Francesca Ieva | Politecnico di Milano (original) (raw)
Papers by Francesca Ieva
Contributions to Statistics, 2010
We describe nature and aims of the Strategic Program "Exploitation, integration and study of curr... more We describe 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 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 perspective.
Aims. To achieve a reduction of time to reperfusion through the organization of an interhospital ... more Aims. To achieve a reduction of time to reperfusion through the organization of an interhospital net- work and the involvement of the Regional Health Authority. Methods. Four major endpoints were identified: institutional governance action, clinical manage- ment of acute ST-elevation myocardial infarction (STEMI), priority actions for cardiac arrest and early defibrillation, actions to avoid the delay related to decision-making, and
The International Journal of Biostatistics, 2015
In this paper we develop statistical methods to compare two independent samples of multivariate f... more In this paper we develop statistical methods to compare two independent samples of multivariate functional data that differ in terms of covariance operators. In particular we generalize the concept of depth measure to this kind of data, exploiting the role of the covariance operators in weighting the components that define the depth. Two simulation studies are carried out to validate the robustness of the proposed methods and to test their effectiveness in some settings of interest. We present an application to Electrocardiographic (ECG) signals aimed at comparing physiological subjects and patients affected by Left Bundle Branch Block. The proposed depth measures computed on data are then used to perform a nonparametric comparison test among these two populations. They are also introduced into a generalized regression model aimed at classifying the ECG signals.
Contributions to Statistics, 2013
In this work we describe design, aims and contents of the ST-segment Elevation Myocardial Infarct... more 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 on going 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.
Advances in Theoretical and Applied Statistics, 2013
We present a pilot data mining analysis on the subset of the Public Health Database (PHD) of Lomb... more We present a pilot data mining analysis on the subset of the Public Health Database (PHD) of Lombardia Region concerning hospital discharge data relative to Acute Myocardial Infarctions without ST segment elevation (NON-STEMI). The analysis is carried out using nonlinear semi-parametric and parametric mixed effects models, in order to detect different patterns of growth in the number of NON-STEMI diagnoses within the 30 largest clinical structures of Lombardia Region, along the time period 2000-2007. The analysis is a seminal example of statistical support to decision makers in clinical context, aimed at monitoring the diffusion of new procedures and the effects of health policy interventions.
Studies of variations in health care utilization and outcome involve the analysis of multilevel d... more Studies of variations in health care utilization and outcome involve the analysis of multilevel data, considering in particular prediction of a specific response, and estimate of covariates effect and components of variance. Those studies quantify the role of contributing factors including patients and providers characteristics and may assess the relationship between health-care process and outcomes. We consider Bayesian generalized linear mixed models to analyze data on patients admitted with ST-elevation myocardial infarction (STEMI) diagnosis in Regione Lombardia hospitals. Clinical registries and administrative databanks were used to predict both in-hospital survival and ST-resolution probability.We fit logit models for the in-hospital survival and ST-resolution probability with groupingeffect (the hospital), under a semiparametric prior. In particular, random effects with dependent Dirichlet process prior are assumed, allowing to include specific hospital-covariates and then en...
Contributions to Statistics, 2012
Biomedical optics express, 2015
We propose a new algorithm, based on a linear regression model, to statistically estimate the hem... more We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.
ABSTRACT Provider profiling is the evaluation process of the performance of hospitals, doctors, a... more ABSTRACT Provider profiling is the evaluation process of the performance of hospitals, doctors, and other medical practitioners, aimed at increasing the quality of medical care. Within this context, performance indicators for assessing quality in healthcare contexts have drawn more and more attention over the last years, since they enable researchers to measure all the relevant features of the healthcare process of interest, including the performances of healthcare providers, clinical outcomes and disease incidence. The purpose of this work is to highlight how advanced statistical methods can be used to model complex data coming from a clinical survey, in order to assess hospitals and healthcare providers performances in treating patients affected by STEMI (ST segment Elevation Myocardial Infarction), a disease with a very high incidence all over the world and where being well timed makes the difference in terms of patients’ survival and later quality of life. The same methods are also used to classify hospitals according to the evaluation of their performances, compared with gold standards and guidelines. To these aims, we fit different models, trying to enhance the grouping structure of data, where the hospital of admission is the grouping factor for the statistical units, represented by patients. In these models we introduce performance indicators in order to adjust for different patterns of care and to compare their effectiveness in treating patients. Also the adjustment for case-mix (i.e., patients’ features at admission) is considered. The clustering structure induced by the model assumptions is then investigated. In particular, we propose three different methods to evaluate hospitals performances: in the first one we estimate the in-hospital survival rates after fitting a Generalized Linear Model, using suitable indexes for testing the presence/absence of outliers; in the second one we fit a Generalized Linear Mixed Effects Model to explain in-hospital survival outcome by means of suitable patient’s covariates and process indicators, with a parametric random effect accounting for hospital influence; then we perform an explorative classification analysis implementing a clustering procedure on the point estimates of hospital effects provided by the model. Finally, in the third case we classify hospitals according to the variance components analysis of the random effect estimates, where nonparametric assumptions have been considered. In fact, the discreteness of the random effect induces an automatic clustering that can be interpreted as identifying hospitals with similar effects on patients’ outcome. The survey we consider for the case study, named STEMI Archive, is a clinical observational registry concerning patients admitted with STEMI diagnosis in any hospital of our regional district, i.e., Regione Lombardia. This registry has been designed and funded within a scientific project, named Strategic Program, aimed at the exploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction. The nearly unanimous agreement of results obtained implementing the three methods on data supports the idea that a real clustering structure in groups exists. Such methods can provide useful decisional support to people in charge with healthcare planning.
Contributions to Statistics, 2010
We describe nature and aims of the Strategic Program "Exploitation, integration and study of curr... more We describe 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 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 perspective.
Aims. To achieve a reduction of time to reperfusion through the organization of an interhospital ... more Aims. To achieve a reduction of time to reperfusion through the organization of an interhospital net- work and the involvement of the Regional Health Authority. Methods. Four major endpoints were identified: institutional governance action, clinical manage- ment of acute ST-elevation myocardial infarction (STEMI), priority actions for cardiac arrest and early defibrillation, actions to avoid the delay related to decision-making, and
The International Journal of Biostatistics, 2015
In this paper we develop statistical methods to compare two independent samples of multivariate f... more In this paper we develop statistical methods to compare two independent samples of multivariate functional data that differ in terms of covariance operators. In particular we generalize the concept of depth measure to this kind of data, exploiting the role of the covariance operators in weighting the components that define the depth. Two simulation studies are carried out to validate the robustness of the proposed methods and to test their effectiveness in some settings of interest. We present an application to Electrocardiographic (ECG) signals aimed at comparing physiological subjects and patients affected by Left Bundle Branch Block. The proposed depth measures computed on data are then used to perform a nonparametric comparison test among these two populations. They are also introduced into a generalized regression model aimed at classifying the ECG signals.
Contributions to Statistics, 2013
In this work we describe design, aims and contents of the ST-segment Elevation Myocardial Infarct... more 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 on going 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.
Advances in Theoretical and Applied Statistics, 2013
We present a pilot data mining analysis on the subset of the Public Health Database (PHD) of Lomb... more We present a pilot data mining analysis on the subset of the Public Health Database (PHD) of Lombardia Region concerning hospital discharge data relative to Acute Myocardial Infarctions without ST segment elevation (NON-STEMI). The analysis is carried out using nonlinear semi-parametric and parametric mixed effects models, in order to detect different patterns of growth in the number of NON-STEMI diagnoses within the 30 largest clinical structures of Lombardia Region, along the time period 2000-2007. The analysis is a seminal example of statistical support to decision makers in clinical context, aimed at monitoring the diffusion of new procedures and the effects of health policy interventions.
Studies of variations in health care utilization and outcome involve the analysis of multilevel d... more Studies of variations in health care utilization and outcome involve the analysis of multilevel data, considering in particular prediction of a specific response, and estimate of covariates effect and components of variance. Those studies quantify the role of contributing factors including patients and providers characteristics and may assess the relationship between health-care process and outcomes. We consider Bayesian generalized linear mixed models to analyze data on patients admitted with ST-elevation myocardial infarction (STEMI) diagnosis in Regione Lombardia hospitals. Clinical registries and administrative databanks were used to predict both in-hospital survival and ST-resolution probability.We fit logit models for the in-hospital survival and ST-resolution probability with groupingeffect (the hospital), under a semiparametric prior. In particular, random effects with dependent Dirichlet process prior are assumed, allowing to include specific hospital-covariates and then en...
Contributions to Statistics, 2012
Biomedical optics express, 2015
We propose a new algorithm, based on a linear regression model, to statistically estimate the hem... more We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.
ABSTRACT Provider profiling is the evaluation process of the performance of hospitals, doctors, a... more ABSTRACT Provider profiling is the evaluation process of the performance of hospitals, doctors, and other medical practitioners, aimed at increasing the quality of medical care. Within this context, performance indicators for assessing quality in healthcare contexts have drawn more and more attention over the last years, since they enable researchers to measure all the relevant features of the healthcare process of interest, including the performances of healthcare providers, clinical outcomes and disease incidence. The purpose of this work is to highlight how advanced statistical methods can be used to model complex data coming from a clinical survey, in order to assess hospitals and healthcare providers performances in treating patients affected by STEMI (ST segment Elevation Myocardial Infarction), a disease with a very high incidence all over the world and where being well timed makes the difference in terms of patients’ survival and later quality of life. The same methods are also used to classify hospitals according to the evaluation of their performances, compared with gold standards and guidelines. To these aims, we fit different models, trying to enhance the grouping structure of data, where the hospital of admission is the grouping factor for the statistical units, represented by patients. In these models we introduce performance indicators in order to adjust for different patterns of care and to compare their effectiveness in treating patients. Also the adjustment for case-mix (i.e., patients’ features at admission) is considered. The clustering structure induced by the model assumptions is then investigated. In particular, we propose three different methods to evaluate hospitals performances: in the first one we estimate the in-hospital survival rates after fitting a Generalized Linear Model, using suitable indexes for testing the presence/absence of outliers; in the second one we fit a Generalized Linear Mixed Effects Model to explain in-hospital survival outcome by means of suitable patient’s covariates and process indicators, with a parametric random effect accounting for hospital influence; then we perform an explorative classification analysis implementing a clustering procedure on the point estimates of hospital effects provided by the model. Finally, in the third case we classify hospitals according to the variance components analysis of the random effect estimates, where nonparametric assumptions have been considered. In fact, the discreteness of the random effect induces an automatic clustering that can be interpreted as identifying hospitals with similar effects on patients’ outcome. The survey we consider for the case study, named STEMI Archive, is a clinical observational registry concerning patients admitted with STEMI diagnosis in any hospital of our regional district, i.e., Regione Lombardia. This registry has been designed and funded within a scientific project, named Strategic Program, aimed at the exploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction. The nearly unanimous agreement of results obtained implementing the three methods on data supports the idea that a real clustering structure in groups exists. Such methods can provide useful decisional support to people in charge with healthcare planning.