ObjectivesClassification criteria are biased towards classifying long-standing disease. We compared the European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR)-2019, Systemic Lupus International Collaborating Clinics (SLICC)-2012 and ACR-1997 criteria in an early (median 48 months) systemic lupus erythematosus (SLE) cohort.MethodsPatients diagnosed with SLE (n=690) or control diseases (n=401). Sensitivity, specificity of the criteria and time-to-classification were calculated. Modified classification algorithms were derived from a random 80% and validated in the remaining 20% of the dataset running multiple iterations.ResultsAt last assessment, sensitivities of ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria were 85.7%, 91.3% and 88.6%, with specificities 93.0%, 93.8% and 97.3%, respectively. Both SLICC and EULAR/ACR enabled earlier classification. Only 76.7% of patients with SLE met all three criteria suggesting non-overlapping groups. Notably, unclassified patients had high prevalence of British Isles Lupus Assessment Group moderate/severe manifestations (43.3%–60%) and SLICC/ACR organ damage (30%–50%). At diagnosis, criteria missed 25.6%–30.5% of patients. Modification of EULAR/ACR and SLICC algorithms to include hypocomplementaemia and/or positive anti-phospholipid antibodies as alternative entry criterion, and/or allow classification with fewer clinical criteria from multiple organs, increased their sensitivity at diagnosis (median 82.0% and 86.2%) and overall (93.7% and 97.1%) with modest decreases in specificity. Importantly, patients who were still missed by the modified criteria had lower incidence of major organ involvement, use of immunosuppressive/biological therapies and organ damage.ConclusionsThe SLICC and EULAR/ACR are more sensitive than the ACR and the EULAR/ACR criteria have superior specificity in early SLE, although patients with significant disease can be missed. Combination and/or modification of the classification algorithms may enhance their sensitivity, allowing earlier classification and treatment of more patients with high disease burden.

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In an epoch where shared decision making is gaining importance, a patient’s commitment to and knowledge about his/her health condition is becoming more and more relevant. Health literacy is one of the most important factors in enhancing the involvement of patients in their care. Nevertheless, other factors can impair patient processing and understanding of health information: psychological aspects and cognitive style may affect the way patients approach, select, and retain information. This paper describes the development and validation of a short and easy to fill-out questionnaire that measures and collects psycho-cognitive information about patients, named ALGA-C. ALGA-C is a multilingual, multidevice instrument, and its validation was carried out in healthy people and breast cancer patients. In addition to the aforementioned questionnaire, a patient profiling mechanism has also been developed. The ALGA-C Profiler enables physicians to rapidly inspect each patient’s individual cognitive profile and see at a glance the areas of concern. With this tool, doctors can modulate the language, vocabulary, and content of subsequent discussions with the patient, thus enabling easier understanding by the patient. This, in turn, helps the patient formulate questions and participate on an equal footing in the decision-making processes. Finally, a preview is given on the techniques under consideration for exploiting the constructed patient profile by a personal health record (PHR). Predefined rules will use a patient’s profile to personalise the contents of the information presented and to customise ways in which users complete their tasks in a PHR system. This optimises information delivery to patients and makes it easier for the patient to decide what is of interest to him/her at the moment.

ObjectivesDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.MethodsFrom a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).ResultsA novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.ConclusionsWe have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.

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Personalized healthcare systems support the provision of timely and appropriate information regarding healthcare options and treatment alternatives. Especially for patients that receive multi-drug treatments a key issue is the minimization of the risk of adverse effects due to drug-drug interactions (DDIs). DDIs may be the result of doctor prescribed drugs but also due to self-medication of conventional drugs, alternative medicines, food habits, alcohol or smoking. It is therefore crucial for personalized health systems, apart from assisting physicians for optimal prescription practices, to also provide appropriate information for individual users for drug-drug interactions or similar information regarding risks for modulation of the ensuing treatment. In this manuscript we describe a DDI service including drug-food, drug-herb and other lifestyle-related factors, developed in the context of a personalized patient empowerment platform. The solution enables guidance to patients for their medication on how to reduce the risk of unwanted drug interactions and side effects in a seamless and transparent way. We present and analyze the implemented services and provide examples on using an alerting service to identify potential DDIs in two different chronic diseases, congestive heart failure and osteoarthritis.

Background The long-term outcome of rheumatoid arthritis (RA) patients who in clinical practice exhibit persistent moderate disease activity (pMDA) despite treatment with biologics has not been adequately studied. Herein, we analyzed the 5-year outcome of the pMDA group and assessed for within-group heterogeneity. Methods We included longitudinally monitored RA patients from the Hellenic Registry of Biologic Therapies with persistent (cumulative time ≥ 50% of a 5-year period) moderate (pMDA, 3.2 < DAS28 ≤ 5.1) or remission/low (pRLDA, DAS28 ≤ 3.2) disease activity. The former was further classified into persistent lower-moderate (plMDA, DAS28 < 4.2) and higher-moderate (phMDA, DAS28 ≥ 4.2) subgroups. Five-year trajectories of functionality (HAQ) were the primary outcome in comparing pRLDA versus pMDA and assessing heterogeneity within the pMDA subgroups through multivariable mixed-effect regression. We further compared serious adverse events (SAEs) occurrence between the two groups. Results We identified 295 patients with pMDA and 90 patients with pRLDA, the former group comprising of plMDA (n = 133, 45%) and phMDA (n = 162, 55%). pMDA was associated with worse 5-year functionality trajectory than pRLDA (+ 0.27 HAQ units, CI 95% + 0.22 to + 0.33; p < 0.0001), while the phMDA subgroup had worse 5-year functionality than plMDA (+ 0.26 HAQ units, CI 95% 0.18 to 0.36; p < 0.0001). Importantly, higher persistent disease activity was associated with more SAEs [pRLDA: 0.2 ± 0.48 vs pMDA: 0.5 ± 0.96, p = 0.006; plMDA: 0.32 ± 0.6 vs phMDA: 0.64 ± 1.16, p = 0.038]. Male gender (p = 0.017), lower baseline DAS28 (p < 0.001), HAQ improvement > 0.22 (p = 0.029), and lower average DAS28 during the first trimester since treatment initiation (p = 0.001) independently predicted grouping into pRLDA. Conclusions In clinical practice, RA patients with pMDA while on bDMARDs have adverse long-term outcomes compared to lower disease activity status, while heterogeneity exists within the pMDA group in terms of 5-year functionality and SAEs. Targeted studies to better characterize pMDA subgroups are needed, in order to assist clinicians in tailoring treatments.

Authorization is an open problem in Ambient Intelligence environments. The difficulty of implementing authorization policies lies in the open and dynamic nature of such environments. The information is distributed among various heterogeneous devices that collect, process, change, and share it. Previous work presented a fully distributed approach for reasoning with conflicts in ambient intelligence systems. This paper extends previous results to address authorization issues in distributed environments. First, the authors present the formal high-level authorization language DEAL to specify access control policies in open and dynamic distributed systems. DEAL has rich expressive power by supporting negative authorization, rule priorities, hierarchical category authorization, and nonmonotonic reasoning. The authors then define the language semantics through Defeasible Logic. Finally, they demonstrate the capabilities of DEAL in a use case Ambient Intelligence scenario regarding a hospital facility.