Plasma neurofilament light admission levels and development of axonal pathology in mild traumatic brain injury.

BACKGROUND: It is known that blood levels of neurofilament light (NF-L) and diffusion-weighted magnetic resonance imaging (DW-MRI) are both associated with outcome of patients with mild traumatic brain injury (mTBI). Here, we sought to examine the association between admission levels of plasma NF-L and white matter (WM) integrity in post-acute stage DW-MRI in patients with mTBI. METHODS: Ninety-three patients with mTBI (GCS ≥ 13), blood sample for NF-L within 24 h of admission, and DW-MRI ≥ 90 days post-injury (median = 229) were included. Mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated from the skeletonized WM tracts of the whole brain. Outcome was assessed using the Extended Glasgow Outcome Scale (GOSE) at the time of imaging. Patients were divided into CT-positive and -negative, and complete (GOSE = 8) and incomplete recovery (GOSE < 8) groups. RESULTS: The levels of NF-L and FA correlated negatively in the whole cohort (p = 0.002), in CT-positive patients (p = 0.016), and in those with incomplete recovery (p = 0.005). The same groups showed a positive correlation with mean MD, AD, and RD (p < 0.001-p = 0.011). In CT-negative patients or in patients with full recovery, significant correlations were not found. CONCLUSION: In patients with mTBI, the significant correlation between NF-L levels at admission and diffusion tensor imaging (DTI) measurements of diffuse axonal injury (DAI) over more than 3 months suggests that the early levels of plasma NF-L may associate with the presence of DAI at a later phase of TBI.

Integrating data from multiple Finnish biobanks and national health-care registers for retrospective studies: Practical experiences.

Aim: This case study aimed to investigate the process of integrating resources of multiple biobanks and health-care registers, especially addressing data permit application, time schedules, co-operation of stakeholders, data exchange and data quality. Methods: We investigated the process in the context of a retrospective study: Pharmacogenomics of antithrombotic drugs (PreMed study). The study involved linking the genotype data of three Finnish biobanks (Auria Biobank, Helsinki Biobank and THL Biobank) with register data on medicine dispensations, health-care encounters and laboratory results. Results: We managed to collect a cohort of 7005 genotyped individuals, thereby achieving the statistical power requirements of the study. The data collection process took 16 months, exceeding our original estimate by seven months. The main delays were caused by the congested data permit approval service to access national register data on health-care encounters. Comparison of hospital data lakes and national registers revealed differences, especially concerning medication data. Genetic variant frequencies were in line with earlier data reported for the European population. The yearly number of international normalised ratio (INR) tests showed stable behaviour over time. Conclusions: A large cohort, consisting of versatile individual-level phenotype and genotype data, can be constructed by integrating data from several biobanks and health data registers in Finland. Co-operation with biobanks is straightforward. However, long time periods need to be reserved when biobank resources are linked with national register data. There is a need for efforts to define general, harmonised co-operation practices and data exchange methods for enabling efficient collection of data from multiple sources.

Digitally Supported Lifestyle Intervention to Prevent Type 2 Diabetes Through Healthy Habits: Secondary Analysis of Long-Term User Engagement Trajectories in a Randomized Controlled Trial.

BACKGROUND: Digital health interventions may offer a scalable way to prevent type 2 diabetes (T2D) with minimal burden on health care systems by providing early support for healthy behaviors among adults at increased risk for T2D. However, ensuring continued engagement with digital solutions is a challenge impacting the expected effectiveness. OBJECTIVE: We aimed to investigate the longitudinal usage patterns of a digital healthy habit formation intervention, BitHabit, and the associations with changes in T2D risk factors. METHODS: This is a secondary analysis of the StopDia (Stop Diabetes) study, an unblinded parallel 1-year randomized controlled trial evaluating the effectiveness of the BitHabit app alone or together with face-to-face group coaching in comparison with routine care in Finland in 2017-2019 among community-dwelling adults (aged 18 to 74 years) at an increased risk of T2D. We used longitudinal data on usage from 1926 participants randomized to the digital intervention arms. Latent class growth models were applied to identify user engagement trajectories with the app during the study. Predictors for trajectory membership were examined with multinomial logistic regression models. Analysis of covariance was used to investigate the association between trajectories and 12-month changes in T2D risk factors. RESULTS: More than half (1022/1926, 53.1%) of the participants continued to use the app throughout the 12-month intervention. The following 4 user engagement trajectories were identified: terminated usage (904/1926, 46.9%), weekly usage (731/1926, 38.0%), twice weekly usage (208/1926, 10.8%), and daily usage (83/1926, 4.3%). Active app use during the first month, higher net promoter score after the first 1 to 2 months of use, older age, and better quality of diet at baseline increased the odds of belonging to the continued usage trajectories. Compared with other trajectories, daily usage was associated with a higher increase in diet quality and a more pronounced decrease in BMI and waist circumference at 12 months. CONCLUSIONS: Distinct long-term usage trajectories of the BitHabit app were identified, and individual predictors for belonging to different trajectory groups were found. These findings highlight the need for being able to identify individuals likely to disengage from interventions early on, and could be used to inform the development of future adaptive interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03156478; https://clinicaltrials.gov/ct2/show/NCT03156478. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-019-6574-y.

Automatically computed rating scales from MRI for patients with cognitive disorders.

OBJECTIVES: The aims of this study were to examine whether visual MRI rating scales used in diagnostics of cognitive disorders can be estimated computationally and to compare the visual rating scales with their computed counterparts in differential diagnostics. METHODS: A set of volumetry and voxel-based morphometry imaging biomarkers was extracted from T1-weighted and FLAIR images. A regression model was developed for estimating visual rating scale values from a combination of imaging biomarkers. We studied three visual rating scales: medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and white matter hyperintensities (WMHs) measured by the Fazekas scale. Images and visual ratings from the Amsterdam Dementia Cohort (ADC) (N = 513) were used to develop the models and cross-validate them. The PredictND (N = 672) and ADNI (N = 752) cohorts were used for independent validation to test generalizability. RESULTS: The correlation coefficients between visual and computed rating scale values were 0.83/0.78 (MTA-left), 0.83/0.79 (MTA-right), 0.64/0.64 (GCA), and 0.76/0.75 (Fazekas) in ADC/PredictND cohorts. When performance in differential diagnostics was studied for the main types of dementia, the highest balanced accuracy, 0.75-0.86, was observed for separating different dementias from cognitively normal subjects using computed GCA. The lowest accuracy of about 0.5 for all the visual and computed scales was observed for the differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Computed scales produced higher balanced accuracies than visual scales for MTA and GCA (statistically significant). CONCLUSIONS: MTA, GCA, and WMHs can be reliably estimated automatically helping to provide consistent imaging biomarkers for diagnosing cognitive disorders, even among less experienced readers. KEY POINTS: • Visual rating scales used in diagnostics of cognitive disorders can be estimated computationally from MRI images with intraclass correlations ranging from 0.64 (GCA) to 0.84 (MTA). • Computed scales provided high diagnostic accuracy with single-subject data (area under the receiver operating curve range, 0.84-0.94).

"OPTImAL": an ontology for patient adherence modeling in physical activity domain.

BACKGROUND: Maintaining physical fitness is a crucial component of the therapeutic process for patients with cardiovascular disease (CVD). Despite the known importance of being physically active, patient adherence to exercise, both in daily life and during cardiac rehabilitation (CR), is low. Patient adherence is frequently composed of numerous determinants associated with different patient aspects (e.g., psychological, clinical, etc.). Understanding the influence of such determinants is a central component of developing personalized interventions to improve or maintain patient adherence. Medical research produced evidence regarding factors affecting patients' adherence to physical activity regimen. However, the heterogeneity of the available data is a significant challenge for knowledge reusability. Ontologies constitute one of the methods applied for efficient knowledge sharing and reuse. In this paper, we are proposing an ontology called OPTImAL, focusing on CVD patient adherence to physical activity and exercise training. METHODS: OPTImAL was developed following the Ontology Development 101 methodology and refined based on the NeOn framework. First, we defined the ontology specification (i.e., purpose, scope, target users, etc.). Then, we elicited domain knowledge based on the published studies. Further, the model was conceptualized, formalized and implemented, while the developed ontology was validated for its consistency. An independent cardiologist and three CR trainers evaluated the ontology for its appropriateness and usefulness. RESULTS: We developed a formal model that includes 142 classes, ten object properties, and 371 individuals, that describes the relations of different factors of CVD patient profile to adherence and adherence quality, as well as the associated types and dimensions of physical activity and exercise. 2637 logical axioms were constructed to comprise the overall concepts that the ontology defines. The ontology was successfully validated for its consistency and preliminary evaluated for its appropriateness and usefulness in medical practice. CONCLUSIONS: OPTImAL describes relations of 320 factors originated from 60 multidimensional aspects (e.g., social, clinical, psychological, etc.) affecting CVD patient adherence to physical activity and exercise. The formal model is evidence-based and can serve as a knowledge tool in the practice of cardiac rehabilitation experts, supporting the process of activity regimen recommendation for better patient adherence.

Comparison of train-of-four ratios measured with Datex-Ohmeda's M-NMT MechanoSensor™ and M-NMT ElectroSensor™.

Neuromuscular blockade is usually monitored using train-of-four (TOF) stimulation pattern. A TOF ratio of higher than 90 % is recommended to reduce the risk of adverse effects after anaesthesia. TOF ratio 90 % is used in clinical practice with all different neuromuscular monitors. Kinemyography (KMG) is one commercialized method to obtain numerical TOF values. We compared the KMG data obtained with Datex M-NMT MechanoSensor™ module, to the EMG data collected with Datex ElectroSensor™, during clinical anaesthesia. Ipsilateral comparisons of the sensors were performed in 20 female patients during clinical procedures in propofol-remifentanil anaesthesia. After initial bolus dose of rocuronium (0.6 mg/kg), the spontaneous recovery of TOF ratio and T1 % were monitored. KMG gave higher TOF values than EMG. The difference was significant at KMG TOF values of 40 % or higher. After anaesthetic induction, but before administration of rocuronium, both TOF sensor values drifted from the TOF value of 1.0, showing either significant spontaneous fade (T1 > T4) or tendency of reverse fade (T1 < T4). KMG overestimates the recovery from neuromuscular blockade when compared with EMG. KMG and EMG cannot be used interchangeably, and TOF ratio 90 % cannot be considered as adequate level of recovery with all monitoring devices.

Telemonitoring and Mobile Phone-Based Health Coaching Among Finnish Diabetic and Heart Disease Patients: Randomized Controlled Trial.

BACKGROUND: There is a strong will and need to find alternative models of health care delivery driven by the ever-increasing burden of chronic diseases. OBJECTIVE: The purpose of this 1-year trial was to study whether a structured mobile phone-based health coaching program, which was supported by a remote monitoring system, could be used to improve the health-related quality of life (HRQL) and/or the clinical measures of type 2 diabetes and heart disease patients. METHODS: A randomized controlled trial was conducted among type 2 diabetes patients and heart disease patients of the South Karelia Social and Health Care District. Patients were recruited by sending invitations to randomly selected patients using the electronic health records system. Health coaches called patients every 4 to 6 weeks and patients were encouraged to self-monitor their weight, blood pressure, blood glucose (diabetics), and steps (heart disease patients) once per week. The primary outcome was HRQL measured by the Short Form (36) Health Survey (SF-36) and glycosylated hemoglobin (HbA1c) among diabetic patients. The clinical measures assessed were blood pressure, weight, waist circumference, and lipid levels. RESULTS: A total of 267 heart patients and 250 diabetes patients started in the trial, of which 246 and 225 patients concluded the end-point assessments, respectively. Withdrawal from the study was associated with the patients' unfamiliarity with mobile phones­of the 41 dropouts, 85% (11/13) of the heart disease patients and 88% (14/16) of the diabetes patients were familiar with mobile phones, whereas the corresponding percentages were 97.1% (231/238) and 98.6% (208/211), respectively, among the rest of the patients (P=.02 and P=.004). Withdrawal was also associated with heart disease patients' comorbidities­40% (8/20) of the dropouts had at least one comorbidity, whereas the corresponding percentage was 18.9% (47/249) among the rest of the patients (P=.02). The intervention showed no statistically significant benefits over the current practice with regard to health-related quality of life­heart disease patients: beta=0.730 (P=.36) for the physical component score and beta=-0.608 (P=.62) for the mental component score; diabetes patients: beta=0.875 (P=.85) for the physical component score and beta=-0.770 (P=.52) for the mental component score. There was a significant difference in waist circumference in the type 2 diabetes group (beta=-1.711, P=.01). There were no differences in any other outcome variables. CONCLUSIONS: A health coaching program supported with telemonitoring did not improve heart disease patients' or diabetes patients' quality of life or their clinical condition. There were indications that the intervention had a differential effect on heart patients and diabetes patients. Diabetes patients may be more prone to benefit from this kind of intervention. This should not be neglected when developing new ways for self-management of chronic diseases. TRIAL REGISTRATION: ClinicalTrials.gov NCT01310491; http://clinicaltrials.gov/ct2/show/NCT01310491 (Archived by WebCite at http://www.webcitation.org/6Z8l5FwAM).

Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland: randomized controlled trial.

BACKGROUND: Heart failure (HF) patients suffer from frequent and repeated hospitalizations, causing a substantial economic burden on society. Hospitalizations can be reduced considerably by better compliance with self-care. Home telemonitoring has the potential to boost patients' compliance with self-care, although the results are still contradictory. OBJECTIVE: A randomized controlled trial was conducted in order to study whether the multidisciplinary care of heart failure patients promoted with telemonitoring leads to decreased HF-related hospitalization. METHODS: HF patients were eligible whose left ventricular ejection fraction was lower than 35%, NYHA functional class ≥2, and who needed regular follow-up. Patients in the telemonitoring group (n=47) measured their body weight, blood pressure, and pulse and answered symptom-related questions on a weekly basis, reporting their values to the heart failure nurse using a mobile phone app. The heart failure nurse followed the status of patients weekly and if necessary contacted the patient. The primary outcome was the number of HF-related hospital days. Control patients (n=47) received multidisciplinary treatment according to standard practices. Patients' clinical status, use of health care resources, adherence, and user experience from the patients' and the health care professionals' perspective were studied. RESULTS: Adherence, calculated as a proportion of weekly submitted self-measurements, was close to 90%. No difference was found in the number of HF-related hospital days (incidence rate ratio [IRR]=0.812, P=.351), which was the primary outcome. The intervention group used more health care resources: they paid an increased number of visits to the nurse (IRR=1.73, P<.001), spent more time at the nurse reception (mean difference of 48.7 minutes, P<.001), and there was a greater number of telephone contacts between the nurse and intervention patients (IRR=3.82, P<.001 for nurse-induced contacts and IRR=1.63, P=.049 for patient-induced contacts). There were no statistically significant differences in patients' clinical health status or in their self-care behavior. The technology received excellent feedback from the patient and professional side with a high adherence rate throughout the study. CONCLUSIONS: Home telemonitoring did not reduce the number of patients' HF-related hospital days and did not improve the patients' clinical condition. Patients in the telemonitoring group contacted the Cardiology Outpatient Clinic more frequently, and on this way increased the use of health care resources. TRIAL REGISTRATION: Clinicaltrials.gov NCT01759368; http://clinicaltrials.gov/show/NCT01759368 (Archived by WebCite at http://www.webcitation.org/6UFxiCk8Z).

Assessing the relevance of mental health factors in fibromyalgia severity: A data-driven case study using explainable AI.

BACKGROUND AND OBJECTIVE: Fibromyalgia is a chronic disease that causes pain and affects patients' quality of life. Current treatments focus on pharmacological therapies for pain reduction. However, patients' psychological well-being is also affected, with depression and pain catastrophizing being common. This research addresses the clinicians' need to assess the influence of mental health factors on FM severity compared to pain factors. METHODS: A co-development study between FM clinicians and data scientists analyzed data from 166 FM-diagnosed patients to assess the influence of mental health factors on FM severity in comparison to pain factors. The study used the Polysymptomatic Distress Scale (PDS) and Fibromyalgia Impact Questionnaire (FIQ) as FM severity indicators and collected 15 variables including regarding demographics, pain intensity perceived, and mental health factors. The team used an author's developed framework to identify the optimal FM severity classifier and explainability by selecting a number of features that lead to obtaining the best classification result. Machine learning classifiers employed in the framework were: decision trees, logistic regression, support vector machines, random forests, AdaBoost, extra trees, and RUSBoost. Explainability analyses were conducted using the following explainable AI techniques: SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Mean Decrease Impurity (MDI). RESULTS: A balanced random forest with 6 features achieved the best performance with PDS (AUC_ROC, mean = 0.81, std = 0.07). Being FIQ the target variable, due to the imbalance in FM severity levels, a binary and a multiclass classification approaches were considered achieving the optimal performance, respectively, a logistic regression classifier (AUC_ROC, mean = 0.83, std = 0.08) with 6 selected features, and a random forest (AUC_ROC, mean = 0.91, std = 0.04) with 8 selected features. Next, the explainability analysis determined mental health factors were found to be more relevant than pain perceived factors for FM severity. CONCLUSIONS: This study's findings, validated by clinicians, are potentially aligned with FM international guidelines that promote non-pharmacological interventions such as promoting mental well-being of FM patients.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review.

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

Late Blood Levels of Neurofilament Light Correlate With Outcome in Patients With Traumatic Brain Injury.

Neurofilament light (NF-L) is an axonal protein that has shown promise as a traumatic brain injury (TBI) biomarker. Serum NF-L shows a rather slow rise after injury, peaking after 1-2 weeks, although some studies suggest that it may remain elevated for months after TBI. The aim of this study was to examine if plasma NF-L levels several months after the injury correlate with functional outcome in patients who have sustained TBIs of variable initial severity. In this prospective study of 178 patients with TBI and 40 orthopedic injury controls, we measured plasma NF-L levels in blood samples taken at the follow-up appointment on average 9 months after injury. Patients with TBI were divided into two groups (mild [mTBI] vs. moderate-to-severe [mo/sTBI]) according to the severity of injury assessed with the Glasgow Coma Scale upon admission. Recovery and functional outcome were assessed using the Extended Glasgow Outcome Scale (GOSE). Higher levels of NF-L at the follow-up correlated with worse outcome in patients with moderate-to-severe TBI (Spearman's rho = -0.18; p < 0.001). In addition, in computed tomography-positive mTBI group, the levels of NF-L were significantly lower in patients with GOSE 7-8 (median 18.14; interquartile range [IQR] 9.82, 32.15) when compared with patients with GOSE <7 (median 73.87; IQR 32.17, 110.54; p = 0.002). In patients with mTBI, late NF-L levels do not seem to provide clinical benefit for late-stage assessment, but in patients with initially mo/sTBI, persistently elevated NF-L levels are associated with worse outcome after TBI and may reflect ongoing brain injury.

Outlier Analysis for Acute Blood Biomarkers of Moderate and Severe Traumatic Brain Injury.

Blood biomarkers have been studied to improve the clinical assessment and prognostication of patients with moderate-severe traumatic brain injury (mo/sTBI). To assess their clinical usability, one needs to know of potential factors that might cause outlier values and affect clinical decision making. In a prospective study, we recruited patients with mo/sTBI (n = 85) and measured the blood levels of eight protein brain pathophysiology biomarkers, including glial fibrillary acidic protein (GFAP), S100 calcium-binding protein B (S100B), neurofilament light (Nf-L), heart-type fatty acid-binding protein (H-FABP), interleukin-10 (IL-10), total tau (T-tau), amyloid ß40 (Aß40) and amyloid ß42 (Aß42), within 24 h of admission. Similar analyses were conducted for controls (n = 40) with an acute orthopedic injury without any head trauma. The patients with TBI were divided into subgroups of normal versus abnormal (n = 9/76) head computed tomography (CT) and favorable (Glasgow Outcome Scale Extended [GOSE] 5-8) versus unfavorable (GOSE <5) (n = 38/42, 5 missing) outcome. Outliers were sought individually from all subgroups from and the whole TBI patient population. Biomarker levels outside Q1 - 1.5 interquartile range (IQR) or Q3 + 1.5 IQR were considered as outliers. The medical records of each outlier patient were reviewed in a team meeting to determine possible reasons for outlier values. A total of 29 patients (34%) combined from all subgroups and 12 patients (30%) among the controls showed outlier values for one or more of the eight biomarkers. Nine patients with TBI and five control patients had outlier values in more than one biomarker (up to 4). All outlier values were > Q3 + 1.5 IQR. A logical explanation was found for almost all cases, except the amyloid proteins. Explanations for outlier values included extremely severe injury, especially for GFAP and S100B. In the case of H-FABP and IL-10, the explanation was extracranial injuries (thoracic injuries for H-FABP and multi-trauma for IL-10), in some cases these also were associated with abnormally high S100B. Timing of sampling and demographic factors such as age and pre-existing neurological conditions (especially for T-tau), explained some of the abnormally high values especially for Nf-L. Similar explanations also emerged in controls, where the outlier values were caused especially by pre-existing neurological diseases. To utilize blood-based biomarkers in clinical assessment of mo/sTBI, very severe or fatal TBIs, various extracranial injuries, timing of sampling, and demographic factors such as age and pre-existing systemic or neurological conditions must be taken into consideration. Very high levels seem to be often associated with poor prognosis and mortality (GFAP and S100B).

Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records.

With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic disease patients, a prediction period of six months was selected. Two major transformer models that rely on learning bidirectional dependencies in sequential data, BERT and XLNet, were trained and compared. To our knowledge, the presented work is the first to apply XLNet on EHR data to predict mortality. The patient histories were formulated as time series consisting of varying types of clinical events, thus enabling the model to learn increasingly complex temporal dependencies. BERT and XLNet achieved an average area under the receiver operating characteristic curve (AUC) of 75.5% and 76.0%, respectively. XLNet surpassed BERT in recall by 9.8%, suggesting that it captures more positive cases than BERT, which is the main focus of recent research on EHRs and transformers.

Associations Between Engagement with the BitHabit Digital Lifestyle Intervention and Changes in Type 2 Diabetes Risk Factors.

Type 2 diabetes (T2D) can be prevented or delayed through a healthy lifestyle. Digital behavior change interventions (DBCIs) may offer cost-effective and scalable means to support lifestyle changes. This study investigated associations between user engagement with a habit-formation-based DBCI, the BitHabit app, and changes in T2D risk factors over 12 months in 963 participants at risk of T2D. User engagement was characterized by calculating use metrics from the BitHabit log data. User ratings were used as a subjective measure of engagement. The use metrics and user ratings were the strongest associated with improvements in diet quality. Weak positive associations were observed between the use metrics and changes in waist circumference and body mass index. No associations were found with changes in physical activity, fasting plasma glucose, or plasma glucose two hours after an oral glucose tolerance test. To conclude, increased use of the BitHabit app can have beneficial impacts on T2D risk factors, especially on diet quality.

Trajectories of interleukin 10 and heart fatty acid-binding protein levels in traumatic brain injury patients with or without extracranial injuries.

Background: Interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) have gained interest as diagnostic biomarkers of traumatic brain injury (TBI), but factors affecting their blood levels in patients with moderate-to-severe TBI are largely unknown. Objective: To investigate the trajectories of IL-10 and H-FABP between TBI patients with and without extracranial injuries (ECI); to investigate if there is a correlation between the levels of IL-10 and H-FABP with the levels of inflammation/infection markers C-reactive protein (CRP) and leukocytes; and to investigate if there is a correlation between the admission level of H-FABP with admission levels of cardiac injury markers, troponin (TnT), creatine kinase (CK), and creatine kinase MB isoenzyme mass (CK-MBm). Materials and methods: The admission levels of IL-10, H-FABP, CRP, and leukocytes were measured within 24 h post-TBI and on days 1, 2, 3, and 7 after TBI. The admission levels of TnT, CK, and CK-MBm were measured within 24 h post-TBI. Results: There was a significant difference in the concentration of H-FABP between TBI patients with and without ECI on day 0 (48.2 ± 20.5 and 12.4 ± 14.7 ng/ml, p = 0.02, respectively). There was no significant difference in the levels of IL-10 between these groups at any timepoints. There was a statistically significant positive correlation between IL-10 and CRP on days 2 (R = 0.43, p < 0.01) and 7 (R = 0.46, p = 0.03) after injury, and a negative correlation between H-FABP and CRP on day 0 (R = -0.45, p = 0.01). The levels of IL-10 or H-FABP did not correlate with leukocyte counts at any timepoint. The admission levels of H-FABP correlated with CK (R = 0.70, p < 0.001) and CK-MBm (R = 0.61, p < 0.001), but not with TnT. Conclusion: Inflammatory reactions during the early days after a TBI do not significantly confound the use of IL-10 and H-FABP as TBI biomarkers. Extracranial injuries and cardiac sources may influence the levels of H-FABP in patients with moderate-to-severe TBI.

Spectral entropy as a measure of hypnosis and hypnotic drug effect of total intravenous anesthesia in children during slow induction and maintenance.

BACKGROUND: We evaluated whether spectral entropy (SpE) can measure the depth of hypnosis and the hypnotic drug effect in children during total intravenous anesthesia. METHODS: Sixty healthy children, aged 3-16 yr, were studied. Anesthesia was induced with an increasing target controlled infusion of propofol, and maintained by a stable remifentanil infusion and variable concentrations of target controlled infusion propofol. Depth of hypnosis was assessed according to the University of Michigan Sedation Scale (UMSS). Estimated plasma (C(p)) and pseudo effect site (C(eff)) propofol concentrations reflected the hypnotic drug effect. Patients were stratified to three age groups. The correlations between SpE versus UMSS, C(p), and C(eff) were analyzed by Prediction Probability (P(k)). The pharmacodynamic relationship between SpE and C(p), and the differences of SpE values between the age groups at the corresponding UMSS levels, were studied. RESULTS: Respective mean P(k) values for the youngest, middle, and oldest age groups were: 1) during induction: SpE versus UMSS 0.87, 0.87, and 0.93; SpE versus C(p) 0.92, 0.95, and 0.97; and SpE versus C(eff) 0.88, 0.94, and 0.95; 2) during maintenance: SpE versus C(eff) 0.86, 0.75, and 0.81. The pharmacodynamic analysis determined an association between SpE and C(p) that followed the E(max) model closely. There were significant differences in SpE values between age groups at corresponding UMSS sedation levels. CONCLUSIONS: SpE measures the level of hypnosis and hypnotic drug effect in children during total intravenous anesthesia. There is an age dependency associated with SpE. Anesthesia should not be steered solely on the basis of SpE.

Application of the PredictAD software tool to predict progression in patients with mild cognitive impairment.

BACKGROUND: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease. METHODS: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. RESULTS: A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. CONCLUSION: Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.

Software tool for improved prediction of Alzheimer's disease.

BACKGROUND: Diagnostic criteria of Alzheimer's disease (AD) emphasize the integration of clinical data and biomarkers. In practice, collection and analysis of patient data vary greatly across different countries and clinics. OBJECTIVE: The goal was to develop a versatile and objective clinical decision support system that could reduce diagnostic errors and highlight early predictors of AD. METHODS: Novel data analysis methods were developed to derive composite disease indicators from heterogeneous patient data. Visualizations that communicate these findings were designed to help the interpretation. The methods were implemented with a software tool that is aimed for daily clinical practice. RESULTS: With the tool, clinicians can analyze available patients as a whole, study them statistically against previously diagnosed cases, and characterize the patients with respect to having AD. The tool is able to work with virtually any patient measurement data, as long as they are stored in electronic format or manually entered into the system. For a subset of patients from the test cohort, the tool was able to predict conversion to AD at an accuracy of 93.6%. CONCLUSION: The software tool developed in this study provides objective information for early detection and prediction of AD based on interpretable visualizations of patient data.

A smart hospital-driven approach to precision pharmacovigilance.

Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study.

Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.