Artif Intell Med - Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores.

Tópicos

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Resumo

TRODUCTION: The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient ICU resource usage and varies considerably. Planning of postoperative ICU admissions is important as ICUs often have no nonoccupied beds available.PROBLEM STATEMENT: Estimation of the ICU bed availability for the next coming days is entirely based on clinical judgement by intensivists and therefore too inaccurate. For this reason, predictive models have much potential for improving planning for ICU patient admission.OBJECTIVE: Our goal is to develop and optimize models for patient survival and ICU length of stay (LOS) based on monitored ICU patient data. Furthermore, these models are compared on their use of sequential organ failure (SOFA) scores as well as underlying raw data as input features.METHODOLOGY: Different machine learning techniques are trained, using a 14,480 patient dataset, both on SOFA scores as well as their underlying raw data values from the first five days after admission, in order to predict (i) the patient LOS, and (ii) the patient mortality. Furthermore, to help physicians in assessing the prediction credibility, a probabilistic model is tailored to the output of our best-performing model, assigning a belief to each patient status prediction. A two-by-two grid is built, using the classification outputs of the mortality and prolonged stay predictors to improve the patient LOS regression models.RESULTS: For predicting patient mortality and a prolonged stay, the best performing model is a support vector machine (SVM) with GA,D=65.9% (area under the curve (AUC) of 0.77) and GS,L=73.2% (AUC of 0.82). In terms of LOS regression, the best performing model is support vector regression, achieving a mean absolute error of 1.79 days and a median absolute error of 1.22 days for those patients surviving a nonprolonged stay.CONCLUSION: Using a classification grid based on the predicted patient mortality and prolonged stay, allows more accurate modeling of the patient LOS. The detailed models allow to support the decisions made by physicians in an ICU setting.

Resumo Limpo

troduct length stay critic ill patient intens care unit icu indic patient icu resourc usag vari consider plan postop icu admiss import icus often nonoccupi bed availableproblem statement estim icu bed avail next come day entir base clinic judgement intensivist therefor inaccur reason predict model much potenti improv plan icu patient admissionobject goal develop optim model patient surviv icu length stay los base monitor icu patient data furthermor model compar use sequenti organ failur sofa score well under raw data input featuresmethodolog differ machin learn techniqu train use patient dataset sofa score well under raw data valu first five day admiss order predict patient los ii patient mortal furthermor help physician assess predict credibl probabilist model tailor output bestperform model assign belief patient status predict twobytwo grid built use classif output mortal prolong stay predictor improv patient los regress modelsresult predict patient mortal prolong stay best perform model support vector machin svm gad area curv auc gsl auc term los regress best perform model support vector regress achiev mean absolut error day median absolut error day patient surviv nonprolong stayconclus use classif grid base predict patient mortal prolong stay allow accur model patient los detail model allow support decis made physician icu set

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