Francesca Ieva

Optical Identification of Subjects at High Risk for Developing Breast Cancer

Abstract. A time-domain multiwavelength (635 to 1060 nm) optical mammography was performed on 147 subjects with recent x-ray mammograms available, and average breast tissue composition (water, lipid, collagen, oxy- and deoxyhemoglobin) and scattering parameters (amplitude a and slope b) were estimated. Correlation was observed between optically derived parameters and mammographic density [Breast Imaging and Reporting Data System (BI-RADS) categories], which is a strong risk factor for breast cancer. A regression logistic model was obtained to best identify high-risk (BI-RADS 4) subjects, based on collagen content and scattering parameters. The model presents a total misclassification error of 12.3%, sensitivity of 69%, specificity of 94%, and simple kappa of 0.84, which compares favorably even with intraradiologist assignments of BI-RADS categories.

Depth Measures for Multivariate Functional Data

In this article, we address the problem of mining and analyzing multivariate functional data. That is, data where each observation is a set of possibly correlated functions. Complex data of this kind is more and more common in many research fields, particularly in the biomedical context. In this work, we propose and apply a new concept of depth measure for multivariate functional data. With this new depth measure it is possible to generalize robust statistics, such as the median, to the multivariate functional framework, which in turn allows the application of outlier detection, boxplots construction, and nonparametric tests also in this more general framework. We present an application to Electrocardiographic (ECG) signals.

Detecting and visualizing outliers in provider profiling via funnel plots and mixed effect models

In this work we propose the use of a graphical diagnostic tool (the funnel plot) to detect outliers among hospitals that treat patients affected by Acute Myocardial Infarction (AMI). We consider an application to data on AMI hospitalizations recorded in the administrative databases of our regional district. The outcome of interest is the in-hospital mortality, a variable indicating if the patient has been discharged dead or alive. We then compare the results obtained by graphical diagnostic tools with those arising from fitting parametric mixed effects models to the same data.

Multilevel models for clinical registers concerning STEMI patients in a complex urban reality: a statistical analysis of MOMI2 survey

In this work we describe statistical analyses conducted on MOMI2 (MOnth MOnitoring Myocardial Infarction in MIlan) survey, a collection of data concerning patients admitted with STEMI (ST-Elevation Myocardial Infarction) diagnosis in one of the hospitals belonging to the Network in Milan urban area. The main goal of the analyses is statistical exploration, description and model of collected data in order to answer specific clinical questions (i.e. whether the result of certain healthcare policy is less or more effective than another one, whether the logistic organization or time scheduling of Emergency Room (ER) and rescue units can be improved, etc). Such results can be used as an effective support to decisional process for clinical and organizational governance. The fundamental result of this study is not only the use of advanced and innovative statistical tools, but also the social impact of the achieved results thanks to the synergic interaction between statisticians and physicians.

Exploitation, integration and statistical analysis of the Public Health Database and STEMI Archive in the Lombardia region

We describe the nature and aims of the Strategic Program “Exploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction”. The main goal of the Programme is the construction and statistical analysis of data coming from the integration of complex clinical and administrative databases concerning patients with Acute Coronary Syndromes treated in the Lombardia region. Clinical data sets arise from observational studies about specific diseases, while administrative data arise from standardised and on-going procedures of data collection. The linkage between clinical and administrative databases enables the Lombardia region to create an efficient global system for collecting and storing integrated longitudinal data, to check them, to guarantee their quality and to study them from a statistical perspective.

Multi-state modelling of repeated hospitalisation and death in patients with heart failure: The use of large administrative databases in clinical epidemiology

In chronic diseases like heart failure (HF), the disease course and associated clinical event histories for the patient population vary widely. To improve understanding of the prognosis of patients and enable health care providers to assess and manage resources, we wish to jointly model disease progression, mortality and their relation with patient characteristics. We show how episodes of hospitalisation for disease-related events, obtained from administrative data, can be used as a surrogate for disease status. We propose flexible multi-state models for serial hospital admissions and death in HF patients, that are able to accommodate important features of disease progression, such as multiple ordered events and competing risks. Fully parametric and semi-parametric semi-Markov models are implemented using freely available software in R. The models were applied to a dataset from the administrative data bank of the Lombardia region in Northern Italy, which included 15,298 patients who had a first hospitalisation ending in 2006 and 4 years of follow-up thereafter. This provided estimates of the associations of age and gender with rates of hospital admission and length of stay in hospital, and estimates of the expected total time spent in hospital over five years. For example, older patients and men were readmitted more frequently, though the total time in hospital was roughly constant with age. We also discuss the relative merits of parametric and semi-parametric multi-state models, and model assessment and comparison.

Contemporary roles of registries in clinical cardiology: when do we need randomized trials?

Clinical registries are established as tools for auditing clinical standards and benchmarking quality improvement initiatives. They also have an emerging role (as electronic health records) in cardiovascular research and, in particular, the conduct of RCTs. While the RCT is accepted as the most robust experimental design, observational data from clinical registries has become increasingly valuable for RCTs. Data from clinical registries may be used to augment results from RCTs, identify patients for recruitment and as an alternative when randomization is not practically possible or ethically desirable. Here the authors appraise the advantages and disadvantages of both methodologies, with the aim of clarifying when their joint use may be successful.

Designing and Mining a Multicenter Observational Clinical Registry Concerning Patients with Acute Coronary Syndromes

In this work we describe design, aims, and contents of the ST-segment Elevation Myocardial Infarction (STEMI) Archive, which is a multicenter observational clinical registry planned within the Strategic Program “Exploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction.” This is an observational clinical registry that collects clinical indicators, process indicators, and outcomes concerning STEMI patients admitted to any hospital of the regional district, one of the most advanced and intensive-care area in Italy. This registry is arranged to be automatically linked to the Public Health Database, the ongoing administrative datawarehouse of Regione Lombardia. Aims and perspectives of this innovative project are discussed, together with feasibility and statistical analyses which are to be performed on it, in order to monitor and evaluate the patterns of care of cardiovascular patients.

Trends in heart failure hospitalizations, patient characteristics, in-hospital and 1-year mortality: A population study, from 2000 to 2012 in Lombardy

BACKGROUND This study was undertaken to evaluate trends in heat failure hospitalizations (HFHs) and 1-year mortality of HFH in Lombardy, the largest Italian region, from 2000 to 2012. METHODS Hospital discharge forms with HF-related ICD-9 CM codes collected from 2000 to 2012 by the regional healthcare service (n=699797 in 370538 adult patients), were analyzed with respect to in-hospital and 1-year mortality; Group (G) 1 included most acute HF episodes with primary cardiac diagnosis (70%); G2 included cardiomyopathies without acute HF codes (17%); and G3 included non-cardiac conditions with HF as secondary diagnosis (13%). Patients experiencing their first HFH since 2005 were analyzed as incident cases (n=216782). RESULTS Annual HFHs number (mean 53830) and in-hospital mortality (9.4%) did not change over the years, the latter being associated with increasing age (p<0.0001) and diagnosis Group (G1 9.1%, G2 5.6%, G3 15.9%, p<0.0001). Incidence of new cases decreased over the years (3.62 [CI 3.58-3.67] in 2005 to 3.13 [CI 3.09-3.17] in 2012, per 1000 adult inhabitants/year, p<0.0001), with an increasing proportion of patients aged ≥85y (22.3% to 31.4%, p<0.0001). Mortality lowered over time in <75y incident cases, both in-hospital (5.15% to 4.36%, p<0.0001) and at 1-year (14.8% to 12.9%, p=0.0006). CONCLUSIONS The overall burden and mortality of HFH appear stable for more than a decade. However, from 2005 to 2012, there was a reduction of new, incident cases, with increasing age at first hospitalization. Meanwhile, both in-hospital and 1-year mortality decreased in patients aged <75y, possibly due to improved prevention and treatment.

Risk prediction for myocardial infarction via generalized functional regression models

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques.