632. Refining measurement of mortality attributable to healthcare associated infections (HAI)
Session: Oral Abstract Session: HAI Reporting: The Devil is in the Details
Thursday, October 8, 2015: 2:00 PM
Room: 5--AB
Background:

Studies of HAI attributable mortality have typically been single center and focus on specific patient populations or types of HAI. Further, national HAI mortality estimates are crude (unadjusted), and have focused only on in-hospital mortality. To evaluate and refine methods of estimating HAI mortality, we obtained post discharge mortality status for a large cohort of patients from a multi-site acute care hospital HAI prevalence survey (PS) conducted in 2011, to compare various crude and adjusted in-hospital and post-discharge mortality measures.

Methods: Using the subset of PS patients on antimicrobial drugs for whom medical records were reviewed for HAIs, outcomes at 90 days post-discharge (died, survived) were obtained from state death certificate registries, then merged with PS data.  Crude in-hospital and 90-day post-discharge outcomes were compared for patients with and without HAIs defined using NHSN methods. Cox proportional hazards (PH) regression (SAS 9.3) was used with hospital length of stay (LOS) in days as the time variable, to evaluate hospital, demographic, and clinical factors associated with in-hospital death.

Results:

Among 2,999 patients (282 with HAI) from 120 hospitals in 7 states, crude in-hospital mortality was higher in patients with HAI than without (13.15 vs 4.9%, p<.001), and remained higher at 90-days post-discharge (21.6% vs 14.7%; p=0.003). The hazard of HAI on death over hospital LOS was not constant, and HAI was included as a time-dependent variable. In multivariable analysis (Table) HAI*time was an independent predictor of in-hospital death.

Factors assessed using multivariate Cox PH modelling

 

Hazard ratio (HR)

p-value

Hospital size

-

a

Location: Critical care

1.84

.004

Sex: Male

-

a

Race: White

-

a

Age (+1 yr)

1.03

<.001

Central line

-

a

Ventilator

1.75

.011

Urinary catheter

1.51

.034

Dialysis

-

a

HAI x log(time)

Varies by LOS

.003

a: Not significant 

Conclusion:

Our results show HAIs significantly impact the risk of death among hospital inpatients, even after accounting for other factors. Additional work to determine the impact of HAIs on post-discharge mortality is necessary. These findings will inform HAI mortality measurement for subsequent PS and more accurate estimation of HAI burden.

Nicola D. Thompson, PhD, MSc1, Lisa Laplace, MPH2, Helen Johnston, MPH3, Brittany Martin, MPH4, Richard Melchreit, MD5, Katherine Ellingson, PhD6, Cathleen Concannon, MPH7, Linn Warnke, RN, MPH8, Susan M. Ray, MD, FIDSA9, Marion Kainer, MBBS, MPH, FSHEA10 and Shelley S. Magill, MD, PhD2, (1)Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, GA, (2)Centers for Disease Control and Prevention, Atlanta, GA, (3)Colorado Department of Public Health and Environment, Denver, CO, (4)California Emerging Infections Program, Oakland, CA, (5)Connecticut Department of Public Health, Hartford, CT, (6)Oregon Health Authority, Portland, OR, (7)Center for Community Health, University of Rochester Medical Center, Rochester, NY, (8)Minnesota Department of Health, St. Paul, MN, (9)Emory University School of Medicine and Georgia Emerging Infections Program, Atlanta, GA, (10)Tennessee Department of Health, Nashville, TN

Disclosures:

N. D. Thompson, None

L. Laplace, None

H. Johnston, None

B. Martin, None

R. Melchreit, None

K. Ellingson, None

C. Concannon, None

L. Warnke, None

S. M. Ray, None

M. Kainer, None

S. S. Magill, None

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