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.

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 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.