini–Hochberg procedure to the p-values of each study separately by determining the ‘individual false discovery rates’ which maximize power subject to a restriction on the (global) false discovery rate. We show how to obtain solutions to the associated optimization problem, provide both theoretical and numerical examples, and compare the method with univariate ones

Sample size calculations for designing clinical proteomic profiling studies using mass spectrometry

In cancer clinical proteomics, MALDI and SELDI profiling are used to search for biomarkers of potentially curable early-stage disease. A given number of samples must be analysed in order to detect clinically relevant differences between cancers and controls, with adequate statistical power. From clinical proteomic profiling studies, expression data for each peak (protein or peptide) from two or more clinically defined groups of subjects are typically available. Typically, both exposure and confounder information on each subject are also available, and usually the samples are not from randomized subjects. Moreover, the data is usually available in replicate. At the design stage, however, covariates are not typically available and are often ignored in sample size calculations. This leads to the use of insufficient numbers of samples and reduced power when there are imbalances in the numbers of subjects between different phenotypic groups. A method is proposed for accommodating information on covariates, data imbalances and designcharacteristics, such as the technical replication and the observational nature of these studies, in sample size calculations. It assumes knowledge of a joint distribution for the protein expression values and the covariates. When discretized covariates are considered, the effect of the covariates enters the calculations as a function of the proportions of subjects with specific attributes. This makes it relatively straightforward (even when pilot data on subject covariates is unavailable) to specify and to adjust for the effect of the expected heterogeneities. The new method suggests certain experimental designs which lead to the use of a smaller number of samples when planning a study. Analysis of data from the proteomic profiling of colorectal cancer reveals that fewer samples are needed when a study is balanced than when it is unbalanced, and when the IMAC30 chip-type is used. The method is implemented in the clippda package and is available in R at: http://www.bioconductor.org/help/bioc-views/release/ bioc/html/clippda.html. © 2012 De Gruyter. All rights reserved

Sample Size Calculations for Designing Clinical Proteomic Profiling Studies Using Mass Spectrometry

In cancer clinical proteomics, MALDI and SELDI profiling are used to search for biomarkers of potentially curable early-stage disease. A given number of samples must be analysed in order to detect clinically relevant differences between cancers and controls, with adequate statistical power. From clinical proteomic profiling studies, expression data for each peak (protein or peptide) from two or more clinically defined groups of subjects are typically available. Typically, both exposure and confounder information on each subject are also available, and usually the samples are not from randomized subjects. Moreover, the data is usually available in replicate. At the design stage, however, covariates are not typically available and are often ignored in sample size calculations. This leads to the use of insufficient numbers of samples and reduced power when there are imbalances in the numbers of subjects between different phenotypic groups. A method is proposed for accommodating information on covariates, data imbalances and design-characteristics, such as the technical replication and the observational nature of these studies, in sample size calculations. It assumes knowledge of a joint distribution for the protein expression values and the covariates. When discretized covariates are considered, the effect of the covariates enters the calculations as a function of the proportions of subjects with specific attributes. This makes it relatively straightforward (even when pilot data on subject covariates is unavailable) to specify and to adjust for the effect of the expected heterogeneities. The new method suggests certain experimental designs which lead to the use of a smaller number of samples when planning a study. Analysis of data from the proteomic profiling of colorectal cancer reveals that fewer samples are needed when a study is balanced than when it is unbalanced, and when the IMAC30 chip-type is used. The method is implemented in the clippda package and is available in R at: http://www.bioconductor.org/help/bioc-views/release/bioc/html/clippda.html.