[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"algorithms\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:algorithms":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,42],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":18,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":30,"lastUpdatePostDateStruct":31,"startDateStruct":34,"completionDateStruct":36,"leadSponsor":38,"locationsCount":41},"100557044","predictive-algorithms-for-critical-rehabilitation-outcomes-100557044",false,"NCT06532994","Predictive Algorithms for Critical Rehabilitation Outcomes","Development and Validation of a Prediction Algorithms to Estimate the Clinical Effect of Early Rehabilitation on ICU Survivors Received Mechanical Ventilation in the ICU","Inclusion Criteria:\n\n1. Age older than 18 years;\n2. Received mechanical ventilation, including endotracheal intubation and tracheostomy, during ICU admission;\n3. Met the rehabilitation intervention indications outlined in the \"Chinese Expert Consensus on Neurocritical Rehabilitation\" during ICU admission and underwent corresponding early rehabilitation interventions, including but not limited to arousal therapy for consciousness disorders, early active\u002Fpassive mobilization, comprehensive pulmonary rehabilitation, etc.;\n4. No mortality events occurred during ICU admission;\n5. Informed consent form signed by family members or the patient.\n\nExclusion Criteria:\n\n1. Pediatric patients under 18 years of age;\n2. Hospitalized patients in the ICU who did not receive mechanical ventilation;\n3. Patients in the ICU who did not undergo early rehabilitation interventions;\n4. mortality events occurred during ICU admission;\n5. Patients transferred out of the ICU due to treatment abandonment by family members;\n6. Family refusal to sign the informed consent form or patient refusal to sign the informed consent form when conscious and competent.","ALL","18 Years","90 Years",{"count":20,"type":21},250,"ESTIMATED","OBSERVATIONAL","An increasing amount of evidence from evidence-based medicine indicates that early rehabilitation intervention for patients receiving mechanical ventilation is safe and feasible, and can promote functional recovery and reduce hospital stay. However, the conscious state, respiratory function, and daily living activities of these patients after being discharged from the ICU vary greatly, and some patients do not show obvious benefits. How to identify which patients may have benefit from early rehabilitation is a key issue that needs to be addressed in critical care rehabilitation. This study aims to investigate the clinical data related to the disease of the ICU survivors who received mechanical ventilation as the research object, by collecting their clinical data when receiving early rehabilitation intervention, and constructing a clinical prediction model for the efficacy of early rehabilitation intervention in the ICU through the selection of optimal regression equation or machine learning algorithm. The application of this model can effectively determine whether ICU inpatients need early rehabilitation intervention, thereby reducing complication rates and improving their quality of life.",[25,26,27,28],"Intensive Care","Mechanical Ventilation","Rehabilitation","Algorithms","RECRUITING","2026-04-15",{"date":32,"type":33},"2026-04-21","ACTUAL",{"date":35,"type":33},"2024-08-01",{"date":37,"type":21},"2026-12-30",{"name":39,"class":40},"Wuhan University","OTHER",1,{"id":43,"slug":44,"hasResults":11,"nctId":45,"briefTitle":46,"officialTitle":47,"acronym":4,"eligibilityCriteria":48,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":49,"targetDuration":4,"studyType":51,"phases":52,"briefSummary":54,"conditions":55,"keywords":59,"overallStatus":64,"whyStopped":4,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":68,"completionDateStruct":70,"leadSponsor":72,"locationsCount":4},"100634349","the-utility-and-feasibility-of-accessible-diarrhea-etiology-prediction-tool-adept-in-an-informal-healthcare-setting-100634349","NCT07538531","The Utility and Feasibility of Accessible Diarrhea Etiology Prediction Tool (ADEPT) in an Informal Healthcare Setting","A Mobile Health Tool to Improve Antibiotics Stewardship Among Village Doctors in Bangladesh","Inclusion Criteria:\n\n* Village Doctor with antibiotic prescribing authority for children presenting with diarrheal illness\n* Practice in trial location subdistrict\n* Self-report treating a minimum of 5 pediatric diarrhea cases per week\n* Willing to participate in ADEPT training, use ADEPT in clinical practice with pediatric diarrhea patients, and to collect, via an electronic tool, data on patient characteristics and clinical management\n\nExclusion Criteria:\n\n\\- Planning to leave study site prior to completion of study",{"count":50,"type":21},30,"INTERVENTIONAL",[53],"NA","Diarrheal disease remains a leading cause of morbidity and mortality for children under 5 globally. Accepted best practice for managing diarrhea in the absence of blood or suspicion of cholera is rehydration, however in resource poor areas antibiotics are still prescribed at high rates due to pressures such as financial incentives, caregiver expectations, and diagnostic uncertainty. Informal healthcare providers often serve as first point of care for pediatric diarrhea patients in low- and middle- income countries (LMICs) and commonly prescribe antibiotics for pediatric diarrhea at high frequencies.\n\nIn this pilot before-after feasibility trial informally trained healthcare providers will use a mobile phone-based application (Accessible Diarrhea Etiology Prediction Tool, ADEPT) which will allow for the exploration of the acceptability, feasibility, and utility of the tool, as well as ADEPTs ability to decrease inappropriate antibiotic prescribing practices.",[56,28,57,58],"Diarrhea Infectious","Decision Support Systems, Clinical","Clinical Decision-making",[60,61,62,63],"Diarrhea","Treatment Algorithm","mHealth","Antimicrobial stewardship","NOT_YET_RECRUITING","2026-04-13",{"date":67,"type":33},"2026-04-20",{"date":69,"type":21},"2026-04-12",{"date":71,"type":21},"2026-06-30",{"name":73,"class":40},"Daniel Leung"]