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Atrial fibrillation (AF) is a common cardiac arrhythmia and affects one to two percent of the population. In this work, we leverage the three-dimensional atrial endocardial unipolar/bipolar voltage map to predict the AF type and recurrence of AF in 1 year. This problem is challenging for two reasons: (1) the unipolar/bipolar voltages are collected at different locations on the endocardium and the shapes of the endocardium vary widely in different patients, and thus the unipolar/bipolar voltage maps need aligning to the same coordinate; (2) the collected dataset size is very limited. To address these issues, we exploit a pretrained 3D point cloud registration approach and finetune it on left atrial voltage maps to learn the geometric feature and align all voltage maps into the same coordinate. After alignment, we feed the unipolar/bipolar voltages from the registered points into a multilayer perceptron (MLP) classifier to predict whether patients have paroxysmal or persistent AF, and the risk of recurrence of AF in 1 year for patients in sinus rhythm. The experiment shows our method classifies the type and recurrence of AF effectively.

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A retrospective cohort study was performed of the Hospital-to-Home (H2H) program, a rapid clinic follow-up program for patients with recent heart failure (HF) admissions at the University of Virginia Health System. There were 6761 hospitalizations among 4685 patients (age 67.5 ± 14.2 years, 43.9% female), and 759 had H2H follow-up. Thirty day mortality after the initial HF hospitalization was lower in H2H patients (1.84% vs 3.13%; P = .049), and this difference remained significant after adjustment in a multivariable logistic regression model (odds ratio = 0.56 [95% CI = 0.31-099]; P = .046). There also was a 24% reduction in readmission days within the first 30 days after the index admission ( P < .0001), and readmission cost savings were found to be greater than the costs of staffing the H2H clinic. In summary, the H2H program is cost-effective, with significant improvements in survival, readmission days, and readmission costs over 30 days.

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BACKGROUND: While advancements in ventricular assist device (VAD) therapy have improved survival and quality of life for select patients with advanced heart failure (HF), variations in provider knowledge and opinions may ultimately serve as barriers to therapy. METHODS AND RESULTS: A 12-item survey assessing experience, knowledge, and perspectives of VAD therapy was sent to 106 practicing cardiologists at three neighboring institutions. We received 34 responses for a total response rate of 32.1%. The majority of respondents elected to refer patients with refractory disease for VAD therapy, while only 29.4% elected to refer when standard medical therapy is withdrawn due to hypotension. CONCLUSIONS: While providers are well-informed on the fundamentals of advanced therapy, identifying patients with advanced HF who may benefit from referral remains an educational challenge. An automated referral program that identifies patients with advanced HF based on validated clinical parameters could increase appropriately timed referrals to HF specialists to further improve survival and quality of life outcomes with advanced therapy.

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OBJECTIVES: The major objective of this research was to propose a simplified approach for the evaluation of medication adherence in chronic pain management patients, using liquid chromatography time-of-flight (TOF) mass spectrometry, performed in parallel with select homogeneous enzyme immunoassays (HEIAs). We called it a "hybrid" approach to urine drug testing. METHODS: The hybrid approach was defined based on anticipated positivity rates, availability of commercial reagents for HEIAs, and assay performance, particularly analytical sensitivity and specificity for drug(s) of interest. Subsequent to implementation of the hybrid approach, time to result was compared with that observed with other urine drug testing approaches. RESULTS: Opioids, benzodiazepines, zolpidem, amphetamine-like stimulants, and methylphenidate metabolite were detected by TOF mass spectrometry to maximize specificity and sensitivity of these 37 drug analytes. Barbiturates, cannabinoid metabolite, carisoprodol, cocaine metabolite, ethyl glucuronide, methadone, phencyclidine, propoxyphene, and tramadol were detected by HEIAs that performed adequately and/or for which positivity rates were very low. Time to result was significantly reduced compared with the traditional approach. CONCLUSIONS: The hybrid approach to urine drug testing provides a simplified and analytically specific testing process that minimizes the need for secondary confirmation.

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Sixty-seven drugs and metabolites were detected in serum or plasma using a fast (7.5 min) liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) method. This method was developed as a blood drug screen, with emphasis on the detection of common drugs of abuse and drugs used to manage chronic pain. Qualitative drug detection may identify a drug exposure, assure patient adherence with prescribed therapy and document abstinence from non-prescribed medications. Compound identification is based on chromatographic retention time, mass, isotope spacing and isotope abundance. Data analysis software (Agilent) generates a compound score based on how well these observed criteria matched theoretical and empirical values. The method was validated using fortified samples and 299 residual patient specimens (920 positive results). All results were confirmed by gas chromatography-MS or LC-tandem MS. The accuracy of positive results (samples meeting all qualitative criteria for retention time, mass and compound score) was >90% for drugs and/or metabolites, except for two benzodiazepines. There were 35 false positive results (seven compounds, 3.8%) that could be distinguished by retention time and/or absence of metabolites. The most frequent was 6-acetylmorphine in the absence of morphine. The LC-TOF-MS targeted screening method presented represents a sensitive and specific technology for drug screening of serum or plasma.

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