Anderson, T.

Athan, E.

Cheng, A.C.

Holmes, N.

Howden, B.

Johnson, P.

Korman, T.

Munckhof, W.

O'Sullivan, M.

Roberts, S.A.

Robinson, J.O.

Turnidge, J.

Van Hal, S.

Warren, S.

English

Research Repository

1088. Prediction Model to Identify Patients at Risk of 30-Day TreatmentFailure in Patients With Staphylococcus aureus BacteremiaNatasha Holmes, MBBS FRACP PhD1; J. Owen Robinson, MD, FRACP2;Sebastian Van Hal, MBBS, FRACP, FRCPA, PhD3; Wendy Munckhof, MBBS, FRACP,FRCPA, PhD4; Eugene Athan, MBBS, FRACP, MPH5; Tony Korman, MBBS, FRACP,FRCPA6; Allen C. Cheng, MBBS, FRACP, MPH, PhD7; Matthew O’Sullivan, MBBS,MMed, FRACP, FRCPA, PhD8; Tara Anderson, MBBS, FRACP9; Sally A. Roberts, MB,ChB, FRCPA10; Sanchia Warren, MBBS, FRACP11; John Turnidge, MBBS, FRACP,FRCPA12; Paul Johnson, MBBS, FRACP, PhD13; Benjamin Howden, MBBS, FRACP,FRCPA, PhD14VANESSA Study Group; 1Department of Infectious Diseases, AustinHealth, Heidelberg Victoria, Australia; 2Department of Microbiology and InfectiousDiseases, Royal Perth Hospital and Fiona Stanley Hospital, Perth Western Australia,Australia; 3Infectious Diseases and Microbiology, Royal Prince Alfred Hospital,Sydney, Australia; 4Infection Management Services, Princess Alexandra Hospital,Woolloongabba Queensland, Australia; 5Infectious Diseases, Barwon Health,Deakin University, Geelong, Australia; 6Department of Infectious Diseases, MonashInfectious Diseases, Clayton Victoria, Australia; 7Infection Prevention and HospitalEpidemiology Unit, The Alfred Hospital, Melbourne, Australia; 8Centre forInfectious Diseases and Microbiology, Westmead Hospital, Westmead New SouthWales, Australia; 9Department of Infectious Diseases, Royal Hobart Hospital,Hobart Tasmania, Australia; 10Labplus, Department of Microbiology, AucklandDistrict Health Board, Auckland, New Zealand; 11Department of InfectiousDiseases, Royal Hobart Hospital, Hobart Tasmania, Australia; 12AustralianCommission on Safety and Quality in Health Care, Sydney New South Wales,Australia; 13Infectious Diseases, Austin Health/University of Melbourne,Melbourne, Australia; 14Microbiological Diagnostic Unit, Parkville Victoria,AustraliaSession: 135. Clinical Infectious Diseases: Bacteremia and EndocarditisFriday, October 28, 2016: 12:30 PMBackground. Clinical or microbiological treatment failure and mortality fromStaphylococcus aureus bacteremia (SAB) remains significant. A predictive model wasdeveloped to identify patients at increased risk of treatment failure in order to improvemanagement strategies.Methods. Adults with monomicrobial SAB from two prospective observationalcohorts were included. Treatment failure was a composite of all-cause mortality, per-sistent bacteremia (3 7 days) and recurrent bacteremia within 30 days. Candidate var-iables were selected a priori based on clinical significance and prior literature, and aparsimonious model was derived using the Akaike Information Criteria with a com-plete case analysis. A sum score was derived for clinical use.Figure 1. Predicted probability of 30-day treatment failure using sum score derived from parsi-monious predictive model.Figure 2. Performance of predictive model sum score versus observed 30-day treatment outcome.Results. Complete data were available for 388 patients, and 20.6% experienced 30-daytreatment failure. The parsimonious multivariable model identified severe sepsis, methi-cillin-resistant S. aureus (MRSA), patient age, and serum albumin as predictors of treat-ment failure. Model calibration (Hosmer-Lemeshow test, p = 0.376) and discrimination(c-statistic 0.752) indicated good performance. A prediction sum score was developedfrom this model (Table 1); Figure 1 shows the predicted probability of treatment failureand Figure 2 shows the sum score according to observed outcomes. The sum score per-formed well (c-statistic 0.750 compared with the multivariable model).Conclusion. The combination of severe sepsis, MRSA, older patient age and lowerserum albumin were predictors of 30-day treatment failure. Although some of thesefactors are non-modifiable, these are useful indicators for the clinician to ensure thepatient receives early appropriate antibiotics and aggressive supportive care to improveclinical outcomes.Table 1. Sum Score From Parsimonious Model to Predict Risk of 30-Day Treatment Q274FailureVariableModelcoefficientRoundedcoefficientScoreassigned*Severe sepsis 1.29308 1.3 +3MRSA 0.84871 0.8 +2Age (per 10 year increment fromage 18 years)0.3843 0.4 +1Albumin (per 5 g/L incrementfrom 10 g/L)−0.4144 −0.4 −1*Possible range of score: −7 to +13Disclosures. N. Holmes, National Health and Medical Research Council: GrantInvestigator, Grant recipient; B. Howden, National Health and Medical ResearchCouncil: Grant Investigator, Grant recipientPoster Abstracts • OFID 2016:1 (Suppl 1) • S201Downloaded from https://academic.oup.com/ofid/article/3/suppl_1/1088/2637335 by Murdoch University user on 30 July 2021

Prediction Model to Identify Patients at Risk of 30-Day Treatment Failure in Patients With Staphylococcus aureus Bacteremia

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Prediction Model to Identify Patients at Risk of 30-Day Treatment Failure in Patients With Staphylococcus aureus Bacteremia

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