Tuberculosis (TB) remains an important problem among end-stage renal disease (ESRD) patients. We reviewed the epidemiology of TB and ESRD, investigations of TB exposures in US dialysis facilities, and published guidelines to inform screening and treatment practices among US ESRD patients. Compared to TB in the general population, ESRD patients have 6-25-fold higher TB incidence rates, and mortality during treatment is 2-3-fold higher. Most TB cases among ESRD patients (~90%) occur among non-US-born persons, and an analysis of genotyping data suggests that 80% of all cases result from latent TB infection (LTBI) reactivation. Published TB contact investigations in dialysis facilities have reported cases among ESRD patients and healthcare workers. However, transmission of TB is rare: there were no reports of secondary cases of TB because of exposure to an index-case patient and there were few TB infections, which was demonstrated by low occurrence of newly positive tuberculin skin tests (12%-16%) and conversions (8%-17%) among contacts. Targeted TB education, screening, and treatment for ESRD patients at highest risk for TB exposure (eg, non-US-born persons), using interferon-gamma release assays and short course LTBI regimens (ie, isoniazid-rifapentine weekly for 12 weeks or rifampin daily for 4 months) may be an effective overall strategy for reducing TB burden in ESRD patients.

OBJECTIVE

Many healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and characterize latent subgroups of high-risk patients based on risk score trajectories.

STUDY DESIGN

Observational study of 7289 patients discharged from Veterans Health Administration (VA) hospitals during a 1-week period in November 2012 and categorized in the top 5th percentile of risk for hospitalization.

METHODS

Using VA administrative data, we calculated weekly risk scores using the validated Care Assessment Needs model, reflecting the predicted probability of hospitalization. We applied the non-parametric k-means algorithm to identify latent subgroups of patients based on the trajectory of patients' hospitalization probability over a 2-year period. We then compared baseline sociodemographic characteristics, comorbidities, health service use, and social instability markers between identified latent subgroups.

RESULTS

The best-fitting model identified two subgroups: moderately high and persistently high risk. The moderately high subgroup included 65% of patients and was characterized by moderate subgroup-level hospitalization probability decreasing from 0.22 to 0.10 between weeks 1 and 66, then remaining constant through the study end. The persistently high subgroup, comprising the remaining 35% of patients, had a subgroup-level probability increasing from 0.38 to 0.41 between weeks 1 and 52, and declining to 0.30 at study end. Persistently high-risk patients were older, had higher prevalence of social instability and comorbidities, and used more health services.

CONCLUSIONS

On average, one third of patients initially identified as high risk stayed at very high risk over a 2-year follow-up period, while risk for the other two thirds decreased to a moderately high level. This suggests that multiple approaches may be needed to address high-risk patient needs longitudinally or intermittently.