A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.
Crit Care Med
; 47(11): 1485-1492, 2019 11.
Article
em En
| MEDLINE
| ID: mdl-31389839
ABSTRACT
OBJECTIVES:
Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.DESIGN:
Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation.SETTING:
Tertiary teaching hospital system in Philadelphia, PA. PATIENTS All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184).INTERVENTIONS:
A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. MEASUREMENT AND MAINRESULT:
Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer.CONCLUSIONS:
Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.
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Base de dados:
MEDLINE
Assunto principal:
Choque Séptico
/
Algoritmos
/
Diagnóstico por Computador
/
Sepse
/
Sistemas de Apoio a Decisões Clínicas
/
Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article