Publications

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.

Numerous microRNAs and their target mRNAs are coexpressed across diverse cell types. However, it is unknown whether they are regulated in a manner independent of or dependent on cellular context. Here, we explored transcriptome-wide targeting and gene regulation by miR-155, whose activation-induced expression plays important roles in innate and adaptive immunity. Through mapping of miR-155 targets through differential iCLIP, mRNA quantification with RNA-seq, and 3' untranslated region (UTR)-usage analysis with poly(A)-seq in macrophages, dendritic cells, and T and B lymphocytes either sufficient or deficient in activated miR-155, we identified numerous targets differentially bound by miR-155. Whereas alternative cleavage and polyadenylation (ApA) contributed to differential miR-155 binding to some transcripts, in most cases, identical 3'-UTR isoforms were differentially regulated across cell types, thus suggesting ApA-independent and cellular-context-dependent miR-155-mediated gene regulation. Our study provides comprehensive maps of miR-155 regulatory networks and offers a valuable resource for dissecting context-dependent and context-independent miRNA-mediated gene regulation in key immune cell types.

OBJECTIVES

To determine why non-mechanically ventilated hospitalized older adults are transferred to long-term acute care (LTAC) hospitals rather than remaining in the hospital.

DESIGN

Observational cohort.

SETTING

National Medicare data.

PARTICIPANTS

Non-mechanically ventilated hospitalized adults aged 65 and older with fee-for-service Medicare in 2012 who were transferred to an LTAC hospital (n=1,831) or had a prolonged hospitalization without transfer (average hospital length of stay or longer of those transferred to an LTAC hospital) and had one of the 50 most common hospital diagnoses leading to LTAC transfer (N=12,875).

MEASUREMENTS

We assessed predictors of transfer using a multilevel model, adjusting for patient-, hospital-, and hospital referral region (HRR)-level factors. We estimated proportions of variance at each level and adjusted hospital- and HRR-specific LTAC transfer rates using sequential models.

RESULTS

The strongest predictor of transfer was being hospitalized near an LTAC hospital (<1.4 vs > 33.6 miles, adjusted odds ratio=6.2, 95% confidence interval (CI)=4.2-9.1). After adjusting for case mix, differences between hospitals explained 15.4% of the variation in LTAC use and differences between regions explained 27.8%. Case mix-adjusted LTAC use was high in the South, where many HRRs had rates between 20.3% and 53.1%, whereas many HRRs were less than 5.4% in the Pacific Northwest, North, and New England. From our fully adjusted model, the median adjusted hospital LTAC transfer rate was 7.2% (interquartile range 2.8-17.5%), with substantial within-region variation (intraclass coefficient=0.25, 95% CI=0.21-0.30).

CONCLUSIONS

Nearly half of the variation in LTAC use is independent of illness severity and is explained by which hospital and what region the individual was hospitalized in. Because of the greater fragmentation of care and Medicare spending with LTAC transfers (because LTAC hospitals generate a separate bundled payment from the hospital), greater attention is needed to define the optimal role of LTAC hospitals in caring for older adults.

Background

The gut-selective nature of vedolizumab has raised questions regarding increased joint pain or arthralgia with its use in inflammatory bowel disease (IBD) patients. As arthralgias are seldom coded and thus difficult to study, few studies have examined the comparative risk of arthralgia between vedolizumab and tumor necrosis factor inhibitor (TNFi). Our objectives were to evaluate the application of natural language processing (NLP) to identify arthralgia in the clinical notes and to compare the risk of arthralgia between vedolizumab and TNFi in IBD.

Methods

We performed a retrospective study using a validated electronic medical record (EMR)-based IBD cohort from 2 large tertiary care centers. The index date was the first date of vedolizumab or TNFi prescription. Baseline covariates were assessed 1 year before the index date; patients were followed 1 year after the index date. The primary outcome was arthralgia, defined using NLP. Using inverse probability of treatment weight to balance the cohorts, we then constructed Cox regression models to calculate the hazard ratio (HR) for arthralgia in the vedolizumab and TNFi groups.

Results

We studied 367 IBD patients on vedolizumab and 1218 IBD patients on TNFi. Patients on vedolizumab were older (mean age, 41.2 vs 34.9 years) and had more prevalent use of immunomodulators (52.3% vs 31.9%) than TNFi users. Our data did not observe a significantly increased risk of arthralgia in the vedolizumab group compared with TNFi (HR, 1.20; 95% confidence interval, 0.97-1.49).

Conclusions

In this large observational study, we did not find a significantly increased risk of arthralgia associated with vedolizumab use compared with TNFi.