CogMe for the Prevention and Early Detection of Delirium

Delirium is a syndrome defined as an acute disturbance of both consciousness and cognition that tends to fluctuate over time and is caused by the physiological consequences of a medical condition. It is a common disorder in acute care settings, in internal medicine units, in post-operative patients and the intensive care unit. Delirium is associated with increased mortality, longer hospital stays, long-term cognitive impairment and increased healthcare costs. The pathophysiology of delirium is multifactorial and is not completely understood. The prevalence of delirium increases with age and is very common in elderly hospitalized patients. In certain departments delirium rates can reach over 40%. However, delirium is underdiagnosed in almost two thirds of cases or misdiagnosed as depression or dementia. Furthermore, it has been previously shown that the diagnosis of delirium is often delayed, and that the recognition and documentation of delirium by physicians and nurses is far from optimal. Early diagnosis of delirium may improve clinical outcome, with shortened duration of symptoms, decreased length of admission and reduced long-term complications. Clinical studies have demonstrated that delirium may be prevented in up to one-third of cases by multifactored non-pharmacological interventions, yet they can be costly to implement and require specially trained staff members. In addition, they do not usually consider physiological parameters. Three recent technological advances now provide opportunities for a new delirium prevention approach. First, over the recent years vital signs monitoring with wearable sensors powered by advanced processing algorithms has become technically feasible. This development may provide opportunities for early detection of delirium and for detection of physiological triggers of delirium such as dehydration, infections, and lack of sleep. Second, recent advances in virtual dialogue systems (e.g. Amazon's Alexa or Apple's Siri) provide new and exciting opportunities for automatic patient interaction. Devices with voice or multimodal communication can be used by older patients with little or no experience in modern mobile technology. Lastly, recent progress in digitized data acquisition, computing infrastructure and algorithm development, now allow artificial intelligence and machine learning applications to expand into areas in medicine that were previously thought to be only the province of human experts. The combination of these three data sources can greatly improve current prediction models and allow for earlier and more accurate delirium prediction. An automated system which could aid with delirium detection and alert clinicians to a possible onset of the syndrome can greatly improve treatment and outcomes for patients. The CogMe system utilizes current technology to provide a holistic and scalable approach for delirium prediction, detection and prevention covering both physiological and cognitive aspects. The system uses wearables for physiological vitals monitoring and communicates with patients by a dedicated tablet app - the CogMe Personal Assistant (PA). In this study, the data collected by the wearables and the CogMe PA, in combination with patient data from the EMR, will be analyzed retrospectively using machine learning techniques (CogMe Data Analytics) to evaluate the ability of the CogMe system to predict and detect delirium.

A Diagnostic for the Early Detection of Bladder Cancer

Environmental exposures, specifically tobacco smoke, increases the risk of many cancers, including bladder cancer. To date, there are no diagnostics capable of detecting bladder cancer early, that is prior to clinical presentation. Because of this severe limitation, nearly 30% of patients initially present with stage 2 and higher bladder cancer. Stage 2 bladder cancer has a 5-year survival of 50%, while stage 3/4 have a 5-year survival of <20%. Ideally, bladder cancer would be preventable. Unfortunate, this has not come to fruition. If these stage 2-4 bladder cancer cases could be detected at Stage 1 (5-yr survival >94%), then its possible to see an improvement in bladder cancer survival rates (21-23). in this study, a urine-based diagnostic that possesses the potential to accurately identify patients who harbor bladder cancer prior to clinical manifestation will be tested.

PSA Glycomics Assay for Early Detection of Prostate Cancer

Early Detection of Progressive Kidney Disease in Preterm Infants

Objectives and Hypotheses: Infants born preterm and of low birth weight are known to be at increased risk for early onset of cardiovascular and renal disease in later life. This has been related to low nephron mass due to inadequate or early termination of glomerulogenesis in utero and during the perinatal period. Risks for subsequent development of hypertension and kidney disease include excessive weight gain during early life with insulin resistance and supplemental high calorie feedings. Specific Aims The long-term goal is for early diagnosis of those infants who are at risk for future development of hypertension and kidney disease so that investigators might intervene to potentially avert progression to adult disease. The objective of this clinical trial is to acquire data on the natural history of neonatal kidney function and size in infants born preterm during the first year of life. This will be done through the use of standard serum and urine markers as well as non-invasive ultrasound technology. The central hypothesis of this clinical trial is that a subgroup of patients born preterm will demonstrate early markers of kidney injury including elevated serum cystatin C, proteinuria and hypertension. This hypothesis has been formulated on the basis of preliminary data from the group studying this question retrospectively in older children born prematurely who have developed overt kidney disease. The rationale for the proposed research is to develop early serum and demographic markers of pre-clinical kidney disease so that early intervention may occur. Study Design. This is a single-center case-controlled prospective observational study with the rationale of evaluating parameters of renal function including proteinuria, microalbuminuria and cystatin C in preterm infants and associating this with kidney size and blood pressure during the first 10 years of life. Demographics including race, gender and growth will provide important perspectives relative to formula and/or breast feeding with/without high calorie supplements during the first year. Part I of the Trial is enrollment from birth with collection of blood, urine and umbilical cords for histomorphometry. Part II will be the "call-back" at 6 to 10 years of age for follow-up assessment of anthropometric and kidney growth, blood pressure and kidney function.

Rarecells Molecular Biomarkers for Early Detection of Lung Cancer

Researching for tumor biomarkers in the blood, circulating tumor cells (CTCs), and circulating tumor DNA (ctDNA) can non-invasively detect signs of cancer without risk to the patient. These are ideal and risk-free methods for monitoring patients and early detection of lung cancer. This study aims to assess the sensitivity of molecular analyses performed on circulating tumor DNA in the blood and on DNA from circulating tumor cells, isolated using the highly sensitive ISET® method. The purpose is to assess two circulating molecular biomarkers in the field of liquid biopsy in patients with lung cancer: Rarecells ISET® CTC-DNA and ctDNA. Subjects eligible for inclusion in the study are individuals diagnosed with operable lung cancer who will undergo biopsy or surgical resection of the tumor. Upon enrolment in the trial, participants will undergo an assessment including low-dose CT scan, isolation of CTCs by the ISET® method, and separation of plasma for analysis of ctDNA.

AIRFRAME: Artificial Intelligence for Recognition of Fetal bRain AnoMaliEs at Second Trimester Fetal Brain Scan

The application of AI in obstetric ultrasound includes three aspects: structure identification, automatic and standardized measurements, and classification diagnosis. Since obstetric ultrasound is time-consuming, the use of AI could also reduce examination time and improve workflow. Study design: this is a multicenter retrospective observational cohort study and subsequent prospective cohort study. The study design will be organized in two different phases. The first phase, the feasibility retrospective study, has the objective to develop and train AI-Algorithm with normal and abnormal images retrospectively acquired during second trimester ultrasound scan from various international fetal medicine centers. The second phase, a prospective clinical validation, has the objective to test the AI-Algorithm in the assessment of basic fetal brain anatomy in a real clinic setting with real patients from each of the participating fetal medicine centers. Setting: Three (3) fetal medicine centers. Participants: singleton pregnant population who underwent ultrasound examination between 19 - 22 weeks of gestation in the participating centers. Primary endpoint: to validate a novel AI-based technology for the automated assessment of the basic anatomy of the fetal brain which could potentially be used to support second trimester screening scan. Secondary endpoints: To improve the performance of the standard second trimester screening of fetal brain anatomy ensuring its reliable sonographic assessment within a shorter time of execution. To detect higher repeatability and reproducibility, allowing to implement the ultrasound screening also in terms of efficiency on a vast scale, optimizing healthcare resources In the first phase of the study, participating fetal medicine centers will search their electronic databases for images of singleton pregnant women who underwent ultrasound imaging at 19+0 - 22+6 weeks of gestation with any fetal brain anomaly. Normal images of the fetal brain at the same gestational age will be provided by the promoting centers - i.e., Fondazione Policlinico A. Gemelli, IRCCS and University of Parma. Clinical, ultrasound, prenatal and postnatal information of each case will be retrieved from patient's medical records and entered an electronic database collection file by the principal investigator from each participating center. The acquired images will be anonymized, saved as DICOM and shared through a dedicated cloud storage system which will be set up by the bioengineering team. Each center will be able to access the web system using a personal ID and password. In the second phase of the study, the algorithm will be prospectively tested and validated in a real clinical setting with real patients from each of the participating fetal medicine centers. Inclusion and exclusion criteria, imaging protocol and data collection will be the same carried out during the retrospective phase.

Early Detection of Malnutrition in Oncology Patients and Elderly Patients

The main goal of the project is the early detection of malnutrition in oncology patients and hospitalised patients 65 years of age and older, as this has a significant effect on the quality of life and the effectiveness of the services provided oncological treatment. Another goal of the project will be the creation of a unified diagnostic proposal process and methodological material for determining malnutrition. This process will then be designed as the standard of nursing practice for patients with an oncological diagnosis and for persons older than 65 years such that, so that an adequate nutritional intervention is started in time, which will lead to a higher tolerance of demanding treatment and thus to increase the quality of life. The project will include testing the procedure on a sample of approx. 1,500 oncological and 500 geriatric patients. The project is supported by the European Social Fund (Operational Program Employment plus) and the state budget of the Czech Republic and is registered by the Ministry of Labour and Social Affairs of the Czech Republic under ID: CZ.03.02.02/00/22_005/0000282.

Early Detection of Relapses in Stage IV Colorectal Cancer Patients

Novel Strategy for Early Detection of Esophageal Squamous Cell Carcinoma

PRIMARY OBJECTIVE: I. To evaluate the sensitivity and specificity of a diagnostic strategy combining esophageal sponge sampling with the 'EsophaCap' sponge device with use of the EsoCAN assay, a novel molecular biomarker assay, among patients with histologically-confirmed ESCC cases and controls. SECONDARY OBJECTIVES: I. To evaluate the sensitivity and specificity of a diagnostic strategy combining esophageal sponge sampling with the 'EsophaCap' sponge device with use of the the EsoCAN assay, among patients with histologically-confirmed ESD and controls. II. To evaluate the safety and feasibility of 'EsophaCap' a swallowable and retrievable sponge, as a non-invasive strategy for screening and early detection of ESCC and its precursor, ESD, in Tanzania. EXPLORATORY OBJECTIVES: I. Sensitivity and specificity of esophageal sponge sampling with standard cytological examination among histologically-confirmed ESCC cases and controls. II. Sensitivity and specificity of esophageal sponge sampling with standard cytological examination among histologically-confirmed ESD cases and controls. III. To examine methylation levels in new and previously identified genes among patients recruited as suspected ESCC cases who are found to have an alternative diagnosis, with the goal of optimizing the EsoCAN Assay. OUTLINE: Each participant will undergo esophageal sponge sampling suing the 'EsophaCap' sponge device. Participants will be on study for up to 38 days.

Early Detection of Treatment Failure in Metastatic Colorectal Cancer Patients

The general objective of this single-centre, prospective observational cohort study in 100 mCRC-LR patients treated with curative intent along standard of care (SOC), is to obtain real-world data on administered therapies, selected complications, and oncological outcomes, while longitudinally collecting biospecimens to enable correlative research investigating early biological markers of treatment resistance and recurrence. Cryopreservation of sequential blood derivatives, tumor tissue, and stool samples will allow investigation of circulating tumor DNA (ctDNA), T-cell receptor repertoire, somatic cancer mutations, immune and other gene expression, gut microbiome, and soluble factors. The first biological marker that will be investigated in correlative research will be longitudinal measurements of ctDNA targeting 30 oncogenes, 23 axons, and 146 hotspots (Follow It assay, Canexia Health). Additional biological markers will be defined in subsequent amendments to this protocol. The results are expected to provide important insights for the design of future trials investigating ways to personalize therapy, such as to: a) avoid the unnecessary use of neoadjuvant or adjuvant systemic chemotherapy, b) avoid morbid hepatectomies in patients unlikely to benefit, c) test novel preoperative therapies in patients more likely to benefit, and d) modulate the intensity of follow-up.