Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

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.

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

Background

While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.

Methods

A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.

Findings

Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.

Interpretation

COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.

Funding

Authors are supported by various funders, with full details stated in the acknowledgement section.

Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma

Background and objectives

Investigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment.

Patients and methods

The retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors' investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systems.

Results

The median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses.

Conclusions

Our AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model.

SurvMaximin: Robust federated approach to transporting survival risk prediction models

OBJECTIVE

For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information.

MATERIALS AND METHODS

For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning.

RESULTS

Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations.

CONCLUSIONS

The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2

OBJECTIVE

To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic.

DESIGN, SETTING AND PARTICIPANTS

This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation.

RESULTS

Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001).

CONCLUSIONS

Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

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.

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.

Preoperative Systemic Inflammatory Biomarkers Are Independent Predictors of Disease Recurrence in ER+ HER2- Early Breast Cancer

The host's immune system plays a crucial role in determining the clinical outcome of many cancers, including breast cancer. Peripheral blood neutrophils and lymphocytes counts may be surrogate markers of systemic inflammation and potentially reflect survival outcomes. The aim of the present study is to assess the role of preoperative systemic inflammatory biomarkers to predict local or distant relapse in breast cancer. In particular we investigated ER+ HER2- early breast cancer, considering its challenging risk stratification. A total of 1,763 breast cancer patients treated at tertiary referral Breast Unit were reviewed. Neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR) and lymphocyte-to-monocyte (LMR) ratios were assessed from the preoperative blood counts. Multivariate analyses for 5-years locoregional recurrence-free (LRRFS), distant metastases-free (DMFS) and disease-free survivals (DFS) were performed, taking into account both blood inflammatory biomarkers and clinical-pathological variables. Low NLR and high LMR were independent predictors of longer LRRFS, DMFS and DFS, and low PLR was predictive of better LRRFS and DMFS in the study population. In 999 ER+ HER2- early breast cancers, high PLR was predictive of worse LRRFS (HR 0.42, p=0.009), while high LMR was predictive of improved LRRFS (HR 2.20, p=0.02) and DFS (HR 2.10, p=0.01). NLR was not an independent factor of 5-years survival in this patients' subset. Inflammatory blood biomarkers and current clinical assessment of the disease were not in agreement in terms of estimate of relapse risk (K-Cohen from -0.03 to 0.02). In conclusion, preoperative lymphocyte ratios, in particular PLR and LMR, showed prognostic relevance in ER+ HER2- early breast cancer. Therefore, they may be used in risk stratification and therapy escalation/de-escalation in patients with this type of tumor.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

Background

Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.

Objective

In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic.

Methods

Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19.

Results

Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain.

Conclusions

Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data

OBJECTIVE

The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.

MATERIALS AND METHODS

Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.

RESULTS

The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.

DISCUSSION

We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.

CONCLUSIONS

We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

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.

Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

Objectives

To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions.

Design

Retrospective cohort study.

Setting

The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe.

Participants

Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2.

Primary and secondary outcome measures

Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction.

Results

Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites.

Conclusions

Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

A dashboard-based system for supporting diabetes care

Objective

To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice.

Methods

The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers.

Results

The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system's capability of identifying and understanding the characteristics of patient subgroups treated at the center.

Conclusion

Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

3039Vulnerability of carotid atherosclerosis: relationship with plaque location, plaque eccentricity and vessel remodeling patterns. Insight from the the MAGNETIC observational study

Conclusions

Carotid atherosclerotic plaque vulnerability seems to be independently associated with longitudinal location, plaque eccentricity and vessel positive remodeling.

Acknowledgement/Funding

Bayer AG, Leverkusen, Germany

Multicentre registry of brain-injured patients with disorder of consciousness: rationale and preliminary data

Diagnostic accuracy and reliable estimation of clinical evolution are challenging issues in the management of patients with disorders of consciousness (DoC). Longitudinal systematic investigations conducted in large cohorts of patients with DoC could make it possible to identify reliable diagnostic and prognostic markers. On the basis of this consideration, we devised a multicentre prospective registry for patients with DoC admitted to ten intensive rehabilitation units. The registry collects homogeneous and detailed data on patients' demographic and clinical features, neurophysiological and neuroimaging findings, and medical and surgical complications. Here we present the rationale and the design of the registry and the preliminary results obtained in 53 patients with DoC (vegetative state or minimally conscious state) enrolled during the first seven months of the study. Data at 6-month post-injury follow-up were available for 46 of them. This registry could be an important tool for collecting high-quality data through the application of rigorous methods, and it could be used in the routine management of patients with DoC admitted to rehabilitation settings.

Risk factors for the development of micro-vascular complications of type 2 diabetes in a single-centre cohort of patients

AIMS

In type 2 diabetes, we aimed at clarifying the role of glycated haemoglobin variability and other risk factors in the development of the main micro-vascular complications: peripheral neuropathy, nephropathy and retinopathy.

METHODS

In a single-centre cohort of 900 patients, glycated haemoglobin variability was evaluated as intra-individual standard deviation, adjusted standard deviation and coefficient of variation of serially measured glycated haemoglobin in the 2-year period before a randomly selected index visit. We devised four models considering different aspects of glycated haemoglobin evolution. Multivariate stepwise logistic regression analysis was performed including the following covariates at the index visit: age, disease duration, body mass index, total cholesterol, high-density lipoprotein cholesterol, triglycerides, sex, smoking habit, hypertension, dyslipidemia, treatment with anti-diabetic drugs, occurrence of macro-vascular events and the presence of another micro-vascular complication.

RESULTS

Males with high mean glycated haemoglobin, long duration of diabetes, presence of macro-vascular events and retinopathy emerged at higher risk for peripheral neuropathy. Development of nephropathy was independently associated with higher glycated haemoglobin variability, older age, male sex, current smoking status, presence of retinopathy, of peripheral neuropathy and of hypertension. Higher mean glycated haemoglobin, younger age, longer duration of diabetes, reduced estimated glomerular filtration rate and the presence of peripheral neuropathy were significantly associated with increased incidence of retinopathy.

CONCLUSION

Glycated haemoglobin variability was associated with increased incidence of nephropathy, while mean glycated haemoglobin emerged as independent risk factor for the development of retinopathy and peripheral neuropathy. The presence of macro-vascular events was positively correlated with peripheral neuropathy. Finally, the occurrence of another micro-vascular complication was found to be a stronger risk factor for developing another micro-vascular complication than the mean or variability of glycated haemoglobin.

Machine Learning Methods to Predict Diabetes Complications

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

Careflow Mining Techniques to Explore Type 2 Diabetes Evolution

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.

Information extraction from Italian medical reports: An ontology-driven approach

OBJECTIVE

In this work, we propose an ontology-driven approach to identify events and their attributes from episodes of care included in medical reports written in Italian. For this language, shared resources for clinical information extraction are not easily accessible.

MATERIALS AND METHODS

The corpus considered in this work includes 5432 non-annotated medical reports belonging to patients with rare arrhythmias. To guide the information extraction process, we built a domain-specific ontology that includes the events and the attributes to be extracted, with related regular expressions. The ontology and the annotation system were constructed on a development set, while the performance was evaluated on an independent test set. As a gold standard, we considered a manually curated hospital database named TRIAD, which stores most of the information written in reports.

RESULTS

The proposed approach performs well on the considered Italian medical corpus, with a percentage of correct annotations above 90% for most considered clinical events. We also assessed the possibility to adapt the system to the analysis of another language (i.e., English), with promising results.

DISCUSSION AND CONCLUSION

Our annotation system relies on a domain ontology to extract and link information in clinical text. We developed an ontology that can be easily enriched and translated, and the system performs well on the considered task. In the future, it could be successfully used to automatically populate the TRIAD database.

Arrhythmogenic Right Ventricular Cardiomyopathy: Clinical Course and Predictors of Arrhythmic Risk

BACKGROUND

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a leading cause of sudden cardiac death, but its progression over time and predictors of arrhythmias are still being defined.

OBJECTIVES

This study sought to describe the clinical course of ARVC and occurrence of life-threatening arrhythmic events (LAE) and cardiovascular mortality; identify risk factors associated with increased LAE risk; and define the response to therapy.

METHODS

We determined the clinical course of 301 consecutive patients with ARVC using the Kaplan-Meier method adjusted to avoid the bias of delayed entry. Predictors of LAE over 5.8 years of follow-up were determined with Cox multivariable analysis. Treatment efficacy was assessed comparing LAE rates during matched time intervals.

RESULTS

A first LAE occurred in 1.5 per 100 person-years between birth and age 20 years, in 4.0 per 100 person-years between ages 21 and 40 years, and in 2.4 per 100 person-years between ages 41 and 60 years. Cumulative probability of a first LAE at follow-up was 14% at 5 years, 23% at 10 years, and 30% at 15 years. Higher risk of LAE was predicted by atrial fibrillation (hazard ratio [HR]: 4.38; p = 0.002), syncope (HR: 3.36; p < 0.001), participation in strenuous exercise after the diagnosis (HR: 2.98; p = 0.028), hemodynamically tolerated sustained monomorphic ventricular tachycardia (HR: 2.19; p = 0.023), and male sex (HR: 2.49; p = 0.012). No difference was observed in the occurrence of LAE before and after treatment with amiodarone, beta-blockers, sotalol, or ablation. A total of 81 patients received an implantable cardioverter-defibrillator, 34 were successfully defibrillated.

CONCLUSIONS

The high risk of life-threatening arrhythmias in patients with ARVC spans from adolescence to advanced age, reaching its peak between ages 21 and 40 years. Atrial fibrillation, syncope, participation in strenuous exercise after the diagnosis of ARVC, hemodynamically tolerated sustained monomorphic ventricular tachycardia, and male sex predicted lethal arrhythmias at follow-up. The lack of efficacy of antiarrhythmic therapy and the life-saving role of the implantable cardioverter-defibrillator highlight the importance of risk stratification for patient management.

EPMA-World Congress 2015

A1 Predictive and prognostic biomarker panel for targeted application of radioembolisation improving individual outcomes in hepatocellular carcinoma

Jella-Andrea Abraham, Olga Golubnitschaja

A2 Integrated market access approach amplifying value of “Rx-CDx”

Ildar Akhmetov

A3 Disaster response: an opportunity to improve global healthcare

Russell J. Andrews, Leonidas Quintana

A4 USA PPPM: proscriptive, profligate, profiteering medicine-good for 1 % wealthy, not for 99 % unhealthy

Russell J. Andrews

A5 The role of IDO in a murine model of gingivitis: predictive and therapeutic potentials

Babak Baban, Jun Yao Liu, Xu Qin, Tailing Wang, Mahmood S. Mozaffari

A6 Specific diets for personalised treatment of diabetes type 2

Viktoriia V. Bati, Tamara V. Meleshko, Olga B. Levchuk, Nadiya V. Boyko

A7 Towards personalized physiotherapeutic approach

Joanna Bauer, Ewa Boerner, Halina Podbielska

A8 Cells, animal, SHIME and in silico models for detection and verification of specific biomarkers of non-communicable chronic diseases

Alojz Bomba, Viktor O. Petrov, Volodymyr G. Drobnych, Rostyslav V. Bubnov, Oksana M. Bykova, Nadiya V. Boyko

A9 INTERACT-chronic care model: Self-treatment by patients with decision support e-Health solution

Hans-Peter Brunner-La Rocca, Lutz Fleischhacker, Olga Golubnitschaja, Frank Heemskerk, Thomas Helms, Tiny Jaarsma, Judita Kinkorova, Jan Ramaekers, Peter Ruff, Ivana Schnur, Emilio Vanoli, Jose Verdu

A10 PPPM in cardiovascular medicine in 2015

Hans-Peter Brunner-La Rocca

A11 Magnetic resonance imaging of nanoparticles in mice, potential for theranostic and contrast media development – pilot results

Rostyslav V. Bubnov, Sergiy A. Grabovetskyi, Olena M. Mykhalchenko, Natalia O. Tymoshok, Oleksandr B. Shcherbakov, Igor P. Semeniv, Mykola Y. Spivak

A12 Ultrasound diagnosis for diabetic neuropathy - comparative study

Rostyslav V. Bubnov, Tetyana V. Ostapenko

A13 Ultrasound for stratification patients with diabetic foot ulcers for prevention and personalized treatment - pilot results

Rostyslav V. Bubnov, Nazarii M. Kobyliak, Nadiya M. Zholobak, Mykola Ya. Spivak

A14 Project ImaGenX – designing and executing a questionnaire on environment and lifestyle risk of breast cancer

John Paul Cauchi

A15 Genomics – a new structural brand of predictive, preventive and personalized medicine or the new driver as well?

Dmitrii Cherepakhin, Marina Bakay, Artem Borovikov, Sergey Suchkov

A16 Survey of questionnaires for evaluation of the quality of life in various medical fields

Barbara Cieślik, Agnieszka Migasiewicz, Maria-Luiza Podbielska, Markus Pelleter, Agnieszka Giemza, Halina Podbielska

A17 Personalized molecular treatment for muscular dystrophies

Sebahattin Cirak

A18 Secondary mutations in circulating tumour DNA for acquired drug resistance in patients with advanced ALK + NSCLC

Marzia Del Re, Paola Bordi, Valentina Citi, Marta Palombi, Carmine Pinto, Marcello Tiseo, Romano Danesi

A19 Recombinant species-specific FcεRI alpha proteins for diagnosis of IgE-mediated allergies in dogs, cats and horses

Lukas Einhorn, Judit Fazekas, Martina Muhr, Alexandra Schoos, Lucia Panakova, Ina Herrmann, Krisztina Manzano-Szalai, Kumiko Oida, Edda Fiebiger, Josef Singer, Erika Jensen-Jarolim

A20 Global methodology for developmental neurotoxicity testing in humans and animals early and chronically exposed to chemical contaminants

Arpiné A. Elnar, Nadia Ouamara, Nadiya Boyko, Xavier Coumoul, Jean-Philippe Antignac, Bruno Le Bizec, Gauthier Eppe, Jenny Renaut, Torsten Bonn, Cédric Guignard, Margherita Ferrante, Maria Liusa Chiusano, Salvatore Cuzzocrea, Gerard O'Keeffe, John Cryan, Michelle Bisson, Amina Barakat, Ihsane Hmamouchi, Nasser Zawia, Anumantha Kanthasamy, Glen E. Kisby, Rui Alves, Oscar Villacañas Pérez, Kim Burgard, Peter Spencer, Norbert Bomba, Martin Haranta, Nina Zaitseva, Irina May, Stéphanie Grojean, Mathilde Body-Malapel, Florencia Harari, Raul Harari, Kristina Yeghiazaryan, Olga Golubnitschaja, Vittorio Calabrese, Christophe Nemos, Rachid Soulimani

A21 Mental indicators at young people with attributes hypertension and pre-hypertension

Maria E. Evsevyeva, Elena A. Mishenko, Zurida V. Kumukova, Evgeniy V. Chudnovsky, Tatyana A. Smirnova

A22 On the approaches to the early diagnosis of stress-induced hypertension in young employees of State law enforcement agencies

Maria E. Evsevyeva, Ludmila V. Ivanova, Michail V. Eremin, Maria V. Rostovtseva

A23 Сentral aortic pressure and indexes of augmentation in young persons in view of risk factors

Maria E. Evsevyeva, Michail V. Eremin, Vladimir I. Koshel, Oksana V. Sergeeva, Nadesgda M. Konovalova

A24 Breast cancer prediction and prevention: Are reliable biomarkers in horizon?

Shantanu Girotra, Olga Golubnitschaja

A25 Flammer Syndrome and potential formation of pre-metastatic niches: A multi-centred study on phenotyping, patient stratification, prediction and potential prevention of aggressive breast cancer and metastatic disease

Olga Golubnitschaja, Manuel Debald, Walther Kuhn, Kristina Yeghiazaryan, Rostyslav V. Bubnov, Vadym M. Goncharenko, Ulyana Lushchyk, Godfrey Grech, Katarzyna Konieczka

A26 Innovative tools for prenatal diagnostics and monitoring: improving individual pregnancy outcomes and health-economy in EU

Olga Golubnitschaja, Jan Jaap Erwich, Vincenzo Costigliola, Kristina Yeghiazaryan, Ulrich Gembruch

A27 Immunohistochemical assessment of APUD cells in endometriosis

Vadym M. Goncharenko, Vasyl O. Beniuk, Olga V. Kalenska, Rostyslav V. Bubnov

A28 Updating personalized management algorithm of endometrial hyperplasia in pre-menopause women

Vadym M. Goncharenko, Vasyl O. Beniuk, Rostyslav V. Bubnov, Olga Melnychuk

A29 The personified treatment approach of polimorbid patients with periodontal inflammatory diseases

Irina A. Gorbacheva, Lyudmila Y. Orekhova, Vadim V. Tachalov

A30 Ukrainian experience in hybrid war – the challenge to update algorithms for personalized care and early prevention of different military injuries

Olena I. Grechanyk, Rizvan Ya. Abdullaiev, Rostyslav V. Bubnov

A31 Tear fluid biomarkers: a comparison of tear fluid sampling and storage protocols

Suzanne Hagan, Eilidh Martin, Ian Pearce, Katherine Oliver

A32 The correlation of dietary habits with gingival problems during menstruation

Cenk Haytac, Fariz Salimov, Servin Yoksul, Anatoly A. Kunin, Natalia S. Moiseeva

A33 Genomic medicine in a contemporary Spanish population of prostate cancer: our experience

Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara, Antonia Angulo, Francisco Javier Machuca Santa-Cruz

A34 Challenges, opportunities and collaborations for personalized medicine applicability in uro-oncological disease

Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara

A35 Metabolic hallmarks of cancer as targets for a personalized therapy

John Ionescu

A36 Influence of genetic polymorphism as a predictor of the development of periodontal disease in patients with gastric ulcer and 12 duodenal ulcer

Alfiya Z. Isamulaeva, Anatoly A. Kunin, Shamil Sh. Magomedov, Aida I. Isamulaeva

A37 Challenges in diabetic macular edema

Tatjana Josifova

A38 Overview of the EPMA strategies in laboratory medicine relevant for PPPM

Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja, Vincenzo Costigliola

A39 EPMA initiative for effective organization of medical travel: European concepts and criteria

Vincenzo Costigliola, Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja

A40 Design and innovation in e-textiles: implications for PPPM

Anthony Kent, Tom Fisher, Tilak Dias

A41 Biobank in Pilsen as a member of national node BBMRI_CZ

Judita Kinkorová, Ondřej Topolčan

A42 Big data in personalized medicine: hype and hope

Matthias Kohl

A43 The 3P approach as the platform of the European Dentistry Department (DPPPD)

Anatoly A. Kunin, Natalia S. Moiseeva

A44 The endometrium cytokine patterns for predictive diagnosis of proliferation severity and cancer prevention

Andrii I. Kurchenko, Vasyl A. Beniuk, Vadym M. Goncharenko, Rostyslav V. Bubnov, Nadiya V. Boyko, Andriy M. Strokan

A45 A monocyte-based in-vitro system for testing individual responses to the implanted material: future for personalized implant construction

Julia Kzhyshkowska, Alexandru Gudima, Ksenia S. Stankevich, Victor D. Filimonov⁴, Harald Klüter, Evgeniya M. Mamontova, Sergei I. Tverdokhlebov

A46 Prediction and prevention of adverse health effects by meteorological factors: Biomarker patterns and creation of a device for self-monitoring and integrated care

Ulyana B. Lushchyk, Viktor V. Novytskyy, Igor P. Babii, Nadiya G. Lushchyk, Lyudmyla S. Riabets, Ivanna I. Legka

A47 Targeting "disease signatures" towards personalized healthcare

Mira Marcus-Kalish, Alexis Mitelpunkt, Tal Galili, Neta Shachar, Yoav Benjamini

A48 Influence of the skin imperfection on the personal quality of life and possible tools for objective diagnosis

Agnieszka Migasiewicz, Markus Pelleter, Joanna Bauer, Ewelina Dereń, Halina Podbielska

A49 The new direction in caries prevention based on the ultrastructure of dental hard tissues and filling materials

Natalia S. Moiseeva, Anatoly A. Kunin, Dmitry A. Kunin

A50 The use of LED radiation in prevention of dental diseases

Natalia S. Moiseeva, Yury A. Ippolitov, Dmitry A. Kunin, Alexei N. Morozov, Natalia V. Chirkova, Nakhid T. Aliev

A51 Status of endothelial progenitor cells in diabetic nephropathy: predictive and preventive potentials

Mahmood S. Mozaffari, Jun Yao Liu, Babak Baban

A52 The status of glucocorticoid-induced leucine zipper protein in salivary gland in Sjögren’s syndrome: predictive and personalized treatment potentials

Mahmood S. Mozaffari, Jun Yao Liu, Rafik Abdelsayed, Xing-Ming Shi, Babak Baban

A53 Maximal aerobic capacity - important quality marker of health

Jaroslav Novák, Milan Štork, Václav Zeman

A54 The EMPOWER project: laboratory medicine and Horizon 2020

Wytze P. Oosterhuis, Elvar Theodorsson

A55 Personality profile manifestations in patient’s attitude to oral care and adherence to doctor’s prescriptions

Lyudmila Y. Orekhova, Tatyana V. Kudryavtseva, Elena R. Isaeva, Vadim V. Tachalov, Ekaterina S. Loboda

A56 Results of an European survey on personalized medicine addressed to directions of laboratory medicine

Mario Pazzagli, Francesca Malentacchi, Irene Mancini, Ivan Brandslund, Pieter Vermeersch, Matthias Schwab, Janja Marc, Ron H.N. van Schaik, Gerard Siest, Elvar Theodorsson, Chiara Di Resta

A57 MCI or early dementia predictive speech based diagnosis techniques

Matus Pleva, Jozef Juhar

A58 Personalized speech based mobile application for eHealth

Matus Pleva, Jozef Juhar

A59 Circulating tumor cell-free DNA as the biomarker in the management of cancer patients

Jiří Polívka jr., Filip Janků, Martin Pešta, Jan Doležal, Milena Králíčková, Jiří Polívka

A60 Complex stroke care – educational programme in Stroke Centre University Hospital Plzen

Jiří Polívka, Alena Lukešová, Nina Müllerová, Petr Ševčík, Vladimír Rohan

A61 Sleep apnea and sleep fragmentation contribute to brain aging

Kneginja Richter, Lence Miloseva, Günter Niklewski

A62 Personalised approach for sleep disturbances in shift workers

Kneginja Richter, Jens Acker, Guenter Niklewski

A63 Medical travel and innovative PPPM clusters: new concept of integration

Olga Safonicheva, Vincenzo Costigliola

A64 Medical travel and women health

Olga Safonicheva

A65 Continuity of generations in the training of specialists in the field of reconstructive microsurgery

Maxim Sautin, Janna Sinelnikova, Sergey Suchkov

A66 Telemonitoring of stroke patients – empirical evidence of individual risk management results from an observational study in Germany

Songül Secer, Stephan von Bandemer

A67 Women’s increasing breast cancer risk with n-6 fatty acid intake explained by estrogen-fatty acid interactive effect on DNA damage: implications for gender-specific nutrition within personalized medicine

Niva Shapira

A68 Cytobacterioscopy of the gingival crevicular fluid as a method for preventive diagnosis of periodontal diseases

Aleksandr Shcherbakov, Anatoly A. Kunin, Natalia S. Moiseeva

A69 Use of specially treated composites in dentistry to avoid violations of aesthetics

Bogdan R. Shumilovich, Zhanna Lipkind, Yulia Vorobieva, Dmitry A. Kunin, Anastasiia V. Sudareva

A70 National eHealth system – platform for preventive, predictive and personalized diabetes care

Ivica Smokovski, Tatjana Milenkovic

A72 The common energy levels of Prof. Szent-Györgyi, the intrinsic chemistry of melanin, and the muscle physiopathology. Implications in the context of Preventive, Predictive, and Personalized Medicine

Arturo Solís-Herrera, María del Carmen Arias-Esparza, Sergey Suchkov

A73 Plurality and individuality of hepatocellular carcinoma: PPPM perspectives

Krishna Chander Sridhar, Olga Golubnitschaja

A74 Strategic aspects of higher medical education reforms to secure newer educational platforms for getting biopharma professionals matures

Maria Studneva, Sihong Song, James Creeden, Мark Мandrik, Sergey Suchkov

A75 Overview of the strategies and activities of the European Federation of Clinical Chemistry and Laboratory Medicine, (EFLM)

Elvar Theodorsson, EFLM

A76 New spectroscopic techniques for point of care label free diagnostics

Syed A. M. Tofail

A77 Tumor markers for personalized medicine and oncology - the role of Laboratory Medicine

Ondřej Topolčan, Judita Kinkorová, Ondřej Fiala, Marie Karlíková, Šárka Svobodová, Radek Kučera, Radka Fuchsová, Vladislav Třeška, Václav Šimánek, Ladislav Pecen, Jan Šoupal, Štěpán Svačina²

A78 Modern medical terminology (MMT) as a driver of the global educational reforms

Evgeniya Tretyak, Maria Studneva, Sergey Suchkov

A79 Juvenile hypertension; the relevance of novel predictive, preventive and personalized assessment of its determinants

Francesca M. Trovato, G. Fabio Martines, Daniela Brischetto, Daniela Catalano, Giuseppe Musumeci, Guglielmo M. Trovato

A80 Proteomarkers Biotech

George Th. Tsangaris, Athanasios K. Anagnostopoulos

A81 Proteomics and mass spectrometry based non-invasive prenatal testing of fetal health and pregnancy complications

George Th. Tsangaris, Athanasios K. Anagnostopoulos

A82 Integrated Ecosystem for an Integrated Care model for Heart Failure (HF) patients including related comorbidities (ZENITH)

José Verdú, German Gutiérrez, Jordi Rovira, Marta Martinez, Lutz Fleischhacker, Donna Green, Arthur Garson, Elena Tamburini, Stefano Cuomo, Juan Martinez-Leon, Teresa Abrisqueta, Hans-Peter Brunner-La Rocca, Tiny Jaarsma, Teresa Arredondo, Cecilia Vera, Giuseppe Fico, Olga Golubnitschaja, Fernando Arribas, Martina Onderco, Isabel Vara, on behalf of ZENITH consortium

A83 Predictive, preventive and personalized medicine in diabetes onset and complication (MOSAIC project)

José Verdú, Francesco Sambo, Barbara Di Camillo, Claudio Cobelli, Andrea Facchinetti, Giuseppe Fico, Riccardo Bellazzi, Lucia Sacchi, Arianna Dagliati, Daniele Segnani, Valentina Tibollo, Manuel Ottaviano, Rafael Gabriel, Leif Groop, Jacqueline Postma, Antonio Martinez, Liisa Hakaste, Tiinamaija Tuomi, Konstantia Zarkogianni, on behalf of MOSAIC consortium

A84 Possibilities for personalized therapy of diabetes using in vitro screening of insulin and oral hypoglycemic agents

Igor Volchek, Nina Pototskaya, Andrey Petrov

A85 The innovative technology for personalized therapy of human diseases based on in vitro drug screening

Igor Volchek, Nadezhda Pototskaya, Andrey Petrov

A86 Bone destruction and temporomandibular joint: predictive markers, pathogenetic aspects and quality of life

Ülle Voog-Oras, Oksana Jagur, Edvitar Leibur, Priit Niibo, Triin Jagomägi, Minh Son Nguyen, Chris Pruunsild, Dagmar Piikov, Mare Saag

A87 Sub-optimal health management – global vision for concepts in medical travel

Wei Wang

A88 Sub-optimal health management: synergic PPPM-TCAM approach

Wei Wang

A89 Innovative technologies for minimal invasive diagnostics

Andreas Weinhäusel, Walter Pulverer, Matthias Wielscher, Manuela Hofner, Christa Noehammer, Regina Soldo, Peter Hettegger, Istvan Gyurjan, Ronald Kulovics, Silvia Schönthaler, Gabriel Beikircher, Albert Kriegner, Stephan Pabinger, Klemens Vierlinger

A90 Rare disease diobanks for personalized medicine

Ayşe Yüzbaşıoğlu, Meral Özgüç, Member of EuroBioBank - European Network of DNA, Cell and Tissue Banks for Rare Diseases

Template for preparation of papers for IEEE sponsored conferences & symposia

To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.

Beyond Cohort Selection: An Analytics-Enabled i2b2

The i2b2 software is a widely adopted solution for secondary use of clinical data for clinical research, specifically designed for cohort identification. i2b2 is still lacking functionalities for data analysis. The aim of this work is to empower the i2b2 framework enabling clinical researchers to perform statistical analyses for accelerating the process of hypothesis testing. To this aim we have developed a flexible extension of i2b2 able to exploit different statistical engines. We have implemented some first applications for basic statistics and survival analyses, exploiting this extension and accessible through suitable user interfaces designed with a special consideration for usability.

Improving Clinical Decisions on T2DM Patients Integrating Clinical, Administrative and Environmental Data

This work describes an integrated informatics system developed to collect and display clinically relevant data that can inform physicians and researchers about Type 2 Diabetes Mellitus (T2DM) patient clinical pathways and therapy adherence. The software we developed takes data coming from the electronic medical record (EMR) of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines the data with administrative, pharmacy drugs (purchased from the local healthcare agency (ASL) of the Pavia area), and open environmental data of the same region. By using different use cases, we explain the importance of gathering and displaying the data types through a single informatics tool: the use of the tool as a calculator of risk factors and indicators to improve current detection of T2DM, a generator of clinical pathways and patients' behaviors from the point of view of the hospital care management, and a decision support tool for follow-up visits. The results of the performed data analysis report how the use of the dashboard displays meaningful clinical decisions in treating complex chronic diseases and might improve health outcomes.

Clinical and research data integration: the i2b2-FSM experience

In order to support and improve the efficiency of clinical research in specific health area, the University of Pavia and the IRCCS Fondazione Salvatore Maugeri of Pavia (FSM) are developing and implementing an i2b2 based platform, designed to collect data coming from hospital clinical practice and scientific research. The work made in FSM is committed to support an affordable, less intrusive and more personalized care, increasing the quality of clinical practice as well as improving the scientific results. Such a aim depends on the application of information and communication technologies and the use of data. An integrated data warehouse has been implemented to support clinicians and researchers in two medical fields with a great impact on the population: oncology and cardiology. Furthermore the data warehouse approach has been tested with administrative information, allowing a financial view of clinical data.

An ICT infrastructure to integrate clinical and molecular data in oncology research

Background

The ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface.

Methods

Onco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system.

Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services.

Results

Onco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts.

Conclusions

Onco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.

CARDIO-i2b2: integrating arrhythmogenic disease data in i2b2

The CARDIO-i2b2 project is an initiative to customize the i2b2 bioinformatics tool with the aim to integrate clinical and research data in order to support translational research in cardiology. In this work we describe the implementation and the customization of i2b2 to manage the data of arrhytmogenic disease patients collected at the Fondazione Salvatore Maugeri of Pavia in a joint project with the NYU Langone Medical Center (New York, USA). The i2b2 clinical research chart data warehouse is populated with the data obtained by the research database called TRIAD. The research infrastructure is extended by the development of new plug-ins for the i2b2 web client application able to properly select and export phenotypic data and to perform data analysis.

R engine cell: integrating R into the i2b2 software infrastructure

Informatics for Integrating Biology and the Bedside (i2b2) is an initiative funded by the NIH that aims at building an informatics infrastructure to support biomedical research. The University of Pavia has recently integrated i2b2 infrastructure with a registry of inherited arrhythmogenic diseases. Within this project, the authors created a novel i2b2 cell, named R Engine Cell, which allows the communication between i2b2 and the R statistical software. As survival analyses are routinely performed by cardiology researchers, the authors have first concentrated on making Kaplan-Meier analyses available within the i2b2 web interface. To this aim, the authors developed a web-client plug-in to select the patient set on which to perform the analysis and to display the results in a graphical, intuitive way. R Engine Cell has been designed to easily support the integration of other R-based statistical analyses into i2b2.

The ONCO-I2b2 project: integrating biobank information and clinical data to support translational research in oncology

The University of Pavia and the IRCCS Fondazione Salvatore Maugeri of Pavia (FSM), has recently started an IT initiative to support clinical research in oncology, called ONCO-i2b2. ONCO-i2b2, funded by the Lombardia region, grounds on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) NIH project. Using i2b2 and new software modules purposely designed, data coming from multiple sources are integrated and jointly queried. The core of the integration process stands in retrieving and merging data from the biobank management software and from the FSM hospital information system. The integration process is based on a ontology of the problem domain and on open-source software integration modules. A Natural Language Processing module has been implemented, too. This module automatically extracts clinical information of oncology patients from unstructured medical records. The system currently manages more than two thousands patients and will be further implemented and improved in the next two years.