In International journal of medical informatics
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.
Deliberato Rodrigo Octávio, Escudero Guilherme Goto, Bulgarelli Lucas, Neto Ary Serpa, Ko Stephanie Q, Campos Niklas Soderberg, Saat Berke, Amaro Edson, Lopes Fabio Silva, Johnson Alistair Ew
Critical care, Hospital mortality, Intensive care, Machine learning, Predictive analysis