S-acylation of Sprouty and SPRED proteins by the S-acyltransferase zDHHC17 involves a novel mode of enzyme-substrate interaction.

S-acylation is an essential post-translational modification, which is mediated by a family of 23 zDHHC enzymes in humans. Several thousand proteins are modified by S-acylation; however, we lack a detailed understanding of how enzyme-substrate recognition and specificity is achieved. Previous work showed that the ankyrin repeat domain of zDHHC17 (ANK17) recognizes a short linear motif, known as the zDHHC ANK binding motif (zDABM) in substrate protein SNAP25, as a mechanism of substrate recruitment prior to S-acylation. Here, we investigated the S-acylation of the Sprouty and SPRED family of proteins by zDHHC17. Interestingly, although Sprouty-2 (Spry2) contains a zDABM that interacts with ANK17, this mode of binding is dispensable for S-acylation, and indeed removal of the zDABM does not completely ablate binding to zDHHC17. Furthermore, the related SPRED3 protein interacts with and is efficiently S-acylated by zDHHC17, despite lacking a zDABM. We undertook mutational analysis of SPRED3 to better understand the basis of its zDABM-independent interaction with zDHHC17. This analysis found that the cysteine-rich SPR domain of SPRED3, which is the defining feature of all Sprouty and SPRED proteins, interacts with zDHHC17. Surprisingly, the interaction with SPRED3 was independent of ANK17. Our mutational analysis of Spry2 was consistent with the SPR domain of this protein containing a zDHHC17-binding site, and Spry2 also showed detectable binding to a zDHHC17 mutant lacking the ANK domain. Thus, zDHHC17 can recognize its substrates through zDABM-dependent and/or zDABM-independent mechanisms, and some substrates display more than one mode of binding to this enzyme.

AI-based preeclampsia detection and prediction with electrocardiogram data.

Introduction: More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings. Methods: Ten-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis. Results: The UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00). Discussion: We conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.

Qualitative feedback from caregivers in a multidisciplinary pediatric neuromuscular clinic.

OBJECTIVE: This study explored family satisfaction and perceived quality of care in a pediatric neuromuscular care clinic to assess the value of the multidisciplinary clinic (MDC) model in delivering coordinated care to children with neuromuscular disorders, such as cerebral palsy. METHODS: Caregivers of 22 patients were administered a qualitative survey assessing their perceptions of clinic efficiency, care coordination, and communication. Surveys were audio-recorded and transcribed. Thematic analysis was completed using both deductive and inductive methods. RESULTS: All caregivers reported that providers adequately communicated next steps in the patient's care, and most reported high confidence in caring for the patient as a result of the clinic. Four major themes were identified from thematic analysis: Care Delivery, Communication, Care Quality, and Family-Centeredness. Caregivers emphasized that the MDC model promoted access to care, enhanced efficiency, promoted provider teamwork, and encouraged shared care planning. Caregivers also valued a physical environment that was suitable for patients with complex needs. CONCLUSION: This study demonstrated that caregivers believed the MDC model was both efficient and convenient for pediatric patients with neuromuscular disorders. This model has the potential to streamline medical care and can be applied more broadly to improve care coordination for children with medical complexity.

Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy.

BACKGROUND: This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy. OBJECTIVE: This study aimed to assess the efficacy of an artificial intelligence-based heart failure detection model for peripartum cardiomyopathy detection. STUDY DESIGN: We first built a deep-learning model for heart failure detection using retrospective data at the University of Tennessee Health Science Center. Cases were adult and nonpregnant female patients with a heart failure diagnosis; controls were adult nonpregnant female patients without heart failure. The model was then tested on an independent cohort of pregnant women at the University of Tennessee Health Science Center with or without peripartum cardiomyopathy. We also tested the model in an external cohort of pregnant women at Atrium Health Wake Forest Baptist. Key outcomes were assessed using the area under the receiver operating characteristic curve. We also repeated our analysis using only lead I electrocardiogram as an input to assess the feasibility of remote monitoring via wearables that can capture single-lead electrocardiogram data. RESULTS: The University of Tennessee Health Science Center heart failure cohort comprised 346,339 electrocardiograms from 142,601 patients. In this cohort, 60% of participants were Black and 37% were White, with an average age (standard deviation) of 53 (19) years. The heart failure detection model achieved an area under the curve of 0.92 on the holdout set. We then tested the ability of the heart failure model to detect peripartum cardiomyopathy in an independent University of Tennessee Health Science Center cohort of pregnant women and an external Atrium Health Wake Forest Baptist cohort of pregnant women. The independent University of Tennessee Health Science Center cohort included 158 electrocardiograms from 115 patients; our deep-learning model achieved an area under the curve of 0.83 (0.77-0.89) for this data set. The external Atrium Health Wake Forest Baptist cohort involved 80 electrocardiograms from 43 patients; our deep-learning model achieved an area under the curve of 0.94 (0.91-0.98) for this data set. For identifying peripartum cardiomyopathy diagnosed ≥10 days after delivery, the model achieved an area under the curve of 0.88 (0.81-0.94) for the University of Tennessee Health Science Center cohort and of 0.96 (0.93-0.99) for the Atrium Health Wake Forest Baptist cohort. When we repeated our analysis by building a heart failure detection model using only lead-I electrocardiograms, we obtained similarly high detection accuracies, with areas under the curve of 0.73 and 0.93 for the University of Tennessee Health Science Center and Atrium Health Wake Forest Baptist cohorts, respectively. CONCLUSION: Artificial intelligence can accurately detect peripartum cardiomyopathy from electrocardiograms alone. A simple electrocardiographic artificial intelligence-based peripartum screening could result in a timelier diagnosis. Given that results with 1-lead electrocardiogram data were similar to those obtained using all 12 leads, future studies will focus on remote screening for peripartum cardiomyopathy using smartwatches that can capture single-lead electrocardiogram data.

A Quality Improvement Project to Improve After-visit Summary Patient Instructions in a Pediatric Multidisciplinary Neuromuscular Program.

Introduction: Multidisciplinary clinics aim to coordinate care between multiple specialties for children with medical complexity yet may result in information overload for caregivers. The after-visit summary (AVS) patient instruction section offers a solution by summarizing visit details and recommendations. No known studies address patient instruction optimization and integration within a multidisciplinary clinic setting. This project aimed to improve the quality of patient instructions to support better postvisit communication between caregivers and providers in a multidisciplinary pediatric neuromuscular program. Methods: A multidisciplinary stakeholder team created a key driver diagram to improve postvisit communication between caregivers and providers in the clinic. The first specific aim was to achieve an 80% completion rate of AVS patient instructions within 6 months. To do so, a standardized electronic medical record "text shortcut" was created for consistent information in each patient's instructions. Feedback on AVS from caregivers was obtained using the Family Experiences with Coordination of Care survey and open-ended interviews. This feedback informed the next specific aim: to reduce medical jargon within patient instructions by 25% over 3 months. Completion rates and jargon use were reviewed using control charts. Results: AVS patient instruction completion rates increased from a mean of 39.4%-85.0%. Provider education reduced mean jargon usage in patient instructions, from 8.2 to 3.9 jargon terms. Conclusions: Provider education and caregiver feedback helped improve patient communication by enhancing AVS compliance and diminishing medical jargon. Interventions to improve AVS patient instructions may enhance patient communication strategies for complex medical visits.

Quantitative Prediction of Right Ventricular Size and Function From the ECG.

BACKGROUND: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. METHODS AND RESULTS: We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031). CONCLUSIONS: Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.

Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts. Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs. Methods: An FCHD single-lead ("lead I" from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen's kappa. Results: The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78. Conclusion: Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.

Epidemiology of pediatric trauma and fractures during and beyond the COVID-19 pandemic.

Purpose: Previous literature has shown decreases in pediatric trauma during the COVID-19 outbreak, but few have analyzed beyond the peak of the pandemic. This study assesses the epidemiology of pediatric trauma cases in a high-volume teaching hospital in New York City before, during, and after the height of the COVID-19 pandemic. Methods: Institutional data on pediatric trauma orthopedic cases from January 1, 2018 to November 30, 2021 were extracted. The following time frames were studied: (1) April 1-June 22 in 2018 and 2019 (pre-pandemic), (2) April 1-June 22, 2020 (peak pandemic), and (3) April 1-June 22, 2021 (post-peak pandemic). Inferential statistics were used to compare patient and trauma characteristics. Results: Compared to the pre-pandemic cohort (n = 6770), the peak pandemic cohort (n = 828) had a greater proportion of fractures (p < 0.01) and had a significantly decreased overall traumas per week rate (p < 0.01) and fractures per week rate (p < 0.01). These decreased trauma (p < 0.01) and fracture rates (p < 0.01) persisted for the post-peak pandemic cohort (n = 2509). Spatial analysis identified zip code clusters throughout New York City with higher rates of emergency department presentation during the peak pandemic compared to pre-pandemic, and these areas aligned with lower-income neighborhoods. Conclusion: During the peak of the pandemic, overall trauma and fracture volumes decreased, the types of prevalent injuries changed, and neighborhoods of different economic resources were variably impacted. These trends have mostly persisted for 12 months post-peak pandemic. This longitudinal analysis helps inform and improve long-term critical care and public health resource allocation for the future. Level of evidence: Level III.

A national analysis of the effect alcohol use disorder has on short-term complications and readmissions following total shoulder arthroplasty.

Background: Alcohol use disorder has been associated with broad health consequences that may interfere with healing after total shoulder arthroplasty. The aim of this study was to explore the impact of alcohol use disorder on readmissions and complications following total shoulder arthroplasty. Methods: We used data from the Healthcare Cost and Utilization Project National Readmissions Database (NRD) from 2016 to 2018. Patients were included based on International Classification of Diseases, 10th Revision (ICD-10) procedure codes for anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA). Patients with an alcohol use disorder (AUD) were identified using the ICD-10 diagnosis code F10.20. Demographics, complications, and 30-day and 90-day readmission were collected for all patients. A univariate logistic regression was performed to investigate AUD as a factor affecting readmission and complication rates. A multivariate logistic regression model was created to assess the impact of alcohol use disorder on complications and readmission while controlling for demographic factors. Results: In total, 164,527 patients were included, and 503 (0.3%) patients had a prior diagnosis of AUD. Revision surgery was more common in patients with an alcohol use disorder (8.8% vs. 6.2%; p = 0.022). Postoperative infection (p = 0.026), dislocation (p = 0.025), liver complications (p < 0.01), and 90-day readmission (p < 0.01) were more common in patients with a diagnosed AUD. On multivariate analysis, patients with an AUD were found to be at increased odds for liver complications (OR: 46.8; 95% CI: [32.8, 66.8]; p < 0.01). Comparatively, mean age, length of stay, and over healthcare costs were also higher for patients with an AUD. Conclusion: Patients with a diagnosis of AUD were more likely to suffer from shoulder dislocation, liver complications, and 90-day readmission, while also being younger and having longer hospital stays. Therefore, surgeons should take caution to anticipate and prevent complications and readmissions following total shoulder arthroplasty in patients with an AUD.

The Impact of COVID-19 on the Timing of Rotator Cuff Repair and Method of Postoperative Follow-up.

Objective Rotator cuff repair (RCR) is one of the most common arthroscopic procedures. Our investigation aims to quantify the impact that the COVID-19 pandemic had on RCR, specifically on patients with acute, traumatic injuries. Methods Institutional records were queried to identify patients who underwent arthroscopic RCR between March 1 st to October 31 st of both 2019 and 2020. Patient demographic, preoperative, perioperative, and postoperative data were collected from electronic medical records. Inferential statistics were used to analyze data. Results Totals of 72 and of 60 patients were identified in 2019 and in 2020, respectively. Patients in 2019 experienced shorter lengths of time from MRI to surgery (62.7 ± 70.5 days versus 115.7 ± 151.0 days; p = 0.01). Magnetic resonance imaging (MRI) scans showed a smaller average degree of retraction in 2019 (2.1 ± 1.3 cm versus 2.6 ± 1.2 cm; p = 0.05) but no difference in anterior to posterior tear size between years (1.6 ± 1.0 cm versus 1.8 ± 1.0 cm; p = 0.17). Less patients in 2019 had a telehealth postoperative consultation with their operating surgeon compared with 2020 (0.0% versus 10.0%; p = 0.009). No significant changes in complications (0.0% versus 0.0%; p > 0.999), readmission (0.0% versus 0.0%; p > 0.999), or revision rates (5.6% versus 0.0%; p = 0.13) were observed. Conclusion From 2019 to 2020, there were no significant differences in patient demographics or major comorbidities. Our data suggests that even though the time from MRI to surgery was delayed in 2020 and telemedicine appointments were necessary, RCR was still performed in a timely manner and with no significant changes in early complications. Level of Evidence III.