Machine learning model for predicting late recurrence of atrial fibrillation after catheter ablation.

Late recurrence of atrial fibrillation (LRAF) in the first year following catheter ablation is a common and significant clinical problem. Our study aimed to create a machine-learning model for predicting arrhythmic recurrence within the first year since catheter ablation. The study comprised 201 consecutive patients (age: 61.8 ± 8.1; women 36%) with paroxysmal, persistent, and long-standing persistent atrial fibrillation (AF) who underwent cryoballoon (61%) and radiofrequency ablation (39%). Five different supervised machine-learning models (decision tree, logistic regression, random forest, XGBoost, support vector machines) were developed for predicting AF recurrence. Further, SHapley Additive exPlanations were derived to explain the predictions using 82 parameters based on clinical, laboratory, and procedural variables collected from each patient. The models were trained and validated using a stratified fivefold cross-validation, and a feature selection was performed with permutation importance. The XGBoost model with 12 variables showed the best performance on the testing cohort, with the highest AUC of 0.75 [95% confidence interval 0.7395, 0.7653]. The machine-learned model, based on the easily available 12 clinical and laboratory variables, predicted LRAF with good performance, which may provide a valuable tool in clinical practice for better patient selection and personalized AF strategy following the procedure.

Cognitive Impairment in Cardiovascular Patients after Myocardial Infarction: Prospective Clinical Study.

(1) Background: Assessment of cognitive function is not routine in cardiac patients, and knowledge on the subject remains limited. The aim of this study was to assess post-myocardial infarction (MI) cognitive functioning in order to determine the frequency of cognitive impairment (CI) and to identify factors that may influence it. (2) Methods: A prospective study included 468 patients hospitalized for MI. Participants were assessed twice: during the first hospitalization and 6 months later. The Mini-Mental State Examination was used to assess the occurrence of CI. (3) Results: Cognitive dysfunction based on the MMSE was found in 37% (N-174) of patients during the first hospitalization. After 6 months, the prevalence of deficits decreased significantly to 25% (N-91) (p < 0.001). Patients with CI significantly differed from those without peri-infarction deficits in the GFR, BNP, ejection fraction and SYNTAX score, while after 6 months, significant differences were observed in LDL and HCT levels. There was a high prevalence of non-cognitive mental disorders among post-MI patients. (4) Conclusions: There is a high prevalence of CI and other non-cognitive mental disorders, such as depression, sleep disorders and a tendency to aggression, among post-MI patients. The analysis of the collected material indicates a significant impact of worse cardiac function expressed as EF and BNP, greater severity of coronary atherosclerosis expressed by SYNTAX results, and red blood cell parameters and LDL levels on the occurrence of CI in the post-MI patient population.

Air pollution and attention in Polish schoolchildren with and without ADHD.

BACKGROUND: Development and functioning of attention-a key component of human cognition-can be affected by environmental factors. We investigated whether long- and short-term exposure to particulate matter with aerodynamic diameter < 10 µm (PM10) and nitrogen dioxide (NO2) are related to attention in 10- to 13-year-old children living in Polish towns recruited in the NeuroSmog case-control study. METHODS: We investigated associations between air pollution and attention separately in children with attention deficit hyperactivity disorder (ADHD, n = 187), a sensitive, at-risk population with impaired attention and in population-based typically developing children (TD, n = 465). Alerting, orienting, and executive aspects of attention were measured using the attention network test (ANT), while inhibitory control was measured with the continuous performance test (CPT). We assessed long-term exposure to NO2 and PM10 using novel hybrid land use regression (LUR) models. Short-term exposures to NO2 and PM10 were assigned to each subject using measurements taken at the air pollution monitoring station nearest to their home address. We tested associations for each exposure-outcome pair using adjusted linear and negative binomial regressions. RESULTS: We found that long-term exposures to both NO2 and PM10 were associated with worse visual attention in children with ADHD. Short-term exposure to NO2 was associated with less efficient executive attention in TD children and more errors in children with ADHD. It was also associated with shorter CPT response times in TD children; however, this effect was accompanied by a trend towards more CPT commission errors, suggestive of more impulsive performance in these subjects. Finally, we found that short-term PM10 exposure was associated with fewer omission errors in CPT in TD children. CONCLUSIONS: Exposure to air pollution, especially short-term exposure to NO2, may have a negative impact on attention in children. In sensitive populations, this impact might be different than in the general population.

Behavioral and Self-reported Data Collected From Smartphones for the Assessment of Depressive and Manic Symptoms in Patients With Bipolar Disorder: Prospective Observational Study.

BACKGROUND: Smartphones allow for real-time monitoring of patients' behavioral activities in a naturalistic setting. These data are suggested as markers for the mental state of patients with bipolar disorder (BD). OBJECTIVE: We assessed the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in patients with BD. METHODS: BDmon, a dedicated mobile app, was developed and installed on patients' smartphones to automatically collect the statistics about their phone calls and text messages as well as their self-assessments of sleep and mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time, 208 [SD 132] days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics on the behavioral data collected automatically from smartphones and the affective symptoms and mood states in patients with BD. RESULTS: Objective behavioral data collected from smartphones were found to be related with the BD states as follows: (1) depressed patients tended to make phone calls less frequently than euthymic patients (ß=-.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (ß=-.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (ß=4.431, P<.001; ß=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (ß=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (ß=3.53, P=.01) and positively correlated to the severity of symptoms (ß=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (ß=.0012, P=.045) and positively correlated to the severity of symptoms (ß=.0017, P=.02); and (6) the number and length of the sent text messages was higher in manic/mixed states as compared to that in the euthymic state (ß=.031, P=.01; ß=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (ß=.116, P<.001; ß=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (ß=-1.452, P<.001) and higher in manic states (ß=.509, P<.001). CONCLUSIONS: Smartphone-based behavioral parameters are valid markers for assessing the severity of affective symptoms and discriminating between mood states in patients with BD. This technology opens a way toward early detection of worsening of the mental state and thereby increases the patient's chance of improving in the course of the illness.

NeuroSmog: Determining the Impact of Air Pollution on the Developing Brain: Project Protocol.

Exposure to airborne particulate matter (PM) may affect neurodevelopmental outcomes in children. The mechanisms underlying these relationships are not currently known. We aim to assess whether PM affects the developing brains of schoolchildren in Poland, a country characterized by high levels of PM pollution. Children aged from 10 to 13 years (n = 800) are recruited to participate in this case-control study. Cases (children with attention deficit hyperactivity disorder (ADHD)) are being recruited by field psychologists. Population-based controls are being sampled from schools. The study area comprises 18 towns in southern Poland characterized by wide-ranging levels of PM. Comprehensive psychological assessments are conducted to assess cognitive and social functioning. Participants undergo structural, diffusion-weighted, task, and resting-state magnetic resonance imaging (MRI). PM concentrations are estimated using land use regression models, incorporating information from air monitoring networks, dispersion models, and characteristics of roads and other land cover types. The estimated concentrations will be assigned to the prenatal and postnatal residential and preschool/school addresses of the study participants. We will assess whether long-term exposure to PM affects brain function, structure, and connectivity in healthy children and in those diagnosed with ADHD. This study will provide novel, in-depth understanding of the neurodevelopmental effects of PM pollution.

Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling.

BACKGROUND: Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence (through real-time monitoring) and providing options for early intervention between outpatient visits. AIMS: The aim of this systematic review is to overview and discuss the studies on the smartphone-based systems that monitor or detect the phase change in BD. We also discuss the challenges concerning predictive modelling. METHODS: Published studies were identified through searching the electronic databases. Predictive attributes reflecting illness activity were evaluated including data from patients' self-assessment ratings and objectively measured data collected via smartphone. Articles were reviewed according to PRISMA guidelines. RESULTS: Objective data automatically collected using smartphones (voice data from phone calls and smartphone-usage data reflecting social and physical activities) are valid markers of a mood state. The articles surveyed reported accuracies in the range of 67% to 97% in predicting mood status. Various machine learning approaches have been analyzed, however, there is no clear evidence about the superiority of any of the approach. CONCLUSIONS: The management of BD could be significantly improved by monitoring of illness activity via smartphone.