Impact of Drugs and Patient Characteristics on Life Expectancy during the Induction Phase of Dialysis.

Various factors affect the prognosis of dialysis patients. Analysis of the drugs used and clinical and demographic characteristics of the patient at the time of dialysis initiation is a useful means of estimating prognosis. In this study, we investigated the drugs used by dialysis patients during the induction phase of dialysis and performed a detailed analysis of variables predictive of prognosis. Patients who underwent dialysis between June 1998 and January 2019 and died during this period were included in the study (n = 118). The induction phase of dialysis was defined as the first month after dialysis began. Dialysis duration was defined as the time between dialysis initiation and death. A univariate regression analysis was performed, with dialysis duration as the objective variable and the drugs used during the induction phase of dialysis, blood laboratory values, age at start of dialysis, sex, body height, body weight, medical history and cause of death as the explanatory variables. In addition, multiple logistic regression analysis with stepwise variable selection of significant factors was performed to determine the factors related to dialysis duration. Antihypertensives, hemoglobin (Hb), and age at start of dialysis were found to have significant effects on dialysis duration. It was posited that antihypertensives prolong dialysis duration, thereby improving life expectancy. The regression model developed allowed estimation of prognosis based on the drugs used during the induction phase of dialysis and patient characteristics. These findings may be used to improve drug adherence in dialysis patients and guide physicians in their treatment.

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.

In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis.