Reducing Broad-Spectrum Antimicrobial Use in Extracorporeal Membrane Oxygenation: Reduce AMMO Study.

BACKGROUND: The use of extracorporeal membrane oxygenation (ECMO) in critically ill adults is increasing. There are currently no guidelines for antimicrobial prophylaxis. We analyzed 7 years of prophylactic antimicrobial use across 3 time series for patients on ECMO at our institution in the development, improvement, and streamlining of our ECMO antimicrobial prophylaxis protocol. METHODS: In this quasi-experimental interrupted time series analysis, we evaluated the impact of an initial ECMO antimicrobial prophylaxis protocol, implemented in 2014, on antimicrobial use and National Healthcare Safety Network-reportable infection rates. Then, following a revision and streamlining of the protocol in November 2018, we reevaluated the same metrics. RESULTS: Our study population included 338 intensive care unit patients who received ECMO between July 2011 and November 2019. After implementation of the first version of the protocol, we did not observe significant changes in antimicrobial use or infection rates in these patients; however, following revision and streamlining of the protocol, we demonstrated a significant reduction in broad-spectrum antimicrobial use for prophylaxis in patients on ECMO without any evidence of a compensatory increase in infection rates. CONCLUSIONS: Our final protocol significantly reduces broad-spectrum antimicrobial use for prophylaxis in patients on ECMO. We propose a standard antimicrobial prophylaxis regimen for patients on ECMO based on current evidence and our experience.Summary: There are no guidelines for antimicrobial prophylaxis in patients on extracorporeal membrane oxygenation (ECMO). A rational approach employing concepts of antimicrobial stewardship can drive logical antimicrobial selection for prophylaxis in patients on ECMO without adversely impacting outcomes.

Machine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit.

BACKGROUND: With the recent change in the definition (Sepsis-3 Definition) of sepsis and septic shock, an electronic search algorithm was required to identify the cases for data automation. This supervised machine learning method would help screen a large amount of electronic medical records (EMR) for efficient research purposes. AIM: To develop and validate a computable phenotype via supervised machine learning method for retrospectively identifying sepsis and septic shock in critical care patients. METHODS: A supervised machine learning method was developed based on culture orders, Sequential Organ Failure Assessment (SOFA) scores, serum lactate levels and vasopressor use in the intensive care units (ICUs). The computable phenotype was derived from a retrospective analysis of a random cohort of 100 patients admitted to the medical ICU. This was then validated in an independent cohort of 100 patients. We compared the results from computable phenotype to a gold standard by manual review of EMR by 2 blinded reviewers. Disagreement was resolved by a critical care clinician. A SOFA score ≥ 2 during the ICU stay with a culture 72 h before or after the time of admission was identified. Sepsis versions as V1 was defined as blood cultures with SOFA ≥ 2 and Sepsis V2 was defined as any culture with SOFA score ≥ 2. A serum lactate level ≥ 2 mmol/L from 24 h before admission till their stay in the ICU and vasopressor use with Sepsis-1 and-2 were identified as Septic Shock-V1 and-V2 respectively. RESULTS: In the derivation subset of 100 random patients, the final machine learning strategy achieved a sensitivity-specificity of 100% and 84% for Sepsis-1, 100% and 95% for Sepsis-2, 78% and 80% for Septic Shock-1, and 80% and 90% for Septic Shock-2. An overall percent of agreement between two blinded reviewers had a k = 0.86 and 0.90 for Sepsis 2 and Septic shock 2 respectively. In validation of the algorithm through a separate 100 random patient subset, the reported sensitivity and specificity for all 4 diagnoses were 100%-100% each. CONCLUSION: Supervised machine learning for identification of sepsis and septic shock is reliable and an efficient alternative to manual chart review.