Septal Myectomy Outcomes in Children and Adolescents With Obstructive Hypertrophic Cardiomyopathy.

BACKGROUND: Little data exist regarding characteristics and outcomes of pediatric patients undergoing septal myectomy. We evaluated this in a large referral population. METHODS: Septal myectomy was performed in 199 consecutive patients aged ≤18 years with obstructive hypertrophic cardiomyopathy from January 1, 1976, to June 30, 2021. RESULTS: Median age was 13 years (interquartile range [IQR], 8-15 years). Left ventricular myectomy approaches included transaortic (163 of 198 [82%]), transapical (16 of 198 [8%]), and combined (19 of 198 [10%]). Right ventricular interventions included myectomy (13 of 199 [7%]) and patch reconstruction of the outflow tract (15 of 199 [8%]). Maximum left ventricular outflow tract gradients decreased after myectomy (prebypass: 50 mm Hg [IQR, 31-73 mm Hg] vs postbypass: 4 mm Hg [IQR, 0-9 mm Hg], P < .001), and this was sustained long-term (5 mm Hg [IQR, 5-10 mm Hg] at 10 years). Iatrogenic aortic and mitral valve injuries occurred in 13 of 199 (7%) and 1 of 199 (1%), respectively; however, all were successfully repaired. Operative mortality was 2 of 199 (1%). The cumulative incidence of redo myectomy was low, at 5.8% at 5 and 8.3% at 10 years. Redo myectomy patients had higher maximum left ventricular outflow tract gradients on echocardiography at predischarge and 1 year and were younger at the index operation (8 years [IQR, 2.5-10 years] vs 13 years [IQR, 9-16 years], P < .001). Overall survival at 10 years was 90%, relative to 47% in a previously reported pediatric nonoperative cohort. CONCLUSIONS: Pediatric septal myectomy provides safe, effective, and durable relief of ventricular outflow tract obstruction. Iatrogenic valve injury remains a low but nonnegligible risk. Recurrent obstruction requiring redo myectomy is infrequent and can be identified early. Long-term survival in this pediatric septal myectomy cohort appears to fare better than pediatric hypertrophic cardiomyopathy cohorts managed nonoperatively.

Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.

BACKGROUND: Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. OBJECTIVE: This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. METHODS: The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. RESULTS: A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). CONCLUSIONS: Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA. .

Sociotechnical Intervention for Improved Delivery of Preventive Cardiovascular Care to Rural Communities: Participatory Design Approach.

BACKGROUND: Clinical practice guidelines recommend antiplatelet and statin therapies as well as blood pressure control and tobacco cessation for secondary prevention in patients with established atherosclerotic cardiovascular diseases (ASCVDs). However, these strategies for risk modification are underused, especially in rural communities. Moreover, resources to support the delivery of preventive care to rural patients are fewer than those for their urban counterparts. Transformative interventions for the delivery of tailored preventive cardiovascular care to rural patients are needed. OBJECTIVE: A multidisciplinary team developed a rural-specific, team-based model of care intervention assisted by clinical decision support (CDS) technology using participatory design in a sociotechnical conceptual framework. The model of care intervention included redesigned workflows and a novel CDS technology for the coordination and delivery of guideline recommendations by primary care teams in a rural clinic. METHODS: The design of the model of care intervention comprised 3 phases: problem identification, experimentation, and testing. Input from team members (n=35) required 150 hours, including observations of clinical encounters, provider workshops, and interviews with patients and health care professionals. The intervention was prototyped, iteratively refined, and tested with user feedback. In a 3-month pilot trial, 369 patients with ASCVDs were randomized into the control or intervention arm. RESULTS: New workflows and a novel CDS tool were created to identify patients with ASCVDs who had gaps in preventive care and assign the right care team member for delivery of tailored recommendations. During the pilot, the intervention prototype was iteratively refined and tested. The pilot demonstrated feasibility for successful implementation of the sociotechnical intervention as the proportion of patients who had encounters with advanced practice providers (nurse practitioners and physician assistants), pharmacists, or tobacco cessation coaches for the delivery of guideline recommendations in the intervention arm was greater than that in the control arm. CONCLUSIONS: Participatory design and a sociotechnical conceptual framework enabled the development of a rural-specific, team-based model of care intervention assisted by CDS technology for the transformation of preventive health care delivery for ASCVDs.