Development and Validation of a Machine Learning Risk-Prediction Model for 30-Day Readmission for Heart Failure Following Transcatheter Aortic Valve Replacement (TAVR-HF Score).

Transcatheter aortic valve replacement (TAVR) is the treatment of choice for patients with severe aortic stenosis across the spectrum of surgical risk. About one-third of 30-day readmissions following TAVR are related to heart failure (HF). Hence, we aim to develop an easy-to-use clinical predictive model to identify patients at risk for HF readmission. We used data from the National Readmission Database (2015-2018) utilizing ICD-10 codes to identify TAVR procedures. Readmission was defined as the first unplanned HF readmission within 30-day of discharge. A machine learning framework was used to develop a 30-day TAVR-HF readmission score. The receiver operator characteristic curve was used to evaluate the predictive power of the model. A total of 92,363 cases of TAVR were included in the analysis. Of the included patients, 3299 (3.6%) were readmitted within 30 days of discharge with HF. Individuals who got readmitted, vs those without readmission, had more emergent admissions during index procedure (33.4% vs 19.8%), electrolyte abnormalities (38% vs 16.7%), chronic kidney disease (34.8% vs 21.2%), and atrial fibrillation (60.1% vs 40.7%). Candidate variables were ranked by importance using a parsimony plot. A total of 7 variables were selected based on predictive ability as well as clinical relevance: HF with reduced ejection fraction (25 points), HF preserved EF (20 points), electrolyte abnormalities (17 points), atrial fibrillation (12 points), Charlson comorbidity index (<6 = 0, 6-8 = 9, 9-10 = 13, >10 = 14 points), chronic kidney disease (7 points), and emergent index admission (5 points). On performance evaluation using the testing dataset, an area under the curve of 0.761 (95% CI 0.744-0.778) was achieved. Thirty-day TAVR-HF readmission score is an easy-to-use risk prediction tool. The score can be incorporated into electronic health record systems to identify at-risk individuals for readmissions with HF following TAVR. However, further external validation studies are needed.

Molecular diagnosis of 22q11.2 deletion and duplication by multiplex ligation dependent probe amplification.

22q11 Deletion syndrome (22q11DS) is the most common microdeletion syndrome in humans, occurring with an incidence of 1 in 4,000. In most cases the submicroscopic deletion spans 3 Mb, but there are a number of other overlapping and non-overlapping deletions that generate a similar phenotype. The majority of the 22q11.2 microdeletions can be ascertained using a standard fluorescence in situ hybridization (FISH) assay probing for TUPLE1 or N25 on 22q11.2. However, this test fails to detect deletions that are either proximal or distal to the FISH probes, and does not provide any information about the length of the deletion. In order to increase the detection rate of 22q11.2 deletion and to better characterize the size and position of such deletions we undertook a study of 22q11.2 cases using multiplex ligation dependent probe amplification (MLPA). We used MLPA to estimate the size of the 22q11.2 deletions in 51 patients positive for TUPLE1 or N25 (FISH) testing, and to investigate 12 patients with clinical features suggestive of 22q11DS and negative FISH results. MLPA analysis confirmed a microdeletion in all 51 FISH-positive samples as well as microduplications in three samples. Further, it allowed us to delineate deletions not previously detected using standard clinical FISH probes in 2 of 12 subjects with clinical features suggestive of 22q11DS. We conclude that MLPA is a cost-effective and accurate diagnostic tool for 22q11DS with a higher sensitivity than FISH alone. Additional advantages of MLPA testing in our study included determination of deletion length and detection of 22q11.2 duplications. (c) 2007 Wiley-Liss, Inc.