Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data

: The prevalence of longstanding chronic diseases has increased worldwide, along with the average age of the population. As a result, an increasing number of people is affected by two or more chronic conditions simultaneously, and healthcare systems are facing the challenge of treating multimorbid patients effectively. Current therapeutic strategies are suited to manage each chronic condition separately, without considering the whole clinical condition of the patient. This approach may lead to suboptimal clinical outcomes and system inefficiencies (e.g. redundant diagnostic tests and inadequate drug prescriptions). We develop a novel methodology based on the joint implementation of data reduction and clustering algorithms to identify patterns of chronic diseases that are likely to co-occur in multichronic patients. We analyse data from a large adult population of multichronic patients living in Tuscany (Italy) in 2019 which was stratified by sex and age classes. Results demonstrate that (i) cardio-metabolic, endocrine, and neuro-degenerative diseases represent a stable pattern of multimorbidity, and (ii) disease prevalence and clustering vary across ages and between women and men. Identifying the most common multichronic profiles can help tailor medical protocols to patients' needs and reduce costs. Furthermore, analysing temporal patterns of disease can refine risk predictions for evolutive chronic conditions

30-day in-hospital mortality after acute myocardial infarction in Tuscany (Italy): An observational study using hospital discharge data

Abstract Background: Coronary heart disease is the leading cause of mortality in the world. One of the outcome indicators recently used to measure hospital performance is 30-day mortality after acute myocardial infarction (AMI). This indicator has proven to be a valid and reproducible indicator of the appropriateness and effectiveness of the diagnostic and therapeutic process for AMI patients after hospital admission. The aim of this study was to examine the determinants of inter-hospital variability on 30-day in-hospital mortality after AMI in Tuscany. This indicator is a proxy of 30-day mortality that includes only deaths occurred during the index or subsequent hospitalizations. Methods: The study population was identified from hospital discharge records (HDRs) and included all patients with primary or secondary ICD-9-CM codes of AMI (ICD-9 codes 410.xx) that were discharged between January 1, 2009 and November 30, 2009 from any hospital in Tuscany. The outcome of interest was 30-day all-cause in-hospital mortality, defined as a death occurring for any reason in the hospital within 30 days of the admission date. Because of the hierarchical structure of the data, with patients clustered into hospitals, random-effects (multilevel) logistic regression models were used. The models included patient risk factors and random intercepts for each hospital. Results: The study included 5,832 patients, 61.90% male, with a mean age of 72.38 years. During the study period, 7.99% of patients died within 30 days of admission. The 30-day in-hospital mortality rate was significantly higher among patients with ST segment elevation myocardial infarction (STEMI) compared with those with non-ST segment elevation myocardial infarction (NSTEMI). The multilevel analysis which included only the hospital variance showed a significant inter-hospital variation in 30-day in-hospital mortality. When patient characteristics were added to the model, the hospital variance decreased. The multilevel analysis was then carried out separately in the two strata of patients with STEMI and NSTEMI. In the STEMI group, after adjusting for patient characteristics, some residual inter-hospital variation was found, and was related to the presence of a cardiac catheterisation laboratory. Conclusion: We have shown that it is possible to use routinely collected administrative data to predict mortality risk and to highlight inter-hospital differences. The distinction between STEMI and NSTEMI proved to be useful to detect organisational characteristics, which affected only the STEMI subgroup. Keywords: Myocardial infarction, Mortality, Cardiovascular risk, Medical record

Looking for the right balance between human and economic costs during COVID-19 outbreak

Since the beginning of Coronavirus 2019 (COVID-19) disease outbreak, there has been a heated debate about public health measures, as they can presumably reduce human costs in the short term but can negatively impact economies and well-being over a longer period. Materials and methods: To study the relationship between health and economic impact of COVID-19, we conducted a secondary research on Italian regions, combining official data (mortality due to COVID-19 and contractions in value added of production for a month of lockdown). Then, we added the tertiles of the number of people tested for COVID-19 and those of health aids to evaluate the correspondence with the outcome measures. Results: Five regions out of 20, the most industrialized northern regions, which were affected both earlier and more severely by the outbreak, registered both mortality and economic value loss above the overall medians. The southern regions, which were affected later and less severely, had low mortality and less economic impact. Conclusions: Our analysis shows that considering health and economic outcomes in the assessment of response to pandemics offers a bigger picture perspective of the outbreak and could allow policymakers and health managers to choose systemic, 'personalized' strategies, in case of a feared second epidemic wave

Patient-reported experience and health-related quality of life in patients with primary Sjögren's syndrome in Europe

Objective. The study aims to provide novel findings on geographic variation in the management of primary Sjögren's syndrome (pSS) in Europe, an underdiagnosed, long-term autoimmune disease. Methods. Starting from the lack of comparable information on patients' experience, quality and efficiency of care delivered to pSS patients in Europe, the approach is to collect and analyse patients reported data from an international survey. To assess and compare access and quality of care to pSS along their care-path we developed and validated a questionnaire administered to a large cohort of pSS patients in selected European countries. Regression models have been applied to survey data to compare quality and volumes of care across Europe. Results. Both follow-up and number of visits with a specialist of the patient are influenced by the severity of the disease with differences among countries. The results show some extent of variations in access and treatments delivered to pSS patients and also their perceived quality of life and satisfaction for SS care in Europe. Conclusion. Findings contribute to support healthcare professionals' decision making and the organisation of care delivery by taking into consideration the patients' point of view and preferences

Phenology of Woody Species Along the Climatic Gradient in West Tropical Africa

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Fitness for purpose of routinely recorded health data to identify patients with complex diseases: The case of Sjögren's syndrome

Background: This study is part of the EU-funded project HarmonicSS, aimed at improving the treatment and diagnosis of primary Sjögren's syndrome (pSS). pSS is an underdiagnosed, long-term autoimmune disease that affects particularly salivary and lachrymal glands. Objectives: We assessed the usability of routinely recorded primary care and hospital claims data for the identification and validation of patients with complex diseases such as pSS. Methods: pSS patients were identified in primary care by translating the formal inclusion and exclusion criteria for pSS into a patient selection algorithm using data from Nivel Primary Care Database (PCD), covering 10% of the Dutch population between 2006 and 2017. As part of a validation exercise, the pSS patients found by the algorithm were compared to Diagnosis Related Groups (DRG) recorded in the national hospital insurance claims database (DIS) between 2013 and 2017. Results: International Classification of Primary Care (ICPC) coded general practitioner (GP) contacts combined with the mention of “Sjögren” in the disease episode titles, were found to best translate the formal classification criteria to a selection algorithm for pSS. A total of 1462 possible pSS patients were identified in primary care (mean prevalence 0.7‰, against 0.61‰ reported globally). The DIS contained 208 545 patients with a Sjögren related DRG or ICD10 code (prevalence 2017: 2.73‰). A total of 2 577 577 patients from Nivel PCD were linked to the DIS database. A total of 716 of the linked pSS patients (55.3%) were confirmed based on the DIS. Conclusion: Our study finds that GP electronic health records (EHRs) lack the granular information needed to apply the formal diagnostic criteria for pSS. The developed algorithm resulted in a patient selection that approximates the expected prevalence and characteristics, although only slightly over half of the patients were confirmed using the DIS. Without more detailed diagnostic information, the fitness for purpose of routine EHR data for patient identification and validation could not be determined

FRI0272 Early damage assessment and prediction of damage accrual in primary sjÖgren's syndrome using salivary gland ultrasonography during 2 years of follow-up

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Detection of primary Sjögren's syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning

Background: Primary Sjögren's Syndrome (pSS) is a rare autoimmune disease that is difficult to diagnose due to a variety of clinical presentations, resulting in misdiagnosis and late referral to specialists. To improve early-stage disease recognition, this study aimed to develop an algorithm to identify possible pSS patients in primary care. We built a machine learning algorithm which was based on combined healthcare data as a first step towards a clinical decision support system. Method: Routine healthcare data, consisting of primary care electronic health records (EHRs) data and hospital claims data (HCD), were linked on patient level and consisted of 1411 pSS and 929,179 non-pSS patients. Logistic regression (LR) and random forest (RF) models were used to classify patients using age, gender, diseases and symptoms, prescriptions and GP visits. Results: The LR and RF models had an AUC of 0.82 and 0.84, respectively. Many actual pSS patients were found (sensitivity LR = 72.3%, RF = 70.1%), specificity was 74.0% (LR) and 77.9% (RF) and the negative predictive value was 99.9% for both models. However, most patients classified as pSS patients did not have a diagnosis of pSS in secondary care (positive predictive value LR = 0.4%, RF = 0.5%). Conclusion: This is the first study to use machine learning to classify patients with pSS in primary care using GP EHR data. Our algorithm has the potential to support the early recognition of pSS in primary care and should be validated and optimized in clinical practice. To further enhance the algorithm in detecting pSS in primary care, we suggest it is improved by working with experienced clinicians