Predictors of outcomes of patients ≥ 80 years old admitted to intensive care units in Poland - a post-hoc analysis of the VIP2 prospective observational study.

INTRODUCTION: Elderly patients pose a significant challenge to intensive care unit (ICU) clinicians. In this study we attempted to characterise the population of patients over 80 years old admitted to ICUs in Poland and identify associations between clinical features and short-term outcomes. MATERIAL AND METHODS: The study is a post-hoc analysis of the Polish cohort of the VIP2 European prospective observational study enrolling patients > 80 years old admitted to ICUs over a 6-month period. Data including clinical features, clinical frailty scale (CFS), geriatric scales, interventions within the ICU, and outcomes (30-day and ICU mortality and length of stay) were gathered. Univariate analyses comparing frail (CFS > 4) to non-frail patients and survivors to non-survivors were performed. Multivariable models with CFS, activities of daily living score (ADL), and the cognitive decline questionnaire IQCODE as predictors and ICU or 30-day mortality as outcomes were formed. RESULTS: A total of 371 patients from 27 ICUs were enrolled. Frail patients had significantly higher ICU (58% vs. 44.45%, P = 0.03) and 30-day (65.61% vs. 54.14%, P = 0.01) mortality compared to non-frail counterparts. The survivors had significantly lower SOFA score, CFS, ADL, and IQCODE than non-survivors. In multivariable analysis CFS (OR 1.15, 95% CI: 1.00-1.34) and SOFA score (OR 1.29, 95% CI: 1.19-1.41) were identified as significant predictors for ICU mortality; however, CFS was not a predictor for 30-day mortality ( P = 0.07). No statistical significance was found for ADL, IQCODE, polypharmacy, or comorbidities. CONCLUSIONS: We found a positive correlation between CFS and ICU mortality, which might point to the value of assessing the score for every patient admitted to the ICU. The older Polish ICU patients were characterised by higher mortality compared to the other European countries.

Diagnostic accuracy of bimanual palpation in bladder cancer patients undergoing cystectomy: A prospective study.

OBJECTIVES: To prospectively assess the concordance of examination under anesthesia (EUA)-based clinical T stage with pathological T stage and diagnostic accuracy of EUA in patients with bladder cancer undergoing cystectomy. METHODS: Consecutive patients with bladder cancer undergoing cystectomy between June 2017 and October 2020 in a single academic center were included in a prospective study. Two urologists performed EUA (one blinded to imaging) before patients underwent cystectomy. We assessed the concordance between clinical T stage in bimanual palpation (index test) and pathological T stage in cystectomy specimens (reference test). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95% confidence intervals (CIs) to detect or exclude locally advanced bladder cancer (pT3b-T4b) in EUA. RESULTS: The data of 134 patients were analyzed. Given that stage pT3a cannot be palpated, for the nonblinded examiner, T staging in EUA was concordant with pT in 107 (79.9%) patients, 20 (14.9%) cases being understaged in EUA and 7 (5.2%) overstaged. For the blinded examiner, staging was correct in 106 (79.1%) patients, 20 (14.9%) cases being understaged and 8 (6%) overstaged. For the nonblinded examiner, sensitivity, specificity, PPV, and NPV of EUA were 55.9% (95% CI 39.2%-72.6%), 93% (88%-98%), 73.1% (56%-90.1%), and 86.1% (79.6%-92.6%), respectively; for the blinded examiner, they were 52.9% (36.2%-69.7%), 93% (88%-98%), 72% (54.4%-89.6%) and 85.3% (78.7%-92%), respectively. Awareness of imaging results did not have a major impact on EUA results. CONCLUSION: Bimanual palpation should still be used for clinical staging, given its specificity, NPV, and that it could correctly determine bladder cancer T stage in 80% of cases.

Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method.

Comprehensive protein function annotation is essential for understanding microbiome-related disease mechanisms in the host organisms. However, a large portion of human gut microbial proteins lack functional annotation. Here, we have developed a new metagenome analysis workflow integrating de novo genome reconstruction, taxonomic profiling, and deep learning-based functional annotations from DeepFRI. This is the first approach to apply deep learning-based functional annotations in metagenomics. We validate DeepFRI functional annotations by comparing them to orthology-based annotations from eggNOG on a set of 1,070 infant metagenomes from the DIABIMMUNE cohort. Using this workflow, we generated a sequence catalogue of 1.9 million nonredundant microbial genes. The functional annotations revealed 70% concordance between Gene Ontology annotations predicted by DeepFRI and eggNOG. DeepFRI improved the annotation coverage, with 99% of the gene catalogue obtaining Gene Ontology molecular function annotations, although they are less specific than those from eggNOG. Additionally, we constructed pangenomes in a reference-free manner using high-quality metagenome-assembled genomes (MAGs) and analyzed the associated annotations. eggNOG annotated more genes on well-studied organisms, such as Escherichia coli, while DeepFRI was less sensitive to taxa. Further, we show that DeepFRI provides additional annotations in comparison to the previous DIABIMMUNE studies. This workflow will contribute to novel understanding of the functional signature of the human gut microbiome in health and disease as well as guiding future metagenomics studies. IMPORTANCE The past decade has seen advancement in high-throughput sequencing technologies resulting in rapid accumulation of genomic data from microbial communities. While this growth in sequence data and gene discovery is impressive, the majority of microbial gene functions remain uncharacterized. The coverage of functional information coming from either experimental sources or inferences is low. To solve these challenges, we have developed a new workflow to computationally assemble microbial genomes and annotate the genes using a deep learning-based model DeepFRI. This improved microbial gene annotation coverage to 1.9 million metagenome-assembled genes, representing 99% of the assembled genes, which is a significant improvement compared to 12% Gene Ontology term annotation coverage by commonly used orthology-based approaches. Importantly, the workflow supports pangenome reconstruction in a reference-free manner, allowing us to analyze the functional potential of individual bacterial species. We therefore propose this alternative approach combining deep-learning functional predictions with the commonly used orthology-based annotations as one that could help us uncover novel functions observed in metagenomic microbiome studies.

Treatment With Multi-Species Probiotics Changes the Functions, Not the Composition of Gut Microbiota in Postmenopausal Women With Obesity: A Randomized, Double-Blind, Placebo-Controlled Study.

Probiotics are known to regulate host metabolism. In randomized controlled trial we aimed to assess whether interventions with probiotic containing following strains: Bifidobacterium bifidum W23, Bifidobacterium lactis W51, Bifidobacterium lactis W52, Lactobacillus acidophilus W37, Levilactobacillus brevis W63, Lacticaseibacillus casei W56, Ligilactobacillus salivarius W24, Lactococcus lactis W19, and Lactococcus lactis W58 affect gut microbiota to promote metabolic effects. By 16S rRNA sequencing we analyzed the fecal microbiota of 56 obese, postmenopausal women randomized into three groups: (1) probiotic dose 2.5 × 109 CFU/day (n = 18), (2) 1 × 1010 CFU/day (n = 18), or (3) placebo (n = 20). In the set of linear mixed-effects models, the interaction between pre- or post-treatment bacterial abundance and time on cardiometabolic parameters was significantly (FDR-adjusted) modified by type of intervention (26 and 19 three-way interactions for the pre-treatment and post-treatment abundance, respectively), indicating the modification of the bio-physiological role of microbiota by probiotics. For example, the unfavorable effects of Erysipelotrichi, Erysipelotrichales, and Erysipelotrichaceae on BMI might be reversed, but the beneficial effect of Betaproteobacteria on BMI was diminished by probiotic treatment. Proinflammatory effect of Bacteroidaceae was alleviated by probiotic administration. However, probiotics did not affect the microbiota composition, and none of the baseline microbiota-related features could predict therapeutic response as defined by cluster analysis. Conclusions: Probiotic intervention alters the influence of microbiota on biochemical, physiological and immunological parameters, but it does not affect diversity and taxonomic composition. Baseline microbiota is not a predictor of therapeutic response to a multispecies probiotic. Further multi-omic and mechanistic studies performed on the bigger cohort of patients are needed to elucidate the cardiometabolic effect of investigated probiotics in postmenopausal obesity.