Procedural performance between two cryoballoon systems for ablation of atrial fibrillation depends on pulmonary vein anatomy.

Background: Cryoballoon ablation is a first-line therapy for atrial fibrillation. We compared the efficacy and safety of two ablation systems and addressed the influence of pulmonary vein (PV) anatomy on performance and outcome. Methods: We consecutively enrolled 122 patients who were planned for first-time cryoballoon ablation. Patients were assigned 1:1 for ablation with the POLARx or the Arctic Front Advance Pro (AFAP) system and followed-up for 12 months. Procedural parameters were recorded during the ablation. Before the procedure, a magnetic resonance angiography (MRA) of the PVs was generated and diameter, area, and shape of each PV ostium were assessed. We applied an evaluated PV anatomical scoring system on our MRA measurement data ranging from 0 (best anatomical combination) to 5. Results: Procedures performed with POLARx were associated with shorter time to balloon temperature -30°C (p < .001), lower balloon nadir temperature (p < .001), and longer thawing time till 0°C (p < .001) in all PVs, however, time to isolation was similar. We observed a decreasing performance with each increase in the score for the AFAP, whereas the POLARx performed constant regardless of the score. At 1 year, AF recurred in 14 of 44 patients treated with AFAP (31.8%) and in 10 of 45 patients treated with POLARx (22.2%) (hazard ratio, 0.61; 95% CI 0.28 to 1.37; p = .225). There was no significant correlation between PV anatomy and clinical outcome. Conclusion: We found significant differences in cooling kinetics, especially when anatomical conditions are difficult. However, both systems have a comparable outcome and safety profile.

Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records.

Importance: Inpatient violence remains a significant problem despite existing risk assessment methods. The lack of robustness and the high degree of effort needed to use current methods might be mitigated by using routinely registered clinical notes. Objective: To develop and validate a multivariable prediction model for assessing inpatient violence risk based on machine learning techniques applied to clinical notes written in patients' electronic health records. Design, Setting, and Participants: This prognostic study used retrospective clinical notes registered in electronic health records during admission at 2 independent psychiatric health care institutions in the Netherlands. No exclusion criteria for individual patients were defined. At site 1, all adults admitted between January 2013 and August 2018 were included, and at site 2 all adults admitted to general psychiatric wards between June 2016 and August 2018 were included. Data were analyzed between September 2018 and February 2019. Main Outcomes and Measures: Predictive validity and generalizability of prognostic models measured using area under the curve (AUC). Results: Clinical notes recorded during a total of 3189 admissions of 2209 unique individuals at site 1 (mean [SD] age, 34.0 [16.6] years; 1536 [48.2%] male) and 3253 admissions of 1919 unique individuals at site 2 (mean [SD] age, 45.9 [16.6] years; 2097 [64.5%] male) were analyzed. Violent outcome was determined using the Staff Observation Aggression Scale-Revised. Nested cross-validation was used to train and evaluate models that assess violence risk during the first 4 weeks of admission based on clinical notes available after 24 hours. The predictive validity of models was measured at site 1 (AUC = 0.797; 95% CI, 0.771-0.822) and site 2 (AUC = 0.764; 95% CI, 0.732-0.797). The validation of pretrained models in the other site resulted in AUCs of 0.722 (95% CI, 0.690-0.753) at site 1 and 0.643 (95% CI, 0.610-0.675) at site 2; the difference in AUCs between the internally trained model and the model trained on other-site data was significant at site 1 (AUC difference = 0.075; 95% CI, 0.045-0.105; P < .001) and site 2 (AUC difference = 0.121; 95% CI, 0.085-0.156; P < .001). Conclusions and Relevance: Internally validated predictions resulted in AUC values with good predictive validity, suggesting that automatic violence risk assessment using routinely registered clinical notes is possible. The validation of trained models using data from other sites corroborates previous findings that violence risk assessment generalizes modestly to different populations.

Transitioning to a Data Driven Mental Health Practice: Collaborative Expert Sessions for Knowledge and Hypothesis Finding.

The surge in the amount of available data in health care enables a novel, exploratory research approach that revolves around finding new knowledge and unexpected hypotheses from data instead of carrying out well-defined data analysis tasks. We propose a specification of the Cross Industry Standard Process for Data Mining (CRISP-DM), suitable for conducting expert sessions that focus on finding new knowledge and hypotheses in collaboration with local workforce. Our proposed specification that we name CRISP-IDM is evaluated in a case study at the psychiatry department of the University Medical Center Utrecht. Expert interviews were conducted to identify seven research themes in the psychiatry department, which were researched in cooperation with local health care professionals using data visualization as a modeling tool. During 19 expert sessions, two results that were directly implemented and 29 hypotheses for further research were found, of which 24 were not imagined during the initial expert interviews. Our work demonstrates the viability and benefits of involving work floor people in the analyses and the possibility to effectively find new knowledge and hypotheses using our CRISP-IDM method.