PI NAME + AFFILIATION: Stephanie K. Mueller, MD MPH, Associate Physician, Brigham and Women’s Hospital (BWH), Instructor of Medicine, Harvard Medical School

POTENTIAL CO-INVESTIGATORS:

• Jeffrey Schnipper MD MPH, BWH
• (Potential for collaboration with site-specific co-investigators): Shani Herzig, Greg Ruhnke, Josh Metlay, Jennifer Myers, Ryan Greysen, Sunil Kripalani, Ed Vasilevskis, Neil Sehgal, Grant Fletcher, Kevin O’Leary,  Edmondo Robinson, Scott Flanders, Peter Lindenauer, Sumant Ranji, Mark Williams, Andy Auerbach, David Meltzer

PROGRAM OVERVIEW/INTRODUCTION: Though commonly undertaken to improve care for patients, transfer from one acute care hospital to another (inter-hospital transfer or IHT) can be costly and expose patients to many potential risks, especially given the severity of illness in this patient population and the discontinuity of care that transfers generate. Despite these risks, substantial gaps remain in knowledge about the outcomes for these patients across a variety of diagnoses, as well as various transfer process factors and patient characteristics that may influence these outcomes and that therefore might be targets for safer transfer practices.

KEY CLINICAL QUESTIONS/EVIDENCE GAPS:

• What are patient, hospital, and transfer process predictors of outcomes among transferred patients?

AIMS + HYPOTHESES:

                Specific Aim: To determine the association between transfer process factors, patient and hospital characteristics, and patient outcomes among patients who undergo IHT, using multi-site data.

                Hypothesis: Certain transfer process factors and patient and hospital characteristics are associated with worse patient outcomes among transferred patients.

PRELIMINARY DATA: We are currently in the process of conducting this same proposed study within a single site (BWH), as part of an AHRQ K08 career development award.  We thus hope to utilize the same methodology, expanding this project to multiple sites, to improve statistical power and the generalizability of our findings. At this time, we have gathered all of the relevant data available from computerized data sources on approximately 26,000 patients transferred to BWH from another acute care hospital between January, 2005 and September, 2013. We are developing a data abstraction tool to conduct concurrent medical record review to gather additional variables, and we will complete our analyses evaluating the association of all variables with clinical outcomes in the coming months.

STUDY DESIGN (including study subjects and setting, comparator groups, data sources, outcomes, analysis plan, power and sample size, limitations, and timeline):

1. Study populations: We plan to include patients at least 18 years old who were transferred to participating site hospitals from another acute care facility between January, 2012 and December, 2016 (pending availability of data available encompassing these years). We aim to exclude patients that left against medical advice and patients that were transferred from any closely affiliated acute care hospital (i.e., those that share medical personnel and medical records systems).

2. Variables (Exposures, Outcomes) + Data Sources:

From Computerized Data Sources (PCORnet + New Requests for extra fields): We aim to gather the majority of our variables (exposure and outcome) via computerized data sources, utilizing fields that PCORnet sites are contributing data to, in addition to some extra fields that we will request for the purposes of this study. These variables can be readily adjusted based on data available at the time of the study (i.e., re-defining the variables, removing/adding variables based on data availability). In addition to listing each variable, I have incorporated which PCORnet table the variable can be found (or can be extrapolated from), or if it is a new variable request:

• Patient demographics (Age, sex, race, insurance) [PCORnet Demographics Table]
• Service of admission (i.e., surgery, medicine) [PCORnet Encounter Table or New Request]
• Level of admission upon arrival (ICU vs. not) [PCORnet Encounter Table or New Request]
• Admission diagnosis (ICD-9) [PCORnet Diagnosis Table]
• Primary diagnosis (ICD-9) [PCORnet Diagnosis Table]
• Comorbidity (Elixhauser) [PCORnet Diagnosis Table]
• Severity of Illness (MS DRG-weight based on year admitted) [PCORnet Encounter Table]
• Number of medications at time of transfer [PCORnet Medications Prescribing Table or New Request]
• Number of prior hospitalizations in prior 12 months [New Request]
• Transferred from a hospital that is in-network/affiliated with receiving hospital (Y/N) [New Request]
• Prior relationship to receiving hospital (defined as PCP and/or any clinic visit in 12 months prior to date of transfer) [New Request]
• Distance between receiving hospital and patient’s home (based on zip code of home and hospital address) [PCORnet Demographics Table]
• Distance between receiving hospital and transferring hospital [New Request (extrapolated from transferring hospital name/address]
• Select laboratory values at time of arrival: hemoglobin, sodium, potassium, creatinine, bicarbonate [PCORnet Lab Results Table]
• Day of week of transfer [PCORnet Encounter Table]
• Time of day of arrival [PCORnet Encounter Table]
• Transfer on a pre-holiday/holiday date (Y/N): Dec 24-25, Dec 31, July 3 [PCORnet Encounter Table]
• Time delay between transfer acceptance and arrival of patient [New Request]
• Hospital unit “busyness” (defined as number of admission and discharges to/from the accepting unit on day of patient arrival) [New Request]
• Hospital team “busyness” (defined as the number of admissions and discharges to/from the accepting team on day of patient arrival) [New Request]
• Characteristics of transferring hospital (if able to merge with AHA files based on name of transferring hospital) [New Request-transferring hospital name]

Outcome variables will also be obtained from computerized data sources and will include:

• Cost of hospitalization (at receiving hospital) [New Request]
• Length of Stay (at receiving hospital) [PCORnet Encounter Table]
• Observed-to-Expected Length of Stay (at receiving hospital) [New Request]
• Transfer to a different service (i.e., medicine to surgery) within 48 hours of admission to receiving hospital [PCORnet Encounter Table or New Request]
• Transfer to a higher level of care (i.e., floor to ICU) within 48 hours of admission to receiving hospital [PCORnet Encounter Table or New Request]
• Mortality: 30-day mortality, death within 3 days of transfer, and in-hospital mortality [PCORnet Death Table]
• Observed-to-Expected Mortality [New Request]
• 30-day Readmission [New Request]

From Manual Medical Record Review: In addition to gathering data from electronic sources (above), we aim to engage willing sites to perform manual chart review on randomly selected patients (100 patients/site, evenly distributed over the time course of the study), to gather variables not readily available from electronic sources. We can provide willing sites previously created data-abstraction tools and definitions to gather these data:

• Reason for transfer
• Appropriateness of accepting service
• Information exchange at time of transfer:
  • Availability/Quality of transfer summary
  • Presence of imaging data

3. Analysis Plan:  All analyses will be stratified by primary diagnosis at time of admission to receiving hospital. We will first assess unadjusted associations between each exposure and outcome, using Fisher exact test for discrete outcomes (mortality), and t-test or Wilcoxon rank sum test for continuous outcomes (cost, length of stay), followed by adjusted analyses with addition of all exposures as covariates using multivariate regression analysis (for cost, linear multivariable regression models (with log-transformation of the outcome, if necessary); for length of stay, Poisson regression (or survival analysis); and for mortality outcomes, logistic multivariable regression). Assuming there will be some frequency of missing variables by site, we will address this with use of dummy variables when missing data accounts for > 10% of the n for categorical variables, and multiple imputation when able for other variables. Additionally, time (in years) will be added to the multivariable regression models as a fixed categorical effect to account for time trends. We will also cluster by hospital.

4. Power and Sample Size: Based on limited data that 30-day mortality of transferred patients ranges from 4-11%, and a conservative 15% exposure for any covariate, we will need a sample size of 59,000 patients to detect a 1% difference in 30-day mortality, with 80% power, and an alpha of 0.05 for any particular variable. We know that there are approximately 7,000 patients transferred to BWH annually. Assuming we include up to 10 sites with similar transfer volume, over the 3 years of data we are asking sites to provide, our sample size will be 210,000, thus ample power for this study, allowing for inclusion of sites with lower transfer volume than expected. In analyses of exposures obtained from medical record review, if 7 sites agree to conduct targeted chart review, with 100 patients/per site, that will give us a sample size of approximately 700 patients. With 700 patients, we will be able to detect 0.7 day difference in length of stay with 84% power (assuming a 5-day average length of stay, following a Poisson distribution, and a 5% type 1 error).

5. Limitations: We understand there may be site-specific limitations to the data we are requesting, and that some of the variable data will be incomplete from each site. We plan to account for this in our statistical analyses, utilizing dummy variables and/or imputations for missing data where appropriate.  We understand that there may be other factors that influence the safety of IHT that cannot be captured quantitatively. Lastly, our analysis of factors only available by medical record review may be underpowered for certain rare exposures and for rare outcomes like mortality. 

6. Timeline: With above data collection, additional data requests for each site, we anticipate the timeline of this project to encompass 6 months for IRB approval and finalization of data collection tools, 9 months for data collection and cleaning, and 6 months for analysis and reporting, including manuscript preparation, presentations at meetings, and other dissemination activities.

CHARACTERISTICS OF PARTICIPATING SITES: As we are aiming to examine a cohort of patients who have undergone inter-hospital transfer, ideal participating sites will be those that accept a moderate to large number of IHT patients annually (i.e., tertiary referral centers).  Each current HOMERuN site meets these criteria.

POTENTIAL FUNDERS: To Be Determined, likely will apply for R01 support from AHRQ, or PCORI support for this project within the next 12-18 months. Other potential funders include foundations such as Commonwealth Fund, Moore Foundation, etc.