Clinical trial NCT02983812: Developing Enhanced Prediction Models
Official title: Developing Enhanced Prediction Models to Identify Patients at Risk for Hospital Readmission by Collecting Patient-Generated Health Data
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Acute Myocardial Infraction or Chest Pain
Chronic Obstructive Pulmonary Disease (COPD)
Congestive Heart Failure (CHF)
Diabetes
Pneumonia
Lead sponsor: University of Pennsylvania
Sponsor class: OTHER
First posted: 2016-12-06
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The study population will be drawn from adults admitted to one of the University of Pennsylvania Health System hospitals. Patients will be invited to participate by a member of the research team prior to hospital discharge.

Brief summary: In this study, patients will be prospectively enrolled for data collection to design prediction models that integrate claims data (inpatient, outpatient, and pharmacy), electronic health record data (on clinical, social, and behavioral indicators), and patient-generated activity data. Patients will be randomized to use either a smartphone or a wearable activity tracking device to capture patient-generated health data.

Detailed description: Many hospital readmissions could be prevented if higher risk patients were identified and effective interventions then targeted towards these individuals. However, most existing claims-based predictive models perform poorly and do not provide timely and actionable information. In this study, researchers will prospectively enroll patients for data collection to design prediction models that integrate claims data (inpatient, outpatient, and pharmacy), electronic health record data (on clinical, social, and behavioral indicators), and use wearable devices or smartphones to collect patient-generated data (physical activity and sleep patterns). Patients will be randomized to use either a smartphone or a wearable activity tracking device to capture patient-generated health data.

Eligibility criteria: Inclusion Criteria:

1. Be 18 years or older
2. Be able to provide informed consent
3. Be admitted to the Hospital of the University of Pennsylvania or Penn Presbyterian Medical Center
4. Have a smartphone or tablet compatible with activity tracking devices
5. Plan to be discharged to home

Exclusion Criteria:

Have no medical condition which prohibits them from ambulating or plan for any medical procedure over the next 6 months that would prohibit them from ambulating.

Primary outcomes:
measure: Hospital readmission within 30 days of discharge
timeFrame: 30 days
Secondary outcomes:
measure: The secondary outcome measures include re-hospitalization within 90 days of discharge.
timeFrame: 90 days
measure: Re-hospitalization within 6 months of discharge
timeFrame: 6 months
measure: Emergency department visits within 6 months of discharge
timeFrame: 6 months
measure: Total health care cost utilization in 6 months after discharge
timeFrame: 6 months
Other outcomes:
Locations:
city: Philadelphia
country: United States
facility: Penn Medicine
state: Pennsylvania
Study officials:
affiliation: University of Pennsylvania
name: Mitesh Patel
role: PRINCIPAL_INVESTIGATOR
References:
citation: Patel MS, Polsky D, Kennedy EH, Small DS, Evans CN, Rareshide CAL, Volpp KG. Smartphones vs Wearable Devices for Remotely Monitoring Physical Activity After Hospital Discharge: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open. 2020 Feb 5;3(2):e1920677. doi: 10.1001/jamanetworkopen.2019.20677. No abstract available.
citation: Evans CN, Volpp KG, Polsky D, Small DS, Kennedy EH, Karpink K, Djaraher R, Mansi N, Rareshide CAL, Patel MS. Prediction using a randomized evaluation of data collection integrated through connected technologies (PREDICT): Design and rationale of a randomized trial of patients discharged from the hospital to home. Contemp Clin Trials. 2019 Aug;83:53-56. doi: 10.1016/j.cct.2019.06.018. Epub 2019 Jun 29.