Diagnostic Techniques and Procedures

The purpose of the Registry is to provide continuing evaluation and periodic reporting of safety and effectiveness of Medtronic market-released products. The Registry data is intended to benefit and support interests of patients, hospitals, clinicians, regulatory bodies, payers, and industry by streamlining the clinical surveillance process and facilitating leading edge performance assessment via the least burdensome approach.

This study evaluates a certified artificial intelligence (AI) based software system that automatically interprets laboratory test results in combination with a structured, dynamically generated medical history questionnaire. The purpose of the study is to determine how accurately and how safely the system assigns a patient to a category of urgency of medical contact, and how closely the interpretations produced by the system correspond to the assessment of an expert physician. Adults treated at two university hospitals in Katowice, Poland, who are referred for laboratory testing, complete an electronic medical history questionnaire after their laboratory results become available. The system then generates an interpretation for each laboratory result, including a category of urgency of medical contact, a suggested medical specialty and suggested further laboratory tests. The attending physician independently records a clinical assessment of the same laboratory results and medical history without access to the output of the system. An independent expert physician subsequently establishes the reference assessment, blinded to the authorship of the assessments being compared. The interpretation report is released to the participant only after the assessment of the attending physician has been recorded and locked. The study is conducted in two phases. The first phase (609 participants) is exploratory and uses the initial version of the system. The second phase (290 participants) is confirmatory and uses the final, frozen version of the system. All outcome measures are assessed in both phases; the pre-specified confirmatory hypothesis test applies to the second phase. The endpoints of this study are properties of the software system, namely the concordance of its output with an expert reference assessment. Health outcomes of participants are not measured.

This study builds AI models that score diagnostic screening tests, and that predict screening results, clinical judgment, and life expectancy. Longevity Metrics collects a battery of clinical tests on each participant, in whole or in part, and follows every participant for life. The sit-to-rise test and the timed walk are scored by hand today, from a person's count. A model scores the same test from video instead. It also measures what no one can count by eye - speed, asymmetry, steadiness - so one capture yields both the original score and additional measurements, intended to enrich the model and strengthen what it predicts. Every test in a participant's record measures the same body, so the tests are correlated: a test that was performed carries information about one that was not. A model trained across the library learns those relationships and estimates a missing result from the results that are present. Each estimate is checked against records where that part was actually measured, and over decades against death and disease through linkage to the 100-Year Human Aging Study (NCT07563777). The hypothesis is that the full battery can eventually be predicted across modalities with high accuracy using a few short video clips, replacing most in-person screening. That would let preventive screening reach people and places a physical laboratory cannot. How far the input can be reduced is the question this study exists to answer. Every model is a physician-reviewed clinical decision aid until it is cleared by the FDA.

The goal of this interrupted time-series analysis is to evaluate the impact of the de-implementation of urine dipsticks as a diagnostic tool for urinary tract infections (UTIs) in hospitalized patients in the North Denmark Region. The main question it aims to answer is: How does de-implementation of urine dipsticks affect the diagnosis and management of UTIs and related disorders? Specifically, does it change the following parameters: * Number and severity of UTI infections (lower and upper UTI, non-severe and severe) * Antibiotic prescription (overall, antibiotic classes, administration routes, duration, dosages) * Number of urine cultures and number of positive urine cultures * Risks of admission to intensive care units and 30-day mortality * Risk of drug toxicity * Length of hospital stay * Risk of admission to intensive care unit * 30-day risk of readmission after discharge * 6-month risks of Clostridioides difficile enterocolitis and de novo antimicrobial resistance in cultures obtained during routine clinical care. Researchers hypothesize that de-implementing urine dipsticks will lead to a reduced frequency of diagnosed cystitis, reduced antibiotic use, and fewer urine cultures without negatively affecting patient mortality or readmission risk. Researchers will compare the outcomes before and after the discontinuation of urine dipsticks across hospitals in the North Denmark Region. Furthermore, results will be compared to another Danish administrative healthcare region where dipsticks are still in use as well as urine culture data from the primary sector in the North Denmark Region. Since this is a registry-based observational study utilizing data from the electronic patient record system in the North Denmark Region, no direct contact will be made with participants.

Tuberculosis (TB) is one of the top ten causes of death worldwide with approximately 10 million cases globally and 1.2 million deaths. Sub-Saharan Africa carries the highest burden of TB. South Africa has one of the highest HIV and TB rates worldwide with an HIV prevalence rate in adults of 19% and a TB case notification rate of 615/100,000 in 2019. Over many years, focus has been paid to pulmonary TB and extrapulmonary TB (EPTB) has received only little attention even though it accounts for almost a quatre of all TB cases. The diagnosis of EPTB remains challenging simply because sample collection requires invasive procedures in the absence of a blood-based diagnostic test. Spinal TB (spondylitis or spondylodiscitis caused by Mycobacterium tuberculosis) - often known as Pott's disease - accounts for up to 10% of EPTB and affects young children, people with HIV-coinfection and elderly, and often leads to lifelong debilitating disease due to devastating deformation of the spine and compression of neural structures. Little is known with regards to the extent of disease and isolated TB spine as well as a disseminated form of TB spine have been described. The latter presents with a spinal manifestation plus disseminations to other organs such as the lungs, pleura, lymph nodes, the GIT or urinary tract or even the brain. In the Spinal TB X cohort, the investigators aim to describe the clinical phenotype of spinal TB using whole body PET/CT and identify a specific gene expression profile for the different stages of dissemination and compare findings to previously described signatures for latent and active pulmonary TB. A blood-based test for spinal TB would lead to earlier diagnosis and treatment in all settings globally and improve treatment outcome of this devastating disease.