Evaluation of Software for Automated Measurement of Adherence to ICP-Monitoring Threshold Guideline.

Challenges inherent in clinical guideline development include a long time lag between the key results and incorporation into best practice and the qualitative nature of adherence measurement, meaning it will have no directly measurable impact. To address these issues, a framework has been developed to automatically measure adherence by clinicians in neurological intensive care units to the Brain Trauma Foundation's intracranial pressure (ICP)-monitoring guidelines for severe traumatic brain injury (TBI).The framework processes physiological and treatment data taken from the bedside, standardises the data as a set of process models, then compares these models against similar process models constructed from published guidelines. A similarity metric (i.e. adherence measure) between the two models is calculated, composed of duration and scale of non-adherence.In a pilot clinical validation test, the framework was applied to physiological/treatment data from three TBI patients exhibiting ICP secondary insults at a local neuro-centre where clinical experts coded key clinical interventions/decisions about patient management.The framework identified non-adherence with respect to drug administration in one patient, with a spike in non-adherence due to an inappropriately high dosage; a second patient showed a high severity of guideline non-adherence; and a third patient showed non-adherence due to a low number of associated events and treatment annotations.

Plasma metanephrines and prospective prediction of tumor location, size and mutation type in patients with pheochromocytoma and paraganglioma.

Objectives: Plasma free metanephrines are commonly used for diagnosis of pheochromocytoma and paraganglioma (PPGLs), but can also provide other information. This multicenter study prospectively examined whether tumor size, location, and mutations could be predicted by these metabolites. Methods: Predictions of tumor location, size, and mutation type, based on measurements of plasma normetanephrine, metanephrine, and methoxytyramine were made without knowledge of disease in 267 patients subsequently determined to have PPGLs. Results: Predictions of adrenal vs. extra-adrenal locations according to increased plasma concentrations of metanephrine and methoxytyramine were correct in 93 and 97% of the respective 136 and 33 patients in who these predictions were possible. Predicted mean tumor diameters correlated positively (p<0.0001) with measured diameters; predictions agreed well for pheochromocytomas but were overestimated for paragangliomas. Considering only patients with mutations, 51 of the 54 (94%) patients with NF1 or RET mutations were correctly predicted with those mutations according to increased plasma metanephrine, whereas no or minimal increase in metanephrine correctly predicted all 71 patients with either VHL or SDHx mutations; furthermore, among the latter group increases in methoxytyramine correctly predicted SDHx mutations in 93% of the 29 cases for this specific prediction. Conclusions: Extents and patterns of increased plasma O-methylated catecholamine metabolites among patients with PPGLs allow predictions of tumor size, adrenal vs. extra-adrenal locations and general types of mutations. Predictions of tumor location are, however, only possible for patients with clearly increased plasma methoxytyramine or metanephrine. Where possible or clinically relevant the predictions are potentially useful for subsequent clinical decision-making.

Adrenomedullary function, obesity and permissive influences of catecholamines on body mass in patients with chromaffin cell tumours.

BACKGROUND: Obesity-associated activation of sympathetic nervous outflow is well documented, whereas involvement of dysregulated adrenomedullary hormonal function in obesity is less clear. This study assessed relationships of sympathoadrenal function with indices of obesity and influences of circulating catecholamines on body mass. METHODS: Anthropometric and clinical data along with plasma and 24-h urine samples were collected from 590 volunteers and 1368 patients tested for phaeochromocytoma and paraganglioma (PPGL), among whom tumours were diagnosed in 210 individuals. RESULTS: Among patients tested for PPGL, those with tumours less often had a body mass index (BMI) above 30 kg/m2 (12 vs. 31%) and more often a BMI under 25 kg/m2 (56 vs. 32%) than those without tumours (P < 0.0001). Urinary outputs of catecholamines in patients with PPGL were negatively related to BMI (r = -0.175, P = 0.0133). Post-operative weight gain (P < 0.0001) after resection of PPGL was positively related to presurgical tumoural catecholamine output (r = 0.257, P = 0.0101). Higher BMI in men and women and percent body fat in women of the volunteer group were associated with lower plasma concentrations and urinary outputs of adrenaline and metanephrine, the former indicating obesity-related reduced adrenaline secretion and the latter obesity-related reduced adrenomedullary adrenaline stores. Daytime activity was associated with substantial increases in urinary adrenaline and noradrenaline excretion, with blunted responses in obese subjects. CONCLUSIONS: The findings in patients with PPGL support an influence of high circulating catecholamines on body weight. Additional associations of adrenomedullary dysfunction with obesity raise the possibility of a permissive influence of the adrenal medulla on the regulation of body weight.

Asymmetric Adrenals: Sexual Dimorphism of Adrenal Tumors.

CONTEXT: Sexual dimorphism has direct consequences on the incidence and survival of cancer. Early and accurate diagnosis is crucial to improve prognosis. OBJECTIVE: This work aimed to characterized the influence of sex and adrenal asymmetry on the emergence of adrenal tumors. METHODS: We conducted a multicenter, observational study involving 8037 patients with adrenal tumors, including adrenocortical carcinoma (ACC), aldosterone-producing adenoma (APA), cortisol-secreting adrenocortical adenomas (CSAs), non-aldosterone-producing adrenal cortical adenoma (NAPACA), pheochromocytoma (PCC), and neuroblastoma (NB), and investigated tumor lateralization according to sex. Human adrenal tissues (n = 20) were analyzed with a multiomics approach that allows determination of gene expression, catecholamine, and steroid contents in a single sample. In addition, we performed a literature review of computed tomography and magnetic resonance imaging-based studies examining adrenal gland size. RESULTS: ACC (n = 1858); CSA (n = 68), NAPACA (n = 2174), and PCC (n = 1824) were more common in females than in males (female-to-male ratio: 1.1:1-3.8:1), whereas NBs (n = 2320) and APAs (n = 228) were less prevalent in females (0.8:1). ACC, APA, CSA, NAPACA, and NB occurred more frequently in the left than in the right adrenal (left-to-right ratio: 1.1:1-1.8:1), whereas PCC arose more often in the right than in the left adrenal (0.8:1). In both sexes, the left adrenal was larger than the right adrenal; females have smaller adrenals than males. CONCLUSION: Adrenal asymmetry in both sexes may be related to the pathogenesis of adrenal tumors and should be considered during the diagnosis of these tumors.

A GDPR-Compliant Dynamic Consent Mobile Application for the Australasian Type-1 Diabetes Data Network.

Australia has a high prevalence of diabetes, with approximately 1.2 million Australians diagnosed with the disease. In 2012, the Australasian Diabetes Data Network (ADDN) was established with funding from the Juvenile Diabetes Research Foundation (JDRF). ADDN is a national diabetes registry which captures longitudinal information about patients with type-1 diabetes (T1D). Currently, the ADDN data are directly contributed from 42 paediatric and 17 adult diabetes centres across Australia and New Zealand, i.e., where the data are pre-existing in hospital systems and not manually entered into ADDN. The historical data in ADDN have been de-identified, and patients are initially afforded the opportunity to opt-out of being involved in the registry; however, moving forward, there is an increased demand from the clinical research community to utilise fully identifying data. This raises additional demands on the registry in terms of security, privacy, and the nature of patient consent. General Data Protection Regulation (GDPR) is an increasingly important mechanism allowing individuals to have the right to know about their health data and what those data are being used for. This paper presents a mobile application being designed to support the ADDN data collection and usage processes and aligning them with GDPR. The app utilises Dynamic Consent-an informed specific consent model, which allows participants to view and modify their research-driven consent decisions through an interactive interface. It focuses specifically on supporting dynamic opt-in consent to both the registry and to associated sub-projects requesting access to and use of the patient data for research purposes.

The ENSAT registry: a digital repository supporting adrenal cancer research.

The very nature of rare diseases means that information is often sparse and highly distributed, and as a result progress in the field is more challenging to conduct. ENSAT-CANCER is an EU-FP7 funded initiative to develop a virtual research environment (VRE) offering a digitally interconnected infrastructure for distributed clinicians specialising in rare adrenal tumours to communicate and collaborate with distributed biomedical research communities. The core of the VRE is a registry that holds vital patient information from specialist centres around Europe, covering different types of adrenal tumours. The VRE also hosts a range of other enabling services including sample barcoding, bio-sample exchange mechanisms, an integrated linkage scheme to other trials and studies, summary statistics and report generation, image hosting - all of which are available in a seamless, security-driven environment. This paper presents the key challenges of this endeavour, the technical solutions that have been developed to address them and reporting the uptake and adoption of the work (currently at 2472 patient records and rising).

Preanalytical Considerations and Outpatient Versus Inpatient Tests of Plasma Metanephrines to Diagnose Pheochromocytoma.

CONTEXT: Sampling of blood in the supine position for diagnosis of pheochromocytoma and paraganglioma (PPGL) results in lower rates of false positives for plasma normetanephrine than seated sampling. It is unclear how inpatient vs outpatient testing and other preanalytical factors impact false positives. OBJECTIVE: We aimed to identify preanalytical precautions to minimize false-positive results for plasma metanephrines. METHODS: Impacts of different blood sampling conditions on plasma metanephrines were evaluated, including outpatient vs inpatient testing, sampling of blood in semi- vs fully recumbent positions, use of cannulae vs direct venipuncture, and differences in outside temperature. A total of 3147 patients at 10 tertiary referral centers were tested for PPGL, including 278 with and 2869 without tumors. Rates of false-positive results were analyzed. RESULTS: Outpatient rather than inpatient sampling resulted in 44% higher plasma concentrations and a 3.4-fold increase in false-positive results for normetanephrine. Low temperature, a semi-recumbent position, and direct venipuncture also resulted in significantly higher plasma concentrations and rates of false-positive results for plasma normetanephrine than alternative sampling conditions, although with less impact than outpatient sampling. Higher concentrations and rates of false-positive results for plasma normetanephrine with low compared with warm temperatures were only apparent for outpatient sampling. Preanalytical factors were without impact on plasma metanephrines in patients with PPGL. CONCLUSION: Although inpatient blood sampling is largely impractical for screening patients with suspected PPGL, other preanalytical precautions (eg, cannulae, warm testing conditions) may be useful. Inpatient sampling may be reserved for follow-up of patients with difficult to distinguish true- from false-positive results.

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study.

BACKGROUND: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. METHODS: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. FINDINGS: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. INTERPRETATION: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. FUNDING: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).

The brain monitoring with Information Technology (BrainIT) collaborative network: EC feasibility study results and future direction.

BACKGROUND: The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain-injured patients and to facilitate a more efficient infrastructure for assessing new health care technology with the primary objective of improving patient care. European Community (EC) funding supported meetings over a year to discuss and define a core dataset to be collected from patients with traumatic brain injury using IT-based methods. We now present the results of a subsequent EC-funded study with the aim of testing the feasibility of collecting this core dataset across a number of European sites and discuss the future direction of this research network. METHODS: Over a 3-year period, data collection client- and web-server-based tools were developed and core data (grouped into nine categories) were collected from 200 head-injured patients by local nursing staff in 22 European neuro-intensive care centres. Data were uploaded through the BrainIT website and random samples of received data were selected automatically by computer for validation by data validation staff against primary sources held in each local centre. Validated data were compared with originally transmitted data and percentage error rates calculated by data category. Feasibility was assessed in terms of the proportion of missing data, accuracy of data collected and limitations reported by users of the IT methods. FINDINGS: Thirteen percent of data files required cleaning. Thirty "one-off" demographic and clinical data elements had significant amounts of missing data (>15%). Validation staff conducted 19,461 comparisons between uploaded database data with local data sources and error rates were commonly less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found. Nearly 10,000 therapies were successfully recorded with start-times but approximately a third had inaccurate or missing "end-times" which limits the analysis of duration of therapy. Over 40,000 events and procedures were recorded but events with long durations (such as transfers) were more likely to have end-times missed. CONCLUSIONS: The BrainIT core dataset is a rich dataset for hypothesis generation and post hoc analyses, provided that studies avoid known limitations in the dataset. Limitations in the current IT-based data collection tools have been identified and have been addressed. In order for multi-centre data collection projects to be viable, the resource intensive validation procedures will require a more automated process and this may include direct electronic access to hospital-based clinical data sources for both validation purposes and for minimising the duplication of data entry. This type of infrastructure may foster and facilitate the remote monitoring of patient management and protocol adherence in future trials of patient management and monitoring.

Enabling secure, distributed collaborations for adrenal tumor research.

Many e-Health strategies rely on the secure integration of datasets that have previously resided in isolated locations, but can now in principle be accessed over the Internet. Of paramount importance in the health domain is the need for the security and privacy of data that is transmitted across these networks. One such collaboration, which spans several specialist centres across France, Germany, Italy and the UK, is ENSAT - the European Network for the Study of Adrenal Tumors. The rarity of the tumors under study means the value of accessing, aggregating and comparing data from many centres is great indeed. However this is especially challenging given that ENSAT require clinical and genomic data to be seamlessly linked, but in such a way that the information governance, ethics and privacy concerns of the patients and associated stakeholders involved are visibly satisfied. Key to this is the clear separation of clinical and genomic data sets and support for rigorous patient-identity protecting access control. This is especially challenging when such data sets exist across different organisational boundaries. In this paper we describe a prototype solution offering a security-oriented tailored portal supported by a layered encryption-driven linkage technology (VANGUARD) that offers precisely such patient-privacy protecting capabilities. We describe the architecture, implementation and use to date of this facility to support the ENSAT adrenal cancer research network.