Your goal is to extract structured information from the user's input that matches the form described below. When extracting information please make sure it matches the type information exactly. Do not add any attributes that do not appear in the schema shown below.

```TypeScript

 { // Information on the clinical trial characteristics from the abstract
 conditions: string // The condition or disease being treated in the clinical trial
 drug_or_intervention: string // The drug or intervention used in the clinical trial
}
```


Please output the extracted information in JSON format. Do not output anything except for the extracted information. Do not add any clarifying information. Do not add any fields that are not in the schema. If the text contains attributes that do not appear in the schema, please ignore them. All output must be in JSON format and follow the schema specified above. Wrap the JSON in <json> tags.



Input:Performance of a new leadless implantable cardiac monitor in detecting and quantifying atrial fibrillation: Results of the XPECT trial.  Current methods for detecting atrial fibrillation (AF) have limited diagnostic yield. Continuous monitoring with automatic arrhythmia detection and classification may improve detection of symptomatic and asymptomatic AF and subsequent patient treatment. The study purpose was to quantify the performance of the first implantable leadless cardiac monitor (ICM) with dedicated AF detection capabilities. Patients (n=247) with an implanted ICM (Reveal XT, Medtronic Inc, Minneapolis, Minn) who were likely to present with paroxysmal AF were selected. A special Holter device stored 46 hours of subcutaneously recorded ECG, ICM markers, and 2 surface ECG leads. The ICM automatic arrhythmia classification was compared with the core laboratory classification of the surface ECG. Of the 206 analyzable Holter recordings collected, 76 (37%) contained at least 1 episode of core laboratory classified AF. The sensitivity, specificity, positive predictive value, and negative predictive value for identifying patients with any AF were 96.1%, 85.4%, 79.3%, and 97.4%, respectively. The AF burden measured with the ICM was very well correlated with the reference value derived from the Holter (Pearson coefficient=0.97). The overall accuracy of the ICM for detecting AF was 98.5%. In this ICM validation study, the dedicated AF detection algorithm reliably detected the presence or absence of AF and the AF burden was accurately quantified. The ICM is a promising new diagnostic and monitoring tool for the clinician to treat AF patients independently of symptoms. Long-term studies are needed to evaluate the clinical benefits of the technology. Output:
{'conditions': 'Atrial Fibrillation|Risk of Cardiac Arrhythmias', 'interventions': 'Other: 46 hrs Holter ECG recording'}