Background\r\n\r\nEvidence suggests there are inconsistencies in patient-reported outcome (PRO) assessment and reporting in clinical trials, which may limit the use of these data to inform patient care. For trials with a PRO endpoint, routine inclusion of key PRO information in the protocol may help improve trial conduct and the reporting and appraisal of PRO results; however, it is currently unclear exactly what PRO-specific information should be included. The aim of this review was to summarize the current PRO-specific guidance for clinical trial protocol developers.\r\n\r\nMethods and Findings\r\n\r\nWe searched the MEDLINE, EMBASE, CINHAL and Cochrane Library databases (inception to February 2013) for PRO-specific guidance regarding trial protocol development. Further guidance documents were identified via Google, Google scholar, requests to members of the UK Clinical Research Collaboration registered clinical trials units and international experts. Two independent investigators undertook title/abstract screening, full text review and data extraction, with a third involved in the event of disagreement. 21,175 citations were screened and 54 met the inclusion criteria. Guidance documents were difficult to access: electronic database searches identified just 8 documents, with the remaining 46 sourced elsewhere (5 from citation tracking, 27 from hand searching, 7 from the grey literature review and 7 from experts). 162 unique PRO-specific protocol recommendations were extracted from included documents. A further 10 PRO recommendations were identified relating to supporting trial documentation. Only 5/162 (3%) recommendations appeared in \u226550% of guidance documents reviewed, indicating a lack of consistency.\r\n\r\nConclusions\r\n\r\nPRO-specific protocol guidelines were difficult to access, lacked consistency and may be challenging to implement in practice. There is a need to develop easily accessible consensus-driven PRO protocol guidance. Guidance should be aimed at ensuring key PRO information is routinely included in appropriate trial protocols, in order to facilitate rigorous collection/reporting of PRO data, to effectively inform patient care.
\n \n\n \n \nBACKGROUND An exaggerated morning blood pressure surge (MBPS) may be associated with stroke and other cardiovascular events, but the threshold at which an MBPS becomes pathological is unclear. This study aimed to systematically review the existing literature and establish the most appropriate definition of pathological MBPS.\r\n\r\nMETHODS A MEDLINE search strategy was adapted for a range of literature databases to identify all prospective studies relating an exaggerated MBPS to cardiovascular endpoints. Hazard ratios (HRs) were extracted and synthesized using random-effects meta-analysis.\r\n\r\nRESULTS The search strategy identified 2,964 unique articles, of which 17 were eligible for the study. Seven different definitions of MBPS were identified; the most common was a prewaking surge (mean blood pressure for 2 hours after wake-up minus mean blood pressure for 2 hours before wake-up; n = 6 studies). Summary meta-analysis gave no clear evidence that prewaking MBPS (defined by a predetermined threshold: >25\u201355mm Hg) was associated with all cardiovascular events (n = 2 studies; HR = 0.94, 95% confidence interval (CI) = 0.39\u20132.28) or stroke (n = 2 studies; HR = 1.26, 95% CI = 0.92\u20131.71). However, using a continuous scale, which has more power to detect an association, there was evidence that a 10 mm Hg increase in MBPS was related to an increased risk of stroke (n = 3 studies; HR = 1.11, 95% CI = 1.03\u20131.20).\r\n\r\nCONCLUSIONS These findings suggest that when measured and analyzed as a continuous variable, increasing levels of MBPS may be associated with increased risk of stroke. Large, protocol-driven individual patient data analyses are needed to accurately define this relationship further.
\n \n\n \n \nThe field of biomarker research has almost reached unmanageable proportions in chronic obstructive pulmonary disease (COPD). The developments of new technology platforms have generated a huge information data base, both cross sectionally and increasingly, longitudinally. The knowledge emerging provides an enormous potential for understanding the disease pathophysiology, for developing markers specific for long-term outcomes, and for developing new therapeutic strategies. However, the excitement must be tempered with an understanding of the limitations of the data collection techniques, and of the variations in disease state, activity, impact, and progression. Nevertheless, the most crucial aspect in interpreting the current literature is the recognition of the relatively superficial characterization of what is a complex group of pathological processes with a common end point of airflow limitation. The current review explores some of these issues together with those areas where real progress appears to have been made, and provides caution on interpretation.
\n \n\n \n \nThere is little doubt that neutrophils, while vital in host defence, have the potential to cause significant tissue damage implicated both in lung and systemic diseases as well as systemic organ failure. Following pathogen recognition receptor activation by agents including formyl-methionyl-leucyl-phenylalanine (fMLP), cigarette smoke, cytokines or complement factor C5a, neutrophils are activated, recruited and participate in tissue breakdown and bacterial clearance via an array of mechanisms including phagocytosis, superoxide burst, extrusion of neutrophil extracellular traps (de-condensed chromatin and antimicrobial protein coated web-like structures), secretion of reactive oxygen and nitrogen species, and extrusion of a range of granule-derived proteases and antimicrobial peptides. These proteases, hydrolases and antimicrobial peptides are thought to contribute to many cellular functions including neutrophil migration, adherence and apoptosis, but their extra-cellular release is also associated with obligate tissue damage (as described in studies of quantum proteolysis1) and inflammation, and neutrophil proteinases in particular have been found to have a myriad of pro-inflammatory effects on bystander tissue to amplify inflammation, favouring further cell recruitment and degranulation.
\n \n\n \n \nBACKGROUND:\r\nDespite the enormous potential for adverse events in primary care, the knowledge base about patient safety in this context is still sparse. The lack of appropriate measurement methods is a key factor limiting the development of research in this field.\r\nOBJECTIVE:\r\nTo identify and characterize available patient reported instruments to measure patient safety in primary care.\r\nMETHODS:\r\nWe conducted a systematic literature review. We searched in bibliographic sources for empirical studies describing the development, evaluation or use of patient reported instruments assessing patient safety in primary care. Study selection and data extraction were independently conducted by two researchers.\r\nRESULTS:\r\nWe identified 28 studies reporting on 23 different instruments. Fifteen instruments were designed for paper-based self-administration, six for phone interview and two consisted in electronic reporting systems. Most instruments focused on specific aspects of patient safety, most commonly on experiences of adverse drug reactions. Face validity was assessed for 10 instruments (43%), three reported construct validity (13%) and three described reliability (13%). Responsiveness was not ascertained.\r\nCONCLUSIONS:\r\nAlthough there is evidence of good psychometric properties for a reduced number of patient reported instruments, currently available instruments do not offer a comprehensive set of resources to measure the effects of interventions to improve patient safety in primary care from a patient perspective. Future research in the field should prioritize (i) the evaluation of the performance of already available instruments and (ii) the development of new instruments that enable an comprehensive assessment of patient safety at general practices.
\n \n\n \n \nResearch on violence against (im)migrant women is emerging, but definitions and classifications of immigrants\r\nare not consistent, which makes comparisons within and between countries difficult if not impossible. This is also\r\nproblematic when studying abused (im)migrant women\u2019s needs and experiences within health care. (Im)migrants\r\nare often subsumed under the umbrella term ethnic minorities, ethnic groups, racial minorities, ethnicity or race or these terms are used interchangeably. This is partly due to the countries\u2019 different historic and political migration trajectories. Although there are similarities between migrants and ethnic minorities (e.g. a shared geographical and cultural heritage), there are also considerable differences (e.g. the act of migrating to another country and, as a consequence, the loss of a social network). An important dimension, which is often neglected when studying migrant women, is the age at migration, which is indicative of the migrant generation a woman belongs to and important since it influences how well she can adjust to a new country, language and culture.\r\n\r\nThe direction of flow of migration (e.g. South-North vs. North-North) may show how similar the country of origin\r\nis from the country of residence (e.g. linguistic proximity between the U.K. and the U.S.) and the different reasons for migration (e.g. work, war; forced vs. voluntary) often affect the legal status. When distinct migrant groups are subsumed and are then compared either with each other, other migrant groups or with non-migrants, this has limitations since it masks heterogeneity. Moreover, domestic violence, being one form of gender violence, needs a broader scope to capture the domestic violence experience of (im)migrant women (e.g. violence committed by extended family members). The following article argues for careful definitions, operalisations, and usage of \u201cmigration\u201d and \u201cdomestic violence\u201d when conducting research on violence against im(migrant) women to make comparison possible and meaningful.\r\n\r\ndoi: 10.4172/2151-6200.S1-008\r\n
\n \n\n \n \nAlthough diet quality is implicated in cardiovascular disease (CVD) risk, few studies have investigated the relation between diet quality and the risks of CVD and mortality in older adults. This study examined the prospective associations between dietary scores and risk of CVD and all-cause mortality in older British men. A total of 3328 men (aged 60\u201379 y) from the British Regional Heart Study, free from CVD at baseline, were followed up for 11.3 y for CVD and mortality. Baseline food-frequency questionnaire data were used to generate 2 dietary scores: the Healthy Diet Indicator (HDI), based on WHO dietary guidelines, and the Elderly Dietary Index (EDI), based on a Mediterranean-style dietary intake, with higher scores indicating greater compliance with dietary recommendations. Cox proportional hazards regression analyses assessed associations between quartiles of HDI and EDI and risk of all-cause mortality, CVD mortality, CVD events, and coronary heart disease (CHD) events. During follow-up, 933 deaths, 327 CVD deaths, 582 CVD events, and 307 CHD events occurred. Men in the highest compared with the lowest EDI quartile had significantly lower risks of all-cause mortality (HR: 0.75; 95% CI: 0.60, 0.94; P-trend = 0.03), CVD mortality (HR: 0.63; 95% CI: 0.42, 0.94; P-trend = 0.03), and CHD events (HR: 0.66; 95% CI: 0.45, 0.97; P-trend = 0.05) but not CVD events (HR: 0.79; 95% CI: 0.60, 1.05; P-trend = 0.16) after adjustment for sociodemographic, behavioral, and cardiovascular risk factors. The HDI was not significantly associated with any of the outcomes. The EDI appears to be more useful than the HDI for assessing diet quality in relation to CVD and morality risk in older men. Encouraging older adults to adhere to the guidelines inherent in the EDI criteria may have public health benefits.
\n \n\n \n \nLetter to the Editor
\n \n\n \n \nObjectives: To determine whether gout increases risk of incident coronary heart disease (CHD), cerebrovascular (CVD) and peripheral vascular disease (PVD) in a large cohort of primary care patients with gout, since there have been no such large studies in primary care.\r\n\r\nMethods: A retrospective cohort study was performed using data from the Clinical Practice Research Datalink (CPRD). Risk of incident CHD, CVD and PVD was compared in 8386 patients with an incident diagnosis of gout, and 39\u2005766 age, sex and registered general practice-matched controls, all aged over 50 years and with no prior vascular history, in the 10\u2005years following incidence of gout, or matched index date (baseline). Multivariable Cox Regression was used to estimate HRs and covariates included sex and baseline measures of age, Body Mass Index, smoking, alcohol consumption, Charlson comorbidity index, history of hypertension, hyperlipidaemia, chronic kidney disease, statin use and aspirin use.\r\n\r\nResults: Multivariable analysis showed men were at increased risk of any vascular event (HRs (95% CIs)) HR 1.06 (1.01 to 1.12), any CHD HR 1.08 (1.01 to 1.15) and PVD HR 1.18 (1.01 to 1.38), while women were at increased risk of any vascular event, HR 1.25 (1.15 to 1.35), any CHD HR 1.25 (1.12 to 1.39), and PVD 1.89 (1.50 to 2.38)) but not any CVD.\r\n\r\nConclusions: In this cohort of over 50s with gout, female patients with gout were at greatest risk of incident vascular events, even after adjustment for vascular risk factors, despite a higher prevalence of both gout and vascular disease in men. Further research is required to establish the reason for this sex difference.
\n \n\n \n \nObjectives: To validate the performance of a set of risk prediction algorithms developed using the QResearch database, in an independent sample from general practices contributing to the Clinical Research Data Link (CPRD).\r\nSetting: Prospective open cohort study using practices contributing to the CPRD database and practices contributing to the QResearch database.\r\nParticipants The CPRD validation cohort consisted of 3.3 million patients, aged 25\u201399\u2005years registered at 357 general practices between 1 Jan 1998 and 31 July 2012. The validation statistics for QResearch were obtained from the original published papers which used a one-third sample of practices separate to those used to derive the score. A cohort from QResearch was used to compare incidence rates and baseline characteristics and consisted of 6.8 million patients from 753 practices registered between 1 Jan 1998 and until 31 July 2013.\r\nOutcome measures: Incident events relating to seven different risk prediction scores: QRISK2 (cardiovascular disease); QStroke (ischaemic stroke); QDiabetes (type 2 diabetes); QFracture (osteoporotic fracture and hip fracture); QKidney (moderate and severe kidney failure); QThrombosis (venous thromboembolism); QBleed (intracranial bleed and upper gastrointestinal haemorrhage). Measures of discrimination and calibration were calculated.\r\nResults: Overall, the baseline characteristics of the CPRD and QResearch cohorts were similar though QResearch had higher recording levels for ethnicity and family history. The validation statistics for each of the risk prediction scores were very similar in the CPRD cohort compared with the published results from QResearch validation cohorts. For example, in women, the QDiabetes algorithm explained 50% of the variation within CPRD compared with 51% on QResearch and the receiver operator curve value was 0.85 on both databases. The scores were well calibrated in CPRD.\r\nConclusions: Each of the algorithms performed practically as well in the external independent CPRD validation cohorts as they had in the original published QResearch validation cohorts.
\n \n\n \n \nBACKGROUND:\r\nGiven the high prevalence of chronic conditions and multimorbidity in the elderly, there is a need to determine which chronic conditions have the greatest impact on health-related quality of life (HRQL) and identify where additional intervention may be required.\r\nOBJECTIVE:\r\nTo explore the impact of a range of common chronic conditions on HRQL in a community-based population aged 65 years or more in the UK.\r\nMETHODS:\r\nSecondary analysis of data derived from a large (n = 5849) cross-sectional study. HRQL was assessed using the EuroQoL EQ-5D. Multivariable models were used to estimate the relative effect of 15 individual common chronic conditions and combinations of these conditions on HRQL.\r\nRESULTS:\r\nMean age of participants was 74.6 years, 49.2% were male. The mean EQ-5D index score was 0.78 (standard deviation 0.2), range -0.43 to 1.00. Overall, 53% (n = 3078) of the cohort reported problems with pain, 39% (n = 2273) with mobility and 9% (n = 529) with self-care. Multivariate modelling demonstrated that impaired HRQL was significantly associated with 13 of the 15 common chronic conditions studied. Clinically meaningful reductions in EQ-5D index scores were observed for osteoarthritis (-0.081, P = 0.0006), neurological disease (-0.172, P < 0.0001) and depression (-0.269, P < 0.001).\r\nCONCLUSIONS:\r\nThis study quantifies the relative impact of 13 common chronic conditions on HRQL in a UK-based community-dwelling ageing population. Findings indicate that osteoarthritis, depression and neurological disease have a strong clinically important negative effect on HRQL. These findings may help clinical decision making and priority setting for management of individuals with multimorbidity.
\n \n\n \n \nAbstract\r\nBACKGROUND:\r\nAround ten percent of the population have been reported as having Chronic Kidney Disease (CKD), which is associated with increased cardiovascular mortality. Few previous studies have ascertained the chronicity of CKD. In the UK, a payment for performance (P4P) initiative incentivizes CKD (stages 3-5) recognition and management in primary care, but the impact of this has not been assessed.\r\nMETHODS AND FINDINGS:\r\nUsing data from 426 primary care practices (population 2,707,130), the age standardised prevalence of stages 3-5 CKD was identified using two consecutive estimated Glomerular Filtration Rates (eGFRs) seven days apart. Additionally the accuracy of practice CKD registers and the relationship between accurate identification of CKD and the achievement of P4P indicators was determined. Between 2005 and 2009, the prevalence of stages 3-5 CKD increased from 0.3% to 3.9%. In 2009, 30,440 patients (1.1% unadjusted) fulfilled biochemical criteria for CKD but were not on a practice CKD register (uncoded CKD) and 60,705 patients (2.2% unadjusted) were included on a practice CKD register but did not fulfil biochemical criteria (miscoded CKD). For patients with confirmed CKD, inclusion in a practice register was associated with increasing age, male sex, diabetes, hypertension, cardiovascular disease and increasing CKD stage (p<0.0001). Uncoded CKD patients compared to miscoded patients were less likely to achieve performance indicators for blood pressure (OR 0.84, 95% CI 0.82-0.86 p<0.001) or recorded albumin-creatinine ratio (OR 0.73, 0.70-0.76, p<0.001).\r\nCONCLUSIONS:\r\nThe prevalence of stages 3-5 CKD, using two laboratory reported eGFRs, was lower than estimates from previous studies. Clinically significant discrepancies were identified between biochemically defined CKD and appearance on practice registers, with misclassification associated with sub-optimal care for some people with CKD.
\n \n\n \n \nLists of clinical codes are the foundation for research undertaken using electronic medical records (EMRs). If clinical code lists are not available, reviewers are unable to determine the validity of research, full study replication is impossible, researchers are unable to make effective comparisons between studies, and the construction of new code lists is subject to much duplication of effort. Despite this, the publication of clinical codes is rarely if ever a requirement for obtaining grants, validating protocols, or publishing research. In a representative sample of 450 EMR primary research articles indexed on PubMed, we found that only 19 (5.1%) were accompanied by a full set of published clinical codes and 32 (8.6%) stated that code lists were available on request. To help address these problems, we have built an online repository where researchers using EMRs can upload and download lists of clinical codes. The repository will enable clinical researchers to better validate EMR studies, build on previous code lists and compare disease definitions across studies. It will also assist health informaticians in replicating database studies, tracking changes in disease definitions or clinical coding practice through time and sharing clinical code information across platforms and data sources as research objects.
\n \n\n \n \nBACKGROUND AND OBJECTIVE:\r\nWe recently showed that a pharmacist-led information technology-based intervention (PINCER) was significantly more effective in reducing medication errors in general practices than providing simple feedback on errors, with cost per error avoided at \u00a379 (US$131). We aimed to estimate cost effectiveness of the PINCER intervention by combining effectiveness in error reduction and intervention costs with the effect of the individual errors on patient outcomes and healthcare costs, to estimate the effect on costs and QALYs.\r\nMETHODS:\r\nWe developed Markov models for each of six medication errors targeted by PINCER. Clinical event probability, treatment pathway, resource use and costs were extracted from literature and costing tariffs. A composite probabilistic model combined patient-level error models with practice-level error rates and intervention costs from the trial. Cost per extra QALY and cost-effectiveness acceptability curves were generated from the perspective of NHS England, with a 5-year time horizon.\r\nRESULTS:\r\nThe PINCER intervention generated \u00a32,679 less cost and 0.81 more QALYs per practice [incremental cost-effectiveness ratio (ICER): -\u00a33,037 per QALY] in the deterministic analysis. In the probabilistic analysis, PINCER generated 0.001 extra QALYs per practice compared with simple feedback, at \u00a34.20 less per practice. Despite this extremely small set of differences in costs and outcomes, PINCER dominated simple feedback with a mean ICER of -\u00a33,936 (standard error \u00a32,970). At a ceiling 'willingness-to-pay' of \u00a320,000/QALY, PINCER reaches 59 % probability of being cost effective.\r\nCONCLUSIONS:\r\nPINCER produced marginal health gain at slightly reduced overall cost. Results are uncertain due to the poor quality of data to inform the effect of avoiding errors.
\n \n\n \n \nOver the past 10 years our team has been involved in a wide range of studies of prescribing in general practice, but one we feel that has really made a difference is the PRACtICe study, which was funded by the General Medical Council.1,2 In this study we took a sample of 15 general practices across England and did a retrospective review of the clinical records of a random sample of over 1700 patients, and over 6000 prescription items. Using a definition of error that focused on clinically important problems,1 we found that one in 20 (5%) prescription items was associated with one or more prescribing or monitoring errors, and that one in 550 prescription items contained what we regarded as a severe error1 (with seriously inadequate monitoring of patients taking warfarin the biggest culprit). We found that per prescription item, errors were more common in children and older people, and that nearly half of patients receiving >10 items over the course of a year were the recipients of an error. The commonest types of error related to incomplete information on the prescription, dose-strength errors, and timing-frequency errors.
\n \n\n \n \nBackground Medication error is an important contributor to patient morbidity and mortality and is associated with inadequate patient safety measures. However, prescribing-safety tools specifically designed for use in general practice are lacking.\r\n\r\nAim To identify and update a set of prescribing-safety indicators for assessing the safety of prescribing in general practice, and to estimate the risk of harm to patients associated with each indicator.\r\n\r\nDesign and setting RAND/UCLA consensus development of indicators in UK general practice.\r\n\r\nMethod Prescribing indicators were identified from a systematic review and previous consensus exercise. The RAND Appropriateness Method was used to further identify and develop the indicators with an electronic-Delphi method used to rate the risk associated with them. Twelve GPs from all the countries of the UK participated in the RAND exercise, with 11 GPs rating risk using the electronic-Delphi approach.\r\n\r\nResults Fifty-six prescribing-safety indicators were considered appropriate for inclusion (overall panel median rating of 7\u20139, with agreement). These indicators cover hazardous prescribing across a range of therapeutic indications, hazardous drug\u2013drug combinations and inadequate laboratory test monitoring. Twenty-three (41%) of these indicators were considered high risk or extreme risk by 80% or more of the participants.\r\n\r\nConclusion This study identified a set of 56 indicators that were considered, by a panel of GPs, to be appropriate for assessing the safety of GP prescribing. Twenty-three of these indicators were considered to be associated with high or extreme risk to patients and should be the focus of efforts to improve patient safety.
\n \n\n \n \nBackground\r\nThe majority of patient contacts occur in general practice but general practice patient safety has been poorly described and under-researched to date compared to hospital settings. Our objective was to produce a set of patient safety tools and indicators that can be used in general practices in any healthcare setting and develop a \u2018toolkit\u2019 of feasible patient safety measures for general practices in England.\r\n\r\nMethods\r\nA RAND/UCLA Appropriateness Method exercise was conducted with a panel of international experts in general practice patient safety. Statements were developed from an extensive systematic literature review of patient safety in general practice. We used standard RAND/UCLA Appropriateness Method rating methods to identify necessary items for assessing patient safety in general practice, framed in terms of the Structure-Process-Outcome taxonomy. Items were included in the toolkit if they received an overall panel median score of \u22657 with agreement (no more than two panel members rating the statement outside a 3-point distribution around the median).\r\n\r\nResults\r\nOf 205 identified statements, the panel rated 101 as necessary for assessing the safety of general practices. Of these 101 statements, 73 covered structures or organisational issues, 22 addressed processes and 6 focused on outcomes.\r\n\r\nConclusions\r\nWe developed and tested tools that can lead to interventions to improve safety outcomes in general practice. This paper reports the first attempt to systematically develop a patient safety toolkit for general practice, which has the potential to improve safety, cost effectiveness and patient experience, in any healthcare system.
\n \n\n \n \nObjective To conduct a fully independent and external validation of a research study based on one electronic health record database, using a different electronic database sampling the same population.\r\n\r\nDesign Using the Clinical Practice Research Datalink (CPRD), we replicated a published investigation into the effects of statins in patients with ischaemic heart disease (IHD) by a different research team using QResearch. We replicated the original methods and analysed all-cause mortality using: (1) a cohort analysis and (2) a case-control analysis nested within the full cohort.\r\n\r\nSetting Electronic health record databases containing longitudinal patient consultation data from large numbers of general practices distributed throughout the UK.\r\n\r\nParticipants CPRD data for 34\u2005925 patients with IHD from 224 general practices, compared to previously published results from QResearch for 13\u2005029 patients from 89 general practices. The study period was from January 1996 to December 2003.\r\n\r\nResults We successfully replicated the methods of the original study very closely. In a cohort analysis, risk of death was lower by 55% for patients on statins, compared with 53% for QResearch (adjusted HR 0.45, 95% CI 0.40 to 0.50; vs 0.47, 95% CI 0.41 to 0.53). In case-control analyses, patients on statins had a 31% lower odds of death, compared with 39% for QResearch (adjusted OR 0.69, 95% CI 0.63 to 0.75; vs OR 0.61, 95% CI 0.52 to 0.72). Results were also close for individual statins.\r\n\r\nConclusions Database differences in population characteristics and in data definitions, recording, quality and completeness had a minimal impact on key statistical outputs. The results uphold the validity of research using CPRD and QResearch by providing independent evidence that both datasets produce very similar estimates of treatment effect, leading to the same clinical and policy decisions. Together with other non-independent replication studies, there is a nascent body of evidence for wider validity.
\n \n\n \n \nLists of clinical codes are the foundation for research undertaken using electronic medical records (EMRs). If clinical code lists are not available, reviewers are unable to determine the validity of research, full study replication is impossible, researchers are unable to make effective comparisons between studies, and the construction of new code lists is subject to much duplication of effort. Despite this, the publication of clinical codes is rarely if ever a requirement for obtaining grants, validating protocols, or publishing research. In a representative sample of 450 EMR primary research articles indexed on PubMed, we found that only 19 (5.1%) were accompanied by a full set of published clinical codes and 32 (8.6%) stated that code lists were available on request. To help address these problems, we have built an online repository where researchers using EMRs can upload and download lists of clinical codes. The repository will enable clinical researchers to better validate EMR studies, build on previous code lists and compare disease definitions across studies. It will also assist health informaticians in replicating database studies, tracking changes in disease definitions or clinical coding practice through time and sharing clinical code information across platforms and data sources as research objects.
\n \n\n \n \n