41 Clinical Trials for Various Conditions
NYU Langone Health outreaches to patients to remind them to schedule their appointments by phone or MyChart message.The proposed study will test different outreach methods using a predictive risk model. The goal is to increase gap closure rate by the end of the year.
A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults.
The purpose of this study is to develop algorithms that will help predict future injury and/or re-injury after being returned to duty from a musculoskeletal injury. After completion of an episode of care with a physical therapist, the subjects will undergo a battery of physical performance tests and fill out associated surveys. The subjects will then be followed for a year to identify the occurrence/re-occurence of any injuries. Based on the performance on the physical evaluation tests, algorithms will be derived using regression analysis to predict injury. Subjects will be recruited from the pool of patients that have recently completed physical rehabilitation in physical therapy clinics for their lower extremity or lumbar/thoracic spine injury.
This research study is performed to compare the accuracy of two methods of lymph node evaluation: research method versus standard method. Standard method is what is usually performed as standard of care where the radiologist evaluates the images overall and decides whether each node seen should or should not be biopsied. In the research method, a second radiologist will evaluate the ultrasound images of the lymph nodes separately, and use a small specific checklist of ultrasound appearance to determine whether each node should or should not be biopsied. Results of both the standard and research method will be used to decide which node(s), if any should be biopsied. Neck ultrasound examination, lymph node evaluation by standard method and subsequent lymph node biopsy are part of the standard clinical care. It is less likely but possible that the research method may identify additional lymph nodes for biopsy to check if that lymph node contains thyroid cancer.
Scoliosis is a three-dimensional deformity affecting the orientation and position of the spine. Locally, the shape of the vertebra is also affected. The most common form is adolescent idiopathic scoliosis (AIS) with a prevalence of 1-3% affecting primarily young adolescent females. AIS can either be treated using a brace and in some cases necessitate surgical correction to prevent progressive deformity. Risk factors for progression include female gender, curve magnitude and location, skeletal maturity and growth velocity. However, these risk factors have been shown to be inconsistent in predicting curve progression. Over the past 6 years, the investigators have developed a predictive model of the final Cobb angle in AIS based on 3D spinal parameters. This analysis was based on a prospective cohort of 195 patients that were enrolled upon their initial visit and followed until maturity. This predictive model has a determination coefficient of 0.702. The proposed new study aims at refining and testing the external validity of this model in a larger cohort. The next step towards using the new model in the clinical setting is to redesign the model and to externally validate the model by measuring the agreement between the new method and the traditional Cobb angle at maturity in a larger multicenter study. The objective of this study is to characterize the risk of scoliosis progression based on local three-dimensional vertebral and pelvic measurements present on initial evaluation. Three-dimensional reconstructions will be derived from stereo-radiographs acquired with a new biplanar low-dose radiographic system installed in all 8 clinical sites (EOS system, EOS-Imaging, Paris). These calibrated radiographs will then be used to reconstruct the vertebrae and intervertebral disks at each level as well as the geometry of the pelvis. A series of local and regional parameters will then be calculated from these 3D reconstructions. Correlation analysis will help determine if intervertebral disk wedging, vertebral wedging, transverse plane rotation or pelvic geometry can be used as early predictors of curve progression in AIS. Identifying a new 3D measure of scoliosis associated with rapid curve progression could help predict which curves need early treatment to prevent further progression. The ultimate goal of this research project will be to validate this new predictive model and finally transfer this new predictive tool in the hands of clinicians treating AIS.
The primary purpose of this prospective cohort study is to develop a simplified risk model for post-discharge nausea and vomiting (PDNV) in adult same-day surgery patients in the US that will allow clinicians to identify those patients who would benefit from prophylactic antiemetic strategies.
The overall objective of this study is to support emergency department management of patients' health-related social needs. This study will measure the impact of a decision support system that informs clinicians about which patients are likely to screen positive for a health-related social need. The system uses statistical models to create a health-related social need risk score for each patient. The main questions, the study aims to answer are: * Does providing emergency department clinicians with risk scores on health-related social needs increase screening and referral activities? * Does providing emergency department clinicians with risk scores on health-related social needs change patients' use of healthcare services? The decision support system with health-related social needs risk scores will be introduced for all adult patients at one emergency department. Screening rates, referrals, and subsequent healthcare encounters will be compared with emergency departments that did not have access to the decision support system.
Aging-related functional declines are thought to be caused by hallmark biological processes that ultimately manifest in physical, mental, and metabolic impairments that compromise healthspan and quality of life. Exercise is a multipotent treatment with promise to mitigate most aging hallmarks, but there is substantial variability in exercisƒe responsiveness. Combining endurance and resistance training in alignment with public health guidelines will be used to better understand variable exercise responsiveness in older adults with the ultimate goal of improving each older adult's capacity to attain the many health benefits of exercise.
This study is a prospective, stepped-wedge implementation trial to test the effects of implementing a Clinical Decision Support (CDS) tool for prediction of septic shock in four Emergency Departments within a pediatric healthcare network. The primary outcome will be the proportion of sepsis patients who receive guideline-concordant septic shock care after implementation of the CDS, and the secondary outcome will be time-to-antibiotic after sepsis recognition.
The investigators are studying the duration it takes surgeons to complete their respective surgical cases. The hospital hopes to improve the overall operating room scheduling accuracy from this project.
This study compares two different methodologies of scheduling cases in the operating room.
This study aims to improve how lab results are communicated to older adults by refining a predictive model that uses electronic health record (EHR) data. The model was originally developed to estimate the risk of chronic kidney disease (CKD) progression. Researchers will use existing health data to test and improve the accuracy of the model and explore how it might be adapted for use in other health conditions. The study does not involve direct interaction with patients and is conducted entirely using de-identified data in a secure environment.
The objective is to provide a mechanism to store information regarding the demographic characteristics; pre-, intra-, and post-transplant laboratories; treatment strategies; complications; and outcomes in patients undergoing hepatic transplantation.
This study will evaluate the effectiveness of SIGHT as a clinical support system to prompt provider/patient discussion and shared decision making regarding the need for genetic testing in the form of a chromosomal microarray. Identifying patients at high predicted probability of needing a test in clinical settings will be examined to determine if it decreases the duration of time to testing and increases diagnostic yield. SIGHT requires only data already collected in routine clinical encounters and is calculated prior to a clinical visit at VUMC.
The purpose of this 32 week study is to use an innovative experimental design known as SMART (Sequential Multiple Assignment Randomized Trial), which will allow us to determine the best way to sequence the delivery of teleexercise (referred to as an adaptive intervention), combined with predictive analytics on participant adherence in a stepped program of physical activity interventions. All 257 participants will have access to a library of recorded video exercise content, and a weekly wellness article. Some participants will receive health coaching calls (1st randomization). Analytic data will be used to determine which participants are responding or not responding to the intervention. Participants not responding after 4 weeks will receive either live one on one or group exercise training (2nd randomization). After 8 weeks, the participant will receive only pre recorded exercise content and articles for another 8 weeks. After final surveys, participants will have open access to the website for another 16 weeks where we will passively observe their fitbit and website data. The study outcomes are: The effectiveness of the adaptive interventions Exploring mediating and moderating variables Sensitivity analysis of the predictive analytics
Hyperthermic Intraperitoneal Chemotherapy (HIPEC) is a well-established alternative for patients with peritoneal surface malignancies. Although HIPEC has a predetermined protocol to manage body temperature, the resultant bladder and core-body temperatures are highly variable and unstable in clinical practice. Such results highlight an incomplete understanding of the thermodynamic processes during HIPEC in humans. Previous clinical and animal investigations have studied abdominal hyperthermia, but a full human model incorporating patient variables, heat delivery, and the impact of the circulatory system and anesthesia in HIPEC has not been established. This project seeks to develop and validate a computational thermodynamic model using prospective real-world data from humans undergoing HIPEC surgery. It is hypothesized that by incorporating patient, anesthetic, and perfusion-related variables in a thermodynamic model, the temperatures inside and outside the abdomen during HIPEC can be predicted.
This study will enroll volunteers in an open-format (outside hospital) setting, to complete novel data collection/analysis of biomarkers, facial images, and audio-recording to establish an optimal set of parameters to predict emergent cases of infection via an early warning score, along with actionable personalized information.
This study aims to predict and minimize post-discharge adverse events (AEs) during care transitions through early identification and escalation of patient-reported symptoms to inpatient and ambulatory clinicians by way of predictive algorithms and clinically integrated digital health apps. We will (1) develop and prospectively validate a predictive model of post-discharge AEs for patients with multiple chronic conditions (MCC); (2) combine, adapt, extend, and iteratively refine our EHR-integrated digital health infrastructure in a series of design sessions with patient and clinician participants; (3) conduct a RCT to evaluate the impact of ePRO monitoring on post-discharge AEs for MCC patients discharged from the general medicine service across Brigham Health; and (4) use mixed methods to evaluate barriers and facilitators of implementation and use as we develop a plan for sustainability, scale, and dissemination.
Predicting response to therapy and disease progression in stage IV NSCLC patients treated with pembrolizumab monotherapy, chemotherapy-pembrolizumab combination therapy or chemotherapy alone in the first-line setting.
This study will involve the evaluation of the cost profile of about approximately 241,000 patients (15,000 who are members of the Generations Plus/ Northern Manhattan Health Network AND 226,000 members of a large self insured union trust fund). We will test the hypothesis that as comorbidity scores exceed three-four, patients will have an exponential increase in average yearly cost with a parallel exponential increase in cost variability. We will examine the relationship between comorbidity and health insurance costs and variability in costs among patients enrolled in health plans.
Aim of the study: The main aim is to collect data of patients with lung cancer, and to perform different analyses on this data. The data contains information on patient and tumor characteristics, imaging, and treatment characteristics. With this data it is possible to improve and validate the predictive model for survival and long term toxicity in lung cancer by multicentric prospective data collection. The long term aim, beyond this specific study, is to build a Decision Support System based on the predictive models validated in this study. Hypothesis: The general hypothesis is that we get a better prediction in terms of AUC (area under the curve) of survival and long term toxicity when we combine multifactorial variables. These variables consist of information from clinical data, imaging data, data related to treatment type and treatment quality.
Serious mental illnesses require years of monitoring and adjustments in treatment. Stress, substance abuse or reduced medication adherence cause rapid worsening of symptoms, with consequences that include job loss, homelessness, suicide, incarceration, and hospitalization. Treatment visits can be infrequent. Illness exacerbations usually occur with no clinician awareness, leaving little opportunity to make treatment adjustments. Tools are needed that quickly detect illness worsening. At least two thirds of Veterans with serious mental illness use a smart phone. These phones generate data that characterize sociability, activity and sleep. Changes in these are warning signs for relapse. Members of this project developed an app that monitors and transmits these mobile data. This project studies passive mobile sensing that allows Veterans to self-track their activities, sociability and sleep; and studies whether this can be used to track symptoms. The project intends to produce a mobile platform that monitors the clinical status of patients, identifies risk for relapse, and allows early intervention.
The purpose of this study is to create models out of tissue samples and treat those models with the same immunotherapy treatment the patient will be receiving, in order to validate this process and to predict responses to therapies and use it to choose the best treatments for people in the future. The researchers will then examine the direct effects of the treatment on those models.
The purpose of this randomized, controlled pilot study is to evaluate the performance of this novel Anemia Controller (vis-à-vis standard of care) for anemia management in hemodialysis patients. Since the Anemia Controller is designed to bring patients to a pre-defined Hgb target level and keep them there, the target population for this study are patients whose Hgb levels are currently not well-controlled (rather than patients who are already relatively stable within the Hgb target range under a standard anemia management algorithm). Specifically, therefore, the target population for this clinical study are chronic hemodialysis patients who are exhibiting Hgb cycling.
The objective of this randomized crossover clinical trial is to 1) assess the efficacy and safety of an automated insulin delivery (AID) system using a Model Predictive Control (MPC) algorithm versus sensor augmented pump therapy (SAP)/Predictive Low Glucose Suspend (PLGS) in people with type 1 diabetes, and 2) assess the impact of different meal macronutrient content on glucose control when using AID and SAP.
This is a pilot study to test the utility of an integrated approach in the management of the anemia of chronic kidney disease through the administration of both an erythropoietic stimulating agent and iron. Subjects will be studied for 6 months during which all iron dosing will be recommended using a computer based tool using model predictive control. Comparisons will be made to the 6 months prior to enrollment in to the study.
This clinical trial is a study to assess the performance of an automated glucose control system (Artificial Pancreas, AP) device in home settings for subjects with type 1 diabetes. Specifically, the investigators will test zone model predictive control AP that will be enhanced by run-to-run optimizations of basal rates (BR) and insulin to carbohydrate ratios (CR).
This clinical trial is a feasibility study to assess the performance of an Artificial Pancreas (AP) device using the Artificial Pancreas System (APS©) platform for subjects with type 1 diabetes. The device is a closed-loop between a DexCom™ SEVEN® PLUS (DexCom™ Corp, San Diego, CA) continuous glucose monitor (CGM) and a OneTouch® Ping® Glucose Monitoring System (Animas Corp, Westchester, PA) subcutaneous insulin delivery pump (CSII). The AP device is controlled by a zone-Model Predictive Control (zone-MPC) algorithm augmented by a safety algorithm named the Health Monitoring System (HMS). The clinical study will include 12 to 20 adults subjects aged 21 to 65 years old.
This is a scientific research study that will look at how a "closed-loop" system and the drug Pramlintide may work together to improve blood sugar control in people with type 1 diabetes mellitus. Pramlintide is approved by the Food and Drug Administration (FDA) and is given as an injection (subcutaneous) that works with insulin to lower blood sugar.
This study will be an extension of the Spinal Cord Injury Vocational Integration Program (SCI-VIP). The study involves research about how to help Veterans with spinal cord injury (SCI) gain employment. Vocational rehabilitation is a special field of service aimed at putting persons with disabilities in the best possible position to become employed. The Veterans Administration has a long history of providing vocational rehabilitation for Veterans with mental health issues and has recently started providing similar services to persons with physical disabilities, including SCI. Past research has shown that vocational rehabilitation is effective in helping some Veterans with spinal cord injury (SCI) gain employment. The extension of this work through PrOMOTE study will establish a large national database of over 2000 Veterans with SCI, containing extensive employment, medical, functional and psychosocial data. The study will analyze both quantitative and qualitative measures to maximize its findings.