Cargando…
P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning
POSTER SESSION 2, SEPTEMBER 22, 2022, 12:30 PM - 1:30 PM: OBJECTIVES: We evaluated the magnitude and factors contributing to poor outcomes among cirrhosis patients with fungal infections (FIs). METHODS: We searched PubMed, Embase, Ovid, and WOS and included articles reporting mortality in cirrhosi...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510012/ http://dx.doi.org/10.1093/mmy/myac072.P305 |
_version_ | 1784797357329612800 |
---|---|
author | Verma, Nipun Singh, Shreya Roy, Akash Valsan, Arun Garg, Pratibha Pradhan, Pranita Chakrabarti, Arunaloke Singh, Meenu |
author_facet | Verma, Nipun Singh, Shreya Roy, Akash Valsan, Arun Garg, Pratibha Pradhan, Pranita Chakrabarti, Arunaloke Singh, Meenu |
author_sort | Verma, Nipun |
collection | PubMed |
description | POSTER SESSION 2, SEPTEMBER 22, 2022, 12:30 PM - 1:30 PM: OBJECTIVES: We evaluated the magnitude and factors contributing to poor outcomes among cirrhosis patients with fungal infections (FIs). METHODS: We searched PubMed, Embase, Ovid, and WOS and included articles reporting mortality in cirrhosis with FIs. We pooled the point and relative-risk (RR) estimates of mortality on random-effects meta-analysis and explored their heterogeneity (I2) on subgroups, meta-regression, and machine learning (ML). We assessed the study quality through New-Castle-Ottawa-Scale and estimate-asymmetry through Eggers regression (CRD42019142782). RESULTS: Of 4345, 34 studies (2134 patients) were included (good/fair/poor quality: 12/21/1). Pooled mortality of FIs was 64.1% (95%CI: 55.4-72.0, 12: 87%, P <.01), which was 2.1 times higher than controls (95%CI: 1.8-2.5, 12:89%, P <.01). Higher CTP (MD: +0.52, 95%CI: 0.27-0.77), MELD (MD: +2.75, 95% CI: 1.21-4.28), organ failures, and increased hospital stay (30 vs. 19 days) was reported among cases with FIs. Patients with ACLF (76.6%, RR: 2.3), and ICU-admission (70.4%, RR: 1.6) had the highest mortality. The risk was maximum for pulmonary-FIs (79.4%, RR: 1.8), followed by peritoneal-FIs (68.3%, RR: 1.7) and fungemia (55%, RR: 1.7). The mortality was higher in FIs than bacterial (RR: 1.7) or no-infections (RR: 2.9). Estimate-asymmetry was evident (P <.05). Up to 8 clusters and 5 outlier studies were identified on ML, and the estimate-heterogeneity was eliminated on excluding such studies. CONCLUSIONS: A substantially worse prognosis, poorer than bacterial infections in cirrhosis patients with FIs indicates an unmet need for improving fungal diagnostics and therapeutics in this population. ACLF and ICU admission should be included in host criteria for defining IFIs. |
format | Online Article Text |
id | pubmed-9510012 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-95100122022-09-26 P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning Verma, Nipun Singh, Shreya Roy, Akash Valsan, Arun Garg, Pratibha Pradhan, Pranita Chakrabarti, Arunaloke Singh, Meenu Med Mycol Oral Presentations POSTER SESSION 2, SEPTEMBER 22, 2022, 12:30 PM - 1:30 PM: OBJECTIVES: We evaluated the magnitude and factors contributing to poor outcomes among cirrhosis patients with fungal infections (FIs). METHODS: We searched PubMed, Embase, Ovid, and WOS and included articles reporting mortality in cirrhosis with FIs. We pooled the point and relative-risk (RR) estimates of mortality on random-effects meta-analysis and explored their heterogeneity (I2) on subgroups, meta-regression, and machine learning (ML). We assessed the study quality through New-Castle-Ottawa-Scale and estimate-asymmetry through Eggers regression (CRD42019142782). RESULTS: Of 4345, 34 studies (2134 patients) were included (good/fair/poor quality: 12/21/1). Pooled mortality of FIs was 64.1% (95%CI: 55.4-72.0, 12: 87%, P <.01), which was 2.1 times higher than controls (95%CI: 1.8-2.5, 12:89%, P <.01). Higher CTP (MD: +0.52, 95%CI: 0.27-0.77), MELD (MD: +2.75, 95% CI: 1.21-4.28), organ failures, and increased hospital stay (30 vs. 19 days) was reported among cases with FIs. Patients with ACLF (76.6%, RR: 2.3), and ICU-admission (70.4%, RR: 1.6) had the highest mortality. The risk was maximum for pulmonary-FIs (79.4%, RR: 1.8), followed by peritoneal-FIs (68.3%, RR: 1.7) and fungemia (55%, RR: 1.7). The mortality was higher in FIs than bacterial (RR: 1.7) or no-infections (RR: 2.9). Estimate-asymmetry was evident (P <.05). Up to 8 clusters and 5 outlier studies were identified on ML, and the estimate-heterogeneity was eliminated on excluding such studies. CONCLUSIONS: A substantially worse prognosis, poorer than bacterial infections in cirrhosis patients with FIs indicates an unmet need for improving fungal diagnostics and therapeutics in this population. ACLF and ICU admission should be included in host criteria for defining IFIs. Oxford University Press 2022-09-20 /pmc/articles/PMC9510012/ http://dx.doi.org/10.1093/mmy/myac072.P305 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Oral Presentations Verma, Nipun Singh, Shreya Roy, Akash Valsan, Arun Garg, Pratibha Pradhan, Pranita Chakrabarti, Arunaloke Singh, Meenu P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning |
title | P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning |
title_full | P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning |
title_fullStr | P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning |
title_full_unstemmed | P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning |
title_short | P305 Cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning |
title_sort | p305 cirrhosis and fungal infections-a cocktail for catastrophe: a systematic review and meta-analysis with machine learning |
topic | Oral Presentations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510012/ http://dx.doi.org/10.1093/mmy/myac072.P305 |
work_keys_str_mv | AT vermanipun p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning AT singhshreya p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning AT royakash p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning AT valsanarun p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning AT gargpratibha p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning AT pradhanpranita p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning AT chakrabartiarunaloke p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning AT singhmeenu p305cirrhosisandfungalinfectionsacocktailforcatastropheasystematicreviewandmetaanalysiswithmachinelearning |