000107413 001__ 107413
000107413 005__ 20230519145428.0
000107413 0247_ $$2doi$$a10.3390/ijerph18168677
000107413 0248_ $$2sideral$$a124734
000107413 037__ $$aART-2021-124734
000107413 041__ $$aeng
000107413 100__ $$aAznar-Gimeno, Rocío
000107413 245__ $$aA clinical decision web to predict ICU admission or death for patients hospitalised with Covid-19 using machine learning algorithms
000107413 260__ $$c2021
000107413 5060_ $$aAccess copy available to the general public$$fUnrestricted
000107413 5203_ $$aThe purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787–0.854) and accurate calibration (slope = 1, intercept = −0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.
000107413 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000107413 590__ $$a4.614$$b2021
000107413 592__ $$a0.814$$b2021
000107413 594__ $$a4.5$$b2021
000107413 591__ $$aPUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH$$b45 / 183 = 0.246$$c2021$$dQ1$$eT1
000107413 593__ $$aPollution$$c2021$$dQ1
000107413 591__ $$aPUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH$$b71 / 210 = 0.338$$c2021$$dQ2$$eT2
000107413 593__ $$aHealth, Toxicology and Mutagenesis$$c2021$$dQ1
000107413 591__ $$aENVIRONMENTAL SCIENCES$$b100 / 279 = 0.358$$c2021$$dQ2$$eT2
000107413 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000107413 700__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis M.
000107413 700__ $$aLabata-Lezaun, Gorka
000107413 700__ $$0(orcid)0000-0003-2755-5500$$aHoyo-Alonso, Rafael del
000107413 700__ $$0(orcid)0000-0001-7296-7307$$aAbadia-Gallego, David$$uUniversidad de Zaragoza
000107413 700__ $$0(orcid)0000-0002-9600-8116$$aPaño-Pardo, Ramón$$uUniversidad de Zaragoza
000107413 700__ $$aEsquillor-Rodrigo, M. José
000107413 700__ $$0(orcid)0000-0001-5932-2889$$aLanas, Ángel$$uUniversidad de Zaragoza
000107413 700__ $$0(orcid)0000-0002-7119-2244$$aSerrano, Trinidad$$uUniversidad de Zaragoza
000107413 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000107413 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000107413 773__ $$g18, 16 (2021), 8677 [20 pp.]$$pInt. j. environ. res. public health$$tInternational journal of environmental research and public health$$x1661-7827
000107413 8564_ $$s4029281$$uhttps://zaguan.unizar.es/record/107413/files/texto_completo.pdf$$yVersión publicada
000107413 8564_ $$s2701668$$uhttps://zaguan.unizar.es/record/107413/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000107413 909CO $$ooai:zaguan.unizar.es:107413$$particulos$$pdriver
000107413 951__ $$a2023-05-18-14:15:23
000107413 980__ $$aARTICLE