The use of FreeStyle libre improves glycaemic control along with reducing diabetes burden and hospital admissions in a socially deprived Northwest English population.

Aim: This retrospective study aimed to use mixed (qualitative and quantitative) methods to evaluate the role of FSL in reducing hospital admissions due to all causes, HbA1c, and reported hypoglycaemic episodes in people with diabetes living in a socially deprived region of Northwest England. Methods: Data were collected retrospectively from previous consultations, which coincided with the 6th -week, 6th -month and annual review including blood tests, hospital admissions due to any cause and reported hypoglycaemia. Also, FSL assessment and satisfaction semi-structured questionnaire was done to assess the impact of FSL on diabetes management and quality of life. Mixed-effects models were used to assess glycaemic control and reductions in hospital admissions and reported hypoglycaemic episodes. Results: Just 127 patients met the inclusion criteria. A multivariate linear mixed model method that analyses HbA1c data longitudinally revealed mean differences (mmol/mol) between baseline and post-FSL measurements, estimated by restricted maximum likelihood method (REML) of 9.64 (six weeks), 7.68 (six months) and 7.58 (annual review); all with a corresponding p-value of < 0.0001. For DKA patients, the bootstrap method revealed a significant reduction in mean HbA1c of 25.5, 95% confidence interval (CI) [8.8, 42.6] mmol/mol. It is demonstrated that FSL use for one year resulted in 59% reduction in hospital admissions and 46% reduction in reported hypoglycaemic episodes. Conclusion: The use of FSL resulted in statistically significant reductions in hospital admissions, HbA1c and reported hypoglycaemic episodes among diabetics in a socially deprived Northwest region of England. These outcomes show a direct association with a higher questionnaire score. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-024-01424-4.

Optimisation of data acquisition towards continuous cardiac Magnetic Resonance Fingerprinting applications.

PURPOSE: Assess and optimise acquisition parameters for continuous cardiac Magnetic Resonance Fingerprinting (MRF). METHODS: Different acquisition schemes (flip angle amplitude, lobe size, T2-preparation pulses) for cardiac MRF were assessed in simulations and phantom and demonstrated in one healthy volunteer. Three different experimental designs were evaluated using central composite and fractional factorial designs. Relative errors for T1 and T2 were calculated for a wide range of realistic T1 and T2 value combinations. The effect of different designs on the accuracy of T1 and T2 was assessed using response surface modelling and Cohen's f calculations. RESULTS: Larger flip angle amplitudes lead to an improvement of T2 accuracy and precision for simulations and phantom experiments. Similar effects could also be shown qualitatively in in-vivo scans. Accuracy and precision of T1 were robust to different design parameters with improved values for faster flip angle variation. Cohen's f showed that T2-preparation pulses influence the accuracy of T2. The number of pulses used is the most important parameter. Without T2-preparation pulses, RMSE were 3.0 ± 8.09 % for T1 and 16.24 ± 14.47 % for T2. Using those pulses reduced the RMSE to 2.3 ± 8.4 % for T1 and 14.11 ± 13.46 % for T2. Nonetheless, even if the improvement is significant, RMSE are still too high for reliable quantification. CONCLUSION: In contrast to previous study using triggered MRF sequences using < 30° flip angles, large flip angle amplitudes led to better results for continuous cardiac MRF sequences. T2-preparation pulse can improve the accuracy of T2 estimation but lead to longer scan times.

Implementation of a medicine management plan (MMP) to reduce medication-related harm (MRH) in older people post-hospital discharge: a randomised controlled trial.

BACKGROUND: Medication-related harm (MRH) is an escalating global challenge especially among older adults. The period following hospital discharge carries high-risk for MRH due to medication discrepancies, limited patient/carer education and support, and poor communication between hospital and community professionals. Discharge Medical Service (DMS), a newly introduced NHS scheme, aims to reduce post-discharge MRH through an electronic communication between hospital and community pharmacists. Our study team has previously developed a risk-prediction tool (RPT) for MRH in the 8-weeks period post discharge from a UK hospital cohort of 1280 patients. In this study, we aim to find out if a Medicines Management Plan (MMP) linked to the DMS is more effective than the DMS alone in reducing rates of MRH. METHOD: Using a randomized control trial design, 682 older adults ≥ 65 years due to be discharged from hospital will be recruited from 4 sites. Participants will be randomized to an intervention arm (individualised medicine management plan (MMP) plus DMS) or a control arm (DMS only) using a 1:1 ratio stratification. Baseline data will include patients' clinical and social demographics, and admission and discharge medications. At 8-weeks post-discharge, a telephone interview and review of GP records by the study pharmacist will verify MRH in both arms. An economic and process evaluation will assess the cost and acceptability of the study methods. DATA ANALYSIS: Univariate analysis will be done for baseline variables comparing the intervention and control arms. A multivariate logistic regression will be done incorporating these variables. Economic evaluation will compare the cost-of-service use among the study arms and modelled to provide national estimates. Qualitative data from focus-group interviews will explore practitioners' understanding, and acceptance of the MMP, DMS and the RPT. CONCLUSION: This study will inform the use of an objective, validated RPT for MRH among older adults after hospital discharge, and provide a clinical, economic, and service evaluation of a specific medicines management plan alongside the DMS in the National Health Service (UK).

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.

Data from the US and UK cystic fibrosis registries support disease modification by CFTR modulation with ivacaftor.

BACKGROUND: Ivacaftor is the first cystic fibrosis transmembrane conductance regulator (CFTR) modulator demonstrating clinical benefit in patients with cystic fibrosis (CF). As ivacaftor is intended for chronic, lifelong use, understanding long-term effects is important for patients and healthcare providers. OBJECTIVE: This ongoing, observational, postapproval safety study evaluates clinical outcomes and disease progression in ivacaftor-treated patients using data from the US and the UK CF registries following commercial availability. METHODS: Annual analyses compare ivacaftor-treated and untreated matched comparator patients for: risks of death, transplantation, hospitalisation, pulmonary exacerbation; prevalence of CF-related complications and microorganisms and lung function changes in a subset of patients who initiated ivacaftor in the first year of commercial availability. Results from the 2014 analyses (2 and 3 years following commercial availability in the UK and USA, respectively) are presented here. RESULTS: Analyses included 1256 ivacaftor-treated and 6200 comparator patients from the USA and 411 ivacaftor-treated and 2069 comparator patients from the UK. No new safety concerns were identified based on the evaluation of clinical outcomes included in the analyses. As part of safety evaluations, ivacaftor-treated US patients were observed to have significantly lower risks of death (0.6% vs 1.6%, p=0.0110), transplantation (0.2% vs 1.1%, p=0.0017), hospitalisation (27.5% vs 43.1%, p<0.0001) and pulmonary exacerbation (27.8% vs 43.3%, p<0.0001) relative to comparators; trends were similar in the UK. In both registries, ivacaftor-treated patients had a lower prevalence of CF-related complications and select microorganisms and had better preserved lung function. CONCLUSIONS: While general limitations of observational research apply, analyses revealed favourable results for clinically important outcomes among ivacaftor-treated patients, adding to the growing body of literature supporting disease modification by CFTR modulation with ivacaftor. EU PAS REGISTRATION NUMBER: EUPAS4270.

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.

Proteomic profiling of urine for the detection of colon cancer.

BACKGROUND: Colorectal cancer is the second most common cause of cancer related death in the developed world. To date, no blood or stool biomarkers with both high sensitivity and specificity for potentially curable early stage disease have been validated for clinical use. SELDI and MALDI profiling are being used increasingly to search for biomarkers in both blood and urine. Both techniques provide information predominantly on the low molecular weight proteome (<15 kDa). There have been several reports that colorectal cancer is associated with changes in the serum proteome that are detectable by SELDI and we hypothesised that proteomic changes would also be detectable in urine. RESULTS: We collected urine from 67 patients with colorectal cancer and 72 non-cancer control subjects, diluted to a constant protein concentration and generated MALDI and SELDI spectra. The intensities of 19 peaks differed significantly between cancer and non-cancer patients by both t-tests and after adjusting for confounders using multiple linear regressions. Logistic regression classifiers based on peak intensities identified colorectal cancer with up to 78% sensitivity at 87% specificity. We identified and independently quantified 3 of the discriminatory peaks using synthetic stable isotope peptides (an 1885 Da fragment of fibrinogen and hepcidin-20) or ELISA (beta2-microglobulin). CONCLUSION: Changes in the urine proteome may aid in the early detection of colorectal cancer.

Multiple testing issues in discriminating compound-related peaks and chromatograms from high frequency noise, spikes and solvent-based noise in LC-MS data sets.

Liquid Chromatography--Mass Spectrometry (LC-MS) is a powerful method for sensitive detection and quantification of proteins and peptides in complex biological fluids like serum. LC-MS produces complex data sets, consisting of some hundreds of millions of data points per sample at a resolution of 0.1 amu in the m/z domain and 7000 data points in the time domain. However, the detection of the lower abundance proteins from this data is hampered by the presence of artefacts, such as high frequency noise and spikes. Moreover, not all of the tens of thousands of the chromatograms produced per sample are relevant for the pursuit of the biomarkers. Thus in analysing the LC-MS data, two critical pre-processing issues arise. Which of the thousands of the: 1. chromatograms per sample are relevant for the detection of the biomarkers?, and 2. signals per chromatogram are truly compound-related? Each of these issues involves assessing the significance (deviation from noise) of multiple observations and the issue of multiple comparisons arises. Current methods disregard the multiplicity and provide no concrete threshold for significance. However, with such procedures, the probability of one or more false-positives is high as the number of tests to be performed is large, and must be controlled. Realizing that the cut-offs for declaring a chromatogram (or a signal) to be compound-related can hugely influence which proteins are detected, it seems natural to define thresholds that are neither arbitrary nor subjective. We suggest the choice of thresholds guided by the critical aim of controlling the False Discovery Rate (FDR) in multiple hypotheses testing for significance over a large set of features produced per sample. This involves the use of the regression diagnostics to characterize the signals of a chromatogram (e.g. as outliers or influential) and to suggest suitable tests statistics for the multiple testing procedures (MTP) for discriminating noise and spikes from true signals. The role of the Generalized Linear Models (GLM) in this MTP is investigated. The method is applied to LC-MS datasets from trypsin-digested serum spiked with varying levels of horse heart cytochrome C (cytoc).