Citations

Abstract Severe aortic valve regurgitation, if not timely treated, can significantly impact patients’ survival both for tricuspid aortic valve and bicuspid aortic valve patients, with the latter being significantly younger. Increased understanding of the root anatomy and its physiology has opened the way to techniques of aortic valve repair surgery. The techniques mainly relate to re-establishing a correct root and annular geometry and eliminating leaflet prolapse. These techniques are applied both in the presence of a dilated and normal root and are equally valid for tricuspid or bicuspid valve. Techniques for repairing a bicuspid valve might vary depending on the different valve phenotypes. Medium and long-term results appear favourable and potentially superior to those of prosthetic replacement in terms of valve-related complications and quality of life. Optimal surgical treatment, especially in younger and selected patients, should aim to avoid aortic valve replacement and its related complications.

Abstract OBJECTIVES The Ross procedure is currently receiving renewed interest. Its function and durability depend on preservation of pulmonary valve anatomy; limited data exist on normal pulmonary valve geometry. The objective was to compare aortic and pulmonary root and cusp dimensions in adults with normal tricuspid aortic and pulmonary valves. METHODS We reviewed 507 coronary computed tomography studies, selecting those with adequate visibility of both pulmonary and aortic roots for further analysis. Diastolic aortic and pulmonary root and cusp dimensions were measured. Root dimensions at different phases of the cardiac cycle were measured in 3 patients. RESULTS We analysed studies of 50 patients with the mean age of 54 years [standard deviation (SD): 16]. In end-diastole, pulmonary root had a smaller sinutubular to basal ring ratio than the aortic root [0.82 (SD: 0.09) vs 1.14 (SD: 0.12), P < 0.001]. Aortic and pulmonary cusps had similar dimensions; however, pulmonary cusp effective height was lower [5.9 mm (SD: 1.6) vs 8.4 mm (SD: 1.2), P < 0.001]. Pulmonary basal ring perimeter was largest at end-diastole and smallest at end-systole, with the relative difference of 23.5% (SD: 2.7). CONCLUSIONS The pulmonary root has a similar cusp size compared to the aortic root, but a different shape, resulting in a lower pulmonary cusp effective height. The perimeter of the pulmonary basal ring changes during the cardiac cycle. These findings suggest that stabilizing the autograft to normal aortic, rather than pulmonary, root dimensions should result in normal autograft cusp configuration. Computed tomography angiography could become a tool for personalized planning of the Ross procedure.

Introduction Knowledge of epidemiologic risk factors, clonal changes associated with MDS development from precursor states, and evolution from one MDS state to another (lower- (LR) or higher-risk (HR) disease or leukemia (AML)) are largely limited to retrospective studies, which may not reflect the true MDS spectrum. The MDS NHS is an established prospective cohort of patients (pts) with suspected MDS enrolled at diverse US sites. Extensive clinical data and biospecimens are collected at baseline and throughout follow-up (F/U), in real time, as pts are followed for life. Here, we assess rates and potential predictors of progression providing a foundation for precise stratification across the MDS spectrum, which may ultimately guide intervention. Methods At baseline, all MDS NHS pts have central pathology review and those assigned to MDS, MDS/MPN overlap, AML<30% blasts, or at-risk of MDS (AR-MDS: CCUS and other at-risk) are followed prospectively. We compared AR-MDS with LR-MDS (MDS and MDS/MPN overlap with IPSS-R ≤ 3.5) and HR-MDS (MDS and MDS/MPN overlap with IPSS-R > 3.5). We defined disease progression as any F/U diagnosis of worsening disease or documented disease-related death. Rates of disease progression were compared between groups using survival analysis methods for cumulative incidence incorporating non-disease death as a competing risk. Associations between progression and predictor variables were explored using univariate tests of association and multivariable survival (Cox regression) models for time-to-progression and logistic models for probability of progression (yes/no). Results The MDS NHS enrolled 2051 pts during June 2016-February 2024, with 54% (1111, above categories) followed prospectively. Median F/U is 1.7 yrs (IQR: 0.8-3.1 yrs, max: 7.9 yrs). For the current analysis, 582 MDS and 505 AR-MDS pts were included. Compared to AR-MDS, MDS patients were more often male (70% v 61%, p<0.01), older (median age: 75 v 72 years, p<0.01), and had lower baseline hemoglobin (median: 10.1 v 11.9 g/dL, p<0.001), platelets (median: 124 x109 v 146 x109 cells/L, p<0.001), and absolute neutrophil counts (median: 1.9 x109 vs 2.8 x109 cells/L, p<0.001). F/U time was similar among AR-MDS and MDS (median: 1.9 vs 1.6 yrs), with both groups having a 75th percentile of 3 yrs and maximum of 7 yrs. A total of 331 deaths were observed (30%), with 145 (44%) of these due to causes related to MDS (myeloid disease and/or cardiovascular disease). Overall, 18.5% had disease progression, with annual rates of 8% (MDS) and 2% (AR-MDS) (p<0.01). Among HR-MDS, progression rates were greatest in the 1st year following diagnosis (33%) and declined thereafter, whereas progression rates remained between 5-8% for LR-MDS and 1-3% for AR-MDS over 5 yrs of F/U. AR-MDS had similar rates of progression within the first 3 yrs from diagnosis as MDS with the lowest IPSS-R category. Among AR-MDS, low hemoglobin (hazard ratio, HR [95% CI]=3.2 [1.4-7.3], p<0.01) was significantly associated with progression. Among MDS, low hemoglobin (HR=4.0 [2.2-7.0], p<0.001), low platelet count (HR=3.1 [1.7-5.6], p<0.001) and higher numbers of mutations (HR=1.13 [1.0-1.2], p<0.01) were significantly associated with progression. Due to the low rate of progression in AR-MDS, meaningful comparisons of mutations could not be completed. In MDS, univariate analyses showed that somatic mutation prevalence in gene pathways for transcription, cell cycle and RAS signaling, and in genes TP53, BCOR, SF3B1, DDX41, PTPN11, SETBP1, and JAK2 were significantly associated with the probability of progression (p<0.05 in all). In multivariable models, adjusting for patient age, sex, and MDS risk level, the probability of progression (PP) was significantly higher among those with vs without somatic mutations in BCOR (PP=63% vs 29%, p=0.004), PTPN11 (PP=67% vs 29%, p=0.009), SETBP1 (PP=61% vs 30%, p=0.02), JAK2 (PP=59% vs 30%, p=0.02), DDX41 (PP=56% vs 30%, p=0.03) and TP53 (PP=43% vs 29%, p=0.04). Conclusions In this large prospective cohort of MDS and pts at risk for MDS, the annual rate of any disease progression was 8% among MDS and 2% among AR-MDS, lower than some retrospective series. Specific cytopenias and genetic factors associated with progression were identified. The MDS NHS provides an incomparable opportunity to address the critical clinical challenges of management and prevention of MDS progression across its spectrum.

Introduction: Myelodysplastic syndromes (MDS) occur due to the acquisition of mutational events over time. Tobacco smoking is a risk factor for MDS and has been associated with distinct genetic mutations in solid cancers. We investigated the association between smoking intensity and duration and the number and types of genetic mutations in MDS using a large prospective cohort. Methods: The prospective NHLBI/NCI National MDS Natural History Study includes individuals with suspected or recently diagnosed MDS enrolled between June 2016-August 2023. Patient (Pt) bone marrow samples underwent central pathology review, resulting in diagnoses including MDS, MDS/myeloproliferative neoplasm overlap (MDS/MPN), or precursor conditions, such as clonal cytopenia of undetermined significance (CCUS). Self-reported smoking data were collected at enrollment. Smoking intensity (i.e., light, medium, and heavy) was defined per CDC definitions. Included mutations were those manually annotated to be pathogenic for MDS. Mutation frequency and prevalence, as well as associations with smoking history and other key covariates, were assessed using univariate tests of association and multivariable regression models for counts and proportions. Rates of disease progression were compared between groups using survival analysis methods for cumulative incidence incorporating non-disease death as a competing risk. Results: Of 1898 evaluable pts, 52% (N=979) had a history of cigarette smoking [MDS: 58% (252/436), MDS/MPN: 57% (52/91), CCUS: 57% (230/405)], and 18% still smoked at diagnosis. The mean ± SD cigarettes per day was 15.8 ± 12.5 and the mean ± SD smoking years was 29.8 ± 16.9 years. Compared to non-smokers, smokers were more often males (68% v 54%, p<0.001) in the 70-79 year age group (45% v 34%, p<0.001). Similar proportions of those with and without a smoking history had MDS (41% v 37%, respectively). Within MDS, there was no association between smoking and MDS prognostic risk per revised international prognostic scoring system. Compared to light smokers [1-10 pack-years (PY)], heavy smokers (30+ PY) were more often male (81% v 62%, p<0.001) and had higher rates of cardiovascular disease (38% v 20%, p<0.001). In univariate analyses, smokers and non-smokers had similar numbers of mutations (mean ± SD: 2.0 ± 2.7 in both groups). Pts with CCUS had the least number of mutations (1.5 ± 1.2), followed by MDS (2.3 ± 1.8), and MDS/MPN overlap (3.3 ± 1.8). Within disease groups, smokers and non-smokers had similar numbers of mutations. Mutation prevalence was higher among smokers v non-smokers in gene pathways for chromatin modification (15% v 11%, p<0.01) and RNA splicing (26% v 19%, p<0.001) and for individual genes ASXL1 (12% v 8%, p<0.01), SF3B1 (9% v 6%, p<0.05), U2AF1 (6% v 3%, p<0.05) and ZRSR2 (2% v 1%, p<0.05). In multivariable regression models, after adjustment for sex, age and disease group, smokers had significantly more mutations on average than non-smokers (2.0 v. 1.4, p=0.04). Moreover, the number of mutations increased significantly with PY smoked (p=0.006), and those at the 75th and 90th percentiles of PY had 1.8 and 3.5 times the number of mutations, respectively, compared to non-smokers, indicating a dose-response relationship. After adjustment, mutation prevalence increased with PY smoked in the gene pathways for chromatin modification and RNA splicing (p<0.03 for both) and for the genes ASXL1 and ETNK1 (p<0.05 in both cases). The five-year cumulative incidence of disease progression was significantly higher among long-term smokers compared to non-smokers/those with shorter smoking history (mean [95% CI] proportion progressed, 20+ years vs <20 years smoking: 27% [19%-36%] vs 18% [13%-24%], p < 0.05). Overall survival was significantly lower for smokers compared to non-smokers for CCUS pts [HR 1.91, 95% CI (1.03-3.55), p=0.04] but not for MDS [HR 1.21, 95% CI (0.53-1.30), p=0.41]. Conclusions: In this prospective, national cohort, tobacco smoking was associated with distinct genetic mutations related to MDS, suggesting smoking potentially contributes to its multi-step molecular pathogenesis. A dose-response relationship was also demonstrated, as was an association with progression and survival. Our data suggest that pts with a new MDS diagnosis who are also smokers should be counseled to stop smoking, as it appears to contribute to the acquisition of new genetic mutations that can lead to progression.

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).

Background: Revision anterior cruciate ligament (ACL) reconstruction has been documented to have inferior outcomes compared with primary ACL reconstruction. The reasons why remain unknown. Purpose: To determine whether surgical factors performed at the time of revision ACL reconstruction can influence a patient’s outcome at 6-year follow-up. Study Design: Cohort study; Level of evidence, 2. Methods: Patients who underwent revision ACL reconstruction were identified and prospectively enrolled between 2006 and 2011. Data collected included baseline patient characteristics, surgical technique and pathology, and a series of validated patient-reported outcome instruments: Knee injury and Osteoarthritis Outcome Score (KOOS), International Knee Documentation Committee (IKDC) subjective form, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Marx activity rating score. Patients were followed up for 6 years and asked to complete the identical set of outcome instruments. Regression analysis was used to control for baseline patient characteristics and surgical variables to assess the surgical risk factors for clinical outcomes 6 years after surgery. Results: A total of 1234 patients were enrolled (716 men, 58%; median age, 26 years), and 6-year follow-up was obtained on 79% of patients (980/1234). Using an interference screw for femoral fixation compared with a cross-pin resulted in significantly better outcomes in 6-year IKDC scores (odds ratio [OR], 2.2; 95% CI, 1.2-3.9; P = .008) and KOOS sports/recreation and quality of life subscale scores (OR range, 2.2-2.7; 95% CI, 1.2-4.8; P < .01). Use of an interference screw compared with a cross-pin resulted in a 2.6 times less likely chance of having a subsequent surgery within 6 years. Use of an interference screw for tibial fixation compared with any combination of tibial fixation techniques resulted in significantly improved scores for IKDC (OR, 1.96; 95% CI, 1.3-2.9; P = .001); KOOS pain, activities of daily living, and sports/recreation subscales (OR range, 1.5-1.6; 95% CI, 1.0-2.4; P < .05); and WOMAC pain and activities of daily living subscales (OR range, 1.5-1.8; 95% CI, 1.0-2.7; P < .05). Use of a transtibial surgical approach compared with an anteromedial portal approach resulted in significantly improved KOOS pain and quality of life subscale scores at 6 years (OR, 1.5; 95% CI, 1.02-2.2; P≤ .04). Conclusion: There are surgical variables at the time of ACL revision that can modify clinical outcomes at 6 years. Opting for a transtibial surgical approach and choosing an interference screw for femoral and tibial fixation improved patients’ odds of having a significantly better 6-year clinical outcome in this cohort.

PURPOSE The adjuvant treatment for stage III colon cancer (CC) is chemotherapy combining fluoropyrimidine (FP) and oxaliplatin (OX). FP regimen plus OX (FPOX) may benefit in high-risk stage II CC. We performed a pooled analysis of pivotal MOSAIC and C-07 studies evaluating FPOX for the treatment of high-risk stage II CC according to prognostic factors, number of high-risk factors, and current clinicopathologic risk classification on the basis of T stage, tumor perforation, and number of lymph nodes examined. PATIENTS AND METHODS One thousand five hundred and ninety-five patients with stage II CC receiving FP or FPOX were pooled. The overall survival (OS) benefit of OX was analyzed using Kaplan-Meier curves and unadjusted Cox models stratified by study. Three thousand and fifty-nine patients with stage III CC were used only for interaction tests between the allocated chemotherapy and stage. RESULTS In the pooled analysis of stage II patients, independent prognostic factors in multivariable analysis were sex, age, perforation/obstruction, and tumor sidedness. There was a significant interaction in OS between stage and allocated chemotherapy with hazard ratios (HRs) of 1.03 for stage II (95% CI, 0.82 to 1.29; P = .813) and 0.82 for stage III (95% CI, 0.73 to 0.92; P = .001; P int = .073). There was no benefit from the addition of OX to FP for any of the prognostic factors. The number of high-risk factors tested was not predictive of OX benefit. According to the currently agreed clinicopathologic risk classification, no OS benefit of OX was observed, as HR was 0.86 (95% CI, 0.63 to 1.18; P = .349). CONCLUSION No OS benefit of adjuvant OX was found in high-risk stage II CC, regardless of the definition used to characterize tumors as having a high risk for recurrence. Hence, our analysis suggests that OX should not be the standard of care for adjuvant chemotherapy for stage II CC, even in high-risk patients.