Predicting hypercapnia and hypoxia by the ventilator's built-in software in children on long-term non-invasive ventilation: A pilot study

IntroductionFollow-up of children on long-term non-invasive ventilation (NIV) could be improved by telemonitoring, using the ventilator's built-in software (BIS) parameters as alternative for in-hospital sleep studies to reduce costs, enhance patient independence and contribute to early detection of infections. This pilot study investigated whether analysis of BIS parameters can predict abnormal nocturnal transcutaneous CO2 (TcCO2) and saturation (SpO2) measurements in children on long-term NIV.MethodsChildren on long-term NIV in follow-up at the Antwerp University Hospital were retrospectively included. Nocturnal TcCO2 and SpO2 measurements were collected together with BIS parameters at three different time points: the night of the sleep study (BIS1), mean values from 48 h (BIS2) and 72 h (BIS3) before the sleep study. Predictions were calculated for following outcome measures: % recording time TcCO2 > 46.9 mmHg (%RT TcCO2; abnormal if ≥2%), recording time SpO2 < 93% (RT SpO2; abnormal if >1 h), abnormal TcCO2 or SpO2, mean TcCO2, mean SpO2.Results69 patients were included. %RT TcCO2 was separately predicted by reached tidal volume2 [OR 0.97 (0.93; 1.00); p = 0.051; AUC = 30%] and reached IPAP1 [OR 1.05 (1.00; 1.10); p = 0.050; AUC = 66%]. Leak1 predicted RT SpO2 [OR 1.21 (1.02; 1.43); p = 0.025; AUC = 84%]. Mean TcCO2 correlated with reached tidal volume2 (R2 0.10, p = 0.033).DiscussionCertain BIS parameters can predict nocturnal hypercapnia and desaturation in children on long-term NIV. Future studies with larger sample sizes are warranted to further investigate the predictive value of the identified BIS parameters

Bone metabolic activity in hyperostosis cranialis interna measured with 18F-fluoride PET

F-18-Fluoride PET/CT is a relatively undervalued diagnostic test to measure bone metabolism in bone diseases. Hyperostosis cranialis interna (HCI) is a (hereditary) bone disease characterised by endosteal hyperostosis and osteosclerosis of the skull and the skull base. Bone overgrowth causes entrapment and dysfunction of several cranial nerves. The aim of this study is to compare standardised uptake values (SUVs) at different sites in order to quantify bone metabolism in the affected anatomical regions in HCI patients. Nine affected family members, seven non-affected family members and nine non-HCI non-family members underwent F-18-fluoride PET/CT scans. SUVs were systematically measured in the different regions of interest: frontal bone, sphenoid bone, petrous bone and clivus. Moreover, the average F-18-fluoride uptake in the entire skull was measured by assessing the uptake in axial slides. Visual assessment of the PET scans of affected individuals was performed to discover the process of disturbed bone metabolism in HCI. F-18-Fluoride uptake is statistically significantly higher in the sphenoid bone and clivus regions of affected family members. Visual assessment of the scans of HCI patients is relevant in detecting disease severity and the pattern of disturbed bone metabolism throughout life. F-18-Fluoride PET/CT is useful in quantifying the metabolic activity in HCI and provides information about the process of disturbed bone metabolism in this specific disorder. Limitations are a narrow window between normal and pathological activity and the influence of age. This study emphasises that F-18-fluoride PET/CT may also be a promising diagnostic tool for other metabolic bone disorders, even those with an indolent course

The Role of Imaging in Measuring Disease Progression and Assessing Novel Therapies in Aortic Stenosis

Aortic stenosis represents a growing health care burden in high-income countries. Currently, the only definitive treatment is surgical or transcatheter valve intervention at the end stages of disease. As the understanding of the underlying pathophysiology evolves, many promising therapies are being investigated. These seek to both slow disease progression in the valve and delay the transition from hypertrophy to heart failure in the myocardium, with the ultimate aim of avoiding the need for valve replacement in the elderly patients afflicted by this condition. Noninvasive imaging has played a pivotal role in enhancing our understanding of the complex pathophysiology underlying aortic stenosis, as well as disease progression in both the valve and myocardium. In this review, the authors discuss the means by which contemporary imaging may be used to assess disease progression and how these approaches may be utilized, both in clinical practice and research trials exploring the clinical efficacy of novel therapies

Clinical Data Miner. Towards More Efficient Clinical Study Support (Clinical Data Miner. Naar een efficiëntere ondersteuning van klinische studies)

Early, accurate diagnosis of disease can dramatically improve prognosis. Clinical diagnostic model research attempts to optimize early diagnosis by designing diagnostic models based on variables obtained by the least invasive means. Diagnostic model research currently involves a complex, multidisciplinary workflow involving data collection by clinicians on the one hand, and data preprocessing and machine-learning by machine-learning experts on the other.Due to the traditional lack of integration between software packages used in this workflow, preparing data for analysis can require considerable manual effort. Following data extraction, data have to be inspected for conversion issues. The absence of information about a Case Report Form (CRF) s structure in extracted data further requires manual guidance during preprocessing. As a result, data analysis is typically only performed once, after the data set reaches a certain predetermined size, based on rules of thumb or Monte Carlo simulations.This thesis presents the Clinical Data Miner (CDM) software framework, which integrates data collection, data preprocessing and machine-learning in a single platform. This integration eliminates the error-prone, time-consuming steps of preparing data for analysis, and enables the automation of preprocessing steps that rely on information about a CRF s structure. The increased automation streamlines the diagnostic model research workflow. With its built-in functionality for generating learning curves, it furthermore provides study coordinators insight into how predictive performance evolves as patient set sizes grow. This allows them to make an informed decision about whether to continue or terminate data collection, thereby respectively avoiding both the creation of weakly performing models, as well as unnecessary data collection.Thus, as Electronic Data Capture (EDC) has done for patient data collection, the CDM software framework s functionality should improve the efficiency of diagnostic model studies.Abstract Contents List of Figures List of Tables 1. Introduction 1.1 Clinical diagnostics 1.2 Clinical diagnostic model research 1.3 Workflow inefficiencies 1.4 Clinical Data Miner 1.4.1 Electronic Data Capture 1.4.2 Data analysis 1.5 Automating machine-learning 1.6 Main contributions 1.7 Chapter-by-chapter overview 2. International Endometrial Tumour Analysis 2.1 Introduction 2.2 Endometrial findings at histopathology 2.3 International Endometrial Tumour Analysis (IETA) consortium 2.4 Ultrasound imaging technology 2.5 Studies 2.6 Data collection 2.7 Conclusion 3. Electronic Data Capture 3.1 Introduction 3.2 Existing software 3.3 Requirements 3.3.1 Visual user interface elements 3.3.2 Case Report Forms 3.3.3 Remote Procedure Calls (RPCs) 3.3.4 Authentication and access control 3.3.5 Database structure 3.4 Software development methodology 3.4.1 Programming language & frameworks 3.4.2 Quality assurance 3.4.3 Software configuration management 3.5 Architecture 3.6 Server setup 3.7 Results 3.8 Conclusion 4. Influence of pictograms on data quality 4.1 Introduction 4.2 Methods 4.2.1 Study design 4.2.2 ImgStudy user interface 4.2.3 MediaStudy user interface 4.2.4 Analysis 4.3 Results 4.3.1 Unenhanced ultrasound 4.3.2 Sonohysterography 4.4 Conclusion 5. Feasibility of automating machine-learning 5.1 Introduction 5.2 Data set 5.3 Classification 5.3.1 Logistic regression 5.3.2 Support Vector Machines 5.3.3 Kernel functions 5.3.4 Least-Squares Support Vector Machines 5.4 Model evaluation 5.5 Learning curves 5.6 Analysis 5.7 Conclusion 6. Data analysis integration 6.1 Introduction 6.2 Data access 6.3 Data preprocessing 6.4 Machine-learning 6.5 Statistical analysis 6.6 Jython interface 6.7 Development methodology 6.8 Conclusion 7. Clinical Data Miner results 7.1 Introduction 7.2 International Endometrial Tumour Analysis 7.2.1 Participants 7.2.2 Inclusions 7.3 Other studies 7.4 Data analysis example scripts 7.4.1 Class distribution 7.4.2 Contingency tables 7.4.3 Learning curves 7.4.4 Model predictions 7.5 Conclusion 8. Conclusions and future research 8.1 Achievements 8.2 Future work 8.3 Dissemination 8.4 Conclusion A. Inter-rater agreement studies A.1 Influence of pictograms A.2 Polycystic ovaries (PCOs) A.3 Uterine anomalies A.4 Endomyometrial junction A.5 International Endometrial Tumour Analysis #2 A.6 Image enhancement B. Case Report Forms B.1 Effect of pictograms on data quality B.1.1 Unenhanced ultrasound B.1.2 Sonohysterography C. Feature selection experiments C.1 Algorithms C.2 Learning curves C.2.1 Performance on the full data set C.2.2 Performance on reduced data sets C.3 Feature selection performance robustness Bibliography Curriculum vitae List of publicationsnrpages: 154status: publishe

Decision support for incentive strategies : a rural development application in Central Africa

In IDL-2492