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ABSTRACT: Social adaptability index (SAI) is the composite index of socioeconomic status based upon employment status, education level, marital status, substance abuse and income. It has been used in the past to define populations at higher risk for inferior clinical outcomes. The objective of this retrospective study was to evaluate the association of the SAI with renal transplant outcome.
We used data from the clinical database at the Beth Israel Deaconess Medical Center Transplant Institute, supplemented with data from United Network for Organ Sharing for the years 2001-09. The association between SAI and graft loss and recipient mortality in renal transplant recipients was studied using Cox model in the entire study population as well as in the subgroups based on age, race, sex and diabetes status.
We analyzed 533 end-stage renal disease patients (mean age at transplant 50.8 ± 11.8 years, 52.2% diabetics, 58.9% males, 71.1% White). Higher SAI on a continuous scale was associated with decreased risk of graft loss [hazard ratio (HR) 0.89, P < 0.05, per 1 point increment in the SAI] and decreased risk of recipient mortality (HR 0.84, P < 0.01, per 1 point increment in the SAI). Higher SAI was also significantly associated with decreased risk for graft loss/recipient mortality in some study subgroups (age 41-65 years, males, non-diabetics).
SAI has an association with graft and recipient survival in renal transplant recipients. It can be helpful in identifying patients at higher risk for inferior transplant outcome as a target population for potential intervention.
Nephrology Dialysis Transplantation 03/2012; 27(3):1239-45. · 3.40 Impact Factor
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American Journal of Kidney Diseases 04/2011; · 5.43 Impact Factor
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ABSTRACT: Prediction of kidney transplant outcome represents an important and clinically relevant problem. Although several prediction models have been proposed based on large, national collections of data, their utility at the local level (where local data distributions may differ from national data) remains unclear. We conducted a comparative analysis that modeled the outcome data of transplant recipients in the national US Renal Data System (USRDS) against a representative local transplant dataset at the University of Utah Health Sciences Center, a regional transplant center. The performance of an identical set of prediction models was evaluated on both national and local data to assess how well national models reflect local outcomes. Compared with the USRDS dataset, several key characteristics of the local dataset differed significantly (e.g., a much higher local graft survival rate; a much higher local percentage of white donors and recipients; and a much higher proportion of living donors). This was reflected in statistically significant differences in model performance. The area under the receiver operating characteristic curve values of the models predicting 1, 3, 5, 7, and 10-year graft survival on the USRDS data were 0.59, 0.63, 0.76, 0.91, and 0.97, respectively. In contrast, in the local dataset, these values were 0.54, 0.58, 0.58, 0.61, and 0.70, respectively. Prediction models trained on a national set of data from the USRDS performed better in the national dataset than in the local data. This might be due to the differences in the data characteristics between the two datasets, suggesting that the wholesale adoption of a prediction model developed on a large national dataset to guide local clinical practice should be done with caution.
ASAIO journal (American Society for Artificial Internal Organs: 1992) 03/2011; 57(3):206-12. · 1.39 Impact Factor
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ABSTRACT: The optimal time of dialysis initiation is unclear. The goal of this analysis was to compare survival outcomes in patients with early and late start dialysis as measured by kidney function at dialysis initiation.
We performed a retrospective analysis of patients entering the U.S. Renal Data System database from January 1, 1995 to September 30, 2006. Patients were classified into groups by estimated GFR (eGFR) at dialysis initiation.
In this total incident population (n = 896,546), 99,231 patients had an early dialysis start (eGFR >15 ml/min per 1.73 m(2)) and 113,510 had a late start (eGFR ≤5 ml/min per 1.73 m(2)). The following variables were significantly (P < 0.001) associated with an early start: white race, male gender, greater comorbidity index, presence of diabetes, and peritoneal dialysis. Compared with the reference group with an eGFR of >5 to 10 ml/min per 1.73 m(2) at dialysis start, a Cox model adjusted for potential confounding variables showed an incremental increase in mortality associated with earlier dialysis start. The group with the earliest start had increased risk of mortality, wheras late start was associated with reduced risk of mortality. Subgroup analyses showed similar results. The limitations of the study are retrospective study design, potential unaccounted confounding, and potential selection and lead-time biases.
Late initiation of dialysis is associated with a reduced risk of mortality, arguing against aggressive early dialysis initiation based primarily on eGFR alone.
Clinical Journal of the American Society of Nephrology 10/2010; 5(10):1828-35. · 5.23 Impact Factor
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ABSTRACT: The reasons for kidney allograft failure subsequent to pancreas after kidney (PAK) are multifactorial; therefore, we examined these factors to identify a meaningful risk assessment that could assist in patient selection.
Five transplant centers in New England collaborated for this multiinstitutional retrospective study of 126 PAK transplantation recipients who had a functioning pancreas allograft 7 days after transplantation. Host factors (age at pancreas transplant, gender, body weight, glomerular filtration rate at 3 months pre-PAK and at 3-, 6-, 9-, and 12-month post-PAK, presence of proteinuria, pre- or post-PAK kidney rejection, pancreas rejection, cytomegalovirus disease, and HbA1C at 6-month post-PAK) and transplant factors (time to PAK, use of induction antibody therapy, and combinations of immunosuppressive medications) were assessed in both univariate and multivariate analyses for the primary outcome of kidney allograft failure.
Of the variables assessed, factors associated with kidney allograft loss after PAK include impaired renal function in the 3 months before PAK, proteinuria, the occurrence of a post-PAK kidney rejection episode, and interval between kidney and pancreas transplantation more than 1 year.
In our analysis, post-PAK kidney allograft loss was strongly associated with glomerular filtration rate less than 45 mL/min pre-PAK, K to P interval of over 1 year, pre-PAK kidney rejection episode, and pre-PAK proteinuria. Diabetic candidates for PAK with any of these conditions should be counseled regarding the risk of post-PAK renal transplant failure.
Transplantation 03/2010; 89(11):1347-53. · 4.00 Impact Factor
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ABSTRACT: While individual socioeconomic factors have been associated with clinical outcome, a composite index has not been developed. In this project, we tested the hypothesis that Social Adaptability Index (SAI) based on employment, education, income, marital status and substance abuse is associated with survival in chronic kidney disease (CKD) patients.
This is a retrospective cohort study of patients with CKD stage 2 or greater. We used the Third National Health and Nutrition Examination Survey (NHANES III) cohort data between 1988 and 1994 including those 18 years or older. Our primary variable of interest is SAI. Each component of SAI (employment status, education, marital status, and substance abuse) has been graded on the scale of 0-3, income has been graded on the scale 0-1. Age, sex, race, diabetes, co-morbidity index, body mass index (BMI), geographic location, haemoglobin, serum creatinine, serum albumin, serum cholesterol and Hba1c were used as covariates in multivariate analysis. The outcome of the study is patient's mortality. The time to death was calculated as time between the first interview by NHANES and death.
We analysed 13 400 subjects with mean age of 50.6 ± 20-53.6% males, 44.4% white, 29.7% African American and 22.1% Mexican American-with 8.5% having diabetes, with an average number of co-morbid conditions of 2.7 ± 1.1. Lower SAI is associated with greater stage of CKD. Higher SAI was associated with decreased mortality [hazard ratio (HR) 0.88, P < 0.001, 95% confidence interval (CI) 0.86-0.89]. When SAI quintiles were analysed, we demonstrated a 'dose-dependent' association between SAI and survival. Subgroup analysis showed that this association of SAI and survival was present in all studied subgroups. The limitations of the study include retrospective design, potential misreporting and misclassification, and reverse causality.
We demonstrated that SAI has a strong and clinically significant association with mortality in CKD patients.
Nephrology Dialysis Transplantation 03/2010; 25(11):3672-81. · 3.40 Impact Factor
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Ogo I Egbuna,
Roger B Davis,
Robyn Chudinski,
Martha Pavlakis,
Christin Rogers,
Phani Molakatalla,
Scott R Johnson,
Seth Karp,
Anthony P Monaco, Hongying Tang,
Douglas W Hanto,
Didier A Mandelbrot
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ABSTRACT: A number of studies have suggested that conversion from calcineurin inhibitors (CNI) to sirolimus (SRL) can improve graft function in renal transplant patients. None of these studies has converted patients to SRL in the absence of steroids.
We describe our experience with 278 renal transplants of which 153 were converted from CNI to SRL. The majority of patients had steroids withdrawn after 6 days. Almost all patients received antithymocyte globulin induction and were maintained on mycophenolate mofetil.
Six months after conversion, patients remaining on SRL therapy had a mean increase in estimated glomerular filtration rate of 6.93 mL/min/1.73 m2 (P<0.0001) compared with preconversion values. SRL-converted patients analyzed by intention-to-treat increased estimated glomerular filtration rate by 5.00 mL/min/1.73 m2 (P=0.0005). Eighty-one percent of patients remaining on SRL had a successful conversion, defined as stable or improved renal function at 6 months. The only factor predictive of unsuccessful conversion was urine protein-to-creatinine ratio more than 1. The benefits of SRL conversion were seen in patients at high immunological risk as well as those at lower risk. Proteinuria increased by a mean of 0.1 (P=0.43) at 6 months. Thirty-six percent of SRL-converted patients experienced adverse effects requiring conversion back to CNI. Rates of rejection, graft loss, and patient death with SRL conversion were low.
The results from our clinical practice suggest that even in the absence of steroids, SRL conversion significantly improves renal function, with acceptable rates of adverse events.
Transplantation 09/2009; 88(5):684-92. · 4.00 Impact Factor
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ABSTRACT: The genetic determinants of acute kidney transplant rejection (AR) are not well studied, and familial aggregation has never been demonstrated. The goal of this retrospective case-control study was to exploit the unique nature of the Utah Population Database (UPDB) to evaluate if AR or rejection-free survival aggregates in families.
We identified 891 recipients with genealogy data in the UPDB with at least one year of follow-up, of which 145 (16.1%) had AR and 77 recipients had biopsy-proven rejection graded >or=1A. We compared the genealogical index of familiality (GIF) in cases and controls (i.e. recipients with random assignment of rejection status).
We did not find evidence for familial clustering of AR in the entire patient population or in the subgroup with early rejection (n = 52). When the subgroup of recipients with rejection grade >or=1A (n = 77) was analysed separately, we observed increased familial clustering (GIF = 3.02) compared to controls (GIF = 1.96), although the p-value did not reach the level of statistical significance (p = 0.17). Furthermore, we observed an increase in familial clustering in recipients who had a rejection-free course (GIF = 2.45) as compared to controls (GIF = 2.08, p = 0.04). When all recipients were compared to non-transplant controls, they demonstrated a much greater degree of familiality (GIF = 2.03 versus GIF 0.63, p < 0.001).
There is a familial component to rejection-free transplant course and trend to familial aggregation in recipients with AR grade 1A or higher. If a genetic association study is performed, there are families in Utah identified in the current study that can be targeted to increase the power of the test.
Nephrology Dialysis Transplantation 04/2009; 24(8):2575-83. · 3.40 Impact Factor
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ABSTRACT: The causative role of alcohol consumption in renal disease is controversial, and its effect on renal graft and recipient survival has not been previously studied.
We analysed the association between pre-transplant [at the time of end-stage renal disease (ESRD) onset] alcohol dependency and renal graft and recipient survival. The United States Renal Data System (USRDS) records of kidney transplant recipients 18 years or older transplanted between 1 January 1995 and 31 December 2002 were examined. We used Kaplan-Meier analysis and Cox regression models adjusted for covariates to analyse the association between pre-transplant alcohol dependency and graft and recipient survival.
In an entire study cohort of 60 523, we identified 425 patients with a history of alcohol dependency. Using Cox models, alcohol dependency was found to be associated with increased risk of death-censored graft failure [hazard ratio (HR) 1.38, P < 0.05] and increased risk of transplant recipient death (HR 1.56, P < 0.001). Subgroup analysis demonstrated an association of alcohol-dependency with recipient survival and death-censored graft survival in males (but not in females), and in both white and non-white racial subgroups.
We concluded that alcohol dependency at the time of ESRD onset is a risk factor for renal graft failure and recipient death.
Nephrology Dialysis Transplantation 04/2007; 22(3):891-8. · 3.40 Impact Factor
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ABSTRACT: Diseases of the central nervous system (CNS) such as meningitis or encephalitis may represent events of public health interest due to emerging infections and/or NIH/CDC Category B priority pathogens. Apart from influencing treatment and management of the index case, some diagnoses such as meningococcal meningitis warrant an immediate public health response. Others such as West Nile Virus may require public education and vector control. Thus early detection of CNS syndromes is of benefit to patients, providers and public health. While computer-based surveillance methods have been used with success in the early detection of respiratory syndromes, there is little data on their use in CNS syndromes. This study analyzed the validity of a hospital emergency department computer-based surveillance system in the early detection of meningitis and encephalitis and determined the test characteristics of selected computer-based rules.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2007;
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AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2007;
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ABSTRACT: Cardiovascular disease (CVD) leads to increased mortality rates among renal transplant recipients; however, its effect on allograft survival has not been well studied. The records from the United States Renal Data System and the United Network for Organ Sharing from January 1, 1995, through December 31, 2002, were examined in this retrospective study. The outcome variables were allograft survival time and recipient survival time. The primary variable of interest was CVD, defined as the presence of at least one of the following: cardiac arrest, myocardial infarction, dysrhythmia, congestive heart failure, ischemic heart disease, peripheral vascular disease, and unstable angina. The Cox models were adjusted for potential confounding factors. Of the 105,181 patients in the data set, 20,371 had a diagnosis of CVD. The presence of CVD had an adverse effect on allograft survival time (HR 1.12, p < 0.001) and recipient survival time (HR 1.41, p < 0.001). Among the subcategories, congestive heart failure (HR 1.14, p < 0.005) and dysrhythmia (HR 1.26, p < 0.05) had adverse effects on allograft survival time. In addition to increasing mortality rates, CVD at the time of end-stage renal disease onset is also a significant risk factor for renal allograft failure. Further research is needed to evaluate the role of specific forms of CVD in allograft and recipient outcome.
ASAIO journal (American Society for Artificial Internal Organs: 1992) 53(5):601-8. · 1.39 Impact Factor
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ABSTRACT: Factors associated with outcome in renal transplant recipients with lupus nephritis have not been studied.
Using the data from the United States Renal Data System of patients transplanted between January 1, 1995 through December 31, 2002 (and followed through December 31, 2003) (n = 2882), we performed a retrospective analysis of factors associated with long-term death-censored graft survival and recipient survival.
The number of pretransplant pregnancies incrementally increased the risk of graft failure [hazard ratio (HR) 1.54, p < 0.05] in the entire subgroup of females and in the subgroup of recipients aged 25-35 yr. Recipient and donor age had an association with both the risk of graft failure (HR 0.96, p < 0.001; HR 1.01, p < 0.005) and recipient death (HR 1.04, p < 0.001; HR 1.01, p < 0.05). Greater graft-failure risk accompanied increased recipient weight (HR 1.01, p < 0.001); African Americans compared with whites (HR 1.55, p < 0.001); greater Charlson comorbidity index (HR 1.17, p < 0.05); and greater panel reactive antibody (PRA) levels (HR 1.06, p < 0.001). Pretransplant peritoneal dialysis as the predominant modality had an association with decreased risk of graft failure (HR 0.49, p < 0.001), while prior transplantation was associated with greater risk of graft failure and recipient death (HR 2.29, p < 0.001; HR 3.59, p < 0.001, respectively) compared with hemodialysis (HD). The number of matched human leukocyte antigens (HLA) antigens and living donors (HR 0.92, p < 0.05; HR 0.64, p < 0.001, respectively) was associated with decreased risk of graft failure. Increased risk of graft failure and recipient death was associated with nonuse of calcineurin inhibitors (HR 1.89, p < 0.005; HR 1.80, p < 0.005) and mycophenolic acid (MPA) (including mycophenolate mofetil and MPA) or azathioprine (HR 1.41, p < 0.05; HR 1.66, p < 0.01). Using both cyclosporine and tacrolimus was associated with increased risk of graft failure (HR 2.09, p < 0.05). Using MPA is associated with greater risk of recipient death compared with azathioprine (HR 1.47, p < 0.05).
In renal transplant recipients with lupus nephritis, multiple pregnancies, multiple blood transfusions, greater comorbidity index, higher body weight, age and African American race of the donor or recipient, prior history of transplantation, greater PRA levels, lower level of HLA matching, deceased donors, and HD in pretransplant period have an association with increased risk of graft failure. Similarly, higher recipient and donor age, prior transplantations, and higher rate of pretransplant transfusions are associated with greater risk of recipient mortality. Using neither cyclosporine nor tacrolimus or using both (compared with tacrolimus) and neither MPA nor azathioprine (compared with azathioprine) was associated with increased risk of graft failure and recipient death. Using MPA is associated with greater risk of recipient death compared with azathioprine. Testing these results in a prospective study might provide important information for clinical practice.
Clinical Transplantation 22(3):263-72. · 1.67 Impact Factor
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Hongying Tang
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ABSTRACT: Background. Prognosis of kidney transplant outcomes, while clinically important, represents a challenging problem. Existing prediction models use the predictors that are available in the post-transplant period. However, the real value of a model is to predict outcomes prior to the transplantation, yet there is little experience in predicting graft survival using pre-transplant variables. Furthermore, the prediction models development using national registry data in previous studies have not been validated in the local clinical environment. This study is one of the first to apply a model derived from aggregate national dataset to a local dataset for validation. Methods. Five classification tree models predicting 1, 3, 5, 7 and 10 years post-transplant graft survival have been derived form the United States Rental Data System (USRDS) data in our previous studies. The models included only those predictors that are available prior to kidney transplantation. In this study, the local clinical data were used to validate the models. The local data sources included the Enterprise Data Warehouse (DW), the Solid Organ Transplant Program, and the Dialysis Program at the University of Utah Health Science Center (UUHSC). A comparative analysis was conducted regarding the data characteristics of the transplant recipients between the national (USRDS) and local (UUHSC) datasets. The performance of the prediction models were evaluated using both the national and local data. Results. In the USRDS dataset, the number of the patients with a long enough follow-up to reach the graft outcome (“fail” or “survive”) at 1, 3, 5, 7, and 10 years post-transplant were 92,844, 73,672, 58,005, 46,791, and 35,279, respectively. In the UUHSC dataset, the numbers of the patients who had known graft outcomes at 1, 3, 5, 7, and 10 years post-transplant were 854, 635, 462, 325, and 213, respectively. We found that the graft survival rates among the transplant recipients from the UUHSC dataset are significantly higher than those from the USRDS dataset (94% vs. 86%l 87% vs. 72%; 77% vs. 54%; 61% vs.36%; and 33% vs. 8% at 1, 3, 5, 7, and 10 years post-transplant, respectively, all p<0.001). The majority of the recipients (>93%) and the donors (>95%) were white at the UUHSC. In addition, the UUHSC dataset showed a significantly higher proportion of living donors than the USRDS dataset at 1 year (42% vs, 21%, p<0.001), 3 years (42% vs. 24%, p<0.001), 5 years 41% vs. 22%, p<0.001), 7 years (40% vs, 20%, p<0.001), and 10 years (39% vs. 18%, p<0.001). Discrimination of the prediction models was measured by the area under the ROC curve (AUC). The AUC values of the models predicting 1,3,5,7, and 10 years graft survival on the USRDS data were 0359, 0.63, 0.76, 0.91, and 0.97, respectively. In contrast, the AUC values of the models predicting 1, 3, 5, 7, and 10 years graft survival on the UUHSC data were 0.54, 0.58, 0.58, 0.61, and 0.70, respectively. Conclusion. The prediction models performed better on the national data than on the local data. This is almost certainly due to the difference in the data characteristics between the two datasets. Researchers routinely extrapolate the results from national studies to local circumstances, but this study is one of the first to show the potential dangers of doing so with real world data. Adopting wholesale a prediction model developed on a large national dataset for local purposes should be done with caution. Master of Science;
Original: University of Utah Spencer S. Eccles Health Sciences Library (no longer available).
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ABSTRACT: Predicting the outcome of kidney transplantation is important in optimizing transplantation parameters and modifying factors related to the recipient, donor, and transplant procedure. As patients with end-stage renal disease (ESRD) secondary to lupus nephropathy are generally younger than the typical ESRD patients and also seem to have inferior transplant outcome, developing an outcome prediction model in this patient category has high clinical relevance. The goal of this study was to compare methods of building prediction models of kidney transplant outcome that potentially can be useful for clinical decision support. We applied three well-known data mining methods (classification trees, logistic regression, and artificial neural networks) to the data describing recipients with systemic lupus erythematosus (SLE) in the US Renal Data System (USRDS) database. The 95% confidence interval (CI) of the area under the receiver-operator characteristic curves (AUC) was used to measure the discrimination ability of the prediction models. Two groups of predictors were selected to build the prediction models. Using input variables based on Weka (a open source machine learning software) supplemented with additional variables of known clinical relevance (38 total predictors), the logistic regression performed the best overall (AUC: 0.74, 95% CI: 0.72-0.77)-significantly better (p < 0.05) than the classification trees (AUC: 0.70, 95% CI: 0.67-0.72) but not significantly better (p = 0.218) than the artificial neural networks (AUC: 0.71, 95% CI: 0.69-0.73). The performance of the artificial neural networks was not significantly better than that of the classification trees (p = 0.693). Using the more parsimonious subset of variables (six variables), the logistic regression (AUC: 0.73, 95% CI: 0.71-0.75) did not perform significantly better than either the classification tree (AUC: 0.70, 95% CI: 0.68-0.73) or the artificial neural network (AUC: 0.73, 95% CI: 0.70-0.75) models. We generated several models predicting 3-year allograft survival in kidney transplant recipients with SLE that potentially can be used in practice. The performance of logistic regression and classification tree was not inferior to more complex artificial neural network. Prediction models may be used in clinical practice to identify patients at risk.
ASAIO journal (American Society for Artificial Internal Organs: 1992) 57(4):300-9. · 1.39 Impact Factor
