[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"deterioration-clinical\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:deterioration-clinical":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,4,0,[8,46,73,91],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":29,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":34,"lastUpdatePostDateStruct":35,"startDateStruct":38,"completionDateStruct":40,"leadSponsor":42,"locationsCount":45},"100641971","optimized-novel-wireless-assessment-of-respiratory-and-circulatory-dirstress---alarm-validation-100641971",false,"NCT07649642","Optimized Novel Wireless Assessment of Respiratory and Circulatory Dirstress - Alarm Validation","Validation of the Accuracy and Relevance of Continuous Vital Sign Monitoring Using an AI-algorithm in Hospitalized Patients","ONWARD - AV","Inclusion criteria:\n\nPatients can be included if they meet all the following criteria:\n\n* Age ≥ 18 years\n* Patients assessed by the clinical staff as having an acute condition with risk for clinical deterioration.\n* Expected admission for more than 24 hours to a general ward.\n\nExclusion criteria:\n\nPatients are excluded if they meet any of the following criteria:\n\n* Having implanted electronic devices (such as pacemaker or Implantable Cardioverter Defibrillator (ICD) device).\n* Known pregnancy or breastfeeding\n* Inability to give informed consent.\n* The participant expected not to cooperate with study procedures.","ALL","18 Years",{"count":20,"type":21},100,"ESTIMATED","INTERVENTIONAL",[24],"NA","The ONWARD study evaluates user experiences from continuous vital sign monitoring alarms sent to the clinical staff in case of deviating vital signs at the general hospital ward. User experiences are evaluated though a 5-point likert questionnaire.",[27,28],"Postoperative Care","Deterioration, Clinical",[30,31,32],"Vital sign","continuous monitoring","automated alerts","NOT_YET_RECRUITING","2026-06-10",{"date":36,"type":37},"2026-06-16","ACTUAL",{"date":39,"type":21},"2026-05-28",{"date":41,"type":21},"2028-03-31",{"name":43,"class":44},"Eske Kvanner Aasvang","OTHER",2,{"id":47,"slug":48,"hasResults":11,"nctId":49,"briefTitle":50,"officialTitle":51,"acronym":52,"eligibilityCriteria":53,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":54,"targetDuration":4,"studyType":56,"phases":4,"briefSummary":57,"conditions":58,"keywords":61,"overallStatus":62,"whyStopped":4,"lastUpdateSubmitDate":63,"lastUpdatePostDateStruct":64,"startDateStruct":66,"completionDateStruct":68,"leadSponsor":70,"locationsCount":72},"100476310","adverse-outcome-of-acute-pulmonary-embolism-by-artificial-intelligence-system-based-on-ct-pulmonary-angiography-100476310","NCT05482269","Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography","Prediction of Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography","PEAICTPA","Inclusion Criteria:\n\n* age of ≥ 18 years and a pulmonary embolism diagnosis based on CT pulmonary angiography\n\nExclusion Criteria:\n\n* pregnancy\n* reception of reperfusion treatment before admission\n* missing data regarding CT parameters, echocardiography, cardiac troponin I (c-Tn I), and N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels.",{"count":55,"type":21},2000,"OBSERVATIONAL","The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.",[59,28,60],"Pulmonary Embolism and Thrombosis","Artificial Intelligence",[59,28,60],"RECRUITING","2026-03-08",{"date":65,"type":37},"2026-03-11",{"date":67,"type":37},"2011-01-01",{"date":69,"type":21},"2026-12-31",{"name":71,"class":44},"Shengjing Hospital",1,{"id":74,"slug":75,"hasResults":11,"nctId":76,"briefTitle":77,"officialTitle":78,"acronym":4,"eligibilityCriteria":79,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":80,"targetDuration":82,"studyType":56,"phases":4,"briefSummary":83,"conditions":84,"keywords":85,"overallStatus":62,"whyStopped":4,"lastUpdateSubmitDate":63,"lastUpdatePostDateStruct":86,"startDateStruct":87,"completionDateStruct":89,"leadSponsor":90,"locationsCount":72},"100446848","a-predictive-tool-for-predicting-adverse-outcomes-in-acute-pulmonary-embolism-patients-using-ctpa-100446848","NCT05098769","A Predictive Tool for Predicting Adverse Outcomes in Acute Pulmonary Embolism Patients Using CTPA.","A Predictive Tool for Predicting Adverse Outcomes in Acute Pulmonary Embolism Patients Using Parameters Obtained by Computed Tomographic Pulmonary Angiography.","Inclusion Criteria:\n\n* age of ≥ 18 years and a PE diagnosis based on CT pulmonary angiography\n\nExclusion Criteria:\n\n* pregnancy\n* reception of reperfusion treatment before admission\n* missing data regarding CT parameters, echocardiography, cardiac troponin I (c-Tn I), and N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels.",{"count":81,"type":21},300,"30 Days","This study collected clinical, laboratory, and CT parameters of acute patients with acute pulmonary embolism from admission to predict adverse outcomes within 30 days after admission into hospital.",[59,28],[59,28],{"date":65,"type":37},{"date":88,"type":37},"2021-01-18",{"date":69,"type":21},{"name":71,"class":44},{"id":92,"slug":93,"hasResults":11,"nctId":94,"briefTitle":95,"officialTitle":95,"acronym":96,"eligibilityCriteria":97,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":98,"targetDuration":4,"studyType":22,"phases":100,"briefSummary":101,"conditions":102,"keywords":4,"overallStatus":62,"whyStopped":4,"lastUpdateSubmitDate":103,"lastUpdatePostDateStruct":104,"startDateStruct":106,"completionDateStruct":108,"leadSponsor":110,"locationsCount":72},"100447587","feasibility-and-initial-clinical-impressions-of-predictive-monitoring-integrated-with-the-race-team-100447587","NCT05108376","Feasibility and Initial Clinical Impressions of Predictive Monitoring Integrated With the RACE Team","VSI","Inclusion Criteria:\n\nAdult patient (greater than or equal to 18 years of age) designated for the most aggressive levels of potential intervention (Category 1 status - Full Care and Category 2 status - Full Care except CPR) who belong to one of the following groups:\n\n* Patients who have undergone high risk elective surgery (Whipple procedures, high risk vascular surgery, high risk general surgery, among others)\n* Malignant haematology or oncology patients at high risk for deterioration\n* Patients with infection admitted from the Emergency Department (ED) to the ward\n* Other high-risk patients determined at the discretion of the clinical team\n\nExclusion Criteria:\n\n* Patients admitted to a unit with higher level monitoring (Neurological Acute Assessment Unit, Acute Monitoring Area, Trauma Step-Down, Intensive Care Unit)\n* Patients with Category III (Full Care except Respiratory or Hemodynamic Life Support, or CPR) or IV status (Comfort Care)",{"count":99,"type":21},140,[24],"Rapid response teams (RRTs) have been adopted by hospitals to provide urgent critical care to hospitalized patients who require quick intervention to prevent further deterioration. Early warning scores (EWS) serve as a method to identify patients requiring RRT assessment by analyzing routinely collected data such as vital signs and laboratory results. The Visensia Safety Index (VSI) is an EWS that uses continuous vital sign monitoring and machine learning to identify the likelihood of deterioration and can be integrated with existing hospital data infrastructure. Initial studies of the VSI have both validated the system and found that patients monitored using VSI had a shorter duration of any instability and fewer episodes of serious and persistent instability. The investigators' recent retrospective analysis at The Ottawa Hospital (TOH) identified that implementation of an EWS could have detected earlier deterioration in over half of the patients identified, potentially preventing subsequent ICU admission, severity of illness, and\u002For mortality. Thus, this study aims to determine the feasibility and potential impact of implementing a portable continuous monitoring system with a VSI trigger at TOH to identify patients at high risk of deterioration.",[28],"2025-05-09",{"date":105,"type":37},"2025-05-14",{"date":107,"type":37},"2023-09-19",{"date":109,"type":21},"2025-09",{"name":111,"class":44},"Ottawa Hospital Research Institute"]