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 (I ) 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, I : 87%, p < .01), which was 2.1 times higher than controls (95% CI: 1.8-2.5, I :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) were 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 in bacterial (RR: 1.7) or no infections (RR: 2.9). Estimate asymmetry was evident (p < 0.05). Up to 8 clusters and 5 outlier studies were identified on ML, and the estimate-heterogeneity was eliminated by 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 the host criteria for defining IFIs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/myc.13482 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Incidence of fall-from-height injuries and predictive factors for severity.
J Osteopath Med
January 2025
McAllen Department of Trauma, South Texas Health System, McAllen, TX, USA.
Context: The injuries caused by falls-from-height (FFH) are a significant public health concern. FFH is one of the most common causes of polytrauma. The injuries persist to be significant adverse events and a challenge regarding injury severity assessment to identify patients at high risk upon admission.
View Article and Find Full Text PDFLiquid-Metal-Based Multichannel Strain Sensor for Sign Language Gesture Classification Using Machine Learning.
ACS Appl Mater Interfaces
January 2025
Centre for Robotics and Automation, Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China.
Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates has been a challenge due to high surface tension. In this paper, a new method is proposed to overcome the difficulties in fabricating liquid-state strain sensors.
View Article and Find Full Text PDFAI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review.
Hum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
A noninvasive urinary microRNA-based assay for the detection of pancreatic cancer from early to late stages: a case control study.
EClinicalMedicine
December 2024
Department of Pathology and Genetics, Laboratory of Cancer Medical Science, Hokuto Hospital, Obihiro, Hokkaido, Japan.
Background: Pancreatic cancer is highly aggressive and has a low survival rate primarily due to late-stage diagnosis and the lack of effective early detection methods. We introduce here a novel, noninvasive urinary extracellular vesicle miRNA-based assay for the detection of pancreatic cancer from early to late stages.
Methods: From September 2019 to July 2023, Urine samples were collected from patients with pancreatic cancer (n = 153) from five distinct sites (Hokuto Hospital, Kawasaki Medical School Hospital, National Cancer Center Hospital, Kagoshima University Hospital, and Kumagaya General Hospital) and non-cancer participants (n = 309) from two separate sites (Hokuto Hospital and Omiya City Clinic).
Machine learning applications in healthcare clinical practice and research.
World J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!