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Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database
Acute kidney injury (AKI) is a common complication of acute illnesses with unfavorable outcomes. This cohort study aimed at constructing prediction models for one-year survival in adult AKI patients based on prognostic nutritional index (PNI), platelet-to-lymphocyte ratio (PLR), neutrophil percentag...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276484/ https://www.ncbi.nlm.nih.gov/pubmed/35836825 http://dx.doi.org/10.1155/2022/5902907 |
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author | Zhou, Lifang Chu, Laping Peng, Junqiong Yin, Shenhan Yu, Yafen |
author_facet | Zhou, Lifang Chu, Laping Peng, Junqiong Yin, Shenhan Yu, Yafen |
author_sort | Zhou, Lifang |
collection | PubMed |
description | Acute kidney injury (AKI) is a common complication of acute illnesses with unfavorable outcomes. This cohort study aimed at constructing prediction models for one-year survival in adult AKI patients based on prognostic nutritional index (PNI), platelet-to-lymphocyte ratio (PLR), neutrophil percentage-to-albumin ratio (NPAR), or neutrophil-to-lymphocyte ratio (NLR), respectively. In total, 6050 patients from Medical Information Mart for Intensive Care III (MIMIC-III) were involved. The least absolute shrinkage and selection operator (LASSO) regression was utilized to screen possible covariates. The samples were randomly divided into the training set and the testing set at a ratio of 7.5 : 2.5, and the prediction models were constructed in the training set by random forest. The prediction values of the models were measured via sensitivity, specificity, negative prediction value (NPV), positive prediction value (PPV), area under the curve (AUC), and accuracy. We found that NLR (OR = 1.261, 95% CI: 1.145–1.388), PLR (OR = 1.295, 95% CI: 1.152–1.445), and NPAR (OR = 1.476, 95% CI: 1.261–1.726) were associated with an increased risk, while PNI (OR = 0.035, 95% CI: 0.020–0.059) was associated with a decreased risk of one-year mortality in AKI patients. The AUC was 0.964 (95% CI: 0.959–0.969) in the training set based on PNI, age, gender, length of stay (LOS) in hospital, platelets (PLT), ethnicity, LOS in ICU, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, glucose, AKI stage, atrial fibrillation (AF), vasopressor, renal replacement therapy (RRT), and mechanical ventilation. The testing set was applied as the internal validation of the model with an AUC of 0.778 (95% CI: 0.754–0.801). In conclusion, PNI accompanied by age, gender, ethnicity, SBP, DBP, heart rate, PLT, glucose, AF, RRT, mechanical ventilation, vasopressor, AKI stage, LOS in ICU, and LOS in hospital exhibited a good predictive value for one-year mortality of AKI patients. |
format | Online Article Text |
id | pubmed-9276484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92764842022-07-13 Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database Zhou, Lifang Chu, Laping Peng, Junqiong Yin, Shenhan Yu, Yafen Evid Based Complement Alternat Med Research Article Acute kidney injury (AKI) is a common complication of acute illnesses with unfavorable outcomes. This cohort study aimed at constructing prediction models for one-year survival in adult AKI patients based on prognostic nutritional index (PNI), platelet-to-lymphocyte ratio (PLR), neutrophil percentage-to-albumin ratio (NPAR), or neutrophil-to-lymphocyte ratio (NLR), respectively. In total, 6050 patients from Medical Information Mart for Intensive Care III (MIMIC-III) were involved. The least absolute shrinkage and selection operator (LASSO) regression was utilized to screen possible covariates. The samples were randomly divided into the training set and the testing set at a ratio of 7.5 : 2.5, and the prediction models were constructed in the training set by random forest. The prediction values of the models were measured via sensitivity, specificity, negative prediction value (NPV), positive prediction value (PPV), area under the curve (AUC), and accuracy. We found that NLR (OR = 1.261, 95% CI: 1.145–1.388), PLR (OR = 1.295, 95% CI: 1.152–1.445), and NPAR (OR = 1.476, 95% CI: 1.261–1.726) were associated with an increased risk, while PNI (OR = 0.035, 95% CI: 0.020–0.059) was associated with a decreased risk of one-year mortality in AKI patients. The AUC was 0.964 (95% CI: 0.959–0.969) in the training set based on PNI, age, gender, length of stay (LOS) in hospital, platelets (PLT), ethnicity, LOS in ICU, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, glucose, AKI stage, atrial fibrillation (AF), vasopressor, renal replacement therapy (RRT), and mechanical ventilation. The testing set was applied as the internal validation of the model with an AUC of 0.778 (95% CI: 0.754–0.801). In conclusion, PNI accompanied by age, gender, ethnicity, SBP, DBP, heart rate, PLT, glucose, AF, RRT, mechanical ventilation, vasopressor, AKI stage, LOS in ICU, and LOS in hospital exhibited a good predictive value for one-year mortality of AKI patients. Hindawi 2022-07-05 /pmc/articles/PMC9276484/ /pubmed/35836825 http://dx.doi.org/10.1155/2022/5902907 Text en Copyright © 2022 Lifang Zhou et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhou, Lifang Chu, Laping Peng, Junqiong Yin, Shenhan Yu, Yafen Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database |
title | Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database |
title_full | Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database |
title_fullStr | Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database |
title_full_unstemmed | Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database |
title_short | Prediction Models for One-Year Survival of Adult Patients with Acute Kidney Injury: A Longitudinal Study Based on the Data from the Medical Information Mart for Intensive Care III Database |
title_sort | prediction models for one-year survival of adult patients with acute kidney injury: a longitudinal study based on the data from the medical information mart for intensive care iii database |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276484/ https://www.ncbi.nlm.nih.gov/pubmed/35836825 http://dx.doi.org/10.1155/2022/5902907 |
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