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La difficoltà di fare memoria. Intervista a Giacomo Verde di Daniela Ielasi (2010), Archivio Giacomo Verde
No full textIntervista a Giacomo Verde di Daniela Ielasi (2010), Archivio Giacomo VerdeGiacomo Verde interviewed by Daniela Ielasi (2010), Archivio Giacomo Verd
Hospitalizations for Mental Health in Migrants and Italian Citizens in the Marche Region Between 2011 and 2023: a Population-Based Study Using Healthcare Utilization Databases (Mighty Project P2022asxkr)
No full textIntroduction
International migration is considered a complex and unstoppable phenomenon. Migrant population is a heterogeneous group of people who experience migration for different reasons and that are considered to be at increased risk of developing mental disorders [1]. Scientific evidence on migrants’ mental health is limited, as is knowledge of their access to and use of specific care services and treatments. As compared to natives, higher rates of involuntary hospitalization were found among migrants in most European countries [1,2].
Aim
The aim of the present study was to compare mental health hospitalization rates in Migrants and Italians in Marche Region during 2011-2023, and to investigate their differences according to demographic and clinical characteristics.
Methods
A cross-sectional population-based study on individuals hospitalized in psychiatric departments of all ages and resident in Marche Region in the period between 2011 and 2023 was conducted, using healthcare utilization databases (Hospital Discharge and Regional Beneficiaries databases). Residents were divided into Migrants from High Migratory Pressure Country (HMPC) and Italians, according to citizenship [3].
The primary diagnosis field of hospital discharge database was used to estimate the prevalence of different types of mental disorder using ICD-9 CM codes (290.*-319.*).
Annual age-standardized hospitalization rates were calculated for HMPC Migrants and Italians using the 2019 Italian population [4] as standard, using the direct method. Rates were stratified by sex and the Standardized Rate Ratios (SRR) with 95% Confidence Interval (95%CI) were calculated by sex and year of observations as ratio between HMPC and Italian rates. All data were processed in compliance with the European (GDPR, EU 2016/679) and national privacy laws (D.lgs. 196/2003 and subsequent amendments).
Results
A total of 59.881 hospitalizations were analyzed, 93.3% of which were of Italians. The mean age was higher in Italians than in HMPC (43.8 y versus 32.2 y). In both populations, hospitalizations most frequently referred to unmarried individuals (62% in Italians, 64% in HMPC) and to individuals with the lowest level of education (45% in Italians, 48% in HMPC).
Hospitalizations originated mainly from Emergency Departments admissions (25.4% Italians, 28.8% HMPC), medical indications (24.8% Italians 20.4% HMPC), prison (13.4% Italians, 13.7% HMPC) or hospitalization at the time of delivery (13.3% Italians 11.4% HMPC).
The most frequent diagnoses were schizophrenia and other functional psychosis (29.6% in Italians vs 31.6% HMPC), alcoholism and toxicomania (15.2% in Italians vs 21.7% HMPC) in both populations. Hospitalizations for depression in Italians and HMPC had similar proportions (11.1% vs 11.2%, respectively) but the frequency was more than twice as high in HMPC women as in HMPC men (8.3% vs 2.9%); the proportions of hospitalizations for mania and bipolar affective disorders hospitalizations were higher in HMPC women than in HMPC men (7.0% vs 2.1%); alcoholism and toxicomania were more frequent in HMPC women than in Italian women (11.5% vs 4.8%), and also than in HMPC men (11.5% vs 10.1%).
Excluding the 2020-2021 (the pandemic period) in which the lowest rates were recorded (2.5-2.5 and 2.3-2.0 for Italians men and women respectively, 1.4-1.7 and 1.6-2.2 for HMPC men and women respectively), the standardized rates (x1000 residents) of hospitalization ranged between 1.7 to 2.6 (in 2023 and 2019, respectively) for HMPC men and between 1.8 to 2.5 (in 2023 and 2017, respectively) for HMPC women; in Italians, rates ranged between 2.6 to 4.2 (2023 and 2011/2012, respectively) for men and between 2.1 to 3.5 (in 2023 and 2011, respectively) for women.
Standardized rate ratios (Figure 1) showed that HMPC reported lower hospitalizations for mental disorders than Italians over the entire study period for both genders. In females, the differences between the two populations were less pronounced than in males.
Conclusion
This study based on healthcare utilization databases allowed to quantify the use of inpatient psychiatric care in both Migrants and Italians in the Marche Region.
Our results showed a lower rate of hospitalization for mental disorders in Migrants than in Italians over the study period, with differences by gender and by type of mental disorder.
Considering that mental disorders are characterized by chronicity, diagnostic-therapeutic difficulties and strong family and social impact, further assessments are needed to identify the reasons for the use of hospitalization versus community-based care services in both populations.
 
Evaluating the Use of Cluster Analysis to Detect and Correct Selection Bias in Single-Center Observational Research on Voluntary Terminations of Pregnancy
No full textIntroduction
Voluntary termination of pregnancy (VTP) has always been debated within the general population. The circumstances leading women to choose VTP are multifaceted, and it remains challenging to identify patterns leading women to opt for this procedure [1, 2]. Up to now, numerous studies have focused on characterizing demographic or psychological profiles of women within specific geographic regions [3, 4]. Due to the sensitive nature of the decision and privacy requirements, studying population clusters in VTP presents inherent methodological limitations. While these challenges can be partially mitigated, they risk a potential selection bias during cohort formation. In these cases, a monocentric study may introduce biases that could arise primarily from the selection of the study centre and secondarily from the difficulty of properly recruiting a sample representative of the entire population.
In this context, cluster analysis conducted first on the general population and subsequently applied to the selected sample could clarify the nature of the selection process that occurred, thereby enabling better estimation of the final conclusions [1, 2]. In general, clustering demonstrated efficiency and feasibility to handle large datasets [5, 6]. In particular, the use of hierarchical clustering procedure is able to choose the optimal number of clusters in the population with a readily available stopping rules, without a priori selection of number of clusters [7]. This approach aligns with contemporary efforts to leverage data-driven methodologies in reproductive health research while addressing ethical considerations inherent to studies of private medical decisions.
Objective
The aim is to evaluate the effectiveness of a clustering method in detecting selection bias within a sample from a monocentric observational study, to generalize the findings from the small sample to the broader population and draw valid inferences from the results.
Methods
We used a retrospective observational database of 6559 women that carried out VTP in Apulia, from January 2023 to January 2024. Data was collected according to the regulations established by the National Surveillance System and were taken from the Health Information System of the Apulia region. The second database was collected, as a cross-sectional, observational study, conducted on 122 women > 18 years who underwent VTP in the Family Planning Unit of the "Di Venere-Fallacara" hospital, between November 2023 and January 2024. The assumption made were that the women of the selected sample are drawn from the same population.
Listwise deletion was applied, to handle missing value. A Multiple Correspondence Analysis (MCA) was first conducted on the population sample to identify the structure of relationships among variables, reducing dimensionality to two principal dimensions. The socio-demographic characteristics and the characteristic of the VTP event were chosen as variables.
We did a hierarchical cluster analysis using Hierarchical Clustering on Principle Components (HCPC) procedure [8,9]. We use the Euclidean metric for calculating distances between observations and the Ward\u27s method. The procedure was applied only on the population dataset. When the cluster has been defined, we predicted the MCA output on the selected sample [11,12].
We selected 10 casual sample of the 111 women from the entire population to compare differences in percentage among clusters. Chi-square tests were employed to compare the proportion of women in each cluster in both samples. We use the z-tests to account the difference in proportions among clusters between the selected sample of women and the population sample [13].
Results with a two-sided p-value < 0.05 were considered statistically significant. The statistical comparisons were done using SAS/STAT® Statistics version 9.4. The cluster analyses were developed using R software version 4.5 [8, 9, 14, 15].
Results
Following listwise deletion, MCA was conducted on a total of 6353 women. The total women in the selected sample with no missing value were 111. The optimal number of clusters was determined using hierarchical clustering procedures implemented through built-in package functions. The p-values across all examined variables are highly significant (p << 0.001), providing robust evidence that the HCPC analysis has identified clinically meaningful clusters, representing authentic subgroups within the population. Table 1 shows the percentage distribution of women across cluster.
Cluster 1 is overrepresented in both samples and subsamples (38% vs. 24%, median 41%). Cluster 2 shows minimal deviation between population and sample (8% vs. 9%), but the median percentage in 10 random subsamples deviates substantially (26%). Cluster 3 is significantly overrepresented in the sample selected (53% vs. 28%, median 27%). Cluster 4 is severely underrepresented in both the samples (0.9% vs. 39%, median 8%).
Conclusions
The selected sample overrepresents Cluster 3 (53% vs. 28%) and nearly excludes Cluster 4 (0.9% vs. 39%). Random subsamples did not align with population proportions, suggesting possible random deviations in the selected sample or from sample size. The observed selection bias might originate from the a priori selection in the monocentric study design. This methodological limitation introduces systematic differences between the sampled cohort and the target population, particularly affecting the generalizability of findings to underrepresented clusters. Beyond ensuring transparency regarding the sample\u27s generalizability, a solution involves implementing covariate balancing techniques [16, 17], balancing alongside synthetic oversampling methods [19] to address overrepresentation of specific variables. This dual approach aligns with causal inference frameworks while mitigating selection bias inherent in monocentric observational designs [20, 21]
Inclusion of Misanalyzed Stepped Wedge Trials in Meta-Analysis: Findings from a Simulation Study
No full textINTRODUCTION
The use of the stepped wedge cluster randomized trial (SWT) design to assess the effect of interventions in real-world settings has gained considerable popularity in recent years [1]. In this design, all clusters begin in the control condition, and the intervention is sequentially and randomly rolled out until all clusters transition into the intervention condition [2]. The unique characteristics of this design–for example, the unidirectional crossover to the intervention, variation in the timing and duration of exposure across clusters, and repeated measurements within clusters–pose additional statistical challenges that must be addressed when defining models to evaluate the effects of interventions [3].
In the context of meta-analysis, the inclusion of outcome data from SWTs requires careful methodological consideration. While analytic approaches for incorporating data from parallel cluster randomized trials have been well-documented [4], these methods are frequently misapplied in practice, with many analyses failing to properly account for clustering [5]. Given the added complexity of SWT designs, the potential for analytical errors, such as model misspecification [6], is expected to be higher. Currently, no established methods exist for synthesizing evidence from SWTs within meta-analyses, underscoring an important methodological gap that warrants attention.
OBJECTIVES
This study aims to examine the effects of including misanalyzed SWT in meta-analysis through a series of simulations.
METHODS
RCT and SWT datasets were simulated. RCT datasets were generated with a balanced two-arm design (treatment and control), assuming a normally distributed treatment effect with a mean difference of 0 and a fixed sample size of 1000 participants per study. SWT datasets were generated based on a repeated cross-sectional design with 5 time points, 50 observations per cluster, an average treatment effect of 0, an error variance (σ2) of 5, and a random cluster effect (τ2) of 1. Eighteen data-generating scenarios were considered, varying in number of clusters (20, 40 or 60), random treatment effect (η2 = 1, 2 or 3), and random time effect (absent (γ2 = 0) or present (γ2 = 1)). For simplicity, no correlation between random effects was included.
Each SWT dataset was analyzed using three linear mixed-effects models:
Unadjusted for time: y ~ treatment + (1 | cluster)
Hussey and Hughes model: y ~ treatment + time + (1 | cluster)
Extended model accounting for random treatment and random time effects, where appropriate:
y ~ treatment + time + (1 | cluster) + (0 + treatment | cluster) + (1 | clustertime)
For each model, the estimated treatment effect and standard error were extracted. Meta-analytic datasets were then constructed, each consisting of 3 RCTs and 1 SWT. To enable direct comparison across the three models, each SWT dataset was included in three separate but matched meta-analytic datasets per scenario. For each scenario, random-effects meta-analysis was done using the Sidik-Jonkman estimator of between-study heterogeneity. Model performance was assessed by calculating the mean of the pooled effect sizes, along with bias, model-based standard error, coverage, and the percentage of statistically significant results out of 1000 pooled effect sizes at p<0.05.
RESULTS
The model unadjusted for time consistently yielded the highest percentage of statistically significant results (based on 1000 meta-analyses per scenario), followed by the Hussey and Hughes and the extended model (Figure 1). Out of 18 scenarios, the unadjusted model exceeded the alpha threshold of 5% in 9 scenarios (50%), whereas neither the Hussey and Hughes model nor the extended model exceeded this threshold in any scenario. Compared to the extended model–considered the correctly specified model in the simulations–the model unadjusted for time yielded, on average, 3.5% more statistically significant results with a maximum difference of 5.8%. The Hussey and Hughes model produced a smaller difference, on average, 1.2% more statistically significant results with a maximum difference of 2.2%.
In terms of coverage, the extended model produced the highest coverage probabilities, while the unadjusted model yielded the lowest. In contrast, the pooled effect size and associated bias averaged over all iterations per scenario were 0 (Monte Carlo standard error ranging from 0 to 0.01), consistent with the treatment effect set during the data generation process.
CONCLUSIONS
The inclusion of SWT data analyzed using misspecified models in meta-analyses can lead to inflated false-positive findings and potentially misleading conclusions about the effect of interventions. Researchers doing meta-analysis that include SWTs should exercise caution in evaluating the appropriateness of the underlying analytic methods to ensure valid and reliable inferences
Effectiveness of Laser Therapy in Adults with Knee Osteoarthritis: A Bivariate Meta-Analysis of Placebo-Controlled Randomized Controlled Trials
No full textBackground: Knee pain represents the second most common musculoskeletal disorder after low back pain [1]. Knee osteoarthritis (OA) represents the leading cause of knee pain, as well as the most common site for OA, with a projected increase of 74.9% [uncertainty interval 59.4-89.9%] in the number of cases by 2050 compared to 2020 [2]. Laser therapy (LT) represents a non-invasive treatment modality that is frequently provided to adults with knee OA due to its anti-inflammatory effects, despite not being recommended in major OA treatment guidelines [3]. Trials usually report measures of function and pain in this population, which are correlated when measured on the same participants. Nevertheless, meta-analyses typically ignore such correlations and perform univariate meta-analyses on the two outcomes independently, which may impact the estimates and their precision [4]. Conversely, multivariate meta-analyses consider the correlation between different outcomes and have the potential for the estimate of one effect to borrow strength from the data on other effects of interest [5].
Objectives: To assess the effectiveness of LT compared to sham LT on function and pain in adults with knee OA, taking advantage of the correlation between the two outcomes.
Methods: PubMed and Embase were systematically searched from inception to May 6th, 2025 for placebo-controlled randomized controlled trials (RCTs) comparing LT to sham LT, alone or in addition to other conservative interventions (e.g., physiotherapy, exercise), in adults with knee OA. Studies were included if they measured function and pain with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Physical Function and Pain subscales, respectively. In case of multiple relevant study groups, the sample size of the control group was split accordingly. Within-group mean changes and corresponding standard deviations of change (SDs) were extracted. When not reported, mean changes were computed and the SD of change was calculated following the Cochrane Handbook guideline [6] and assuming a correlation of 0.5 between baseline and post-treatment values.
A frequentist random-effect bivariate meta-analysis was performed on function and pain at the end of treatment, assuming a common correlation between outcomes (r=0.824, from external reference [7]) for all included studies. Sensitivity analyses assuming different correlation values (i.e., 0.2, 0.4, 0.6) were also performed. Univariate random-effects meta-analyses for the two outcomes disjointly were performed estimating the between-study heterogeneity using the restricted maximum likelihood estimator (REML) and computing the 95% confidence interval (95%CI) using the Hartung-Knapp (HK) method. Mean differences (MD) and 95%CI were calculated for both univariate and bivariate meta-analysis. Bivariate estimates were compared with univariate estimates and the impact of bivariate meta-analysis was assessed using the Borrowing of Strength (BoS) index [5] and the estimated number of additional studies using correlated evidence [8]. All the analysis were performed in R version 4.4.1.
Results: From 233 individual records identified, 14 RCTs (16 effect sizes) involving 728 adults with knee OA were included. Main bivariate meta-analyses supported the effectiveness of LT compared to sham LT (function: MD -3.57, 95%CI -5.27 to -1.86; pain: MD -1.37, 95%CI -2.12 to -0.61). Similar estimates have been computed in univariate meta-analyses and in sensitivity analyses (Figure 1).
Compared to univariate meta-analyses, the main model improved the precision of the estimates (i.e., reduced standard error), with a BoS of 15.5% and 9.5% for function and pain, respectively. The extra information gained by using correlated evidence is similar to finding direct evidence from approximately three and two additional studies, respectively. When considering sensitivity analyses, bivariate models improved the precision of the estimate for function for every assumed correlation, while bivariate models with lower correlations (i.e., r<0.4) resulted in reduced precision and, consequently, larger confidence intervals compared to the univariate model for pain. Heterogeneity statistics were similar in univariate and bivariate models.
Conclusions: Compared to sham LT, the current findings support that LT improves function and pain in adults with knee OA, irrespective of the meta-analytic model considered. Despite univariate and multivariate models provide very similar results, with apparently little information gained from considering the correlation between the two outcomes, the latter may improve the precision of the estimates. Nevertheless, the extent to which multivariate meta-analyses may provide more precise estimates and thus narrower confidence intervals depends on the assumed correlation and the considered outcome. Future research, considering situations where studies do not provide information on all outcomes or provide different measures of selected outcomes (thus requiring standardization of the effect estimates), or extending the bivariate meta-analysis to more than two outcomes, may shed light on the impact of a multivariate meta-analytic approach compared to separate univariate meta-analyses in the field.
 
Informative Censoring in CDK4/6 Inhibitor Adjuvant Therapy for Early Breast Cancer: A Sensitivity Analysis of Invasive Disease-Free Survival in the monarchE Trial
No full textINTRODUCTION
The estimation of survival curves and hazard functions relies on the assumption of non-informative censoring—that is, the censoring time for an individual provides no further information about that person’s likelihood of survival at a future time, had the individual continued the study [1]. This assumption is inherently untestable [2] and, if violated, may introduce bias in the estimation of treatment effects, such as the hazard ratio (HR). This issue is particularly relevant in oncology trials evaluating disease recurrence or progression, where follow-up may be affected by treatment-related toxicity. Patients with early toxicity or clinical deterioration may be more likely to discontinue treatment and withdraw from follow-up, introducing potentially informative censoring.
This topic has gained attention in the interpretation of the monarchE trial—a large phase III study evaluating adjuvant CDK4/6 inhibitor in early breast cancer patients with hormone receptor-positive, HER2-negative, high-risk of recurrence —where the magnitude of benefit in invasive disease-free survival (IDFS) (HR = 0.68; 95% CI: 0.60-0.77) was not paralleled by an overall survival (OS) advantage (HR = 0.90; 95% CI: 0.75-1.09 ). Informative censoring due to toxicity-related dropouts has been suggested as a possible explanation for this discordance, potentially resulting in an overestimation of the IDFS benefit [3,4].
A previous reanalysis of the monarchE data applied a sensitivity analysis by balancing reverse Kaplan–Meier curves over the entire 72-month follow-up period, treating all excess censored patients in the experimental arm as if they had experienced the event [3]. This approach yielded an attenuated IDFS HR of 0.82 (95%CI: 0.72-0.94), casting doubt on the magnitude of the reported benefit. However, this strategy implicitly assumes that all censoring is informative, including late censoring events that are more plausibly administrative that may overcorrect the estimate.
AIM
We reanalysed the monarchE trial data by describing and comparing censoring patterns between treatment arms, then estimating the potential bias on the IDFS HR through clinically motivated sensitivity analyses.
METHODSIn the absence of individual patient data, pseudo-individual data were reconstructed from published Kaplan–Meier curves using Guyot’s algorithm [6].
To describe censoring patterns and compare arms, reverse Kaplan–Meier curves were used [7], which invert the roles of events and censoring and estimate the probability of remaining under observation over time, assuming no events occur.
Two sensitivity analyses were conducted using clinically motivated time intervals: i) the first sensitivity analysis (24 months) considered 0–24 months interval, corresponding to treatment duration, during which toxicity could cause dropouts; ii) the second sensitivity analysis (48 months) considered 0–48 months interval, representing the minimum time before administrative censoring could occur. Before 48 months, all censoring must be attributed to loss to follow-up.
For each analysis, the difference in censored patients between treatment arms—defined as the \u27excess\u27 censoring—was calculated. Assuming that this excess censoring could be attributed to treatment-related toxicity, an equal number of censored patients matching this excess were then randomly selected from the experimental arm and reclassified as having experienced the event at their censoring time. A new HR was subsequently estimated.
RESULTSIn the first 24 months, there were 27 excess censored patients in the experimental group (214 vs. 187), with 186 and 268 events in the experimental and control arms, respectively. Over 48 months, the difference in censoring decreased to 23 (351 vs. 328), with 362 and 523 events, respectively.
Reverse Kaplan–Meier analysis showed a marked increase in censoring rates from 48 months onwards, consistent with the expected start of administrative censoring. The censoring rates were similar between arms during the entire study period.
When reclassifying the excess censored patients in the 24-month window in the experimental arm as events, the IDFS HR changed from 0.68 (95% CI: 0.60–0.77) to 0.72 (95% CI: 0.64–0.82). Similar results were obtained in the 48-month analysis, with an HR of 0.72 (95% CI: 0.63–0.81).
To observe a less relevant effect (defined as an IDFS HR higher then 0.80), it was necessary to reclassify 80 censored patients in the experimental arm as events within the first 24 months—the duration of treatment, whose toxicity is suspected to cause the bias. This is a very large number, especially considering that just 186 events were actually observed in the same time interval.
CONCLUSIONS
Informative censoring is a potentially important source of bias in oncology trials. Reverse Kaplan–Meier curves help visualize temporal patterns in censoring, and sensitivity analyses based on clinically justified time windows provide realistic estimates of the possible bias. In the monarchE trial, the observed IDFS benefit appears robust even under conservative scenarios. Therefore, the discrepancy between IDFS and OS does not seem to be attributable to informative censoring and may be better explained by prolonged post-recurrence survival, as the discontinuation of CDK4/6 inhibitors at the time of progression limits their impact beyond recurrence. Extended survival after recurrence can make it inherently difficult to detect an OS benefit, even when a true delay in recurrence—as reflected by improved IDFS—is present [8]. Even where OS remains unchanged between treatment arms, improvements in IDFS can still provide meaningful value, extending the time patients remain free from chronic disease and its psychological, social, and financial burdens.
 
Clinical Outcomes and Survival Differences in People with Cystic Fibrosis Living in Europe
No full textINTRODUCTION
Cystic fibrosis (CF) is the one of the most common severe genetic disease in the world. Although advances in care have positively affected CF outcomes, both in terms of lung function and survival in higher-income countries (HIC), the situation remains alarming in lower-income countries (LIC) [1,2].
AIMS
This study aims in comparing characteristics of pwCF and their clinical outcomes in European countries with different income and to compare the situation before and after the introduction of CFTR modulators, a new class of drugs that completely transformed the landscape of CF.
METHODS
PwCF carrying the F508del mutation on at least 1 allele enrolled in the European Cystic Fibrosis Society Patient Registry (ECFSPR) were evaluated in 2017 and in 2022 according to 3 groups created by terciles of the gross national income (GNI) per capita of European countries included in the study (Low Income Countries -LIC-, Middle Income Countries -MIC-, High Income Countries -HIC-) for predicted percent forced expiratory volume (ppFEV1), underweight, and chronic Pseudomonas aeruginosa (Pa) infection. Survival was evaluated in 2013-2017 and 2018-2022. Generalised linear models and Cox regression models were fitted. Prediction of the median survival age were obtained from the regression model, according to the values of the covariates.
RESULTS
From the 31,723 pwCF reported in ECFSPR in 2022, 13.5% lived in LIC, 19.9% in MIC, and 66.6% in HIC. pwCF living in LIC had a significantly lower median survival age, reduced ppFEV1, higher prevalence of Pa infection and underweight compared to pwCF from MIC and HIC. Although some improvements have been observed between 2017 and 2022 in all country groups, the gap between lower and higher income countries became even larger in recent years, after the introduction of CFTR modulator.
Data modeling indicated that the effect of the country group almost “disappear” in the adjusted regression model, indicating that in an ideal situation where underweight status, chronic Pseudomonas Aeruginosa infection, use of CFTR modulator is the same among different countries, also their median survival age will be the same. Model prediction showed that avoiding underweight and Pa infection would increase survival by 42 years for pwCF living in LIC. Access to CFTR modulators would further increase their survival by 15 to 29 years depending on their nutrition and infection status, resulting in a survival up to 82 years in the best case scenario.
CONCLUSIONS
Big differences of survival in pwCF are observed according to the country where they live. The reduced survival of PwCF living in LICs can be improved with improvement in standard of care, such as optimal nutrition, anti-infectious care, access to CFTR modulators
Discontinuity of Sacubitril/Valsartan in a Cohort of Hospitalised Individuals for Heart Failure
No full textINTRODUCTION
Heart failure (HF) is a complex, multifactorial clinical syndrome that originates from an alteration in the pump function of the heart, either systolic and/or diastolic. This condition typically manifests itself with symptoms such as dyspnoea, easy fatigability and water retention, which are often responsible for a reduced quality of life. The prevalence of HF increases exponentially with increasing age [1] and is on of the most main cause of hospitalisation of geriatric population. This is associated with a high mortality risk and a significant burden of comorbidities [2-3]. The management of these patients is particularly complex and relevant in public health terms. Over the years, guidelines for the management of heart failure have undergone some modifications following the introduction of new drugs. Specifically, from 2021 the guidelines ESC [4] suggest, as first-line for HF patients, the use of sacubitril/valsartan. The efficacy of sacubitril/valsartan has been investigated HF in two randomizd clinical trial (RCT): PARADIGM-HF (Prospective Comparison of Angiotensin Receptor–Neprilysin Inhibitor with Angiotensin-Converting–Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure Trial) [5] and PARAGON-HF (Prospective Comparison of Angiotensin Receptor–Neprilysin Inhibitor with Angiotensin-Receptor Blockers Global Outcomes in HF with Preserved Ejection Fraction) [6].
However, few real world data is available about persistence of this pharmacological treatment.
AIM
To investigate the therapeutic discontinuity of sacubitril/valsartan in a cohort of individuals hospitalised for heart failure using data from the healthcare databases of the Lombardy region.
METHODS
The study was conducted according to a retrospective cohort design. Patients in the Lombardy Region aged between 40 and 80 years with a hospitalisation for heart failure (ICD9-CM codes: 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.91, 404.03, 404.13, 404.93, 427.4x and 428.x) in the period 2021-2022 were identified. For each subject, the first hospitalisation for HF was considered and the discharge date was taken as the index date. Patients with a hospitalisation for heart failure in the 5 years preceding the index date were excluded. The final cohort included all patients with a fill of sacubitril/valsartan in the first 90 days after index date.
All sacubitril/valsartan prescriptions dispensed to cohort members during follow-up were identified. The duration of each prescription was calculated by dividing the total amount of drug prescribed by the DDDs (Defined Daily Dose). From the index prescription onwards a patient was considered to be discontinuing (primary outcome) if the time lapse between the end of the prescription and the start of the next one was greater than or equal to 90 days. The first day of non-coverage of the drug was considered as the event date. A sensitivity analysis was performed considering a reduce length of 60 days for the window used to define discontinuation.
Each cohort member was followed from the index date until 31 December 2023. The proportion of discontinuation was estimated with its 95% confidence interval (95% CI). A log-binomial model was implemented to investigate the determinants of discontinuation including the following variables: gender, age at index date, the use of antihypertensive, antidiabetic, statins and antidepressants as well as Multisource Comorbidity Score (MC score at 3 classes) evaluated in the 5 years before index date. The relative risk (RR) and its confidence interval (95% CI) was reported.
RESULTS
Final cohort was composed by 1985 patients with a first admission for HF in the period 2021-2022. The mean age was 65 (SD 10) years and 77% were male. Of these, 27% were in treatment with antidiabetics while 45% with statins and 74% with antihypertensive drugs. In addition, 11% were treated with antidepressants. Patients in the lowest MC score class were 1177 (59%) while those in the highest class were 323 (16%). The S/V discontinuation proportion was 20% (95% CI 18% to 22%). We observed that for discontinuers the proportion of alternative pharmacological drug (ACE, ARB, CCB, BB and Diuretics) during the 90 days with no S/V treatment were lower respect to their proportion during the 3 months after index date suggesting either a clinician indication or the onset of collateral effect.
The model showed that increasing age was statistically associated with a lower risk of discontinuation (RR 0.987; 95% CI 0.976 to 0.998, p-value = 0.019) while the use of antidepressants increased the risk (RR 1.430; 95% CI 1.072 to 1.91, p-value = 0.015). When the outcome was defined by considering 60 days, age was no longer significant.
CONCLUSIONS
Preliminary data based on few years after ESC guidelines showed a quite high S/V discontinuation proportion similar to that reported in other studies [7]. Young age and use of antidepressant seems to increase the risk to interrupt an efficacy treatment like S/V. Continuous monitoring of healthcare data, in the next years, will allow efficiently to evaluate the effectiveness and persistence to S/V treatment
«Mai tutto è veramente detto». Memoria e riscrittura nelle prose di “Senza l’onore delle armi” [pp. 36-49]
No full textL’articolo si propone di rileggere dal punto di vista tematico e stilistico i cinque racconti di guerra e prigionia riuniti sotto il titolo di Senza l’onore delle armi, soffermandosi poi su alcuni passi in cui la memoria e la scrittura vengono riattivate senza che mai si esaurisca la materia della narrazione.The article aims to reread thematically and stylistically the five short stories of war and imprisonment gathered under the title of Senza l’onore delle armi, focusing then on passages in which memory and writing are reactivated without ever exhausting the subject matter of the narrative.
 
Metrical fairy-tales. Mario Benedetti’s anapests in Vanno via i tuoi occhi
No full textThis paper presents a metrical and thematic analysis of Vanno via i tuoi occhi by Mario Benedetti, starting from a single detail: the epigraph from La luna e i falò, «era morta la vecchia del Nido». The first section explores the connection between tragic aspects in Pavese and elegiac elements in Benedetti, focusing on the mythical and fairy-tale features. The aim is to determine whether Pavese’s influence extends to the metrical structure. The second section develops a generative account using Bracketed Grid Theory by Fabb and Halle (2008). The goal is to hypothesize an underlying anapaestic structure and connect it with fairy-tale and elegy elements.Il saggio propone un’analisi metrica e tematica di Vanno via i tuoi occhi, a partire da un dettaglio apparentemente esterno al testo: la citazione in epigrafe tratta da La luna e i falò, «era morta la vecchia del Nido». Nella prima sezione si esplora il rapporto fra il tragico in Pavese e l’elegiaco in Benedetti, con particolare attenzione all’immaginario fiabesco che caratterizza il testo scelto. Per tutto il saggio si inseguirà la seguente domanda: e se l’influenza di Pavese agisse a un livello metrico? Nella seconda sezione si applica al verso di Vanno via i tuoi occhi la Teoria della Griglia con Parentesi di Fabb e Halle (2008), ipotizzando un concetto di metricità latente per collegarlo – attraverso l’anapesto – alla favola e all’elegia