SEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries

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dc.contributor.authorDeliberato R.O.
dc.contributor.authorEscudero G.G.
dc.contributor.authorBulgarelli L.
dc.contributor.authorNeto A.S.
dc.contributor.authorKo S.Q.
dc.contributor.authorCampos N.S.
dc.contributor.authorSaat B.
dc.contributor.authorAmaro E.
dc.contributor.authorLopes F.S.
dc.contributor.authorJohnson A.E.
dc.date.accessioned2024-03-12T23:51:17Z
dc.date.available2024-03-12T23:51:17Z
dc.date.issued2019
dc.description.abstract© 2019 Elsevier B.V.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.
dc.description.volume131
dc.identifier.doi10.1016/j.ijmedinf.2019.103959
dc.identifier.issn1872-8243
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35166
dc.relation.ispartofInternational Journal of Medical Informatics
dc.rightsAcesso Restrito
dc.subject.otherlanguageCritical care
dc.subject.otherlanguageHospital mortality
dc.subject.otherlanguageIntensive care
dc.subject.otherlanguageMachine learning
dc.subject.otherlanguagePredictive analysis
dc.titleSEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries
dc.typeArtigo
local.scopus.citations11
local.scopus.eid2-s2.0-85072181862
local.scopus.subjectCritical care
local.scopus.subjectHospital mortality
local.scopus.subjectIntensive care
local.scopus.subjectLength of hospital stays
local.scopus.subjectLow and middle income countries
local.scopus.subjectReceiver operating curves
local.scopus.subjectSimple logistic regressions
local.scopus.subjectStandardized mortality ratios
local.scopus.subjectBenchmarking
local.scopus.subjectBrazil
local.scopus.subjectCritical Illness
local.scopus.subjectDeveloping Countries
local.scopus.subjectFemale
local.scopus.subjectHospital Mortality
local.scopus.subjectHumans
local.scopus.subjectIntensive Care Units
local.scopus.subjectMachine Learning
local.scopus.subjectMale
local.scopus.subjectMiddle Aged
local.scopus.subjectModels, Statistical
local.scopus.subjectPredictive Value of Tests
local.scopus.subjectRetrospective Studies
local.scopus.subjectSeverity of Illness Index
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072181862&origin=inward
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