Developing and verifying a multivariate model to predict the survival probability after coronary artery bypass grafting in patients with coronary atherosclerosis based on the MIMIC-III database

Li, Chengzhuo; Xu, Fengshuo; Han, Didi; Zheng, Shuai; Ma, Wen; Yang, Rui; Wang, Zichen; Liu, Yue; Lyu, Jun

doi:10.1016/j.hrtlng.2021.11.009

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Secondly, some indicators were not included in the model because of the high rate of missing data, which decreased the comprehensiveness of the model. The accuracy of the model can be further improved if these missing variables, such as alanine aminotransferase, serum calcium ions and oedema, are added in future studies [ 33 ]. Thirdly, we only used the data for the internal verification of our model, and other external data still need to be used to further verify its performance and clinical applicability.…”

Section: Discussionmentioning

confidence: 99%

Construction of risk prediction model for hyponatremia in patients with acute decompensated heart failure

Gong,

Zhou,

Huang

et al. 2023

BMC Cardiovasc Disord

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Background Patients with Heart failure (HF) commonly have a water-electrolyte imbalance due to various reasons and mechanisms, and hyponatremia is one of the most common types. However, currently, there are very few local studies on hyponatremia risk assessment in patients with acute decompensated heart failure (ADHF), and there is a lack of specific screening tools. The aim of this study is to identify a prediction model of hyponatremia in patients with acute decompensated heart failure (ADHF) and verify the prediction effect of the model. Methods A total of 532 patients with ADHF were enrolled from March 2014 to December 2019. Univariate and multivariate logistic regression analyses were performed to investigate the independently associated risk factors of hyponatremia in patients with ADHF. The prediction model of hyponatremia in patients with ADHF was constructed by R software, and validation of the model was performed using the area under the receiver operating characteristic curve (AUC) and calibration curves. Results A total of 65 patients (12.2%) had hyponatremia in patients with ADHF. Multivariate logistic regression analysis demonstrated that NYHA cardiac function classification (NYHA III vs II, OR = 12.31, NYHA IV vs II, OR = 11.55), systolic blood pressure (OR = 0.978), serum urea nitrogen (OR = 1.046) and creatinine (OR = 1.006) were five independent prognostic factors for hyponatremia in patients with ADHF. The AUC was 0.757; The calibration curve was near the ideal curve, which showed that the model can accurately predict the occurrence of hyponatremia in patients with ADHF. Conclusions The prediction model constructed in our study has good discrimination and accuracy and can be used to predict the occurrence of hyponatremia in patients with ADHF.

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Section: Discussionmentioning

confidence: 99%

Construction of risk prediction model for hyponatremia in patients with acute decompensated heart failure

Gong,

Zhou,

Huang

et al. 2023

BMC Cardiovasc Disord

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Construction of Risk Prediction Model for Hyponatremia in Patients with Acute Decompensated Heart Failure

Gong

Zhou

Huang

et al. 2022

Preprint

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Background: Patients with Heart failure（HF）commonly have a water-electrolyte imbalance due to various reasons and mechanisms, and hyponatremia is one of the most common types. However, currently, there are very few local studies on hyponatremia risk assessment in patients with acute decompensated heart failure (ADHF), and there is a lack of specific screening tools. The aim of this study is to identify a prediction model of hyponatremia in patients with acute decompensated heart failure (ADHF) and verify the prediction effect of the model. Methods: A total of 532 patients with ADHF were enrolled from March 2014 to December 2019. Univariate and multivariate logistic regression analyses were performed to investigate the independently associated risk factors of hyponatremia in patients with ADHF. The prediction model of hyponatremia in patients with ADHF was constructed by R software, and validation of the model was performed using the area under the receiver operating characteristic curve (AUC) and calibration curves. Results: A total of 65 patients (12.2%) had hyponatremia in patients with ADHF. Multivariate logistic regression analysis demonstrated that NYHA cardiac function classification (NYHA III vs II, OR = 12.31, NYHA IV vs II, OR = 11.55), systolic blood pressure (OR = 0.978), serum urea nitrogen (OR= 1.046) and creatinine (OR = 1.006) were five independent prognostic factors for hyponatremia in patients with ADHF. The AUC was 0.757; The calibration curve was near the ideal curve, which showed that the model can accurately predict the occurrence of hyponatremia in patients with ADHF. Conclusions: The prediction model constructed in our study has good discrimination and accuracy and can be used to predict the occurrence of hyponatremia in patients with ADHF.

show abstract

Developing and verifying a multivariate model to predict the survival probability after coronary artery bypass grafting in patients with coronary atherosclerosis based on the MIMIC-III database

Cited by 2 publications

References 31 publications

Construction of risk prediction model for hyponatremia in patients with acute decompensated heart failure

Construction of risk prediction model for hyponatremia in patients with acute decompensated heart failure

Construction of Risk Prediction Model for Hyponatremia in Patients with Acute Decompensated Heart Failure

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