Learning high-order interactions for polygenic risk prediction.

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.

Total embedding of spermatic cord and hilar soft tissue in orchiectomy for seminoma: does the extensive sampling improve pathologic risk factors?

Pure seminomas represent the majority of testicular germ cell tumors and accurate diagnosis and staging require an accurate sampling of radical orchiectomy specimens. The aim of our study is to find the most informative gross sampling method for orchiectomy specimens. We performed the extensive sampling of 88 radical orchiectomy specimens embedding in their entirety testicular hilum, rete testis, hilar soft tissue, and spermatic cord. We examined the impact of this procedure on tumor stage, prognostic parameters (lymphovascular invasion and infiltration of rete testis, epididymis, tunica vaginalis, and spermatic cord), and their relationship with recurrence. Eighty-eight seminomas from 88 radical orchiectomies were sampled. Seventy-seven cases (87.5%) presented as clinical stage I and 11 cases (12.5%) as clinical stage II. The follow-up period range was 18-54 months and 82 patients (93.2%) had a minimum of 2-year follow-up. Tumor size ranged from 0.4 to 16 cm (mean 3.6) requiring a mean of 7.1 sections for entire tumoral sampling. Epididymis required 2 to 8 sections (mean 3.3), and hilum and hilar soft tissues 2 to 9 sections (mean 3.4). Epididymal infiltration and lymphovascular invasion resulted significant at multivariate analysis generating a receiver operating characteristic (ROC) curve with area under curve of 0.778. All the other parameters (except for pagetoid rete testis infiltration) were significant to predict metastasis only at univariate analysis. Extensive sampling of radical orchiectomy specimens does not improve the accuracy of staging in pure seminomas. Lymphovascular invasion and epididymal infiltration are useful to predict metastasis.

Stability over time of the "hospital effect" on 30-day unplanned readmissions: Evidence from administrative data.

Past studies showed that hospital characteristics affect hospital performance in terms of 30-day unplanned readmissions, proving the existence of a "hospital effect". However, the stability over time of this effect has been under-investigated. This study offers new evidence about the stability over time of the hospital effect on 30-day unplanned readmissions. Using 78,907 heart failure (HF) records collected from 116 hospitals in the Lombardy Region (Northern Italy) over three years (2010-2012), this study analysed hospital performance in terms of 30-day unplanned readmissions. Hospitals with unusually high and low readmission rates were identified through multi-level regression that combined both patient and hospital covariates in each year. Our results confirm that although hospital covariates - and the connected managerial choices - affect the 30-day unplanned readmissions of a specific year, their effect is not stable in the short-term (3 years). This has important implications for pay-for-performance schemes and quality improvement initiatives.

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity.

AIM: To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). MATERIALS AND METHODS: Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ≥10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). RESULTS: Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. CONCLUSIONS: Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models.

Treatment prescription, adherence, and persistence after the first hospitalization for heart failure: A population-based retrospective study on 100785 patients.

BACKGROUND: This study evaluates, in a real-world setting, to what extent the recommended therapies by international guidelines, are prescribed after a first hospitalization for heart failure (HF), and to analyse adherence and persistence, and the effect of treatment adherence on mortality and re-hospitalization. METHODS: From the Lombardy healthcare administrative database, we analysed patients discharged after their incident HF, from 2000 to 2012. Adherence was defined as the proportion of days covered (PDC) ≥80% adjusted for hospitalizations and persistence as the absence of discontinuation of therapy for >30 days. A logit model was used to determine the effect of patients' adherence on mortality and readmissions. RESULTS: Of 100422 HF patients (52% males, age 75 ± 12 years), 86846 (87%) had a prescription for angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers (ACE/ARBs), 64135 (64%) for beta-blockers (BB), and 36893 (37%) for mineralocorticoid receptor antagonists (MRAs), as mono-, bi- or tri-therapy. In patients on monotherapy, PDC was 78 ± 22% for ACE/ARBs, 69 ± 29% for BB and 54 ± 29% for MRAs; in those on bi-therapy, PDC was 63 ± 31% for ACEI/ARBs+BB, 41 ± 29% for ACEI/ARBs+MRAs, and 40 ± 26% for MRAs+BB; for patients on tri-therapy, PDC was 42 ± 28%. Medication persistence was present in 47% of patients treated with ACEI/ARBs, in 35% of patients treated with BB and in 14% of patients treated with MRAs. Re-hospitalizations and in mortality were significantly reduced in adherent patients (p < 0.000). CONCLUSIONS: Polypharmacy is associated with an increased rate of non-adherence and non-persistence in incident HF. Non-adherence is associated with an increased risk of mortality and re-hospitalizations.

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort.

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.

Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model.

BACKGROUND: Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. METHODS: Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion. RESULTS: We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death). CONCLUSIONS: The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance.

Non-parametric frailty Cox models for hierarchical time-to-event data.

We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation-Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.

Impact of in-hospital cardiac rehabilitation on mortality and readmissions in heart failure: A population study in Lombardy, Italy, from 2005 to 2012.

AIMS: The 2016 European guidelines for the diagnosis and treatment of heart failure classified cardiac rehabilitation as a mandatory class I intervention. We aimed to analyse in heart failure patients the impact of an in-hospital cardiac rehabilitation programme on all-cause mortality and readmissions. METHODS: From the Lombardy healthcare administrative database, we analysed in patients with incident heart failure, from 2005 to 2012, the number of all hospitalisations, cardiac rehabilitation admissions, post-discharge deaths, outpatient drug prescriptions and visits. We divided patients into hospitalised for heart failure in acute care only (group A) versus patients with one or more admission to cardiac rehabilitation for an in-hospital cardiac rehabilitation programme (group B). RESULTS: Of 140,552 incident cases, 100,843 (71%) were in group A and 39,709 (29%) in group B. Patients in group B had 3.26 ± 1.78 admissions to acute care before referral to an in-hospital cardiac rehabilitation programme. Male gender, age in women and comorbidities (more than two) were higher in group B ( P < 0.0001). Patients in group B had a higher number of interventional procedures ( P < 0.0001), drug prescription and outpatient visit rate ( P < 0.0001). Total mortality was 30% in group A versus 29% in group B. At Cox and logistic regression analyses, after adjustment for covariates, group B had a significantly lower risk of mortality (hazard ratio 0.5768, 95% confidence interval 0.5650-0.5888, P < 0.0001) and readmissions (0.7997, 0.7758-0.8244, P < 0.0001) than group A. CONCLUSION: This study showed in a large population of heart failure patients that in-hospital cardiac rehabilitation is associated with a reduction of all-cause mortality and rehospitalisations in heart failure. Given its potential significant benefit, referral of heart failure patients to an in-hospital cardiac rehabilitation programme should be promoted.

Serum human epididymis protein 4 vs. carbohydrate antigen 125 in ovarian cancer follow-up.

BACKGROUND: The addition of human epididymis protein 4 (HE4) to carbohydrate antigen 125 (CA125) in ovarian cancer (OC) assessment has been proposed. We compared the clinical value of biomarker changes in a prospective series of patients undergoing OC monitoring. METHODS: We studied 43 patients (79% post-menopausal), followed for 3.5 ±â€¯3.1 years. Serous OC was prevalent (53.5%), with 81.4% of patients diagnosed at late stages. Both cut-offs and reference change values (RCV) were used for assessing significant marker changes. RESULTS: The use of cut-offs for CA125 and HE4 interpretation appeared equally fitting the evaluation of disease progression defined according to running guidelines, performing better than RCV criterion. However, both markers were simultaneously over cut-offs only in 46% of samples and changed in agreement in 35% of cases. The inspection of individual longitudinal trends indicated as main causes of disagreement the influence of renal impairment on HE4 concentrations and the more significant rate of decrease of CA125 vs. HE4 concentrations early after treatment. CA125 and HE4 changes according to RCV were not predictive of OC progression. CONCLUSIONS: CA125 appears the most reliable biomarker for OC monitoring, whereas HE4 contributes additional information only in a minority of cases.

Urn models for response-adaptive randomized designs: a simulation study based on a non-adaptive randomized trial.

Recently, response-adaptive designs have been proposed in randomized clinical trials to achieve ethical and/or cost advantages by using sequential accrual information collected during the trial to dynamically update the probabilities of treatment assignments. In this context, urn models-where the probability to assign patients to treatments is interpreted as the proportion of balls of different colors available in a virtual urn-have been used as response-adaptive randomization rules. We propose the use of Randomly Reinforced Urn (RRU) models in a simulation study based on a published randomized clinical trial on the efficacy of home enteral nutrition in cancer patients after major gastrointestinal surgery. We compare results with the RRU design with those previously published with the non-adaptive approach. We also provide a code written with the R software to implement the RRU design in practice. In detail, we simulate 10,000 trials based on the RRU model in three set-ups of different total sample sizes. We report information on the number of patients allocated to the inferior treatment and on the empirical power of the t-test for the treatment coefficient in the ANOVA model. We carry out a sensitivity analysis to assess the effect of different urn compositions. For each sample size, in approximately 75% of the simulation runs, the number of patients allocated to the inferior treatment by the RRU design is lower, as compared to the non-adaptive design. The empirical power of the t-test for the treatment effect is similar in the two designs.

Immunomodulatory Drugs in the Context of Autologous Hematopoietic Stem Cell Transplantation Associate With Reduced Pro-tumor T Cell Subsets in Multiple Myeloma.

Immunomodulatory drugs (IMiDs) are effective therapeutics for multiple myeloma (MM), where in different clinical settings they exert their function both directly on MM cells and indirectly by modulating immune cell subsets, although with not completely defined mechanisms. Here we studied the role of IMiDs in the context of autologous hematopoietic stem cell transplantation on the T cell subset distribution in the bone marrow of newly diagnosed MM patients. We found that after transplantation pro-tumor Th17-Th1 and Th22 cells and their related cytokines were lower in patients treated with IMiDs during induction chemotherapy compared to untreated patients. Of note, lower levels of IL-17, IL-22, and related IL-6, TNF-α, IL-1ß, and IL-23 in the bone marrow sera correlated with treatment with IMiDs and favorable clinical outcome. Collectively, our results suggest a novel anti-inflammatory role for IMiDs in MM.

Regional variation in hospitalisation and mortality in heart failure: comparison of England and Lombardy using multistate modelling.

Heart failure (HF) is a common, serious chronic condition with high morbidity, hospitalisation and mortality. The healthcare systems of England and the northern Italian region of Lombardy share important similarities and have comprehensive hospital administrative databases linked to the death register. We used them to compare admission for HF and mortality for patients between 2006 and 2012 (n = 37,185 for Lombardy, 234,719 for England) with multistate models. Despite close similarities in age, sex and common comorbidities of the two sets of patients, in Lombardy, HF admissions were longer and more frequent per patient than in England, but short- and medium-term mortality was much lower. English patients had more very short stays, but their very elderly also had longer stays than their Lombardy counterparts. Using a three-state model, the predicted total time spent in hospital showed large differences between the countries: women in England spent an average of 24 days if aged 65 at first admission and 19 days if aged 85; in Lombardy these figures were 68 and 27 days respectively. Eight-state models suggested disease progression that appeared similar in each country. Differences by region within England were modest, with London patients spending more time in hospital and having lower mortality than the rest of England. Whilst clinical practice differences plausibly explain these patterns, we cannot confidently disentangle the impact of alternatives such as coding, casemix, and the availability and use of non-hospital settings. We need to better understand the links between rehospitalisation frequency and mortality.

A multi-state approach to patients affected by chronic heart failure : The value added by administrative data.

Healthcare administrative databases are becoming more and more important and reliable sources of clinical and epidemiological information. They are able to track several interactions between a patient and the public healthcare system. In the present study, we make use of data extracted from the administrative data warehouse of Regione Lombardia, a region located in the northern part of Italy whose capital is Milan. Data are within a project aiming at providing a description of the epidemiology of Heart Failure (HF) patients at regional level, to profile health service utilization over time, and to investigate variations in patient care according to geographic area, socio-demographic characteristic and other clinical variables. We use multi-state models to estimate the probability of transition from (re)admission to discharge and death adjusting for covariates which are state dependent. To the best of our knowledge, this is the first Italian attempt of investigating which are the effects of pharmacological and outpatient cares covariates on patient's readmissions and death. This allows to better characterise disease progression and possibly identify what are the main determinants of a hospital admission and death in patients with Heart Failure.

Laparoscopic Retroperitoneal Lymph Node Dissection for Clinical Stage I Nonseminomatous Germ Cell Tumors of the Testis: Safety and Efficacy Analyses at a High Volume Center.

PURPOSE: The prognosis of stage I nonseminomatous germ cell tumor of the testis is favorable. Early and late side effects of treatment may affect quality of life and survival. We determined the tolerability, safety and efficacy of laparoscopic retroperitoneal lymph node dissection in patients with stage I nonseminomatous germ cell tumor of the testis at a high volume center. MATERIALS AND METHODS: Unilateral laparoscopic retroperitoneal lymph node dissection was prospectively recorded in 225 patients from 2000 to 2014. Since 2007, patients have been treated at a multidisciplinary clinic and were proposed surgery as an alternative to surveillance or adjuvant chemotherapy. The indication for adjuvant chemotherapy changed during the study period. Descriptive statistics and regression analyses were used to evaluate the domains of safety and oncologic outcomes. RESULTS: A total of 221 patients were evaluable. Median operative time was 200 minutes. Conversion to open surgery was done in 20 cases (9%). A median of 14 nodes (IQR 11-20) was retrieved. Grade greater than 2 complications in 8 cases (3.6%) increased as the number of retrieved nodes increased. Antegrade ejaculation was maintained in 98.6% of patients. Nodal metastases were found in 29 patients (13%), of whom 7 underwent adjuvant chemotherapy. There were 14 recurrences (6.3%), including 8 of 192 (4.2%) associated with no nodal metastases and 6 of 22 (27.3%) associated with nodal metastases in patients not undergoing adjuvant chemotherapy. At regression analyses lymph node ratio was the only significant factor predictive of recurrence and of the administration of any chemotherapy (each p <0.001). Operative time, the number of retrieved nodes and conversions improved with time. CONCLUSIONS: In the context of a high volume center laparoscopic retroperitoneal lymph node dissection was safe and its oncologic efficacy was comparable to that of open surgery. Select patients with stage I nonseminomatous germ cell tumor could be offered laparoscopic retroperitoneal lymph node dissection as an alternative to other options.

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.

Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study.

Several techniques are being investigated as a complement to screening mammography, to reduce its false-positive rate, but results are still insufficient to draw conclusions. This initial study explores time domain diffuse optical imaging as an adjunct method to classify non-invasively malignant vs benign breast lesions. We estimated differences in tissue composition (oxy- and deoxyhemoglobin, lipid, water, collagen) and absorption properties between lesion and average healthy tissue in the same breast applying a perturbative approach to optical images collected at 7 red-near infrared wavelengths (635-1060 nm) from subjects bearing breast lesions. The Discrete AdaBoost procedure, a machine-learning algorithm, was then exploited to classify lesions based on optically derived information (either tissue composition or absorption) and risk factors obtained from patient's anamnesis (age, body mass index, familiarity, parity, use of oral contraceptives, and use of Tamoxifen). Collagen content, in particular, turned out to be the most important parameter for discrimination. Based on the initial results of this study the proposed method deserves further investigation.

Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure.

We analyse data collected from the administrative datawarehouse of an Italian regional district (Lombardia) concerning patients affected by Chronic Heart Failure. The longitudinal data gathering for each patient hospital readmissions in time, as well as patient-specific covariates, is studied as a realization of non homogeneous Poisson process. Since the aim behind this study is to identify groups of patients behaving similarly in terms of disease progression and then healthcare consumption, we conjectured the time segments between two consecutive hospitalizations to be Weibull distributed in each hidden cluster. Adding a frailty term to take into account the within subjects unknown variability, the corresponding patient-specific hazard functions are reconstructed. Therefore, the comprehensive distribution for each time to event variable is modelled as a Weibull Mixture. We are then able to easily interpret the related hidden groups as healthy, sick, and terminally ill subjects.

Validation of the Italian Version of the Haemophilia Activities List.

BACKGROUND: The aim of this study was to provide an Italian version of the Haemophilia Activities List (HAL) and check its reliability in Italian medical centers. METHODS: The Italian version of this assessment was administered to 80 patients (aged 18-65 years) affected by haemophilia A and B (moderate or severe). The validation was accomplished by comparing it to the revised and expanded Arthritis Impact Measurement Scales (AIMS2). RESULTS: The internal consistency of the Italian version of the HAL had statistically high results: Cronbach's α 0.957-0.579. The highest internal consistency was measured in the domains 'leg functionality' and in the overall points of the HAL questionnaire. The correlation between the AIMS2, which has been translated into Italian, and the version of the HAL questionnaire that we proposed, yielded good results for the following correlations: AIMS2 all and HAL overall (r = 0.64), AIMS2 physical function and HAL overall (r = 0.66), AIMS2 pain and HAL overall (r = 0.66). CONCLUSION: The Italian version of the HAL questionnaire presents both internal coherence and convergent validity. It can be used in addition to other functional tests to measure outcomes in moderate and severe haemophiliac diseases or to determine the quality of life as observed in the everyday life of patients.

Methodological issues on the use of administrative data in healthcare research: the case of heart failure hospitalizations in Lombardy region, 2000 to 2012.

BACKGROUND: Administrative data are increasingly used in healthcare research. However, in order to avoid biases, their use requires careful study planning. This paper describes the methodological principles and criteria used in a study on epidemiology, outcomes and process of care of patients hospitalized for heart failure (HF) in the largest Italian Region, from 2000 to 2012. METHODS: Data were extracted from the administrative data warehouse of the healthcare system of Lombardy, Italy. Hospital discharge forms with HF-related diagnosis codes were the basis for identifying HF hospitalizations as clinical events, or episodes. In patients experiencing at least one HF event, hospitalizations for any cause, outpatient services utilization, and drug prescriptions were also analyzed. RESULTS: Seven hundred one thousand, seven hundred one heart failure events involving 371,766 patients were recorded from 2000 to 2012. Once all the healthcare services provided to these patients after the first HF event had been joined together, the study database totalled about 91 million records. Principles, criteria and tips utilized in order to minimize errors and characterize some relevant subgroups are described. CONCLUSIONS: The methodology of this study could represent the basis for future research and could be applied in similar studies concerning epidemiology, trend analysis, and healthcare resources utilization.