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Objectives: To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015., Design: Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording., Setting: 400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES)., Main Outcomes: The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45)., Results: Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines., Conclusions: Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

This is the second in a series of two papers regarding the construction of data quality (DQ) assured repositories, based on population data from Electronic Health Records (EHR), for the reuse of information on infant feeding from birth until the age of two. This second paper describes the application of the computational process of constructing the first quality-assured repository for the reuse of information on infant feeding in the perinatal period, with the aim of studying relevant questions from the Baby Friendly Hospital Initiative (BFHI) and monitoring its deployment in our hospital. The construction of the repository was carried out using 13 semi-automated procedures to assess, recover or discard clinical data. The initial information consisted of perinatal forms from EHR related to 2048 births (Facts of Study, FoS) between 2009 and 2011, with a total of 433,308 observations of 223 variables. DQ was measured before and after the procedures using metrics related to eight quality dimensions: predictive value, correctness, duplication, consistency, completeness, contextualization, temporal-stability, and spatial-stability. Once the predictive variables were selected and DQ was assured, the final repository consisted of 1925 births, 107,529 observations and 73 quality-assured variables. The amount of discarded observations mainly corresponds to observations of non-predictive variables (52.90%) and the impact of the de-duplication process (20.58%) with respect to the total input data. Seven out of thirteen procedures achieved 100% of valid births, observations and variables. Moreover, 89% of births and ~98% of observations were consistent according to the experts׳ criteria. A multidisciplinary approach along with the quantification of DQ has allowed us to construct the first repository about infant feeding in the perinatal period based on EHR population data., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

This is the first paper of a series of two regarding the construction of data quality (DQ) assured repositories for the reuse of information on infant feeding from birth until two years old. This first paper justifies the need for such repositories and describes the design of a process to construct them from Electronic Health Records (EHR). As a result, Part 1 proposes a computational process to obtain quality-assured datasets represented by a canonical structure extracted from raw data from multiple EHR. For this, 13 steps were defined to ensure the harmonization, standardization, completion, de-duplication, and consistency of the dataset content. Moreover, the quality of the input and output data for each of these steps is controlled according to eight DQ dimensions: predictive value, correctness, duplication, consistency, completeness, contextualization, temporal-stability and spatial-stability. The second paper of the series will describe the application of this computational process to construct the first quality-assured repository for the reuse of information on infant feeding in the perinatal period aimed at the monitoring of clinical activities and research., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Low biomedical Data Quality (DQ) leads into poor decisions which may affect the care process or the result of evidence-based studies. Most of the current approaches for DQ leave unattended the shifting behaviour of data underlying concepts and its relation to DQ. There is also no agreement on a common set of DQ dimensions and how they interact and relate to these shifts. In this paper we propose an organization of biomedical DQ assessment based on these concepts, identifying characteristics and requirements which will facilitate future research. As a result, we define the Data Quality Vector compiling a unified set of DQ dimensions (completeness, consistency, duplicity, correctness, timeliness, spatial stability, contextualization, predictive value and reliability), as the foundations to the further development of DQ assessment algorithms and platforms.

Object: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases., Materials and Methods: Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed., Results: Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns., Conclusion: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T (1)H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.