Objective and design: A clinicopathological score has been proposed by Trouillas et al. to predict the evolution of pituitary adenomas. Aim of our study was to perform an independent external validation of this score and identify other potential predictor of post-surgical outcome. Methods: The study sample included 566 patients with pituitary adenomas, specifically 253 FSH/LH-secreting, 147 GH-secreting, 85 PRL-secreting, 72 ACTH-secreting and 9 TSH-secreting tumours with at least 3-year post-surgical follow-up. Results: In 437 cases, pituitary adenomas were non-invasive, with low (grade 1a: 378 cases) or high (grade 1b: 59 cases) proliferative activity. In 129 cases, tumours were invasive, with low (grade 2a: 87 cases) or high (grade 2b: 42 cases) proliferative activity. During the follow-up (mean: 5.8 years), 60 patients developed disease recurrence or progression, with a total of 130 patients with pituitary disease at last follow-up. Univariate analysis demonstrated a significantly higher risk of disease persistence and recurrence/progression in patients with PRL-, ACTH-and FSH/LH-secreting tumours as compared to those with somatotroph tumours, and in those with high proliferative activity (grade 1b and 2b) or >1 cm diameter. Multivariate analysis confirmed tumour type and grade to be independent predictors of disease-free-survival. Tumour invasion, Ki-67 and tumour type were the only independent prognostic factors of disease-free survival. Conclusions: Our data confirmed the validity of Trouillas' score, being tumour type and grade independent predictors of disease evolution. Therefore, we recommend to always consider both features, together with tumour histological subtype, in the clinical setting to early identify patients at higher risk of recurrence.

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Aims Depression in type 2 diabetes may heavily affect the course of the disease. In this study, we investigated, among new cases with type 2 diabetes, the incidence and clinical predictors of depression and determined the extent to which depression constitutes a risk factor for acute and long-term diabetes complications and mortality. Methods In this population-based retrospective cohort study, incident cases of type 2 diabetes without a prior history of depression were identified from the administrative databases of the Emilia-Romagna Region, Italy, between 2008 and 2017 and followed up until 2020. Logistic regression models were used to identify the predictors of depression. Cox regression models were used to estimate the risk of acute complications over three years, and the risk of long-term complications and mortality over ten years. Results Incident cases with type 2 diabetes were 30,815, of whom 5146 (16.7%) developed depression. The predictors of depression onset were as follows: female sex, age > 65 years, living in rural areas and comorbid diseases. Depression in type 2 diabetes was associated with a 2.3-fold risk of developing acute complications, 1.6-fold risk of developing long-term complications and 2.8-fold mortality risk. Conclusions Our findings highlight that depression is associated with an increased risk for complications in type 2 diabetes and mortality and should not be neglected. Therefore, it is important to promote screening activities and introduce targeted and personalized treatment for depression in order to reduce the risk of poor short- and long-term outcomes of diabetes.

ObjectivesTo develop a population-based risk stratification model (COVID-19 Vulnerability Score) for predicting severe/fatal clinical manifestations of SARS-CoV-2 infection, using the multiple source information provided by the healthcare utilisation databases of the Italian National Health Service.DesignRetrospective observational cohort study.SettingPopulation-based study using the healthcare utilisation database from five Italian regions.ParticipantsBeneficiaries of the National Health Service, aged 18–79 years, who had the residentship in the five participating regions. Residents in a nursing home were not included. The model was built from the 7 655 502 residents of Lombardy region.Main outcome measureThe score included gender, age and 29 conditions/diseases selected from a list of 61 conditions which independently predicted the primary outcome, that is, severe (intensive care unit admission) or fatal manifestation of COVID-19 experienced during the first epidemic wave (until June 2020). The score performance was validated by applying the model to several validation sets, that is, Lombardy population (second epidemic wave), and the other four Italian regions (entire 2020) for a total of about 15.4 million individuals and 7031 outcomes. Predictive performance was assessed by discrimination (areas under the receiver operating characteristic curve) and calibration (plot of observed vs predicted outcomes).ResultsWe observed a clear positive trend towards increasing outcome incidence as the score increased. The areas under the receiver operating characteristic curve of the COVID-19 Vulnerability Score ranged from 0.85 to 0.88, which compared favourably with the areas of generic scores such as the Charlson Comorbidity Score (0.60). A remarkable performance of the score on the calibration of observed and predicted outcome probability was also observed.ConclusionsA score based on data used for public health management accurately predicted the occurrence of severe/fatal manifestations of COVID-19. Use of this score may help health decision-makers to more accurately identify high-risk citizens who need early preventive or treatment interventions.

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Background Estimating the morbidity of a population is strategic for health systems to improve healthcare. In recent years administrative databases have been increasingly used to predict health outcomes. In 1992, Von Korff proposed a Chronic Disease Score (CDS) to predict 1-year mortality by only using drug prescription data. Because pharmacotherapy underwent many changes over the last 3 decades, the original version of the CDS has limitations. The aim of this paper is to report on the development of the modified version of the CDS. Methods The modified CDS (M-CDS) was developed using 33 variables (from drug prescriptions within two-year before 01/01/2018) to predict one-year mortality in Bologna residents aged �50 years. The population was split into training and testing sets for internal validation. Score weights were estimated in the training set using Cox regression model with LASSO procedure for variables selection. The external validation was carried out on the Imola population. The predictive ability of M-CDS was assessed using ROC analysis and compared with that of the Charlson Comorbidity Index (CCI), that is based on hospital data only, and of the Multisource Comorbidity Score (MCS), which uses hospital and pharmaceutical data. Results The predictive ability of M-CDS was similar in the training and testing sets (AUC 95% CI: training [0.760-0.770] vs. testing [0.750-0.772]) and in the external population (Imola AUC 95% CI [0.756-0.781]). M-CDS was significantly better than CCI (M-CDS AUC = 0.761, 95% CI [0.750-0.772] vs. CCI-AUC = 0.696, 95% CI [0.681-0.711]). No significant difference was found between M-CDS and MCS (MCS AUC = 0.762, 95% CI [0.749-0.775]).

Aims A wide range of post‐radiotherapy (RT) vascular lesions can occur, ranging from benign lymphangiomatous papules of the skin (BLAPs), to atypical vascular lesions (AVLs) and post‐RT angiosarcomas (ASs). The relationship between benign and malignant post‐RT breast lesions and their prognostic features are still controversial. The aims of this study were to investigate the relationship between benign and malignant mammary post‐RT vascular lesions and to define post‐RT AS prognostic features. Methods and results Seventy‐four post‐RT vascular lesion cases were obtained and stained with antibodies against CD34, CD31, D2‐40, Ki67, and c‐Myc. Mutational analysis was performed by deep sequencing for the following genes: KRAS, NRAS, HRAS, BRAF, PIK3CA, TP53, NOTCH1, PTEN, CDKN2A, EGFR, AKT1, CTNNB1, hTERT, and PTPRB. Post‐RT AS cases were graded according to a previously reported breast AS grading system. AVL cases showed a low number of HRAS and hTERT mutations, whereas post‐RT AS cases showed a high frequency of EGFR, TP53, HRAS and hTERT mutations. On follow‐up, all BLAP and AVL patients were alive with no evidence of disease. Post‐RT AS 5‐year overall survival declined with the increase in grade, as follows: 85.7% for grade 1, 83.3% for grade 2, and 40.4% for grade 3. Conclusions Our findings confirm that BLAP and AVL have a good prognosis, and that post‐RT AS prognosis is strongly related to histological grading. On molecular analysis, AVL and post‐RT AS shared HRAS and hTERT mutations, suggesting a relationship between the two lesions.

Risk equalization is a fundamental tool in health plan payment in many countries. Data availability often constrains the feasible models. This paper proposes, implements and quantifies the gains of a risk equalization scheme which incorporates risk sharing in a data poor context. Risk sharing relies on total spending data likely available for purposes of payment, potentially increasing feasibility of an effective payment design. To examine incentives for risk selection, alternative models are evaluated in terms of fit at individual, insurer, and group level. Using Chile's private health insurance market as case study, we show that modest amount of risk sharing greatly improves fit even in simple demographic-based risk equalization.Expanding the model's formula to include morbidity-based adjustors and risk sharing redirects compensations at insurer level and reduces opportunity to engage in profitable risk selection at group level. Our emphasis on feasibility may make alternatives proposed attractive to countries facing data-availability constraints.