How can we reward you? A compliance and reward ontology (CaRO) for eliciting quantitative reward rules for engagement in mHealth app and healthy behaviors.

OBJECTIVE: When developing mHealth apps with point reward systems, knowledge engineers and domain experts should define app requirements capturing quantitative reward patterns that reflect patient compliance with health behaviors. This is a difficult task, and they could be aided by an ontology that defines systematically quantitative behavior goals that address more than merely the recommended behavior but also rewards for partial compliance or practicing the behavior more than recommended. No ontology and algorithm exist for defining point rewards systematically. METHODS: We developed an OWL ontology for point rewards that leverages the Basic Formal Ontology, the Behaviour Change Intervention Ontology and the Gamification Domain Ontology. This Compliance and Reward Ontology (CaRO) allows defining temporal elementary reward patterns for single and multiple sessions of practicing a behavior. These could be assembled to define more complex temporal patterns for persistence behavior over longer time intervals as well as logical combinations of simpler reward patterns. We also developed an algorithm for calculating the points that should be rewarded to users, given data regarding their actual performance. A natural language generation algorithm generates from ontology instances app requirements in the form of user stories. To assess the usefulness of the ontology and algorithms, information system students who are trained to be system analysts/knowledge engineers evaluated whether the ontology and algorithms can improve the requirement elicitation of point rewards for compliance patterns more completely and correctly. RESULTS: For single-session rewards, the ontology improved formulation of two of the six requirements as well as the total time for specifying them. For multi-session rewards, the ontology improved formulation of five of the 11 requirements. CONCLUSION: CaRO is a first attempt of its kind, and it covers all of the cases of compliance and reward pattern definitions that were needed for a full-scale system that was developed as part of a large European project. The ontology and algorithm are available at https://github.com/capable-project/rewards.

Effect of Sustained Clinical Remission on the Risk of Lupus Flares and Impaired Kidney Function in Patients With Lupus Nephritis.

Introduction: This retrospective study on patients with biopsy-proven lupus nephritis (LN) aimed to assess the probability of sustained clinical remission (sCR) and to investigate sCR effects on disease flares and impaired kidney function (IKF). Methods: sCR was defined as clinical-Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) = 0 and estimated glomerular filtration rate (eGFR) >60 ml/min per 1.73 m2 lasting ≥1 year; IKF: eGFR <60 ml/min per 1.73 m2 for >3 months. We analyzed the probability of achieving and maintaining sCR, and the yearly risk of flare. Cox models were used to identify predictors of sCR and IKF with variables analyzed as time-dependent covariates when appropriate. Results: Of 303 patients followed-up with for 14.8 (interquartile range: 9.8-22) years, 257 (84.8%) achieved sCR. The probability of achieving sCR progressively increased over time reaching 90% at 15 years. Baseline age (hazard ratio [HR]: 1.017; 95% confidence interval [CI]: 0.005-1.029; P = 0.004), hydroxychloroquine intake (HR: 1.385; 95% CI: 1.051-1.825; P = 0.021), and absence of arterial hypertension (HR: 0.699; 95% CI: 0.532-0.921; P = 0.011) were independent predictors of sCR. Among patients who achieved sCR, 142 (55.3%) developed a lupus flare after a median time of 3.6 (2.3-5.9) years. In the remaining 115 patients, sCR persisted for 9.5 (5.8-14.5) years. The probability of sCR to persist at 15 years was 38%. SLE flare risk decreased to 10%, 5%, and 2% in patients with sCR lasting <5, 5 to 10, and >10 years, respectively. At the last observation, 57 patients (18.81%) had IKF. sCR achievement (HR: 0.18, P < 0.001) and its duration (HR: 0.83, P < 0.001) were protective against IKF. Conclusion: sCR is an achievable target in LN management and protects against IKF. The longer the sCR, the higher the chance of its persistence and the lower the risk of SLE flares.

Education in Health Informatics: Perspectives from the Italian Society for Biomedical Informatics (SIBIM).

The evolution of socio-technological habits together with the widespread demand of post-acute and chronic treatments outside hospital boundaries drove the increased demand of medical informatics experts to develop tools for and support healthcare professionals. The recent COVID-19 pandemic further highlighted the need of physicians able to manage diseases virtually and remotely. Moreover, healthcare professionals need to access to innovative techniques and procedures to manage biomedical data, cloud-based communication, and data sharing procedures, often connected to innovative devices to support an effective precision in the health treatments. In this paper we report the experiences of the Italian Biomedical Informatics Society (SIBIM), in the definition and promotion of eHealth educational topics in medical and health professions teaching programs, as well as in bioengineering schools, showing how SIBIM members' efforts have been applied towards increasing the level of eHealth contents in medical schools.

Corrigendum to "Effect of Sustained Clinical Remission on the Risk of Lupus Flares and Impaired Kidney Function in Patients With Lupus Nephritis" [Kidney International Reports Volume 9, Issue 4, April 2024, Pages 1047-1056].

[This corrects the article DOI: 10.1016/j.ekir.2024.01.016.].

An eHealth App (CAPABLE) Providing Symptom Monitoring, Well-Being Interventions, and Educational Material for Patients With Melanoma Treated With Immune Checkpoint Inhibitors: Protocol for an Exploratory Intervention Trial.

BACKGROUND: Since treatment with immune checkpoint inhibitors (ICIs) is becoming standard therapy for patients with high-risk and advanced melanoma, an increasing number of patients experience treatment-related adverse events such as fatigue. Until now, studies have demonstrated the benefits of using eHealth tools to provide either symptom monitoring or interventions to reduce treatment-related symptoms such as fatigue. However, an eHealth tool that facilitates the combination of both symptom monitoring and symptom management in patients with melanoma treated with ICIs is still needed. OBJECTIVE: In this pilot study, we will explore the use of the CAPABLE (Cancer Patients Better Life Experience) app in providing symptom monitoring, education, and well-being interventions on health-related quality of life (HRQoL) outcomes such as fatigue and physical functioning, as well as patients' acceptance and usability of using CAPABLE. METHODS: This prospective, exploratory pilot study will examine changes in fatigue over time in 36 patients with stage III or IV melanoma during treatment with ICI using CAPABLE (a smartphone app and multisensory smartwatch). This cohort will be compared to a prospectively collected cohort of patients with melanoma treated with standard ICI therapy. CAPABLE will be used for a minimum of 3 and a maximum of 6 months. The primary endpoint in this study is the change in fatigue between baseline and 3 and 6 months after the start of treatment. Secondary end points include HRQoL outcomes, usability, and feasibility parameters. RESULTS: Study inclusion started in April 2023 and is currently ongoing. CONCLUSIONS: This pilot study will explore the effect, usability, and feasibility of CAPABLE in patients with melanoma during treatment with ICI. Adding the CAPABLE system to active treatment is hypothesized to decrease fatigue in patients with high-risk and advanced melanoma during treatment with ICIs compared to a control group receiving standard care. The Medical Ethics Committee NedMec (Amsterdam, The Netherlands) granted ethical approval for this study (reference number 22-981/NL81970.000.22). TRIAL REGISTRATION: ClinicalTrials.gov NCT05827289; https://clinicaltrials.gov/study/NCT05827289. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49252.

The role of basophil activation test and component-resolved diagnostics in the workup of egg allergy in children at low risk for severe allergic reactions: A real-life study.

BACKGROUND: The gold standard for diagnosing egg allergy in children is the oral food challenge (OFC). However, OFCs are time-consuming and risky procedures. Our study aimed to evaluate the utility of the basophil activation test (BAT) and component-resolved diagnostic in the diagnostic workup of children with egg allergy. METHODS: Overall, 86 children aged 6 months to 17 years, suspected of egg allergy, underwent OFC with boiled egg according to international standardized protocols. BAT and specific immunoglobulin E (sIgE) testing to component egg proteins (Gal d 1-4) were also performed. RESULTS: Of the 22 children who reacted to boiled egg, only one experienced anaphylaxis during the challenge. BAT was performed in samples obtained by 75 of the 86 patients of our cohort. Egg white and yolk protein extracts induced CD63 upregulation in the egg-allergic (EA) children compared with sensitized children that tolerated boiled egg (we registered an overall mean of CD63 expression in the EA population of 44.4% [SD 34.1] for egg white and 34.7% [SD 31.3] for egg yolk vs. 12.5% [SD 19.1] and 10.0% [SD 16.0] in sensitized children). BAT could discriminate between true egg allergy and egg sensitization in our population. As a second-line diagnostic step, the positivity of BAT for egg white or Gal d 1-sIgE resulted in a 40.9% OFC reduction, especially for those with a positive outcome. CONCLUSION: The BAT may be implemented in the diagnostic workup of egg allergy in children and, in a stepwise approach, separately or combined with Gal d 1-sIgE, may predict the allergic status and reduce the number of positive OFCs in children with egg allergy at low risk for severe reactions.

Eosinophilic cationic protein (ECP) in the clinical work-up of chronic cough.

BACKGROUND: Chronic cough is a common symptom, addressed in the clinical setting by empirical treatment together with some laboratory investigations. The aim of the present study was to investigate the value of testing eosinophilic cationic protein (ECP) serum levels combined with other diagnostic procedures and empirical treatment in the diagnostic workup of chronic cough. METHODS: In this study, we evaluated 194 patients with chronic cough. No subject had received any anti-inflammatory treatment before clinical evaluation, and none was an active smoker. ECP was measured with a commercially available fluoroenzyme immunoassay and results were expressed as µg/L. RESULTS: The analysis of variance showed that mean ECP level differs among the various diagnosis categories (P<0.001). Mean ECP level was significantly higher in asthmatic patients, particularly in the active disease. CONCLUSIONS: Serum ECP concentration could represent a useful biomarker in the clinical work-up of chronic cough, managing to differentiate asthma from other chronic disorders.

Serum Aspergillus-specific IgE, IgG and IgG4 immunoglobulins assessment in the work-up of invasive pulmonary aspergillosis: a prospective cohort study.

BACKGROUND: Positive microbiological fungal culture from bronchoalveolar-lavage-fluid (BAL) for Aspergillus or tissue biopsy and the detection of high levels of Aspergillus Galactomannan (GM) are commonly considered standard for diagnosing Invasive Pulmonary Aspergillosis (IPA). However, Aspergillus infection induces both cellular and humoral immune responses, characterized by the production of specific immunoglobulins, which can be easily detected in serum and accurately measured. This study hypothesized that Aspergillus-specific IgE, IgG, including IgG4, assays could be adopted as a rapid preliminary screening tool in patients with suspected Aspergillus-related lung disease in order to help in the identification of patients who require more invasive procedures (bronchoscopy, biopsy). METHODS: We prospectively stored 447 serum specimens of patients admitted for suspected IPA from 1 January 2010 to 31 July 2021. Serum total IgE and serum IgE, IgG and IgG4 specific for Aspergillus fumigatus and Aspergillus niger were determined for each sample. In addition, bronchoscopy with BAL for microbiologic culture and Aspergillus Galactomannan (GM) antigen were performed in all patients. RESULTS: Patients with IPA, diagnosed by detection of a positive BAL culture for Aspergillus and/or a positive GM, showed higher serum levels of specific Aspergillus fumigatus and Aspergillus niger immunoglobulins. Serum-specific Aspergillus fumigatus IgG at a cut-off of 22.6 mgA/L showed the highest sensitivity in predicting IPA, though quite moderate (AUC 0.62). Nonetheless, the simultaneous presence of values below the cut-off of Aspergillus IgE, IgG and IgG4 showed a negative predictive value greater than 90% both towards positive BAL culture and positive GM. CONCLUSIONS: In patients with suspected IPA, Aspergillus-specific immunoglobulins assay could be tested as a preliminary screening tool to support more invasive procedures, i.e. BAL.

The Bio-Diversity and the Role of Gut Microbiota in Postmenopausal Women with Luminal Breast Cancer Treated with Aromatase Inhibitors: An Observational Cohort Study.

The interactions between aromatase inhibitors (AI) in breast cancer (BC) and gut microbiota (GM) have not been completely established yet. The aim of the study is to evaluate the bio-diversity of GM and the relationship between GM, inflammation and tumor-infiltrating lymphocytes (TILs) in postmenopausal women with BC during adjuvant AI treatment compared to women with disease relapse during or after one year of AI therapy ("endocrine-resistant"). We conducted a monocenter observational case-control study. Eighty-four women with BC (8 cases, 76 controls) were enrolled from 2019 to 2021. We observed a significant difference in the mean microbial abundance between the two groups for the taxonomic rank of order (p 0.035) and family (p 0.029); specifically, the case group showed higher diversity than the control group. Veillonella reached its maximum abundance in cases (p 0.022). Cytokine levels were compared among the groups created considering the TILs levels. We obtained a statistically significant difference (p 0.045) in IL-17 levels among the groups, with patients with low TILs levels showing a higher median value for IL-17 (0.15 vs. 0.08 pg/mL). Further studies about the bio-diversity in women with BC may lead to the development of new biomarkers and targeted interventions.

Developing the Digital Healthcare Workforce in Italy: The SIBIM Experience.

The digital healthcare workforce is usually composed of two major types of professionals: the healthcare workers, who are the users of eHealth, and the health informatics developers, who are usually computer scientists, biomedical engineers, or other technical experts. Health informatics educators have the responsibility to develop the appropriate skills for both, acting within their specific curricula. Here we present the experience of the Italian Society of Biomedical Informatics (SIBIM) and show that, whereas the technical curricula are widely covered with a large range of topics, the eHealth education in medical curricula is often limited to simple bioengineering and informatics skills, thus suggesting that eHealth associations and organizations at the national level should focus their efforts towards increasing the level of eHealth contents in medical schools.

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital "Istituti Clinici Salvatore Maugeri" in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

Obstructive Sleep Apnea Home-Monitoring Using a Commercial Wearable Device.

Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. To allow an easier monitoring of the treatment efficacy at home, this work explores the possibility of using one of those commercial smart-bands. To this aim, we studied the signals provided by PSG and a Fitbit smart-band on 26 consecutive patients, admitted to the hospital after the diagnosis of OSA, and submitted to ventilation or positional treatment. They underwent monitoring for three nights (basal, titration, and control). We developed both a visualization software allowing doctors to visually compare the two hypnograms, and a set of statistics for assessing the concordance of the two methods. Results indicate that Fitbit can detect normal sleep patterns, while it is less able to detect the abnormal ones.

Personalising Symptoms Reporting in Telemonitoring Applications for Cancer Patients.

Patient reported outcomes have been shown to be predictive of cancer patients' prognosis, and their monitoring through electronic applications have been shown to positively impact survival. On the other hand, patient apps in general show a number of criticalities that often lead patients to abandon their use. One of them is usability. A scarce attention to usability during app development leads to unsatisfactory user experience. In this work, we present an algorithm to facilitate patient symptoms reporting, by personalising the list of symptoms according to their probability of occurrence in the specific patient. This avoids searching long lists of items, thus decreasing the patients' burden in symptom reporting.

Beyond ISN/RPS Lupus Nephritis Classification: Adding Chronicity Index to Clinical Variables Predicts Kidney Survival.

Background: A renewed interest for activity and chronicity indices as predictors of lupus nephritis (LN) outcome has emerged. Revised National Institutes of Health activity and chronicity indices have been proposed to classify LN lesions, but they should be validated by future studies. The aims of this study were (1) to detect the histologic features associated with the development of kidney function impairment (KFI), and (2) to identify the best clinical-histologic model to predict KFI at time of kidney biopsy. Methods: Patients with LN who had more than ten glomeruli per kidney biopsy specimen were admitted to the study. Univariate and multivariate logistic regression and Cox proportional hazards models were used to investigate whether activity and chronicity indices could predict KFI development. Results: Among 203 participants with LN followed for 14 years, correlations were found between the activity index, and its components, and clinical-laboratory signs of active LN at baseline. The chronicity index was correlated with serum creatinine. Thus, serum creatinine was significantly and directly correlated with both activity and chronicity indices. In the multivariate analysis, glomerulosclerosis (OR, 3.05; 95% CI, 1.17 to 7.91; P=0.02) and fibrous crescents (OR, 6.84; 95% CI, 3.22 to 14.52; P<0.001) associated with either moderate/severe tubular atrophy (OR, 3.17; 95% CI, 1.04 to 9.64; P=0.04), or with interstitial fibrosis (OR, 2.36; 95% CI, 1.05 to 5.32; P=0.04), predicted KFI. Considering both clinical and histologic features, serum creatinine (OR, 1.68; 95% CI, 1.31 to 2.15; P<0.001), arterial hypertension (OR, 4.64; 95% CI, 1.90 to 11.32; P<0.001), glomerulosclerosis (OR, 2.12; 95% CI, 1.00 to 4.50; P=0.05), and fibrous crescents (OR, 5.18; 95% CI, 2.43 to 11.04; P<0.001) independently predicted KFI. Older age (P<0.001) and longer delay between clinical onset of LN and kidney biopsy (P<0.001) were significantly correlated with baseline chronicity index. Conclusions: The chronicity index and its components, but not the activity index, were significantly associated with an impairment of kidney function. The Cox model showed that serum creatinine, arterial hypertension, chronic glomerular lesions, and delay in kidney biopsy predicted KFI. These data reinforce the importance of timely kidney biopsy in LN.

Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Asymmetry at Disease Onset Is Not a Predictor of Parkinson's Disease Progression.

BACKGROUND: Even though a significant fraction of Parkinson's disease (PD) patients presents with only minor or no motor asymmetry, the motor symptoms in PD typically start on one side of the body and worse symptoms on the side of the disease onset usually persist long after the disease has become clinically bilateral. The asymmetric presentation of PD has been studied over the years, with some studies showing slower progression in PD subjects with asymmetric disease presentation. In other studies, however, it was not possible to relate the asymmetry to disease progression. OBJECTIVE: The main objective of the present study was to assess the effect of asymmetry at disease onset on disease progression. METHODS: Using the data available in the Parkinson's Progression Markers Initiative (PPMI) database, at baseline, 423 subjects with de-novo PD were included in the study. Instead of dichotomizing the subjects in asymmetric and symmetric, we kept the asymmetry index and the non-motor, disability, and motor progression at one-, three-, and five-year follow-up continuous. Pearson's r correlational analysis and the coefficient of determination R2 were used to correlate asymmetry indices and disease progression. RESULTS: There was no correlation between neither clinically, nor DatSCAN defined asymmetry and non-motor, motor, and disability progression in the de-novo PD subjects with a 5-year follow-up. CONCLUSION: Asymmetry at disease onset does not predict progression of PD. Further studies are needed to investigate whether early detection of asymmetry on clinical grounds could successfully distinguish between PD and symmetric types of atypical parkinsonism in the early stages of the disease.

Impaired Glucose-Insulin Metabolism in Multisystem Inflammatory Syndrome Related to SARS-CoV-2 in Children.

An interaction between metabolic glucose impairment and coronavirus disease 2019 is reported. The development of a severe multisystem inflammatory syndrome in children (MIS-C) related to SARS-CoV-2 infection has been described. We evaluated the impact of MIS-C on glycemic patterns in pediatric patients. A group of 30 children and adolescents affected by MIS-C were considered; all patients were normal weight. Clinical and biochemical assessments, including surrogate markers of insulin resistance (IR) such as homeostasis model analysis-IR (HOMA-IR) and triglyceride-glucose (TyG) indexes, were recorded. Patients were also invited to undergo an intermittently scanned continuous glucose monitoring (isCGM). HOMA-IR index was calculated in 18 patients (60%), of which 17 (94%) revealed a pathological value. TyG index was computed for all patients and pathological values were detected in all cases. In 15 patients, isCGM data were recorded on average for 9 days (±3 days). Overall, average glucose was 105 mg/dL (±16 mg/dL) and average time spent in the 70-180 mg/dL range (TIR) was 93.76%, with nearly 10% of glucose readings in the 141-180 mg/dL range; glycemic fluctuations over the hyperglycemic threshold were detected in four patients. Regular glucose monitoring may be useful to prevent metabolic imbalance and obtain a better outcome.

Exploring the inter-subject variability in the relationship between glucose monitoring metrics and glycated hemoglobin for pediatric patients with type 1 diabetes.

OBJECTIVES: Despite the widespread diffusion of continuous glucose monitoring (CGM) systems, which includes both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), an effective application of CGM technology in clinical practice is still limited. The study aimed to investigate the relationship between isCGM-derived glycemic metrics and glycated hemoglobin (HbA1c), identifying overall CGM targets and exploring the inter-subject variability. METHODS: A group of 27 children and adolescents with type 1 diabetes under multiple daily injection insulin-therapy was enrolled. All participants used the isCGM Abbott's FreeStyle Libre system on average for eight months, and clinical data were collected from the Advanced Intelligent Distant-Glucose Monitoring platform. Starting from each HbA1c exam date, windows of past 30, 60, and 90 days were considered to compute several CGM metrics. The relationships between HbA1c and each metric were explored through linear mixed models, adopting an HbA1c target of 7%. RESULTS: Time in Range and Time in Target Range show a negative relationship with HbA1c (R2>0.88) whereas Time Above Range and Time Severely Above Range show a positive relationship (R2>0.75). Focusing on Time in Range in 30-day windows, random effect represented by the patient's specific intercept reveals a high variability compared to the overall population intercept. CONCLUSIONS: This study confirms the relationship between several CGM metrics and HbA1c; it also highlights the importance of an individualized interpretation of the CGM data.

Enhancing the IDEAS Framework with Ontology: Designing Digital Interventions for Improving Cancer Patients' Wellbeing.

Developing effective digital interventions to help patients form healthy habits is a challenging goal. IDEAS is a step-by-step framework that allows developers to draw ideas from intended users and behavioral theories, and ideate implementation strategies for them, followed by rapid prototype development. Based on our long experience with developing generic knowledge-based clinical decision support systems (CDSS) and integrating them with electronic health records (EHR) to deliver patient-specific advice, we observed a challenge that IDEAS is not addressing: the semantic detailing of the clinical knowledge behind the digital intervention and relevant patient data that could be used to personalize the digital intervention. To close the gap, we augmented two steps of IDEAS with an ontology that structures the target behavior as classes, derived from HL7 Fast Healthcare Interoperability Resources standard. We exemplify the augmented IDEAS with a case study taken from the Horizon 2020 CAPABLE project, that uses Fogg's Tiny Habits behavioral model to improve the sleep of cancer patients via Tai Chi.