[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"risk-assessment\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:risk-assessment":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,7,0,[8,49,78,116,144,173,200],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":22,"studyType":23,"phases":4,"briefSummary":24,"conditions":25,"keywords":28,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":48},"100634581","adult-disease-risk-prediction-using-wearables-hearing-and-health-data-100634581",false,"NCT07541547","Adult Disease Risk Prediction Using Wearables, Hearing, and Health Data","A Study to Build a Disease Risk Prediction Model for Adults by Integrating Data From Wearable Devices, Hearing Tests, and Multiple Health Databases","Inclusion Criteria:\n\n* Adults aged 18 years and older\n* Living in the community in Taiwan\n* Able to understand the study procedures and provide written informed consent\n* Able and willing to complete the study questionnaire, hearing assessment, and wearable device monitoring procedures\n* Has access to a smartphone and is able to install and use the study-related application, with assistance from study staff if needed\n\nExclusion Criteria:\n\n* Diagnosis of dementia\n* Too frail or has other health conditions that make participation in the study procedures not feasible\n* Bilateral deafness without use of any hearing assistive device\n* Does not have a smartphone or is unable to use a smartphone application required for the study procedures",true,"ALL","18 Years",{"count":20,"type":21},1500,"ESTIMATED","2 Weeks","OBSERVATIONAL","This prospective cohort study aims to develop and validate a personalized disease risk prediction model for adults by integrating multiple sources of health data. The study will recruit community-dwelling adults aged 18 years and older in Taiwan. After providing informed consent, participants will complete a structured questionnaire, undergo pure tone hearing testing, and wear a smartwatch for 2 weeks to collect continuous physiological data, including heart rate and physical activity. With participant authorization, the study will also collect data from personal health records and national health insurance databases to allow longer-term follow-up of health outcomes.\n\nThe main goals of the study are to examine the relationships among hearing, lifestyle factors, and wearable device data; to identify combinations of risk factors associated with progression from health to subclinical or chronic disease states; and to develop analytical methods for integrating heterogeneous health data from questionnaires, physiological monitoring, hearing tests, and medical databases. Machine learning methods will be used to identify important predictors and build risk prediction models.\n\nThe study hypothesis is that combining hearing measures, lifestyle information, wearable physiological data, and longitudinal medical record data will improve the ability to identify individuals at higher risk of future disease compared with using a single source of information alone. The long-term objective is to support early risk identification, personalized health management, and prevention strategies in community adults.",[26,27],"Chronic Disease","Risk Assessment",[29,27,26,30,31,32,33,34,35],"Wearable Devices","Disease Prediction","Machine Learning","Cohort Studies","Physiological Monitoring","Personal Health Records","Health Insurance Claims","RECRUITING","2026-04-13",{"date":39,"type":40},"2026-04-21","ACTUAL",{"date":42,"type":40},"2026-01-09",{"date":44,"type":21},"2029-01",{"name":46,"class":47},"National Health Research Institutes, Taiwan","OTHER",1,{"id":50,"slug":51,"hasResults":11,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":55,"eligibilityCriteria":56,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":57,"targetDuration":59,"studyType":23,"phases":4,"briefSummary":60,"conditions":61,"keywords":4,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":68,"lastUpdatePostDateStruct":69,"startDateStruct":71,"completionDateStruct":73,"leadSponsor":75,"locationsCount":48},"100633517","cardiac-magnetic-resonance-clinical-prediction-model-dilated-cardiomyopathy-100633517","NCT07527715","Cardiac Magnetic Resonance-Clinical Prediction Model-Dilated Cardiomyopathy","Study on Risk Early Warning of Clinical Prediction Model Based on Multi-Parameter Stress Perfusion Cardiac Magnetic Resonance in Adverse Prognosis of Dilated Cardiomyopathy","MPS-CMR-DCM","Inclusion Criteria\n\n1.An elevated left ventricular end-diastolic volume indexed to body surface area and reduced LVEF, compared with published age- and gender-specific reference values Exclusion Criteria\n\n1. significant coronary artery disease (CAD), defined as a stenosis of ˃50% in a major coronary artery\n2. infiltrative disease\n3. valvular cardiomyopathy\n4. arrhythmogenic cardiomyopathy\n5. congenital heart disease",{"count":58,"type":21},2000,"6 Months","Dilated cardiomyopathy (DCM) is a common and serious heart disease characterized by left ventricular enlargement and impaired pumping function, with adverse prognosis (including heart failure, arrhythmia, heart-related hospitalization, and death) being a major concern for patients. Currently, a critical gap exists in accurately predicting which DCM patients are at high risk of these severe outcomes, limiting targeted clinical care.\n\nThis observational, non-invasive study aims to develop and validate a clinical prediction model for early risk warning of adverse prognosis in DCM patients. The model integrates multi-parameter stress perfusion cardiac magnetic resonance (MP stress perfusion CMR)-a safe, high-resolution imaging technique that assesses cardiac structure, function, blood perfusion, and tissue damage under mild stress-and standard clinical data (e.g., age, gender, blood pressure, and routine heart test results).\n\nThe model will be trained and tested using follow-up data from hundreds of DCM patients, with the analysis identifying patterns in CMR and clinical data associated with adverse outcomes. Once validated for accuracy, the model will provide doctors with personalized risk scores to prioritize care for high-risk patients (e.g., early intervention, close monitoring) and avoid over-treatment for lower-risk individuals.\n\nBeyond clinical application, the study will enhance understanding of DCM progression, laying the groundwork for improved diagnostic tools, more effective treatments, and better strategies to prevent DCM-related complications, ultimately improving patient quality of life and reducing mortality.",[62,63,64,65,66,27,67],"Cardiomyopathy, Dilated","Prognosis","Magnetic Resonance Imaging, Cardiac","Death, Sudden, Cardiac","Heart Failure","Stress Perfusion","2026-04-09",{"date":70,"type":40},"2026-04-14",{"date":72,"type":40},"2021-12-01",{"date":74,"type":21},"2038-12-01",{"name":76,"class":77},"Shandong Provincial Hospital","OTHER_GOV",{"id":79,"slug":80,"hasResults":11,"nctId":81,"briefTitle":82,"officialTitle":83,"acronym":4,"eligibilityCriteria":84,"healthyVolunteers":11,"sex":17,"minAge":85,"maxAge":4,"enrollmentInfo":86,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":88,"conditions":89,"keywords":98,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":107,"lastUpdatePostDateStruct":108,"startDateStruct":110,"completionDateStruct":112,"leadSponsor":114,"locationsCount":48},"100592034","frailty-and-muscle-strength-tests-in-older-adults-undergoing-major-surgery-100592034","NCT06988176","Frailty and Muscle Strength Tests in Older Adults Undergoing Major Surgery","Feasibility and Correlation of Functional Muscle Strength Tests and Objective Frailty Measures With Clinical Frailty Scale in Patients Undergoing Major Surgery","Inclusion Criteria:\n\nParticipants may be eligible for this study if they meet the following conditions:\n\n* Age 65 years or older.\n* Scheduled for elective (planned, non-emergency) major abdominal surgery, including colorectal, hepatobiliary, gynecologic, or urologic procedures.\n* Able to attend a routine preoperative evaluation visit.\n* Able and willing to complete brief assessments for muscle strength, walking, breathing strength, memory, and nutrition.\n\nExclusion Criteria:\n\nParticipants will not be eligible for the study if they have any of the following conditions:\n\n* Surgery is an emergency procedure.\n* Unable to walk independently (for example, dependent on a wheelchair or bed-bound).\n* Significant cognitive impairment that prevents understanding or completing study tests.","65 Years",{"count":87,"type":21},100,"The goal of this observational study is to learn if simple tests for frailty and muscle strength can help predict which older adults (age 65 and older) are at higher risk for problems after major abdominal surgery.\n\nThe main questions it aims to answer are:\n\n* Do measures of frailty and muscle strength, taken before surgery, predict complications after surgery?\n* Can these tests be easily done during a routine pre-surgical visit?\n\nParticipants will:\n\n* Complete brief tests measuring muscle strength, breathing strength, physical function, nutrition status, body composition, and memory during a regular pre-surgical clinic appointment.\n* Allow researchers to review their medical records 30 and 90 days after surgery to identify any complications or health problems.",[90,91,92,93,94,95,27,96,97],"Frailty","Sarcopenia","Sarcopenia in Elderly","Muscle Strength","Post Operative Complications","Nutrition Assessment","Elderly (People Aged 65 or More)","Hand Strength",[90,99,100,101,97,102,103,27,95,104,105,106],"sarcopenia","elective abdominal surgery","Muscle strength","Inspiratory muscle strength","Post operative complications","Elderly patients","Cognitive Function","Physical functional performance","2026-01-27",{"date":109,"type":40},"2026-01-28",{"date":111,"type":40},"2025-06-19",{"date":113,"type":21},"2026-08",{"name":115,"class":47},"University of North Carolina, Chapel Hill",{"id":117,"slug":118,"hasResults":11,"nctId":119,"briefTitle":120,"officialTitle":121,"acronym":4,"eligibilityCriteria":122,"healthyVolunteers":11,"sex":17,"minAge":123,"maxAge":124,"enrollmentInfo":125,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":127,"conditions":128,"keywords":132,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":135,"lastUpdatePostDateStruct":136,"startDateStruct":138,"completionDateStruct":140,"leadSponsor":142,"locationsCount":48},"100602907","remote-monitoring-of-asthma-in-children-and-young-people-100602907","NCT07129616","Remote Monitoring of Asthma in Children and Young People","Remote Monitoring of Asthma in Children and Young People - Reducing Risk of Asthma Attack Using a Connected Patient Approach","Inclusion Criteria:\n\n* Children and Young People with a diagnosis of asthma (coded as asthma or suspected asthma) or a prescription of inhaled corticosteroid in the prior 2 years.\n\nExclusion Criteria:\n\n* Alternative non-asthma diagnosis that would require inhaled steroid\n* cystic fibrosis\n* bronchiectasis\n* primary ciliary dyskinaesia","5 Years","17 Years",{"count":126,"type":21},900,"The objective of this study is to determine whether healthcare data and remotely collected patient data can accurately predict asthma attacks in children and young people aged 5-17 years. The main outcome is:\n\nwhen using this new system, is there a reduction in asthma attacks compared with a historic average.\n\nThe whole population of children and young people with asthma will have routine healthcare data monitored, with a subset of people with high risk asthma asked to participate in a more detail study involving remotely monitored data.",[129,130,131,27,31],"Asthma Childhood","Asthma Attack","Remote Monitoring",[133,134],"asthma attack risk reduction","remote monitoring","2026-01-20",{"date":137,"type":40},"2026-01-22",{"date":139,"type":40},"2025-11-20",{"date":141,"type":21},"2027-03-01",{"name":143,"class":47},"University of Edinburgh",{"id":145,"slug":146,"hasResults":11,"nctId":147,"briefTitle":148,"officialTitle":149,"acronym":4,"eligibilityCriteria":150,"healthyVolunteers":11,"sex":151,"minAge":18,"maxAge":4,"enrollmentInfo":152,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":154,"conditions":155,"keywords":158,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":164,"lastUpdatePostDateStruct":165,"startDateStruct":167,"completionDateStruct":169,"leadSponsor":171,"locationsCount":48},"100615029","comparing-cci-and-possum-for-predicting-oncogynecologic-surgery-complications-100615029","NCT07287280","Comparing CCI and POSSUM for Predicting Oncogynecologic Surgery Complications","A Comparison of the Charlson Comorbidity Index (CCI) vs POSSUM Score for Prediction of Perioperative Complications in Patients Undergoing Oncogynecologic Surgery","Inclusion Criteria:\n\n* Age \\> 18 years\n* Patient underwent elective onco-gynecologic surgery\n\nExclusion Criteria:\n\n* Patient required emergency surgery from any indication\n* Patient chart that not contained primary outcome data eg. absent of the anesthetic record","FEMALE",{"count":153,"type":21},300,"With the global rates of gynecologic cancers on the rise, optimizing perioperative care is imperative. Accurate risk prediction is essential for enhancing patient care, directing preoperative interventions, and facilitating informed decision-making in oncology. This research compares two widely-used risk assessment tools: the Charlson Comorbidity Index (CCI) and the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM), in predicting perioperative outcomes. The CCI predominantly addresses comorbidities, providing simplicity and broad applicability, while POSSUM incorporates both physiological and operative factors for a more comprehensive risk assessment. Despite their application across various surgical specialties, the specific utility of these tools in onco-gynecologic surgery remains insufficiently explored. The study aims to evaluate the effectiveness of CCI and POSSUM in predicting perioperative complications, with a focus on the incidence of these complications, length of hospital stay, and 30-day mortality. The implementation of these risk tools may enhance multidisciplinary risk management, thus improving patient outcomes in gynecologic oncology surgery.",[156,27,157],"Gynecologic Surgical Procedures","Perioperative Care",[159,160,161,162,163],"Gynecologic oncology surgery","Perioperative complications","Charlson comorbidity index","POSSUM","Mortality","2025-12-16",{"date":166,"type":40},"2025-12-17",{"date":168,"type":40},"2025-09-01",{"date":170,"type":21},"2027-10-31",{"name":172,"class":47},"Mahidol University",{"id":174,"slug":175,"hasResults":11,"nctId":176,"briefTitle":177,"officialTitle":178,"acronym":4,"eligibilityCriteria":179,"healthyVolunteers":11,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":180,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":182,"conditions":183,"keywords":186,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":190,"lastUpdatePostDateStruct":191,"startDateStruct":193,"completionDateStruct":195,"leadSponsor":197,"locationsCount":199},"100573015","risk-factors-for-complications-after-cranioplasty-100573015","NCT06740773","Risk Factors for Complications After Cranioplasty","A Retrospective Study to Identify Risk Factors for Complications After Cranioplasty","Inclusion Criteria:\n\n* Patients who underwent cranioplasty in the Department of Neurosurgery at Qilu Hospital of Shandong University, Tangdu Hospital of Air Force Medical University, or Daping Hospital, Army Military Medical University between January 1, 2015, and July 31, 2023\n* Diagnosed with cranial defects\n* Possessed complete and accessible electronic medical records\n* Patients had no prior history of cranioplasty\n\nExclusion Criteria:\n\n* Patients with a prior history of cranioplasty\n* Severe comorbidities (such as serious cardiac, liver, kidney and immune system dysfunction)\n* Congenital cranial defects\n* Severe missing data",{"count":181,"type":21},1000,"Cranial defects often result from brain injuries, hemorrhages, strokes, or brain tumors. These conditions can increase pressure inside the skull, and if left untreated, may lead to dangerous complications like brain herniation. To manage this, a common procedure called decompressive craniectomy is performed to reduce intracranial pressure. While this surgery often stabilizes the patient's condition, it leaves a cranial defect that exposes the brain to external risks, including pressure fluctuations and potential damage. In severe cases, patients with larger defects may develop complications such as sinking skin flap syndrome.\n\nCranial reconstruction, also known as cranioplasty, is an important procedure to restore the skull's structure and protect the brain. This surgery can improve brain function, stabilize intracranial pressure, and enhance the patient's appearance. While cranioplasty is a standard neurosurgical procedure, it has a relatively high risk of complications compared to other brain surgeries. Common complications include infections, bleeding, hydrocephalus, and seizures. In severe cases, complications may lead to the failure of the reconstruction.\n\nUnderstanding the factors that contribute to complications after cranioplasty is crucial for neurosurgeons to improve outcomes and reduce risks. This study aims to identify these factors and develop predictive models for postoperative complications of cranioplasty.",[184,185,27],"Cranioplasty","Postoperative Complications",[184,185,187,188,189],"Risk factors","Retrospective Study","predictive model","2025-06-02",{"date":192,"type":40},"2025-06-05",{"date":194,"type":40},"2024-12-17",{"date":196,"type":21},"2025-12-31",{"name":198,"class":47},"Qilu Hospital of Shandong University",3,{"id":201,"slug":202,"hasResults":11,"nctId":203,"briefTitle":204,"officialTitle":205,"acronym":206,"eligibilityCriteria":207,"healthyVolunteers":11,"sex":17,"minAge":85,"maxAge":4,"enrollmentInfo":208,"targetDuration":4,"studyType":209,"phases":210,"briefSummary":212,"conditions":213,"keywords":218,"overallStatus":221,"whyStopped":4,"lastUpdateSubmitDate":222,"lastUpdatePostDateStruct":223,"startDateStruct":225,"completionDateStruct":227,"leadSponsor":229,"locationsCount":4},"100505523","preoperative-focused-cardiac-ultrasound-in-hip-fracture-surgery-precho-100505523","NCT05862493","Preoperative Focused Cardiac Ultrasound in Hip Fracture Surgery (PrEcho)","Preoperative Focused Cardiac Ultrasound in Hip Fracture Surgery - Study Protocol for a Randomized Controlled Trial","PrEcho","Inclusion Criteria:\n\n* Patients ≥ 65 years of age, with American Socieity of Anesthesiologists (ASA) physical status classification 2-4, that are scheduled for acute hip fracture surgery (ICD-codes s72.0, s72.00, s72.01, s72.1, s72.2)\n\nExclusion Criteria:\n\n* Metastatic cancer and\u002For suspect pathological fracture.\n* Concurrent other fracture\u002Fsurgery.\n* Reoperation within 72 hours from primary operation.\n* Severe dementia.\n* Preoperative echocardiography for other reason than participation in the study.",{"count":87,"type":21},"INTERVENTIONAL",[211],"NA","The primary objective of this study is to investigate the impact of preoperative focused transthoracic ultrasound (FOCUS) on intraoperative hypotension and postoperative complications in hip fracture surgery. Our hypothesis is that a preoperative FOCUS along with a hemodynamic optimization protocol will reduce the occurrence of intraoperative drops in blood pressure and post-operative complications.",[214,215,27,216,217],"Echocardiography","Hip Fractures","Anesthesia","Hemodynamic Instability",[219,220],"FOCUS","Point-of-care echocardiography","NOT_YET_RECRUITING","2023-12-07",{"date":224,"type":40},"2023-12-08",{"date":226,"type":21},"2024-02",{"date":228,"type":21},"2026-12",{"name":230,"class":47},"Umeå University"]