Automated Measurement of Adherence to Traumatic Brain Injury (TBI) Guidelines using Neurological ICU Data

Using a combination of physiological and treatment information from neurological ICU data-sets, adherence to traumatic brain injury (TBI) guidelines on hypotension, intracranial pressure (ICP) and cerebral perfusion pressure (CPP) is calculated automatically. The ICU output is evaluated to capture pressure events and actions taken by clinical staff for patient management, and are then re-expressed as simplified process models. The official TBI guidelines from the Brain Trauma Foundation are similarly evaluated, so the two structures can be compared and a quantifiable distance between the two calculated (the measure of adherence). The methods used include: the compilation of physiological and treatment information into event logs and subsequently process models; the expression of the BTF guidelines in process models within the real-time context of the ICU; a calculation of distance between the two processes using two algorithms (“Direct” and “Weighted”) building on work conducted in th e business process domain. Results are presented across two categories each with clinical utility (minute-by-minute and single patient stays) using a real ICU data-set. Results of two sample patients using a weighted algorithm show a non-adherence level of 6.25% for 42 mins and 56.25% for 708 mins and non-adherence of 18.75% for 17 minutes and 56.25% for 483 minutes. Expressed as two combinatorial metrics (duration/non-adherence (A) and duration * non-adherence (B)), which together indicate the clinical importance of the non-adherence, one has a mean of A=4.63 and B=10014.16 and the other a mean of A=0.43 and B=500.0

Evaluating clinical variation in traumatic brain injury data

Current methods of clinical guideline development have two large challenges: 1) there is often a long time-lag between the key results and publication into recommended best practice and 2) the measurement of adherence to those guidelines is often qualitative and difficult to standardise into measurable impact. In an age of ever-increasing volumes of accurate data captured at the bedside in specialist intensive care units, this thesis explores the possibility of constructing a technology that can interpret that data and present the results as a quantitative and immediate measure of guideline adherence. Applied to the Traumatic Brain Injury (TBI) domain, and specifically to the management of ICP and CPP, a framework is developed that makes use of process models to measure the adherence of clinicians to three specific TBI guidelines. By combining models constructed from physiological and treatment ICU data, and those constructed from guideline text, a distance is calculated between the two, and patterns of guideline adherence are inferred from this distance. The framework has been developed into an online application capable of producing adherence output on most standardised ICU datasets. This application has been applied to the Brain-IT and MIMIC III repositories and evaluated on the Philips ICCA bedside monitoring system. Patterns of guideline adherence are presented in a variety of ways including minute-by-minute windowing, tables of non-adherence instances, statistical distribution of instances, and a severity chart summarising the impact of non-adherence in a single number

Levodopa therapy in Parkinson’s disease: Influence on liquid chromatographic tandem mass spectrometricbased 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 (freeþconjugated) 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

Deep Learning Approaches Applied to Image Classification of Renal Tumors: A Systematic Review

Renal cancer is one of the ten most common cancers in the population that affects 65,000 new patients a year. Nowadays, to predict pathologies or classify tumors, deep learning (DL) methods are effective in addition to extracting high-performance features and dealing with segmentation tasks. This review has focused on the different studies related to the application of DL techniques for the detection or segmentation of renal tumors in patients. From the bibliographic search carried out, a total of 33 records were identified in Scopus, PubMed and Web of Science. The results derived from the systematic review give a detailed description of the research objectives, the types of images used for analysis, the data sets used, whether the database used is public or private, and the number of patients involved in the studies. The first paper where DL is applied compared to other types of tumors was in 2019 which is relatively recent. Public collection and sharing of data sets are of utmost importance to increase research in this field as many studies use private databases. We can conclude that future research will identify many benefits, such as unnecessary incisions for patients and more accurate diagnoses. As research in this field grows, the amount of open data is expected to increase.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This article is based upon work from COST Action HARMONISATION (CA20122). This research has been partially funded by the Spanish Government by the project PID2021-127275OB-I00, FEDER “Una manera de hacer Europa”

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.The research leading to these results has received funding from the following sources: The Seventh Framework Programme (FP7/2007–2013) under grant agreement n° 259735 awarded to F B, H T and G E. The study has further been supported by the Deutsche Forschungsgemeinschaft (DFG) within the CRC/Transregio 205/1 ‘The Adrenal: Central Relay in Health and Disease’ to M F, M R, J L, G E, and F B. The authors are grateful to all patients who participated in this research and to Christina Brugger, Katharina Langton and Denise Kaden for excellent technical assistance.S

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 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 body mass index (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

Laryngocele: a rare complication of surgical tracheostomy

BACKGROUND: A laryngocele is usually a cystic dilatation of the laryngeal saccule. The etiology behind its occurrence is still unclear, but congenital and acquired factors have been implicated in its development. CASE PRESENTATION: We present a rare case of laryngocele occurring in a 77-year-old Caucasian woman. The patient presented with one month history of altered voice, no other associated symptoms were reported. The medical history of the patient included respiratory failure secondary to childhood polio at the age of ten; the airway management included a surgical tracheostomy. Flexible naso-laryngoscopy revealed a soft mass arising from the posterior pharyngeal wall obscuring the view of the posterior commissure and vocal folds. The shape of the mass altered with respiration and on performing valsalva maneuver. A plain lateral neck radiograph revealed a large air filled sac originating from the laryngeal cartilages and extending along the posterior pharyngeal wall. The patient was then treated by endoscopic laser marsupialization and reviewed annually. We discuss the complications of tracheostomy and the pathophysiology of laryngoceles and in particular the likely aetiological factors in this case. CONCLUSION: A laryngocele presenting in a female patient with tracheostomy is extremely rare and has not been to date reported in the world literature. A local mechanical condition may be the determinant factor in the pathogenesis of the disease

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)

e-Enabling international cancer research: lessons being learnt in the ENS@T-CANCER Project

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