A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients

In this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to data arising from a clinical registry called STEMI Archive. A realistic quantification of the relationship between outcomes can be problematic for several reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient’s condition at admission time. Moreover, they can also directly influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent or biased parameter estimates. Secondly, covariate-outcomes relationships can exhibit nonlinear patterns. Provided that proper statistical methods for model fitting in such framework are available, it is possible to employ a simultaneous estimation approach to account for unobservable confounders. Such a framework can also provide flexible covariate structures and model the whole conditional distribution of the response

Mining discharge letters for diagnoses validation and quality assessment

We present two projects where text mining techniques are applied to free text documents written by clinicians. In the first, text mining is applied to discharge letters related to patients with diag-noses of acute myocardial infarction (by ICD9CM coding). The aim is extracting information on diagnoses to validate them and to integrate administrative databases. In the second, text mining is applied to discharge letters related to patients that received a diagnosis of heart failure (by ICD9CM coding). The aim is assessing the presence of follow-up instructions of doctors to patients, as an aspect of information continuity and of the continuity and quality of care. Results show that text mining is a promising tool both for diagnoses validation and quality of care as-sessment

Performance assessment using mixed effects models: a case study on coronary patient care

Provider profiling is the process of evaluation of the performance of hospitals, doctors and other medical practitioners in order to increase the quality of medical care. This paper reports statistical analyses carried out on data arising from a regular survey concerning patients admitted with an ST-elevation myocardial infarction diagnosis in one of a number of hospitals in the Milan area. The main aim is to determine process indicators to be used in health-care evaluation. Effective statistical support for clinical and organizational governance is obtained by analysing and modelling data from clinical registries. A grouping structure and a consequent ranking of hospitals is investigated, taking into account the fact that this kind of survey data are intrinsically grouped at first level by where patients are hospitalized.We compare three different techniques for hospital classification based, respectively, on: (a) traditional comparison of survival rates; (b) analysis of variance components in fitted generalized linear mixed effects models; and (c) non-parametric random effects estimation

Multilevel modeling of heterogeneity in math achievements: different class- and school-effects across Italian regions

Catching the differences in educational attainments between groups of students and across schools is becoming increasingly interesting. With the aim of assessing the extent of these differences in the context of Italian educational system, the paper applies multilevel modeling to a dataset containing detailed information of students\u2019 math attainments at grade 6 of primary school in the year 2011/12, provided by the Italian Institute for the Evaluation of Educational System (Invalsi)

Multivariate functional data depth measure based on variance-covariance operators

We introduce a generalization of the simplicial depth measure to multivariate functional data, exploiting the role of the variance-covariance operators in weighting the components that define the depth. We propose the use of this nonparametric method for supervised classification purpose

Depth measures for multivariate functional data with data-driven weights

The notion of statistical depth have recently been extended to the case of multivariate functional data. Its definition involves the choice of proper weights, averaging the univariate functional depths of each component. The choice of weights is crucial and must be carefully done according to the problem at hand. We describe a procedure that, starting from data, allows to compute a set of weights which are suitable for classification based on depths. These weights incorporate information on distances between covariance operators of the sub-populations.We show the validity of our strategy through a case study in which we perform supervised classification on ECG traces referring to both physiological and pathological subjects

A Bayesian random effects model for survival probabilities after acute myocardial infarction

Studies of variations in health care utilization and outcome involve the analysis of multi-level clustered data, considering in particular the estimation of a cluster-specific adjusted response, covariates e\uaeect and components of variance. Besides reporting on the extent of observed variations, those studies quantify the role of contributing factors including patients' and providers' characteristics. In addition, they may assess the relationship between health care process and outcomes. In this article we present a case-study, considering a Bayesian hierarchical generalized linear model, to analyze MOMI2 (Month Monitoring Myocardial Infarction in Milan) data on patients admitted with ST-elevation myocardial infarction diagnosis; both clinical registries and administrative databanks were used to predict survival probabilities. The major contributions of the paper consist in the comparison of the performance of the health care providers, as well as in the assessment of the role of patients' and providers' characteristics on survival outcome. In particular, we obtain posterior estimates of the regression parameters, as well as of the random effects parameters (the grouping factor is the hospital the patients were admitted to), through an MCMC algorithm. The choice of covariates is achieved in a Bayesian fashion as a preliminary step. Some issues about model fitting are discussed through the use of predictive tail probabilities and Bayesian residuals

Exploitation, integration and statistical analysis of Public Health Database and STEMI archive in Lombardia Region

We describe nature and aims of the Strategic Program \u201dExploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction\u201d. The main goal of the Program 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 Lombardia Region. Clinical data sets arise from observational studies about specific diseases, while administrative data arise from standardized and on-going procedures of data collection. The linkage between clinical and administrative databases enables Lombardia Region to create an efficient global system for collecting and storing integrated longitudinal data, to check them, to guarantee for their quality and to study them from a statistical perspectiv

Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival

Bayesian semiparametric logit models are fitted to grouped data related to in-hospital survival outcome of patients hospitalized with an ST -segment elevation myocardial infarction diagnosis. Dependent Dirichlet process priors are considered for modelling the random-effects distribution of the grouping factor (hospital of admission), to provide a cluster analysis of the hospitals. The clustering structure is highlighted through the optimal random partition that minimizes the posterior expected value of a suitable loss function. There are two main goals of the work: to provide model-based clustering and ranking of the providers according to the similarity of their effect on patients\u2019 outcomes, and to make reliable predictions on the survival outcome at the patient\u2019s level, even when the survival rate itself is strongly unbalanced. The study is within a project, named the \u2018Strategic program of Regione Lombardia\u2019, and is aimed at supporting decisions in healthcare policies

The Milano Network for Acute Coronary Syndromes and Emergency Services

Cardiologic emergences need specific equipment, a specific organization and operative networks connecting every single loop of the intervention chaim. The Authors' experience confirm the extreme usefulness of the ECG transmission in supporting the emergency system, the Milan network is trying to expand the use of thus technique also to the MSB as experimental. This study showed also big importance for the evaluation of quality standards, as the simple knowledge of our performances for treatments appeared a strong incentive to monitor and improve decisional strategies