Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Contemporary lipid-lowering management and risk of cardiovascular events in homozygous familial hypercholesterolaemia: insights from the Italian LIPIGEN Registry

Aims: The availability of novel lipid-lowering therapies (LLTs) has remarkably changed the clinical management of homozygous familial hypercholesterolaemia (HoFH). The impact of these advances was evaluated in a cohort of 139 HoFH patients followed in a real-world clinical setting. Methods and results: The clinical characteristics of 139 HoFH patients, along with information about LLTs and low-density lipoprotein cholesterol (LDL-C) levels at baseline and after a median follow-up of 5 years, were retrospectively retrieved from the records of patients enrolled in the LIPid transport disorders Italian GEnetic Network-Familial Hypercholesterolaemia (LIPIGEN-FH) Registry. The annual rates of major atherosclerotic cardiovascular events (MACE-plus) during follow-up were compared before and after baseline. Additionally, the lifelong survival free from MACE-plus was compared with that of the historical LIPIGEN HoFH cohort. At baseline, LDL-C level was 332 ± 138 mg/dL. During follow-up, the potency of LLTs was enhanced and, at the last visit, 15.8% of patients were taking quadruple therapy. Consistently, LDL-C decreased to an average value of 124 mg/dL corresponding to a 58.3% reduction (Pt < 0.001), with the lowest value (∼90 mg/dL) reached in patients receiving proprotein convertase subtilisin/kexin type 9 inhibitors and lomitapide and/or evinacumab as add-on therapies. The average annual MACE-plus rate in the 5-year follow-up was significantly lower than that observed during the 5 years before baseline visit (21.7 vs. 56.5 per 1000 patients/year; P = 0.0016). Conclusion: Our findings indicate that the combination of novel and conventional LLTs significantly improved LDL-C control with a signal of better cardiovascular prognosis in HoFH patients. Overall, these results advocate the use of intensive, multidrug LLTs to effectively manage HoFH

Roadmap on optical sensors

Optical sensors and sensing technologies are playing a more and more important role in our modern world. From micro-probes to large devices used in such diverse areas like medical diagnosis, defence, monitoring of industrial and environmental conditions, optics can be used in a variety of ways to achieve compact, low cost, stand-off sensing with extreme sensitivity and selectivity. Actually, the challenges to the design and functioning of an optical sensor for a particular application requires intimate knowledge of the optical, material, and environmental properties that can affect its performance. This roadmap on optical sensors addresses different technologies and application areas. It is constituted by twelve contributions authored by world-leading experts, providing insight into the current state-of-the-art and the challenges their respective fields face. Two articles address the area of optical fibre sensors, encompassing both conventional and specialty optical fibres. Several other articles are dedicated to laser-based sensors, micro- and nano-engineered sensors, whispering-gallery mode and plasmonic sensors. The use of optical sensors in chemical, biological and biomedical areas is discussed in some other papers. Different approaches required to satisfy applications at visible, infrared and THz spectral regions are also discussed

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Patterns and trends of transcatheter aortic valve implantation in Italy: Insights from RISPEVA

Aims Clinical trials have shown that transcatheter aortic valve implantation for aortic stenosis compares favorably to surgical replacement in high-risk patients and is superior to medical therapy in those at prohibitive risk. There is uncertainty however on patterns and trends in transcatheter aortic valve implantation, especially focusing on Italy. Methods The RISPEVA study is a prospective Italian registry including 21 institutions. Patients have been enrolled since late 2012, and data collection includes several baseline, procedural, in-hospital, and follow-up details. For the present analysis on patterns and trends, we focused on patients enrolled between 2012 and 2015, and as primary variable on the prevalence of high versus prohibitive surgical risk, limiting our scope to procedural outcomes. Results A total of 1157 patients were included. The temporal breakdown was 376 (33%) patients enrolled in 2013, 408 (35%) in 2014, and 373 (32%) in 2015. Several patient features differed over time, including risk score, peripheral artery disease, end-stage pulmonary disease, and prior valvuloplasty (all P < 0.05). Several procedural features differed significantly over time, including sheath size, use of general anesthesia, Prostar closure device, predilation, antiembolic device, new TAVI device, and multiple prostheses (all P < 0.05). No significant temporal differences were found for major clinical outcomes, whereas the occurrence of moderate or severe postprocedural regurgitation and pacemaker dependency decreased over the years (both P < 0.05). Conclusion According to the RISPEVA results, the Italian uptake of TAVI is steady, with evident trends toward less invasive approaches and fitter patients

Prosthesis-patient mismatch following transcatheter aortic valve replacement for degenerated transcatheter aortic valves: the TRANSIT-PPM international project

Background: A severe prosthesis-patient mismatch (PPM) is associated with adverse outcomes following transcatheter aortic valve replacement (TAVR) for de novo aortic stenosis or a failed surgical bioprosthesis. The impact of severe PPM in patients undergoing TAV-in-TAVR is unknown. Aim: We sought to investigate the incidence and 1-year outcomes of different grades of PPM in patients undergoing TAV-in-TAVR. Materials and methods: The TRANSIT-PPM is an international registry, including cases of degenerated TAVR treated with a second TAVR. PPM severity, as well as in-hospital, 30-day, and 1-year outcomes were defined according to the Valve Academic Research Consortium-3 (VARC-3) criteria. Results: Among 28 centers, 155 patients were included. Severe PPM was found in 6.5% of patients, whereas moderate PPM was found in 14.2% of patients. The rate of severe PPM was higher in patients who underwent TAV-in-TAVR with a second supra-annular self-expanding (S-SE) TAVR (10%, p = 0.04). Specifically, the rate of severe PPM was significantly higher among cases of a SE TAVR implanted into a balloon-expandable (BE) device (19%, p = 0.003). At 1-year follow-up, the rate of all-cause mortality, and the rate of patients in the New York Heart Association (NYHA) class III/IV were significantly higher in the cohort of patients with severe PPM (p = 0.016 and p = 0.0001, respectively). Almost all the patients with a severe PPM after the first TAVR had a failed < 23 mm BE transcatheter heart valve (THV): the treatment with an S-SE resolved the severe PPM in the majority of the cases. Conclusion: After TAV-in-TAVR, in a fifth of the cases, a moderate or severe PPM occurred. A severe PPM is associated with an increased 1-year all-cause mortality. Clinical trial registration: [https://clinicaltrials.gov], identifier [NCT04500964]

Transcatheter Aortic Valve Replacement for Degenerated Transcatheter Aortic Valves: The TRANSIT International Project

Background: Transcatheter aortic valve replacement (TAVR) has determined a paradigm shift in the treatment of patients with severe aortic stenosis. However, the durability of bioprostheses is still a matter of concern, and little is known about the management of degenerated TAV. We sought to evaluate the outcomes of patients with a degenerated TAV treated by means of a second TAVR. Methods: The TRANSIT is an international registry that included cases of degenerated TAVR from 28 centers. Among around 40 000 patients treated with TAVR in the participating centers, 172 underwent a second TAVR: 57 (33%) for a mainly stenotic degenerated TAV, 97 (56%) for a mainly regurgitant TAV, and 18 (11%) for a combined degeneration. Overall, the rate of New York Heart Association class III/IV at presentation was 73.5%. Results: Valve Academic Research Consortium 2 device success rate was 79%, as a consequence of residual gradient (14%) or regurgitation (7%). At 1 month, the overall mortality rate was 2.9%, while rates of new hospitalization and New York Heart Association class III/IV were 3.6% and 7%, respectively, without significant difference across the groups. At 1 year, the overall mortality rate was 10%, while rates of new hospitalization and New York Heart Association class III/IV were 7.6% and 5.8%, respectively, without significant difference across the groups. No cases of valve thrombosis were recorded. Conclusions: Selected patients with a degenerated TAV may be safely and successfully treated by means of a second TAVR. This finding is of crucial importance for the adoption of the TAVR technology in a lower risk and younger population. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04500964