Search

Hip fracture is a condition associated with ageing and frailty, with an associated prevalence of 7 per 10000 population in Spain. Evidence suggests that factors in the healthcare process can influence clinical outcomes, so the creation of a national registry is an opportunity to monitor and improve this process. In this regards, Electronic Health Record (EHR) can provide a large amount of data, that can be used to populate the Spanish National Hip Fracture Registry (RNFC, by its acronym in Spanish). However, this reuse of the EHR requires a prior effort in modelling and standardization to build the extraction, transformation, and loading (ETL) processes in a flexible, transparent, and scalable manner. In this work, a robust EHR reuse methodology is implemented to obtain EHR-derived data for the RNFC. The main result of this work was the design and implementation of an EHR data reuse methodology, which was able to load 1279 hip fracture cases and almost 68% of the required concepts from the RNFC.

Background: Hemovigilance (HV) is usually based on voluntary reports (passive HV). Our aim is to ascertain credible incidence, severity, and mortality of transfusion-associated adverse events (TAAEs) using an active HV program., Study Design and Methods: Prospective cohort study to estimate transfusion risk after 46,488 transfusions in 5830 patients, using an active HV program with follow-up within the first 24 h after transfusion. We compared these results to those with the previously established passive HV program during the same 30 months of the study. We explored factors associated with the occurrence of TAAEs using generalized estimating equations models., Results: With the active HV program TAAEs incidence was 57.3 (95% CI, 50.5-64.2) and mortality 1.1 (95% CI, 0.13-2.01) per 10,000 transfusions. Incidence with the new surveillance model was 14.0 times higher than with the passive. Most events occurred when transfusions had already finished (60.2%); especially pulmonary events (80.4%). Three out of five deaths and 50.3% of severe TAAEs were pulmonary. In the multivariate analysis surgical patients had half TAAEs risk when compared to medical patients (OR, 0.53; 95% CI, 0.34-0.78) and women had nearly twice the risk of a pulmonary event compared to men (OR, 1.84; 95% CI, 1.03-3.32). Patient's age, blood component type, or blood component shelf-life were unrelated to TAAEs risk., Discussion: Active hemovigilance programs provide additional data which may lead to better recognition and understanding of TAAEs and their frequency and severity., (© 2023 The Authors. Transfusion published by Wiley Periodicals LLC on behalf of AABB.)

Background: During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable., Objectives: This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization., Methods: The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML., Results: First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined., Conclusions: This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization., Objective: This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time., Material and Methods: The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020., Results: An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts., Conclusion: This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Our primary goal was to study the use of outpatient attendances by lung cancer patients in Hospital Universitario Puerta de Hierro Majadahonda (HUPHM), Spain, by leveraging our Electronic Patient Record (EPR) and structured clinical registry of lung cancer cases as well as assessing current Data Science methods and tools., Methods/patients: We applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) to integrate and analyze activity data extracted from the EPR (9.3 million records) and clinical data of lung cancer patients from a previous registry that was curated into a new, structured database based on REDCap. We have described and quantified factors with an influence in outpatient care use from univariate and multivariate points of view (through Poisson and negative binomial regression)., Results: Three cycles of CRISP-DM were performed resulting in a curated database of 522 lung cancer patients with 133 variables which generated 43,197 outpatient visits and tests, 1538 ER visits and 753 inpatient admissions. Stage and ECOG-PS at diagnosis and Charlson Comorbidity Index were major contributors to healthcare use. We also found that the patients' pattern of healthcare use (even before diagnosis), the existence of a history of cancer in first-grade relatives, smoking habits, or even age at diagnosis, could play a relevant role., Conclusions: Integrating activity data from EPR and clinical structured data from lung cancer patients and applying CRISP-DM has allowed us to describe healthcare use in connection with clinical variables that could be used to plan resources and improve quality of care.

Introduction and Objectives: Chronic kidney disease has a high prevalence and economic impact, and an increased risk of hospitalization. Although there are public regional and country registries, we have not found references to estimate the impact of renal replacement therapy (RRT) on hospital admissions., Methods: We obtained authorization from the ethics committee and health authorities to integrate the REMER [Madrid Kidney Disease Registry] (2013-2014) and Minimum Basic Data Set (2013-2015) databases and to analyze the admissions during the first year of RRT., Results: 767 patients started RRT in all the hospitals of our region across all RRT modalities. More than a third of the patients start dialysis during a hospital admission. This unplanned start, more common in HD than PD, shows relevant differences in patient profile or admission characteristics. Without considering this initial episode, almost 60% of patients were admitted during their first year. The hospitalization rate was 1.2admissions/patient, higher in HD than in TX or PD; the mean length of stay was 8.6days. The estimated cost of admissions during the first year is €12,006/patient. Our analysis ensures the exhaustive inclusion of all episodes and accurate estimation based on the discharge form., Conclusion: The impact of RRT on hospitals has been underestimated and is very relevant when calculating the total cost of RRT. Results from other countries cannot be extrapolated due to differences in the health system and patient profile. The integration of clinical databases could open up an opportunity that needs only institutional support for its development., (Copyright © 2019 Sociedad Española de Nefrología. Published by Elsevier España, S.L.U. All rights reserved.)

Background: Liquid biopsy has evolved from being a promising line to becoming a validated approach for biomarker testing. However, its utility for individualization of therapy has been scarcely reported. In this study, we show how monitoring levels of EGFR mutation in plasma can be useful for the individualization of treatment., Results: Longitudinal EGFR mutation levels in plasma always correlated with tumor response ascertained by RECIST criteria. Moreover, decreasing EGFR mutation levels were detected in all patients benefiting from locoregional radiotherapy, whereas the opposite occurred when a patient progressed soon after radiotherapy treatment. Similarly, increasing EGFR mutation levels anticipated disease progression after TKI dose reduction, discontinuation of treatment, or reduced bioavailability due to drug interactions. In addition, EGFR mutation levels were useful to monitor treatment outcome of new therapies and constituted a decisive factor when the clinical situation of the patient did not correlate with responses ascertained by radiologist. Finally, our results indicate that cancer associated body fluids (pleural, pericardial or cerebrospinal fluid) are certainly a suitable source for biomarker testing that can extend EGFR mutation detection to biofluids other than blood., Materials and Methods: A total of 180 serial plasma samples from 18 non-small-cell lung cancer patients who carried an activating EGFR mutation were investigated by digital PCR., Conclusions: Monitoring levels of EGFR mutation in plasma allows resolving doubts that frequently arise in daily clinical practice and constitutes a major step towards achieving personalized medicine., Competing Interests: CONFLICTS OF INTEREST The authors have declare no conflicts of interest.

Background: The identification of anaplastic lymphoma kinase (ALK) rearrangements is found in approximately 5% of non-small-cell lung cancers (NSCLCs). However, the development of liquid biopsies as a diagnostic tool is less developed in these cases. This study investigates the use of CTCs during treatment, together with an extended follow-up to correlate with clinical evolution., Patients and Methods: A total of 13 patients out of a cohort of 212 patients with lung adenocarcinoma, presented ALK rearrangements (6%) confirmed by tumor biopsy. A total of 60 serial blood samples were collected from these patients who were prospectively enrolled in the study., Results: All patients had a positive CTC count at baseline (mean = 3). The median follow-up was 9 months (range 1-17 months). Three patients underwent surgery and their CTC counts decreased after the procedure but still remained detectable. After radiotherapy, 3 cases showed an average decrease of 5 CTCs. A total of 6 patients were treated with ALK inhibitors and a partial response was observed in 3 of them, who also presented decreased CTC counts. The other 3 patients presented primary resistance, and their CTC counts were higher than those obtained prior to progression., Conclusion: We believe that the use of CTCs for dynamic monitoring of NSCLC with ALK rearrangement and to detect disease persistence or recurrence may be a reliable technique. CTC counts may also have potential use to monitor the efficacy of ALK inhibitors, facilitating detection of resistance to treatment., Competing Interests: CONFLICTS OF INTEREST The authors have no potential conflicts of interest to disclose.