La implementación de la guía de práctica clínica

The primary goal of this study was to provide new CKD monitoring suggestions, by teasing out patients’ characteristics that were associated with more frequent outpatient ap- pointments and hospitalizations. To do that, we compared and contrasted regions based

on characteristics that significantly contribute towards individuals’ appointment utilization differences. PVD/CVD and diabetes states, and the time since dialysis onset were the three characteristics whose mean group values had orderings most consistent with the frequencies of outpatient appointments and hospitalizations (Table22). Those three characteristics were selected in six regions; heart failure and the time since the onset of heart failure were selected in four.

There exists a plethora of work on developing risk stratification models, whose main goal is to highlight patient groups with more complex and, therefore, more costly health needs. Such models include the hierarchical condition categories (HCC), adjusted clinical groups (ACGs), elder risk assessment, chronic comorbidity count (CCC), Carlson comorbidity index, and Minnesota tiering [36]. The primary commonality of those risk adjustment models is that all of them contain some patient comorbidity information. Still, the way comorbidity information is incorporated into the models differs. Some measures count the number of chronic conditions that fit certain characteristics (CCC and Minnesota tiering). Others group them based on whether they are stable or unstable (ACGs).

Our observations suggest that policymakers should focus on the type of comorbidities, not only on their number. We believe that they should pay attention first to whether patients have chronic peripheral and/or cardiovascular disease and diabetes, and then on whether they have been diagnosed with heart failure and cirrhosis, because there was a clear relationship between the differences among group resource utilizations and the prevalence of those comorbidities among patients. Focusing on diabetes is particularly important, because between 2000 and 2008, the percentage of Veterans with renal failure and diabetes increased by 197% and 34%, respectively [122]. Therefore, failing to improve the treatment of renal failure patients with diabetes could result in a significant increase in resource demand. A possible way of addressing that problem might be to develop a policy aimed at preventing the onset of diabetes and PVD/CVD in CKD veterans.

While preventing the onset of comorbidities could be difficult to achieve, a more doable goal might involve the creation or the improvement of existing comorbidity-management rules for individuals with chronic kidney disease. An example of the potential associated with an update in CKD policies was reported by Kaiser Permanente Hawaii, where the CKD state

of patients referred to a nephrologist varied significantly [59]. A risk stratification model, and improved communication channels between generalists and nephrologists, addressed the variability problem, and resulted in a significant reduction of late nephrologist referrals, which substantially increased the number of patients with functioning arteriovenous fistulas at dialysis initiation. Similarly, Schmidt et al. [102] argue that, even though long term survival rates in early and late referrals are not significantly different, the distress caused by the unexpected diagnosis and dialysis initiation could be very emotionally and physically demanding, which makes early referrals preferable.

While many papers on early referrals of CKD patients focus on the importance of nephrol- ogist care, our results highlight the importance of referring patients to cardiologists and endocrinologists, who are expected to provide better care for patients with vascular and diabetes complications. A rise in such referrals might result in an increase in demand for more specialized outpatient appointments, but may help prevent hospitalizations caused by sub-optimal disease management.

Figure 13: Regions Stimulus Coordinates

Unfortunately, our data set contains information only about Veterans with chronic kidney disease. Because the population of Veterans is predominantly male, and older than the

general population, it is possible that our findings may not apply as they stand to the entire US CKD population. Still, our results and methodology could be used to determine whether patients’ laboratory results for the general population could be used to stratify individuals into distinct groups, to see if patient clusters have different resource utilization rates.

Furthermore, we observed geographical variability in clusters and clustering predictors, which might be partially explained by the regional variability of patient populations, because dissimilarities are driven by complex, and possibly interacting, factors. Note that close geographical proximity (FL #1 and FL #4) does not necessarily imply similarity (Fig. 13). The degree of medical professionals’ adherence to guidelines is an attractive attribute, which we have not measured in this study, and which might further explain the differences across facilities. This would suggest the need for further examination of whether hospitaliza- tions across facilities are significantly different after controlling for patients’ characteristics, as well as practitioners’ adherence to guidelines.

In this study, we showed that, regardless of the facility’s geographical location, patients’ comorbidity states are usually the primary contributors towards individuals’ appointment utilizations. The two comorbidities with the greatest regional selection rates are PCV/CVD and diabetes, followed by heart failure and cirrhosis. Those selection frequencies highlight the need for the improvement of chronic kidney disease referral policies, not only between generalists and nephrologists, but also between generalists and both cardiologists and en- docrinologists. The data appear to suggest that more specialized care might be beneficial in reducing the number of hospitalizations among CKD patients with PVD/CVD and diabetes comorbidities.