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...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.展开更多
文摘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.
