We read with great interest the recent paper by Kalantar-Zadeh et al. [1] based on a comparison of malnutritioninflammation markers for outcome predictability. We appreciate its high level in the matter of clinical usefulness and statistical power, but would like to add some critical notes.
In our opinion, Kalantar-Zadeh et al. placed insufficient emphasis on the significance of the Charlson co-morbidity index (CCI), despite the fact that the z-statistics show the CCI to be the most potent predictive factor in mortality and hospitalization.
Moreover, the method used to calculate CCIspecifically, the exclusion of factors pertaining to age and kidney diseasediffers significantly from that employed by Charlson et al. [2] and Fried et al. [3] in their studies of general populations and dialysis patients, respectively. The absence of these factors makes it difficult to compare the results of Kalantar-Zadeh et al. with those of previous studies using CCI.
Furthermore, the final multivariate model incorporates the malnutritioninflammation score, or MIS, alongside the CCI, even though both indices take into account the same set of diseases: myocardial infarction, chronic obstructive pulmonary disease, major neurology sequelae and malignancies. Similarly, diabetes mellitus was included in the model as an independent factor despite being accounted for in the CCI; the same for serum albumin which is already accounted for in MIS. These overlapping factors could bias the results of the final analysis.
In order to avoid the duplication of similar factors within one model, we suggest comparing several different models, each with its own set of non-overlapping factors, and determining which is most accurate by estimating the 2 log likelihood (2LL) statistics for each in the Cox analysis. This procedure compares the 2LL for different models fitted to the same set of survival data, assuming that the smaller the 2LL value, the better the agreement between the model and observed data. The difference between models will be termed statistically significant with P<0.05 if it is >3.841.
We used this approach in a retrospective evaluation of survival among 213 non-diabetic and 45 diabetic haemodialysis patients based on the following factors: diabetes mellitus, age, authentic CCI and modified CCI (unpublished data). In our survey, CCI was better than diabetes in predicting survival, but equal to diabetes and age (Table 1). In order to analyse CCI and diabetes together, we modified the CCI by excluding the weight of diabetes with end-organ damage (calculated at 2). This constellation of factors improves the predictive power of the model. Next, we increased the weight of diabetes with end-stage organ damage to 3 on the assumptions that Charlson et al. could underestimate diabetes weight, having performed their research with only 13 patients with diabetes (2.2% of their total population), and that diabetic patients on haemodialysis could suffer from a more pronounced degree of angiopathy than diabetics without renal failure. This modified CCI model achieved the same predictive power as the previous scenario without neglecting any of the components of CCI. (Interestingly, increasing the weight of diabetes to 4 did not improve the model's predictive power.)
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Another important point is that Kalantar-Zadeh et al. fail to resolve whether inflammationmalnutrition markers correlate with CCI, or with any co-morbidities in particular, and whether differences in predictors of mortality and hospitalization may be found between diabetics and non-diabetics. However, these factors may considerably influence the inflammationmalnutrition markers.
Conflict of interest statement. None declared.
1 Research Institute of Transplantology and Artificial Organs Department of Nephrology Issues of Transplanted Kidney2 City Hospital # 52 Moscow City Nephrology Center Moscow Russian Federation Email: bgab{at}orc.ru
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