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Delayed allograft function (DGF) is a common adverse event in postrenal transplantation. The etiology of DGF is thought to include both nonimmunologic (donor age, cold ischemia time, and recipient race) and immunologic factors. We examined the association of DGF with amino acid mismatches at 66 variable sites of the HLA-A molecule in a prospective cohort study of 697 renal transplant recipients of deceased donors. Using a multivariate logistic regression model adjusted for nonimmunologic risk factors, we show that combinations of a few amino acid mismatches at crucial sites of HLA-A molecules were associated with DGF. In Caucasian recipients, a mismatch at position 62, 95, or 163, all known to be functionally important within the antigen recognition site, was associated with an increased risk for DGF. Furthermore, a decreased risk for DGF was associated with a mismatch at HLA-A family-specific sites (149, 184, 193, or 246), indicating that evolutionary features of HLA-A polymorphism separating HLA-A families and lineages among donor-recipient pairs may correlate with the magnitude of alloreactivity influencing the development of DGF. These findings suggest that amino acid polymorphisms at functionally important positions at the antigen recognition site of the HLA-A molecule have a significant influence on DGF. HLA alleles | kidney transplantation | HLA mismatches | MHC

We conducted a case-control study of adults with bacteremic pneumococcal pneumonia to identify factors associated with macrolide resistance. Study participants were identified through population-based surveillance in a 5-county region surrounding [...]

In traditional regression modeling, to control for confounding by a variable one must include it in the structural part of the statistical model. Marginal structural models are a flexible new set of causal models. The estimation methods used to estimate model parameters use weighting to control for confounding; this allows more flexibility in choosing covariates for inclusion in the structural model and allows the model to more precisely reflect the scientific questions of interest. An important example of this is in multicenter observational studies where there is confounding by cluster. We illustrate these points with data from a study of surgery to provide vascular access for hemodialysis and a study comparing different timings for coronary angioplasty. KEY WORDS: Causality; Prediction; Weighting., 1. INTRODUCTION One of the main uses of regression models has been to estimate the effect of a treatment or exposure on some outcome of interest. This use has been [...]

This article addresses unique causal issues in the context of a randomized study on improving adherence to best practice guidelines by primary care physicians (PCP's) in treating their depressed patients. The study assessed an encouragement strategy to improve PCP guideline adherence. In this context, we compare two causal approaches: the conditional-compliance (CC) Bayesian latent class and the conditional-observable (CO) structural mean model methods. The CC methods estimate contrasts between randomized encouragement and no-encouragement arms [intent-to-treat (ITT) estimand] given latent PCP guideline complier classes. The CO methods estimate contrasts between PCP guideline adherence and nonadherence conditions (as-treated estimand) given observed PCP adherence status. The CC ITT estimand for patients with PCP compliers equals the CO as-treated estimand depending on assumptions. One such assumption pertains to the absence of physician defiers, who do the opposite of what they are encouraged to do in treating patients for depression. We relate these two estimands to each other in our clinical context when the no-defier assumption is not plausible. In other contexts, previous statistical literature has appropriately assumed the absence of defiers. However, indications in the behavioral literature, anecdotal evidence in the study, and results from the data analysis and simulations suggest that defiers do exist in the context of physician-based interventions in primary care. Both simulation and empirical results show that even with a small estimated proportion of defiers under Bayesian model assumptions, inference is sensitive to different assumptions about this class of PCP noncompliers. KEY WORDS: Defiers; Exclusion restriction; G-estimation; Instrumental variables; Intent-to-treat; Latent class model; Noncompliance; Structural mean model., 1. INTRODUCTION We consider several unique causal modeling issues in a randomized study of an encouragement strategy for treating depression in primary care patients. In general, encouragement strategies target the [...]

The effectiveness of a kidney transplant from a living donor can be improved if the patient has not received dialysis. In a study of 8,481 patients, those who did not receive dialysis were more likely to have a functioning transplant even years after the operation.

Mitral-valve prolapse (MVP) appears to be no more common among young stroke patients than among healthy people. MVP is a defect in the mitral valve of the heart that can permit blood to backflow during the heart beat. Earlier research showed a high prevalence of this condition among stroke patients under 46 years of age. Researchers compared the mitral valves of 213 young stroke patients and 263 healthy people. Only 2% of stroke patients had MVP, compared to 2.7% of the healthy people. MVP may have been overdiagnosed in the past, when echocardiographic diagnosis was less advanced.

Estrogen replacement therapy does not appear to significantly reduce mortality rates from heart disease in women who have a low risk of the disease. This is one of the conclusions of the Nurses' Health Study, which has followed 121,700 female nurses since 1976. By 1994, 3,637 had died. Those who used estrogen replacement therapy had a lower risk of death compared to those who never had. However, the increased risk of breast cancer in hormone users reduced that benefit over time. Hormone users with risk factors for heart disease benefitted the most, whereas those without risk factors benefitted little.

Kidney transplant recipients who have high blood pressure may have a higher risk of graft rejection. This was the conclusion of researchers who analyzed the risk factors for graft failure in 277 kidney transplant recipients. People of color and those with non-diabetic kidney disease were more likely to reject the kidney. Also, the risk of rejection increased 15%-30% for every 10 mm increase in blood pressure.