Administrative databases have become an important tool to monitor diseases. Patients with epilepsy could be traced using disease-specific codes and prescriptions, but formal validation is required to obtain an accurate case definition. The aim of the study was to correlate administrative data on epilepsy with an independent source of patients with epilepsy in a district of Lombardy, Northern Italy, from 2000 to 2008.Data of nearly 320 600 inhabitants in the district of Lecco collected from the Drug Administrative Database of the Lombardy Region were analysed. Among them were included patients who fulfilled the International Classification of Diseases 9 (ICD-9) codes and/or the disease-specific exemption code for epilepsy and those who had at least one EEG record and took antiepileptic drugs (AEDs) as monotherapy or in variable combinations. To ascertain epilepsy cases, 11 general practitioners (GPs) with 15 728 affiliates were contacted. Multiple versions of the diagnostic algorithm were developed using different logistic regression models and all combinations of the four independent variables.Among the GP affiliates, 71 (4.5/1000) had a gold standard diagnosis of epilepsy. The best and most conservative algorithm included EEG and selected treatment schedules and identified 61/71 patients with epilepsy (sensitivity 85.9%, CI 76.0% to 92.2%) and 15 623/15 657 patients without epilepsy (specificity 99.8%,CI 99.7% to 99.8%). The positive and negative predictive values were 64.2% and 99.9%. Sensitivity (86.7%) and the positive predictive value (68.4%) increased only slightly when patients with single seizures were included.A diagnostic algorithm including EEG and selected treatment schedules is only moderately sensitive for the detection of epilepsy and seizures. These findings apply only to the Northern Italian scenario.
Meta-analyses have found conflicting evidence on the link between antipsychotics and cerebrovascular events (CVEs). The primary aim of this study was to evaluate the association between any antipsychotic prescription and CVEs in Italian elderly; second, to compare the effect of typical and atypical antipsychotics on CVEs; and third, to investigate the effect of antipsychotics on CVEs in the subgroup of persons coprescribed with acetylcholinesterase inhibitors (AChEIs). Administrative claims from community-dwelling people aged 65 to 94 years living in Northern Italy were analyzed using a retrospective case-control design, from 2003 to 2005. The primary outcome measure was a hospital discharge diagnosis of CVEs during 2005. Four age-, sex-, and local health unit-matched control subjects were identified for each case. Antihypertensive drugs, anticoagulants, platelet inhibitors, antidiabetic drugs, lipid-lowering drugs, and AChEI were used as covariates in conditional logistic regression models testing the odds ratio (OR) for CVEs due to antipsychotics use. Three thousand eight hundred fifty-five cases of CVEs were identified and matched with 15,420 control subjects. In multiadjusted models, the association of any antipsychotics, typical or atypical with CVEs, was not significant. When antipsychotics were categorized according to the number of boxes prescribed during the observational period, being prescribed with at least 19 boxes of typical antipsychotics was significantly associated with CVEs (OR, 2.4; 95% confidence interval, 1.08-5.5). An interaction was found between any antipsychotic and AChEI coprescription on CVEs (OR, 0.46; 95% confidence interval, 0.23-0.92). In conclusion, only typical antipsychotics were associated with an increased odd of CVEs, but the association was duration dependent. Persons prescribed simultaneously with AChEI and antipsychotics may be at a lower risk of CVEs.
To assess how lipid-lowering drugs (LLDs) are administered in the hospitalized patients aged 65 and older and their association with clinical outcomes according to their health-related profiles.This is a retrospective study based on data from REPOSI (REgistro POliterapie SIMI - Italian Society of Internal Medicine) register, an Italian network of internal medicine hospital wards.A total of 4642 patients with a mean age of 79 years enrolled between 2010 and 2018.Socio-demographic characteristics, functional abilities, cognitive skills, laboratory parameters and comorbidities were used to investigate the health state profiles by using multiple correspondence analysis and clustering. Logistic regression was used to assess whether LLD prescription was associated with patients' health state profiles and with short-term mortality.Four clusters of patients were identified according to their health state: two of them (Cluster III and IV) were the epitome of frailty conditions with poor short-term outcomes, whereas the others included healthier patients. The average prevalence of LLD use was 27.6%. The lowest prevalence was found among the healthier patients in Cluster I and among the oldest frail patients with severe functional and cognitive impairment in Cluster IV. The highest prevalence was among multimorbid patients in Cluster III (OR=4.50, 95% CI=3.76-5.38) characterized by a high cardiovascular risk. Being prescribed with LLDs was associated with a lower 3-month mortality, even after adjusting for cluster assignment (OR=0.59; 95% CI = 0.44-0.80).The prevalence of LLD prescription was low and in overall agreement with guideline recommendations and with respect to patients' health state profiles.
Background Recently we defined a user-friendly tool (FADOI-COMPLIMED scores—FCS) to assess complexity of patients hospitalized in medical wards. FCS-1 is an average between the Barthel Index and the Exton-Smith score, while FCS-2 is obtained by using the Charlson score. The aim of this paper is to assess the ability of the FCS to predict mortality in-hospital and after 1-3-6-12-months. In this perspective, we performed comparisons with the validated Multidimensional Prognostic Index (MPI). Methods It is a multicenter, prospective observational study, enrolling patients aged over 40, suffering from at least two chronic diseases and consecutively admitted to Internal Medicine departments. For each patient, data from 13 questionnaires were collected. Survival follow-up was conducted at 1-3-6-12 months after discharge. The relationships between cumulative incidences of death with FCS were investigated with logistic regression analyses. ROC curve analyses were performed in order to compare the predictiveness of the logistic models based on FCS with respect to those with MPI taken as reference. Results A cohort of 541 patients was evaluated. A 10-point higher value for FCS-1 and FCS-2 leads to an increased risk of 1-year death equal to 25.0% and 27.1%, respectively. In case of in-hospital mortality, the relevant percentages were 63.1% and 15.3%. The logistic model based on FCS is significantly more predictive than the model based on MPI (which requires an almost doubled number of items) for all the time-points considered. Conclusions Assessment of prognosis of patients has the potential to guide clinical decision-making and lead to better care. We propose a new, efficient and easy-to-use instrument based on FCS, which demonstrated a good predictive power for mortality in patients hospitalized in medical wards. This tool may be of interest for clinical practice, since it well balances feasibility (requiring the compilation of 34 items, taking around 10 minutes) and performance.
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