Brian J. Moore

Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index

Objective: We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness. Data Sources: We used a large analysis file built from all-payer hospital administrative data in the Healthcare Cost and Utilization Project State Inpatient Databases from 18 states in 2011 and 2012. Methods: The final models were derived with bootstrapped replications of backward stepwise logistic regressions on each outcome. Odds ratios and index weights were generated for each Elixhauser comorbidity to create a single index score per record for mortality and readmissions. Model validation was conducted with c-statistics. Results: Our index scores performed as well as using all 29 Elixhauser comorbidity variables separately. The c-statistic for our index scores without inclusion of other covariates was 0.777 (95% confidence interval, 0.776–0.778) for the mortality index and 0.634 (95% confidence interval, 0.633–0.634) for the readmissions index. The indices were stable across multiple subsamples defined by demographic characteristics or clinical condition. The addition of other commonly used covariates (age, sex, expected payer) improved discrimination modestly. Conclusions: These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.

Trends in Opioid-related Inpatient Stays Shifted After the Us Transitioned to Icd-10-cm Diagnosis Coding in 2015

Background: Trend analyses of opioid-related inpatient stays depend on the availability of comparable data over time. In October 2015, the US transitioned diagnosis coding from International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to ICD-10-CM, increasing from ∼14,000 to 68,000 codes. This study examines how trend analyses of inpatient stays involving opioid diagnoses were affected by the transition to ICD-10-CM. Subjects: Data are from Healthcare Cost and Utilization Project State Inpatient Databases for 14 states in 2015−2016, representing 26% of acute care inpatient discharges in the US. Study Design: We examined changes in the number of opioid-related stays before, during, and after the transition to ICD-10-CM using quarterly ICD-9-CM data from 2015 and quarterly ICD-10-CM data from the fourth quarter of 2015 and the first 3 quarters of 2016. Results: Overall, stays involving any opioid-related diagnosis increased by 14.1% during the ICD transition—which was preceded by a much lower 5.0% average quarterly increase before the transition and followed by a 3.5% average increase after the transition. In stratified analysis, stays involving adverse effects of opioids in therapeutic use showed the largest increase (63.2%) during the transition, whereas stays involving abuse and poisoning diagnoses decreased by 21.1% and 12.4%, respectively. Conclusions: The sharp increase in opioid-related stays overall during the transition to ICD-10-CM may indicate that the new classification system is capturing stays that were missed by ICD-9-CM data. Estimates of stays involving other diagnoses may also be affected, and analysts should assess potential discontinuities in trends across the ICD transition.

Cost comparison between uterine-sparing fibroid treatments one year following treatment

BackgroundTo compare one-year all-cause and uterine fibroid (UF)-related direct costs in patients treated with one of the following three uterine-sparing procedures: magnetic resonance-guided focused ultrasound (MRgFUS), uterine artery embolization (UAE) and myomectomy.MethodsThis retrospective observational cohort study used healthcare claims for several million individuals with healthcare coverage from employers in the MarketScan Database for the period 2003–2010. UF patients aged 25–54 on their first UF procedure (index) date with 366-day baseline experience, 366-day follow-up period, continuous health plan enrollment during baseline and follow-up, and absence of any baseline UF procedures were included in the final sample. Cost outcomes were measured by allowed charges (sum of insurer-paid and patient-paid amounts). UF-related cost was defined as difference in mean cost between study cohorts and propensity-score-matched control cohorts without UF. Multivariate adjustment of cost outcomes was conducted using generalized linear models.ResultsThe study sample comprised 14,426 patients (MRgFUS = 14; UAE = 4,092; myomectomy = 10,320) with a higher percent of older patients in MRgFUS cohort (71% vs. 50% vs. 12% in age-group 45–54, P < 0.001). Adjusted all-cause mean cost was lowest for MRgFUS (

Impact of Health System Affiliation on Hospital Resource Use Intensity and Quality of Care

19,763; 95% CI:

Young Adults, Health Insurance Expansions and Hospital Services Utilization

10,425-

The Impact of Patient Incentives on Comprehensive Diabetes Care Services and Medical Expenditures

38,694) followed by myomectomy (

Engagement in health and wellness: An online incentive-based program

20,407; 95% CI:

Author Response for Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index

19,483-

Admissions after discharge from an emergency department for chest symptoms

21,381) and UAE (

Length of stay in EDs: variation across classifications of clinical condition and patient discharge disposition

25,019; 95% CI: