UM CRMC RecuR Score Pilot

Hypertension Clinical Trial

Official title:

Readmission Risk Score (RecuR Score) Pilot at The University of Maryland Charles Regional Medical Center (UM CRMC)

Clinical Trial Details — Status: Withdrawn

Administrative data

• NCT number: NCT05765903
• Other study ID #: HP-00104036
• Status: Withdrawn
• Phase: N/A
• Start date: March 31, 2024
• Est. completion date: September 2026

Study information

• Verified date: February 2024
• Source: University of Maryland, Baltimore
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This study will look to implement a plan for enhanced transitional care for patients at high risk of unplanned hospital readmission in hopes of reducing their risk for readmission in the first 30 days post discharge from an inpatient encounter. Hospital readmissions are an undesirable occurrence that can increase cost for hospitals, and can cause further negative outcomes for patients. Identifying factors that increase a patient's chances of being readmitted to the hospital, as well as developing an intervention to effectively reduce this risk, has historically been challenging. Our new method uses a combination of common features such as diagnosis and length of hospital stay, with a novel artificial intelligence (AI) algorithm, the RecuR Score model developed by the University of Maryland Medical System, that identifies patients at the highest risk of having an unplanned hospital readmission. Participants identified as higher risk will then be enrolled into our pilot where they will be randomized to receive either the standard of care treatment or an enhanced protocol that includes additional disease education, coordination of home health services, and a focus on their readmission during existing multidisciplinary team huddles. The main goal of this study is to reduce unplanned hospital readmission within 30 days of initial discharge, in those most at risk of being readmitted, using the aforementioned novel methods for identifying these participants and a transitional care intervention. This success of this goal will be analyzed across different readmission risk levels in the study population. Secondary goals of this study include reducing unplanned hospital readmission within 90 days, reducing 30-day post-discharge mortality, and reducing 30- and 90-day emergency department (ED) usage after an initial hospitalization.

Description:

1. BACKGROUND Hospital readmission is an adverse health outcome that incurs significant cost to the healthcare ecosystem. While undesired, unplanned hospital readmission within 30 days of discharge is not uncommon. To improve care quality and reduce unnecessary healthcare costs, in 2013, the Center for Medicare and Medicaid Services (CMS) launched the Hospital Readmissions Reduction Program (HRRP) as part of the Value Based Purchasing (VBP) program to encourage better discharge care coordination. Under the HRRP program, hospitals with high readmission rate incur a payment reduction of up to 3 percent. Since the launch of the HRRP program, reducing hospital readmissions has elevated to a strategic priority of hospitals. Best practices to effectively reduce hospital readmissions while maintaining a healthy operating margin are sought after by hospitals across the country. Studies on most optimal intervention structure and intensity have yet to identify a single effective strategy, and the effectiveness and external validity of interventions in the literature remains uncertain. Across all patients at University of Maryland Charles Regional Medical Center (UM CRMC) between January 2019 and January 2022, the unplanned hospital readmission rate was 11% and this value is as high as 30% across certain highest risk groups. UM CRMC has implemented a Transitional Care Program with Nurse Navigators since 2011 that focuses on patients that are typically known to have a higher rate of readmission (patients whose primary reason for admission is Diabetes, Congestive Heart Failure [CHF], Chronic Obstructive Pulmonary Disease [COPD] and Hypertension). Despite genuine efforts to manage these patients and provide additional support to these patients prior to discharge and post-discharge, the readmission rate at UM CRMC has remained relatively unchanged over the past five years between 7-15% (mean 11% ± 1.5%) with no sustained year-over-year improvement. At the University of Maryland Medical System (UMMS), we have developed an artificial intelligence (AI)-powered risk score called the RecuR Score (Readmission Risk Score). The RecuR Score estimates the risk of 30-day unplanned readmission for patients both in-house and during the 30 days after inpatient discharge. Patients are grouped in one of five score levels (1-5), where a RecuR Score of 1 indicates the lowest risk of readmission and a RecuR Score of 5 indicates the highest risk of readmission. This risk score is retrained monthly using data from patients with encounters at UMMS hospitals. The target population is inpatients, currently in-house non-inpatients (Emergency Department, Observation Unit) who might become inpatients, and previous inpatients within 30 days of discharge. The score uses data from the UMMS electronic health record system (EHR), CRISP (Chesapeake Regional Information System for our Patients - the state-designated Health Information Exchange for Maryland), commercial and non-commercial claims, and the U.S. Census Bureau. A comparison of the performance of the RecuR Score compared to LACE and HOSPITAL on the same patients showed that the Area Under the Receiver Operating Characteristic Curve, sometimes known as the Area Under the Curve (AUC), of the RecuR Score significantly outperforms the other two metrics, even prior to discharge. While LACE is only available at discharge, HOSPITAL is described as most accurate at discharge, and even then, it is outperformed by the RecuR Score at 48 hours post-arrival when the RecuR Score is not at its best The literature review shows that efforts to reduce readmission rates are not consistently effective, and it has been difficult to extract a set of interventions that reliably reduces readmissions. Our team theorizes that efforts to reduce readmission rates are not effective because the patients are not adequately stratified into risk categories resulting in interventions not being used on the patients who will benefit the most from the interventions. This pilot addresses this issue by identifying patients at higher risk of readmission using the RecuR Score. The RecuR Score accurately identifies patients at high risk of readmission with an area under the ROC curve of 0.83. This higher risk population (limited to selected principal diagnoses and other inclusion and exclusion criteria) has a higher readmission rate (19.4%) than the hospital's overall readmission rate (11%), which results in a greater opportunity to reduce the readmission rate for the target population. To address the issue of identifying the most effective interventions, our team also theorizes that the interventions used are not robust, meaning that the impact of the intervention is insufficient. For example, most readmission intervention programs focus on phone calls post-discharge without considering a more complete view of the patient's situation. To address this issue, this pilot is implementing more complex interventions such as additional educational materials, a focus on the patient's readmission risk during interdisciplinary medical team huddles/care transition rounds, and multiple home healthcare programs that cover a broad spectrum of potential interventions. The expectation is that by accurately identifying the higher risk patients and having a broader view of the patients' situation with multiple interventions, we can reduce the 30-day unplanned readmission rate. 2. STUDY OBJECTIVES Offering more complex interventions that are higher intensity than those currently universally provided at UM CRMC. By targeting patients who are at a higher risk of readmission using a novel AI-based risk score, the RecuR Score, resources can be best allocated to those who need them most. These more intense interventions include additional educational materials, emphasis on a patient's readmission risk during their multidisciplinary team huddle, and home health services. For the study, we will only be targeting patients with a high readmission risk (based on the patient's RecuR Score), to test the efficacy of the standardized use of these resources. The first primary hypothesis for this study is that using a novel UMMS algorithm (RecuR Score) to identify patients at a higher risk of unplanned hospital readmission combined with enhanced pre-discharge and follow-up care interventions including, additional educational material about their health, a focus on their readmission risk during interdisciplinary team huddles, and home health care, can reduce 30-day unplanned hospital readmissions in this high-risk group by 30%. The second primary hypothesis in this fallback design trial, is that using the aforementioned enhanced interventions, there will be a 30% reduction of unplanned hospital readmission risk in patients determined to be a medium-high risk of readmission (RecuR Score level 2 or 3). 3. METHODS This is a parallel-group, two-arm, prospective, randomized, non-blinded, fallback design, controlled superiority study of the impact of a new transitional care model for patients determined to be at higher risk of 30-day unplanned hospital readmission conducted at UM CRMC. UM CRMC is an approximately 100-bed community hospital located in Charles County, Maryland and is part of the University of Maryland Medical System. Patients will be 1:1 (equally) randomized to Arm 1 or Arm 2 using a stratified randomization method with stratification by RecuR Score. Participants assigned to Arm 1 will receive the activities in Intervention "A" and the participants assigned to Arm 2 will receive the activities in Intervention "A" AND the activities in Intervention "B." As there is an apparent difference between Arm 1 and Arm 2, neither the patients nor providers will be blinded to the study assignment. Informed consent will be obtained and documented for this study. The target patient population is admitted patients (inpatients) at UM CRMC with a high readmission risk (RecuR Score) and/or specific admission diagnoses that are amenable to peri- and post-discharge interventions, where the goal is to reduce the readmission rate of this high-risk population. This study will use a fallback design with two primary endpoints. The first sequential primary endpoint is 30-day post-discharge unplanned hospital readmission in the overall study population. The second sequential primary endpoint is 30-day post-discharge unplanned hospital readmission in the medium-high risk population (RecuR Score 2 & 3). The significance level (0.05) will be divided evenly between the two primary endpoints, for a significance level of 0.025 for the first primary endpoint and a reserved 0.025 significance level for the second primary endpoint. 4. DATA COLLECTION The patient's Electronic Health Record (EHR) from Epic will be the main source of information about the patient's demographics, admission diagnoses, length of stay, and outcomes data like subsequent hospital encounters in the 90 days post-discharge. The CRISP ("Chesapeake Regional Information System for our Patients") Admission Discharge Transfer (ADT) tool is the source that provides what hospital encounters the enrolled patients have post-discharge if they do not occur at UMMS facilities. 5. DATA ANALYSIS As this is a fallback method design, we will first test the first primary endpoint against a 0.025 significance level. If the first test shows statistical significance, we will be able to add the unused alpha level from the first test to the reserved alpha level, for significance level of 0.05 to test the second primary endpoint. If this test fails to show statistical significance, we will proceed and test the second primary endpoint at the reserved 0.025 significance level. Patient characteristics will be summarized and compared between Arm 1 and Arm 2 for both the overall and RecuR Score 2 and 3 populations. Most results will be compared using a test of difference for two proportions. If the data are multi-categoric, the Mantel-Haenszel method will be used for comparisons. If an event is rare (<5%), the Fisher exact test will be used. Any continuous data will be compared using a t-test or Wilcoxon Rank Sum statistic. If data are greatly skewed, additional methods might be needed. These analyses will be used to present baseline and background characteristics, demonstrating that the analysis is balanced and representative of the general population, or pointing to areas where primary analyses might need to be adjusted to account for imbalances. All analyses of results for primary and secondary endpoints will be based on the Intent-to-Treat principle, where results are computed based on the Arm that the patient was randomized to rather than which interventions they actually received. All comparisons will be done for both the overall and medium-high risk study populations, assuming the study continues to the second primary endpoint. Comparison of hospital readmission will be performed using either z-tests, or Fischer's Exact test, depending on the rarity of the outcome of interest. If imbalances are noted in the baseline characteristics, adjusted regression models will be tested where indicated. Subgroup analysis will be performed, including a specific focus on differing readmission rates between the various RecuR Score risk levels.

Recruitment information / eligibility

• Status: Withdrawn
• Enrollment: 0
• Est. completion date: September 2026
• Est. primary completion date: September 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria

• Patient is in Observation (and is expected to be admitted) or is admitted as an Inpatient Encounter. Consider eligible patients in any unit except Emergency Department.
• Patient has RecuR Score available 24 hours after start of data collection in EHR.
• Patient is at least 18 years of age.
• Participant is willing and able to provide informed consent for the trial.
• Participant has a RecuR Score greater than or equal to 3; OR Participant has a RecuR Score greater than or equal to 2 and length of stay greater than 10 days; OR Participant has a RecuR Score greater than or equal to 2 with admitting diagnosis of COPD, CHF, Diabetes with elevated HbA1c, Hypertension, or pneumonia; OR Participant has any RecuR Score AND current admission is a readmission where participant was not enrolled during any prior admission. Exclusion Criteria
• Patients who were enrolled in the pilot during an earlier inpatient hospital encounter.
• Patients with encounters having length of stay less than 48 hours or greater than 30 days.
• Patients who are not expected to be discharged to "home", e.g., patients who were admitted from skilled nursing facility (SNF) and are expected to be discharged to SNF. Use Admission Source (or disposition field) as an indicator of who may not be discharged home.
• Patients with an admission diagnosis of Septicemia.
• Patients who lack capacity to sign the consent and participate in the study.
• Patients who are not fluent English.
• Patients who are already receiving home health care.
• Patients who the nursing team believes will require home health care post- hospitalization. Post-Hoc Exclusion Criteria
• Patients who leave against medical advice.

Related Conditions & MeSH terms

• Heart Failure
• Hypertension
• Patient Readmission
• Pneumonia
• Pulmonary Disease, Chronic Obstructive

Intervention

Other:
• Standard of Care
Diagnosis education. Follow-appointment scheduling assistance. Offer resources in the community. Offer weekly follow-up calls for one month. Social Determinants of Health (SDoH) assessment.
• Enhanced Care
Additional educational training using computer tablet devices, such as iPads. Focus on readmission risk. Set-up Home Health Services (HHS), Mobile Integrated Healthcare (MIH) or Resources, Education and Access to Community Health (REACH).

Locations

Country	Name	City	State
United States	University of Maryland Charles Regional Medical Center	La Plata	Maryland

Sponsors (2)

Lead Sponsor	Collaborator
University of Maryland, Baltimore	University of Maryland Medical System

Country where clinical trial is conducted

United States, 

References & Publications (12)

Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8. — View Citation

Damery S, Combes G. Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: a retrospective cohort study. BMJ Open. 2017 Jul 13;7(7):e016921. doi: 10.1136/bmjopen-2017-016921. — View Citation

Dhalla IA, O'Brien T, Morra D, Thorpe KE, Wong BM, Mehta R, Frost DW, Abrams H, Ko F, Van Rooyen P, Bell CM, Gruneir A, Lewis GH, Daub S, Anderson GM, Hawker GA, Rochon PA, Laupacis A. Effect of a postdischarge virtual ward on readmission or death for high-risk patients: a randomized clinical trial. JAMA. 2014 Oct 1;312(13):1305-12. doi: 10.1001/jama.2014.11492. — View Citation

Donze JD, Williams MV, Robinson EJ, Zimlichman E, Aujesky D, Vasilevskis EE, Kripalani S, Metlay JP, Wallington T, Fletcher GS, Auerbach AD, Schnipper JL. International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions. JAMA Intern Med. 2016 Apr;176(4):496-502. doi: 10.1001/jamainternmed.2015.8462. — View Citation

Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011 Oct 18;155(8):520-8. doi: 10.7326/0003-4819-155-8-201110180-00008. — View Citation

Jenq GY, Doyle MM, Belton BM, Herrin J, Horwitz LI. Quasi-Experimental Evaluation of the Effectiveness of a Large-Scale Readmission Reduction Program. JAMA Intern Med. 2016 May 1;176(5):681-90. doi: 10.1001/jamainternmed.2016.0833. — View Citation

Kripalani S, Chen G, Ciampa P, Theobald C, Cao A, McBride M, Dittus RS, Speroff T. A transition care coordinator model reduces hospital readmissions and costs. Contemp Clin Trials. 2019 Jun;81:55-61. doi: 10.1016/j.cct.2019.04.014. Epub 2019 Apr 25. — View Citation

Leppin AL, Gionfriddo MR, Kessler M, Brito JP, Mair FS, Gallacher K, Wang Z, Erwin PJ, Sylvester T, Boehmer K, Ting HH, Murad MH, Shippee ND, Montori VM. Preventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trials. JAMA Intern Med. 2014 Jul;174(7):1095-107. doi: 10.1001/jamainternmed.2014.1608. — View Citation

Marafino BJ, Escobar GJ, Baiocchi MT, Liu VX, Plimier CC, Schuler A. Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study. BMJ. 2021 Aug 11;374:n1747. doi: 10.1136/bmj.n1747. — View Citation

McWilliams A, Roberge J, Anderson WE, Moore CG, Rossman W, Murphy S, McCall S, Brown R, Carpenter S, Rissmiller S, Furney S. Aiming to Improve Readmissions Through InteGrated Hospital Transitions (AIRTIGHT): a Pragmatic Randomized Controlled Trial. J Gen Intern Med. 2019 Jan;34(1):58-64. doi: 10.1007/s11606-018-4617-1. Epub 2018 Aug 14. — View Citation

van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, Austin PC, Forster AJ. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010 Apr 6;182(6):551-7. doi: 10.1503/cmaj.091117. Epub 2010 Mar 1. — View Citation

Wang H, Robinson RD, Johnson C, Zenarosa NR, Jayswal RD, Keithley J, Delaney KA. Using the LACE index to predict hospital readmissions in congestive heart failure patients. BMC Cardiovasc Disord. 2014 Aug 7;14:97. doi: 10.1186/1471-2261-14-97. — View Citation

* Note: There are 12 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Number of overall participants with 30-day post-discharge hospital readmission	This is the first primary endpoint of this fallback design study. It measures the number of participants with a hospital readmission in the 30 days post-hospital discharge in the overall study population.	30 days post-hospital discharge
Primary	Number of moderate-high risk participants with 30-day post-discharge hospital readmission	This is the second primary endpoint of this fallback design study. It measures the number of moderate-high risk participants, those having a RecuR Score of 2 or 3, with a hospital readmission in the 30 days post-hospital discharge.	30 days post-hospital discharge
Secondary	30 day post-discharge mortality	Number of participants with 30 day post-discharge mortality	30 days post-hospital discharge
Secondary	30 day post-discharge unplanned hospital readmission	Number of participants with 30 day post-discharge unplanned hospital readmission	30 days post-hospital discharge
Secondary	90-day post-discharge unplanned hospital readmission	Number of participants with 90 day post-discharge unplanned hospital readmission	90 days post-hospital discharge
Secondary	30-day post-discharge emergency department usage	Number of participants with 30 day post-discharge emergency department usage	30 days post-hospital discharge
Secondary	90-day post-discharge emergency department usage	Number of participants with 90 day post-discharge emergency department usage	90 days post-hospital discharge

External Links

•   Community-based Care Transitions Program | CMS Innovation Center
•   Hospital Readmissions Reduction Program (HRRP)
•   National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2017
•   Overview of Clinical Conditions With Frequent and Costly Hospital Readmissions by Payer, 2018
•   Overview of U.S. Hospital Stays in 2016: Variation by Geographic Region