BACKGROUND: Renal Registries play a key role in assessing quality of care and outcomes of renal replacement therapy and comparisons of outcomes between groups should adjust for differences in comorbidities. This study aimed to describe patterns of missing comorbidity data and differences in survival between patients with comorbidity data returned and those with missing comorbidity data. METHODS: Trends in comorbidity data returns by year (1998-2006) and within centres were examined using descriptive statistics. Survival of patients was described using Kaplan-Meier graphs (log-rank tests) and hazard ratios were calculated using Cox proportional hazard models. Last follow-up was at 31 December 2007. A range of sensitivity analyses were carried out, including multiple imputation. RESULTS: Among 34,059 patients, there were 62% who had no comorbidity data. The completeness of comorbidity data increased markedly from 17% in 1998 to 47% in 2003, but had fallen back to 37% by the year 2006. Those with a missing comorbidity generally do considerably worse than those without the comorbidity and in most cases more closely follow the survival curve of those with the comorbidity. Multiple imputation analysis suggested that those with missing information on comorbidity have higher prevalence of comorbidity than seen in those with available data. Treating missing comorbidity entries as indication of absent comorbidity (i.e. a tick only if yes policy) would lead to an attenuation of the effect of comorbidity on survival. CONCLUSIONS: Missing data lead to difficulties in performing between centre comparisons. A 'tick if present policy' in comorbidity data collection should be discouraged. Much more work is needed to fully understand why levels of missing comorbidity data are so high and to identify strategies to improve recording.
BACKGROUND: Renal Registries play a key role in assessing quality of care and outcomes of renal replacement therapy and comparisons of outcomes between groups should adjust for differences in comorbidities. This study aimed to describe patterns of missing comorbidity data and differences in survival between patients with comorbidity data returned and those with missing comorbidity data. METHODS: Trends in comorbidity data returns by year (1998-2006) and within centres were examined using descriptive statistics. Survival of patients was described using Kaplan-Meier graphs (log-rank tests) and hazard ratios were calculated using Cox proportional hazard models. Last follow-up was at 31 December 2007. A range of sensitivity analyses were carried out, including multiple imputation. RESULTS: Among 34,059 patients, there were 62% who had no comorbidity data. The completeness of comorbidity data increased markedly from 17% in 1998 to 47% in 2003, but had fallen back to 37% by the year 2006. Those with a missing comorbidity generally do considerably worse than those without the comorbidity and in most cases more closely follow the survival curve of those with the comorbidity. Multiple imputation analysis suggested that those with missing information on comorbidity have higher prevalence of comorbidity than seen in those with available data. Treating missing comorbidity entries as indication of absent comorbidity (i.e. a tick only if yes policy) would lead to an attenuation of the effect of comorbidity on survival. CONCLUSIONS: Missing data lead to difficulties in performing between centre comparisons. A 'tick if present policy' in comorbidity data collection should be discouraged. Much more work is needed to fully understand why levels of missing comorbidity data are so high and to identify strategies to improve recording.
Authors: Alexandra J Greenberg; Angela L Falisi; Lila J Finney Rutten; Wen-Ying Sylvia Chou; Vaishali Patel; Richard P Moser; Bradford W Hesse Journal: J Med Internet Res Date: 2017-06-02 Impact factor: 7.076