• Like
  • Save
Predicting the Risk of Clostridium Difficile Infections Following an Outpatient Visit KUNTZ
Upcoming SlideShare
Loading in...5
×
 

Predicting the Risk of Clostridium Difficile Infections Following an Outpatient Visit KUNTZ

on

  • 459 views

Pharmacoepidemiology

Pharmacoepidemiology

Statistics

Views

Total Views
459
Views on SlideShare
459
Embed Views
0

Actions

Likes
0
Downloads
6
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • As background, Clostridium difficile infection is a gastrointestinal infection associated with significant morbidity in the United States. C diff is traditionally considered to be a healthcare associated infection although it is becoming more common in ambulatory populations. The primary risk factors for CDI are advanced age, underlying comorbidity, and antimicrobial use. First, you are likely wondering who will benefit from being able to accurately predict a patient’s risk for CDI.1) Vaccine developers – to determine clinical trial eligibility2) In turn, if or when a C. difficile vaccine is developed, healthcare providers could determine which patients would benefit from vaccination. 3) Finally, physicians might use the risk score to identify patients for more judicious utilization of antimicrobials or risk management for CDI.
  • To develop the risk score, we conducted a retrospective cohort study among KPNW patients who had an index outpatient visit for any reason between July 2005 and September 2008. We then followed these patients for one year--until the end of follow-up or the first occurrence of CDI. C. difficile was identified through toxin tests in combination with treatment for CDI or through ICD-9 codes. We then identified potential predictors for CDI based on the current scientific literature and input from clinicians. As we were designing the risk score to be pragmatic and useful in clinical practice, we also tried to select predictors which can be easily obtained during patient care, through electronic health records. Predictors included: healthcare utilization—hospitalization, stay in a nursing home; medication use, specifically antimicrobials and gastric acid suppressants; and history of immunosuppression, renal dialysis, and chemotherapeutic procedures or therapies. These were measured during the 60-day baseline period before the index outpatient visit. The presence or absence of comorbid conditions were measured in the one year prior to the index visit.We used Cox regression to evaluate baseline patient characteristics that might predict CDI in the one year following an outpatient visit. The risk score results from the translation of the coefficients from the Cox regression into a points-based system, in which a higher number of points indicates a higher risk of CDI. First, the linear predictor in the Cox model was mapped to the corresponding one-year risk for CDI. Following this, the components of the linear predictor were rescaled to an arbitrary axis in which a score of zero points was assigned to the lowest-risk category for each variable, with increasing points counted for proportionate increases in the linear predictor. These risk score points approximate the exact hazard ratio for CDI. We calculated the observed risk of CDI for each decile of patients’ predicted risks of CDI to measure discrimination and calibration. The observed and predicted risks were then plotted using failure curves.Following development of the risk score at KPNW, we validated and recalibrated the risk score using a retrospective cohort of KPCO members with an index outpatient visit during the same time period. We used the same patient characteristics for the development and validation risk scores.

Predicting the Risk of Clostridium Difficile Infections Following an Outpatient Visit KUNTZ Predicting the Risk of Clostridium Difficile Infections Following an Outpatient Visit KUNTZ Presentation Transcript

  • Predicting The Risk of Clostridium difficile Infections Following an Outpatient Visit: Development And External Validation of a Pragmatic, Prognostic Risk Score Jennifer L. Kuntz, PhD Kaiser Permanente Northwest Center for Health Research May 2, 2012© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
  • Research question How accurately can routinely collected, patient characteristics predict the one-year risk of C. difficile infection (CDI) among patients having a routine outpatient healthcare visit?© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
  • Methods  Retrospective cohort study of KPNW patients with an index outpatient visit between 2005 and 2008  Outcome: Time to first occurrence of CDI during the one year after an index outpatient visit  We modeled the occurrence of CDI using Cox regression and translated regression coefficients into risk score points.  We calculated and plotted the observed one-year CDI risk for each decile of predicted risk.  The risk score was validated and recalibrated using a KPCO cohort and the same patient characteristics.© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
  • Results: Development of the risk score The failure curves show the incidence of CDI during the first year after an outpatient visit among KPNW cohort members. The curves show the observed risk (solid lines) and the predicted risk (dotted lines) of CDI according to deciles of predicted risk.© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
  • Assigning risk score points to patients: Translating the tool into practice at KPNW A 65-year-old male patient in our cohort who was recently hospitalized for 8 days. This patient has diabetes and has recently used a fluoroquinolone. Age 65 (51 pts) + Hospitalization of 8 days (47 pts) + Diabetes (8 pts) + Fluoroquinolone use (27 pts) = 133 points© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
  • Results: Validation of the risk score The failure curves show 0.020 the incidence of CDI during the first year after an outpatient visit 0.015 among KPCO cohortCumulative Risk of Infection members. 0.010 The curves show the observed risk (solid lines) and the predicted 0.005 risk (dotted lines) of CDI according to deciles of predicted risk. 0.000 0 2 4 6 8 10 12© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH Months
  • Results: Validation of the risk score Each point indicates the predicted risk plotted against the observed risk among patients in each decile of predicted risk.© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
  • Conclusions  We developed a pragmatic risk score which:  Successfully discriminated between patients at the highest and lowest one-year risk for CDI.  Provided predictions which agreed closely with the observed risk for CDI.  Provided important information about the risk for CDI among patients who would likely benefit the most from clinician recognition of this risk.© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
  • Acknowledgements  Kaiser Permanente Northwest  Funding source Eric S. Johnson, PhD Amanda F. Petrik, MS Sanofi pasteur David H. Smith, PhD Micah L. Thorp, DO, MPH Xiuhai Yang, MS  Kaiser Permanente Colorado Marsha A. Raebel, PharmD Karen A. Glenn  Decision Research Nancy Neil, PhD© 2012, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH