Platelet-to-lymphocyte ratio (PLR) was established showing the poor prognosis in several diseases, such as malignancies and cardiovascular diseases. But limited study has been conducted about the prognostic value of PLR on the long-term renal survival of patients with Immunoglobulin A nephropathy (IgAN).
Methods
We performed an observational cohort study enrolling patients with biopsy-proven IgAN recorded from November 2011 to March 2016. The definition of composite endpoint was eGFR decrease by 50%, eGFR?<?15 mL/min/1.73 m2, initiation of dialysis, or renal transplantation. Patients were categorized by the magnitude of PLR tertiles into three groups. The Kaplan–Meier curves and multivariate Cox models were performed to determine the association of PLR with the renal survival of IgAN patients.
Results
330 patients with a median age of 34.0 years were followed for a median of 47.4 months, and 27 patients (8.2%) had reached the composite endpoints. There were no differences among the three groups (PLR?<?106, 106?≤?PLR?≤?137, and PLR?>?137) in demographic characteristics, mean arterial pressure (MAP), proteinuria, and estimated glomerular filtration rate (eGFR) at baseline. The Kaplan–Meier curves showed that the PLR?>?137 group was significantly more likely to poor renal outcomes than the other two groups. Using univariate and multivariate cox regression analyses, we found that PLR?>?137 was an independent prognostic factor for poor renal survival in patients with IgAN. Subgroup analysis revealed that the PLR remained the prognostic value for female patients or patients with eGFR less than 60 mL/min/1.73 m2.
Conclusions
Our results underscored that baseline PLR was an independent prognostic factor for poor renal survival in patients with IgAN, especially for female patients or those patients with baseline eGFR less than 60 mL/min/1.73 m2.
For massive survival data, we propose a subsampling algorithm to efficiently approximate the estimates of regression parameters in the additive hazards model. We establish consistency and asymptotic normality of the subsample‐based estimator given the full data. The optimal subsampling probabilities are obtained via minimizing asymptotic variance of the resulting estimator. The subsample‐based procedure can largely reduce the computational cost compared with the full data method. In numerical simulations, our method has low bias and satisfactory coverage probabilities. We provide an illustrative example on the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program. 相似文献