Pitfalls in Analyzing FHIR Data from Different University Hospitals.

The German Medical Informatics Initiative has agreed on a HL7 FHIR-based core data set as the common data model that all 37 university hospitals use for their patient's data. These data are stored locally at the site but are centrally queryable for researchers and accessible upon request. This infrastructure is currently under construction, and its functionality is being tested by so-called Projectathons. In the 6th Projectathon, a clinical hypothesis was formulated, executed in a multicenter scenario, and its results were analyzed. A number of oddities emerged in the analysis of data from different sites. Biometricians, who had previously performed analyses in prospective data collection settings such as clinical trials or cohorts, were not consistently aware of these idiosyncrasies. This field report describes data quality problems that have occurred, although not all are genuine errors. The aim is to point out such circumstances of data generation that may affect statistical analysis.

"fhircrackr": An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis.

BACKGROUND: The growing interest in the secondary use of electronic health record (EHR) data has increased the number of new data integration and data sharing infrastructures. The present work has been developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats. OBJECTIVES: The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise. METHODS: We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license. RESULTS: The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms. CONCLUSION: The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

Effect of Automated Telephone Infectious Disease Consultations to Nonacademic Hospitals on 30-Day Mortality Among Patients With Staphylococcus aureus Bacteremia: The SUPPORT Cluster Randomized Clinical Trial.

Importance: Staphylococcus aureus bacteremia (SAB) is a common and potentially severe infectious disease (ID). Retrospective studies and derived meta-analyses suggest that bedside infectious disease consultation (IDC) for SAB is associated with improved survival; however, such IDCs might not always be possible because of the lack of ID specialists, particularly at nonacademic hospitals. Objectives: To investigate whether unsolicited telephone IDCs (triggered by an automated blood stream infection reporting system) to nonacademic hospitals improved 30-day all-cause mortality in patients with SAB. Design, Setting, and Participants: This patient-blinded, multicenter, interventional, cluster randomized, controlled, crossover clinical trial was conducted in 21 rural, nonacademic hospitals in Thuringia, Germany. From July 1, 2016, to December 31, 2018, 1029 blood culture reports were assessed for eligibility. A total of 386 patients were enrolled, whereas 643 patients were not enrolled for the following reasons: death before enrollment (n = 59); palliative care (n = 41); recurrence of SAB (n = 9); discharge from the hospital before enrollment (n = 77); age younger than 18 years (n = 5); duplicate report from a single patient (n = 26); late report (n = 17); blood culture reported during the washout phase (n = 48); and no signed informed consent for other or unknown reasons (n = 361). Interventions: During the ID intervention phase, ID specialists from Jena University Hospital provided unsolicited telephone IDCs to physicians treating patients with SAB. During the control phase, patients were treated according to local standards. Crossover was performed after including 15 patients or, at the latest, 1 year after the first patient was included. Main Outcomes and Measures: Thirty-day all-cause mortality. Results: A total of 386 patients (median [IQR] age, 75 [63-82] years; 261 [67.6%] male) were included, with 177 randomized to the IDC group and 209 to the control group. The 30-day all-cause mortality rate did not differ between the IDC and control groups (relative risk reduction [RRR], 0.12; 95% CI, -2.17 to 0.76; P = .81). No evidence was found of a difference in secondary outcomes, including 90-day mortality (RRR, 0.17; 95% CI, -0.59 to 0.57; P = .62), 90-day recurrence (RRR, 0.10; 95% CI, -2.51 to 0.89; P = .89), and hospital readmission (RRR, 0.04; 95% CI, -0.63 to 0.48; P = .90). Exploratory evidence suggested that indicators of quality of care were potentially realized more often in the IDC group than in the control group (relative quality improvement, 0.16; 95% CI, 0.08-0.26; P = .01). Conclusions and Relevance: In this cluster randomized clinical trial, unsolicited telephone IDC, although potentially enhancing quality of care, did not improve 30-day all-cause mortality in patients with SAB. Trial Registration: drks.de Identifier: DRKS00010135.

Prediction of treatment outcome in patients suffering from chronic tinnitus - from individual characteristics to early and long-term change.

BACKGROUND AND OBJECTIVE: Despite the availability of successful treatment approaches for chronic tinnitus, it has proven difficult to predict who profits from treatment and it is still an open question if it is possible at all. We tried to overcome methodological shortcomings and to predict treatment outcome indicated by questionnaires measuring tinnitus distress. METHODS: This is an observational, prospective cohort study. Lasso and post-selection inference methods were used to predict treatment outcome in patients suffering from chronic tinnitus (N = 747). Patients were treated for five consecutive days in an interdisciplinary setting according to guidelines. RESULTS: Early change, i.e. a positive response after the screening day, as well as change due to treatment was predicted by several psychopathological variables, but also tinnitus-related factors. Female gender as an example was a predictor for change due to treatment. In general, therapy success both for early change and change due to treatment cannot be predicted satisfactorily as indicated by a high mean cross-validation error (for early change: 9.83, for change due to treatment: 14.40). Analyzing sub-groups separated by tinnitus severity to reduce heterogeneity did not improve the situation and for patients with high tinnitus severity no predictors at all could be reported (cross-validated error: 11.62 for the low quartile, 13.38 for the low-medium quartile, and 15.61 for the medium-high quartile). CONCLUSION: Several psychopathological and tinnitus-related variables predicted early and long-term change. Nevertheless, also overcoming methodological shortcomings to predict treatment success did not lead to satisfactory results, but rather emphasizes the high heterogeneity of chronic tinnitus.

Introduction of BD Vacutainer® Barricor™ tubes in clinical biobanking and application of amino acid and cytokine quality indicators to Barricor plasma.

OBJECTIVES: The use of BD Vacutainer® Barricor™ tubes (BAR) can reduce turnaround time (TAT) and improve separation of plasma from cellular components using a specific mechanical separator. Concentrations of amino acids (AAs) and cytokines, known to be labile during pre-analytical time delays, were compared in heparin (BAR, BD Heparin standard tube [PST]), EDTA and serum gel tubes (SER) to validate previously identified quality indicators (QIs) in BAR. METHODS: Samples of healthy individuals (n=10) were collected in heparin, EDTA and SER tubes and exposed to varying pre- and post-centrifugation delays at room temperature (RT). Cytokines (interleukin [IL]-8, IL-16 and sCD40L) were analyzed by enzyme-linked immunosorbent assay (ELISA) and AAs were characterized by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). RESULTS: All QIs, AAs/AA ratio and cytokines increased during prolonged blood storage in heparin plasma (PST, BAR) and SER tubes. Comparison of 53 h/1 h pre-centrifugation delay resulted in an increase in taurine (Tau) and glutamic acid (Glu) concentrations by more than three times, soluble CD40L increased by 13.6, 9.2 and 4.3 fold in PST, BAR-CTRL and BAR-FAST, and IL-8 increased even more by 112.8 (PST), 266.1 (BAR-CTRL), 268.1 (BAR-FAST) and 70.0 (SER) fold, respectively. Overall, compared to prolonged blood storage, effects of post-centrifugation delays were less pronounced in all tested materials. CONCLUSIONS: BAR tubes are compatible with the use of several established QIs and can therefore be used in clinical biobanking to reduce pre-analytical TAT without compromising QIs and thus pre-analytical sample quality analysis.

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy.

INTRODUCTION: The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure. METHODS AND ANALYSIS: In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested. ETHICS AND DISSEMINATION: Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: DRKS00014330.

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey.

BACKGROUND: The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE: This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS: A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS: The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS: Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

Minimum infusion rate of alfaxalone for total intravenous anaesthesia after sedation with acepromazine or medetomidine in cats undergoing ovariohysterectomy.

OBJECTIVE: To determine the induction doses, then minimum infusion rates of alfaxalone for total intravenous anaesthesia (TIVA), and subsequent, cardiopulmonary effects, recovery characteristics and alfaxalone plasma concentrations in cats undergoing ovariohysterectomy after premedication with butorphanol-acepromazine or butorphanol-medetomidine. STUDY DESIGN: Prospective randomized blinded clinical study. ANIMALS: Twenty-eight healthy cats. METHODS: Cats undergoing ovariohysterectomy were assigned into two groups: together with butorphanol [0.2 mg kg(-1) intramuscularly (IM)], group AA (n = 14) received acepromazine (0.1 mg kg(-1) IM) and group MA (n = 14) medetomidine (20 µg kg(-1) IM). Anaesthesia was induced with alfaxalone to effect [0.2 mg kg(-1) intravenously (IV) every 20 seconds], initially maintained with 8 mg kg(-1)  hour(-1) alfaxalone IV and infusion adjusted (±0.5 mg kg(-1)  hour(-1) ) every five  minutes according to alterations in heart rate (HR), respiratory rate (fR ), Doppler blood pressure (DBP) and presence of palpebral reflex. Additional alfaxalone boli were administered IV if cats moved/swallowed (0.5 mg kg(-1) ) or if fR >40 breaths minute(-1) (0.25 mg kg(-1) ). Venous blood samples were obtained to determine plasma alfaxalone concentrations. Meloxicam (0.2 mg kg(-1) IV) was administered postoperatively. Data were analysed using linear mixed models, Chi-squared, Fishers exact and t-tests. RESULTS: Alfaxalone anaesthesia induction dose (mean ± SD), was lower in group MA (1.87 ± 0.5; group AA: 2.57 ± 0.41 mg kg(-1) ). No cats became apnoeic. Intraoperative bolus requirements and TIVA rates (group AA: 11.62 ± 1.37, group MA: 10.76 ± 0.96 mg kg(-1)  hour(-1) ) did not differ significantly between groups. Plasma concentrations ranged between 0.69 and 10.76 µg mL(-1) . In group MA, fR , end-tidal carbon dioxide, temperature and DBP were significantly higher and HR lower. CONCLUSION AND CLINICAL RELEVANCE: Alfaxalone TIVA in cats after medetomidine or acepromazine sedation provided suitable anaesthesia with no need for ventilatory support. After these premedications, the authors recommend initial alfaxalone TIVA rates of 10 mg kg(-1)  hour(-1) .