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.

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.

The burden of recurrent respiratory tract infections in adult population: a population-based study in primary care.

BACKGROUND: Viral infections are the main original cause of recurrent respiratory tract infections (RRTIs), but their complications and recurrences are due to bacteria as well. While some operational definitions and epidemiology of RRTIs are reported in paediatrics, no similar definitions have been proposed for adults. AIM: To assess the epidemiology and characteristics of RRTIs in the adult population. DESIGN AND SETTING: Cohort study in the primary care setting. METHODS: Using the Health Search Database, we selected a cohort of patients aged 18 years or older between 2002 and 2022. Yearly, we counted upper and lower respiratory tract infections (RTIs) per patient. We investigated 2 cut-offs defining RRTIs, nominally 3+ RRTIs/patient/year or greater than the mean value of RTIs/patient/year. The associations between these two event definitions and the correlates defining the patients' vulnerability were assessed by estimating a logistic regression model. RESULTS: Over the study years, the mean number of RTIs/patient/year ranged from 0.07-0.16 or 1.10-1.13 events, when the denominator was formed by the overall population or those diagnosed with RTIs, respectively. When the analysis was focussed on 2022, we obtained 0.2% (1.3% among those with RTIs) or 13% (11.3% among those with RTIs) cases of RRTIs, using a cut-off of 3+ or >=0.16 events (mean value/patient), respectively. Consistent associations were found for these two operational definitions and the investigated clinical correlates. CONCLUSION: We provided evidence on the epidemiology and concurrent/predisposing factors of RRTIs in adults. These data should support health authorities and general practitioners for the application of the most appropriate preventive and/or treatment strategies.

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.

Time-varying association between herpes zoster infection and subsequent occurrence of stroke.

OBJECTIVES: The aim of this study was to investigate the shape of the time-varying relationship between herpes zoster infection, nominally shingles, and the occurrence of stroke. STUDY DESIGN: Retrospective cohort study. METHODS: Using the Italian Health Search Database, a cohort of patients aged ≥18 years who were registered between 2002 and 2021 was selected. In this cohort, a nested case-control analysis was used to model the time-varying distance (in months) between the dates of shingles and post-herpetic stroke, using a regression cubic spline, based on the odds of the occurrence of stroke compared with those without shingles. RESULTS: The dataset comprised 42,513 cases (51.1% males; mean age [stanndard deviation {SD}]: 71.0 [11.8] years) and 425,124 related controls (51.1% males; mean age [SD]: 70.9 [12] years). In the first 12 months following shingles diagnosis, a rapid increase in the risk of stroke was observed, reaching an odds ratio of 1.31 (95% confidence interval: 1.21-1.41); subsequently, there was some risk reduction and a new symmetric increase within the first 4.2 years of follow-up, thus shaping a bimodal distribution. Then, a new increase in the stroke risk was reported, although less steep, which was followed by a regular risk reduction (still 10% higher compared with those without shingles), resulting in a right-skewed relationship between the time from the shingles diagnosis and the occurrence of stroke. This association was no longer statistically significant 13.1 years after shingles diagnosis. CONCLUSIONS: This study demonstrated that the risk of post-herpetic stroke has a short- and long-term association according to a risk continuum relationship. These findings confirm the relevance of vaccination coverage for herpes zoster.

Examining the effectiveness and duration of adjuvanted vs. non-adjuvanted influenza vaccines in protecting older adults against symptomatic SARS-CoV-2 infection.

Annual influenza vaccination is one of the main public health measures able to drastically reduce the burden of this infectious disease. Some evidence suggests 'trained immunity' triggered by influenza vaccine might reduce the risk of SARS-CoV-2 infection. Adjuvanted influenza vaccines are known to induce a broader cross-reactive immunity. No studies investigated the effect of adjuvanted vs. non-adjuvanted influenza vaccines on the risk of symptomatic SARS-CoV-2 infection. A case-control analysis nested in a cohort of subjects aged ≥65 years and immunized with adjuvanted or non-adjuvanted influenza vaccines was conducted. Although no statistically significant (OR = 0.87; P = .082) difference between the two vaccine types was observed for the 9-month follow-up period, a 17% (OR = 0.83; P = .042) reduction in the odds of COVID-19 was observed for adjuvanted vaccines with a 6-month follow-up. Further evidence is needed, but these results might have implications given the complexity of the upcoming winter seasons, in which the co-occurrence of influenza, SARS-CoV-2 and other respiratory infections (e.g., syncytial virus) might be unpredictable.

To retrieve values of albuminuria to define the size of CKD patients eligible to SGLT-2Is: An explorative analysis using a primary care database.

PURPOSE: To address missingness of albuminuria values, which establish the eligibility to SGLT-2Is for patients with CKD, using the multiple imputation (MI) method. METHODS: We selected patients aged 18 or older and diagnosed with CKD in a primary care database. Those with severe CKD and/or previously treated with SGLT-2Is were excluded. Then, we collected all available information on albuminuria within 90 days the measure of GFR. A value of albumin-creatinine ratio (ACR) ranging 200-5000 mg/g or otherwise was the response variable on which we ran MI. Using logistic regression, odds ratios (OR) and related 95% confidence intervals (CI) were estimated for each covariate toward the response variable for both full and imputed dataset. RESULTS: The determinants showed consistent estimates between the full and imputed datasets as demonstrated by the overlaps of the CIs and the similar point estimates. As expected, there were some exceptions, such as diabetes (OR of 1.2 vs. 0.5) and use of diabetic medications (OR of 1.0 vs. 2.1) and/or statins (OR of 1.1 vs. 1.8). CONCLUSIONS: Besides being a reminder for GPs to prescribe and register albuminuria in certain patients' categories, these determinants might be translated into an operational algorithm to input ACR values in administrative data sources. Scenarios for the reimbursement criteria regulating SGLT-2Is to treat CKD would be therefore simulated on more inferable estimates.

Time lapses between distribution of influenza vaccines to health authorities and their administration by General Practitioners (GPs) to older adults: a retrospective study over five influenza seasons in Italy.

BACKGROUND: Delays in influenza vaccine delivery and administration can hinder vaccine coverage and protection. This study examines the differentials in distributing and administering adjuvanted trivalent (aTIV) and quadrivalent influenza vaccines (aQIV) to older adults in Italy's primary care setting and its potential impact on hospitalization risk over 5 epidemic seasons. METHODS: Using a primary care database, individuals aged ≥ 65 years were selected. The proportion of vaccine distribution to regional authorities and subsequent administration by GPs was estimated using census data. Using quantile (median) regression, we examined the relationship between velocities of vaccine distribution and administration (doses/week) and the incidence of hospitalizations. RESULTS: Over the 5 influenza seasons, the velocity of distribution and administration of aTIV/aQIV ranged 341-833 and 152-270 median doses/week; no trend was yielded for the difference between these velocities (p = 0.189) or vaccine coverage (p = 0.142). An association was observed for each differential dose/week between distributed and administered vaccines and all-cause hospitalizations with a 10% increase in 2017-2018, 54% in 2018-2019, and 12% in 2020-2021 season. CONCLUSIONS: These findings highlight the importance of minimizing the time lapse between vaccine distribution and administration to mitigate the impact of influenza and address factors that contribute to vaccination barriers.

Epidemiology of pediatric psoriasis: a population-based study using two Italian data sources.

BACKGROUND: Psoriasis can be associated with certain comorbidities. This information is important for family pediatricians (FPs) and general practitioners (GPs) who have a key role in the identification and management of skin diseases. This study aimed to assess the incidence and prevalence rates of pediatrics psoriasis and its association with specific comorbidities. METHODS: A retrospective cohort study was performed in patients aged less than 18 years registered in two Italian primary care databases (Pedianet and HSD) between 2015 and 2019. Prevalence and incidence of psoriasis were estimated, and a case-control design was adopted to assess specific comorbidities in psoriasis patients. RESULTS: The annual prevalence rate of psoriasis was 0.2% in Pedianet and between 0.5% and 0.7% in HSD. The incidence rate ranged from 0.47 to 0.58 and from 1.3 to 1.77 per 1000 person-years in Pedianet and HSD, respectively. Allergic rhinitis, asthma, celiac disease, other malabsorption disease and non-infective cutaneous diseases showed a statistically significant association with psoriasis in Pedianet, while no statistically significant difference was found in HSD. CONCLUSION: Given the FP-GP transition of patients, there is a need for accurate registration of clinical correlates, enabling GPs to implement strategies to minimize the lifetime risk of psoriatic progression.

[Severe Covid-19: to recognize subjects at risk, from the territory to specialist services.] / Covid-19 severo: riconoscere i soggetti a rischio, dal territorio ai servizi specialistici.

During the Covid-19 pandemic, territorial services and general practitioners in particular played a central role in identifying suspected cases and contact tracing. Vulnerability criteria were defined to identify patients at risk of developing severe forms of infection, which were later used to direct patients towards appropriate mitigation measures and prioritize access to vaccinations. The identification of individuals at risk of severe Covid-19 remains crucial, especially for particularly vulnerable patients with oncohematological and cardiovascular conditions, who require specific preventive and therapeutic strategies.

Outpatient healthcare costs associated with overweight and obesity in Italy.

BACKGROUND: To evaluate outpatient healthcare expenditure associated with different levels of BMI and glucose metabolism alterations. METHODS: The study is based on a representative national sample of adults, with data obtained from electronic clinical records of 900 Italian general practitioners. Data relative to the year 2018 were analyzed. The study population was classified according to BMI (normal weight, overweight, and obesity classes 1, 2, and 3) and glucose metabolism status (normoglycemia - NGT; impaired fasting glucose - IFG; diabetes mellitus - DM). Outpatient health expenditures include diagnostic tests, specialist visits, and drugs. RESULTS: Data relative to 991,917 adults were analyzed. Annual per capita expenditure rose from 252.2 Euro among individuals with normal weight to 752.9 Euro among those with class 3 obesity. The presence of obesity determined an excess cost, particularly among younger individuals. Within each BMI class, the presence of IFG or DM2 identified subgroups of individuals with substantially higher healthcare expenditures. CONCLUSIONS: Outpatient healthcare costs markedly increased with increasing BMI in all age categories, particularly among individuals below 65. Addressing the double burden of excess weight and hyperglycemia represents a significant challenge and a healthcare priority.

Epidemiology of invasive meningococcal disease and its sequelae: a population-based study in Italian primary care, 2000-2019.

BACKGROUND: Invasive meningococcal disease (IMD) is a severe infectious disease. Although effective preventive and therapeutical strategies are available, the fatality rate remains high in the general population, with an occurrence of meningococcal-related severe sequelae involving 10-20% of survivors. Given the crucial role of general practitioners in recognizing and preventing IMD and its related sequelae, we aim to assess the burden of these conditions in primary care. METHODS: Using an Italian primary care database, the incidence rate of IMD was calculated in the period 2000-2019 by capturing the first diagnosis registered during follow-up. As far as meningococcal-related sequelae are concerned, we identified and clinically evaluated each potential sequela during the first 3 months, from 3 to 12 months, and up to 36 months. RESULTS: Among 508 patients diagnosed with IMD, 403 (incidence rate: 0.24 per 10,000 person-years) comprised those diagnosed with IMD in patients aged 15 years or older. We ascertained 104 sequelae (20.4%); 76% of them occurred in those aged 25 or older; 42, 27, and 35 were assessed as short-, medium-, or long-term sequelae, respectively. Overall, 4.7% of IMD patients reported physical sequelae, while 12.2% and 5.7% of patients reported neurological and psychological sequelae, respectively. CONCLUSION: Our study showed that a substantial proportion of IMD and related sequelae occur in individuals aged over 25, with a non-negligible burden for healthcare systems. As for the paediatric population, effective communication on the relevance of meningococcal vaccination in adults should be proficiently fostered.

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.

To support the use of NT-proBNP to better detect heart failure in patients with type 2 diabetes.

PURPOSE: Heart failure (HF) is a chronic disease that causes approximately 300,000 and 250,000 deaths per year in Europe and United States, respectively. Type 2 Diabetes Mellitus (T2DM) is one the major risk factors of HF, and the investigation of NT-proBNP might support the early identification of HF in T2DM sufferers. Nevertheless, this parameter is poorly investigated. Thus, we aimed to demographically and clinically characterize diabetic patients which were prescribed with NT-proBNP in the primary care setting. METHODS: Using a primary care database, we formed a cohort of patients aged ≥18 years diagnosed with T2DM between 2002 and 2021. A multivariate Cox model was adopted to assess the determinants associated with the prescription of NT-proBNP. RESULTS: Among 167,961 T2DM patients, 7558 (4.5%, 95% CI: 4.4-4.6) were prescribed with NT-proBNP. Males and increasing age were expectedly associated with a higher propensity to be prescribed with NT-proBNP. In addition, a significant association was found for those suffering from obesity, ischemic cardiomyopathy, stroke, atrial fibrillation, hypertension, and with a Charlson Index of 2+. CONCLUSION: These determinants might contribute to investigate the NT-proBNP in T2DM sufferers. A decision support system to appropriately ease the prescription of NT-proBNP might be therefore implemented in primary care settings.

A methodology to assess the population size and estimate the needed resources for new licensed medications by combining clinical and administrative databases: The example of glycated haemoglobin in type 2 diabetes.

PURPOSE: To develop and validate a model to estimate glycated haemoglobin (HbA1c) values in patients with type 2 diabetes mellitus (T2DM) using a clinical data source, with the aim to apply this equation to administrative databases. METHODS: Using a primary care and administrative Italian databases, namely the Health Search database (HSD) and the ReS (Ricerca e Salute) database, we selected all patients aged 18 years or older on 31 December 2018 being diagnosed with T2DM and without prior prescription of sodium-glucose cotransporter-2 (SGLT-2) inhibitors. We included patients prescribed with and adherent to metformin. HSD was used to develop and test (using 2019 data as well) the algorithm imputing HbA1c values ≥7% according to a series of covariates. The algorithm was gathered by combining beta-coefficients being estimated by logistic regression models using complete case (excluding missing values) and imputed (after multiple imputation) dataset. The final algorithm was applied to ReS database using the same covariates. RESULTS: The tested algorithms were able to explain 17%-18% variation in assessing HbA1c values. Good discrimination (70%) and calibration were obtained as well. The best algorithm (three) cut-offs, namely those providing correct classifications ranging 66%-70% was therefore calculated and applied to ReS database. By doing so, from 52 999 (27.9, 95% CI: 27.7%-28.1%) to 74 250 (40.1%, 95% CI: 38.9%-39.3%) patients were estimated with HbA1c ≥7%. CONCLUSION: Through this methodology, healthcare authorities should be able to quantify the population eligible to a new licensed medication, such as SGLT-2 inhibitors, and to simulate scenarios to assess reimbursement criteria according to precise estimates.