A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease
BackgroundProviding intensive care is increasingly expensive, and the aim of this study was to construct a risk column line graph (nomograms)for prolonged length of stay (LOS) in the intensive care unit (ICU) for patients with chronic obstructive pulmonary disease (COPD).MethodsThis study included 4...
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1177786/full |
| _version_ | 1797785626619150336 |
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| author | Hongtao Cheng Jieyao Li Fangxin Wei Xin Yang Shiqi Yuan Xiaxuan Huang Fuling Zhou Jun Lyu Jun Lyu |
| author_facet | Hongtao Cheng Jieyao Li Fangxin Wei Xin Yang Shiqi Yuan Xiaxuan Huang Fuling Zhou Jun Lyu Jun Lyu |
| author_sort | Hongtao Cheng |
| collection | DOAJ |
| description | BackgroundProviding intensive care is increasingly expensive, and the aim of this study was to construct a risk column line graph (nomograms)for prolonged length of stay (LOS) in the intensive care unit (ICU) for patients with chronic obstructive pulmonary disease (COPD).MethodsThis study included 4,940 patients, and the data set was randomly divided into training (n = 3,458) and validation (n = 1,482) sets at a 7:3 ratio. First, least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Second, a prediction model was constructed using multifactorial logistic regression analysis. Third, the model was validated using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, calibration plots, and decision-curve analysis (DCA), and was further internally validated.ResultsThis study selected 11 predictors: sepsis, renal replacement therapy, cerebrovascular disease, respiratory failure, ventilator associated pneumonia, norepinephrine, bronchodilators, invasive mechanical ventilation, electrolytes disorders, Glasgow Coma Scale score and body temperature. The models constructed using these 11 predictors indicated good predictive power, with the areas under the ROC curves being 0.826 (95%CI, 0.809–0.842) and 0.827 (95%CI, 0.802–0.853) in the training and validation sets, respectively. The Hosmer-Lemeshow test indicated a strong agreement between the predicted and observed probabilities in the training (χ2 = 8.21, p = 0.413) and validation (χ2 = 0.64, p = 0.999) sets. In addition, decision-curve analysis suggested that the model had good clinical validity.ConclusionThis study has constructed and validated original and dynamic nomograms for prolonged ICU stay in patients with COPD using 11 easily collected parameters. These nomograms can provide useful guidance to medical and nursing practitioners in ICUs and help reduce the disease and economic burdens on patients. |
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| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-13T00:56:42Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj.art-08733a8599cb4675881d2f3518cfb4d32023-07-06T16:59:26ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-07-011010.3389/fmed.2023.11777861177786A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary diseaseHongtao Cheng0Jieyao Li1Fangxin Wei2Xin Yang3Shiqi Yuan4Xiaxuan Huang5Fuling Zhou6Jun Lyu7Jun Lyu8School of Nursing, Jinan University, Guangzhou, ChinaIntensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, ChinaSchool of Nursing, Jinan University, Guangzhou, ChinaSchool of Nursing, Jinan University, Guangzhou, ChinaDepartment of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, ChinaDepartment of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, ChinaDepartment of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, ChinaGuangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, ChinaBackgroundProviding intensive care is increasingly expensive, and the aim of this study was to construct a risk column line graph (nomograms)for prolonged length of stay (LOS) in the intensive care unit (ICU) for patients with chronic obstructive pulmonary disease (COPD).MethodsThis study included 4,940 patients, and the data set was randomly divided into training (n = 3,458) and validation (n = 1,482) sets at a 7:3 ratio. First, least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Second, a prediction model was constructed using multifactorial logistic regression analysis. Third, the model was validated using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, calibration plots, and decision-curve analysis (DCA), and was further internally validated.ResultsThis study selected 11 predictors: sepsis, renal replacement therapy, cerebrovascular disease, respiratory failure, ventilator associated pneumonia, norepinephrine, bronchodilators, invasive mechanical ventilation, electrolytes disorders, Glasgow Coma Scale score and body temperature. The models constructed using these 11 predictors indicated good predictive power, with the areas under the ROC curves being 0.826 (95%CI, 0.809–0.842) and 0.827 (95%CI, 0.802–0.853) in the training and validation sets, respectively. The Hosmer-Lemeshow test indicated a strong agreement between the predicted and observed probabilities in the training (χ2 = 8.21, p = 0.413) and validation (χ2 = 0.64, p = 0.999) sets. In addition, decision-curve analysis suggested that the model had good clinical validity.ConclusionThis study has constructed and validated original and dynamic nomograms for prolonged ICU stay in patients with COPD using 11 easily collected parameters. These nomograms can provide useful guidance to medical and nursing practitioners in ICUs and help reduce the disease and economic burdens on patients.https://www.frontiersin.org/articles/10.3389/fmed.2023.1177786/fullchronic obstructive pulmonary diseaseintensive care unitlength of staynomogramsprolonged intensive care unit stays |
| spellingShingle | Hongtao Cheng Jieyao Li Fangxin Wei Xin Yang Shiqi Yuan Xiaxuan Huang Fuling Zhou Jun Lyu Jun Lyu A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease Frontiers in Medicine chronic obstructive pulmonary disease intensive care unit length of stay nomograms prolonged intensive care unit stays |
| title | A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease |
| title_full | A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease |
| title_fullStr | A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease |
| title_full_unstemmed | A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease |
| title_short | A risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease |
| title_sort | risk nomogram for predicting prolonged intensive care unit stays in patients with chronic obstructive pulmonary disease |
| topic | chronic obstructive pulmonary disease intensive care unit length of stay nomograms prolonged intensive care unit stays |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1177786/full |
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