Evaluation of the SIPAT instrument to assess psychosocial risk in heart transplant candidates: A retrospective single center study.

OBJECTIVES: We evaluated the reliability of the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT) in heart transplant (HT) recipients and explored its usefulness in predicting post-transplant outcomes. BACKGROUND: Pre-transplant psychosocial and behavioral risk is associated with post-transplant clinical outcomes. SIPAT is a risk assessment tool created for pre-transplant psychosocial evaluation. METHODS: Via retrospective chart review, three examiners applied the SIPAT to 51 adult HT recipients. Examiners blinded to SIPAT scores extracted data and interviewed clinicians for one-year post-transplant outcomes. Analysis included Intra-class correlation coefficient (ICC), Pearson's correlation coefficient and Chi-square. RESULTS: SIPAT demonstrated strong inter-rater reliability (ICC = 0.89, 95% CI = 0.76-0.96). Compared to those with SIPAT ratings of "Excellent/Good", the "Minimally Acceptable Candidate/High Risk" group was more likely to miss clinic visits (p = 0.004). CONCLUSIONS: The SIPAT tool had strong IRR. Less favorable SIPAT ratings were associated with nonadherence to clinic visits. Further study is warranted to determine association of SIPAT ratings to clinical outcomes.

Preoperative assessment of high-risk candidates to predict survival after heart transplantation.

BACKGROUND: Alternate waiting list strategies expand listing criteria for patients awaiting heart transplantation (HTx). We retrospectively analyzed clinical events and outcome of patients listed as high-risk recipients for HTx. METHODS AND RESULTS: We analyzed 822 adult patients who underwent HTx of whom 111 patients met high-risk criteria. Clinical data were collected from medical records and outcome factors calculated for 61 characteristics. Significant factors were summarized in a prognostic score. Age >65 years (67%) and amyloidosis (19%) were the most common reasons for alternate listing. High-risk recipients were older (63.2±10.2 versus 51.4±11.8 years; P<0.001), had more renal dysfunction, prior cancer, and smoking. Survival analysis revealed lower post-HTx survival in high-risk recipients (82.2% versus 87.4% at 1-year; 59.8% versus 76.3% at 5-year post-HTx; P=0.0005). Prior cerebral vascular accident, albumin <3.5 mg/dL, re-HTx, renal dysfunction (glomerular filtration rate <40 mL/min), and >2 prior sternotomies were associated with poor survival after HTx. A prognostic risk score (CARRS [CVA, albumin, re-HTx, renal dysfunction, and sternotomies]) derived from these factors stratified survival post-HTx in high-risk (3+ points) versus low-risk (0-2 points) patients (87.9% versus 52.9% at 1-year; 65.9% versus 28.4% at 5-year post-HTx; P<0.001). Low-risk alternate patients had survival comparable with regular patients (87.9% versus 87.0% at 1-year and 65.9% versus 74.5% at 5-year post-HTx; P=0.46). CONCLUSIONS: High-risk patients had reduced survival compared with regular patients post-HTx. Among patients previously accepted for alternate donor listing, application of the CARRS score identifies patients with unacceptably high mortality after HTx and those with a survival similar to regularly listed patients.

Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation.

Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information-theoretic, reverse-engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B-cells (NFκB), Fas (TNFRSF6)-associated via death domain (FADD) and c-AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first-neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P < 0.001, Fisher's exact test). Our results confirm the accuracy of ARACNe to reconstruct the PBMC transcriptional network and show the utility of systems biological approaches to identify possible molecular targets and biomarkers.

The economic implications of noninvasive molecular testing for cardiac allograft rejection.

Endomyocardial biopsy is the mainstay for monitoring cardiac allograft rejection. A noninvasive strategy--peripheral blood gene expression profiling of circulating leukocytes--is an alternative with proven benefits, but unclear economic implications. Financial data were obtained from five cardiac transplant centers. An economic evaluation was conducted to compare the costs of outpatient biopsy with those of a noninvasive approach to monitoring cardiac allograft rejection. Hospital outpatient biopsy costs averaged 3297 US dollars, excluding reimbursement for professional fees. Costs to Medicare and private payers averaged 3581 US dollars and 4140 US dollars, respectively. A noninvasive monitoring test can reduce biopsy utilization. The savings to health care payers in the United States can be conservatively estimated at approximately 12.0 million US dollars annually. Molecular testing using gene expression profiling of peripheral circulating leukocytes is a new technology that offers physicians a noninvasive, less expensive alternative to endomyocardial biopsy for monitoring allograft rejection in cardiac transplant patients.

The heart-allocation simulation model: a tool for comparison of transplantation allocation policies.

BACKGROUND: Numerous studies have investigated prognostic factors for the survival of transplant candidates waiting for a donor organ, but little is known about the impact of allocation policies on waiting list outcome. Simulation models would allow a comparison of different policies for allocating donor hearts on pretransplant outcome. METHODS: A model was built for the Eurotransplant waiting list for heart transplantation. Survival and delisting distributions were estimated from the Eurotransplant transplant candidate inflow between 1995 and 2000 (n=7,142). Other characteristics were obtained directly from the transplant candidate inflow of 1999 and 2000 (n=2,097) and the donor organs of 1998 and 1999 (n=1,520). Overall and subgroup waiting list mortality were estimated for allocation policies differing by ABO blood group, border, and clinical profile rules. RESULTS: The model estimated that international organ exchange reduces waiting list mortality in the different countries by 1.9% to 12.4%. An allocation policy incorporating the initial clinical profile of the transplant candidates further reduced waiting list mortality by 1.7%. Changing ABO rules toward identical matching yielded a slightly more equitable survival for the different groups, without an overall effect on mortality. The best possible allocation policy is the policy where organs are allocated to patients that are at highest risk of dying, and withholding organs from patients that would eventually delist because of improvement. CONCLUSIONS: Patients benefit from international organ exchange and by a heart allocation scheme based on clinical profiles. Timely delisting of patients who are-temporarily-too well for transplantation is the best waiting list policy.