Williams syndrome is a rare congenital disorder affecting connective tissue and the cardiovascular and central nervous systems. Pediatric patients diagnosed with Williams syndrome face significant risk for cardiac collapse and death when undergoing anesthesia. We sought to evaluate our institution's historical practices, evaluate individual risk stratification, and create detailed standardized perianesthesia guidelines for management of this population, particularly during noncardiac procedures. The study included a retrospective chart review of pediatric patients with Williams syndrome who received anesthesia over a 10-year period at a single institution. A total of 23 patients underwent 46 procedures. At time of procedure, median age was 5.8 years (range, 0.4-17.6 yr), and the majority (n = 19) had a "low" Williams syndrome risk category and required anesthesia for noncardiac procedures. Most (61%) had no cardiac involvement beyond mild supravalvar or branch pulmonary artery stenosis. No intraprocedure adverse cardiac events were identified. One patient experienced an adverse cardiac event approximately 60 minutes postanesthesia emergence which included ST segment depression and development of refractory ventricular fibrillation, necessitating deployment of venoarterial extracorporeal membrane oxygenation. This patient was eventually stabilized and was taken to the operating room 3 days later for definitive severe supravalvar aortic stenosis repair. Patients with Williams syndrome have a reported risk for sudden cardiac death that is 25 to 100 times greater than that of the age-matched general population. The incidence of adverse cardiac events in our cohort of patients with Williams syndrome undergoing anesthesia was lower compared with previous studies, which reported rates ranging from 4.2% to 11%. These findings support the idea that risk stratification and institutional practice guidelines can aid anesthesia providers in making informed decisions, and standardization of pre-, intra-, and postprocedural care according to existing guidelines may further reduce risks. Our review offers valuable insight into historical anesthesia management and contributes to a broader interdisciplinary understanding of care provision for this high-risk group.
Abstract Inherited bone marrow failure syndromes (IBMFS) are a group of monogenic diseases of diverse pathogenesis manifesting as single or multilineage cytopenia typically due to hypoproliferative dyshematopoiesis. Accurately diagnosing IBMFS is challenging given the overlapping clinicopathological features between individual genetic syndromes as well as with acquired BMFS (e.g. immune aplastic anemia). Accurate genetic diagnosis in IBMFS is critical and most commonly involves targeted panel DNA sequencing or whole exome sequencing. These approaches do not cover the full spectrum of possible genomic abnormalities which in turn may contribute to a significant proportion of patients with IBMFS remaining undiagnosed. We aimed to comprehensively evaluate the unbiased upfront approach of whole genome transcriptome sequencing (WGTS) in patients with suspected IBMFS (the IBMDx study). The IBMDx study aimed to (i) determine the diagnostic rate and clinical impact of upfront WGTS (ii) assess the acceptability of WGTS to patients and physicians through an implementation science framework (iii) evaluate the health-economic impact and cost-effectiveness of the approach and (iv) discover and functionally characterize novel genes and variants in IBMFS. 237 patients were enrolled from March 2022 to March 2025. The median age of the cohort was 30 years (range 6m-78 years; M:F 0.93:1). 70/237 (29.5%) were under 18 years. WGS was performed exclusively on non-hematological DNA (hair follicle DNA [n=178], skin biopsy/cultured skin fibroblasts [n=59]). Trio WGS was performed in 18 families. WGS was performed by a clinically accredited service and results returned to physicians/patients in real time for patient management and segregation/predictive testing as required. A genomic diagnosis for the hematological phenotype was established in 88/237 (37.1%) patients. Diagnoses made included hereditary thrombocytopenia (n=19), telomere biology disorders (TBDs) (n=14), Diamond-Blackfan anemia (n=12), primary red cell disorders (n=6), severe congenital neutropenia (n=6), Shwachman Diamond Syndrome (n=5) and Fanconi anemia (n=4). Targeted sequencing of the hematological compartment demonstrated clonal hematopoiesis in 32/217 (14.7%) patients. In addition, four patients had genetic diagnoses made of symptomatic and clinically significant diseases unrelated to the hematological phenotype (TAP2, IRF2BP2, ACADM and mosaic trisomy 7). Secondary genomic findings requiring further management were detected in 7 patients (TNNI3, MSH6, Monosomy X mosaic, BRCA1, BRIP1, ATM, FBN1). WGTS revealed novel genomic abnormalities not previously established in IBMFS such as retrotransposon-mediated gene disruption, polyadenylation site loss and disruption of novel regulatory regions. Novel genomic abnormalities in genes associated with Diamond-Blackfan anaemia (RPL31), TBDs (TERT/TERC) and hereditary thrombocytopenia (TPM4) underwent in vivo and in vitro functional assessment to inform pathogenicity. Finally, novel associations were identified with primary IBMFS-like presentations (THRA) as well as definitive evidence of gene-disease association were established (MEIS1, TUBB). Using a formal Theoretical Framework of Acceptability, WGTS was found to be highly acceptable to patients and carers as evidenced by positive perceptions of clinicians, alignment with healthcare expectations, and minimal effort required to participate. However, concern regarding equity of access to technology for all patients with suspected IBMFS was a recurrent theme amongst patients and carers interviewed. Health economic analysis showed that the cost of achieving maximum diagnostic yield, incorporating varied diagnostic strategies, to be between $7,800 - $8,200 USD per hematological diagnosis. In summary, we have comprehensively evaluated upfront WGTS in a large cohort of adult and pediatric patients with suspected IBMFS and have found this approach has a high diagnostic rate (37.1%), is highly acceptable to patients, and uncovered multiple genomic abnormalities that would have been missed with more traditional diagnostic approaches. Moreover, we have uncovered new genomic mechanisms of disease and genes associated with IBMFS leading to new areas of research into the underlying biology of these challenging diseases.
Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier).
