TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse.

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.

Making EHRs Trustable: A Quality Analysis of EHR-Derived Datasets for COVID-19 Research.

One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried out.

Building an i2b2-Based Population Repository for COVID-19 Research.

Reuse of Electronic Health Records (EHRs) for specific diseases such as COVID-19 requires data to be recorded and persisted according to international standards. Since the beginning of the COVID-19 pandemic, Hospital Universitario 12 de Octubre (H12O) evolved its EHRs: it identified, modeled and standardized the concepts related to this new disease in an agile, flexible and staged way. Thus, data from more than 200,000 COVID-19 cases were extracted, transformed, and loaded into an i2b2 repository. This effort allowed H12O to share data with worldwide networks such as the TriNetX platform and the 4CE Consortium.

Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models.

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.

Reconstructing the patient's natural history from electronic health records.

The automatic extraction of a patient's natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient's medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information. In this paper, we introduce a new Natural Language Processing (NLP) framework, capable of extracting the aforementioned elements from EHRs written in Spanish using rule-based methods. We focus on building medical timelines, which include disease diagnosis and its progression over time. By using a large dataset of EHRs comprising information about patients suffering from lung cancer, we show that our framework has an adequate level of performance by correctly building the timeline for 843 patients from a pool of 989 patients, achieving a precision of 0.852.

Impacto del primer año de tratamiento sustitutivo renal en la hospitalización de una comunidad autónoma / Impact of first year renal replacement therapy on the hospital admissions of a regional public health system

INTRODUCCIÓN Y OBJETIVOS: La enfermedad renal crónica tiene una alta prevalencia y coste, así como un mayor riesgo de ingreso. Disponemos de registros públicos y obligatorios, pero no hay referencias recientes para estimar el impacto que el tratamiento sustitutivo renal (TSR) tiene en la actividad hospitalaria. MÉTODOS: Tras las autorizaciones pertinentes, hemos integrado las bases de datos REMER (2013-2014) y CMBDH (2013-2015) para analizar la actividad hospitalaria durante el primer año de TSR. RESULTADOS: Un total de 767 pacientes iniciaron TSR en los 7 hospitales de tercer nivel de la Comunidad de Madrid. Más de una tercera parte lo hicieron de forma no programada durante un ingreso. Este inicio es más frecuente en HD que en DP, pero existen diferencias clínicas relevantes en edad y en comorbilidad. Descartando este primer episodio, casi el 60% de pacientes ingresan durante el primer año. La tasa de ingreso es de 1,2 ingresos/paciente, más alta en HD que en TX y DP; la estancia media es de 8,6días. El coste agregado de los ingresos del primer año es de 12.006 €/paciente. Nuestro análisis asegura la inclusión exhaustiva de todos los episodios y la estimación precisa de costes. CONCLUSIONES: El impacto del TSR en la actividad hospitalaria ha sido infraestimado y es una parte importante del coste global del TSR. Los resultados de la literatura internacional no pueden extrapolarse a nuestro país por las diferencias en el modelo sanitario y perfil de paciente. La integración de bases de datos clínicas es técnicamente viable y podría abrir una vía inmensa de información que solo requiere apoyo institucional para su desarrollo

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 1.2 admissions/patient, higher in HD than in TX or PD; the mean length of stay was 8.6 days. 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

Impact of first year renal replacement therapy on the hospital admissions of a regional public health system. / Impacto del primer año de tratamiento sustitutivo renal en la hospitalización de una comunidad autónoma.

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.

Profiling Lung Cancer Patients Using Electronic Health Records.

If Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We here report on a first integration of an NLP framework for the analysis of clinical records of lung cancer patients making use of a telephone assistance service of a major Spanish hospital. We specifically show how some relevant data, about patient demographics and health condition, can be extracted; and how some relevant analyses can be performed, aimed at improving the usefulness of the service. We thus demonstrate that the use of EHR texts, and their integration inside a data analysis framework, is technically feasible and worth of further study.

Concordance between circulating tumor cells and clinical status during follow-up in anaplastic lymphoma kinase (ALK) non-small-cell lung cancer patients.

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.

Dynamic circulating tumor DNA quantificaton for the individualization of non-small-cell lung cancer patients treatment.

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.