Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment
Background: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted...
| Main Authors: | , , , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2022
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| Online Access: | http://hdl.handle.net/10835/13431 |
| _version_ | 1789408383368953856 |
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| author | Lea-Pereira, María Carmen Amaya Pascasio, Laura Martínez Sánchez, Patricia Rodríguez Salvador, María del Mar Galván-Espinosa, José Téllez-Ramírez, Luis Reche Lorite, Fernando Sánchez, María-José García-Torrecillas, Juan Manuel |
| author_facet | Lea-Pereira, María Carmen Amaya Pascasio, Laura Martínez Sánchez, Patricia Rodríguez Salvador, María del Mar Galván-Espinosa, José Téllez-Ramírez, Luis Reche Lorite, Fernando Sánchez, María-José García-Torrecillas, Juan Manuel |
| author_sort | Lea-Pereira, María Carmen |
| collection | DSpace |
| description | Background: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted of a clinical–administrative database, reflecting all cases of non-reperfused ischaemic stroke admitted to Spanish hospitals during the period 2008–2012. A predictive model based on logistic regression was developed on a training cohort and later validated by the “hold-out” method. Complementary machine learning techniques were also explored. Results: The resulting model had the following nine variables, all readily obtainable during initial care. Age (OR 1.069), female sex (OR 1.202), readmission (OR 2.008), hypertension (OR 0.726), diabetes (OR 1.105), atrial fibrillation (OR 1.537), dyslipidaemia (0.638), heart failure (OR 1.518) and neurological symptoms suggestive of posterior fossa involvement (OR 2.639). The predictability was moderate (AUC 0.742, 95% CI: 0.737–0.747), with good visual calibration; Pearson’s chi-square test revealed non-significant calibration. An easily consulted risk score was prepared. Conclusions: It is possible to create a predictive model of mortality for patients with ischaemic stroke from which important advances can be made towards optimising the quality and efficiency of care. The model results are available within a few minutes of admission and would provide a valuable complementary resource for the neurologist. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-13431 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-134312023-10-30T14:04:50Z Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment Lea-Pereira, María Carmen Amaya Pascasio, Laura Martínez Sánchez, Patricia Rodríguez Salvador, María del Mar Galván-Espinosa, José Téllez-Ramírez, Luis Reche Lorite, Fernando Sánchez, María-José García-Torrecillas, Juan Manuel predictive model risk score mortality stroke vascular neurology Background: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted of a clinical–administrative database, reflecting all cases of non-reperfused ischaemic stroke admitted to Spanish hospitals during the period 2008–2012. A predictive model based on logistic regression was developed on a training cohort and later validated by the “hold-out” method. Complementary machine learning techniques were also explored. Results: The resulting model had the following nine variables, all readily obtainable during initial care. Age (OR 1.069), female sex (OR 1.202), readmission (OR 2.008), hypertension (OR 0.726), diabetes (OR 1.105), atrial fibrillation (OR 1.537), dyslipidaemia (0.638), heart failure (OR 1.518) and neurological symptoms suggestive of posterior fossa involvement (OR 2.639). The predictability was moderate (AUC 0.742, 95% CI: 0.737–0.747), with good visual calibration; Pearson’s chi-square test revealed non-significant calibration. An easily consulted risk score was prepared. Conclusions: It is possible to create a predictive model of mortality for patients with ischaemic stroke from which important advances can be made towards optimising the quality and efficiency of care. The model results are available within a few minutes of admission and would provide a valuable complementary resource for the neurologist. 2022-03-10T18:05:56Z 2022-03-10T18:05:56Z 2022-03-08 info:eu-repo/semantics/article 1660-4601 http://hdl.handle.net/10835/13431 10.3390/ijerph19063182 en https://www.mdpi.com/1660-4601/19/6/3182 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | predictive model risk score mortality stroke vascular neurology Lea-Pereira, María Carmen Amaya Pascasio, Laura Martínez Sánchez, Patricia Rodríguez Salvador, María del Mar Galván-Espinosa, José Téllez-Ramírez, Luis Reche Lorite, Fernando Sánchez, María-José García-Torrecillas, Juan Manuel Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment |
| title | Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment |
| title_full | Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment |
| title_fullStr | Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment |
| title_full_unstemmed | Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment |
| title_short | Predictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatment |
| title_sort | predictive model and mortality risk score during admission for ischaemic stroke with conservative treatment |
| topic | predictive model risk score mortality stroke vascular neurology |
| url | http://hdl.handle.net/10835/13431 |
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