Serum sodium level variability as a prognosticator in older adults.

Our aim was to explore biological variation of serum sodium levels as a method of quantifying health risk in older adults. We investigated whether dynamic changes in serum sodium levels could provide additional prognostic information to standard predictors of mortality in older people. Analysis of routinely collected clinical datasets containing information on demographics, hospitalisation, biochemistry, haematology and physical function for Dundee in-patient rehabilitation services, between 1999 and 2011. Older people admitted to inpatient rehabilitation following an acute medical or surgical hospitalisation. Five dynamic measures of sodium levels homeostasis - minimum, maximum, standard deviation, and minimum and maximum deviation from mean - were derived for each individual, using biochemistry data from the year preceding their rehabilitation discharge. Cox regression models tested for associations with time to death. Covariates included age, sex, discharge Barthel score, co-morbid diagnoses, haemoglobin, albumin and eGFR. 3021 patients were included (mean age 84 years, 1776 (58.8%) females). 1651 (54.7%) patients experienced hyponatraemia and 446 (14.8%) became hypernatraemic. Mean sodium was correlated with all mean, minimum and SD of sodium. Kaplan-Meier survival curves showed that those without sodium perturbations had the best mortality outcomes, whilst those with both hyponatremia and hypernatremia had the worst. Multivariate Cox regression showed that standard deviation and hypernatraemia were significant predictors of death in non-adjusted models, but not fully adjusted models. All dynamic measures of dysnatraemia were associated with increased mortality risk, but failed to add predictive value to established static measures after adjusting for covariates.

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.

BACKGROUND: Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. OBJECTIVE: In this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data. METHODS: This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records. RESULTS: We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature. CONCLUSIONS: There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Association between kidney function, rehabilitation outcome, and survival in older patients discharged from inpatient rehabilitation.

BACKGROUND: Chronic kidney disease (CKD) is common in older people, but it is unclear if it affects survival and rehabilitation outcomes independent of comorbid conditions and physical function in this population. STUDY DESIGN: Cohort analysis of prospective, routinely collected, linked clinical data sets. SETTING & PARTICIPANTS: Patients discharged from a single inpatient geriatric rehabilitation center over a 12-year period. PREDICTORS: Admission estimated glomerular filtration rate (eGFR) category as a predictor of improvement in the 20-point Barthel score (activities of daily living measure) during rehabilitation; discharge eGFR category and Barthel score as predictors of survival postdischarge. OUTCOMES: Survival postdischarge was modeled using Cox regression analyses, unadjusted and adjusted for age, sex, morbidities (ischemic heart disease, chronic obstructive pulmonary disease, stroke, diabetes, and heart failure), Barthel score and eGFR category on discharge, and serum calcium, hemoglobin, and albumin levels. The effect of admission eGFR category on change in Barthel score during admission was modeled using analysis of covariance, adjusted for admission, Barthel score, and comorbid conditions. RESULTS: 3,012 patients were included; mean age, 84 years. 2,394 patients died during a mean follow-up of 8.3 years. Compared with patients with eGFR of 60 to 89mL/min/1.73m(2), adjusted HRs for death were 1.26 (95% CI, 1.13-1.40), 1.45 (95% CI, 1.29-1.63), and 1.68 (95% CI, 1.42-1.99) for eGFR categories of 45 to 59, 30 to 44, and <30mL/min/1.73m(2), respectively. The relationship between discharge Barthel score and survival was similar within each discharge eGFR category (HRs of 0.95, 0.93, 0.92, 0.95, and 0.90 per Barthel score point within eGFR categories of ≥90, 60-89, 45-59, 30-44, and <30mL/min/1.73m(2); P for interaction = 0.2). Similar improvements in Barthel score between admission and discharge were seen for each admission eGFR category. LIMITATIONS: Single-center study using routinely collected clinical data. CONCLUSIONS: eGFR category and Barthel score are independent risk markers for survival in older rehabilitation patients, but advanced CKD does not preclude successful rehabilitation.

eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial.

OBJECTIVE: The Learning Health System (LHS) requires integration of research into routine practice. 'eSource' or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a solution to the rising costs of research. We aimed to create and validate an eSource solution that would be readily extensible as part of a LHS. MATERIALS AND METHODS: The EU FP7 TRANSFoRm project's approach is based on dual modelling, using the Clinical Research Information Model (CRIM) and the Clinical Data Integration Model of meaning (CDIM) to bridge the gap between clinical and research data structures, using the CDISC Operational Data Model (ODM) standard. Validation against GCP requirements was conducted in a clinical site, and a cluster randomised evaluation by site nested into a live clinical trial. RESULTS: Using the form definition element of ODM, we linked precisely modelled data queries to data elements, constrained against CDIM concepts, to enable automated patient identification for specific protocols and pre-population of electronic case report forms (e-CRF). Both control and eSource sites recruited better than expected with no significant difference. Completeness of clinical forms was significantly improved by eSource, but Patient Related Outcome Measures (PROMs) were less well completed on smartphones than paper in this population. DISCUSSION: The TRANSFoRm approach provides an ontologically-based approach to eSource in a low-resource, heterogeneous, highly distributed environment, that allows precise prospective mapping of data elements in the EHR. CONCLUSION: Further studies using this approach to CDISC should optimise the delivery of PROMS, whilst building a sustainable infrastructure for eSource with research networks, trials units and EHR vendors.

Slower Decline in C-Reactive Protein after an Inflammatory Insult Is Associated with Longer Survival in Older Hospitalised Patients.

BACKGROUND: Enhancing biological resilience may offer a novel way to prevent and ameliorate disease in older patients. We investigated whether changes in C-reactive protein (CRP), as a dynamic marker of the acute inflammatory response to diverse stressors, may provide a way to operationalize the concept of resilience in older adults. We tested this hypothesis by examining whether such changes could predict prognosis by identifying which individuals are at greater risk of 6-month mortality. METHODS: Analysis of prospective, routinely collected datasets containing data on hospitalization, clinical chemistry and rehabilitation outcomes for rehabilitation inpatients between 1999 and 2011. Maximum CRP response during acute illness and CRP recovery indices (time and slope of CRP decay to half maximum, and to <50mg/L if peak values were greater than 50mg/L) was derived from biochemistry data. 6-month survival plots were conducted on quartiles of CRP recovery indices. Cox proportional hazards models were used to test univariate and multivariate predictors of 6-month mortality. Covariates included age, sex, number of medications, serum calcium, haemoglobin level, renal function, and the presence of previous myocardial infarction, stroke, chronic heart failure, COPD and diabetes. RESULTS: 3723 patients, mean age 84 years, were included. 1535 (41%) were male and 733 (20%) died during six-month follow-up. The lower an individual's peak CRP reading, and the longer the time taken for their CRP to fall, the better their 6-month survival. The time for CRP to reach half of its maximum value was the best dynamic CRP index of survival (HR 0.93 per week, 95% CI 0.89 to 0.98; p = 0.004); this remained significant even after adjustment for maximum CRP level and covariates listed above. CONCLUSION: CRP recovery indices are associated with survival in older people; further work is required to explain differences in physiology between patients with a fast and slow CRP recovery.

Cardiovascular risk factors associated with the metabolic syndrome are more prevalent in people reporting chronic pain: results from a cross-sectional general population study.

To explore whether chronic pain is associated with cardiovascular risk factors and identify whether increased distribution or intensity of pain is associated with cardiovascular risk, participants in Generation Scotland: The Scottish Family Health study completed pain questionnaires recording the following: presence of chronic pain, distribution of pain, and intensity of chronic pain. Blood pressure, lipids, blood glucose, smoking history, waist-hip ratio, and body mass index were recorded; Framingham 10-year coronary heart disease (CHD) risk scores were calculated and a diagnosis of metabolic syndrome derived. Associations between chronic pain and cardiovascular risk were explored. Of 13,328 participants, 1100 (8.3%) had high CHD risk. Chronic pain was reported by 5209 (39%), 1294 (9.7%) reported widespread chronic pain, and 707 (5.3%) reported high-intensity chronic pain. In age- and gender-adjusted analyses, chronic pain was associated with elevated CHD risk scores (odds ratio 1.11, 95% confidence interval 1.01-1.23) and the metabolic syndrome (odds ratio 1.42, 95% confidence interval 1.24-1.62). Multivariate analyses identified dyslipidaemia, age, gender, smoking, obesity, and high waist-hip ratio as independently associated with chronic pain. Within the chronic pain subgroup, widespread pain did not confer any additional cardiovascular disease risk. However, cardiovascular disease risk factors contributing to metabolic syndrome were more prevalent in those reporting high-intensity chronic pain. This large population-based study has demonstrated that chronic pain, and in particular high-intensity chronic pain, is associated with an increased prevalence of cardiovascular risk factors and metabolic syndrome. The 10-year CHD risk score and metabolic syndrome correlate well with increased pain intensity, but not with widespread pain.

A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.

OBJECTIVE: Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method. MATERIALS AND METHODS: We developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures. RESULTS: Our unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project. CONCLUSIONS: We present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.