Pretest results indicated a mean of 6.31 [CI 95: 5.83, 6.79] patients on a statin, with a 3-month post-test mean of 7.34 [CI 95: 6.90, 7.79], a statistically significant difference (P < .001). The 3-year post mean was 7.43 [CI 95: 6.96, 7.89] and although not statistically significant from the 3-month result, is statistically significant compared with the pretest mean (P = .001) (Figure 2).
Pretest results indicated a mean of 8.40 [CI 95: 7.91, 8.89] patients with a recent A1C with a 3-month post-test mean of 8.99 [CI 95: 8.63, 9.35], which was statistically significant (P = .001). The 3-year post-test mean was 8.81 [CI 95: 8.39, 9.23], which was not significantly lower than the 3-month post-test but also was not significantly higher than the pretest mean. The post-test improvement over pretest was not maintained for A1C, but rather diminished over the course of 3 years (Figure 2).
Pretest results indicated a mean of 4.86 [CI 95: 4.13, 5.6] patients with a UACR with a post-test mean of 6.86 [CI 95: 6.17, 7.55], which was a statistically significant difference (P < .001). The 3-year post mean was 6.98 [CI 95: 6.25, 7.7] and, although not statistically significant compared with the 3-month post-test, was statistically significantly higher than the pretest mean (P < .001) (Figure 2).
Pretest results indicated a mean of 3.29 [CI 95: 2.63, 3.95] patients who had CKD stage documented with a post-test mean of 5.91 [CI 95: 5.26, 6.56], which was statistically significant (P < .001). The 3-year post mean was 6.33 [CI 95: 5.65, 7.02], which was not statistically significantly higher than the 3-month post-test but was statistically significantly higher than the pretest mean (P < .001) (Figure 2).
OTC medication caution lists
Pretest results indicated a mean of 2.63 [CI 95: 1.83, 3.43] patients given an OTC medication caution list with a post-test mean of 6.72 [CI 95: 6.05, 7.40], a statistically significant difference (P < .001). The 3-year post mean was 5.29 [CI 95: 4.45, 6.13], which was not statistically significant when compared with the 3-month post-test (P = .01) but was statistically significantly higher than the pretest mean (P < .001) (Figure 2).
Smoking history documentation
Pretest results indicated a mean of 5.33 [CI 95: 4.66, 6] patients with a documented smoking history in the chart, with a post-test mean of 5.64 [CI 95: 4.94, 6.33], which was not statistically significant in the original cohort. The 3-year post mean was 8.69 [CI 95: 8.23, 9.15] and was statistically significantly higher than both the pretest and 3-month post-test (P < .001)(Figure 2).
KIB was developed to improve screening for kidney disease in patients with diabetes. The program encourages clinicians to use methods developed by the NKDEP to slow the progression of CKD. The original cohort who completed KIB showed statistically significant changes in evaluating and managing patients with diabetes and CKD.7 This 3-year follow-up study was designed to assess the potential to induce longitudinal practice changes in CKD care through a quality improvement project using the six parameters (described earlier) known to slow CKD progression.
At 3 years postintervention, clinicians reviewed their practice via chart survey to see if the initial changes were maintained. In four of the six interventions (statins, A1C, UACR, and CKD staging), the statistically significant post-test improvements were maintained at 3 years with no further interventions. For OTC medication caution lists, the results diminished somewhat over time but remained statistically significant when compared with pretest levels. Although the 3-year post-test did not increase further, improvements observed at 3-month post-test were sustained for 3 years after KIB. In other words, a simple, short intervention made in practices by PAs and NPs continued to show positive results 3 years later.
One of the original parameters, smoking history documentation, had not changed significantly during the original study.7 In an unexpected result, smoking history documentation actually increased at 3 years postintervention and now showed statistically significant results (P < .001). One of the reasons that the original cohort did not show improvement in documenting smoking history may have been an issue with the wording of the question on the original evaluation form, “Does the patient have a smoking history?” For the 3-year post-test, the question was clarified and changed to, “Does the patient have a smoking history documented in the chart?” This may have led to a more accurate assessment of the intervention and the significant increase in reported documentation at the 3-year-post-test mark.
Four interventions maintained statistically significant improvements at both the 3-month post-test and 3-year post-test: statin use, UACR measured within the last year, CKD stage, and OTC medication caution list.
Documentation of CKD stage in patients with diabetes is key to early identification of disease progression. CKD progression can be slowed by monitoring A1C and UACR and educating patients about OTC medications they should use with caution or avoid.2
Short-term quality improvement interventions have been criticized as not powerful enough to effect long-term practice change. The results of this study demonstrate the potential for a single-intervention quality improvement program for PAs and NPs to cause sustained improvements in the clinical care of patients with diabetes and CKD. NKDEP has shown that incorporation of the six quality measures analyzed in our study has the power to slow CKD progression.2 Slowing disease progression not only translates into improved patient outcomes but also has the potential to dramatically reduce healthcare costs, because 20% of the Medicare budget supports patients with CKD and healthcare costs rise as patients' glomerular filtration rate falls.3
In the absence of survey data from a control group of clinicians who did not participate in the KIB project, we cannot definitively exclude the possibility that the observed changes in practice were not attributable to influences outside the quality improvement intervention, such as self-initiated learning or other continuing medical education (CME) relating to managing patients with diabetes and CKD. To screen for any signal of non-KIB CME participation that may have influenced the sustained changes in practice observed in this study, we asked participants during the 3-year longitudinal follow-up whether they had engaged in CKD-related CME. Because only 7.3% indicated involvement in external CKD learning, the improvements in clinical practice seen at 3-month and 3-year follow-up were felt to be the result of KIB involvement. Excluding participants who did not submit chart abstraction at 3 years, longitudinal follow-up did not appear to significantly affect findings, as using a standard ANOVA to analyze all data available yielded the same pattern of findings. Given the novel introduction of practice improvement-specific CMEs for PAs and NPs at the time of the original KIB enrollment, it is possible that enrollment self-selected for providers highly motivated to make practice improvement. Most respondents were practicing PAs, so further studies within the larger population of CKD providers, including NPs, will help define the applicability of our findings to specific demographic subgroups and identify which practice behaviors are most responsive to quality improvement interventions. Data were diverse, with participation from primary care and specialty clinicians in 30 states. Future studies also might incorporate blinded chart abstraction to avoid participant bias in reporting positive results.
The KIB intervention showed successful improvements from pretest measures at 3 years after the initial intervention for five of six interventions. This study shows that CKD education for PAs and NPs has the potential to significantly change clinician behavior and increase the use of interventions to slow kidney disease. Furthermore, the study shows that sustained behavioral changes are possible with a short-term quality improvement project.
3. National Institutes of Health, National Institute of Diabetes
and Digestive and Kidney Diseases, US Renal Data System. 2017 USRDS annual data report: epidemiology of kidney disease
in the United States. Bethesda, MD, 2017.
4. Grams ME, Chow EK, Segev DL, Coresh J. Lifetime incidence of CKD
stages 3-5 in the United States. Am J Kidney Dis
6. National Kidney Foundation. KDOQI clinical practice guideline for diabetes
: 2012 update. Am J Kidney Dis
7. Thomsen K, Zuber K, Davis J, Thomas G. Improving treatment for patients with chronic kidney disease
8. Silver SA, McQuillan R, Harel Z, et al How to sustain change and support continuous quality improvement
. Clin J Am Soc Nephrol
Keywords:Copyright © 2019 American Academy of Physician Assistants
CKD; diabetes; longitudinal; intervention; quality improvement; kidney disease