A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their ...susceptibility to a particular disease or response to a specific treatment. In this work, with an illustrative example on type 2 diabetes we showed how the increasing ability to access and analyzed open data from randomized clinical trials (RCTs) allows to build Machine Learning applications in a framework of personalized medicine. An ensemble machine learning predictive model is first developed and then applied to estimate the expected treatment response according to the medication that would be prescribed. Machine learning is quickly becoming indispensable to bridge science and clinical practice, but it is not sufficient on its own. A collaborative effort is requested to clinicians, statisticians, and computer scientists to strengthen tools built on machine learning to take advantage of this evidence flow.
Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability ...distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering "prior elicitation" as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the "Probability and Statistics" area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts' elicitation, more efforts are needed to ensure their diffusion also in applied settings.
To analyze the prevalence of homologous recombination deficiency (HRD) in patients with pancreatic ductal adenocarcinoma (PDAC).
We conducted a systematic review and meta-analysis of the prevalence ...of HRD in PDAC from PubMed, Scopus, and Cochrane Library databases, and online cancer genomic data sets. The main outcome was pooled prevalence of somatic and germline mutations in the better characterized HRD genes (
,
,
,
,
,
,
, and the
genes). The secondary outcomes were prevalence of germline mutations overall, and in sporadic and familial cases; prevalence of germline
mutations in Ashkenazi Jewish (AJ); and prevalence of HRD based on other definitions (ie, alterations in other genes, genomic scars, and mutational signatures). Random-effects modeling with the Freeman-Tukey transformation was used for the analyses. PROSPERO registration number: (CRD42020190813).
Sixty studies with 21,842 participants were included in the systematic review and 57 in the meta-analysis. Prevalence of germline and somatic mutations was
: 0.9%,
: 3.5%,
: 0.2%,
: 2.2%,
: 0.3%,
: 0.5%,
: 0.0%, and
: 0.1%. Prevalence of germline mutations was
: 0.9% (2.4% in AJ),
: 3.8% (8.2% in AJ),
: 0.2%,
: 2%,
: 0.3%, and
: 0.4%. No significant differences between sporadic and familial cases were identified. HRD prevalence ranged between 14.5%-16.5% through targeted next-generation sequencing and 24%-44% through whole-genome or whole-exome sequencing allowing complementary genomic analysis, including genomic scars and other signatures (surrogate markers of HRD).
Surrogate readouts of HRD identify a greater proportion of patients with HRD than analyses limited to gene-level approaches. There is a clear need to harmonize HRD definitions and to validate the optimal biomarker for treatment selection. Universal HRD screening including integrated somatic and germline analysis should be offered to all patients with PDAC.
The present study aimed to provide a descriptive analysis of the nutrient profile of ultra-processed foods (UPFs) marketed in Italy according to three front-of-pack labeling (FOPL) schemes ...implemented by France, i.e., the Nutriscore; by the United Kingdom, i.e., Multiple Traffic Lights (MTL); and by Italy, i.e., the NutrInform battery. The analysis was made in fourteen food product categories, corresponding to 124 foods. The application of the Nutriscore scheme showed that a significant proportion of foods (23%) were awarded an A or B. Furthermore, the analysis according to the MTL showed that food products that were above the threshold (“red”) for fat, saturated fats, sugars, and salt ranged from 13% to 31%. Interestingly, even though all foods considered in the analysis were UPF, they were heterogeneous in nutritional composition, as demonstrated by the FOPL schemes applied, showing that UPF represent a heterogeneous group of foods with different characteristics. Such a finding may have relevant implications for epidemiological studies that analyze the association between UPF consumption and health outcomes, suggesting the need for better characterization of the effects of UPF intake on human health.
Abstract
Aims
Quantitative echocardiography parameters are seldom used to grade tricuspid regurgitation (TR) severity due to relative paucity of validation studies and lack of prognostic data. To ...assess the relationship between TR severity and the composite endpoint of death and hospitalization for congestive heart failure (CHF); and to identify the threshold values of vena contracta width (VCavg), effective regurgitant orifice area (EROA), regurgitant volume (RegVol), and regurgitant fraction (RegFr) to define low, intermediate, and high-risk TR based on patients’ outcome data.
Methods and results
A cohort of 296 patients with at least mild TR underwent 2D, 3D, and Doppler echocardiography. We built statistical models (adjusted for age, NYHA class, left ventricular ejection fraction, and pulmonary artery systolic pressure) for VCavg, EROA, RegVol, and RegFr to study their relationships with the hazard of outcome. The tertiles of the derived hazard values defined the threshold values of the quantitative parameters for TR severity grading. During 47-month follow-up, 32 deaths and 72 CHF occurred. Event-free rate was 14%, 48%, and 93% in patients with severe, moderate, and mild TR, respectively. Severe TR was graded as VCavg > 6 mm, EROA > 0.30 cm2, RegVol > 30 mL, and RegF > 45%.
Conclusion
This outcome study demonstrates the prognostic value of quantitative parameters of TR severity and provides prognostically meaningful threshold values to grade TR severity in low, intermediate, and high risk.
Northern Italy has been the first European area affected by the COVID-19 pandemic and related social restrictive measures. We sought to evaluate the impact of the COVID-19 outbreak on PICU admissions ...in Northern Italy, using data from the Italian Network of Pediatric Intensive Care Units Registry. We included all patients admitted to 4 PICUs from 8-weeks-before to 8-weeks-after February 24
th
, 2020, and those admitted in the same period in 2019. Incidence rate ratios (IRR) evaluating incidence rate differences between pre- and post-COVID-19 periods in 2020 (IRR-1), as well as between the post-COVID-19-period with the same period in 2019 (IRR-2), were computed using zero-inflated negative binomial or Poisson regression modeling. A total of 1001 admissions were included. The number of PICU admissions significantly decreased during the COVID-19 outbreak compared to pre-COVID-19 and compared to the same period in 2020 (IRR-1 0.63 95%CI 0.50–0.79; IRR-2 0.70 CI 0.57–0.91). Unplanned and medical admissions significantly decreased (IRR-1 0.60 CI 0.46–0.70; IRR-2 0.67 CI 0.51–0.89; and IRR-1 0.52, CI 0.40–0.67; IRR-2 0.77 CI 0.58–1.00, respectively). Intra-hospital, planned (potentially delayed by at least 12 h), and surgical admissions did not significantly change. Patients admitted for respiratory failure significantly decreased (IRR-1 0.55 CI 0.37–0.77; IRR-2 0.48 CI 0.33–0.69).
Conclusions
: Unplanned and medical PICU admissions significantly decreased during COVID-19 outbreak, especially those for respiratory failure.
What is Known:
• Northern Italy has been the first European area affected by the COVID-19 pandemic.
• Although children are relatively spared from the severe COVID-19 disease, the pediatric care system has been affected by social restrictive measures, with a reported 73
–
88% reduction in pediatric emergency department admissions.
What is New:
• Unplanned and medical PICU admissions significantly decreased during the COVID-19 outbreak compared to pre-COVID-19 and to the same period in 2019, especially those for respiratory failure. Further studies are needed to identify associated factors and new prevention strategies.
The outbreak poses a relevant burden on hospital resources, with a marked increase in the intensive care unit (ICU) occupancy rates 1. SEE PDF These findings suggest that testing also ...asymptomatic/mild symptomatic patients would help reduce the proportion of most severe cases eventually requiring ICU and thus limiting the risk of saturation of ICU units. Regression modeling strategies with applications to linear models, logistic and ordinal regression and survival analysis (2nd Edition).
The clinical course in idiopathic pulmonary fibrosis (IPF) is highly heterogeneous, with some patients having a slow progression and others an accelerated clinical and functional decline. This study ...aims to clinically characterize the type of progression in IPF and to investigate the pathological basis that might account for the observed differences in disease behavior. Clinical and functional data were analyzed in 73 IPF patients, followed long-time as candidates for lung transplantation. The forced vital capacity (FVC) change/year (< or ≥10% predicted) was used to define "slow" or "rapid" disease progression. Pathological abnormalities were quantified in the explanted lung of 41 out of 73 patients undergoing lung transplantation. At diagnosis, slow progressors (n = 48) showed longer duration of symptoms and lower FVC than rapid progressors (n = 25). Eleven slow and 3 rapid progressors developed an acute exacerbation (AE) during follow-up. Quantitative lung pathology showed a severe innate and adaptive inflammatory infiltrate in rapid progressors, markedly increased compared to slow progressors and similar to that observed in patients experiencing AE. The extent of inflammation was correlated with the yearly FVC decline (r = 0.52, p = 0.005). In conclusion an innate and adaptive inflammation appears to be a prominent feature in the lung of patients with IPF and could contribute to determining of the rate of disease progression.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Atrial functional mitral regurgitation (aFMR) has a peculiar pathophysiology that may have distinctive outcomes. We investigated the impact of transcatheter edge-to-edge repair in aFMR compared with ...other FMR etiologies. The GIOTTO (GIse registry Of Transcatheter treatment of MR) is a multicenter, prospective study enrolling patients with symptomatic MR treated with MitraClip up to 2020. We categorized patients with FMR as aFMR, ischemic FMR (iFMR), and nonischemic ventricular FMR (niFMR). The clinical end points were defined according to the Mitral Valve Academic Research Consortium. Of 1,153 patients, 6% had aFMR, 47% iFMR, and 47% niFMR. Patients with aFMR were older, mostly women, and had a higher atrial fibrillation rate. They had better left ventricular ejection fraction and smaller left ventricular volumes, with no difference in mitral effective regurgitant orifice area. The acute device and procedural success rates were similar among the groups. At the longest available follow-up (median 478 days, interquartile range 91 to 741 days), the rate of MR ≥2+ was similar among the groups. Patients with aFMR had a lower rate of cardiovascular death and heart failure than patients with iFMR (hazard ratio HR 0.43, p = 0.02) and niFMR (HR 0.45, p = 0.03). The aFMR etiology remained independently associated with the composite outcome, together with postprocedural MR ≤1+ (HR 0.63, p <0.01) and peripheral arteriopathy (HR 1.82, p = 0.003). The results of this GIOTTO subanalysis suggested that aFMR is less prevalent and associated with better outcomes compared with other causes of FMR treated by transcatheter edge-to-edge repair. Postprocedural MR >1+, peripheral vasculopathy, non-aFMR were independent predictors of worse outcomes.
Prediction of major arrhythmic events (MAEs) in dilated cardiomyopathy represents an unmet clinical goal. Computational models and artificial intelligence (AI) are new technological tools that could ...offer a significant improvement in our ability to predict MAEs. In this proof-of-concept study, we propose a deep learning (DL)-based model, which we termed Deep ARrhythmic Prevention in dilated cardiomyopathy (DARP-D), built using multidimensional cardiac magnetic resonance data (cine videos and hypervideos and LGE images and hyperimages) and clinical covariates, aimed at predicting and tracking an individual patient's risk curve of MAEs (including sudden cardiac death, cardiac arrest due to ventricular fibrillation, sustained ventricular tachycardia lasting ≥30 s or causing haemodynamic collapse in <30 s, appropriate implantable cardiac defibrillator intervention) over time. The model was trained and validated in 70% of a sample of 154 patients with dilated cardiomyopathy and tested in the remaining 30%. DARP-D achieved a 95% CI in Harrell's C concordance indices of 0.12-0.68 on the test set. We demonstrate that our DL approach is feasible and represents a novelty in the field of arrhythmic risk prediction in dilated cardiomyopathy, able to analyze cardiac motion, tissue characteristics, and baseline covariates to predict an individual patient's risk curve of major arrhythmic events. However, the low number of patients, MAEs and epoch of training make the model a promising prototype but not ready for clinical usage. Further research is needed to improve, stabilize and validate the performance of the DARP-D to convert it from an AI experiment to a daily used tool.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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