Temporal validation of a Generalized Additive2 Model (GA2M) to assess the risk of Chronic Kidney Disease (CKD).

OBJECTIVE: To assess the temporal validity of a model predicting the risk of Chronic Kidney Disease (CKD) using Generalized Additive2 Models (GA2M). MATERIALS: We adopted the Italian Health Search Database (HSD) with which the original algorithm was developed and validated by comparing different machine learnings models. METHODS: We selected all patients aged >=15 being active in HSD in 2019. They were followed up until December 2022 so being updated with three years of data collection. Those with prior diagnosis of CKD were excluded. A GA2M-based algorithm for CKD prediction was applied to this cohort in order to compare observed and predicted risk. Area Under Curve (AUC) and Average Precision (AP) were calculated. RESULTS: We obtained an AUC and AP equal to 88% and 30%, respectively. DISCUSSION: The prediction accuracy of the algorithm was largely consistent with that obtained in our prior work which was based on a different time-window for data collection. We therefore underlined and demonstrated the relevance of temporal validation for this prediction tool. CONCLUSION: The GA2M confirmed its high accuracy in prediction of CKD. As such, the respective patient- and population-based informatic tools might be implemented in primary care.

Development and validation of a score assessing the risk of severe asthma in primary care.

OBJECTIVE: To develop and validate the Asthma Severity-Health Search (AS-HScore), predicting severe asthma risk in Italian primary care. According to the current asthma treatment guidelines, the AS-HScore intended to serve as a clinical decision support system (CDSS) for General Practitioners (GPs). METHODS: Using the Health Search Database (HSD), a cohort of 32,917 asthma-diagnosed patients between 2013 and 2021 was identified. The AS-HScore was developed using multivariable Cox regression in a two-part cohort: development and validation. Candidate determinants were estimated and linearly combined to form the score; its predictive accuracy was evaluated in the validation sub-cohort. RESULTS: AS-HScore performance in the validation cohort revealed a 73% area under the curve (i.e. discrimination power) and a 22% pseudo-R2 (explained variation). Calibration slope of 1.07 indicated strong calibration without rejecting the equivalence hypothesis (p = 0.157). Estimating a mean 10% (SD: 6.8%) 1-year risk of severe asthma, GPs might be provided with risk thresholds for patient categorization. CONCLUSION: The AS-HScore emerges as an accurate tool predicting severe asthma risk in the Italian primary care. It therefore shows promising application to enhance asthma care by early identification of severe cases. Implementing a score-based CDSS for Italian GPs holds potential for significantly improving asthma management and patients' outcomes.

Development and validation of a prediction score to assess the risk of incurring in COPD-related exacerbations: a population-based study in primary care.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the fourth most important cause of death in high-income countries. Inappropriate use of COPD inhaled therapy, including the low adherence (only 10 %-40 % of patients reporting an adequate compliance) may shrink or even nullify the proven benefits of these medications. As such, an accurate prediction algorithm to assess at national level the risk of COPD exacerbation might be relevant for general practictioners (GPs) to improve patient's therapy. METHODS: We formed a cohort of patients aged 45 years or older being diagnosed with COPD in the period between January 2013 to December 2021. Each patient was followed until occurrence of COPD exacerbation up to the end of 2021. Sixteen determinants were adopted to assemble the CopdEX(CEX)-Health Search(HS)core, which was therefore developed and validated through the related two sub-cohorts. RESULTS: We idenfied 63763 patients aged 45 years or older being diagnosed with COPD (mean age: 67.8 (SD:11.7); 57.7 % males).When the risk of COPD exacerbation was estimated via CEX-HScore, its predicted value was equal to 14.22 % over a 6-month event horizon. Discrimination accuracy and explained variation were equal to 66 % (95 % CI: 65-67 %) and 10 % (95 % CI: 9-11 %), respectively. The calibration slope did not significantly differ from the unit (p = 0.514). CONCLUSIONS: The CEX-HScore was featured by fair accuracy for prediction of COPD-related exacerbations over a 6-month follow-up. Such a tool might therefore support GPs to enhance COPD patients' care, and improve their outcomes by facilitating personalized approaches through a score-based decision support system.

A Vulnerability Index to Assess the Risk of SARS-CoV-2-Related Hospitalization/Death: Urgent Need for an Update after Diffusion of Anti-COVID Vaccines.

Background: There are algorithms to predict the risk of SARS-CoV-2-related complications. Given the spread of anti-COVID vaccination, which sensibly modified the burden of risk of the infection, these tools need to be re-calibrated. Therefore, we updated our vulnerability index, namely, the Health Search (HS)-CoVulnerabiltyIndex (VI)d (HS-CoVId), to predict the risk of SARS-CoV-2-related hospitalization/death in the primary care setting. Methods: We formed a cohort of individuals aged ≥15 years and diagnosed with COVID-19 between 1 January and 31 December 2021 in the HSD. The date of COVID-19 diagnosis was the study index date. These patients were eligible if they had received an anti-COVID vaccine at least 15 days before the index date. Patients were followed up from the index date until one of the following events, whichever came first: COVID-19-related hospitalization/death (event date), end of registration with their GPs, and end of the study period (31 December 2022). To calculate the incidence rate of COVID-19-related hospitalization/death, a patient-specific score was derived through linear combination of the coefficients stemming from a multivariate Cox regression model. Its prediction performance was evaluated by obtaining explained variation, discrimination, and calibration measures. Results: We identified 2192 patients who had received an anti-COVID vaccine from 1 January to 31 December 2021. With this cohort, we re-calibrated the HS-CoVId by calculating optimism-corrected pseudo-R2, AUC, and calibration slope. The final model reported a good predictive performance by explaining 58% (95% CI: 48-71%) of variation in the occurrence of hospitalizations/deaths, the AUC was 83 (95% CI: 77-93%), and the calibration slope did not reject the equivalence hypothesis (p-value = 0.904). Conclusions: Two versions of HS-CoVId need to be differentially adopted to assess the risk of COVID-19-related complications among vaccinated and unvaccinated subjects. Therefore, this functionality should be operationalized in related patient- and population-based informatic tools intended for general practitioners.

How to support general practitioners to better detect sarcopenia among older adults: a nested case-control analysis.

PURPOSE: This study explores correlations of sarcopenia and its proxies, such as history of falls, asthenia, and ambulation issues, with frailty levels among older adults in primary care. METHODS: In a cohort of 546,590 patients aged 60 years or older, "definite" sarcopenia cases were operationally defined through the use of non-specific diagnostic codes coupled with inspection of free-texts. Proxies of sarcopenia, such as falls history, asthenia, and ambulation issues were considered as well. Frailty was calculated using an Index intended to primary care. RESULTS: Overall, 171 definite sarcopenia cases were found, rising to 51,520 cases when including proxies (9.4% prevalence). There was a significant association between severe frailty and increased odds of sarcopenia, consistently observed across different event definitions. CONCLUSIONS: Sarcopenia was strongly associated with severe frailty in primary care. The history of falls, asthenia, and ambulation issues were reliable proxies to raise the suspect of sarcopenia. Improved strategies for sarcopenia detection, focusing on specific indicators within severely frail individuals, are warranted.

Development and validation of a prediction score to assess the risk of depression in primary care.

BACKGROUND: Major depression is the most frequent psychiatric disorder and primary care is a crucial setting for its early recognition. This study aimed to develop and validate the DEP-HScore as a tool to predict depression risk in primary care and increase awareness and investigation of this condition among General Practitioners (GPs). METHODS: The DEP-HScore was developed using data from the Italian Health Search Database (HSD). A cohort of 903,748 patients aged 18 years or older was selected and followed until the occurrence of depression, death or end of data availability (December 2019). Demographics, somatic signs/symptoms and psychiatric/medical comorbidities were entered in a multivariate Cox regression to predict the occurrence of depression. The coefficients formed the DEP-HScore for individual patients. Explained variance (pseudo-R2), discrimination (AUC) and calibration (slope estimating predicted-observed risk relationship) assessed the prediction accuracy. RESULTS: The DEP-HScore explained 18.1 % of the variation in occurrence of depression and the discrimination value was equal to 67 %. With an event horizon of three months, the slope and intercept were not significantly different from the ideal calibration. LIMITATIONS: The DEP-HScore has not been tested in other settings. Furthermore, the model was characterized by limited calibration performance when the risk of depression was estimated at the 1-year follow-up. CONCLUSIONS: The DEP-HScore is reliable tool that could be implemented in primary care settings to evaluate the risk of depression, thus enabling prompt and suitable investigations to verify the presence of this condition.

To predict the risk of chronic kidney disease (CKD) using Generalized Additive2 Models (GA2M).

OBJECTIVE: To train and test a model predicting chronic kidney disease (CKD) using the Generalized Additive2 Model (GA2M), and compare it with other models being obtained with traditional or machine learning approaches. MATERIALS: We adopted the Health Search Database (HSD) which is a representative longitudinal database containing electronic healthcare records of approximately 2 million adults. METHODS: We selected all patients aged 15 years or older being active in HSD between January 1, 2018 and December 31, 2020 with no prior diagnosis of CKD. The following models were trained and tested using 20 candidate determinants for incident CKD: logistic regression, Random Forest, Gradient Boosting Machines (GBMs), GAM, and GA2M. Their prediction performances were compared by calculating Area Under Curve (AUC) and Average Precision (AP). RESULTS: Comparing the predictive performances of the 7 models, the AUC and AP for GBM and GA2M showed the highest values which were equal to 88.9%, 88.8% and 21.8%, 21.1%, respectively. These 2 models outperformed the others including logistic regression. In contrast to GBMs, GA2M kept the interpretability of variable combinations, including interactions and nonlinearities assessment. DISCUSSION: Although GA2M is slightly less performant than light GBM, it is not "black-box" algorithm, so being simply interpretable using shape and heatmap functions. This evidence supports the fact machine learning techniques should be adopted in case of complex algorithms such as those predicting the risk of CKD. CONCLUSION: The GA2M was reliably performant in predicting CKD in primary care. A related decision support system might be therefore implemented.

Early identification of chronic kidney disease: it is time to enhance patient and population-based informatics tools for general practitioners.

Chronic kidney disease (CKD) is a global public health issue that can lead to several complications such as, kidney failure, cerebro/cardiovascular disease, and death. There is a well-documented "awareness gap" among general practitioners (GPs) to recognize CKD. As shown by estimates stemming from the Health Search Database (HSD) of the Italian College of General Practitioners and Primary Care (SIMG), no substantial changes were observed in terms of the incident rate of CKD over the last 10 years. Namely, 10.3-9.5 per 1000 new cases of CKD were estimated in 2012 and 2021, respectively. Thus, strategies to reduce under-recognized cases are needed. Early identification of CKD might improve patient's quality of life and clinical outcomes. In this context, patient- and population-based informatic tools may support both opportunistic and systematic screening of patients at greater risk of CKD. As such, the new effective pharmacotherapies for CKD would be proficiently administered. To this aim, these two complimentary tools have been developed and will be further implemented by GPs. The effectiveness of these instruments in identifying the condition at an early stage and reducing the burden of CKD on the national health system needs to be verified according to the new regulations on medical device (MDR: (EU) 2017/745).

Frailty detection among primary care older patients through the Primary Care Frailty Index (PC-FI).

The prompt identification of frailty in primary care is the first step to offer personalized care to older individuals. We aimed to detect and quantify frailty among primary care older patients, by developing and validating a primary care frailty index (PC-FI) based on routinely collected health records and providing sex-specific frailty charts. The PC-FI was developed using data from 308,280 primary care patients ≥ 60 years old part of the Health Search Database (HSD) in Italy (baseline 2013-2019) and validated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K; baseline 2001-2004), a well-characterized population-based cohort including 3363 individuals ≥ 60 years old. Potential health deficits part of the PC-FI were identified through ICD-9, ATC, and exemption codes and selected through an optimization algorithm (i.e., genetic algorithm), using all-cause mortality as the main outcome for the PC-FI development. The PC-FI association at 1, 3 and 5 years, and discriminative ability for mortality and hospitalization were tested in Cox models. The convergent validity with frailty-related measures was verified in SNAC-K. The following cut-offs were used to define absent, mild, moderate and severe frailty: < 0.07, 0.07-0.14, 0.14-0.21, and ≥ 0.21. Mean age of HSD and SNAC-K participants was 71.0 years (55.4% females). The PC-FI included 25 health deficits and showed an independent association with mortality (hazard ratio range 2.03-2.27; p < 0.05) and hospitalization (hazard ratio range 1.25-1.64; p < 0.05) and a fair-to-good discriminative ability (c-statistics range 0.74-0.84 for mortality and 0.59-0.69 for hospitalization). In HSD 34.2%, 10.9% and 3.8% were deemed mildly, moderately, and severely frail, respectively. In the SNAC-K cohort, the associations between PC-FI and mortality and hospitalization were stronger than in the HSD and PC-FI scores were associated with physical frailty (odds ratio 4.25 for each 0.1 increase; p < 0.05; area under the curve 0.84), poor physical performance, disability, injurious falls, and dementia. Almost 15% of primary care patients ≥ 60 years old are affected by moderate or severe frailty in Italy. We propose a reliable, automated, and easily implementable frailty index that can be used to screen the primary care population for frailty.

Development and internal validation of a prognostic model for 15-year risk of Alzheimer dementia in primary care patients.

BACKGROUND: The exploitation of routinely collected clinical health information is warranted to optimize the case detection and diagnostic workout of Alzheimer's disease (AD). We aimed to derive an AD prediction score based on routinely collected primary care data. METHODS: We built a cohort selecting 199,978 primary care patients 60 + part of the Health Search Database between January 2002 and 2009, followed up until 2019 to detect incident AD cases. The cohort was randomly divided into a derivation and validation sub-cohort. To identify AD and non-AD cases, we applied a clinical algorithm that involved two clinicians. According to a nested case-control design, AD cases were matched with up to 10 controls based on age, sex, calendar period, and follow-up duration. Using the derivation sub-cohort, 32 potential AD predictors (sociodemographic, clinical, drug-related, etc.) were tested in a logistic regression and selected to build a prediction model. The predictive performance of this model was tested on the validation sub-cohort by mean of explained variation, calibration, and discrimination measurements. RESULTS: We identified 3223 AD cases. The presence of memory disorders, hallucinations, anxiety, and depression and the use of NSAIDs were associated with future AD. The combination of the predictors allowed the production of a predictive score that showed an explained variation (pseudo-R2) for AD occurrence of 13.4%, good calibration parameters, and an area under the curve of 0.73 (95% CI: 0.71-0.75). In accordance with this model, 7% of patients presented with a high-risk score for developing AD over 15 years. CONCLUSION: An automated risk score for AD based on routinely collected clinical data is a promising tool for the early case detection and timely management of patients by the general practitioners.

Clinical Decision Support System (CDSS) in primary care: from pragmatic use to the best approach to assess their benefit/risk profile in clinical practice.

Clinical Decision Support Systems (CDSSs) are computer-based tools intended to support physicians in clinical decision making. MilleDSS is an illustrative example for the Italian context. It is featured by four domains of GP-software interaction, such as clinical management and follow-up evaluation, prescribing appropriateness and clinical risk, prevention strategies and medical computerized stewardship on scientific update and training. MilleDSS registered 23,222 accesses in early September 2021. In specific, the sections on prevention and training were viewed 19,440 and 21,797 times, respectively.The Medical Device Regulation (MDR: (EU) 2017/745) indicates that clinical evidence needs to be provided for any software intended to medical purpose. Clinical research on CDSS effectiveness will be therefore conducted through epidemiological studies. In theory, this generation of evidence would follow the pyramid of evidence as per medications approval but, given the large use and constant update of CDSS for daily clinical practice, attentions should be posed on the most cost-effective study.

Predicting the risk of severe COVID-19 outcomes in primary care: development and validation of a vulnerability index for equitable allocation of effective vaccines.

BACKGROUND: General practitioners (GPs) need a valid, user-friendly tool to identify patients most vulnerable to COVID-19, especially in the hypothesis of a booster vaccine dose. The aim of this study was to develop and validate a GP-friendly prognostic index able to forecast severe COVID-19 outcomes in primary care. Indeed, no such prognostic score is as yet available in Italy. RESEARCH DESIGN AND METHODS: In this retrospective cohort study, a representative sample of 47,868 Italian adults were followed up for 129,000 person-months. The study outcome was COVID-19-related hospitalization and/or death. Candidate predictors were chosen on the basis of systematic evidence and current recommendations. The model was calibrated by using Cox regression. Both internal and external validations were performed. RESULTS: Age, sex and several clinical characteristics were significantly associated with severe outcomes. The final multivariable model explained 60% (95%CI 58-63%) of variance for COVID-19-related hospitalizations and/or deaths. The area under the receiver-operator curve (AUC) was 84% (95% CI: 83-85%). On applying the index to an external cohort, the AUC was 94% (95% CI: 93-95%). CONCLUSIONS: This index is a reliable prognostic tool that can help GPs to prioritize their patients for preventive and therapeutic interventions.

The changing face of family medicine in the COVID and post-COVID era.

This article describes the prospective changes and the fundamental values of the relationships between family doctors, patients and community according to an ethical-social concept of medicine. New aspects of the organization of the activity and of the roles of family doctors are reported in order to build hypotheses pointing to a modern and efficient management of patients in the coming the post-COVID era.

Drug interactions in users of tablet vs. oral liquid levothyroxine formulations: a real-world evidence study in primary care.

PURPOSE: Several medications may interact with levothyroxine (LT4) intestinal absorption or metabolism, thus reducing its bioavailability. We investigated the variability of thyroid stimulating hormone (TSH) levels and prescribed daily dosages (PDDs) of LT4 before and during potential drug-drug interactions (DDIs) in users of tablets vs. oral liquid LT4 formulations. METHODS: By using the Italian general practice Health Search Database (HSD), we retrospectively selected adult patients with at least one LT4 prescription from 2012 to 2015 and at least 1 year of clinical history recorded. The incident prescription of interacting medications (e.g., proton pump inhibitors, calcium or iron salts) was the index date. Analysis was carried out using a self-controlled study design. RESULTS: Overall, 3965 users of LT4 formed the study cohort (84.1% women, mean age 56 ± 16.5 years). TSH variability on the entry date was greater among liquid LT4 users than in those prescribed with tablets as shown by the difference between 75th and 25th centile, which were 3.01 and 3.8, respectively. The incidence rate ratio (IRR) for TSH variability did not differ between groups, before and during exposure to DDIs. In contrast, PDDs less likely increased during the exposure to DDI with oral liquid LT4 compared with tablets (IRR = 0.84; 95% CI: 0.77-0.92), especially in patients with post-surgical hypothyroidism (IRR = 0.75; 95% CI: 0.64-0.85). CONCLUSIONS: In clinical practice, the use of oral liquid LT4 is not associated with increased PDDs, compared with tablets formulation, during exposure to DDIs. These results support the need for individualizing LT4 formulation to prescribe, especially in patients with various comorbidities and complex therapeutic regimens.

Medication patterns in chronic spontaneous urticaria: results from a nationwide investigation in the primary care setting in Italy.

BACKGROUND: Only a few studies have assessed treatment usage in clinical practice among patients with chronic spontaneous urticaria (CSU). METHODS: The aim of this study was to evaluate patterns of medications used for CSU, analyzing the electronic patient records contained in the Italian general practice Health Search IMS Health Longitudinal Patient Database (HSD). The study period was from January 2002 to December 2013. RESULTS: The most frequent prescriptions during the year following CSU diagnosis were H1-antihistamines (78.47%) and corticosteroids (49.20%). Different medications were given to a limited number of CSU patients. CONCLUSIONS: The results of our study have shown that H1-antihistamines were the most prescribed drugs to treat CSU, reflecting their role as the mainstay of CSU treatment. Systemic corticosteroids continue to be frequently prescribed for CSU in clinical practice. Alternative drugs were used only in very few patients, suggesting the possibility that severe refractory cases of CSU are undertreated in clinical practice.

Monitoring compliance with standards of care for chronic diseases using healthcare administrative databases in Italy: Strengths and limitations.

BACKGROUND: A recent comprehensive report on healthcare quality in Italy published by the Organization of Economic Co-operation and Development (OECD) recommended that regular monitoring of quality of primary care by means of compliance with standards of care for chronic diseases is performed. A previous ecological study demonstrated that compliance with standards of care could be reliably estimated on regional level using administrative databases. This study compares estimates based on administrative data with estimates based on GP records for the same persons, to understand whether ecological fallacy played a role in the results of the previous study. METHODS: We compared estimates of compliance with diagnostic and therapeutic standards of care for type 2 diabetes (T2DM), hypertension and ischaemic heart disease (IHD) from administrative data (IAD) with estimates from medical records (MR) for the same persons registered with 24 GP's in 2012. Data were linked at an individual level. RESULTS: 32,688 persons entered the study, 12,673 having at least one of the three diseases according to at least one data source. Patients not detected by IAD were many, for all three conditions: adding MR increased the number of cases of T2DM, hypertension, and IHD by +40%, +42%, and +104%, respectively. IAD had imperfect sensitivity in detecting population compliance with therapies (adding MR increased the estimate, from +11.5% for statins to +14.7% for antithrombotics), and, more substantially, with diagnostic recommendations (adding MR increased the estimate, from +23.7% in glycated hemoglobin tests, to +50.5% in electrocardiogram). Patients not detected by IAD were less compliant with respect to those that IAD correctly identified (from -4.8 percentage points in proportion of IHD patients compliant with a yearly glycated hemoglobin test, to -40.1 points in the proportion of T2DM patients compliant with the same recommendation). IAD overestimated indicators of compliance with therapeutic standards (significant differences ranged from 3.3. to 3.6 percentage points) and underestimated indicators of compliance with diagnostic standards (significant differences ranged from -2.3 to -14.1 percentage points). CONCLUSION: IAD overestimated the percentage of patients compliant with therapeutic standards by less than 6 percentage points, and underestimated the percentage of patients compliant with diagnostic standards by a maximum of 14 percentage points. Therefore, both discussions at local level between GP's and local health unit managers and discussions at central level between national and regional policy makers can be informed by indicators of compliance estimated by IAD, which, based on those results, have the ability of signalling critical or excellent clusters. However, this study found that estimates are partly flawed, because a high number of patients with chronic diseases are not detected by IAD, patients detected are not representative of the whole population of patients, and some categories of diagnostic tests are markedly underrecorded in IAD (up to 50% in the case of electrocardiograms). Those results call to caution when interpreting IAD estimates. Audits based on medical records, on the local level, and an interpretation taking into account information external to IAD, on the central level, are needed to assess a more comprehensive compliance with standards.

Poor adherence to chronic obstructive pulmonary disease medications in primary care: Role of age, disease burden and polypharmacy.

AIM: The aim of the present study was to assess the occurrence and determinants of poor adherence to pharmacological treatment in a cohort of primary care patients with chronic obstructive pulmonary disease (COPD), paying special attention to the role of age, comorbidity and polypharmacy. METHODS: We identified a cohort of COPD patients using the primary care Italian Health Search - IMS Longitudinal Patient Database. We assessed 1-year adherence to COPD maintenance pharmacotherapy (encompassing inhaled corticosteroids, long-acting beta agonists and long-acting anticholinergics). Poor adherence was defined as <80% of proportion of days covered by pharmacological treatment over a 1-year period. RESULTS: Of 22 505 patients (mean age 67.3 ± 13.2; 41.3% women) entering the study, 17 486 (77.7%) were poorly adherent. According to multivariate analysis, poor adherence is less likely in older adults (OR 0.51, 95% CI 0.40-0.66), in non-smokers (OR 0.77, 95% CI 0.69-0.86) and in those with a history of alcohol abuse (OR 0.74, 95% CI 0.58-0.94). Higher comorbidity (OR 1.43, 95% CI 1.13-1.80) was positively associated with poor adherence. Polypharmacy was associated with poor adherence only in patients aged ≥65 years (OR 1.34, 95% CI 1.13-1.59). Finally, COPD severity was associated with a reduced likelihood of poor adherence (OR 0.20, 95% CI 0.07-0.61 for stage IV). CONCLUSIONS: The present findings show that poor medication adherence is common in patients with COPD receiving long-term treatment. The interaction between age and polypharmacy, and the role of comorbidity suggest a pivotal role of biological age as a steering determinant of poor adherence. Geriatr Gerontol Int 2017; 17: 2500-2506.

Effectiveness of adherence to lipid lowering therapy on LDL-cholesterol in patients with very high cardiovascular risk: A real-world evidence study in primary care.

BACKGROUND AND AIMS: Despite management guidelines advocating statin/ezetimibe use in very high cardiovascular risk (CV) conditions, adherence to this therapy is still suboptimal and LDL-C target attainment unsatisfactory. We aimed to investigate the level of adherence to statin/ezetimibe and LDL-C target achievement rates in an unselected very high CV risk population in primary care setting in Italy. METHODS: We performed a retrospective population-based study using the Health Search IMS Health Longitudinal Patient Database (HSD), including adult patients at very high CV risk, newly treated with statin, ezetimibe or their combination, with 3 and 6 months of follow-up. RESULTS: Although the large majority of patients had previous major CV events (99.9%), only 61% and 55.14% resulted adherent (Proportion of Days Covered, PDC≥80%) after 3 and 6 months, respectively. High adherence entailed almost a three times higher probability to reach the therapeutic LDL-C target (3 months: OR = 2.26 [95% [CI]: 1.88 to 2.72]; 6-months: OR = 2.74 [95% CI: 2.27 to 3.31]). The odds to treat to LDL-C target was greater for simvastatin-ezetimibe fixed combination, simvastatin, atorvastatin and rosuvastatin, in decreasing order. Finally, poor adherence was slightly more prevalent among patients treated with less effective statins, and at both low and maximal dosage regimens. CONCLUSIONS: This population-based study showed that adherence to statin therapy is poor even among patients who have already experienced a CV event. Failure to achieve recommended LDL-C levels appears imputable to the use of moderate doses and low to standard efficacy statins.

Prescription Appropriateness of Cyproterone Acetate/Ethinylestradiol in Primary Care: A Population-Based Study in Italy.

BACKGROUND AND OBJECTIVE: We evaluated the prescription appropriateness of cyproterone acetate in combination with ethinylestradiol (CPA/EE) in a primary care setting before and after the 2013 European Medicines Agency's (EMA) recommendation relating to the risk profile of CPA/EE. METHODS: Data were obtained from the Health Search IMS Health Longitudinal Patient Database (HSD). We compared the results from 2011 to 2012 with the results of 2014, namely before and after the 2013 EMA recommendation, and investigated the burden of concurrent use of CPA/EE and other hormonal contraceptives (HCs) and the reported indication at the time of CPA/EE prescription. RESULTS: Overall, 1189 new users of CPA/EE were identified: 495 in 2011, 446 in 2012, and 261 in 2014. Concomitant use of CPA/EE and other HCs was found in 1% of cases in 2011-2012 and 2% of cases in 2014, while potential concomitant use was observed in 4% of cases in 2011 and 2% of cases in 2012 and 2014. A recent (within 365 days) diagnosis of any hyperandrogenic condition was detected in 22 and 24% of CPA/EE users in 2011 and 2012, respectively, and in 24% of cases in 2014. The percentage of CPA/EE users with a recent acne diagnosis and/or treatment was 19, 21, and 18% in 2011, 2012, and 2014, respectively. CONCLUSIONS: Apart from an overall reduction in CPA/EE prescriptions in the HSD, no substantial difference was found in terms of the proportions of patients diagnosed with acne or other hyperandrogenic conditions and/or potential concomitant use of HCs before and after the EMA recommendation.

The Burden of Chronic Heart Failure in Primary Care in Italy.

INTRODUCTION: Chronic heart failure (CHF) is a major public health concern. From a public health perspective, the epidemiology of CHF needs to be distinguished from that of its related acute form. Data stemming from primary care are crucial to better know and update the prevalence and incidence rates of CHF. AIM: To update the epidemiology of CHF in an Italian primary care setting and to describe socio-demographic, lifestyle, and clinical characteristics of these patients. METHODS: A population-based study was conducted among 800 Italian general practitioners collecting data in a dedicated database. Information was extracted from adult subjects with a diagnosis of CHF from 2002 to 2013, and the prevalence and incidence rate of CHF were calculated. The study population was described in terms of socio-demographic, lifestyle, and clinical characteristics. RESULTS: A total of 13,633 patients with CHF were identified. Overall, the prevalence of CHF was 1.25% (95% CI 1.23-1.27), and the incidence rate was 1.99 per 1000 person-years (95% CI 1.81-2.08). In this population, smoking, alcohol use, and obesity were present in 2.93, 0.45, and 10.80% of cases, respectively. Hypertension (58.40%), chronic kidney disease (51.36%), dyslipidaemia (44.62%), ischaemic heart disease (25.75%), and atrial fibrillation (25.32%) were the most represented comorbidities. CONCLUSION: This study provides an updated epidemiological scenario of CHF in a primary care setting in Italy. These data may be useful to weight the social and economic impact of CHF and to plan strategies for improving the clinical care of CHF in general practice.