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.

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.

Epidemiology of Parkinson's Disease: A Population-Based Study in Primary Care in Italy.

BACKGROUND: There are no studies on prevalence, incidence and comorbidities of Parkinson's disease (PD) in the Italian population. METHODS: The database of 700 Italian general practitioners (population, 923,356) was investigated. All patients with International Classification of Diseases Ninth Revision - Clinical Modification (ICD-9-CM) diagnosis of PD during the period 2002-2012 were included. Parkinsonisms were excluded. Clinical conditions preceding PD were identified through ICD-9-CM codes. The Charlson Comorbidity Index was used. PD crude and standardized prevalence and annual incidence were calculated. Crude and adjusted hazard ratios were calculated for comorbidities. RESULTS: A total of 2,204 patients (1,140 men, 1,064 women, age 22-95 years) were included. The crude prevalence of PD was 239/100,000. Prevalence increased exponentially with age. Standardized prevalence was 233 (95% CI 232-235). One hundred ninety-four patients were newly diagnosed, giving a crude incidence of 22/100,000 and a standardized incidence of 23.1/100,000 (95% CI 22.9-23.2). Incidence increased steadily until age 75-84 years and then decreased. Older age, cardiovascular and gastrointestinal disorders, diabetes, and restless-legs syndrome were associated with increased PD risk and smoking and hypersomnia with decreased PD risk. The Charlson Comorbidity Index was associated with PD risk with a documented gradient. CONCLUSIONS: Prevalence and incidence of PD in Italy are in line with studies with the highest case ascertainment. PD risk varies with the number and type of comorbidities.

Epidemiology of iron deficiency anaemia in four European countries: a population-based study in primary care.

OBJECTIVES: Iron deficiency anaemia (IDA) is a global public health concern, being responsible for about 800 000 deaths per year worldwide. To date, few studies have investigated the epidemiology of IDA in Europe. This study therefore aimed to assess the incidence rate and determinants of IDA in four European countries. METHODS: Demographic and clinical information was obtained from four national primary care databases, respectively, for Italy, Belgium, Germany and Spain. IDA-related determinants were estimated using multivariable Cox regression. RESULTS: The annual incidence rates of IDA ranged between 7.2 and 13.96 per 1000 person-years. The estimates were higher in Spain and Germany. Females, younger and older patients were at greater risk of IDA, as well as those suffering from gastrointestinal diseases, pregnant women and those with history of menometrorrhagia, and aspirin and/or antacids users. A Charlson Index >0 was a significant determinant of IDA in all countries. CONCLUSIONS: The use of primary care databases allowed us to assess the incidence rate and determinants of IDA in four European countries. Given the crucial role of general practitioners in the diagnosis and management of this condition, our findings may contribute to increase the awareness of IDA among physicians as well as to reduce its occurrence among at-risk patients.

Development and Validation of a Score for Adjusting Health Care Costs in General Practice.

OBJECTIVE: To develop and validate the Italian Health Search Morbidity (HSM) Index to adjust health care costs in general practice. METHODS: The study population comprised 1,076,311 patients registered in the Health Search CSD Longitudinal Patient Database between January 1, 2008, and December 31, 2010. We randomly selected 538,254 and 538,057 patients to form the development and validation cohorts, respectively. To ensure model convergence, 5% of the aforementioned cohorts were selected randomly to create development and validation samples. The outcome was the total direct health care costs covered by the national health system. Interaction between age and sex, chronic diseases, and acute diseases were entered in a multilevel generalized linear latent mixed model with random intercepts (province of residence and general practitioner) to identify determinants associated with increased or decreased costs. The estimated coefficients were linearly combined to create the HSM Index for individual patients. The score was applied to the validation sample, and measures of predictive accuracy, explained variance, and the observed/predicted ratio were computed to evaluate the model's accuracy. RESULTS: The mean yearly cost was €414.57 per patient, and the HSM Index had a median value of 5.08 (25th-75th range 4.44-5.98). The HSM Index explained 50.17% of the variation in costs. Concerning calibration, in 80% of the population, the margin of error in the estimation of costs was around 10%. CONCLUSIONS: The HSM Index is a reliable case-mix system that could be implemented in general practice for costs adjustment. This tool should ensure fairer scrutiny of resource use and allocation of budgets among general practitioners.

Prevalence and incidence of epilepsy in Italy based on a nationwide database.

OBJECTIVES: To estimate the prevalence and incidence of epilepsy in Italy using a national database of general practitioners (GPs). METHODS: The Health Search CSD Longitudinal Patient Database (HSD) has been established in 1998 by the Italian College of GPs. Participants were 700 GPs, representing a population of 912,458. For each patient, information on age and sex, EEG, CT scan, and MRI was included. Prevalent cases with a diagnosis of 'epilepsy' (ICD9CM: 345*) were selected in the 2011 population. Incident cases of epilepsy were identified in 2011 by excluding patients diagnosed for epilepsy and convulsions and those with EEG, CT scan, MRI prescribed for epilepsy and/or convulsions in the previous years. Crude and standardized (Italian population) prevalence and incidence were calculated. RESULTS: Crude prevalence of epilepsy was 7.9 per 1,000 (men 8.1; women 7.7). The highest prevalence was in patients <25 years and ≥75 years. The incidence of epilepsy was 33.5 per 100,000 (women 35.3; men 31.5). The highest incidence was in women <25 years and in men 75 years or older. CONCLUSIONS: Prevalence and incidence of epilepsy in this study were similar to those of other industrialized countries. HSD appears as a reliable data source for the surveillance of epilepsy in Italy.

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 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.

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.

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.

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.

Development and validation of a predictive score for diagnosing prostate cancer in primary care.

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.

Supporting vaccine (co)-administration decisions: Development and validation of a tool for assessing the risk of severe outcomes due to lower respiratory tract infections.

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.

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.

Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey.

BACKGROUND: Administrative databases are widely available and have been extensively used to provide estimates of chronic disease prevalence for the purpose of surveillance of both geographical and temporal trends. There are, however, other sources of data available, such as medical records from primary care and national surveys. In this paper we compare disease prevalence estimates obtained from these three different data sources. METHODS: Data from general practitioners (GP) and administrative transactions for health services were collected from five Italian regions (Veneto, Emilia Romagna, Tuscany, Marche and Sicily) belonging to all the three macroareas of the country (North, Center, South). Crude prevalence estimates were calculated by data source and region for diabetes, ischaemic heart disease, heart failure and chronic obstructive pulmonary disease (COPD). For diabetes and COPD, prevalence estimates were also obtained from a national health survey. When necessary, estimates were adjusted for completeness of data ascertainment. RESULTS: Crude prevalence estimates of diabetes in administrative databases (range: from 4.8% to 7.1%) were lower than corresponding GP (6.2%-8.5%) and survey-based estimates (5.1%-7.5%). Geographical trends were similar in the three sources and estimates based on treatment were the same, while estimates adjusted for completeness of ascertainment (6.1%-8.8%) were slightly higher. For ischaemic heart disease administrative and GP data sources were fairly consistent, with prevalence ranging from 3.7% to 4.7% and from 3.3% to 4.9%, respectively. In the case of heart failure administrative estimates were consistently higher than GPs' estimates in all five regions, the highest difference being 1.4% vs 1.1%. For COPD the estimates from administrative data, ranging from 3.1% to 5.2%, fell into the confidence interval of the Survey estimates in four regions, but failed to detect the higher prevalence in the most Southern region (4.0% in administrative data vs 6.8% in survey data). The prevalence estimates for COPD from GP data were consistently higher than the corresponding estimates from the other two sources. CONCLUSION: This study supports the use of data from Italian administrative databases to estimate geographic differences in population prevalence of ischaemic heart disease, treated diabetes, diabetes mellitus and heart failure. The algorithm for COPD used in this study requires further refinement.

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).

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.

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.

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.