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.

Pheochromocytoma and paraganglioma: clinical feature-based disease probability in relation to catecholamine biochemistry and reason for disease suspicion.

OBJECTIVE: Hypertension and symptoms of catecholamine excess are features of pheochromocytomas and paragangliomas (PPGLs). This prospective observational cohort study assessed whether differences in presenting features in patients tested for PPGLs might assist establishing likelihood of disease. DESIGN AND METHODS: Patients were tested for PPGLs because of signs and symptoms, an incidental mass on imaging or routine surveillance due to previous history or hereditary risk. Patients with (n = 245) compared to without (n = 1820) PPGLs were identified on follow-up. Differences in presenting features were then examined to assess the probability of disease and relationships to catecholamine excess. RESULTS: Hyperhidrosis, palpitations, pallor, tremor and nausea were 30-90% more prevalent (P < 0.001) among patients with than without PPGLs, whereas headache, flushing and other symptoms showed little or no differences. Although heart rates were higher (P < 0.0001) in patients with than without PPGLs, blood pressures were not higher and were positively correlated to BMI, which was lower (P < 0.0001) in patients with than without PPGLs. From these differences in clinical features, a score system was established that indicated a 5.8-fold higher probability of PPGLs in patients with high than low scores. Higher scores among patients with PPGLs were associated, independently of tumor size, with higher biochemical indices of catecholamine excess. CONCLUSIONS: This study identifies a complex of five signs and symptoms combined with lower BMI and elevated heart rate as key features in patients with PPGLs. Prevalences of these features, which reflect variable tumoral catecholamine production, may be used to triage patients according to likelihood of disease.

Missed clinical clues in patients with pheochromocytoma/paraganglioma discovered by imaging.

CONTEXT: Pheochromocytomas and paragangliomas (PPGLs) are rare but potentially harmful tumors that can vary in their clinical presentation. Tumors may be found due to signs and symptoms, as part of a hereditary syndrome or following an imaging procedure. OBJECTIVE: To investigate potential differences in clinical presentation between PPGLs discovered by imaging (iPPGLs), symptomatic cases (sPPGLs) and those diagnosed during follow-up because of earlier disease/known hereditary mutations (fPPGL). DESIGN: Prospective study protocol, which has enrolled patients from 6 European centers with confirmed PPGLs. SETTING AND PATIENTS: Data were analyzed from 235 patients (37% iPPGLs, 36% sPPGLs, 27% fPPGLs) and compared for tumor volume, biochemical profile, mutation status, presence of metastases and self-reported symptoms. RESULTS: iPPGL patients were diagnosed at a significantly higher age than fPPGLs (p<0.001), found to have larger tumors (p=0.003) and higher metanephrine and normetanephrine levels at diagnosis (p=0.021). Significantly lower than in sPPGL, there was a relevant number of self-reported symptoms in iPPGL (2.9 vs. 4.3 symptoms, p<0.001). In 16.2% of iPPGL, mutations in susceptibility genes were detected, although this proportion was lower than in fPPGL (60.9%) and sPPGL (21.5%). CONCLUSIONS: Patients with PPGLs detected by imaging were older, have higher tumor volume and more excessive hormonal secretion in comparison to those found as part of a surveillance program. Presence of typical symptoms indicates that in a relevant proportion of those patients the PPGL diagnosis had been delayed. Précis: Pheochromocytoma/paraganglioma discovered by imaging are often symptomatic and carry a significant proportion of germline mutations in susceptibility genes.

Plasma methoxytyramine: clinical utility with metanephrines for diagnosis of pheochromocytoma and paraganglioma.

CONTEXT: Measurements of plasma methoxytyramine, the O-methylated dopamine metabolite, are useful for detecting rare dopamine-producing pheochromocytomas and paragangliomas (PPGLs) and head and neck paragangliomas (HNPGLs), but utility for screening beyond that achieved using standard measurements of normetanephrine and metanephrine is unclear. OBJECTIVE: Evaluation of the additional utility of methoxytyramine compared to plasma normetanephrine and metanephrine for diagnosis of PPGLs and HNPGLs. DESIGN: Comparative prospective study. METHODS: Comparison of mass spectrometric-based measurements of plasma methoxytyramine, normetanephrine and metanephrine in 1963 patients tested for PPGLs at six tertiary medical centers according to reference intervals verified in 423 normotensive and hypertensive volunteers. RESULTS: Of the screened patients, 213 had PPGLs and 38 HNPGLs. Using an upper cut-off of 0.10 nmol/L for methoxytyramine, 0.45 nmol/L for metanephrine and age-specific upper cut-offs for normetanephrine, diagnostic sensitivity with the addition of methoxytyramine increased from 97.2% to 98.6% for patients with PPGLs and from 22.1% to 50.0% for patients with HNPGLs, with a small decrease in specificity from 95.9% to 95.1%. Addition of methoxytyramine did not significantly alter areas under receiver operating characteristic curves for patients with PPGLs (0.984 vs 0.991), but did increase (P < 0.05) areas for patients with HNPGLs (0.627 vs 0.801). Addition of methoxytyramine also increased the proportion of patients with PPGLs who showed highly positive predictive elevations of multiple metabolites (70.9% vs 49.3%). CONCLUSIONS: While the benefit of additional measurements of plasma methoxytyramine for the detection of PPGLs is modest, the measurements do assist with positive confirmation of disease and are useful for the detection of HNPGLs.

Development of grid frameworks for clinical trials and epidemiological studies.

E-Health initiatives such as electronic clinical trials and epidemiological studies require access to and usage of a range of both clinical and other data sets. Such data sets are typically only available over many heterogeneous domains where a plethora of often legacy based or in-house/bespoke IT solutions exist. Considerable efforts and investments are being made across the UK to upgrade the IT infrastructures across the National Health Service (NHS) such as the National Program for IT in the NHS (NPFIT) [1]. However, it is the case that currently independent and largely non-interoperable IT solutions exist across hospitals, trusts, disease registries and GP practices - this includes security as well as more general compute and data infrastructures. Grid technology allows issues of distribution and heterogeneity to be overcome, however the clinical trials domain places special demands on security and data which hitherto the Grid community have not satisfactorily addressed. These challenges are often common across many studies and trials hence the development of a re-usable framework for creation and subsequent management of such infrastructures is highly desirable. In this paper we present the challenges in developing such a framework and outline initial scenarios and prototypes developed within the MRC funded Virtual Organisations for Trials and Epidemiological Studies (VOTES) project [2].

Major prognostic role of Ki67 in localized adrenocortical carcinoma after complete resection.

BACKGROUND: Recurrence of adrenocortical carcinoma (ACC) even after complete (R0) resection occurs frequently. OBJECTIVE: The aim of this study was to identify markers with prognostic value for patients in this clinical setting. DESIGN, SETTING, AND PARTICIPANTS: From the German ACC registry, 319 patients with the European Network for the Study of Adrenal Tumors stage I-III were identified. As an independent validation cohort, 250 patients from three European countries were included. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Clinical, histological, and immunohistochemical markers were correlated with recurrence-free (RFS) and overall survival (OS). RESULTS: Although univariable analysis within the German cohort suggested several factors with potential prognostic power, upon multivariable adjustment only a few including age, tumor size, venous tumor thrombus (VTT), and the proliferation marker Ki67 retained significance. Among these, Ki67 provided the single best prognostic value for RFS (hazard ratio [HR] for recurrence, 1.042 per 1% increase; P < .0001) and OS (HR for death, 1.051; P < .0001) which was confirmed in the validation cohort. Accordingly, clinical outcome differed significantly between patients with Ki67 <10%, 10-19%, and ≥20% (for the German cohort: median RFS, 53.2 vs 31.6 vs 9.4 mo; median OS, 180.5 vs 113.5 vs 42.0 mo). Using the combined cohort prognostic scores including tumor size, VTT, and Ki67 were established. Although these scores discriminated slightly better between subgroups, there was no clinically meaningful advantage in comparison with Ki67 alone. CONCLUSION: This largest study on prognostic markers in localized ACC identified Ki67 as the single most important factor predicting recurrence in patients following R0 resection. Thus, evaluation of Ki67 indices should be introduced as standard grading in all pathology reports of patients with ACC.

Verification of step-and-shoot IMRT delivery using a fast video-based electronic portal imaging device.

We present an investigation into the use of a fast video-based electronic portal-imaging device (EPID) to study intensity modulated radiation therapy (IMRT) delivery. The aim of this study is to test the feasibility of using an EPID system to independently measure the orchestration of collimator leaf motion and beam fluence; simultaneously measuring both the delivered field fluence and shape as it exits the accelerator head during IMRT delivery. A fast EPID that consists of a terbium-doped gadolinium oxysulphide (GdO2S:Tb) scintillator coupled with an inexpensive commercial 30 frames-per-second (FPS) CCD-video recorder (16.7 ms shutter time) was employed for imaging IMRT delivery. The measurements were performed on a Varian 2100 C/D linear accelerator equipped with a 120-leaf multileaf-collimator (MLC). A characterization of the EPID was performed that included measurements of spatial resolution, linac pulse-rate dependence, linear output response, signal uniformity, and imaging artifacts. The average pixel intensity for fields imaged with the EPID was found to be linear in the delivered monitor units of static non-IMRT fields between 3x3 and 15x15 cm2. A systematic increase of the average pixel intensity was observed with increasing field size, leading to a maximum variation of 8%. Deliveries of a clinical step-and-shoot mode leaf sequence were imaged at 600 MU/min. Measurements from this IMRT delivery were compared with experimentally validated MLC controller log files and were found to agree to within 5%. An analysis of the EPID image data allowed identification of three types of errors: (1) 5 out of 35 segments were undelivered; (2) redistributing all of the delivered segment MUs; and (3) leaf movement during segment delivery. Measurements with the EPID at lower dose rates showed poor agreement with log files due to an aliasing artifact. The study was extended to use a high-speed camera (1-1000 FPS and 10 micros shutter time) with our EPID to image the same delivery to demonstrate the feasibility of imaging without aliasing artifacts. High-speed imaging was shown to be a promising direction toward validating IMRT deliveries with reasonable image resolution and noise.

An extensive log-file analysis of step-and-shoot intensity modulated radiation therapy segment delivery errors.

We present a study to evaluate the monitor unit (MU), dosimetric, and leaf-motion errors found in the delivery of 91 step-and-shoot IMRT treatment plans performed at three nominal dose rates using a dual modality high energy Linac (Varian 2100 C/D, Varian Medical Systems Inc., Palo Alto, CA) equipped with a 120-leaf multileaf collimator (MLC). The analysis was performed by studying log files generated by the MLC controller system. Recent studies by our group have validated that the automatically generated MLC log files accurately record the actual system delivery. A total of 635 beams were delivered at three nominal dose rates: 100, 300, and 600 MU/min. The log files were manually retrieved and analysis software was developed to extract the recorded MU delivery and leaf positions for each segment. Our analysis revealed that the magnitude of segment MU errors were independent of the planned segment MUs. Segment MU errors were found to increase with dose rate having maximum errors per segment of +/-1.8 MU at 600 MU/min, +/-0.8 MU at 300 MU/min, and +/-0.5 MU at 100 MU/min. The total absolute MU error in each plan was observed to increase with the number of plan segments, with the trend increasing more rapidly for higher dose rates. Three dimensional dose distributions were recomputed based on the observed segment MU errors for three plans with large cumulative absolute MU errors. Comparison with the original treatment plans indicated no clinically significant consequences due to these errors. In addition, approximately 80% of the total segment deliveries reported at least one collimator leaf moving at least 1 mm (projected at isocenter) during segment delivery. Such errors occur near the end of segment delivery and have been previously observed by our group using a fast video-based electronic portal imaging device. At 600 MU/min, between 5% and 23% of the plan MUs were delivered during leaf motion that had exceeded a 1 mm position tolerance. These leaf motion errors were not included in the treatment plan recalculations performed in this study.

Levodopa therapy in Parkinson's disease: influence on liquid chromatographic tandem mass spectrometric-based measurements of plasma and urinary normetanephrine, metanephrine and methoxytyramine.

BACKGROUND: Medication-related interferences with measurements of catecholamines and their metabolites represent important causes of false-positive results during diagnosis of phaeochromocytomas and paragangliomas (PPGLs). Such interferences are less troublesome with measurements by liquid chromatography with tandem mass-spectrometry (LC-MS/MS) than by other methods, but can still present problems for some drugs. Levodopa, the precursor for dopamine used in the treatment of Parkinson's disease, represents one potentially interfering medication. METHODS: Plasma and urine samples, obtained from 20 Parkinsonian patients receiving levodopa, were analysed for concentrations of catecholamines and their O-methylated metabolites by LC-MS/MS. Results were compared with those from a group of 120 age-matched subjects and 18 patients with PPGLs. RESULTS: Plasma and urinary free and deconjugated (free + conjugated) methoxytyramine, as well as urinary dopamine, showed 22- to 148-fold higher (P < 0.0001) concentrations in patients receiving levodopa than in the reference group. In contrast, plasma normetanephrine, urinary noradrenaline and urinary free and deconjugated normetanephrine concentrations were unaffected. Plasma free metanephrine, urinary adrenaline and urinary free and deconjugated metanephrine all showed higher (P < 0.05) concentrations in Parkinsonian patients than the reference group, but this was only a problem for adrenaline. Similar to normetanephrine, plasma and urinary metanephrine remained below the 97.5 percentiles of the reference group in almost all Parkinsonian patients. CONCLUSIONS: These data establish that although levodopa treatment confounds identification of PPGLs that produce dopamine, the therapy is not a problem for use of LC-MS/MS measurements of plasma and urinary normetanephrine and metanephrine to diagnose more commonly encountered PPGLs that produce noradrenaline or adrenaline.

Federating distributed clinical data for the prediction of adverse hypotensive events.

The ability to predict adverse hypotensive events, where a patient's arterial blood pressure drops to abnormally low (and dangerous) levels, would be of major benefit to the fields of primary and secondary health care, and especially to the traumatic brain injury domain. A wealth of data exist in health care systems providing information on the major health indicators of patients in hospitals (blood pressure, temperature, heart rate, etc.). It is believed that if enough of these data could be drawn together and analysed in a systematic way, then a system could be built that will trigger an alarm predicting the onset of a hypotensive event over a useful time scale, e.g. half an hour in advance. In such circumstances, avoidance measures can be taken to prevent such events arising. This is the basis for the Avert-IT project (http://www.avert-it.org), a collaborative EU-funded project involving the construction of a hypotension alarm system exploiting Bayesian neural networks using techniques of data federation to bring together the relevant information for study and system development.