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Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases
BACKGROUND: Previous studies have reported very different mortality rates among cancer patients in the intensive care unit (ICU), implying different clinical subtypes. We aimed to reveal the clinical subtypes and demonstrate the importance of segregating the patients in clinical research, and to rep...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859733/ https://www.ncbi.nlm.nih.gov/pubmed/33553306 http://dx.doi.org/10.21037/atm-20-4634 |
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author | Gao, Shaowei Wang, Yaqing Yang, Lu Wang, Zhongxing Huang, Wenqi |
author_facet | Gao, Shaowei Wang, Yaqing Yang, Lu Wang, Zhongxing Huang, Wenqi |
author_sort | Gao, Shaowei |
collection | PubMed |
description | BACKGROUND: Previous studies have reported very different mortality rates among cancer patients in the intensive care unit (ICU), implying different clinical subtypes. We aimed to reveal the clinical subtypes and demonstrate the importance of segregating the patients in clinical research, and to report the ICD-level mortality of cancer patients in the ICU. METHODS: Two ICU databases (MIMIC-III and eICU) were utilized to identify cancer patients. Mortality based on ICD-level diagnoses were calculated, and K-means clustering was used to identify different clinical subtypes in the MIMIC database. Clinical characteristics and outcomes were compared among subtypes, and the calibration of SAPS II and APACHE IV among different subtypes was evaluated. RESULTS: In total, 6,505 (13.8%) cancer patients of the MIMIC database and 7,351 (4.9%) ones in eICU database, were enrolled in the study. Metastasis involving pleura, metastasis involving the liver, and acute myeloid leukemia were in the top 5 diagnoses with the highest mortality in both databases. Clinical subtypes identified by K-means clustering were closely associated with admission type (elective or emergency) and clinical service provider (surgical or medical). In a four-cluster pattern, nearly all patients in the first cluster were elective admissions (99.1%), whereas in the rest of the clusters, most were emergency admissions (93.7%). Most surgical patients were in the 1+2 clusters (92.0%) and most medical patients were in the 3+4 clusters (93.5%). Most characteristics and outcomes as well as the calibration of SAPS II and APACHE IV scoring systems were significantly different among clinical subtypes. CONCLUSIONS: Different clinical subtypes can be well identified by admission type and clinical service provider among ICU patients with cancer. Caution should be exercised when considering these patients as a whole population both in clinical practice and research. Moreover, APACHE IV has better calibration than SAPS II for cancer patients at low risk of mortality in the ICU. |
format | Online Article Text |
id | pubmed-7859733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-78597332021-02-05 Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases Gao, Shaowei Wang, Yaqing Yang, Lu Wang, Zhongxing Huang, Wenqi Ann Transl Med Original Article BACKGROUND: Previous studies have reported very different mortality rates among cancer patients in the intensive care unit (ICU), implying different clinical subtypes. We aimed to reveal the clinical subtypes and demonstrate the importance of segregating the patients in clinical research, and to report the ICD-level mortality of cancer patients in the ICU. METHODS: Two ICU databases (MIMIC-III and eICU) were utilized to identify cancer patients. Mortality based on ICD-level diagnoses were calculated, and K-means clustering was used to identify different clinical subtypes in the MIMIC database. Clinical characteristics and outcomes were compared among subtypes, and the calibration of SAPS II and APACHE IV among different subtypes was evaluated. RESULTS: In total, 6,505 (13.8%) cancer patients of the MIMIC database and 7,351 (4.9%) ones in eICU database, were enrolled in the study. Metastasis involving pleura, metastasis involving the liver, and acute myeloid leukemia were in the top 5 diagnoses with the highest mortality in both databases. Clinical subtypes identified by K-means clustering were closely associated with admission type (elective or emergency) and clinical service provider (surgical or medical). In a four-cluster pattern, nearly all patients in the first cluster were elective admissions (99.1%), whereas in the rest of the clusters, most were emergency admissions (93.7%). Most surgical patients were in the 1+2 clusters (92.0%) and most medical patients were in the 3+4 clusters (93.5%). Most characteristics and outcomes as well as the calibration of SAPS II and APACHE IV scoring systems were significantly different among clinical subtypes. CONCLUSIONS: Different clinical subtypes can be well identified by admission type and clinical service provider among ICU patients with cancer. Caution should be exercised when considering these patients as a whole population both in clinical practice and research. Moreover, APACHE IV has better calibration than SAPS II for cancer patients at low risk of mortality in the ICU. AME Publishing Company 2021-01 /pmc/articles/PMC7859733/ /pubmed/33553306 http://dx.doi.org/10.21037/atm-20-4634 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Gao, Shaowei Wang, Yaqing Yang, Lu Wang, Zhongxing Huang, Wenqi Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases |
title | Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases |
title_full | Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases |
title_fullStr | Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases |
title_full_unstemmed | Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases |
title_short | Characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases |
title_sort | characteristics and clinical subtypes of cancer patients in the intensive care unit: a retrospective observational study for two large databases |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859733/ https://www.ncbi.nlm.nih.gov/pubmed/33553306 http://dx.doi.org/10.21037/atm-20-4634 |
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