Search

BackgroundThe harmonization and standardization of digital medical information for research purposes is a challenging and ongoing collaborative effort. Current research data repositories typically require extensive efforts in harmonizing and transforming original clinical data. The Fast Healthcare Interoperability Resources (FHIR) format was designed primarily to represent clinical processes; therefore, it closely resembles the clinical data model and is more widely available across modern electronic health records. However, no common standardized data format is directly suitable for statistical analyses, and data need to be preprocessed before statistical analysis. ObjectiveThis study aimed to elucidate how FHIR data can be queried directly with a preprocessing service and be used for statistical analyses. MethodsWe propose that the binary JavaScript Object Notation format of the PostgreSQL (PSQL) open source database is suitable for not only storing FHIR data, but also extending it with preprocessing and filtering services, which directly transform data stored in FHIR format into prepared data subsets for statistical analysis. We specified an interface for this preprocessor, implemented and deployed it at University Hospital Erlangen-Nürnberg, generated 3 sample data sets, and analyzed the available data. ResultsWe imported real-world patient data from 2016 to 2018 into a standard PSQL database, generating a dataset of approximately 35.5 million FHIR resources, including “Patient,” “Encounter,” “Condition” (diagnoses specified using International Classification of Diseases codes), “Procedure,” and “Observation” (laboratory test results). We then integrated the developed preprocessing service with the PSQL database and the locally installed web-based KETOS analysis platform. Advanced statistical analyses were feasible using the developed framework using 3 clinically relevant scenarios (data-driven establishment of hemoglobin reference intervals, assessment of anemia prevalence in patients with cancer, and investigation of the adverse effects of drugs). ConclusionsThis study shows how the standard open source database PSQL can be used to store FHIR data and be integrated with a specifically developed preprocessing and analysis framework. This enables dataset generation with advanced medical criteria and the integration of subsequent statistical analysis. The web-based preprocessing service can be deployed locally at the hospital level, protecting patients’ privacy while being integrated with existing open source data analysis tools currently being developed across Germany.

Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiological dynamics with age in many biomarkers. Creation of reference intervals using conventional approaches requires the sampling of healthy individuals, which is opposed by ethical and practical considerations in children, due to the need for a large number of blood samples from healthy children of all ages, including neonates and young infants. This limits the availability and quality of pediatric reference intervals, and ultimately negatively impacts pediatric clinical decision-making. Data mining approaches use laboratory test results and clinical information from hospital information systems to create reference intervals. The extensive number of available test results from laboratory information systems and advanced statistical methods enable the creation of pediatric reference intervals with an unprecedented age-related accuracy for children of all ages. Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals.

Introduction: Neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and lymphocyte‐to‐monocyte ratio (LMR) are emerging biomarkers for systemic inflammation and have been shown to predict morbidity and mortality for several diseases. However, lack of pediatric reference intervals (RIs) prevents their comprehensive use in patient care and medical research. Material and Methods: We calculated reference intervals and corresponding confidence intervals for NLR, PLR, and LMR from birth to 18 years using a data‐mining approach: We analyzed 232 746 blood counts from 60 685 patients performed during patient care and excluded patients with elevated C‐reactive protein and procalcitonin. Test results were separated according to age and sex, and the distribution of physiological ratios was estimated using an indirect approach (refineR). Additionally, we investigated the ratios' diagnostic benefit for different inflammatory diseases (acute appendicitis, asthma, Bell's palsy, Henoch‐Schonlein purpura, and cystic fibrosis) using the newly obtained reference intervals. Results: We estimated age‐ and sex‐specific reference intervals from birth to adulthood for NLR, PLR, and LMR. Analyses in pediatric inflammatory diseases showed that PLR and LMR were poor markers to detect the examined inflammatory diseases, while NLR was significantly increased in patients with appendicitis and asthma. Conclusion: We provide pediatric reference intervals for NLR, PLR, and LMR to improve the interpretation of these biomarkers in children. [ABSTRACT FROM AUTHOR]

Objective To establish reference intervals (RIs) for alkaline phosphatase (ALP) levels in Pakistani children using an indirect data mining approach. Methods ALP levels analyzed on a Siemens Advia 1800 analyzer using the International Federation of Clinical Chemistry's photometric method for both inpatients and outpatients aged 1 to 17 years between January 2013 and December 2017, including patients from intensive care units and specialty units, were retrieved. RIs were calculated using a previously validated indirect algorithm developed by the German Society of Clinical Chemistry and Laboratory Medicine's Working Group on Guide Limits. Results From a total of 108,845 results, after the exclusion of patients with multiple specimens, RIs were calculated for 24,628 males and 18,083 females with stratification into fine-grained age groups. These RIs demonstrate the complex age- and sex-related ALP dynamics occurring during physiological development. Conclusion The population-specific RIs serve to allow an accurate understanding of the fluctuations in analyte activity with increasing age and to support clinical decision making. [ABSTRACT FROM AUTHOR]

Acute lymphoblastic leukemia is the most common malignancy in childhood and requires prolonged oral maintenance chemotherapy to prevent disease relapse after remission induction with intensive intravenous chemotherapy. In maintenance therapy, drug doses of 6-mercaptopurine (6-MP) and methotrexate (MTX) are adjusted to achieve sustained antileukemic activity without excessive myelosuppression. However, uncertainty exists regarding timing and extent of drug dose responses and optimal dose adaptation strategies. We propose a novel comprehensive mathematical model for 6-MP and MTX pharmacokinetics, pharmacodynamics and myelosuppression in acute lymphoblastic maintenance therapy. We personalize and cross-validate the mathematical model using clinical data and propose a real-time algorithm to predict chemotherapy responses with a clinical decision support system as a potential future application. [ABSTRACT FROM AUTHOR]