Cargando…
A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage
OBJECTIVES: To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation. METHODS: The cohorts of 253 aSAH patients were divided into the HAP group (n = 64) and the non-HAP group (n = 189)...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764336/ https://www.ncbi.nlm.nih.gov/pubmed/36561302 http://dx.doi.org/10.3389/fneur.2022.1034313 |
_version_ | 1784853254088163328 |
---|---|
author | Hu, Sheng-Qi Hu, Jian-Nan Chen, Ru-Dong Yu, Jia-Sheng |
author_facet | Hu, Sheng-Qi Hu, Jian-Nan Chen, Ru-Dong Yu, Jia-Sheng |
author_sort | Hu, Sheng-Qi |
collection | PubMed |
description | OBJECTIVES: To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation. METHODS: The cohorts of 253 aSAH patients were divided into the HAP group (n = 64) and the non-HAP group (n = 189). Univariate and multivariate logistic regression were performed to identify risk factors. A logistic model (Model-Logit) was established based on the independent risk factors. We used risk factor categories to develop a model (Model-Cat). Receiver operating characteristic curves were generated to determine the cutoff values. Areas under the curves (AUCs) were calculated to assess the accuracy of models and single factors. The Delong test was performed to compare the AUCs. RESULTS: The multivariate logistic analysis showed that the age [p = 0.012, odds ratio (OR) = 1.059, confidence interval (CI) = 1.013–1.107], blood glucose (BG; >7.22 mmol/L; p = 0.011, OR = 2.781, CI = 1.263–6.119), red blood distribution width standard deviation (RDW-SD; p = 0.024, OR = 1.118, CI = 1.015–1.231), and Glasgow coma scale (GCS; p < 0.001, OR = 0.710, CI = 0.633–0.798) were independent risk factors. The Model-Logit was as follows: Logit(P) = −5.467 + 0.057 (*) Age + 1.023 (*) BG (>7.22 mmol/L, yes = 1, no = 0) + 0.111 (*) RDW-SD−0.342 (*) GCS. The AUCs values of the Model-Logit, GCS, age, BG (>7.22 mmol/L), and RDW-SD were 0.865, 0.819, 0.634, 0.698, and 0.625, respectively. For clinical use, the Model-Cat was established. In the Model-Cat, the AUCs for GCS, age, BG, and RDW-SD were 0.850, 0.760, 0.700, 0.641, and 0.564, respectively. The AUCs of the Model-Logit were insignificantly higher than the Model-Cat (Delong test, p = 0.157). The total points from −3 to 4 and 5 to 14 were classified as low- and high-risk levels, respectively. CONCLUSIONS: Age, BG (> 7.22 mmol/L), GCS, and RDW-SD were independent risk factors for HAP in aSAH patients. The Model-Cat was convenient for practical evaluation. The aSAH patients with total points from 5 to 14 had a high risk for HAP, suggesting the need for more attention during treatment. |
format | Online Article Text |
id | pubmed-9764336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97643362022-12-21 A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage Hu, Sheng-Qi Hu, Jian-Nan Chen, Ru-Dong Yu, Jia-Sheng Front Neurol Neurology OBJECTIVES: To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation. METHODS: The cohorts of 253 aSAH patients were divided into the HAP group (n = 64) and the non-HAP group (n = 189). Univariate and multivariate logistic regression were performed to identify risk factors. A logistic model (Model-Logit) was established based on the independent risk factors. We used risk factor categories to develop a model (Model-Cat). Receiver operating characteristic curves were generated to determine the cutoff values. Areas under the curves (AUCs) were calculated to assess the accuracy of models and single factors. The Delong test was performed to compare the AUCs. RESULTS: The multivariate logistic analysis showed that the age [p = 0.012, odds ratio (OR) = 1.059, confidence interval (CI) = 1.013–1.107], blood glucose (BG; >7.22 mmol/L; p = 0.011, OR = 2.781, CI = 1.263–6.119), red blood distribution width standard deviation (RDW-SD; p = 0.024, OR = 1.118, CI = 1.015–1.231), and Glasgow coma scale (GCS; p < 0.001, OR = 0.710, CI = 0.633–0.798) were independent risk factors. The Model-Logit was as follows: Logit(P) = −5.467 + 0.057 (*) Age + 1.023 (*) BG (>7.22 mmol/L, yes = 1, no = 0) + 0.111 (*) RDW-SD−0.342 (*) GCS. The AUCs values of the Model-Logit, GCS, age, BG (>7.22 mmol/L), and RDW-SD were 0.865, 0.819, 0.634, 0.698, and 0.625, respectively. For clinical use, the Model-Cat was established. In the Model-Cat, the AUCs for GCS, age, BG, and RDW-SD were 0.850, 0.760, 0.700, 0.641, and 0.564, respectively. The AUCs of the Model-Logit were insignificantly higher than the Model-Cat (Delong test, p = 0.157). The total points from −3 to 4 and 5 to 14 were classified as low- and high-risk levels, respectively. CONCLUSIONS: Age, BG (> 7.22 mmol/L), GCS, and RDW-SD were independent risk factors for HAP in aSAH patients. The Model-Cat was convenient for practical evaluation. The aSAH patients with total points from 5 to 14 had a high risk for HAP, suggesting the need for more attention during treatment. Frontiers Media S.A. 2022-12-06 /pmc/articles/PMC9764336/ /pubmed/36561302 http://dx.doi.org/10.3389/fneur.2022.1034313 Text en Copyright © 2022 Hu, Hu, Chen and Yu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Hu, Sheng-Qi Hu, Jian-Nan Chen, Ru-Dong Yu, Jia-Sheng A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage |
title | A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage |
title_full | A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage |
title_fullStr | A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage |
title_full_unstemmed | A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage |
title_short | A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage |
title_sort | predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764336/ https://www.ncbi.nlm.nih.gov/pubmed/36561302 http://dx.doi.org/10.3389/fneur.2022.1034313 |
work_keys_str_mv | AT hushengqi apredictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage AT hujiannan apredictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage AT chenrudong apredictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage AT yujiasheng apredictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage AT hushengqi predictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage AT hujiannan predictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage AT chenrudong predictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage AT yujiasheng predictivemodelusingriskfactorcategoriesforhospitalacquiredpneumoniainpatientswithaneurysmalsubarachnoidhemorrhage |