Citations

AbstractAchievement of a complete response (CR) in multiple myeloma (MM) correlates with improvement in survival outcomes; however, its impact on prognostic variables at baseline outside of clinical trial settings is not well described. We sought to determine the impact of achieving a CR within 2 years from diagnosis, its effect on the prognostic value of fluorescence in situ hybridization (FISH) and International Staging System (ISS) risk, and examined additional predictors of outcome among those achieving a CR in a routine clinical setting. We evaluated 1869 newly diagnosed MM patients who had ≥ 2 monoclonal protein immunofixation studies in the serum and urine available within 24 months from diagnosis, categorizing those with ≥ 2 negative serum and urine immunofixations as achieving CR. With a landmark at 24 months, median progression‐free survival (PFS) for CR versus non‐CR patients was 29.8 versus 20.9 months (p ≤ .0002); median overall survival (OS) was 104 versus 70 months (p < .0001). The impact of achieving a CR was retained after adjusting for FISH, ISS, sex, transplant status, and involved heavy chain. Baseline FISH and ISS stage were not associated with PFS or OS among patients achieving a CR. The following variables were found as predictors of inferior OS within the CR cohort: age > 75 years, male gender, hypoalbuminemia, and non‐immunoglobulin G involved heavy chain. Our study confirms that achievement of CR within 2 years from diagnosis is associated with improvement in survival outcomes and neutralization of the impact of FISH and ISS risk, thereby confirming observations from the clinical trial setting among a clinical practice cohort.

AbstractPurposeExternal validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Machine learning analysis of the Norwegian Knee Ligament Register (NKLR) recently led to the development of a tool capable of estimating the risk of anterior cruciate ligament (ACL) revision (https://swastvedt.shinyapps.io/calculator_rev/). The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR).MethodsThe primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For external validation, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables included graft choice, femur fixation device, KOOS QOL score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration.ResultsIn total, 10,922 DKLR patients were included for analysis. Average follow‐up time or time‐to‐revision was 8.4 (± 4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68–0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years.ConclusionThe NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in‐clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre‐operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown.Level of evidenceIII.

Abstract Study Objectives Post-traumatic stress disorder (PTSD) and rapid eye movement (REM) sleep behavior disorder (RBD) share some common features including prominent nightmares and sleep disturbances. We aimed to comparatively analyze REM sleep without atonia (RSWA) between patients with chronic PTSD with and without dream enactment behavior (DEB), isolated RBD (iRBD), and controls. Methods In this retrospective study, we comparatively analyzed 18 PTSD with DEB (PTSD+DEB), 18 PTSD without DEB, 15 iRBD, and 51 controls matched for age and sex. We reviewed medical records to determine PTSD clinical features and quantitatively analyzed RSWA. We used nonparametric analyses to compare clinical and polysomnographic features. Results PTSD patients, both with and without DEB, had significantly higher RSWA than controls (all p &lt; .025, excepting submentalis phasic duration in PTSD+DEB). Most RSWA measures were also higher in PTSD+DEB than in PTSD without DEB patients (all p &lt; .025). Conclusions PTSD patients have higher RSWA than controls, whether DEB is present or not, indicating that REM sleep atonia control is abnormal in chronic PTSD. Further prospective studies are needed to determine whether neurodegenerative risk and disease markers similar to RBD might occur in PTSD patients.

Abstract Background and Aims Despite that hepatic fibrosis often affects the liver globally, spatial distribution can be heterogeneous. This study aimed to investigate the effect of liver stiffness (LS) heterogeneity on concordance between MR elastography (MRE)‐based fibrosis staging and biopsy staging in patients with NAFLD. Approach and Results We retrospectively evaluated data from 155 NAFLD patients who underwent liver biopsy and 3 Tesla MRE and undertook a retrospective validation study of 169 NAFLD patients at three hepatology centers. Heterogeneity of stiffness was assessed by measuring the range between minimum and maximum MRE‐based LS measurement (LSM). Variability of LSM was defined as the stiffness range divided by the maximum stiffness value. The cohort was divided into two groups (homogenous or heterogeneous), according to whether variability was below or above the average for the training cohort. Based on histopathology and receiver operating characteristic (ROC) analysis, optimum LSM thresholds were determined for MRE‐based fibrosis staging of stage 4 (4.43, kPa; AUROC, 0.89) and stage ≥3 (3.93, kPa; AUROC, 0.89). In total, 53 had LSM above the threshold for stage 4. Within this group, 30 had a biopsy stage of <4. In 86.7% of these discordant cases, variability of LSM was classified as heterogeneous. In MRE‐based LSM stage ≥3, 88.9% of discordant cases were classified as heterogeneous. Results of the validation cohort were similar to those of the training cohort. Conclusions Discordance between biopsy‐ and MRE‐based fibrosis staging is associated with heterogeneity in LSM, as depicted with MRE.

Purpose To develop a machine learning algorithm to predict total charges after ambulatory hip arthroscopy and create a risk‐adjusted payment model based on patient comorbidities. Methods A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective hip arthroscopy between 2015 and 2016. Features included in initial models consisted of patient characteristics, medical comorbidities, and procedure‐specific variables. Models were generated to predict total charges using 5 algorithms. Model performance was assessed by the root‐mean‐square error, root‐mean‐square logarithmic error, and coefficient of determination. Global variable importance and partial dependence curves were constructed to show the impact of each input feature on total charges. For performance benchmarking, the best candidate model was compared with a multivariate linear regression using the same input features. Results A total of 5,121 patients were included. The median cost after hip arthroscopy was $19,720 (interquartile range, $12,399‐$26,439). The gradient‐boosted ensemble model showed the best performance (root‐mean‐square error, $3,800 [95% confidence interval, $3,700‐$3,900]; logarithmic root‐mean‐square error, 0.249 [95% confidence interval, 0.24‐0.26]; R 2 = 0.73). Major cost drivers included total hours in facility less than 12 or more than 15, longer procedure time, performance of a labral repair, age younger than 30 years, Elixhauser Comorbidity Index (ECI) of 1 or greater, African American race, residence in extreme urban and rural areas, and higher household and neighborhood income. Conclusions The gradient‐boosted ensemble model effectively predicted total charges after hip arthroscopy. Few modifiable variables were identified other than anesthesia type; nonmodifiable drivers of total charges included duration of care less than 12 hours or more than 15 hours, operating room time more than 100 minutes, age younger than 30 years, performance of a labral repair, and ECI greater than 0. Stratification of patients based on the ECI highlighted the increased financial risk borne by physicians via flat reimbursement schedules given variable degrees of comorbidities. Level of Evidence Level III, retrospective cohort study.

Introduction. Pathogen-associated molecular patterns’ (PAMPs) are microbial signatures that are recognized by host myeloid C-type lectin receptors (CLRs). These CLRs interact with micro-organisms via their carbohydrate recognition domains (CRDs) and engage signalling pathways within the cell resulting in pro-inflammatory and microbicidal responses. Gap statement. In this article, we extend our laboratory study of additional CLRs that recognize fungal ligands against Pneumocystis murina and Pneumocystis carinii and their purified major surface glycoproteins (Msgs). Aim. To study the potential of newly synthesized hFc-CLR fusions on binding to Pneumocystis and its Msg. Methods. A library of new synthesized hFc-CLR fusions was screened against Pneumocystis murina and Pneumocystis carinii organisms and their purified major surface glycoproteins (Msgs) found on the respective fungi via modified ELISA. Immunofluorescence assay (IFA) was implemented and quantified to verify results. mRNA expression analysis by quantitative PCR (q-PCR) was employed to detect respective CLRs found to bind fungal organisms in the ELISA and determine their expression levels in the mouse immunosuppressed Pneumocystis pneumonia (PCP) model. Results. We detected a number of the CLR hFc-fusions displayed significant binding with P. murina and P. carinii organisms, and similarly to their respective Msgs. Significant organism and Msg binding was observed for CLR members C-type lectin domain family 12 member A (CLEC12A), Langerin, macrophage galactose-type lectin-1 (MGL-1), and specific intracellular adhesion molecule-3 grabbing non-integrin homologue-related 3 (SIGNR3). Immunofluorescence assay (IFA) with the respective CLR hFc-fusions against whole P. murina life forms corroborated these findings. Lastly, we surveyed the mRNA expression profiles of the respective CLRs tested above in the mouse immunosuppressed Pneumocystis pneumonia (PCP) model and determined that macrophage galactose type C-type lectin (Mgl-1), implicated in recognizing terminal N-acetylgalactosamine (GalNAc) found in the glycoproteins of microbial pathogens was significantly up-regulated during infection. Conclusion. The data herein add to the growing list of CLRs recognizing Pneumocystis and provide insights for further study of organism/host immune cell interactions.

Purpose To develop and internally validate a machine‐learning algorithm to reliably predict cost after anterior cruciate ligament reconstruction (ACLR). Methods A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective ACLR from 2015 to 2016. Features included in initial models consisted of patient characteristics (age, sex, insurance status, income, medical comorbidities as classified by the Clinical Classifications Software diagnosis code) as well as intraoperative variables (type of anesthesia and procedure‐specific factors). Models were generated to predict total charges using 4 algorithms: random forest, extreme gradient boost, elastic net penalized regression, and support vector machines with radial kernels. Training was performed with 10‐fold cross‐validation followed by internal validation via 0.632 bootstrapping. Model discriminative performance was assessed by area under the receiver operating characteristic curve, calibration, and the Brier score. Decision curve analysis was performed to demonstrate the net benefit of using the final model in practice. Results In total, 7,311 patients undergoing ambulatory ACLR were included. The random forest model demonstrated the best performance assessed via internal validation (area under the curve = 0.85), calibration, and the Brier score (0.208). Cost incurred was influenced by anesthesia type, operating room time, and number of chronic comorbidities. Decision curve analysis revealed a net benefit for use of the random forest model and the model was integrated into a web‐based open‐access application. Conclusions The random forest model predicted cost after ambulatory ACLR using a large, statewide database with good performance. The top variables found to predict increased charges were general anesthesia, operating room time, meniscal repair, self‐pay insurance, patient neighborhood characteristics, and number of chronic conditions. Level of Evidence III, retrospective cohort study.

AbstractThromboses are prevalent in POEMS syndrome, but few risk factors for POEMS‐associated thrombosis have been identified. The objective of this study is to identify novel risk factors for POEMS‐associated thrombosis. In this retrospective cohort of 230 POEMS patients, 27% developed thrombosis. Arterial events were slightly more common than venous. Stroke accounted for 26% of all thromboses and 53% of arterial events. There were differences in baseline features between the thrombosis group and the no thrombosis group, and these were driven by patients with arterial thrombosis. Risk factors for arterial thrombosis included thrombocytosis, elevated hemoglobin/hematocrit, extravascular volume overload, and splenomegaly. Hyperprolactinemia appeared to be a risk factor for venous thrombosis. The risk of thrombosis was most striking among men with elevated hemoglobin (32% vs. 5%, p < .001) and hematocrit (42% vs. 5%, p < .001) compared to men without. Most thromboses occurred prior to POEMS directed therapy, and most that occurred during therapy happened within 3 months of diagnosis. Twenty‐one percent of patients with thrombosis had recurrence. In recognition of high overall rates of thrombosis in this population, all patients with POEMS syndrome should receive prophylactic antiplatelet therapy, and clinicians should consider anticoagulation in patients with risk factors for POEMS‐associated thrombosis.

Abstract Background: There has been a steady improvement in the survival of patients with multiple myeloma (MM) due to better understanding of the pathophysiology, use of novel agents, and risk-adapted therapy. These improvements were largely observed in clinical trials and tertiary center populations and questions remain about the generalizability of these findings to patients in community settings. Here, we compared the experience of patients seen at Mayo Clinic, Rochester (MAYO) and those followed in the Surveillance, Epidemiology, and End Results Program (SEER), to gain a better understanding of current trends in survival. Methods: We studied all patients with MM diagnosed between 01/2004 and 12/2018 seen at MAYO and those followed in SEER (18 registry research data 2000-2018, 11/2020 submission). For comparison with SEER, MAYO patients who were alive at the end of SEER follow-up (12/31/2018) were censored. For the estimation of relative survival, patients alive at the end of 2019 were censored to align with the published United States general population rate tables. Overall survival (OS) estimates were calculated using the method described by Kaplan and Meier. Multivariable-adjusted proportional hazards regression models were used to assess the association between the 5-year interval of diagnosis and overall survival. Expected OS estimates were calculated based on United States general population rate tables from the Human Mortality Database using a conditional approach. Standardized mortality ratios (SMR) were calculated by dividing the number of observed deaths by the number of expected deaths in age- and sex-matched general United States population controls. Linear regression models were fit to test for trends in SMRs over time. Results: The median age at diagnosis was 3 years lower in patients seen at MAYO compared to patients followed in SEER. The proportion of patients 75 years and older at the time of diagnosis was higher in SEER (26.3%) compared to MAYO (15.3%). Median OS was 6.7 years (95% CI 6.3-7.1) for MAYO compared to 4.6 years (95% CI 4.5-4.7) in SEER (Figure 1). When stratifying OS by year of diagnosis (in 5-year-intervals: 2004-2008, 2009-2013, 2014-2018), improvements were seen in both patient populations over time, albeit to a greater extent in patients seen at MAYO (Figures 2 and 3). The results were consistent after adjusting for age and sex in both MAYO and SEER. There were improvements in OS across all three 5-year intervals and age groups in both patient populations. In MAYO, these improvements were most pronounced during the second 5-year interval for younger patients (&lt; 65 years) and more pronounced during the last 5-year interval for older patients (&gt; 65 years). In SEER, the improvements were more gradual across the three 5-year intervals, but it was noted that the relative improvements in younger patients were greater than in older patients during the last 5-year interval. Early mortality decreased over time in both patient populations: from 11% to 6% at MAYO and from 23% to 16% in SEER (Figure 4). To examine the observed improvements in the context of the survival trends of the general population, we stratified the SMRs by patient population, age at diagnosis, and year of diagnosis, and a lack of improvement in excess mortality was observed among older patients (&gt;75 years) in both populations over time (Figure 5). Three- and five-year overall survival estimates were higher in patients seen at MAYO compared to patients followed in SEER, but the survival advantage diminished with increasing follow-up time (Figure 6). On further sub-group analysis within MAYO patients, excess mortality was observed for all International staging system (ISS) stages and patients with high-risk (HR) cytogenetics. Stratifying the SMRs by age and risk group, decreases in excess mortality were most pronounced among older patients, particularly those with indolent disease (Figure 7). Conclusions: Survival outcomes in MM continued to improve in both patient populations. These improvements were more pronounced in MAYO compared to SEER, which may be partially explained by differences in access to specialized care. The most pronounced improvements were observed among younger patients and those with favorable disease characteristics. Future efforts need to focus on improving therapies for patients with unfavorable disease characteristics and on increasing access to specialized care in community settings. Figure 1 Figure 1. Disclosures Kapoor: Glaxo SmithKline: Research Funding; Takeda: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Sanofi: Consultancy; Pharmacyclics: Consultancy; Ichnos Sciences: Research Funding; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; Karyopharm: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy. Dingli: Alexion: Consultancy; Apellis: Consultancy; Janssen: Consultancy; Novartis: Research Funding; GSK: Consultancy; Sanofi: Consultancy. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Lin: Novartis: Consultancy; Celgene: Consultancy, Research Funding; Juno: Consultancy; Takeda: Research Funding; Gamida Cell: Consultancy; Vineti: Consultancy; Merck: Research Funding; Bluebird Bio: Consultancy, Research Funding; Legend: Consultancy; Sorrento: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding. Dispenzieri: Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Research Funding; Oncopeptides: Consultancy; Alnylam: Research Funding; Pfizer: Research Funding. Kumar: Tenebio: Research Funding; Amgen: Consultancy, Research Funding; Oncopeptides: Consultancy; Bluebird Bio: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.