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Objective: To develop and validate a score to predict the 90-day risk of hospitalization/death in patients with low respiratory tract infections (LRTIs) with the aim to support clinical decision making on vaccine (co)-administration., Methods: We formed a cohort of patients aged 18 years or older being diagnosed with LRTIs in the period between January 1, 2012 and December 31, 2022. Each patient was followed until occurrence of respiratory-related hospitalization/death up to the end of the study period (December 31, 2022). Along with age and sex, forty determinants were adopted to assemble the respiratory tract infection (RTI)-Health Search (HS) core using the development sub-cohort. The prediction accuracy of the score was therefore assessed in the validation sub-cohort., Results: We identified 252,319 patients being diagnosed with LRTIs (females: 54.7 %; mean age: 60 (SD:18.1)). When the risk of LRTIs-related hospitalizations/deaths was estimated via RTI-HScore, its predicted value was equal to 1.4 % over a 90-day event horizon. The score showed explained variation and discrimination accuracy were equal to 45 % (95 % CI: 44-47 %) and 81 % (95 % CI: 79-84 %), respectively. The calibration slope did not significantly differ from the unit (p = 0.8314)., Conclusions: The RTI-HScore was featured by good accuracy for prediction of LRTIs-related complications over a 90-day follow-up. Such a tool might therefore support general practitioners to enhance patients' care by facilitating approaches for (co)-administration of vaccines for respiratory infections through a score-based decision support system., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FL, EM and IC provided consulting for protocol preparation for epidemiological studies and data analyses for GSK, Pfizer, Moderna, Sanofi, MSD and Seqirus. AR and CC provided clinical consulting for GSK, Pfizer, Moderna, Sanofi, MSD and Seqirus. AD provided consultancies and received speaker fees from CSL Seqirus, GSK and SD Biosensor. GI provided consultancies and/or received grants for conducting experimental and/or observational studies for GSK, Sanofi, MSD, CSL Seqirus and Pfizer. IC is an employee at Genomedics SRL., (Copyright © 2024. Published by Elsevier Ltd.)

Background : In recent years, Europe has experienced a significant increase in pertussis cases. One reason behind this rise is the decline in diphtheria-tetanus-pertussis (dTap) booster coverage among adults. Currently, Italy lacks a reliable monitoring system to track pertussis infections and vaccine coverage among adults. We therefore evaluated the reliability of a primary care framework to respond to this need. Methods : Using an Italian primary care database for individuals aged 15 or above, we determined the pertussis infection notification rate and dTap vaccine/booster coverage for the timeframe of 2009-2022. Results : In the overall population, we obtained a lifetime occurrence rate of pertussis infections of 7.52 per 10,000 individuals. The annual incidence rates of pertussis infections ranged from 0.008 to 0.001 per 10,000 person-years between 2009 and 2022. A rising trend in dTap vaccine coverage rate (ranging from 8.72 to 16.54 vaccines per 10,000 individuals) was observed during the same period. Notably, those aged 65 or older, smokers, and/or individuals with immunodeficiencies were more likely to receive the dTap vaccine compared to the general population. Conclusions : Given the organization of the Italian public health system, this primary care network might act as a reliable epidemiological monitoring system to keep track of pertussis infections and dTap vaccine coverage in adults. Pertussis cases were underreported, and there was a low uptake of vaccines and boosters. Therefore, it is crucial to closely monitor pertussis notifications and dTap administrations and develop intervention strategies at the national level to enhance vaccine-related prevention.

Background: Prostate cancer (PCa) represents the fifth cause of death in the male population worldwide. The prostate-specific antigen (PSA) test demonstrated poor accuracy to assess the presence of PCa. Thus, the PSA testing paradigm should be moved from the systematic screening approach to the early identification of men who are harbouring clinically significant disease. Accurate clinical-based tools to predict PCa should therefore be developed for general practice. We derived and validated a PCa predictive score using a primary care data source., Methods: Using the Italian Health Search Database, we formed a cohort of men aged 45-90 years in the period between 2002 and 2015. These patients were followed up until 31 December 2022. Those with less than a 5-year follow-up were excluded. The cohort was randomly divided into 'derivation' and 'validation' samples in a 1:1 ratio. Along with the demographic and clinical determinants forming the score, we investigated the role of PSA kinetics in the prediction accuracy., Results: In a cohort of 529,082 men aged 45+ years, we identified 14,524 cases of PCa (incidence rate = 2.71 per 1000 person-years; 95% confidence interval = 2.67-2.80). The prediction accuracy of the PCa-HScore featured an explained variation of 12% and a discrimination power of 70%. The calibration slope was almost equal to 1 (p = 0.951, tested for equivalence against the 'perfect' slope) and the PSA kinetics did not improve the prediction accuracy., Conclusions: The PCa-HScore might guide the prescription of PSA and/or other clinical strategies in those men reporting certain levels of risk. A related decision support system could therefore be implemented in primary care., (© 2024 John Wiley & Sons Ltd.)

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-R 2 ), 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., Competing Interests: Declaration of competing interest F.L. and E.M. provided consultancies in protocol preparation for epidemiological studies and data analyses for Angelini, GSK, Lundbeck and Takeda; C.C. provided clinical consultancies for Angelini, GSK, Lundbeck and Takeda; I.C. is an employee of Genomedics Srl; and G.C. and V.R. have no conflict of interest to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (© 2024. The Author(s), under exclusive licence to European Geriatric Medicine Society.)

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., Competing Interests: Declaration of competing interest FL and EM provided consultancies in protocol preparation for epidemiological studies and data analyses for GSK, AstraZeneca, and Chiesi. FPL provided clinical consultances for GSK, AstraZeneca, and Chiesi. CC provided clinical and scientific consultances for epidemiological studies for GSK, AstraZeneca, and Chiesi. CM received fees as a speaker from AstraZeneca, Sanofi, Novartis, GSK, Chiesi, Menarini, Zambon, Berlin-Chemie. FDM has received honoraria for lectures at national and international meetings, served as a consultant, and received financial support for research from AstraZeneca, Boehringer Ingelheim, Chiesi Farmaceutici, Guidotti/Malesci, GSK, Menarini, Neopharmed Gentili, Novartis, Sanofi and Zambon. EA and MG are employees at AstraZeneca., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: To assess the temporal validity of a model predicting the risk of Chronic Kidney Disease (CKD) using Generalized Additive 2 Models (GA 2 M)., 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 GA 2 M-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 GA 2 M confirmed its high accuracy in prediction of CKD. As such, the respective patient- and population-based informatic tools might be implemented in primary care., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FL, EM and IC provided consultations in protocol preparation for epidemiological studies and data analyses for Dr Shär, Astra Zeneca, Mundipharma and MSD. IC and LN are employees at Genomedics. CC provided clinical consultations for Dr Shär, Astra Zeneca, Mundipharma and MSD., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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-R 2 (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.

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-R 2 , 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.

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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).