Abstract: BACKGROUND AND AIMS: Low physical activity is common in hemodialysis (HD) patients and is associated with adverse outcomes in this population (poor health-related quality of life, cardiovascular [CV] disease, death). Prior studies show that hypothyroidism is highly prevalent in HD patients, and is associated with worse self-reported physical function. METHOD: In a substudy of the ongoing multi-center NIH THYROID-HD Trial, we examined baseline physical activity scores determined by the Human Activity Profile (HAP), a validated 94-item instrument assessing daily activities across a wide range of energy expenditures, in HD patients with TSH levels in the high-normal (TSH >3–5 mIU/L) and subclinical hypothyroid range (TSH >5–10 mIU/L). The HAP was used to derive the Maximum Activity Score (MAS) and Adjusted Activity Score (AAS), representing greatest and mean estimated energy expenditures, respectively (range 0–94, segmented to low [<52], moderate [54–73] and high [>74] scores). RESULTS: Among 100 HD patients who underwent baseline HAP assessment, the mean ± SD MAS and AAS scores were 48 ± 22 and 23 ± 27,  respectively; median (IQR) MAS and AAS scores were 49 (31–64) and 9 (0–39), respectively (Figure 1). In the overall cohort, 83% had low, 10% moderate and 7% high AAS scores. MAS and AAS scores were significantly lower in patients who had underlying diabetes versus those without diabetes (Table 1). There was also a trend towards lower (worse) MAS and AAS scores among older versus younger patients (≥65 versus >65 years, respectively) and patients with underlying coronary artery disease (CAD) versus those without CAD. In logistic regression analyses, older age (+∆ 1-year increments) was associated with higher likelihood of low MAS scores, and diabetes trended towards a significant association (defined as < median of observed values) (ref: MAS ≥ median): ORs [95% confidence intervals (95% CIs)]: 1.04 (1.01–1.09); P = 0.005 and 2.57 (0.96–6.86); P = 0.06, respectively. Similarly, older age (+∆ 1-year increments) and diabetes were each associated with higher likelihood of low AAS scores (defined as < 52) (ref: moderate-high AAS scores): ORs (95% CIs): 1.06 (1.02–1.11); P = 0.01 and 4.83 (14.6–16.0); P = 0.01, respectively. CONCLUSION: In this substudy of the NIH THYROID-HD Trial, HAP scores in HD patients with high-normal and subclinical hypothyroid range TSH levels were lower than observed in prior historical dialysis cohorts that did not have underlying thyroid dysfunction. Additionally, older age and diabetes were each associated with worse HAP scores. Further research is needed to determine the impact of thyroid hormone replacement on improving physical activity and function in this population, particularly those of elder age and with underlying diabetes.

Profiling analysis aims to evaluate health care providers, including hospitals, nursing homes, or dialysis facilities among others with respect to a patient outcome, such as 30-day unplanned hospital readmission or mortality. Fixed effects (FE) profiling models have been developed over the last decade, motivated by the overall need to (a) improve accurate identification or flagging of under-performing providers, (b) relax assumptions inherent in random effects (RE) profiling models, and (c) take into consideration the unique disease characteristics and care/treatment processes of end-stage kidney disease (ESKD) patients on dialysis. In this paper, we review the current state of FE methodologies and their rationale in the ESKD population and illustrate applications in four key areas: profiling dialysis facilities for (1) patient hospitalizations over time (longitudinally) using standardized dynamic readmission ratio (SDRR), (2) identification of dialysis facility characteristics (e.g., staffing level) that contribute to hospital readmission, and (3) adverse recurrent events using standardized event ratio (SER). Also, we examine the operating characteristics with a focus on FE profiling models. Throughout these areas of applications to the ESKD population, we identify challenges for future research in both methodology and clinical studies.

Background

The value of chronic kidney disease (CKD) screening in the general population remains unclear but may be beneficial in populations with high disease prevalence. We examined risk factors for albuminuria among participants in a state-wide CKD screening program in Hawaii.

Methods

The National Kidney Foundation of Hawaii Kidney Early Detection Screening (NKFH-KEDS) program held 19 CKD screening events from 2006 to 2012. Participants rotated through 5 stations during which sociodemographic, blood glucose, urine albumin-to-creatinine ratio (ACR), and spot urine albumin data were collected. Multivariate logistic regression analyses (adjusted for age, sex, race/ethnicity, body mass index [BMI]) were used to identify clinical predictors of abnormal ACR (≥30 μg/mg) and abnormal spot urine albumin (>20 mg/L) levels.

Results

Among 1,190 NKFH-KEDS participants who met eligibility criteria, 13 and 49% had abnormal ACR and urine albumin levels, respectively. In multivariate logistic regression analyses, participants of older age (>65 years), Asian and Pacific Islander race/ethnicity, BMI ≥30 kg/m2, and with hypertension had higher risk of abnormal ACR. Being of older age; Asian, Pacific Islander, and Mixed race/ethnicity; and having diabetes was associated with higher risk of abnormal urine albumin levels in adjusted analyses.

Conclusions

NKFH-KEDS participants of older age; Asian and Pacific Islander race/ethnicity; and with obesity, hypertension, and diabetes had higher risk of kidney damage defined by elevated ACR and urine albumin levels. Further studies are needed to determine whether targeted screening programs can result in timely identification of CKD and implementation of interventions that reduce cardiovascular disease, death, and progression to end-stage renal disease.