Improving Infant Hydrocephalus Outcomes in Uganda: A Longitudinal Prospective Study Protocol for Predicting Developmental Outcomes and Identifying Patients at Risk for Early Treatment Failure after ETV/CPC

Infant hydrocephalus poses a severe global health burden; 80% of cases occur in the developing world where patients have limited access to neurosurgical care. Surgical treatment combining endoscopic third ventriculostomy and choroid plexus cauterization (ETV/CPC), first practiced at CURE Children’s Hospital of Uganda (CCHU), is as effective as standard ventriculoperitoneal shunt (VPS) placement while requiring fewer resources and less post-operative care. Although treatment focuses on controlling ventricle size, this has little association with treatment failure or long-term outcome. This study aims to monitor the progression of hydrocephalus and treatment response, and investigate the association between cerebral physiology, brain growth, and neurodevelopmental outcomes following surgery. We will enroll 300 infants admitted to CCHU for treatment. All patients will receive pre/post-operative measurements of cerebral tissue oxygenation (SO2), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) using frequency-domain near-infrared combined with diffuse correlation spectroscopies (FDNIRS-DCS). Infants will also receive brain imaging, to monitor tissue/ventricle volume, and neurodevelopmental assessments until two years of age. This study will provide a foundation for implementing cerebral physiological monitoring to establish evidence-based guidelines for hydrocephalus treatment. This paper outlines the protocol, clinical workflow, data management, and analysis plan of this international, multi-center trial.

Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

Codeveloping an effective EMPA to maturity in an acute NHS Trust: an implementer report

IntroductionUniversity Hospitals Leicester has codeveloped, with Nervecentre, an Electronic Prescribing and Medicines Administration System that meets specific clinical and interoperability demands of the National Health Service (NHS).MethodsThe system was developed through a frontline-led and agile approach with a project team consisting of clinicians, Information Technology (IT) specialists and the vendor’s representatives over an 18-month period.ResultsThe system was deployed successfully with more than a thousand transcriptions during roll-out. Despite the high caseload and novelty of the system, there was no increase in error rates within the first 3 months of roll-out. Healthcare professionals perceived the new system as efficient with improved clinical workflow, and safe through an integrated medication alert system.DiscussionThis case study demonstrates how NHS trusts can successfully co-develop, with vendors, new IT systems which meet interoperability standards such as Fast Healthcare Interoperability Resources, while improving front line clinical experience.ConclusionAlternative methods to the ‘big bang’ deployment of IT projects, such as ‘gradual implementation’, must be demonstrated and evaluated for their ability to deliver digital transformation projects in the NHS successfully.

COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

Axially rigid steerable needle with compliant active tip control

Steerable instruments allow for precise access to deeply-seated targets while sparing sensitive tissues and avoiding anatomical structures. In this study we present a novel omnidirectional steerable instrument for prostate high-dose-rate (HDR) brachytherapy (BT). The instrument utilizes a needle with internal compliant mechanism, which enables distal tip steering through proximal instrument bending while retaining high axial and flexural rigidity. Finite element analysis evaluated the design and the prototype was validated in experiments involving tissue simulants and ex-vivo bovine tissue. Ultrasound (US) images were used to provide visualization and shape-reconstruction of the instrument during the insertions. In the experiments lateral tip steering up to 20 mm was found. Manually controlled active needle tip steering in inhomogeneous tissue simulants and ex-vivo tissue resulted in mean targeting errors of 1.4 mm and 2 mm in 3D position, respectively. The experiments show that steering response of the instrument is history-independent. The results indicate that the endpoint accuracy of the steerable instrument is similar to that of the conventional rigid HDR BT needle while adding the ability to steer along curved paths. Due to the design of the steerable needle sufficient axial and flexural rigidity is preserved to enable puncturing and path control within various heterogeneous tissues. The developed instrument has the potential to overcome problems currently unavoidable with conventional instruments, such as pubic arch interference in HDR BT, without major changes to the clinical workflow.

Reducing the Electrogram Review Burden Imposed by Insertable Cardiac Monitors

Background: Insertable cardiac monitors (ICMs) are essential for ambulatory arrhythmia diagnosis. However, definitive diagnoses still require time-consuming, manual adjudication of electrograms (EGMs). Objective: To evaluate the clinical impact of selecting only key EGMs for review. Methods: Retrospective analyses of randomly selected Abbott Confirm Rx devices with ≥90 days of remote transmission history was performed, with each EGM adjudicated as true or false positive (TP, FP). For each device, up to 3 “key EGMs” per arrhythmia type per day were prioritized for review based on ventricular rate and episode duration. The reduction in EGMs and TP days (patient-days with at least 1 TP EGM), and any diagnostic delay (from the first TP), were calculated vs. reviewing all EGMs. Results: In 1,000 ICMs over a median duration of 8.1 months, at least one atrial fibrillation (AF), tachycardia, bradycardia, or pause EGM was transmitted by 424, 343, 190, and 325 devices, respectively, with a total of 95716 EGMs. Approximately 90% of episodes were contributed by 25% of patients. Key EGM selection reduced EGM review burden by 43%, 66%, 77%, and 50% (55% overall), while reducing TP days by 0.8%, 2.1%, 0.2%, and 0.0%, respectively. Despite reviewing fewer EGMs, 99% of devices with a TP EGM were ultimately diagnosed on the same day vs. reviewing all EGMs. Conclusions: Key EGM selection reduced the EGM review substantially with no delay-to-diagnosis in 99% of patients exhibiting true arrhythmias. Implementing these rules in the Abbott patient care network may accelerate clinical workflow without compromising diagnostic timelines.

Development and Pilot Implementation of a Patient Oriented Discharge Summary for Critically Ill Patients (PODS-ICU)

Background - Patients leaving the intensive care unit (ICU) often experience gaps in care due to deficiencies in discharge communication. This study aimed to develop an ICU specific patient-oriented discharge summary tool (PODS-ICU) and pilot test the tool for acceptability and feasibility.Methods - Patient-partners, ICU clinicians, and researchers met to discuss ICU patients’ specific informational needs and design the PODS-ICU through several cycles of iterative revisions. Research team nurses piloted the PODS-ICU with patient and family-caregiver participants in two ICUs in Calgary, Canada. Follow-up surveys on the PODS-ICU and its impact on discharge were administered to participants and ICU nurses.Results – Fifteen patient and family-caregiver participants were administered the PODS-ICU. Most participants felt that their discharge from the ICU was good or better (n=13), and some (n=9) participants reported a good understanding of why the patient was in ICU. Most participants (n=12) reported that they understood ICU events and impacts on the patient’s health. ICU nurses reported that the PODS-ICU was “not reasonable” in their daily clinical workflow due to “time constraint”. Conclusions - PODS-ICU improves patients and family-caregivers’ understanding of ICU events and health-implications but requires better integration with existing care processes to be feasible. Patient or Public Contribution – This work involved patient partners (i.e., individuals with lived experience as patients or family-caregivers) in tool development, study design, participant recruitment, and manuscript preparation.

Acute Infectious Gastroenteritis: The Causative Agents, Omics-Based Detection of Antigens and Novel Biomarkers

Acute infectious gastroenteritis (AGE) is among the leading causes of mortality in children less than 5 years of age worldwide. There are many causative agents that lead to this infection, with rotavirus being the commonest pathogen in the past decade. However, this trend is now being progressively replaced by another agent, which is the norovirus. Apart from the viruses, bacteria such as Salmonella and Escherichia coli and parasites such as Entamoeba histolytica also contribute to AGE. These agents can be recognised by their respective biological markers, which are mainly the specific antigens or genes to determine the causative pathogen. In conjunction to that, omics technologies are currently providing crucial insights into the diagnosis of acute infectious gastroenteritis at the molecular level. Recent advancement in omics technologies could be an important tool to further elucidate the potential causative agents for AGE. This review will explore the current available biomarkers and antigens available for the diagnosis and management of the different causative agents of AGE. Despite the high-priced multi-omics approaches, the idea for utilization of these technologies is to allow more robust discovery of novel antigens and biomarkers related to management AGE, which eventually can be developed using easier and cheaper detection methods for future clinical setting. Thus, prediction of prognosis, virulence and drug susceptibility for active infections can be obtained. Case management, risk prediction for hospital-acquired infections, outbreak detection, and antimicrobial accountability are aimed for further improvement by integrating these capabilities into a new clinical workflow.

Methodology for clinical genotyping of CYP2D6 and CYP2C19

Many antidepressants, atomoxetine, and several antipsychotics are metabolized by the cytochrome P450 enzymes CYP2D6 and CYP2C19, and guidelines for prescribers based on genetic variants exist. Although some laboratories offer such testing, there is no consensus regarding validated methodology for clinical genotyping of CYP2D6 and CYP2C19. The aim of this paper was to cross-validate multiple technologies for genotyping CYP2D6 and CYP2C19 against each other, and to contribute to feasibility for clinical implementation by providing an enhanced range of assay options, customizable automated translation of data into haplotypes, and a workflow algorithm. AmpliChip CYP450 and some TaqMan single nucleotide variant (SNV) and copy number variant (CNV) data in the Genome-based therapeutic drugs for depression (GENDEP) study were used to select 95 samples (out of 853) to represent as broad a range of CYP2D6 and CYP2C19 genotypes as possible. These 95 included a larger range of CYP2D6 hybrid configurations than have previously been reported using inter-technology data. Genotyping techniques employed were: further TaqMan CNV and SNV assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel, and, for samples with CYP2D6 hybrid configurations, long-range polymerase chain reactions (L-PCRs) with Sanger sequencing and Luminex. Agena MassARRAY was also used for CYP2C19. This study has led to the development of a broader range of TaqMan SNV assays, haplotype phasing methodology with TaqMan adaptable for other technologies, a multiplex genotyping method for efficient identification of some hybrid haplotypes, a customizable automated translation of SNV and CNV data into haplotypes, and a clinical workflow algorithm.