TY - JOUR
T1 - SEVERITAS
T2 - An externally validated mortality prediction for critically ill patients in low and middle-income countries
AU - Deliberato, Rodrigo Octávio
AU - Escudero, Guilherme Goto
AU - Bulgarelli, Lucas
AU - Neto, Ary Serpa
AU - Ko, Stephanie Q.
AU - Campos, Niklas Soderberg
AU - Saat, Berke
AU - Amaro, Edson
AU - Lopes, Fabio Silva
AU - Johnson, Alistair E. W.
PY - 2019/11
Y1 - 2019/11
N2 - Objective: Severity of illness scores used in critical care for benchmarking, quality assurance and risk stratification have been mainly created in high-income countries. In low and middle-income countries (LMICs), they cannot be widely utilized due to the demand for large amounts of data that may not be available (e.g. laboratory results). We attempt to create a new severity prognostication model using fewer variables that are easier to collect in an LMIC. Setting: Two intensive care units, one private and one public, from São Paulo, Brazil Patients: An ICU for the first time. Interventions: None. Measurements and Mains results: The dataset from the private ICU was used as a training set for model development to predict in-hospital mortality. Three different machine learning models were applied to five different blocks of candidate variables. The resulting 15 models were then validated on a separate dataset from the public ICU, and discrimination and calibration compared to identify the best model. The best performing model used logistic regression on a small set of 10 variables: highest respiratory rate, lowest systolic blood pressure, highest body temperature and Glasgow Coma Scale during the first hour of ICU admission; age; prior functional capacity; type of ICU admission; source of ICU admission; and length of hospital stay prior to ICU admission. On the validation dataset, our new score, named SEVERITAS, had an area under the receiver operating curve of 0.84 (0.82 – 0.86) and standardized mortality ratio of 1.00 (0.91–1.08). Moreover, SEVERITAS had similar discrimination compared to SAPS-3 and better discrimination than the simplified TropICS and R-MPM. Conclusions: Our study proposes a new ICU mortality prediction model using simple logistic regression on a small set of easily collected variables may be better suited than currently available models for use in low and middle-income countries.
AB - Objective: Severity of illness scores used in critical care for benchmarking, quality assurance and risk stratification have been mainly created in high-income countries. In low and middle-income countries (LMICs), they cannot be widely utilized due to the demand for large amounts of data that may not be available (e.g. laboratory results). We attempt to create a new severity prognostication model using fewer variables that are easier to collect in an LMIC. Setting: Two intensive care units, one private and one public, from São Paulo, Brazil Patients: An ICU for the first time. Interventions: None. Measurements and Mains results: The dataset from the private ICU was used as a training set for model development to predict in-hospital mortality. Three different machine learning models were applied to five different blocks of candidate variables. The resulting 15 models were then validated on a separate dataset from the public ICU, and discrimination and calibration compared to identify the best model. The best performing model used logistic regression on a small set of 10 variables: highest respiratory rate, lowest systolic blood pressure, highest body temperature and Glasgow Coma Scale during the first hour of ICU admission; age; prior functional capacity; type of ICU admission; source of ICU admission; and length of hospital stay prior to ICU admission. On the validation dataset, our new score, named SEVERITAS, had an area under the receiver operating curve of 0.84 (0.82 – 0.86) and standardized mortality ratio of 1.00 (0.91–1.08). Moreover, SEVERITAS had similar discrimination compared to SAPS-3 and better discrimination than the simplified TropICS and R-MPM. Conclusions: Our study proposes a new ICU mortality prediction model using simple logistic regression on a small set of easily collected variables may be better suited than currently available models for use in low and middle-income countries.
KW - Critical care
KW - Hospital mortality
KW - Intensive care
KW - Machine learning
KW - Predictive analysis
UR - http://www.scopus.com/inward/record.url?scp=85072181862&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2019.103959
DO - 10.1016/j.ijmedinf.2019.103959
M3 - Article
C2 - 31539837
AN - SCOPUS:85072181862
SN - 1386-5056
VL - 131
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
M1 - 103959
ER -