Score chart to predict 1-year mortality risk.

<p>Points correspond to each predictor value and are added to give a score.</p><p>eGFR, estimated glomerular filtration rate; ESA, erythropoiesis-stimulating agent</p><p>^aItems related to diabetes and renal disease were excluded from the original Charlson Comorbidity Index in the present study.</p><p>Score chart to predict 1-year mortality risk.</p

Multivariable predictors of 1-year mortality and associated risk scoring system.

<p>eGFR, estimated glomerular filtration rate; ESA, erythropoiesis-stimulating agent</p><p>^aOriginal β-coefficients multiplied by heuristic shrinkage factor to improve predictions for future patients.</p><p>^bScores assigned by dividing the shrunken β-coefficients by 0.568 and rounding to nearest integer.</p><p>^cItems related to diabetes and renal disease were excluded from the original Charlson Comorbidity Index in the present study.</p><p>Multivariable predictors of 1-year mortality and associated risk scoring system.</p

Process of the multiple imputation and derivation of the prediction rule.

<p>(1) Five multiply imputed datasets were created using original data. (2) Backward elimination was separately applied to each of the five imputed datasets, resulting in five sets of selected predictors. (3) Predictors that were selected in all of the five data sets were chosen as the final set of selected predictors, with exclusion of some predictors based on balance between number of candidate predictors with number of outcomes (deaths) and discussion according to clinical relevance. (4) The logistic regression with the selected six predictors was separately applied to each of the five imputed data sets, giving five sets of β-coefficients of the six predictors. (5) To avoid overfitting, each of five sets of β-coefficients of the six predictors were shrunken using heuristic shrinkage factor. Then, the mean for each of the five estimates for β-coefficients of the final model were taken and variances of the five estimates were pooled according to Rubin’s rules. (6) The shrunken β-coefficients of the predictors in the final model divided by two-fifths of the two small β-coefficients in the model and rounded up to the nearest integer to give a simple point score.</p

Predicted mortality risks and observed proportions for ranges of total scores.

<p>Prognostic score calculated form the following six items well predicts 1-year mortality for patients initiating haemodialysis: high eGFR level (>7 mL/min per 1.73 m²), low serum albumin levels, high calcium levels, high modified Charlson Comorbidity Index, low performance status, and no use of ESA. The modified Charlson Comorbidity Index was excluded items related to diabetes and renal disease from the original Charlson Comorbidity Index in the present study.</p

Retained predictors in each of 5 imputed dataset and choice of the predictors.

<p>Predictors listed in this table were entered to logistic regression model with backward elimination procedure. X indicates predictors retained after backward elimination procedure in each of the five imputed dataset. Predictors which retained all of the five imputed dataset were considered as candidate predictors of final prediction model. After further discussion based on clinical relevance, six predictors were chosen as the final prediction model.</p><p>eGFR, estimated glomerular filtration rate; CNS, central nervous system; ESA, erythropoiesis-stimulating agent</p><p>^aItems related to diabetes and renal disease were excluded from the original Charlson Comorbidity Index in the present study.</p><p>Retained predictors in each of 5 imputed dataset and choice of the predictors.</p

Candidate predictors and outcome variables.

<p>Continuous variables represented as median with interquartile range in parentheses.</p><p>eGFR, estimated glomerular filtration rate; CNS, central nervous system; ESA, erythropoiesis-stimulating agent</p><p>^aItems related to diabetes and renal disease were excluded from the original Charlson Comorbidity Index in the present study.</p><p>Candidate predictors and outcome variables.</p

Agreement between the predicted mortality risks and the observed proportions.

<p>The short-dashed line (“Apparent”) indicates the agreement between predicted mortality risks and observed proportions of the original model. The sold line (“Bias-corrected”) indicates the agreement between predicted mortality risks and observed proportions of the bootstrap model.</p

Association between dialysis session length and change in mental health or physical functioning at one year after study initiation.

<p>Association between dialysis session length and change in mental health or physical functioning at one year after study initiation.</p

Shorter dialysis session length was not associated with lower mental health and physical functioning in elderly hemodialysis patients: Results from the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS)

<div><p>Background</p><p>Health-related quality of life (HRQOL) is often prioritized over long-term survival in elderly patients. Although a longer dialysis session length (DSL) has been shown to reduce mortality, its effects on improving the HRQOL are unknown.</p><p>Methods</p><p>Using data from the Japan Dialysis Outcomes and Practice Patterns Study (J-DOPPS), patients aged ≥ 65 years on maintenance hemodialysis were enrolled. DSL was categorized as short (<210 minutes), medium (210–240 minutes), or long (>240 minutes). The primary outcomes were changes in mental health (ΔMH) and physical functioning (ΔPF) scores assessed using the Japanese version of SF-12, in one year. The differences in the ΔMH and ΔPF among the three groups were assessed via regression (beta) coefficients derived using a linear regression model.</p><p>Results</p><p>Of 1,187 patients at baseline, 319 (26.9%) had a short length, 686 (57.8%) a medium length, and 182 (15.3%) a long length. We assessed the ΔMH data from 793 patients and the ΔPF data from 738. No significant differences in the ΔMH were noted for the short or long groups compared with the medium group (score difference: 0.26, 95% confidence interval [CI]: -4.17 to 4.69 for short; score difference: -1.15, 95% CI: -6.17 to 3.86 for long). Similarly, no significant differences were noted for these groups versus the medium group in ΔPF either (score difference: -1.43, 95% CI: -6.73 to 3.87 for short; score difference: -1.71, 95% CI: -7.63 to 4.22 for long).</p><p>Conclusions</p><p>A shorter DSL might have no adverse effects on MH or PF for elderly patients.</p></div

Multiple imputation using predictive mean matching (pmm) for censoring during study follow-up.

<p>Multiple imputation using predictive mean matching (pmm) for censoring during study follow-up.</p