Citations

Abstract Background The exclusion/occlusion of the left atrial appendage (LAA) is a treatment option for atrial fibrillation (AF) patients who are at high risk of stroke and high risk of bleeding. As the role of the LAA is not well understood or explored, this study aims to assess its role on flow dynamics in the left atrium. Methods Computational fluid dynamics (CFD) simulations were carried out for nine AF patients before and after LAA exclusion. The flow parameters investigated included the LA velocities, Time Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), Relative Residence Time (RRT), and Pressure in the LA. Results This study shows that, on average, a decrease in TAWSS (1.82 ± 1.85 Pa to 1.27 ± 0.96 Pa, p < 0.05) and a slight increase in OSI (0.16 ± 0.10 to 0.17 ± 0.10, p < 0.05), RRT (1.87 ± 1.84 Pa −1 to 2.11 ± 1.78 Pa −1 , p < 0.05), and pressure (−19.2 ± 6.8 mmHg to −15.3 ± 8.3 mmHg, p < 0.05) were observed in the LA after the exclusion of the LAA, with a decrease in low‐magnitude velocities. Conclusion The exclusion of the LAA seems to be associated with changes in LA flow dynamics. Further studies are needed to elucidate the clinical implications of these changes.

Background: Literature supports including occupational therapists on primary care teams. Few occupational therapists practice in this setting. The health care community needs more information regarding the benefits of occupational therapists on the team. Method: We completed an 18-month pilot integrating an occupational therapist in a primary care team, tracking conditions treated by the occupational therapist, visit duration, reimbursement, and level of primary care providers’ (PCP) satisfaction. We then evaluated outcomes via a retrospective chart review. Results: Visits were brief, averaging 35 min. Longer visits did not improve outcomes. Occupational therapists were reimbursed by Medicare (47%), commercial (47%), both (6%). PCPs referred numerous conditions, and > 50% related to musculoskeletal pain. Most of the patients, 84%, reported partial (30%) or complete (54%) improvement; 60% required one-time treatment; and 40% were referred for further treatment on-site or outside the department for additional or specialty treatment. After the pilot, most of the PCPs felt an occupational therapist on the team was absolutely essential (n = 14; 61%), or very important (n = 7; 30%). Conclusion: Embedded occupational therapy treatment resulted in symptom improvement in 84% of patients across a variety of conditions, and brief visits were effective. Occupational therapist services were reimbursed across insurance plans. After working with an occupational therapist, 91% of the PCPs viewed the occupational therapist as a very important or essential team member.

PURPOSE Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Key Points Kidney stones are common in patients with autosomal dominant polycystic kidney disease compared with matched controls with CKD. The increase in imaging seen in patients with autosomal dominant polycystic kidney disease correlates with the kidney stone burden in these patients. Background Patients with autosomal dominant polycystic kidney disease (ADPKD) may have more imaging studies than patients with other forms of CKD. We characterized the imaging burden of patients with ADPKD relative to a CKD population to determine which factors lead to increased imaging in patients with ADPKD. Methods We retrospectively reviewed patients seen at Yale Nephrology between January 2012 and January 2021. We collected demographic, clinical, and imaging data through automated query and manual chart review. Eight hundred seven patients with ADPKD were matched to 4035 CKD controls on the basis of criteria of sex, race, ethnicity, CKD stage, hypertension, and diabetes, but not age. The number of abdominal imaging studies were compared between ADPKD and CKD groups, and the effect of kidney stone diagnosis was further evaluated. Chi-square and t tests were used to evaluate demographic variables, and Kruskal Wallis and negative binomial regression models were used to evaluate differences between abdominal imaging studies. Results Patients with ADPKD had a greater number of total abdominal imaging studies ( P < 0.0001), ultrasounds ( P < 0.0001), and magnetic resonance imagings ( P = 0.02) compared with controls. In patients with preserved renal function (eGFR >60 ml/min per m 2 ), these differences persisted. Kidney stones were significantly more common among patients with ADPKD ( P < 0.0001). In multivariable assessment of imaging study counts using a negative binomial model controlling for kidney stones, ADPKD was no longer a significant predictor. In patients with ADPKD, pyelonephritis; cyst complications; lower eGFR; diabetes; coronary artery disease; kidney stones; lower body mass index; and being male, Black, and younger increased the likelihood of having more imaging studies. Conclusions The higher prevalence of abdominal imaging studies in patients with ADPKD correlated with the increased incidence of kidney stones observed in this population.

Purpose: Improved predictive tools are desired in classic Hodgkin lymphoma (cHL) to improve prognostication and to better risk stratify patients (pts) for clinical trials. In younger pts who tolerate intensive therapies, focus has been on limiting late treatment toxicity through risk-adapted, PET-guided treatment strategy. In older pts, acute treatment toxicities are limiting factors for optimal disease control. Due to these fundamental differences, predictive tools may perform differently in younger and older pts. In particular, tools developed in younger pts may be suboptimal in older populations and vice versa. The primary objective of this study was to assess the performance of the A-HIPI (development was limited to 18-65 years of age) against the international prognostic score (IPS) for modelling overall survival (OS) and progression-free survival (PFS) in younger and older pts with cHL. Methods: This study utilized data from the population-based, nationwide Danish National Lymphoma registry. Pts with advanced-stage cHL (Ann Arbor stage III-IV or IIB with extra nodal disease) were included and categorized by age in two groups: ages ≤65 years vs. &gt;65 years. In the primary analysis, all pts regardless of therapy were included. A sensitivity analysis restricting to pts starting on BEACOPP or ABVD was performed (Only ABVD in the &gt;65 cohort). Concordance index (C-index) and calibration were used to assess model performance and generalizability. Calibration was visualized using calibration plots and quantified by calibration in the large (CITL) and calibration slope (CS) at 5-years post diagnosis. Results: A total of 893 advanced-stage HL pts fulfilled the inclusion criteria (634 in the ≤65 cohort and 259 in the &gt;65 cohort). The 5-year OS and PFS in the &gt;65-year cohort were 44% (Confidence intervals (CI); 37-51%) and 35% (CI; 30-42%), respectively. Fifty-six pts in the &gt;65 cohort were classified as low-intermediate-risk (IPS; 1-2), 128 intermediate-risk (IPS; 3-4), and 75 high-risk (IPS; 5-7). For the ≤65 cohort, the 5-year OS and PFS was 88% (CI; 85-90%) and 76% (CI; 73-80%), respectively. Twenty-three pts were classified as low-risk (IPS; 0), 244 low-intermediate-risk, 286 intermediate-risk, and 81 high-risk. The A-HIPI had a C-index of 0.74 and 0.63 for OS and PFS, respectively in the ≤65 years cohort vs. 0.65 and 0.64 for OS and PFS, respectively, in the &gt;65 cohort. The IPS had a C-index in the ≤65 cohort of 0.66 and 0.60 for OS and PFS, respectively, vs. 0.58 and 0.59 for OS and PFS, respectively, in the &gt;65 cohort (i.e., the IPS performed better in the ≤65 cohort in terms of OS and PFS but was inferior to the A-HIPI). When restricting to pts treated with ABVD or BEACOPP, the A-HIPI C-index in the &gt;65 cohort (116 pts) were reduced to 0.55 for OS and 0.54 for PFS. For the ≤65 cohort (595 pts), the A-HIPI C-index was stable at 0.75 and 0.62 for OS and PFS, respectively. In terms of A-HIPI calibration in the &gt;65 cohort, CITL and CS were -0.78 and 0.39 for OS vs. -0.98 and 0.80 for PFS. In the ≤65 cohort, CITL and CS were -0.06 and 0.99 for OS vs. 0.21 and 1.18 for PFS. Visual inspection of the calibration plot (Figure 1) for the &gt;65 cohort showed that the A-HIPI underestimated the observed proportion for both OS and PFS (i.e., the observed events were higher than the predicted). Calibration plot for the ≤65 cohort (Figure 2) indicated that the A-HIPI accurately modelled the observed proportion for both OS and PFS (i.e., the predicted probability was consistent with the observed proportion). Conclusion: The newly developed A-HIPI was validated in a large population-based cohort of Danish pts with advanced-stage cHL. Performance of the A-HIPI was high in the ≤65 cohort and superior to the IPS for OS prediction. Calibration of A-HIPI was excellent in pts ages ≤65 years. However, A-HIPI performed significantly worse among pts &gt;65 years and attained poor performance in pts receiving ABVD or BEACOPP, which is not surprising since patients older than 65 were not included in the development. This study emphasizes the importance of further efforts to develop or update models that apply specifically in older individuals. The inclusion of additional variables reflecting comorbidities and pts fitness into a prognostic model may be necessary to effectively deliver accurate prognoses and risk assessments for the older pt.

Patients with autosomal dominant polycystic kidney disease benefit from specialized care over their lifetimes, starting with diagnosis of the condition with ongoing discussion of both the renal course and extra-renal issues. Both renal and extra-renal issues may continue to cause major morbidity even after successful kidney transplant or initiation of RRT, and extra-renal disease aspects should always be considered as part of routine management. In this review, we will focus on updates in pain/depression screening, cardiac manifestations, liver and pancreatic cysts, kidney stone management, and genetic counseling. In some instances, we have shared our current clinical practice rather than an evidence-based guideline. We anticipate more standardization of care after the release of the Kidney Disease Improving Global Outcomes guidelines for management in autosomal dominant polycystic kidney disease later this year.

Genetic testing in nephrology is becoming increasingly important to diagnose patients and to provide appropriate care. This is especially true for autosomal dominant polycystic kidney disease (ADPKD) because this is a common cause of kidney failure and genetically complex. In addition to the major genes, PKD1 and PKD2 , there are at least six minor loci, and phenotypic, and in some cases, genetic overlap with other cystic disorders. Targeted next-generation sequencing, a low-cost, high-throughput technique, has made routine genetic testing viable in nephrology clinics. Appropriate pre- and post-testing genetic counseling is essential to the testing process. Carefully assessing variants is also critical, with the genetic report classifying variants in accordance with American College of Medical Genetics and Genomics guidelines. However, variant of uncertain significance (VUSs) may pose a significant challenge for the ordering clinician. In ADPKD, and particularly within PKD1 , there is high allelic heterogeneity; no single variant is present in more than 2% of families. The Mayo/Polycystic Kidney Disease Foundation variant database, a research tool, is the best current database of PKD1 and PKD2 variants containing over 2300 variants identified in individuals with polycystic kidney disease, but novel variants are often identified. In patients with a high pretest probability of ADPKD on the basis of clinical criteria, but no finding of a pathogenic (P) or likely pathogenic (LP) variant in a cystic kidney gene, additional evaluation of cystic gene VUS can be helpful. In this case-based review, we propose an algorithm for the assessment of such variants in a clinical setting and show how some can be reassigned to a diagnostic grouping. When assessing the relevance of a VUS, we consider both patient/family-specific and allele-related factors using population and variant databases and available prediction tools, as well as genetic expertise. This analysis plus further family studies can aid in making a genetic diagnosis.

Abstract Background Dobutamine stress echocardiography (DSE) is recommended to distinguish between true-severe and pseudo-severe aortic stenosis (AS) in patients with low flow low-gradients and left ventricular ejection fraction (LVEF) &lt;50%. However, DSE has mostly been tested in the setting of LVEF&lt;35%. Purpose The purpose of this study was to examine the diagnostic accuracy of DSE according to aortic valve calcification (AVC) score, in patents with low-flow, low-gradient AS across a wide range of LVEF. Methods Patients with aortic mean gradient &lt;40 mmHg, aortic valve area (AVA) &lt;1.0 cm2, and stroke volume index (SVi) ≤35 mL/m2 undergoing DSE were identified from three prospectively collected patient cohorts, and stratified according to LVEF; &lt;35%, 35-50% and &gt;50%. Severe AS was defined as AVC score ≥2000 AU among men, and ≥1200 AU for women. Recommendation for severe AS according to DSE, are an AVA &lt;1.0 cm2 combined with an aortic mean gradient ≥40 mmHg or peak velocity ≥4 m/s. Receiver operating characteristic curves for DSE-derived AVA, mean gradient and Vmax were used to identify optimal cut-off points for each LVEF subgroup. Results Of the 221 patients included in the study, 78 (35%) presented with LVEF &lt;35%, 67 (30%) with LVEF 35-50%, and 76 (34%) with LVEF &gt;50%. Baseline characteristics are presented in the table. Based on AVC, severe AS was present in 102 (46%) of patients. Using recommended thresholds for severe AS for AVC and DSE, led to a sensitivity of 49%, a specificity of 84% and an accuracy of 67% for DSE for all patients with LVEF &lt;50%, but with significant heterogeneity between groups figure. AVA performed rather uniformly across LVEF subgroups; (AUC =0.68 vs 0.62 vs 0.54, p=0.36) LVEF&lt;35%, LVEF 35-50%, LVEF&gt;50% respectively and displayed a rather uniform optimal cut-off (1.0 cm2 vs 0.9 cm2 vs 0.8 cm2). However, mean gradient and Vmax during DSE, both showed significantly diagnostic heterogeneity across LVEF groups (AUC= 0.90 vs 0.67 vs 0.65, p=&lt;0.001 and AUC= 0.90 vs 0.66 vs 0.60, p=&lt;0.001) with different optimal thresholds (30 mmHg & 3.8 m/s, 45 mmHg & 4.3 m/s and 37mmHg & 4 m/s, LVEF&lt;35%, LVEF 35-50%, LVEF&gt;50%, respectively). Conclusion Our study shows, that the association between DSE gradients and AS severity assessed by AVC demonstrates important heterogeneity depending on LVEF. While DSE had the highest accuracy in patients with LVEF&lt;35% both patients with LVEF 35-50%, and patients with LVEF&gt;50% showed significantly less concordance between DSE and AVC.