Citations

Abstract Lenalidomide‐containing (R) triplet and quadruplet regimens are the standard of care for multiple myeloma (MM) and have been shown to increase the risk of thrombosis. The association between thromboembolism (TE) and survival in the novel multidrug era is not yet delineated. In this study, we evaluated the incidence of TE during the first year of MM diagnosis, its association with the type of induction regimen, and its impact on overall survival. We studied 672 newly diagnosed MM (NDMM) patients who received a triplet or quadruplet lenalidomide‐based induction at the Mayo Clinic, Rochester. TE was diagnosed in 83 patients (12.4%). Of these, 56 (8.3%) had a deep venous thrombosis (DVT), 23 (3.4%) had a pulmonary embolism (PE) with or without the DVT, and 4 (0.6%) patients had a stroke. Carfilzomib‐Rd (KRd) had the highest risk of TE (21.1%, 18/85), followed by quadruplets (11.1%, 5/45), bortezomib‐Rd (9.6%, 51/531), and 0/11 (0%), treated with other lenalidomide‐containing regimens. The difference in TE risk between KRd and the other regimens was statistically significant (OR = 2.6, p < .01). Nine patients developed a TE before being exposed to any treatment. Survival was significantly lower among patients that developed a TE (66 vs. 133 months, p < .01). The association of TE with reduced survival demonstrated in univariate analysis (HR = 2.2, 95% CI = 1.6–3.3) was maintained in the multivariable analysis adjusted for high‐risk interphase fluorescence in situ hybridization (FISH), sex, age, receipt of an upfront transplant, the response at induction, and the International Staging System (ISS) (HR = 2.61, CI = 1.74–3.9). We conclude that TE is an important aspect of MM management, and effective management is especially relevant in the novel treatment era.

AbstractBackgroundDyadic (self and study partner) subjective reports of an individual’s recent cognitive and functional decline are an efficient approach to facilitate identification of older adults with, and at risk for, Mild Cognitive Impairment (MCI). Such methods can be easily adapted for remote administration, but few studies have investigated the ability of remotely‐collected, unsupervised dyadic subjective reports to accurately detect MCI and cognitive decline.MethodsIn 289 older adults enrolled in the Brain health Registry (BHR), with clinically‐confirmed diagnoses of Cognitively‐unimpaired (CU) or MCI, we identified the optimal combination of remote measures to accurately distinguish CU from MCI. We then externally validated our predictive model in 217 participants enrolled in a separate cohort, Validation of Online Methods to Predict Cognitive Decline (eVAL). Predictors included Cogstate Brief Battery One Card Learning (OCL) accuracy; self‐ and study partner‐reported memory concern (SMCs); performance on online adaptations of Everyday Cognition Scale (ECog, self‐ and study partner‐report versions), Geriatric Depression Scale Short Form (GDS); family history of AD, age, gender, and education. We calculated 10‐fold cross validated area under the ROC curves (cvAUC), 95% confidence intervals (CI), and the percentage of participants correctly classified (accuracy) for all models. Likelihood ratio (LR) tests were used to compare model fits.ResultsIn the BHR cohort, models including dyad‐report ECog, family history of AD, GDS, and Cogstate OCL scores distinguished CU from MCI with the highest accuracy (cvAUC = 0.90; 95% CI = 0.83, 0.92; accuracy = 85.5%). Models excluding study partner‐report ECog distinguished diagnostic groups with moderate accuracy (cvAUC = 0.83; 95% CI = 0.78, 0.85; accuracy = 75.9%) When applied to the validation cohort, the optimal training model including dyadic report performed with moderate accuracy ( Table 2 ). In the validation cohort, inclusion of study partner (but not self) report SMC, in addition to dyadic‐report ECog, improved model fit (LR test p<0.01).ConclusionsRemotely‐collected dyadic subjective measures, along with other remote measures, accurately distinguish CU from MCI. Subjective cognitive and functional decline and concerns about memory problems independently contribute to diagnostic discrimination. Results support future utilization of remote dyadic subjective measures to efficiently identify older adults with MCI for prodromal AD clinical trials, and in clinical care settings.

Abstract POEMS syndrome is a rare paraneoplastic syndrome and therapies are directed against plasma cells that produce the proteins that cause this syndrome. Novel therapies are widely used in multiple myeloma aiming for plasma cell eradication. However, data on their use in POEMS syndrome are lacking. In this case series, we provide the Mayo Clinic experience in treating 16 patients with relapsed POEMS syndrome with novel agents (daratumumab, carfilzomib, pomalidomide, and elotuzumab). The patients were treated with a doublet including dexamethasone (N = 5) (31%) or in various combinations with other agents: DRd (N = 6), DC(V)d (N = 3), KRd (N = 3), KPd (N = 1), DP(V)d (N = 5), and EloRd (N = 1). The outcomes with novel agent therapies were favorable. Overall, twelve patients treated with daratumumab-based therapies (86%) and five patients treated with carfilzomib-based therapies (83%) responded to therapy. Among patients treated with daratumumab based therapies (N = 14), 9 patients achieved CR/VGPRH, 7 patients achieved CRV, and 5 patients achieved CRP. Among patients treated with carfilzomib-based therapies (N = 6), 3 patients achieved CR/VGPRH, and one achieved PRH. At a median follow-up of 38 months since starting of the novel agent (IQR 24–57), 15 of the patients (93%) are still alive, and the median TTNT was not reached. None of the patients discontinued therapy due to adverse events and no deaths occurred on therapy. Novel therapies were safe with 7 events of hospitalization due to pneumonia (4 in daratumumab-based therapies and 3 on carfilzomib-based therapies), and 4 patients were hospitalized due to volume overload. Three patients experienced infusion-related reactions (IRR) to the first dose of IV daratumumab. The response rate to novel agents was high, and the responses were deep. Novel agent therapies were safe, and no death case occurred on therapy. Future studies are needed to clarify the optimal sequence of novel agents and the best combination.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.