Defining migraine days, based on longitudinal E-diary data.

BACKGROUND: There is a need for standardization of the definition of a migraine day for clinical and research purposes. METHODS: We prospectively compared different definitions of a migraine day with E-diary data of n = 1494 patients with migraine. We used a baseline definition based on migraine characteristics with a duration of ≥4 hours OR triptan intake (independently from its effect) OR (visual) aura lasting 5-60 minutes. RESULTS: Of all migraine days defined by triptan intake only, 66.2% had a duration <4 hours. Adjusting the headache duration criterion to ≥30 minutes led to a decrease in days defined by triptan intake only and resulted in a 5.4% increase in total migraine days (equals 0.45 migraine day increase in monthly migraine days). These additional migraine days had a median duration of 2.5 hours. CONCLUSION: We propose to define a migraine day as follows: 1) (a) headache duration ≥30 minutes; (b) matching ≥2 of four characteristics: unilateral, pulsating, moderate to severe pain, aggravation by or causing avoidance of routine physical activity; and (c) during headache ≥1 of the following: nausea and/or vomiting, photophobia and phonophobia or 2) (visual) aura duration 5-60 minutes or 3) a day with headache for which acute migraine-specific medication is used irrespective of its effect.

The added value of ferritin levels and genetic markers for the prediction of haemoglobin deferral.

BACKGROUND AND OBJECTIVES: On-site haemoglobin deferral for blood donors is sometimes necessary for donor health but demotivating for donors and inefficient for the blood bank. Deferral rates could be reduced by accurately predicting donors' haemoglobin status before they visit the blood bank. Although such predictive models have been published, there is ample room for improvement in predictive performance. We aim to assess the added value of ferritin levels or genetic markers as predictor variables in haemoglobin deferral prediction models. MATERIALS AND METHODS: Support vector machines with and without this information (the full and reduced model, respectively) are compared in Finland and the Netherlands. Genetic markers are available in the Finnish data and ferritin levels in the Dutch data. RESULTS: Although there is a clear association between haemoglobin deferral and both ferritin levels and several genetic markers, predictive performance increases only marginally with their inclusion as predictors. The recall of deferrals increases from 68.6% to 69.9% with genetic markers and from 79.7% to 80.0% with ferritin levels included. Subgroup analyses show that the added value of these predictors is higher in specific subgroups, for example, for donors with minor alleles on single-nucleotide polymorphism 17:58358769, recall of deferral increases from 73.3% to 93.3%. CONCLUSION: Including ferritin levels or genetic markers in haemoglobin deferral prediction models improves predictive performance. The increase in overall performance is small but may be substantial for specific subgroups. We recommend including this information as predictor variables when available, but not to collect it for this purpose only.

Explainable haemoglobin deferral predictions using machine learning models: Interpretation and consequences for the blood supply.

BACKGROUND AND OBJECTIVES: Accurate predictions of haemoglobin (Hb) deferral for whole-blood donors could aid blood banks in reducing deferral rates and increasing efficiency and donor motivation. Complex models are needed to make accurate predictions, but predictions must also be explainable. Before the implementation of a prediction model, its impact on the blood supply should be estimated to avoid shortages. MATERIALS AND METHODS: Donation visits between October 2017 and December 2021 were selected from Sanquin's database system. The following variables were available for each visit: donor sex, age, donation start time, month, number of donations in the last 24 months, most recent ferritin level, days since last ferritin measurement, Hb at nth previous visit (n between 1 and 5), days since the nth previous visit. Outcome Hb deferral has two classes: deferred and not deferred. Support vector machines were used as prediction models, and SHapley Additive exPlanations values were used to quantify the contribution of each variable to the model predictions. Performance was assessed using precision and recall. The potential impact on blood supply was estimated by predicting deferral at earlier or later donation dates. RESULTS: We present a model that predicts Hb deferral in an explainable way. If used in practice, 64% of non-deferred donors would be invited on or before their original donation date, while 80% of deferred donors would be invited later. CONCLUSION: By using this model to invite donors, the number of blood bank visits would increase by 15%, while deferral rates would decrease by 60% (currently 3% for women and 1% for men).

First results of a ferritin-based blood donor deferral policy in the Netherlands.

BACKGROUND: Whole blood donors are at risk of becoming iron deficient. To monitor iron stores, Sanquin implemented a new deferral policy based on ferritin levels, in addition to the traditional hemoglobin measurements. METHODS: Ferritin levels are determined in every fifth donation, as well as in all first-time donors. Donors with ferritin levels <15 ng/mL (WHO threshold) are deferred for 12 months; those ≥15 and ≤30 ng/mL for 6 months. The first results were analyzed and are presented here. RESULTS: The results show that 25% of women (N = 20151, 95% CI 24%-25%) and 1.6% of men (N = 10391, 95% CI 1.4%-1.8%) have ferritin levels ≤30 ng/mL at their first blood center visit. For repeat (non-first-time) donors, these proportions are higher: 53% of women (N = 28329, 95% CI 52%-54%) and 42% of men (N = 31089, 95% CI 41%-43%). After a 6-month deferral, in 88% of returning women (N = 3059, 95% CI 87%-89%) and 99% of returning men (N = 3736, 95% CI 98%-99%) ferritin levels were ≥15 ng/mL. After a 12-month deferral, in 74% of returning women (N = 486, 95% CI 70%-78%) and 95% of returning men (N = 479, 95% CI 94%-97%) ferritin levels increased to ≥15 ng/mL. CONCLUSION: Deferral of donors whose pre-donation ferritin levels were ≤30 ng/mL might prevent donors from returning with ferritin levels <15 ng/mL. This policy is promising to mitigate effects of repeated donations on iron stores.

Simultaneous discovery of cancer subtypes and subtype features by molecular data integration.

MOTIVATION: Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis. The molecular mechanisms are reflected by subtype-specific mutational and expression features. Data-driven subtyping is a complex problem as subtyping and identifying the molecular mechanisms that drive carcinogenesis are confounded problems. Many current integrative subtyping methods use global mutational and/or expression tumor profiles to group tumor samples in subtypes but do not explicitly extract the subtype-specific features. We therefore present a method that solves both tasks of subtyping and identification of subtype-specific features simultaneously. Hereto our method integrates` mutational and expression data while taking into account the clonal properties of carcinogenesis. Key to our method is a formalization of the problem as a rank matrix factorization of ranked data that approaches the subtyping problem as multi-view bi-clustering RESULTS: We introduce a novel integrative framework to identify subtypes by combining mutational and expression features. The incomparable measurement data is integrated by transformation into ranked data and subtypes are defined as multi-view bi-clusters We formalize the model using rank matrix factorization, resulting in the SRF algorithm. Experiments on simulated data and the TCGA breast cancer data demonstrate that SRF is able to capture subtle differences that existing methods may miss. AVAILABILITY AND IMPLEMENTATION: The implementation is available at: https://github.com/rankmatrixfactorisation/SRF CONTACT: kathleen.marchal@intec.ugent.be, siegfried.nijssen@cs.kuleuven.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authority defied: need for cognitive closure influences regulatory control when resisting authority.

The present studies examined whether differences in need for cognitive closure (NCC) were related to differences in regulatory control when confronted with authority. In two studies, levels of regulatory control were measured when participants resisted (Study 1; N = 46) or prepared to resist the influence attempt of an authority figure (Study 2; N = 50). Results showed that resisting the influence attempt from a high-authority figure was more depleting for participants higher in NCC compared to individuals lower in NCC. However, when they were given instructions and time to prepare the act of resistance, individuals high in NCC actually showed an increase in regulatory control. Authority is usually viewed as a general principle of influence; however, the present studies suggest that there are individual differences that influence how people may experience interactions with authorities.

Assessment of implicit sexual associations in non-incarcerated pedophiles.

Offences committed by pedophiles are crimes that evoke serious public concern and outrage. Although recent research using implicit measures has shown promise in detecting deviant sexual associations, the discriminatory and predictive quality of implicit tasks has not yet surpassed traditional assessment methods such as questionnaires and phallometry. The current research extended previous findings by examining whether a combination of two implicit tasks, the Implicit Association Task (IAT) and the Picture Association Task (PAT), was capable of differentiating pedophiles from non-pedophiles, and whether the PAT, which allows separate analysis for male, female, boy and girl stimulus categories, was more sensitive to specific sexual associations in pedophiles than the IAT. A total of 20 male self-reported pedophiles (10 offender and 10 non-offenders) and 20 male self-reported heterosexual controls completed the two implicit measures. Results indicated that the combination of both tasks produced the strongest results to date in detecting implicit pedophilic preferences (AUC = .97). Additionally, the PAT showed promise in decomposing the sexual associations in pedophiles. Interestingly, as there was an equal distribution of offenders and non-offenders in the pedophile group, it was possible to test for implicit association differences between these groups. This comparison showed no clear link between having these implicit sexual associations and actual offending.

Empathy is a beautiful thing: empathy predicts imitation only for attractive others.

Research shows that we spontaneously imitate people. Moreover, empathy predicts the degree of this non-conscious imitation. Little is known, however, if or how this expression of empathy is influenced by stable physical characteristics of our interaction-partners. In two studies, we tested whether attractiveness of others moderated the relation between empathy and imitation. While seeing a woman performing joystick movements, participants either imitated, or non-imitated these movements. Results showed that the higher participants empathy score, the faster they imitated an attractive person. The level of empathy did not predict the degree of imitation of unattractive targets. The findings demonstrate that the expression of empathy through imitation can be moderated by attractiveness, thereby introducing a new dimension to the conditionality of empathy.

Attentional and affective processing of sexual stimuli in women with hypoactive sexual desire disorder.

Hypoactive sexual desire disorder (HSDD) is the most common sexual problem in women. From an incentive motivation perspective, HSDD may be the result of a weak association between sexual stimuli and rewarding experiences. As a consequence, these stimuli may either lose or fail to acquire a positive meaning, resulting in a limited number of incentives that have the capacity to elicit a sexual response. According to current information processing models of sexual arousal, sexual stimuli automatically activate meanings and if these are not predominantly positive, processes relevant to the activation of sexual arousal and desire may be interrupted. Premenopausal U.S. and Dutch women with acquired HSDD (n = 42) and a control group of sexually functional women (n = 42) completed a single target Implicit Association Task and a Picture Association Task assessing automatic affective associations with sexual stimuli and a dot detection task measuring attentional capture by sexual stimuli. Results showed that women with acquired HSDD displayed less positive (but not more negative) automatic associations with sexual stimuli than sexually functional women. The same pattern was found for self-reported affective sex-related associations. Participants were slower to detect targets in the dot detection task that replaced sexual images, irrespective of sexual function status. As such, the findings point to the relevance of affective processing of sexual stimuli in women with HSDD, and imply that the treatment of HSDD might benefit from a stronger emphasis on the strengthening of the association between sexual stimuli and positive meaning and sexual reward.

Generating synthetic mixed discrete-continuous health records with mixed sum-product networks.

OBJECTIVE: Privacy is a concern whenever individual patient health data is exchanged for scientific research. We propose using mixed sum-product networks (MSPNs) as private representations of data and take samples from the network to generate synthetic data that can be shared for subsequent statistical analysis. This anonymization method was evaluated with respect to privacy and information loss. MATERIALS AND METHODS: Using a simulation study, information loss was quantified by assessing whether synthetic data could reproduce regression parameters obtained from the original data. Predictors variable types were varied between continuous, count, categorical, and mixed discrete-continuous. Additionally, we measured whether the MSPN approach successfully anonymizes the data by removing associations between background and sensitive information for these datasets. RESULTS: The synthetic data generated with MSPNs yielded regression results highly similar to those generated with original data, differing less than 5% in most simulation scenarios. Standard errors increased compared to the original data. Particularly for smaller datasets (1000 records), this resulted in a discrepancy between the estimated and empirical standard errors. Sensitive values could no longer be inferred from background information for at least 99% of tested individuals. DISCUSSION: The proposed anonymization approach yields very promising results. Further research is required to evaluate its performance with other types of data and analyses, and to predict how user parameter choices affect a bias-privacy trade-off. CONCLUSION: Generating synthetic data from MSPNs is a promising, easy-to-use approach for anonymization of sensitive individual health data that yields informative and private data.

Associations Between Symptoms, Donor Characteristics and IgG Antibody Response in 2082 COVID-19 Convalescent Plasma Donors.

Many studies already reported on the association between patient characteristics on the severity of COVID-19 disease outcome, but the relation with SARS-CoV-2 antibody levels is less clear. To investigate this in more detail, we performed a retrospective observational study in which we used the IgG antibody response from 11,118 longitudinal antibody measurements of 2,082 unique COVID convalescent plasma donors. COVID-19 symptoms and donor characteristics were obtained by a questionnaire. Antibody responses were modelled using a linear mixed-effects model. Our study confirms that the SARS-CoV-2 antibody response is associated with patient characteristics like body mass index and age. Antibody decay was faster in male than in female donors (average half-life of 62 versus 72 days). Most interestingly, we also found that three symptoms (headache, anosmia, nasal cold) were associated with lower peak IgG, while six other symptoms (dry cough, fatigue, diarrhoea, fever, dyspnoea, muscle weakness) were associated with higher IgG concentrations.

Evaluating privacy of individuals in medical data.

Although data protection is compulsory when personal data is shared, there is no systematic method available to evaluate to what extent each individual is at risk of a privacy breach. We use a collection of measures that quantify how much information is needed to uncover sensitive information. Combined with visualization techniques, our approach can be used to perform a detailed privacy analysis of medical data. Because privacy is evaluated per variable, these adjustments can be made while incorporating how likely it is that these variables will be exploited to uncover sensitive information in practice, as is mandatory in the European Union. Additionally, the analysis of privacy can be used to evaluate to what extent knowledge on specific variables in the data can contribute to privacy breaches, which can subsequently guide the use of anonymization techniques, such as generalization.

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms.

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables. Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed. Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53-0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77-0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88-0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00-0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge). Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

A KNIME-Based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules.

Recently, the photomotor response (PMR) of zebrafish embryos was reported as a robust behavior that is useful for high-throughput neuroactive drug discovery and mechanism prediction. Given the complexity of the PMR, there is a need for rapid and easy analysis of the behavioral data. In this study, we developed an automated analysis workflow using the KNIME Analytics Platform and made it freely accessible. This workflow allows us to simultaneously calculate a behavioral fingerprint for all analyzed compounds and to further process the data. Furthermore, to further characterize the potential of PMR for mechanism prediction, we performed PMR analysis of 767 neuroactive compounds covering 14 different receptor classes using the KNIME workflow. We observed a true positive rate of 25% and a false negative rate of 75% in our screening conditions. Among the true positives, all receptor classes were represented, thereby confirming the utility of the PMR assay to identify a broad range of neuroactive molecules. By hierarchical clustering of the behavioral fingerprints, different phenotypical clusters were observed that suggest the utility of PMR for mechanism prediction for adrenergics, dopaminergics, serotonergics, metabotropic glutamatergics, opioids, and ion channel ligands.

Effects of a priori liking on the elicitation of mimicry.

Mimicry and prosocial feelings are generally thought to be positively related. However, the conditions under which mimicry and liking are related largely remain unspecified. We advance this specification by examining the relationship between mimicry and liking more thoroughly. In two experiments, we manipulated an individual's a priori liking for another and investigated whether it influenced mimicry of that person. Our experiments demonstrate that in the presence of a reason to like a target, automatic mimicry is increased. However, mimicry did not decrease when disliking a target. These studies provide further evidence of a link between mimicry and liking and extend previous research by showing that a certain level of mimicry even occurs when mimicry behavior is inconsistent with one's goals or motivations.

Executive functioning and imitation: Increasing working memory load facilitates behavioural imitation.

If perceptual and bodily states are closely linked and if perceiving action automatically leads to corresponding activations in one's own motor system, then why do not we imitate all the time? There is evidence suggesting that executive functioning (EF) may play a moderating role in inhibiting overt imitation [e.g. Luria, A. R. (1966). Higher cortical functions in man. New York: Basic Books]. In an experiment we tested this idea. 48 participants received either a high or low working memory (WM) load and were instructed to respond to either a finger cue or spatial cue with a finger movement. Results indicate that occupying WM facilitates reaction times to finger cues while responses to simple spatial cues are unchanged. The findings suggest that imitation is indeed a dominant response and EF is needed to inhibit the spontaneous tendency to imitate.