Integration Of Clinical Parameters Research Articles

Systematic review and meta-analysis of the prognostic value of 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and/or computed tomography (CT)-based radiomics in head and neck cancer

Systematic review and meta-analysis of the prognostic value of 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and/or computed tomography (CT)-based radiomics in head and neck cancer

The Role of Early Serum Biomarkers and Clinical Rating Scales in the Prediction of Delayed Cerebral Ischaemia and Short-Term Outcome after Aneurysmal Subarachnoid Haemorrhage: Single Centre Experience.

Considering the variety of complications that arise after aneurysmal subarachnoid haemorrhage (aSAH) and the complex pathomechanism of delayed cerebral ischaemia (DCI), the task of predicting the outcome assumes a profound complexity. Therefore, there is a need to develop early predictive and decision-making models. This study explores the effect of serum biomarkers and clinical scales on patients' outcomes and their interrelationship with DCI and systemic complications in aSAH. This was a retrospective analysis including aSAH patients admitted to the Wroclaw University Hospital (Wrocław, Poland) from 2011 to 2020. A good outcome was defined as a modified Rankin Scale (mRS) score of 0-2. The prediction of the development of DCI and poor outcome was conducted using logistic regression as a standard model (SM) and random forest as a machine learning method (ML). A cohort of 174 aSAH patients were included in the analysis. DCI was diagnosed in 79 (45%) patients. Significant differences between patients with poor vs. good outcome were determined from their levels of albumin (31 ± 7 vs. 35 ± 5 (g/L); p < 0.001), D-dimer (3.0 ± 4.5 vs. 1.5 ± 2.8 (ng/mL); p < 0.001), procalcitonin (0.2 ± 0.4 vs. 0.1 ± 0.1 (ng/mL); p < 0.001), and glucose (169 ± 69 vs. 137 ± 48 (nmol/L); p < 0.001). SM for DCI prediction included the Apache II scale (odds ratio [OD] 1.05; 95% confidence interval [CI] 1.00-1.09) and albumin level (OD 0.88; CI 0.82-0.95). ML demonstrated that low albumin level, high Apache II scale, increased D-dimer and procalcitonin levels had the highest predictive values for DCI. The integration of clinical parameters and scales with a panel of biomarkers may effectively facilitate the stratification of aSAH patients, identifying those at high risk of secondary complications and poor outcome.

Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism.

The aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA). 269 patients from the prospective German Conn Registry with PA were included in this study. After segmentation of adrenal glands in native CT images, radiomic features were calculated. The study population consisted of a training (n = 215) and a validation (n = 54) cohort. The k = 25 best radiomic features, selected using maximum-relevance minimum-redundancy (MRMR) feature selection, were used to train a baseline random forest model to predict the result of AVS from imaging alone. In a second step, clinical parameters were integrated. Model performance was assessed via area under the receiver operating characteristic curve (ROC AUC). Permutation feature importance was used to assess the predictive value of selected features. Radiomics features alone allowed only for moderate discrimination of the location of aldosterone overproduction with a ROC AUC of 0.57 for unilateral left (UL), 0.61 for unilateral right (UR), and 0.50 for bilateral (BI) aldosterone overproduction (total 0.56, 95% CI: 0.45-0.65). Integration of clinical parameters into the model substantially improved ROC AUC values (0.61 UL, 0.68 UR, and 0.73 for BI, total 0.67, 95% CI: 0.57-0.77). According to permutation feature importance, lowest potassium value at baseline and saline infusion test (SIT) were the two most important features. Integration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA.

Gut bacterial dysbiosis and instability is associated with the onset of complications and mortality in COVID-19

ABSTRACT There is a growing debate about the involvement of the gut microbiome in COVID-19, although it is not conclusively understood whether the microbiome has an impact on COVID-19, or vice versa, especially as analysis of amplicon data in hospitalized patients requires sophisticated cohort recruitment and integration of clinical parameters. Here, we analyzed fecal and saliva samples from SARS-CoV-2 infected and post COVID-19 patients and controls considering multiple influencing factors during hospitalization. 16S rRNA gene sequencing was performed on fecal and saliva samples from 108 COVID-19 and 22 post COVID-19 patients, 20 pneumonia controls and 26 asymptomatic controls. Patients were recruited over the first and second corona wave in Germany and detailed clinical parameters were considered. Serial samples per individual allowed intra-individual analysis. We found the gut and oral microbiota to be altered depending on number and type of COVID-19-associated complications and disease severity. The occurrence of individual complications was correlated with low-risk (e.g., Faecalibacterium prausznitzii) and high-risk bacteria (e.g., Parabacteroides ssp.). We demonstrated that a stable gut bacterial composition was associated with a favorable disease progression. Based on gut microbial profiles, we identified a model to estimate mortality in COVID-19. Gut microbiota are associated with the occurrence of complications in COVID-19 and may thereby influencing disease severity. A stable gut microbial composition may contribute to a favorable disease progression and using bacterial signatures to estimate mortality could contribute to diagnostic approaches. Importantly, we highlight challenges in the analysis of microbial data in the context of hospitalization.

Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

SUMMARYAlthough clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-free, automated pipeline enabling integration of categorical and numeric data spanning clinical and multi-omic profiles for unsupervised clustering to identify disease subsets. Using simulations and real-world data from The Cancer Genome Atlas, we demonstrate that MANAclust’s feature selection algorithms are accurate and outperform competitors. We also apply MANAclust to a clinically and multi-omically phenotyped asthma cohort. MANAclust identifies clinically and molecularly distinct clusters, including heterogeneous groups of “healthy controls” and viral and allergy-driven subsets of asthmatic subjects. We also find that subjects with similar clinical presentations have disparate molecular profiles, highlighting the need for additional testing to uncover asthma endotypes. This work facilitates data-driven personalized medicine through integration of clinical parameters with multi-omics. MANAclust is freely available at https://bitbucket.org/scottyler892/manaclust/src/master/.

Integration of clinical parameters, genotype and epistaxis severity score to guide treatment for hereditary hemorrhagic telangiectasia associated bleeding

BackgroundHereditary Hemorrhagic Telangiectasia (HHT) is a rare inherited disorder characterized by development of mucocutaneous telangiectases and visceral organ arteriovenous malformations, which can lead to recurrent, spontaneous bleeding and development of iron deficiency anemia. The primary objective of this study was to ascertain the relationship between epistaxis severity scores (ESS), laboratory values, genotype, and phenotype in HHT. Our secondary objective was to assess efficacy of systemic antifibrinolytic therapy in reducing ESS in HHT.MethodologyWe conducted a retrospective review of patients seen at the UNC HHT Center from January 1, 2009 to February 28, 2015. ESS, demographics, and results of genetic testing were abstracted from the medical record. Response to antifibrinolytic therapy was evaluated by comparing pre-post ESS.ResultsOne hundred and forty nine patients were eligible with 116 having genetic testing and 33 without. Age, hemoglobin and ferritin levels were predictive of ESS. Of the 116 patients that underwent genetic testing: 63 had an ACVRL1 mutation, 40 had an ENG mutation, 2 had a SMAD4 mutation, and 11 patients had no pathologic HHT genetic variation detected. Compared to patients without a detectable HHT-associated genetic variation, patients with a HHT-associated genetic variation had higher ESS scores (p < 0.05). Neither ESS nor genotype was predictive of pulmonary or brain AVMs. Twenty-four HHT patients with ESS > 4 were started on antifibrinolytic therapy (tranexamic acid or aminocaproic acid) and had a post-treatment ESS recorded. All patients had a decrease in ESS of > 0.71 (minimal meaningful difference), but patients taking antifibrinolytics displayed larger decreases. No patients on antifibrinolytics experienced a VTE with median follow up of 13 months.ConclusionsWe demonstrate that the ESS correlates with age, hemoglobin and ferritin. Additionally, we demonstrate that HHT patients with genetic mutations have higher ESS scores. Our data demonstrate that antifibrinolytics are effective in decreasing epistaxis severity and safe with long-term use in HHT patients.

A novel urine peptide biomarker-based algorithm for the prognosis of necrotising enterocolitis in human infants

ObjectiveNecrotising enterocolitis (NEC) is a major source of neonatal morbidity and mortality. The management of infants with NEC is currently complicated by our inability to accurately identify those at risk...