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Transcatheter pulmonary valve replacement (TPVR) with the Harmony valve (Medtronic, Inc.) was recently approved to treat postoperative native outflow tract pulmonary regurgitation. While the 22 mm Harmony valve Early Feasibility Study demonstrated ventricular tachycardia (VT) in only 5% of patients, little is known about ventricular arrhythmias after TPVR with the larger 25 mm valve (TPV25).A single center review was performed of patients with TPV25 implant from 2020 to 2021. Demographic, cardiac, procedural, and postimplant cardiac telemetry data were collected and compared between patients who did and did not have peri-implant ventricular arrhythmia.Thirty patients underwent TPV25 at a median age of 30 years. On postimplant telemetry, VT events were documented in 12 patients (40%); 11 nonsustained VT (NSVT) (median 3 episodes per patient and 6 beats per episode, maximum 157 episodes) and 1 sustained VT (3%), with Torsades de Pointes secondary to a short coupled premature ventricular contraction (PVC). VT events were associated with annular valve positioning (p lt; 0.001) and increased postimplant PVC burden (p lt; 0.0001), but there was no association between VT and other demongraphic, historical, or procedural factors. The frequency of NSVT events fell from 3/h from 0 to 12 h postimplant to 0.5/hr from 12 to 24 h (p lt; 0.001).VT occurred commonly (40%) in the first 24 h after TPV25 implant, with self-limited NSVT in 11 of 12 patients and 1 patient with cardiac arrest secondary to Torsades de Pointes. VT only occurred with annular valve positioning. Larger, longer-term studies are needed to determine risk factors for and natural history of post-TPVR VT.

We have previously shown that patients with a chromosome 22q11 microdeletion are at risk for prolonged respiratory failure after pulmonary artery reconstruction surgery compared with those with normal genotype. We sought to describe preexisting airway abnormalities in this patient population and examine relationships between airway abnormalities and outcomes.Single-center retrospective chart review from Society of Thoracic Surgery and Pediatric Cardiac Critical Care Consortium databases and the electronic medical record.Lucile Packard Children's Hospital at Stanford from September 2017 to February 2019.All patients undergoing pulmonary artery reconstruction surgery were considered for inclusion.We identified 127 patients meeting study inclusion criteria. Thirty-nine patients met specific criteria and underwent screening preoperative bronchoscopy including microdirect laryngoscopy and lower airway examination. Postoperative bronchoscopy was performed at the discretion of the intensive care team.Airway abnormalities were detected in 25/26 of children (96%) with a chromosome 22q11 deletion who underwent preoperative bronchoscopy. Upper and lower airway pathologies were found in 19/25 (73%) and 21/25 (81%) patients, respectively, and it was common for patients to have more than one abnormality. Presence of 22q11 deletion was associated with longer duration of mechanical ventilation (9.1 vs 4.3 d; p = 0.001), use of noninvasive positive pressure support (13 vs 6 d; p = 0.001), and longer hospital stays (30 vs 14 d; p = 0.002). These outcomes were worse when compared with patients with known airway abnormalities who did not have 22q11 deletion.Preexisting upper and lower airway pathologies are common in patients with a chromosome 22q11 deletion who undergo pulmonary artery reconstruction surgery. Despite similar postoperative hemodynamics and outcomes as their counterparts without 22q11 deletion, 22q11 deletion is associated with more postoperative respiratory complications not entirely explained by preexisting airway abnormalities.

Kawasaki disease (KD) is the leading cause of acquired heart disease in children. A key objective of research in KD is to reduce the risk of long-term cardiovascular sequelae by expediting timely diagnosis. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile illnesses. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US.We sought to determine if our algorithmic approach applied to an Asian cohort. This study involved 418 KD and 259 febrile controls (FC) from the Chang Gung Memorial Hospital in Taiwan. Our diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.We assessed the performance of our KD diagnostic algorithm with a single center Asian cohort. This work demonstrates the applicability of our algorithmic approach and diagnostic portability, providing evidence to support the launch of an adequately powered, multicenter study for future Asian application in the emergency department setting. If deployed in Asia, our tool promises a cost-effective diagnostic approach to allow the timely management of Asian KD patients even in the absence of KD experts, to potentially enhance the outcome for KD patients and reduce the risk of coronary artery aneurysms.

Objective To evaluate whether longitudinal measurements of serological adipokines and sphingolipids can predict preeclampsia early in gestation. Design Retrospective multi-omics discovery and longitudinal validation. Setting Maternity units in two US hospitals. Methods A multi-omics approach integrating genomic and lipidomic discoveries was employed to identify leptin (Lep) and ceramide (Cer) as novel PE early gestational biomarkers. The levels of placental growth factor (PlGF), soluble fms-like tyrosine kinase (sFlt-1), Lep, and Cer in maternal sera were then determined by enzyme-linked immunosorbent and liquid chromatography-tandem mass spectrometric assays. Main outcome measures Interval from positive prediction to confirmative diagnosis. Results Genomic meta-analysis compiled six PE placental cohorts with 78 PE and 95 non-PE control placentas. The Testing Cohort included sera from 7 non-PE and 8 PE women collected at confirmatory diagnosis. The Validation Cohort included sera from 20 non-PE and 20 PE women collected longitudinally through gestation. Our findings revealed a marked elevation of maternal serum Leptin/Ceramide (d18:1/25:0) ratio from early gestation (a median of 23 weeks) when comparing later PE-complicated with uncomplicated pregnancies. The maternal Lep/Cer (d18:1/25:0) ratio significantly outperformed the established sFlt-1/PlGF ratio in predicting PE for sensitivity (85% vs. 40%), positive predictive value (89% vs. 42%), and AUC (0.92 vs. 0.52) from 5 to 25 weeks of gestation. Conclusions Non-invasive longitudinal assessment by serological evaluation of Lep/Cer (d18:1/25:0) ratio can case find early pregnancies at risk of preeclampsia, outperforming sFlt-1/PlGF ratio test. Tweetable abstract Non-invasive longitudinal assessment by serological evaluation of Lep and Cer ratio can predict preeclampsia early in gestation.

BACKGROUND Suicide is the 10th leading cause of death in the US. Over the past 15 years, the total suicide rate has increased 24% from 10.5 to 13.0 per 100,000 people. In Massachusetts the rate of death by suicide is three times the rate of homicide deaths. Approximately 60% of suicides die on the first attempt. Of the remaining 40% who survive the index attempt and receive emergency or hospital level of care, rates of subsequent completed suicide are exceptionally high, ranging from 2.3% to 4%. A recent study determined that risk factors for repeat suicide attempt and suicide differed, with alcohol use, younger age and cluster B personality disorders among the attempters and older age and alcohol use among the suicide completers. This data is from a small sample in Catalonia, Spain and whether it is generalizable to populations in the USA is yet to be determined. An early warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and the contribution of repeated attempts to the risk of eventual death by suicide. OBJECTIVE In this study we sought to develop an early warning system (EWS) for high risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Importantly, this EWS was designed to support case managers, primary care and mental health care practitioners participating in accountable care programs. The continuous use of the system in this program will help assess the ongoing EWS effectiveness. METHODS Data from individual patient electronic health records (EHRs) from the Berkshire Health System located in Pittsfield, MA. Advanced machine-learning algorithms and Deep Neural Networks were utilized in the process of feature selection and model building. A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following one-year time period. Risk scores were subjected to individual level analysis in order to aid in the interpretation of the results for health care providers managing the at-risk cohorts. RESULTS The one-year suicide attempt risk model attained an area under the curve (AUC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the “very high risk” category was 60 times greater than the population baseline when tested in the prospective cohorts, 10 times greater in the “high risk” group, and 5 times greater in the “medium risk” bin. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socio-economic determinants were recognized as significant features associated with incident suicide attempt. CONCLUSIONS Utilizing a single EHR dataset, an EWS for the determination of risk for suicide attempt was successfully developed and prospectively validated using deep learning modeling techniques.

BACKGROUND As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. OBJECTIVE The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. METHODS Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual. RESULTS The 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension. CONCLUSIONS With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.

Background: The outcome of hospitalized patients suffering clinical decline is contingent upon timely recognition. Current algorithms utilize select clinical variables for predictive modeling but have generally excluded congenital heart disease due to heterogeneity of patient and disease. The purpose of this study is to develop a model to predict decline in congenital heart disease patients. Methods: We analyzed all patients admitted to the Heart Center at Stanford Children’s Health between 9/1/2009 and 12/31/2012. We profiled the phenotypic changes in the EMR and measured entropy defined as the degree of fluctuation in all EMR values at 3-hour intervals. Predictive modeling was performed to identify temporal and directional relationships between entropy and the occurrence of a critical event, compared to the entropy of matched control patients. The entropy index was derived retrospectively in the first two-thirds of patients, and applied prospectively in the final third to determine predictability. Results: A total of 2,658 encounters occurred. A total of 71 (2.6%) critical events were observed as defined by calls for rapid response team, code blue, and ECMO. Peak entropy within the EMR was observed at mean of 15 (±5.4) hours prior to critical events (Figure). In the prospective arm, the area under the ROC curve for predicting critical events was 0.80 (95% confidence interval [CI]: 0.75-0.83). The sensitivity and specificity derived from the ROC curve was 68.4% and 74.6%, respectively. Only 2% of EMR variables were found in all cases and 14% of all critical events shared 3 or more common variables. Conclusion: Common EMR variables that cluster uniquely in relation to critical events among children hospitalized for cardiac disease are rare, which poses a challenge to early warning systems that are based on specific variables. Leveraging the EMR to detect increased entropy as a novel predictor of crisis event in this population merits further investigation.