Accuracy of VO2 estimation according to the widely used Krakau formula for the prediction of cardiac output.

BACKGROUND: Invasive cardiac output (CO) is measured with the thermodilution (TD) or the indirect Fick method (iFM) in right heart catheterization (RHC). The iFM estimates CO using approximation formulas for oxygen consumption ([Formula: see text]O2), but there are significant discrepancies (> 20%) between both methods. Although regularly applied, the formula proposed by Krakau has not been validated. We compared the CO discrepancies between the Krakau formula with the reference (TD) and three established formulas and investigated whether alterations assessed in cardiac magnetic resonance imaging (CMR) determined the extent of the deviations. METHODS: This retrospective study included 188 patients aged 63 ± 14 years (30% women) receiving both CMR and RHC. The CO was measured with TD or with the iFM using the formulas by Krakau, LaFarge, Dehmer, and Bergstra for [Formula: see text]O2 estimation (iFM-K/-L/-D/-B). Percentage errors were calculated as twice the standard deviation of the difference between two CO methods divided by their means; a cut-off of < 30% was regarded as acceptable. The iFM and TD-derived CO ratio was built, and deviations > 20% were counted. Logistic regression analyses were performed to identify determinants of a deviation of > 20%. RESULTS: The TD-derived CO (5.5 ± 1.7 L/min) was significantly different from all iFM (K: 4.8 ± 1.6, L: 4.3 ± 1.6; D: 4.8 ± 1.5 L/min; B: 5.4 ± 1.8 L/min all p < 0.05). The iFM-K-CO differed from all methods (p < 0.001) except iFM­D (p = 0.19). Percentage errors between TD-CO and iFM-K/-L/-D/-B were all beyond the acceptance limit (44/45/44/43%), while percentage errors between iFM­K and other iFM were all < 16%. None of the parameters measured in CMR was predictive of a discrepancy of > 20% between both methods. CONCLUSION: The Krakau formula was comparable to other iFM in estimating CO levels, but none showed satisfactory agreement with the TD method. Improved derivation cohorts for [Formula: see text]O2 estimation are needed that better reflect today's patients undergoing RHC.

Usability of a mHealth Solution using Speech Recognition for Point-of-care Diagnostic Management.

The administrative burden for physicians in the hospital can affect the quality of patient care. The Service Center Medical Informatics (SMI) of the University Hospital Würzburg developed and implemented the smartphone-based mobile application (MA) ukw.mobile1 that uses speech recognition for the point-of-care ordering of radiological examinations. The aim of this study was to examine the usability of the MA workflow for the point-of-care ordering of radiological examinations. All physicians at the Department of Trauma and Plastic Surgery at the University Hospital Würzburg, Germany, were asked to participate in a survey including the short version of the User Experience Questionnaire (UEQ-S) and the Unified Theory of Acceptance and Use of Technology (UTAUT). For the analysis of the different domains of user experience (overall attractiveness, pragmatic quality and hedonic quality), we used a two-sided dependent sample t-test. For the determinants of the acceptance model, we employed regression analysis. Twenty-one of 30 physicians (mean age 34 ± 8 years, 62% male) completed the questionnaire. Compared to the conventional desktop application (DA) workflow, the new MA workflow showed superior overall attractiveness (mean difference 2.15 ± 1.33), pragmatic quality (mean difference 1.90 ± 1.16), and hedonic quality (mean difference 2.41 ± 1.62; all p < .001). The user acceptance measured by the UTAUT (mean 4.49 ± 0.41; min. 1, max. 5) was also high. Performance expectancy (beta = 0.57, p = .02) and effort expectancy (beta = 0.36, p = .04) were identified as predictors of acceptance, the full predictive model explained 65.4% of its variance. Point-of-care mHealth solutions using innovative technology such as speech-recognition seem to address the users' needs and to offer higher usability in comparison to conventional technology. Implementation of user-centered mHealth innovations might therefore help to facilitate physicians' daily work.

Sustained Increase in Serum Glial Fibrillary Acidic Protein after First ST-Elevation Myocardial Infarction.

Acute ischemic cardiac injury predisposes one to cognitive impairment, dementia, and depression. Pathophysiologically, recent positron emission tomography data suggest astroglial activation after experimental myocardial infarction (MI). We analyzed peripheral surrogate markers of glial (and neuronal) damage serially within 12 months after the first ST-elevation MI (STEMI). Serum levels of glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) were quantified using ultra-sensitive molecular immunoassays. Sufficient biomaterial was available from 45 STEMI patients (aged 28 to 78 years, median 56 years, 11% female). The median (quartiles) of GFAP was 63.8 (47.0, 89.9) pg/mL and of NfL 10.6 (7.2, 14.8) pg/mL at study entry 0-4 days after STEMI. GFAP after STEMI increased in the first 3 months, with a median change of +7.8 (0.4, 19.4) pg/mL (p = 0.007). It remained elevated without further relevant increases after 6 months (+11.7 (0.6, 23.5) pg/mL; p = 0.015), and 12 months (+10.3 (1.5, 22.7) pg/mL; p = 0.010) compared to the baseline. Larger relative infarction size was associated with a higher increase in GFAP (ρ = 0.41; p = 0.009). In contrast, NfL remained unaltered in the course of one year. Our findings support the idea of central nervous system involvement after MI, with GFAP as a potential peripheral biomarker of chronic glial damage as one pathophysiologic pathway.

Unlocking the PACS DICOM Domain for its Use in Clinical Research Data Warehouses.

Clinical Data Warehouses (DWHs) are used to provide researchers with simplified access to pseudonymized and homogenized clinical routine data from multiple primary systems. Experience with the integration of imaging and metadata from picture archiving and communication systems (PACS), however, is rare. Our goal was therefore to analyze the viability of integrating a production PACS with a research DWH to enable DWH queries combining clinical and medical imaging metadata and to enable the DWH to display and download images ad hoc. We developed an application interface that enables to query the production PACS of a large hospital from a clinical research DWH containing pseudonymized data. We evaluated the performance of bulk extracting metadata from the PACS to the DWH and the performance of retrieving images ad hoc from the PACS for display and download within the DWH. We integrated the system into the query interface of our DWH and used it successfully in four use cases. The bulk extraction of imaging metadata required a median (quartiles) time of 0.09 (0.03-2.25) to 12.52 (4.11-37.30) seconds for a median (quartiles) number of 10 (3-29) to 103 (8-693) images per patient, depending on the extraction approach. The ad hoc image retrieval from the PACS required a median (quartiles) of 2.57 (2.57-2.79) seconds per image for the download, but 5.55 (4.91-6.06) seconds to display the first and 40.77 (38.60-41.63) seconds to display all images using the pure web-based viewer. A full integration of a production PACS with a research DWH is viable and enables various use cases in research. While the extraction of basic metadata from all images can be done with reasonable effort, the extraction of all metadata seems to be more appropriate for subgroups.

Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse.

BACKGROUND: Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of information extraction (IE) demands a high developmental effort, we used ad hoc IE instead. This technique queries information and extracts it on the fly from texts contained in the CDW. METHODS: We present a generalizable approach of ad hoc IE for pharmacotherapy (medications and their daily dosage) presented in hospital discharge letters. We added import and query features to the CDW system, like error tolerant queries to deal with misspellings and proximity search for the extraction of the daily dosage. During the data integration process in the CDW, negated, historical and non-patient context data are filtered. For the replication studies, we used a drug list grouped by ATC (Anatomical Therapeutic Chemical Classification System) codes as input for queries to the CDW. RESULTS: We achieve an F1 score of 0.983 (precision 0.997, recall 0.970) for extracting medication from discharge letters and an F1 score of 0.974 (precision 0.977, recall 0.972) for extracting the dosage. We replicated three published medical trend studies for hypertension, atrial fibrillation and chronic kidney disease. Overall, 93% of the main findings could be replicated, 68% of sub-findings, and 75% of all findings. One study could be completely replicated with all main and sub-findings. CONCLUSION: A novel approach for ad hoc IE is presented. It is very suitable for basic medical texts like discharge letters and finding reports. Ad hoc IE is by definition more limited than conventional IE and does not claim to replace it, but it substantially exceeds the search capabilities of many CDWs and it is convenient to conduct replication studies fast and with high quality.

Patient's and physician's awareness of kidney disease in coronary heart disease patients - a cross-sectional analysis of the German subset of the EUROASPIRE IV survey.

BACKGROUND: Chronic kidney disease (CKD) is a common comorbid condition in coronary heart disease (CHD). CKD predisposes the patient to acute kidney injury (AKI) during hospitalization. Data on awareness of kidney dysfunction among CHD patients and their treating physicians are lacking. In the current cross-sectional analysis of the German EUROASPIRE IV sample we aimed to investigate the physician's awareness of kidney disease of patients hospitalized for CHD and also the patient's awareness of CKD in a study visit following hospital discharge. METHODS: All serum creatinine (SCr) values measured during the hospital stay were used to describe impaired kidney function (eGFRCKD-EPI < 60 ml/min/1.73m2) at admission, discharge and episodes of AKI (KDIGO definition). Information extracted from hospital discharge letters and correct ICD coding for kidney disease was studied as a surrogate of physician's awareness of kidney disease. All patients were interrogated 0.5 to 3 years after hospital discharge, whether they had ever been told about kidney disease by a physician. RESULTS: Of the 536 patients, 32% had evidence for acute or chronic kidney disease during the index hospital stay. Either condition was mentioned in the discharge letter in 22%, and 72% were correctly coded according to ICD-10. At the study visit in the outpatient setting 35% had impaired kidney function. Of 158 patients with kidney disease, 54 (34%) were aware of CKD. Determinants of patient's awareness were severity of CKD (OReGFR 0.94; 95%CI 0.92-0.96), obesity (OR 1.97; 1.07-3.64), history of heart failure (OR 1.99; 1.00-3.97), and mentioning of kidney disease in the index event's hospital discharge letter (OR 5.51; 2.35-12.9). CONCLUSIONS: Although CKD is frequent in CHD, only one third of patients is aware of this condition. Patient's awareness was associated with kidney disease being mentioned in the hospital discharge letter. Future studies should examine how raising physician's awareness for kidney dysfunction may improve patient's awareness of CKD.

Fine-grained information extraction from German transthoracic echocardiography reports.

BACKGROUND: Information extraction techniques that get structured representations out of unstructured data make a large amount of clinically relevant information about patients accessible for semantic applications. These methods typically rely on standardized terminologies that guide this process. Many languages and clinical domains, however, lack appropriate resources and tools, as well as evaluations of their applications, especially if detailed conceptualizations of the domain are required. For instance, German transthoracic echocardiography reports have not been targeted sufficiently before, despite of their importance for clinical trials. This work therefore aimed at development and evaluation of an information extraction component with a fine-grained terminology that enables to recognize almost all relevant information stated in German transthoracic echocardiography reports at the University Hospital of Würzburg. METHODS: A domain expert validated and iteratively refined an automatically inferred base terminology. The terminology was used by an ontology-driven information extraction system that outputs attribute value pairs. The final component has been mapped to the central elements of a standardized terminology, and it has been evaluated according to documents with different layouts. RESULTS: The final system achieved state-of-the-art precision (micro average.996) and recall (micro average.961) on 100 test documents that represent more than 90 % of all reports. In particular, principal aspects as defined in a standardized external terminology were recognized with f 1=.989 (micro average) and f 1=.963 (macro average). As a result of keyword matching and restraint concept extraction, the system obtained high precision also on unstructured or exceptionally short documents, and documents with uncommon layout. CONCLUSIONS: The developed terminology and the proposed information extraction system allow to extract fine-grained information from German semi-structured transthoracic echocardiography reports with very high precision and high recall on the majority of documents at the University Hospital of Würzburg. Extracted results populate a clinical data warehouse which supports clinical research.

Improved Interpretation of Pulmonary Artery Wedge Pressures through Left Atrial Volumetry-A Cardiac Magnetic Resonance Imaging Study.

BACKGROUND: The pulmonary artery wedge pressure (PAWP) is regarded as a reliable indicator of left ventricular end-diastolic pressure (LVEDP), but this association is weaker in patients with left-sided heart disease (LHD). We compared morphological differences in cardiac magnetic resonance imaging (CMR) in patients with heart failure (HF) and a reduced left ventricular ejection fraction (LVEF), with or without elevation of PAWP or LVEDP. METHODS: We retrospectively identified 121 patients with LVEF < 50% who had undergone right heart catheterization (RHC) and CMR. LVEDP data were available for 75 patients. RESULTS: The mean age of the study sample was 63 ± 14 years, the mean LVEF was 32 ± 10%, and 72% were men. About 53% of the patients had an elevated PAWP (>15 mmHg). In multivariable logistic regression analysis, NT-proBNP, left atrial ejection fraction (LAEF), and LV end-systolic volume index independently predicted an elevated PAWP. Of the 75 patients with available LVEDP data, 79% had an elevated LVEDP, and 70% had concomitant PAWP elevation. By contrast, all but one patient with elevated PAWP and half of the patients with normal PAWP had concomitant LVEDP elevation. The Bland-Altman plot revealed a systematic bias of +5.0 mmHg between LVEDP and PAWP. Notably, LAEF was the only CMR variable that differed significantly between patients with elevated LVEDP and a PAWP ≤ or >15 mmHg. CONCLUSIONS: In patients with LVEF < 50%, a normal PAWP did not reliably exclude LHD, and an elevated LVEDP was more frequent than an elevated PAWP. LAEF was the most relevant determinant of an increased PAWP, suggesting that a preserved LAEF in LHD may protect against backward failure into the lungs and the subsequent increase in pulmonary pressure.

Using a Clinical Data Warehouse to Calculate and Present Key Metrics for the Radiology Department: Implementation and Performance Evaluation.

BACKGROUND: Due to the importance of radiologic examinations, such as X-rays or computed tomography scans, for many clinical diagnoses, the optimal use of the radiology department is 1 of the primary goals of many hospitals. OBJECTIVE: This study aims to calculate the key metrics of this use by creating a radiology data warehouse solution, where data from radiology information systems (RISs) can be imported and then queried using a query language as well as a graphical user interface (GUI). METHODS: Using a simple configuration file, the developed system allowed for the processing of radiology data exported from any kind of RIS into a Microsoft Excel, comma-separated value (CSV), or JavaScript Object Notation (JSON) file. These data were then imported into a clinical data warehouse. Additional values based on the radiology data were calculated during this import process by implementing 1 of several provided interfaces. Afterward, the query language and GUI of the data warehouse were used to configure and calculate reports on these data. For the most common types of requested reports, a web interface was created to view their numbers as graphics. RESULTS: The tool was successfully tested with the data of 4 different German hospitals from 2018 to 2021, with a total of 1,436,111 examinations. The user feedback was good, since all their queries could be answered if the available data were sufficient. The initial processing of the radiology data for using them with the clinical data warehouse took (depending on the amount of data provided by each hospital) between 7 minutes and 1 hour 11 minutes. Calculating 3 reports of different complexities on the data of each hospital was possible in 1-3 seconds for reports with up to 200 individual calculations and in up to 1.5 minutes for reports with up to 8200 individual calculations. CONCLUSIONS: A system was developed with the main advantage of being generic concerning the export of different RISs as well as concerning the configuration of queries for various reports. The queries could be configured easily using the GUI of the data warehouse, and their results could be exported into the standard formats Excel and CSV for further processing.

Diagnosing post-capillary hypertension in patients with left heart disease: impact of new guidelines.

BACKGROUND: In 2022, the definition of pulmonary hypertension (PH) in the presence of left heart disease was updated according to the new joint guidelines of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS). The impact of the new ESC/ERS definition on the prevalence of post-capillary PH (pc-PH) and its subgroups of isolated post-capillary (Ipc-PH) and combined pre- and post-capillary PH (Cpc-PH) in patients with left heart disease is unclear. METHODS: We retrospectively identified N = 242 patients with left heart disease with available data on right heart catheterisation (RHC) and cardiac magnetic resonance imaging (CMR). The proportion of pc-PH and its subgroups was calculated according to the old and new ESC/ERS PH definition. As the old definition did not allow the exact allocation of all patients with pc-PH into a respective subgroup, unclassifiable patients (Upc-PH) were regarded separately. RESULTS: Seventy-six out of 242 patients had pc-PH according to the new ESC/ERS definitions, with 72 of these patients also meeting the criteria of the old definition. Using the old definition, 50 patients were diagnosed with Ipc-PH, 4 with Cpc-PH, and 18 with Upc-PH. Applying the new definition, Ipc-PH was diagnosed in 35 patients (4 newly), and Cpc-PH in 41 patients. No CMR parameter allowed differentiating between Ipc-PH and Cpc-PH, regardless of which guideline version was used. CONCLUSION: Applying the new ESC/ERS 2022 guideline definitions mildly increased the proportion of patients diagnosed with pc-PH (+ 5.5%) but markedly increased Cpc-PH diagnoses. This effect was driven by the allocation of patients with formerly unclassifiable forms of post-capillary PH to the Cpc-PH subgroup and a significant shift of patients from the Ipc-PH to the Cpc-PH subgroup. Distribution of post-capillary pulmonary hypertension (pc-PH) subgroups according to the European Society of Cardiology/European Respiratory Society (ESC/ERS) PH guidelines from 2015 and 2022 in N = 242 patients with left heart disease.

Features of metabolic syndrome and inflammation independently affect left ventricular function early after first myocardial infarction.

BACKGROUND: A high body mass index (BMI) is often associated with metabolic syndrome, which is accompanied by systemic low-grade chronic inflammation. Here, we analyzed whether BMI, other components of metabolic syndrome, and/or inflammatory markers correlate with left ventricular geometry, function, and infarct size as assessed by serial cardiac magnetic resonance imaging (MRI) after a first (clinically evident) ST-elevation MI (STEMI). METHODS: Within the Etiology, Titre-Course, and effect on Survival (ETiCS) study, cardiac MRI conducted 7-9 days and 12 months after MI enabled longitudinal characterization of patients with a first STEMI along with serial routine blood counts and multiplex cytokine measurements. RESULTS: Of 91 locally included STEMI patients, 47% were overweight (25 kg/m2 < BMI < 30 kg/m2) and 24% were obese (BMI ≥ 30 kg/m2). No patient died during 12 months of follow-up. Left ventricular ejection fraction (LVEF), measured 7-9 days after STEMI, was significantly lower in overweight (49.5 ± 7.1%) and obese (45.8 ± 12.0%) patients than in the normal weight group (56.2 ± 7.7%). Along with BMI (T = -3.8; p < 0.001), hemoglobin A1c (HbA1c; T = -3.1; p = 0.004) and peak C-reactive protein (T = -2.6; p = 0.013) emerged as independent predictors of worse LVEF 7-9 days post MI (R2 = 0.45). Only peak C-reactive protein (T = -4.4; p < 0.001), but not parameters of the metabolic syndrome, predicted worse LVEF 12 months after STEMI (R2 = 0.20). CONCLUSION: Both BMI and HbA1c correlated negatively with LVEF only early, but not late after STEMI. Peak CRP evolved as strongest predictor of cardiac function at all time points independent of the metabolic syndrome.

Point-of-Care Speech-Recognition Based mHealth Solution to Facilitate Physicians' Daily Work.

We developed and implemented a smartphone-based mobile application that uses speech recognition for the point-of-care ordering of radiological examinations. 21 out of 30 physicians completed a usability questionnaire including the Short version of the User Experience Questionnaire (UEQ-S) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The mobile application showed high user acceptance and superior user experience when compared to the conventional workflow. Due to the high usability of our mHealth solution, it might help to facilitate the physician's daily work.

Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution.

A deep integration of routine care and research remains challenging in many respects. We aimed to show the feasibility of an automated transformation and transfer process feeding deeply structured data with a high level of granularity collected for a clinical prospective cohort study from our hospital information system to the study's electronic data capture system, while accounting for study-specific data and visits. We developed a system integrating all necessary software and organizational processes then used in the study. The process and key system components are described together with descriptive statistics to show its feasibility in general and to identify individual challenges in particular. Data of 2051 patients enrolled between 2014 and 2020 was transferred. We were able to automate the transfer of approximately 11 million individual data values, representing 95% of all entered study data. These were recorded in n = 314 variables (28% of all variables), with some variables being used multiple times for follow-up visits. Our validation approach allowed for constant good data quality over the course of the study. In conclusion, the automated transfer of multi-dimensional routine medical data from HIS to study databases using specific study data and visit structures is complex, yet viable.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

Calculating Key Figures for Radiology Departments Using a Clinical Data Warehouse - A Technical Case Report.

Optimizing the utilization of radiology departments is one of the primary objectives for many hospitals. To support this, a solution has been developed, which at first transforms the export of different Radiological Information Systems (RIS) into the data format of a clinical data warehouse (CDW). Additional features, like for example the time between the creation of a radiologic request and the finalization of the diagnosis for the created images, can then be defined using a simple interface and are calculated and saved in the CDW as well. Finally, the query language of the CDW can be used to create custom reports with all the RIS data including the calculated features and export them into the standard formats Excel and CSV. The solution has been successfully tested with data from two German hospitals.

Translating openEHR Models to FHIR.

openEHR and FHIR are two competing clinical data modeling and data exchange standards, that are commonly seen as mostly incompatible. However, the two have quite much in common and bridging approaches between the two worlds can serve the benefit of both communities. In the presented work, the data models of openEHR are translated into FHIR data models.

Coagulation factor XIII activity predicts left ventricular remodelling after acute myocardial infarction.

AIMS: Acute myocardial infarction (MI) is the major cause of chronic heart failure. The activity of blood coagulation factor XIII (FXIIIa) plays an important role in rodents as a healing factor after MI, whereas its role in healing and remodelling processes in humans remains unclear. We prospectively evaluated the relevance of FXIIIa after acute MI as a potential early prognostic marker for adequate healing. METHODS AND RESULTS: This monocentric prospective cohort study investigated cardiac remodelling in patients with ST-elevation MI and followed them up for 1 year. Serum FXIIIa was serially assessed during the first 9 days after MI and after 2, 6, and 12 months. Cardiac magnetic resonance imaging was performed within 4 days after MI (Scan 1), after 7 to 9 days (Scan 2), and after 12 months (Scan 3). The FXIII valine-to-leucine (V34L) single-nucleotide polymorphism rs5985 was genotyped. One hundred forty-six patients were investigated (mean age 58 ± 11 years, 13% women). Median FXIIIa was 118% (quartiles, 102-132%) and dropped to a trough on the second day after MI: 109% (98-109%; P < 0.001). FXIIIa recovered slowly over time, reaching the baseline level after 2 to 6 months and surpassed baseline levels only after 12 months: 124% (110-142%). The development of FXIIIa after MI was independent of the genotype. FXIIIa on Day 2 was strongly and inversely associated with the relative size of MI in Scan 1 (Spearman's ρ = -0.31; P = 0.01) and Scan 3 (ρ = -0.39; P < 0.01) and positively associated with left ventricular ejection fraction: ρ = 0.32 (P < 0.01) and ρ = 0.24 (P = 0.04), respectively. CONCLUSIONS: FXIII activity after MI is highly dynamic, exhibiting a significant decline in the early healing period, with reconstitution 6 months later. Depressed FXIIIa early after MI predicted a greater size of MI and lower left ventricular ejection fraction after 1 year. The clinical relevance of these findings awaits to be tested in a randomized trial.

Implementation of a HL7-CQL Engine Using the Graph Database Neo4J.

The Clinical Quality Language (CQL) is a useful tool for defining search requests for data stores containing FHIR data. Unfortunately, there are only few execution engines that are able to evaluate CQL queries. As FHIR data represents a graph structure, the authors pursue the approach of storing all data contained in a FHIR server in the graph database Neo4J and to translate CQL queries into Neo4J's query language Cypher. The query results returned by the graph database are retranslated into their FHIR representation and returned to the querying user. The approach has been positively tested on publicly available FHIR servers with a handcrafted set of example CQL queries.

Query Translation Between openEHR and i2b2.

Secondary use of electronic health records using data warehouses (DW) has become an attractive approach to support clinical research. In order to increase the volume of underlying patient data DWs at different institutions can be connected to research networks. Two obstacles to connect a DW to such a network are the syntactical differences between the involved DW technologies and differences in the data models of the connected DWs. The current work presents an approach to tackle both problems by translating queries from the DW system openEHR into queries from the DW system i2b2 and vice versa. For the subset of queries expressible in the query languages of both systems, the presented approach is well feasible.