The complete mitochondrial genome of the Indian leafwing butterfly Kallima paralekta (insecta: Lepidoptera: Nymphalidae).

The Indian leafwing butterfly Kallima paralekta (Horsfield, 1829) (Nymphalidae) is an Asian forest-dwelling, leaf-mimic. Genome skimming by Illumina sequencing permitted assembly of a complete circular mitogenome of 15,200 bp from K. paralekta consisting of 79.5% AT nucleotides, 22 tRNAs, 13 protein-coding genes, two rRNAs and a control region in the typical butterfly gene order. Kallima paralekta COX1 features an atypical CGA start codon, while ATP6, COX1, COX2, ND4, ND4L, and ND5 exhibit incomplete stop codons completed by 3' A residues added to the mRNA. Phylogenetic reconstruction places K. paraleckta within the monophyletic genus Kallima, sister to Mallika in the subfamily Nymphalinae. These data support the monophyly of tribe Kallimini and contribute to the evolutionary systematics of the Nymphalidae.

Variation in mortality rates after admission to long-term acute care hospitals for ventilator weaning.

PURPOSE: We sought to examine variation in long-term acute care hospital (LTACH) quality based on 90-day in-hospital mortality for patients admitted for weaning from mechanical ventilation. METHODS: We developed an administrative risk-adjustment model using data from Medicare claims. We validated the administrative model against a clinical model using data from LTACHs participating in a 2002 to 2003 clinical registry. We then used our validated administrative model to assess national variation in 90-day in-hospital mortality rates in LTACHs from 2013. RESULTS: The administrative risk-adjustment model was derived using data from 9447 patients admitted to 221 LTACHs in 2003. The model had good discrimination (C statistic=0.72) and calibration. Compared to a clinically derived model using data from 1163 patients admitted to 14 LTACHs, the administrative model generated similar performance estimates. National variation in risk-adjusted mortality was assessed using data from 20,453 patients admitted to 380 LTACHs in 2013. LTACH-specific risk-adjusted mortality rates varied from 8.4% to 48.1% (median: 24.2%, interquartile range: 19.7%-30.7%). CONCLUSIONS: LTACHs vary widely in mortality rates, underscoring the need to better understand the sources of this variation and improve the quality of care for patients requiring long-term ventilator weaning.

The Effect of Intensive Care Unit Admission Patterns on Mortality-based Critical Care Performance Measures.

RATIONALE: Current mortality-based critical care performance measurement focuses on intensive care unit (ICU) admissions as a single group, conflating low-severity and high-severity ICU patients for whom performance may differ and neglecting severely ill patients treated solely on hospital wards. OBJECTIVES: To assess the relationship between hospital performance as measured by risk-standardized mortality for severely ill ICU patients, less severely ill ICU patients, and severely ill patients outside the ICU. METHODS: Using a statewide, all-payer dataset from the Pennsylvania Healthcare Cost Containment Council, we analyzed discharge data for patients with nine clinical conditions with frequent ICU use. Using a validated severity-of-illness measure, we categorized hospitalized patients as either high severity (predicted probability of in-hospital death in top quartile) or low severity (all others). We then created three mutually exclusive groups: high-severity ICU admissions, low-severity ICU admissions, and high-severity ward patients. We used hierarchical logistic regression to generate hospital-specific 30-day risk-standardized mortality rates for each group and then compared hospital performance across groups using Spearman's rank correlation. MEASUREMENTS AND MAIN RESULTS: We analyzed 87 hospitals with 22,734 low-severity ICU admissions (mean per hospital, 261 ± 187), 10,991 high-severity ICU admissions (mean per hospital, 126 ± 105), and 6,636 high-severity ward patients (mean per hospital, 76 ± 48). We found little correlation between hospital performance for high-severity ICU patients versus low-severity ICU patients (ρ = 0.15; P = 0.17). There were 29 hospitals (33%) that moved up or down at least two quartiles of performance across the ICU groups. There was weak correlation between hospital performance for high-severity ICU patients versus high-severity ward patients (ρ = 0.25; P = 0.02). There were 24 hospitals (28%) that moved up or down at least two quartiles of performance across the high-severity groups. CONCLUSIONS: Hospitals that perform well in caring for high-severity ICU patients do not necessarily also perform well in caring for low-severity ICU patients or high-severity ward patients, indicating that risk-standardized mortality rates for ICU admissions as a whole offer only a narrow window on a hospital's overall performance for critically ill patients.

ICU Telemedicine and Critical Care Mortality: A National Effectiveness Study.

BACKGROUND: Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain. OBJECTIVES: To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals. RESEARCH DESIGN: We performed a multicenter retrospective case-control study using 2001-2010 Medicare claims data linked to a national survey identifying US hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 nonadopting hospitals (controls) based on size, case-mix, and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach. RESULTS: A total of 132 adopting case hospitals were matched to 389 similar nonadopting control hospitals. The preadoption and postadoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, P=0.07) and control hospitals (23.5% vs. 23.7%, P<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios=0.96; 95% CI, 0.95-0.98; P<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios=1.01; interquartile range, 0.85-1.12; range, 0.45-2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions postadoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (P<0.001) and be located in urban areas (P=0.04) compared with other hospitals. CONCLUSIONS: Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.

Adoption and de-adoption of drotrecogin alfa for severe sepsis in the United States.

PURPOSE: Drotrecogin alfa was a landmark drug for treatment of severe sepsis, yet little is known about how it was adopted and de-adopted during its 10-year period of availability. METHODS: We used hospitalization data on fee-for-service Medicare beneficiaries from 2002 to 2011 to characterize trends in the use of drotrecogin alfa in the United States. RESULTS: Drotrecogin alfa use peaked at 5.87 per 1000 severe sepsis hospitalizations in 2003 and then steadily declined to 0.94 administrations per 1000 severe sepsis hospitalizations in 2010. Large teaching hospitals were more likely to use drotrecogin alfa than small, nonteaching hospitals. The addition of "add-on payments" to hospitals for using drotrecogin alfa in 2002 was associated with significantly increased use (P < .0001), and the withdrawal of those payments in 2004 was associated significantly decreased use (P < .0001). Neither the publication of international sepsis guidelines with favorable drotrecogin alfa recommendations (in 2004 and 2008) nor the publication of a clinical trial focused on drotrecogin alfa (in 2005) were associated with consistent changes use (P > .05). CONCLUSIONS: Drotrecogin alfa use declined over time, with marked changes in use associated with drug-specific financial incentives but not the publication of clinical practice guidelines or clinical trials.

A Comparison of Free-Standing versus Co-Located Long-Term Acute Care Hospitals.

BACKGROUND: Long-term acute care hospitals (LTACs) provide specialized treatment for patients with chronic critical illness. Increasingly LTACs are co-located within traditional short-stay hospitals rather than operated as free-standing facilities, which may affect LTAC utilization patterns and outcomes. METHODS: We compared free-standing and co-located LTACs using 2005 data from the United States Centers for Medicare & Medicaid Services. We used bivariate analyses to examine patient characteristics and timing of LTAC transfer, and used propensity matching and multivariable regression to examine mortality, readmissions, and costs after transfer. RESULTS: Of 379 LTACs in our sample, 192 (50.7%) were free-standing and 187 (49.3%) were co-located in a short-stay hospital. Co-located LTACs were smaller (median bed size: 34 vs. 66, p <0.001) and more likely to be for-profit (72.2% v. 68.8%, p = 0.001) than freestanding LTACs. Co-located LTACs admitted patients later in their hospital course (average time prior to transfer: 15.5 days vs. 14.0 days) and were more likely to admit patients for ventilator weaning (15.9% vs. 12.4%). In the multivariate propensity-matched analysis, patients in co-located LTACs experienced higher 180-day mortality (adjusted relative risk: 1.05, 95% CI: 1.00-1.11, p = 0.04) but lower readmission rates (adjusted relative risk: 0.86, 95% CI: 0.75-0.98, p = 0.02). Costs were similar between the two hospital types (mean difference in costs within 180 days of transfer: -$3,580, 95% CI: -$8,720 -$1,550, p = 0.17). CONCLUSIONS: Compared to patients in free-standing LTACs, patients in co-located LTACs experience slightly higher mortality but lower readmission rates, with no change in overall resource use as measured by 180 day costs.

Effect of public reporting on intensive care unit discharge destination and outcomes.

RATIONALE: Public reporting of hospital performance is designed to improve healthcare outcomes by promoting quality improvement and informing consumer choice, but these programs may carry unintended consequences. OBJECTIVE: To determine whether publicly reporting in-hospital mortality rates for intensive care unit (ICU) patients influenced discharge patterns or mortality. METHODS: We performed a retrospective cohort study taking advantage of a natural experiment in which California, but not other states, publicly reported hospital-specific severity-adjusted ICU mortality rates between 2007 and 2012. We used multivariable logistic regression adjusted for patient, hospital, and regional characteristics to compare mortality rates and discharge patterns between California and states without public reporting for Medicare fee-for-service ICU admissions from 2005 through 2009 using a difference-in-differences approach. MEASUREMENTS AND MAIN RESULTS: We assessed discharge patterns using post-acute care use and acute care hospital transfer rates and mortality using in-hospital and 30-day mortality rates. The study cohort included 936,063 patients admitted to 646 hospitals. Compared with control subjects, admission to a California ICU after the introduction of public reporting was associated with a reduced odds of post-acute care use in post-reform year 2 (ratio of odds ratios [ORs], 0.94; 95% confidence interval [CI], 0.91-0.96) and increased odds of transfer to another acute care hospital in both post-reform years (year 1: ratio of ORs, 1.08; 95% CI, 1.01-1.16; year 2: ratio of ORs, 1.43; 95% CI, 1.33-1.53). There were no significant differences in in-hospital or 30-day mortality. CONCLUSIONS: Public reporting of ICU in-hospital mortality rates was associated with changes in discharge patterns but no change in risk-adjusted mortality.

Hospital factors associated with discharge bias in ICU performance measurement.

OBJECTIVE: Performance assessments based on in-hospital mortality for ICU patients can be affected by discharge practices such that differences in mortality may reflect variation in discharge patterns rather than quality of care. Time-specific mortality rates, such as 30-day mortality, are preferred but are harder to measure. The degree to which the difference between 30-day and in-hospital ICU mortality rates-or "discharge bias"-varies by hospital type is unknown. The aim of this study was to quantify variation in discharge bias across hospitals and determine the hospital characteristics associated with greater discharge bias. DESIGN: Retrospective cohort study. SETTING: Nonfederal Pennsylvania hospital discharges in 2008. PATIENTS: Eligible patients were 18 years old or older and admitted to an ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used logistic regression with hospital-level random effects to calculate hospital-specific risk-adjusted 30-day and in-hospital mortality rates. We then calculated discharge bias, defined as the difference between 30-day and in-hospital mortality rates, and used multivariable linear regression to compare discharge bias across hospital types. A total of 43,830 patients and 134 hospitals were included in the analysis. Mean (SD) risk-adjusted hospital-specific in-hospital and 30-day ICU mortality rates were 9.6% (1.3) and 12.7% (1.5), respectively. Hospital-specific discharge biases ranged from -1.3% to 6.6%. Discharge bias was smaller in large hospitals compared with small hospitals, making large hospitals appear comparatively worse from a benchmarking standpoint when using in-hospital mortality instead of 30-day mortality. CONCLUSIONS: Discharge practices bias in-hospital ICU mortality measures in a way that disadvantages large hospitals. Accounting for discharge bias will prevent these hospitals from being unfairly disadvantaged in public reporting and pay-for-performance.