scholarly journals Effect of Nosocomial Bloodstream Infections on Mortality, Length of Stay, and Hospital Costs in Older Adults

2014 ◽  
Vol 62 (2) ◽  
pp. 306-311 ◽  
Author(s):  
Keith S. Kaye ◽  
Dror Marchaim ◽  
Ting-Yi Chen ◽  
Timothy Baures ◽  
Deverick J. Anderson ◽  
...  
Keyword(s):  
Older Adults ◽  
Length Of Stay ◽  
Hospital Costs ◽  
Bloodstream Infections

scholarly journals Attributable Costs of Enterococcal Bloodstream Infections in a Nonsurgical Hospital Cohort

2010 ◽  
Vol 31 (1) ◽  
pp. 28-35 ◽  
Author(s):  
Anne M. Butler ◽  
Margaret A. Olsen ◽  
Liana R. Merz ◽  
Rebecca M. Guth ◽  
Keith F. Woeltje ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Propensity Score ◽  
Medical Center ◽  
Academic Medical Center ◽  
Hospital Costs ◽  
Bloodstream Infections ◽  
Generalized Least Squares ◽  
Vancomycin Resistance ◽  
Independent Effect ◽  
Matched Pairs

Background.Vancomycin-resistantEnterococcus(VRE) bloodstream infections (BSIs) are associated with increased morbidity and mortality.Objective.To determine the hospital costs and length of stay attributable to VRE BSI and vancomycin-sensitiveEnterococcus(VSE) BSI and the independent effect of vancomycin resistance on hospital costs.Methods.A retrospective cohort study was conducted of 21,154 nonsurgical patients admitted to an academic medical center during the period from 2002 through 2003. Using administrative data, attributable hospital costs (adjusted for inflation to 2007 US dollars) and length of stay were estimated with multivariate generalized least-squares (GLS) models and propensity score-matched pairs.Results.The cohort included 94 patients with VRE BSI and 182 patients with VSE BSI. After adjustment for demographics, comorbidities, procedures, nonenterococcal BSI, and early mortality, the costs attributable to VRE BSI were $4,479 (95% confidence interval [CI], $3,500-$5,732) in the standard GLS model and $4,036 (95% CI, $3,170-$5,140) in the propensity score-weighted GLS model, and the costs attributable to VSE BSI were $2,250 (95% CI, $l,758-$2,880) in the standard GLS model and $2,023 (95% CI, $1,588-$2,575) in the propensity score-weighted GLS model. The median values of the difference in costs between matched pairs were $9,949 (95% CI, $1,579-$24,693) for VRE BSI and $5,282 (95% CI, $2,042-$8,043) for VSE BSI. The costs attributable to vancomycin resistance were $1,713 (95% CI, $1,338-$2,192) in the standard GLS model and $1,546 (95% CI, $1,214-$1,968) in the propensity score-weighted GLS model. Depending on the statistical method used, attributable length of stay estimates ranged from 2.2 to 3.5 days for patients with VRE BSI and from 1.1 to 2.2 days for patients with VSE BSI.Conclusions.VRE BSI and VSE BSI were independently associated with increased hospital costs and increased length of stay. Vancomycin resistance was associated with increased costs.

scholarly journals Differences in Hospital Costs among Octogenarians and Nonagenarians Following Primary Total Joint Arthroplasty

Geriatrics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 26
Author(s):  
Christopher Fang ◽  
Andrew Hagar ◽  
Matthew Gordon ◽  
Carl T. Talmo ◽  
David A. Mattingly ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Total Joint Arthroplasty ◽  
Hospital Costs ◽  
Cost Effective ◽  
Joint Arthroplasty ◽  
Nursing Facilities ◽  
Activity Based Costing ◽  
Skilled Nursing ◽  
Value Based Care ◽  
Hospital Expenses

The proportion of patients over the age of 90 years continues to grow, and the anticipated demand for total joint arthroplasty (TJA) in this population is expected to rise concomitantly. As the country shifts to alternative reimbursement models, data regarding hospital expenses is needed for accurate risk-adjusted stratification. The aim of this study was to compare total in-hospital costs following primary TJA in octogenarians and nonagenarians, and to determine the primary drivers of cost. This was a retrospective analysis from a single institution in the U.S. We used time-drive activity-based costing (TDABC) to capture granular total hospital costs for each patient. 889 TJA’s were included in the study, with 841 octogenarians and 48 nonagenarians. Nonagenarians were more likely to undergo total hip arthroplasty (THA) (70.8% vs. 42.4%; p < 0.0001), had higher ASA classification (2.6 vs. 2.4; p = 0.049), and were more often privately insured (35.4% vs. 27.8%; p = 0.0001) as compared to octogenarians. Nonagenarians were more often discharged to skilled nursing facilities (56.2% vs. 37.5%; p = 0.0011), experienced longer operating room (OR) time (142 vs. 133; p = 0.0201) and length of stay (3.7 vs. 3.1; p = 0.0003), and had higher implant and total in-hospital costs (p < 0.0001 and 0.0001). Multivariate linear regression showed implant cost (0.700; p < 0.0001), length of stay (0.546; p < 0.0001), and OR time (0.288; p < 0.0001) to be the strongest associations with overall costs. Primary TJA for nonagenarians was more expensive than octogenarians. Targeting implant costs, length of stay, and OR time can reduce costs for nonagenarians in order to provide cost-effective value-based care.

scholarly journals Temporal Changes in Central-Line–Associated Bloodstream Infection Time Between Events, 2017–2018 Versus 2015–2016

2020 ◽  
Vol 41 (S1) ◽  
pp. s403-s404
Author(s):  
Jonathan Edwards ◽  
Katherine Allen-Bridson ◽  
Daniel Pollock
Keyword(s):  
Length Of Stay ◽  
Patient Care ◽  
Average Length ◽  
Bloodstream Infections ◽  
Central Line ◽  
Significant Shift ◽  
Care Hospital ◽  
Average Length Of Stay ◽  
Period 2 ◽  
Hospital Bed

Background: The CDC NHSN surveillance coverage includes central-line–associated bloodstream infections (CLABSIs) in acute-care hospital intensive care units (ICUs) and select patient-care wards across all 50 states. This surveillance enables the use of CLABSI data to measure time between events (TBE) as a potential metric to complement traditional incidence measures such as the standardized infection ratio and prevention progress. Methods: The TBEs were calculated using 37,705 CLABSI events reported to the NHSN during 2015–2018 from medical, medical-surgical, and surgical ICUs as well as patient-care wards. The CLABSI TBE data were combined into 2 separate pairs of consecutive years of data for comparison, namely, 2015–2016 (period 1) and 2017–2018 (period 2). To reduce the length bias, CLABSI TBEs were truncated for period 2 at the maximum for period 1; thereby, 1,292 CLABSI events were excluded. The medians of the CLABSI TBE distributions were compared over the 2 periods for each patient care location. Quantile regression models stratified by location were used to account for factors independently associated with CLABSI TBE, such as hospital bed size and average length of stay, and were used to measure the adjusted shift in median CLABSI TBE. Results: The unadjusted median CLABSI TBE shifted significantly from period 1 to period 2 for the patient care locations studied. The shift ranged from 20 to 75.5 days, all with 95% CIs ranging from 10.2 to 32.8, respectively, and P < .0001 (Fig. 1). Accounting for independent associations of CLABSI TBE with hospital bed size and average length of stay, the adjusted shift in median CLABSI TBE remained significant for each patient care location that was reduced by ∼15% (Table 1). Conclusions: Differences in the unadjusted median CLABSI TBE between period 1 and period 2 for all patient care locations demonstrate the feasibility of using TBE for setting benchmarks and tracking prevention progress. Furthermore, after adjusting for hospital bed size and average length of stay, a significant shift in the median CLABSI TBE persisted among all patient care locations, indicating that differences in patient populations alone likely do not account for differences in TBE. These findings regarding CLABSI TBEs warrant further exploration of potential shifts at additional quantiles, which would provide additional evidence that TBE is a metric that can be used for setting benchmarks and can serve as a signal of CLABSI prevention progress.Funding: NoneDisclosures: None

scholarly journals 53. Incidence of Bloodstream Infections and Outcomes in Patients with Severe COVID-19 Pneumonia

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S156-S157
Author(s):  
Aikaterini Papamanoli ◽  
Jeanwoo Yoo ◽  
Azad Mojahedi ◽  
Robin Jacob ◽  
Prabhjot Grewal ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Medical Records ◽  
Bloodstream Infections ◽  
Blood Stream ◽  
Blood Cultures ◽  
High Flow ◽  
University Hospital ◽  
Skin Microbiota ◽  
Prolonged Recovery ◽  
The Impact

Abstract Background Coronavirus disease 19 (COVID-19) leading to acute respiratory distress syndrome is associated with need for intensive care (IC), mechanical ventilation (MV), and prolonged recovery. These patients are thus predisposed to blood stream infections which can worsen outcomes. This risk may be aggravated by adjunctive therapies. Methods We reviewed the medical records of all adults admitted to Stony Brook University Hospital, NY, from March 1 to April 15, 2020 with severe COVID-19 pneumonia (requiring high-flow O2). Patients who received MV or died within 24h were excluded. Patients were followed until death or hospital discharge. We reviewed positive blood cultures (PBC) for pathogenic microorganisms, and calculated the incidence of bacteremia, rates of infective endocarditis (IE), and impact on mortality. Microbes isolated only once and belonging to groups defined as commensal skin microbiota were labelled as contaminants. We also examined the impact of adjunctive therapies with immunosuppressive potential (steroids and tocilizumab), on bacteremia. Results A total of 469 patients with severe COVID-19 pneumonia were included (Table 1). Of these, 199 (42.4%) required IC and 172 (36.7%) MV. Median length of stay was 13 days (8–22) and 94 (20.0%) had PBC. Of these, 43 were considered true pathogens (bacteremia), with predominance of E. faecalis and S. epidermidis, and 51 were considered contaminants (Table 2). The incidence of bacteremia (43/469, 9.2%) was 5.1 per 1000 patient-days (95%CI 3.8–6.4). An echocardiogram was performed in 21 patients, 1 had an aortic valve vegetation (IE) by methicillin sensitive S. aureus. Bacteremia rates were nonsignificantly higher with steroids (5.9 vs 3.7 per 1000 patient-days; P=0.057). Use of tocilizumab was not associated with bacteremia (5.8 vs 4.8 per 1000 patient-days; P=0.28). Mortality was nonsignificantly higher in patients with (15/43, 34.9%) vs. without (108/426, 25.4%) bacteremia (P=0.20). Length of stay was the strongest predictor of bacteremia, with risk increasing by 7% (95%CI 6%-9%, P&lt; 0.001) per additional day. Cohort Characteristics of Patients with Severe COVID-19 Pneumonia on High-Flow O2 (N= 469) All Microorganisms Isolated from Blood Cultures Conclusion The incidence of bacteremia was relatively low and IE was uncommon in this study of severe COVID-19 patients. Risk of bacteremia increased with longer hospital stay and with steroids use, but not with tocilizumab. Disclosures All Authors: No reported disclosures

Abstract WP182: Trends in Acute Ischemic Stroke Hospitalizations and Risk Factors Among Young Adults: 12 Years of Nationally Representative Data

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Urvish K Patel ◽  
Priti Poojary ◽  
Vishal Jani ◽  
Mandip S Dhamoon
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Young Adults ◽  
Ischemic Stroke ◽  
Length Of Stay ◽  
Acute Ischemic Stroke ◽  
Stroke Risk ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Stroke Risk Factors

Background: There is limited recent population-based data of trends in acute ischemic stroke (AIS) hospitalization rates among young adults (YA). Rising prevalence of stroke risk factors may increase stroke rates in YA. We hypothesized that 1) stroke hospitalizations and mortality among YA are increasing over time (2000-2011), 2) besides traditional stroke risk factors, non-traditional factors are associated with stroke in YA, 3) stroke hospitalization among YA is associated with higher mortality, length of stay (LOS), and cost. Methods: In the Nationwide Inpatient Sample database (years 2000-2011), adult hospitalizations for AIS and concurrent diagnoses were identified by ICD-9-CM codes; the analytic cohort constituted all AIS hospitalizations. We performed weighted analysis using chi-square, t-test, and Jonckheere trend test. Multivariable survey regression models evaluated interactions between age group (18-45 vs. >45 years) and traditional and non-traditional risk factors, with outcomes including mortality, LOS, and cost. Models were adjusted for race, sex, Charlson’s Comorbidity Index, primary payer, location and teaching status of hospital, and admission day. Results: Among 5220960 AIS hospitalizations, 231858 (4.4%) were YA. On trend analysis, proportion of YA amongst AIS increased from 3.6% in 2000 to 4.7% in 2011 (p<0.0001) but mortality in YA decreased from 3.7% in 2000 to 2.6% in 2011, compared to 7.1% in 2000 to 4.6% in 2011 (p<0.0001) among older adults. Non-traditional, especially behavioral, risk factors were more common among YA, and LOS and cost were higher (Table). Conclusion: There was a trend for higher proportion of YA among AIS hospitalizations, though there was a decreasing mortality trend over 10 years. Behavioral risk factors were more common among YA, and there was an increased length of stay and cost. AIS in YA may require different preventive approaches compared to AIS among older adults.

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