Cargando…
Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients
Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through th...
Autores principales: | , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432169/ https://www.ncbi.nlm.nih.gov/pubmed/34501406 http://dx.doi.org/10.3390/jcm10173959 |
_version_ | 1783751101557768192 |
---|---|
author | Carpio, Jacqueline Del Marco, Maria Paz Martin, Maria Luisa Ramos, Natalia de la Torre, Judith Prat, Joana Torres, Maria J. Montoro, Bruno Ibarz, Mercedes Pico, Silvia Falcon, Gloria Canales, Marina Huertas, Elisard Romero, Iñaki Nieto, Nacho Gavaldà, Ricard Segarra, Alfons |
author_facet | Carpio, Jacqueline Del Marco, Maria Paz Martin, Maria Luisa Ramos, Natalia de la Torre, Judith Prat, Joana Torres, Maria J. Montoro, Bruno Ibarz, Mercedes Pico, Silvia Falcon, Gloria Canales, Marina Huertas, Elisard Romero, Iñaki Nieto, Nacho Gavaldà, Ricard Segarra, Alfons |
author_sort | Carpio, Jacqueline Del |
collection | PubMed |
description | Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. Methods. Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. Results. The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0–91.0) and a specificity of 80.5 (95% CI 80.2–80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi(2): 16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859–0.863), a sensitivity of 83.0 (95% CI 80.5–85.3) and a specificity of 76.5 (95% CI 76.2–76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi(2): 15.42, p: 0.052). Conclusions. Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients. |
format | Online Article Text |
id | pubmed-8432169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84321692021-09-11 Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients Carpio, Jacqueline Del Marco, Maria Paz Martin, Maria Luisa Ramos, Natalia de la Torre, Judith Prat, Joana Torres, Maria J. Montoro, Bruno Ibarz, Mercedes Pico, Silvia Falcon, Gloria Canales, Marina Huertas, Elisard Romero, Iñaki Nieto, Nacho Gavaldà, Ricard Segarra, Alfons J Clin Med Article Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. Methods. Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. Results. The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0–91.0) and a specificity of 80.5 (95% CI 80.2–80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi(2): 16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859–0.863), a sensitivity of 83.0 (95% CI 80.5–85.3) and a specificity of 76.5 (95% CI 76.2–76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi(2): 15.42, p: 0.052). Conclusions. Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients. MDPI 2021-08-31 /pmc/articles/PMC8432169/ /pubmed/34501406 http://dx.doi.org/10.3390/jcm10173959 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Carpio, Jacqueline Del Marco, Maria Paz Martin, Maria Luisa Ramos, Natalia de la Torre, Judith Prat, Joana Torres, Maria J. Montoro, Bruno Ibarz, Mercedes Pico, Silvia Falcon, Gloria Canales, Marina Huertas, Elisard Romero, Iñaki Nieto, Nacho Gavaldà, Ricard Segarra, Alfons Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients |
title | Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients |
title_full | Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients |
title_fullStr | Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients |
title_full_unstemmed | Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients |
title_short | Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients |
title_sort | development and validation of a model to predict severe hospital-acquired acute kidney injury in non-critically ill patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432169/ https://www.ncbi.nlm.nih.gov/pubmed/34501406 http://dx.doi.org/10.3390/jcm10173959 |
work_keys_str_mv | AT carpiojacquelinedel developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT marcomariapaz developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT martinmarialuisa developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT ramosnatalia developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT delatorrejudith developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT pratjoana developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT torresmariaj developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT montorobruno developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT ibarzmercedes developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT picosilvia developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT falcongloria developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT canalesmarina developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT huertaselisard developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT romeroinaki developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT nietonacho developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT gavaldaricard developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients AT segarraalfons developmentandvalidationofamodeltopredictseverehospitalacquiredacutekidneyinjuryinnoncriticallyillpatients |