Study population and groups
After approval from the University of Washington Institutional Review Board, we conducted a retrospective cohort study on patients who received liver transplants from January 1, 2004 to April 1, 2008 who survived to discharge from the transplant admission and thus received outpatient immunosuppressive medications. Those patients initially taking tacrolimus were our study population. Patients were followed for one year. All recipients received immunosuppressive induction therapy with anti-thymocyte globulin (ATG) or basiliximab, depending on surgeon preference and drug availability. Subsequently, patients initially received tacrolimus monotherapy, with an initial target level of 10 mg/dl. The target levels decreased over the year to levels of 5 to 7 mg/dl. Patients with renal dysfunction at the time of their transplant, who need lower levels of tacrolimus, initially received mycophenolate mofetil (MMF) or prednisone or a combination of both added to their immunosuppressive regimen, depending on surgeon preference.
The study involves a before/after design, which included one group of patients before implementation of the automated clinical management system and another after implementation of the system. One group received immunosuppressive management using the paper charting system transplanted from January 1, 2004 to November 30, 2006. Another group, transplanted from December 1, 2006 to April 1, 2008, was tracked with the automated clinical management system.
The primary goal of our study was to determine significant recipient, intraoperative, donor, and postoperative factors associated with clinically important endpoints. The primary endpoints chosen were rejection episodes and tacrolimus toxicity episodes. We posit that if levels of tacrolimus were allowed to remain elevated for longer periods of time, that those patients would experience more side effects of tacrolimus. Likewise, if levels of tacrolimus were allowed to remain inadequate for longer periods of time, then more of those patients could experience rejection episodes. The mortality rate and readmission rate per patient following liver transplantation were also followed for secondary endpoints.
The endpoints were determined within the first year following transplantation. Rejection episodes required treatment for clinical rejection and were confirmed by biopsy. A patient was counted as having rejection if one or multiple rejection episodes occurred during the year. Tacrolimus toxicity events included seizures, tremors, mental confusion, or severe acute renal dysfunction that resolved upon discontinuation of tacrolimus. A patient was determined to have a tacrolimus toxicity if one or multiple episodes occurred during the year.
Additional secondary endpoints were the number of deaths and the number of readmissions to the transplant hospital for each patient, starting at the time of discharge from the transplant admission and continuing for 1 year following transplantation.
Factors for analysis
Baseline liver recipient data collected for our review included age, gender, race, use of interpreter, primary liver disease diagnosis including re-transplantation, receipt of exception model for end-stage liver disease points for hepatocellular carcinoma, cerebral or cardiac and vascular disease (as documented by angiograms), renal disease (as documented by creatinine clearance ≤60 ml/min or evaluation by a nephrologist for renal dysfunction prior to liver transplantation), diabetes mellitus (denoted by requirement for insulin therapy), hypertension (denoted by requirement for antihypertensive therapy), body mass index, Status 1 designation (in the intensive care unit and expected to live ≤7 days), presence of a transjugular intrahepatic portosystemic shunt, requirement for dialysis immediately prior to liver transplantation, and date of transplant. Pre-transplant laboratory data of serum creatinine, total bilirubin, and cholesterol levels were obtained.
Intraoperative and donor data obtained included blood type match between donor and recipient (identical, compatible, or incompatible), split liver versus whole liver allograft, liver transplant alone or with simultaneous kidney transplant, cold ischemia time (the time duration from placement of the liver on ice following the liver's removal from the donor to removal of the liver from ice prior to transplantation) and warm ischemia time (the time duration from removal of the liver from ice prior to transplantation to circulation of blood into the transplanted liver), and the amount of packed red blood cells (PRBC) transfused during the transplant procedure. Donor liver information collected included donor type (donation after cardiac death or donation after brain death), age, gender, race, body mass index, and percent fat in the donor liver.
Post-transplantation data included immunosuppressive induction therapy (ATG or basiliximab) and choice of maintenance immunosuppressive therapy in an intention to treat analysis (tacrolimus with or without MMF and/or prednisone). The method of post-transplant immunosuppressive management (paper charting system or automated management system) was recorded for each patient. Those patients transplanted from January 1, 2004 to November 30, 2006 were followed by the paper charting system. The pre-existing process of using the paper charting system for managing immunosuppressive therapy consisted of the following steps:
- determining when laboratory results would be available;
- transcribing the results into a paper spreadsheet in the recipient satellite transplant record;
- batching the results for several patients;
- finding a physician to review the results and write orders;
- calling the liver transplant recipient to inform the patient to make necessary changes.
Several steps in this process could be problematic, including a delay in knowing when laboratory results were ready for review, not finding the satellite chart, and difficulty in finding a physician to review the laboratory results and prescribe medication changes.
Automated clinical management system
Patients transplanted from December 1, 2006 to April 1, 2008 were followed with the automated system. This system consists of three computer screens in the EHR system that consolidates all clinical information to expedite immunosuppressive review. The EHR system resides on a secure server with access via the internet 24 hours per day. When laboratory results are available, the patient's name is automatically added to an Immuno Daily List. This list includes not only the patient's name but also that patient's transplant coordinator's name and which physician is to review the results. By selecting the patient's name on the Immuno Daily List screen, the Immuno MD Review screen appears that includes the following fields: patient name, date of transplant, age, diagnosis, cytomegalovirus status of donor and recipient, pathology report for last biopsy, date of last rejection episode, any protocols applicable to that patient, comments for target goals for immunosuppressive medications, current immunosuppressive therapy, and all laboratory results with immunosuppressive drug levels. The physician can review this screen and type his/her orders, and the orders appear on the Immuno Daily List. The transplant coordinator reviews the orders, calls the recipient, and notes any changes. All orders and the specific nurse and physician making the medications changes are recorded automatically and authenticated. All dosages are recorded in a master list that appears on a third screen, Immuno Medications.
Cost predictions for the formal cost-effectiveness analysis were determined through interviews with the transplant coordinators and administrative staff regarding the paper and automated systems. The average time for the nursing staff to collect the data, find a physician and present the results, and contact the patient were estimated for the paper system. The average time required in using the automated system was determined by following several patients. The probabilities for the various clinical events were determined from the two study groups. The quality of life years were determined by consensus.
The cost for developing the automated clinical system was determined by the programming costs. The cost of a transplant coordinator's salary was obtained by converting the average salary and benefits from a yearly salary to an hourly salary assuming a 40-hour work week. All costs were standardized for the year 2008.
Continuous variables were given as the mean±SD, and categorical variables were presented as percentages. After checking for normal distributions, the Student's t test was used for testing continuous variables, and the Fisher's Exact test was used for categorical variables. An autoregressive integrated moving average (ARIMA) analysis was used to determine if a change in endpoints occurred over time. Logistic regression was used to determine univariable and multivariable factors associated with the endpoints. To avoid overfitting in both univariable and multivariable logistic regression modeling, clinical reasoning was used to choose the clinical variables best associated with the endpoint. To determine how well each model fit the clinical endpoints, receiver operating characteristic (ROC) curves were developed to determine area under the curve (AUC) values for each multivariable logistic regression model. The statistical software package used was JMP V.7.0.2 (SAS Institute, Inc). p Values< 0.05 were considered significant. TreeAge Suite Pro Healthcare V.1.4.1 (TreeAge Software, Inc) was used to create a decision tree model to determine cost-effectiveness between the new automated clinical management system and the standard paper charting system.