Ensuring phenotyping algorithms using national electronic health records are FAIR:Meeting the needs of the cardiometabolic research community

Phenotyping algorithms enable the extraction of clinically-relevant information (such as diagnoses, prescription information, or a blood pressure measurement) from electronic health records for use in research. They have enormous potential and wide-ranging utility in research to improve disease understanding, health, and healthcare provision. While great progress has been achieved over the past years in standardising how genomic data are represented and curated (e.g. VCF files for variants), phenotypic data are significantly more fragmented and lack a common representation approach. This lack of standards creates challenges, including a lack of comparability, transparency and reproducibility, and limiting the subsequent use of phenotyping algorithms in other research studies. The FAIR guiding principles for scientific data management and stewardship state that digital assets should be findable, accessible, interoperable and reusable, yet the current lack of phenotyping algorithm standards means that phenotyping algorithms are not FAIR. We have therefore engaged with the community to address these challenges, including defining standards for the reporting and sharing of phenotyping algorithms. Here we present the results of our engagement with the community to identify and explore their requirements and outline our recommendations to ensure FAIR phenotyping algorithms are available to meet the needs of the cardiometabolic research community

Ensuring phenotyping algorithms using national electronic health records are FAIR:Meeting the needs of the cardiometabolic research community

Phenotyping algorithms enable the extraction of clinically-relevant information (such as diagnoses, prescription information, or a blood pressure measurement) from electronic health records for use in research. They have enormous potential and wide-ranging utility in research to improve disease understanding, health, and healthcare provision. While great progress has been achieved over the past years in standardising how genomic data are represented and curated (e.g. VCF files for variants), phenotypic data are significantly more fragmented and lack a common representation approach. This lack of standards creates challenges, including a lack of comparability, transparency and reproducibility, and limiting the subsequent use of phenotyping algorithms in other research studies. The FAIR guiding principles for scientific data management and stewardship state that digital assets should be findable, accessible, interoperable and reusable, yet the current lack of phenotyping algorithm standards means that phenotyping algorithms are not FAIR. We have therefore engaged with the community to address these challenges, including defining standards for the reporting and sharing of phenotyping algorithms. Here we present the results of our engagement with the community to identify and explore their requirements and outline our recommendations to ensure FAIR phenotyping algorithms are available to meet the needs of the cardiometabolic research community

Metformin in non-diabetic hyperglycaemia: the GLINT feasibility RCT.

BACKGROUND: The treatment of people with diabetes with metformin can reduce cardiovascular disease (CVD) and may reduce the risk of cancer. However, it is unknown whether or not metformin can reduce the risk of these outcomes in people with elevated blood glucose levels below the threshold for diabetes [i.e. non-diabetic hyperglycaemia (NDH)]. OBJECTIVE: To assess the feasibility of the Glucose Lowering In Non-diabetic hyperglycaemia Trial (GLINT) and to estimate the key parameters to inform the design of the full trial. These parameters include the recruitment strategy, randomisation, electronic data capture, postal drug distribution, retention, study medication adherence, safety monitoring and remote collection of outcome data. DESIGN: A multicentre, individually randomised, double-blind, parallel-group, pragmatic, primary prevention trial. Participants were individually randomised on a 1 : 1 basis, blocked within each site. SETTING: General practices and clinical research facilities in Cambridgeshire, Norfolk and Leicestershire. PARTICIPANTS: Males and females aged ≥ 40 years with NDH who had a high risk of CVD. INTERVENTIONS: Prolonged-release metformin (500 mg) (Glucophage® SR, Merck KGaA, Bedfont Cross, Middlesex, UK) or the matched placebo, up to three tablets per day, distributed by post. MAIN OUTCOME MEASURES: Recruitment rates; adherence to study medication; laboratory results at baseline and 3 and 6 months; reliability and acceptability of study drug delivery; questionnaire return rates; and quality of life. RESULTS: We sent 5251 invitations, with 511 individuals consenting to participate. Of these, 249 were eligible and were randomised between March and November 2015 (125 to the metformin group and 124 to the placebo group). Participants were followed up for 0.99 years [standard deviation (SD) 0.30 years]. The use of electronic medical records to identify potentially eligible individuals in individual practices was resource intensive. Participants were generally elderly [mean age 70 years (SD 6.7 years)], overweight [mean body mass index 30.1 kg/m2 (SD 4.5 kg/m2)] and male (88%), and the mean modelled 10-year CVD risk was 28.8% (SD 8.5%). Randomisation, postal delivery of the study drug and outcome assessment using registers/medical records were feasible and acceptable to participants. Most participants were able to take three tablets per day, but premature discontinuation of the study drug was common (≈30% of participants by 6 months), although there were no differences between the groups. All randomised participants returned questionnaires at baseline and 67% of participants returned questionnaires by the end of the study. There was no between-group difference in Short Form questionnaire-8 items or EuroQol-5 Dimensions scores. Compared with placebo, metformin was associated with small improvements in the mean glycated haemoglobin level [-0.82 mmol/mol, 95% confidence interval (CI) -1.39 to -0.24 mmol/mol], mean estimated glomerular filtration rate (2.31 ml/minute/1.73 m2, 95% CI -0.2 to 4.81 ml/minute/1.73 m2) and mean low-density lipoprotein cholesterol level (-0.11 mmol/l, 95% CI -0.25 to 0.02 mmol/l) and a reduction in mean plasma vitamin B12 level (-16.4 ng/l, 95% CI -32.9 to -0.01 ng/l). There were 35 serious adverse events (13 in the placebo group, 22 in the metformin group), with none deemed to be treatment related. LIMITATIONS: Changes to sponsorship reduced the study duration, the limited availability of information in medical records reduced recruitment efficiency and discontinuation of study medication exceeded forecasts. CONCLUSIONS: A large, pragmatic trial comparing the effects of prolonged-release metformin and placebo on the risk of CVD events is potentially feasible. However, changes to the study design and conduct are recommended to enable an efficient scaling up of the trial. Recommendations include changing the inclusion criteria to recruit people with pre-existing CVD to increase the recruitment and event rates, using large primary/secondary care databases to increase recruitment rates, conducting follow-up remotely to improve efficiency and including a run-in period prior to randomisation to optimise trial adherence. TRIAL REGISTRATION: Current Controlled Trials ISRCTN34875079. FUNDING: The project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 18. See the NIHR Journals Library website for further project information. Merck KGaA provided metformin and matching placebo

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p

Multivariable prognostic modelling to improve prediction of colorectal cancer recurrence:the PROSPeCT trial

OBJECTIVE: Improving prognostication to direct personalised therapy remains an unmet need. This study prospectively investigated promising CT, genetic, and immunohistochemical markers to improve the prediction of colorectal cancer recurrence.MATERIAL AND METHODS: This multicentre trial (ISRCTN 95037515) recruited patients with primary colorectal cancer undergoing CT staging from 13 hospitals. Follow-up identified cancer recurrence and death. A baseline model for cancer recurrence at 3 years was developed from pre-specified clinicopathological variables (age, sex, tumour-node stage, tumour size, location, extramural venous invasion, and treatment). Then, CT perfusion (blood flow, blood volume, transit time and permeability), genetic (RAS, RAF, and DNA mismatch repair), and immunohistochemical markers of angiogenesis and hypoxia (CD105, vascular endothelial growth factor, glucose transporter protein, and hypoxia-inducible factor) were added to assess whether prediction improved over tumour-node staging alone as the main outcome measure.RESULTS: Three hundred twenty-six of 448 participants formed the final cohort (226 male; mean 66 ± 10 years. 227 (70%) had ≥ T3 stage cancers; 151 (46%) were node-positive; 81 (25%) developed subsequent recurrence. The sensitivity and specificity of staging alone for recurrence were 0.56 [95% CI: 0.44, 0.67] and 0.58 [0.51, 0.64], respectively. The baseline clinicopathologic model improved specificity (0.74 [0.68, 0.79], with equivalent sensitivity of 0.57 [0.45, 0.68] for high vs medium/low-risk participants. The addition of prespecified CT perfusion, genetic, and immunohistochemical markers did not improve prediction over and above the clinicopathologic model (sensitivity, 0.58-0.68; specificity, 0.75-0.76).CONCLUSION: A multivariable clinicopathological model outperformed staging in identifying patients at high risk of recurrence. Promising CT, genetic, and immunohistochemical markers investigated did not further improve prognostication in rigorous prospective evaluation.CLINICAL RELEVANCE STATEMENT: A prognostic model based on clinicopathological variables including age, sex, tumour-node stage, size, location, and extramural venous invasion better identifies colorectal cancer patients at high risk of recurrence for neoadjuvant/adjuvant therapy than stage alone.KEY POINTS: Identification of colorectal cancer patients at high risk of recurrence is an unmet need for treatment personalisation. This model for recurrence, incorporating many patient variables, had higher specificity than staging alone. Continued optimisation of risk stratification schema will help individualise treatment plans and follow-up schedules.</p