An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the "Suggested Diagnosis" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.

A continuous-time Markov model approach for modeling myelodysplastic syndromes progression from cross-sectional data.

The integration of both genomics and clinical data to model disease progression is now possible, thanks to the increasing availability of molecular patients' profiles. This may lead to the definition of novel decision support tools, able to tailor therapeutic interventions on the basis of a "precise" patients' risk stratification, given their health status evolution. However, longitudinal analysis requires long-term data collection and curation, which can be time demanding, expensive and sometimes unfeasible. Here we present a clinical decision support framework that combines the simulation of disease progression from cross-sectional data with a Markov model that exploits continuous-time transition probabilities derived from Cox regression. Trajectories between patients at different disease stages are stochastically built according to a measure of patient similarity, computed with a matrix tri-factorization technique. Such trajectories are seen as realizations drawn from the stochastic process driving the transitions between the disease stages. Eventually, Markov models applied to the resulting longitudinal dataset highlight potentially relevant clinical information. We applied our method to cross-sectional genomic and clinical data from a cohort of Myelodysplastic syndromes (MDS) patients. MDS are heterogeneous clonal hematopoietic disorders whose patients are characterized by different risks of Acute Myeloid Leukemia (AML) development, defined by an international score. We computed patients' trajectories across increasing and subsequent levels of risk of developing AML, and we applied a Cox model to the simulated longitudinal dataset to assess whether genomic characteristics could be associated with a higher or lower probability of disease progression. We then used the learned parameters of such Cox model to calculate the transition probabilities of a continuous-time Markov model that describes the patients' evolution across stages. Our results are in most cases confirmed by previous studies, thus demonstrating that simulated longitudinal data represent a valuable resource to investigate disease progression of MDS patients.

Patient similarity for precision medicine: A systematic review.

Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research.

Temporal electronic phenotyping by mining careflows of breast cancer patients.

In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data.

Clusters of individuals recovering from an exacerbation of chronic obstructive pulmonary disease and response to in-hospital pulmonary rehabilitation.

INTRODUCTION AND OBJECTIVES: Due to the present low availability of pulmonary rehabilitation (PR) for individuals recovering from a COPD exacerbation (ECOPD), we need admission priority criteria. We tested the hypothesis that these individuals might be clustered according to baseline characteristics to identify subpopulations with different responses to PR. METHODS: Multicentric retrospective analysis of individuals undergone in-hospital PR. Baseline characteristics and outcome measures (six-minute walking test - 6MWT, Medical Research Council scale for dyspnoea -MRC, COPD assessment test -CAT) were used for clustering analysis. RESULTS: Data analysis of 1159 individuals showed that after program, the proportion of individuals reaching the minimal clinically important difference (MCID) was 85.0%, 86.3%, and 65.6% for CAT, MRC, and 6MWT respectively. Three clusters were found (C1-severe: 10.9%; C2-intermediate: 74.4%; C3-mild: 14.7% of cases respectively). Cluster C1-severe showed the worst conditions with the largest post PR improvements in outcome measures; C3-mild showed the least severe baseline conditions, but the smallest improvements. The proportion of participants reaching the MCID in ALL three outcome measures was significantly different among clusters, with C1-severe having the highest proportion of full success (69.0%) as compared to C2-intermediate (48.3%) and C3-mild (37.4%). Participants in C2-intermediate and C1-severe had 1.7- and 4.6-fold increases in the probability to reach the MCID in all three outcomes as compared to those in C3-mild (OR = 1.72, 95% confidence interval [95% CI] = 1.2 - 2.49, p = 0.0035 and OR = 4.57, 95% CI = 2.68 - 7.91, p < 0.0001 respectively). CONCLUSIONS: Clustering analysis can identify subpopulations of individuals recovering from ECOPD associated with different responses to PR. Our results may help in defining priority criteria based on the probability of success of PR.

Improving Keyword-Based Topic Classification in Cancer Patient Forums with Multilingual Transformers.

Online forums play an important role in connecting people who have crossed paths with cancer. These communities create networks of mutual support that cover different cancer-related topics, containing an extensive amount of heterogeneous information that can be mined to get useful insights. This work presents a case study where users' posts from an Italian cancer patient community have been classified combining both count-based and prediction-based representations to identify discussion topics, with the aim of improving message reviewing and filtering. We demonstrate that pairing simple bag-of-words representations based on keywords matching with pre-trained contextual embeddings significantly improves the overall quality of the predictions and allows the model to handle ambiguities and misspellings. By using non-English real-world data, we also investigated the reusability of pretrained multilingual models like BERT in lower data regimes like many local medical institutions.

Epigenetic analysis of the critical region I for premature ovarian failure: demonstration of a highly heterochromatic domain on the long arm of the mammalian X chromosome.

BACKGROUND: X chromosome rearrangements defined a critical region for premature ovarian failure (POF) that extended for >15 Mb in Xq. It has been shown previously that the region could be divided into two functionally distinct portions and suggested that balanced translocations interrupting its proximal part, critical region 1 (CR1), could be responsible for POF through downregulation of ovary expressed autosomal genes translocated to the X chromosome. RESULTS AND CONCLUSION: This study reports that such position effect can indeed be demonstrated by analysis of breakpoint regions in somatic cells of POF patients and by the finding that CR1 has a highly heterochromatic organisation, very different from that of the euchromatic autosomal regions involved in the rearrangements. The chromatin organisation of the POF CR1 is likely to be responsible for the epigenetic modifications observed in POF patients. The characteristics of CR1 and its downregulation in oocytes may very well explain its role in POF and the frequency of the POF phenotype in chromosomal rearrangements involving Xq. This study also demonstrates a large and evolutionary conserved domain of the long arm of the X chromosome, largely corresponding to CR1, that may have structural or functional roles, in oocyte maturation or in X chromosome inactivation.

Ontology-Driven Real World Evidence Extraction from Clinical Narratives.

Unstructured clinical notes contain a huge amount of information. We investigated the possibility of harvesting such information through an NLP-based approach. A manually curated ontology is the only resource required to handle all the steps of the process leading from clinical narrative to a structured data warehouse (i2b2). We have tested our approach at the Papa Giovanni XXIII hospital in Bergamo (Italy) on pathology reports collected since 2008.

Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia.

OBJECTIVE: Computing patients' similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge. MATERIALS AND METHODS: In this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise and data sparsity, and (2) on a real data set coming from an acute myeloid leukemia (AML) study. The results obtained are finally compared with the ones of traditional similarity calculation methods. RESULTS: In the analysis of the synthetic data set, where the ground truth is known, we measured the capability of reconstructing the correct clusters, while in the AML study we evaluated the Kaplan-Meier curves obtained with the different clusters and measured their statistical difference by means of the log-rank test. In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data. DISCUSSION: In case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. We demonstrated how this approach can be efficiently applied to discover meaningful patient similarities and therefore may be considered a reliable data driven strategy for the definition of new research hypothesis for precision oncology. CONCLUSION: The better performance of the proposed approach presents an advantage over previous methods to provide accurate patient similarities supporting precision medicine.

Trusting telemedicine: A discussion on risks, safety, legal implications and liability of involved stakeholders.

OBJECTIVES: The main purpose of the article is to raise awareness among all the involved stakeholders about the risks and legal implications connected to the development and use of modern telemedicine systems. Particular focus is given to the class of "active" telemedicine systems, that imply a real-world, non-mediated, interaction with the final user. A secondary objective is to give an overview of the European legal framework that applies to these systems, in the effort to avoid defensive medicine practices and fears, which might be a barrier to their broader adoption. METHODS: We leverage on the experience gained during two international telemedicine projects, namely MobiGuide (pilot studies conducted in Spain and Italy) and AP@home (clinical trials enrolled patients in Italy, France, the Netherlands, United Kingdom, Austria and Germany), whose development our group has significantly contributed to in the last 4 years, to create a map of the potential criticalities of active telemedicine systems and comment upon the legal framework that applies to them. Two workshops have been organized in December 2015 and March 2016 where the topic has been discussed in round tables with system developers, researchers, physicians, nurses, legal experts, healthcare economists and administrators. RESULTS: We identified 8 features that generate relevant risks from our example use cases. These features generalize to a broad set of telemedicine applications, and suggest insights on possible risk mitigation strategies. We also discuss the relevant European legal framework that regulate this class of systems, providing pointers to specific norms and highlighting possible liability profiles for involved stakeholders. CONCLUSIONS: Patients are more and more willing to adopt telemedicine systems to improve home care and day-by-day self-management. An essential step towards a broader adoption of these systems consists in increasing their compliance with existing regulations and better defining responsibilities for all the involved stakeholders.

Multicentre registry of brain-injured patients with disorder of consciousness: rationale and preliminary data.

Diagnostic accuracy and reliable estimation of clinical evolution are challenging issues in the management of patients with disorders of consciousness (DoC). Longitudinal systematic investigations conducted in large cohorts of patients with DoC could make it possible to identify reliable diagnostic and prognostic markers. On the basis of this consideration, we devised a multicentre prospective registry for patients with DoC admitted to ten intensive rehabilitation units. The registry collects homogeneous and detailed data on patients' demographic and clinical features, neurophysiological and neuroimaging findings, and medical and surgical complications. Here we present the rationale and the design of the registry and the preliminary results obtained in 53 patients with DoC (vegetative state or minimally conscious state) enrolled during the first seven months of the study. Data at 6-month post-injury follow-up were available for 46 of them. This registry could be an important tool for collecting high-quality data through the application of rigorous methods, and it could be used in the routine management of patients with DoC admitted to rehabilitation settings.

Diagnostic value of PRND gene expression profiles in astrocytomas: relationship to tumor grades of malignancy.

Doppel (PRND) is a paralogue of the mammalian prion (PRNP) gene. It is abundant in testis and, unlike PRNP, it is expressed at low levels in the adult central nervous system (CNS). Besides, doppel overexpression correlates with some prion-disease pathological features, such as ataxia and death of cerebellar neurons. Recently, ectopic expression of doppel was found in two different tumor types, specifically in glial and haematological cancers. In order to address clinical important issues, PRND mRNA expression was investigated in a panel of 111 astrocytoma tissue samples, histologically classified according to the World Health Organization (WHO) criteria (6 grade I pilocytic astrocytomas, 15 grade II low-grade astrocytomas, 26 grade III anaplastic astrocytomas and 64 grade IV glioblastoma multiforme). Real-time PRND gene expression profiling, after normalisation with GAPDH, revealed large differences between low (WHO I and II) and high grade (III and IV) of malignancy (P<0.001). Extensive differences in PRND gene expression were also found within each grade of malignancy, suggesting that PRND mRNA quantitation might be useful to distinguish astrocytoma subtypes, and important in disease stratification and in the assessment of specific treatment strategies.

Automatic pedigree reconstruction for genetic studies in isolated populations.

This paper describes a tool implemented to automatically reconstruct the pedigree of an isolated population of Northern Italy with the aim of supporting genetic studies. The goal of such studies is to analyze genealogic, clinical and genetic data for genetic dissection of complex diseases. In this context the reconstruction of the population pedigree is fundamental to verify that such population is a genetic isolate and obtain the parental relationships among the individuals participating to the study. The algorithm presented in the paper, from heterogeneous data sources (demographic municipal and parish archives and other data sources), derives the pedigree applying several heuristic rules in a predefined order. One of the main difficulties in performing such task stands in the "record linkage" process that requires the definition of a sufficiently general strategy for managing the ambiguities caused by missing or imprecise/erroneous input data. The paper, finally, presents and discusses the preliminary results obtained by reconstructing the pedigree of four villages from the data collected during the first eighteen months of project.

The M2DM Project--the experience of two Italian clinical sites with clinical evaluation of a multi-access service for the management of diabetes mellitus patients.

OBJECTIVES: This paper presents a multi-access service for the management of diabetes mellitus patients and the results of its assessment in two Italian clinical sites. METHODS: The service was evaluated for one year in order to prove the advantages of these kind of systems from different points of view. In this paper the clinical, usability and technical outcomes are presented. RESULTS: The evaluation results show that, thanks to the high flexibility of the implemented service, the telemedicine management of diabetes patients is feasible, well accepted by patients and clinically effective. However, in Italy the problem of quantifying the reimbursement rate of telematic services and the impact they have on the organization are factors that may hamper their introduction in routine clinical practice. CONCLUSIONS: The evaluation study showed that the telemedicine intervention has been satisfactory both for physicians because it allows to constantly monitor the patients' blood glucose level and for patients because it strengthens their motivation to self-monitor the metabolic situation.

A proposed semantic framework for diabetes education content management, customisation and delivery within the M2DM project.

M2DM (multi access services for telematic management of diabetes mellitus, ) is an EU-funded telemedicine project that aims at increasing the quality of diabetes care by improving communication between patients and caregivers. As part of this project, we have undertaken the initial work of describing the necessary requirements (framework) of an advanced educational component for M2DM in accordance with the latest Semantic Web concepts. This paper describes our proposed semantic framework for educational content management, customisation and delivery. A big internet challenge today is to find and push situation and user-specific quality knowledge to users based on their actual individual needs, circumstances and profiles at any given time. We believe that the semantic framework presented in this paper could be a good step towards meeting this challenge. Benefits for users, both developers and end users, of adopting such framework are also discussed. The ideas discussed in this paper could be easily adapted to other similar services besides M2DM and to different health topics besides diabetes mellitus.

Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses.

The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying data mining techniques to FHR parameters, obtained from recordings in a population of 122 fetuses (61 healthy and 61 IUGRs), through standard CTG non-stress test. We computed N=12 indices (N=4 related to time domain FHR analysis, N=4 to frequency domain and N=4 to non-linear analysis) and normalized them with respect to the gestational week. We compared, through a 10-fold crossvalidation procedure, 15 data mining techniques in order to select the more reliable approach for identifying IUGR fetuses. The results of this comparison highlight that two techniques (Random Forest and Logistic Regression) show the best classification accuracy and that both outperform the best single parameter in terms of mean AUROC on the test sets.

[From the Mailing List SIN: expected and unexpected professional risks for the nephrologists--reflections from an outbreak of Burkholderia cepacia bacteremia in a hemodialysis unit]. / Dalla Mailing-List soci SIN (ML-SIN). I rischi professionali attesi e inattesi del nefrologo--(Riflessioni da una emergenza iniziata la Notte di Natale).

An outbreak of bacteremia in 20 hemodialysed patients who developed central venous catheter (CVC) infection related to Burkholderia cepacia is reported, introducing medical and professional responsibilities in nephrology units. The cepacia was documented in the blood stream, in the CVC biofilm, in the water supply and in the distribution. This and other confounding factors delayed the identification of the contamination source. Finally, it was isolated, clonally identical to that found in the blood stream, from ammonium chloride solution used to disinfect the skin and distributed in a sterile disposable kit. Burkholderia cepacia was clonally different in blood with respect to water. The possible differing responsibilities in the organizational steps of nephrology activity are discussed.

[The Dialysis Outcomes and Practice Patterns Study (DOPPS): results of the Italian cohort]. / Lo studio DOPPS (Dialysis Outcomes and Practice Patterns Study): risultati della coorte italiana.

BACKGROUND: The Dialysis Outcomes and Practice Patterns Study (DOPPS) is an international prospective, longitudinal, observational study examining the relationship between dialysis unit practices and outcomes for hemodialysis (HD) patients in seven developed countries France, Germany, Italy, Spain, United Kingdom, Japan and the United States. Results of the DOPPS in Italy are the subject of this report. METHODS: A national representative sample of 20 dialysis units (21 in Germany) was randomly selected in each of the European DOPPS countries (Euro-DOPPS). In these units, the HD in-center patients were included on a facility census, and their survival rates continuously monitored. A representative sample of incident (269 in Italy, 1553 in the Euro-DOPPS) and prevalent (600 in Italy, 3038 in the Euro-DOPPS) patients was randomly selected from the census for more detailed longitudinal investigation with regard to medical history, laboratory values and hospital admission. RESULTS: Comparing the Italian and Euro-DOPPS cohorts we found comparable mean age for prevalent patients (61.4 vs. 59.5 yrs), but incident patients were older in Italy. Italian prevalent patients had less cardiovascular disease, more satisfactory nutritional status and more frequent use of native vascular access. These data were associated with a comparable mortality (15.7 vs. 16.3 deaths/100 patient yrs), but morbidity was lower in Italy. Kt/V levels were comparable in the two cohorts (1.32 vs. 1.37), but 35% of Italian patients showed a Kt/V below the recommended target. Moreover, hemoglobin levels were below 11 g/dL in 60% of Italian patients. CONCLUSIONS: The DOPPS results bring to light several positive aspects and the opportunity for further possible improvements for Italian patients, but at the same time highlight some critical points that could represent a risk for dialysis quality.

From data to the decision: A software architecture to integrate predictive modelling in clinical settings.

The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component (Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.