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In observational studies, one of the main difficulties consists in the comparison of treat- ment effects. In fact, receiving a treatment is not a “purely random” event, and there could be relevant differences between treatment groups. Propensity score is a popular tool to account for this source of bias. However, its use requires a careful modelization of the dependence relationships of the treatment on the covariates. In this work, we consider a general setting with multiple treatments and discrete multi-valued outcome. We propose to estimate the propensity score by using Bayesian Networks and, based on this, we develop an inferential methodology to evaluate the treatments effect. The performance of the pro- posed approach have been studied through a simulation study with very promising results.

CPX-351, a liposomal combination of cytarabine plus daunorubicin, has been approved for the treatment of adults with newly diagnosed, therapy-related acute myeloid leukemia (AML) or AML with myelodysplasia-related changes, because it improves survival and outcome of patients who received hematopoietic stem cell transplant compared with the continuous infusion of cytarabine plus daunorubicin (referred to as “7 + 3” combination). Because gut dysbiosis occurring in patients with AML during induction chemotherapy heavily affects the subsequent phases of therapy, we have assessed whether the superior activity of CPX-351 vs “7 + 3” combination in the real-life setting implicates an action on and by the intestinal microbiota. To this purpose, we have evaluated the impact of CPX-351 and “7 + 3” combination on mucosal barrier function, gut microbial composition and function, and antifungal colonization resistance in preclinical models of intestinal damage in vitro and in vivo and fecal microbiota transplantation. We found that CPX-351, at variance with “7 + 3” combination, protected from gut dysbiosis, mucosal damage, and gut morbidity while increasing antifungal resistance. Mechanistically, the protective effect of CPX-351 occurred through pathways involving both the host and the intestinal microbiota, namely via the activation of the aryl hydrocarbon receptor–interleukin-22 (IL-22)–IL-10 host pathway and the production of immunomodulatory metabolites by anaerobes. This study reveals how the gut microbiota may contribute to the good safety profile, with a low infection-related mortality, of CPX-351 and highlights how a better understanding of the host-microbiota dialogue may contribute to pave the way for precision medicine in AML.

Purpose: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Objectives Enlarged main pulmonary artery diameter (MPAD) resulted to be associated with pulmonary hypertension and mortality in a non-COVID-19 setting. The aim was to investigate and validate the association between MPAD enlargement and overall survival in COVID-19 patients. Methods This is a cohort study on 1469 consecutive COVID-19 patients submitted to chest CT within 72 h from admission in seven tertiary level hospitals in Northern Italy, between March 1 and April 20, 2020. Derivation cohort (n = 761) included patients from the first three participating hospitals; validation cohort (n = 633) included patients from the remaining hospitals. CT images were centrally analyzed in a core-lab blinded to clinical data. The prognostic value of MPAD on overall survival was evaluated at adjusted and multivariable Cox's regression analysis on the derivation cohort. The final multivariable model was tested on the validation cohort. Results In the derivation cohort, the median age was 69 (IQR, 58-77) years and 537 (70.6%) were males. In the validation cohort, the median age was 69 (IQR, 59-77) years with 421 (66.5%) males. Enlarged MPAD (>= 31 mm) was a predictor of mortality at adjusted (hazard ratio, HR [95%CI]: 1.741 [1.253-2.418], p < 0.001) and multivariable regression analysis (HR [95%CI]: 1.592 [1.154-2.196], p = 0.005), together with male gender, old age, high creatinine, low well-aerated lung volume, and high pneumonia extension (c-index [95%CI] = 0.826 [0.796-0.851]). Model discrimination was confirmed on the validation cohort (c-index [95%CI] = 0.789 [0.758-0.823]), also using CT measurements from a second reader (c-index [95%CI] = 0.790 [0.753;0.825]). Conclusion Enlarged MPAD (>= 31 mm) at admitting chest CT is an independent predictor of mortality in COVID-19.

In observational studies evaluating the treatment effect on a given out- come, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to esti- mate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.

As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensinconverting enzyme 2 (ACE2), but whether or not renin-angiotensin-aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.

CD4+ type 1 T regulatory (Tr1) cells are induced in the periphery and have a pivotal role in promoting and maintaining tolerance. The absence of surface markers that uniquely identify Tr1 cells has limited their study and clinical applications. By gene expression profiling of human Tr1 cell clones, we identified the surface markers CD49b and lymphocyte activation gene 3 (LAG-3) as being stably and selectively coexpressed on mouse and human Tr1 cells. We showed the specificity of these markers in mouse models of intestinal inflammation and helminth infection and in the peripheral blood of healthy volunteers. The coexpression of CD49b and LAG-3 enables the isolation of highly suppressive human Tr1 cells from in vitro anergized cultures and allows the tracking of Tr1 cells in the peripheral blood of subjects who developed tolerance after allogeneic hematopoietic stem cell transplantation. The use of these markers makes it feasible to track Tr1 cells in vivo and purify Tr1 cells for cell therapy to induce or restore tolerance in subjects with immune-mediated diseases. © 2013 Nature America, Inc. All rights reserved

Wiskott-Aldrich syndrome (WAS) is an inherited immunodeficiency caused by mutations in the gene encoding WASP, a protein regulating the cytoskeleton. Hematopoietic stem/progenitor cell (HSPC) transplants can be curative but, when matched donors are unavailable, infusion of autologous HSPCs modified ex vivo by gene therapy is an alternative approach. We used a lentiviral vector encoding functional WASP to genetically correct HSPCs from three WAS patients and reinfused the cells after reduced-intensity conditioning regimen. All three patients showed stable engraftment of WASP-expressing cells and improvements in platelet counts, immune functions, and clinical score. Vector integration analyses revealed highly polyclonal and multilineage haematopoiesis resulting from the gene-corrected HSPCs. Lentiviral gene therapy did not induce selection of integrations near oncogenes, and no aberrant clonal expansion was observed after 20 to 32 months. Although extended clinical observation is required to establish long-term safety, lentiviral gene therapy represents a promising treatment for WAS

Background Data: Studies comparing the costs of colorectal resection by laparoscopic (LPS) and open approaches are small sized or not randomized. The main purpose of this study is to compare the hospital costs of LPS and open colorectal surgery in a large series of randomized patients. Methods: A total of 517 patients with colorectal disease were randomly assigned to LPS (n = 258) or open (n = 259) resection. The following costs were calculated: surgical instruments, operative room (OR) occupation, routine care, postoperative morbidity, and length of hospital stay (LOS). Follow-up for postoperative morbidity was carried out for 30 days after hospital discharge. Results: Operative time was 37 minutes longer in the LPS group. Overall morbidity rate was 18.2% (47 of 258) in the LPS versus 34.7% (90 of 259) in the open group (P = 0.0005). The mean LOS was 9.9 (2.6) days in the LPS group and 12.4 (3.9) days in the open group (P < 0.0001). The additional OR charge in the LPS group was €1171 per patient randomized (€864 due to surgical instruments and €307 due to longer time). The saving in the LPS group was €1046 per patient randomized (€401 due to shorter LOS and €645 due to the lower cost of postoperative complications). The net balance resulted in €125 extra cost per patient allocated to the LPS group. Conclusions: The present cost-benefit analysis showed a slight additional cost in the LPS group. The better postoperative short-term outcome in patients receiving LPS had a key role to nearly balance the operative room charges due to laparoscopy. Copyright © 2005 by Lippincott Williams & Wilkins.