Cargando…
A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit
OBJECTIVES: To improve the quality of invasive pulmonary aspergillosis (IPA) management for intensive care unit (ICU) patients using a practical diagnostic scoring model. METHODS: This nested case-control study aimed to determine the incidence of IPA in 405 ICU patients, between July 2012 and June 2...
Autores principales: | , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Saudi Medical Journal
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402463/ https://www.ncbi.nlm.nih.gov/pubmed/30723858 http://dx.doi.org/10.15537/smj.2019.2.22940 |
_version_ | 1783400405523234816 |
---|---|
author | Rozaliyani, Anna Sedono, Rudyanto Jusuf, Anwar Rumende, Cleopas M. Aniwidyaningsih, Wahju Burhan, Erlina Prasenohadi, Handayani, Diah Yunihastuti, Evy Siagian, Forman E. Jayusman, Achmad M. Rusli, Adria Sungkar, Saleha Prihartono, Joedo Hagen, Ferry Meis, Jacques F. Wahyuningsih, Retno |
author_facet | Rozaliyani, Anna Sedono, Rudyanto Jusuf, Anwar Rumende, Cleopas M. Aniwidyaningsih, Wahju Burhan, Erlina Prasenohadi, Handayani, Diah Yunihastuti, Evy Siagian, Forman E. Jayusman, Achmad M. Rusli, Adria Sungkar, Saleha Prihartono, Joedo Hagen, Ferry Meis, Jacques F. Wahyuningsih, Retno |
author_sort | Rozaliyani, Anna |
collection | PubMed |
description | OBJECTIVES: To improve the quality of invasive pulmonary aspergillosis (IPA) management for intensive care unit (ICU) patients using a practical diagnostic scoring model. METHODS: This nested case-control study aimed to determine the incidence of IPA in 405 ICU patients, between July 2012 and June 2014, at 6 hospitals in Jakarta, Indonesia. Phenotypic identifications and galactomannan (GM) tests of sera and lung excreta were performed in mycology laboratory, Parasitology Department, Faculty of Medicine, Universitas Indonesia in Jakarta, Indonesia. RESULTS: The incidence of IPA in the ICUs was 7.7% (31 of 405 patients). A scoring model used for IPA diagnosis showed 4 variables as the most potential risk factors: lung excreta GM index (score 2), solid organ malignancy (score 2), pulmonary tuberculosis (score 2), and systemic corticosteroids (score 1). Patients were included in a high-risk group if their score was >2, and in a low-risk group if their score was <2. CONCLUSION: This study provides a novel diagnosis scoring model to predict IPA in ICU patients. Using this model, a more rapid diagnosis and treatment of IPA may be possible. The application of the diagnosis scoring should be preceded by specified pre-requisites. |
format | Online Article Text |
id | pubmed-6402463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Saudi Medical Journal |
record_format | MEDLINE/PubMed |
spelling | pubmed-64024632019-03-16 A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit Rozaliyani, Anna Sedono, Rudyanto Jusuf, Anwar Rumende, Cleopas M. Aniwidyaningsih, Wahju Burhan, Erlina Prasenohadi, Handayani, Diah Yunihastuti, Evy Siagian, Forman E. Jayusman, Achmad M. Rusli, Adria Sungkar, Saleha Prihartono, Joedo Hagen, Ferry Meis, Jacques F. Wahyuningsih, Retno Saudi Med J Original Article OBJECTIVES: To improve the quality of invasive pulmonary aspergillosis (IPA) management for intensive care unit (ICU) patients using a practical diagnostic scoring model. METHODS: This nested case-control study aimed to determine the incidence of IPA in 405 ICU patients, between July 2012 and June 2014, at 6 hospitals in Jakarta, Indonesia. Phenotypic identifications and galactomannan (GM) tests of sera and lung excreta were performed in mycology laboratory, Parasitology Department, Faculty of Medicine, Universitas Indonesia in Jakarta, Indonesia. RESULTS: The incidence of IPA in the ICUs was 7.7% (31 of 405 patients). A scoring model used for IPA diagnosis showed 4 variables as the most potential risk factors: lung excreta GM index (score 2), solid organ malignancy (score 2), pulmonary tuberculosis (score 2), and systemic corticosteroids (score 1). Patients were included in a high-risk group if their score was >2, and in a low-risk group if their score was <2. CONCLUSION: This study provides a novel diagnosis scoring model to predict IPA in ICU patients. Using this model, a more rapid diagnosis and treatment of IPA may be possible. The application of the diagnosis scoring should be preceded by specified pre-requisites. Saudi Medical Journal 2019-02 /pmc/articles/PMC6402463/ /pubmed/30723858 http://dx.doi.org/10.15537/smj.2019.2.22940 Text en Copyright: © Saudi Medical Journal http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Rozaliyani, Anna Sedono, Rudyanto Jusuf, Anwar Rumende, Cleopas M. Aniwidyaningsih, Wahju Burhan, Erlina Prasenohadi, Handayani, Diah Yunihastuti, Evy Siagian, Forman E. Jayusman, Achmad M. Rusli, Adria Sungkar, Saleha Prihartono, Joedo Hagen, Ferry Meis, Jacques F. Wahyuningsih, Retno A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit |
title | A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit |
title_full | A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit |
title_fullStr | A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit |
title_full_unstemmed | A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit |
title_short | A novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit |
title_sort | novel diagnosis scoring model to predict invasive pulmonary aspergillosis in the intensive care unit |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402463/ https://www.ncbi.nlm.nih.gov/pubmed/30723858 http://dx.doi.org/10.15537/smj.2019.2.22940 |
work_keys_str_mv | AT rozaliyanianna anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT sedonorudyanto anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT jusufanwar anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT rumendecleopasm anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT aniwidyaningsihwahju anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT burhanerlina anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT prasenohadi anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT handayanidiah anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT yunihastutievy anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT siagianformane anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT jayusmanachmadm anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT rusliadria anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT sungkarsaleha anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT prihartonojoedo anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT hagenferry anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT meisjacquesf anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT wahyuningsihretno anoveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT rozaliyanianna noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT sedonorudyanto noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT jusufanwar noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT rumendecleopasm noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT aniwidyaningsihwahju noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT burhanerlina noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT prasenohadi noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT handayanidiah noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT yunihastutievy noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT siagianformane noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT jayusmanachmadm noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT rusliadria noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT sungkarsaleha noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT prihartonojoedo noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT hagenferry noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT meisjacquesf noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit AT wahyuningsihretno noveldiagnosisscoringmodeltopredictinvasivepulmonaryaspergillosisintheintensivecareunit |