Citations

Total joint arthroplasty (TJA) implants are composed of metals, ceramics, and/or polyethylene. Studies suggest that the debris released from metal implants may possess neurotoxic properties with reports of neuropsychiatric symptoms and memory deficits, which could be relevant to Alzheimer’s disease and related dementias. This exploratory study examined the cross-sectional correlation of blood metal concentrations with cognitive performance and neuroimaging findings in a convenience sample of 113 TJA patients with history of elevated blood metal concentrations of either titanium, cobalt and/or chromium. Associations with neuroimaging measures were observed but not with cognitive scores. Larger studies with longitudinal follow-up are warranted.

Background: Bortezomib, a proteasome inhibitor, has shown efficacy in the treatment of newly diagnosed and relapsed light chain (AL) amyloidosis, and the combination of bortezomib, cyclophosphamide and dexamethasone is a commonly used regimen in AL. Ixazomib is the first oral proteasome inhibitor to be approved, and the combination of ixazomib with cyclophosphamide and dexamethasone is an all oral effective regimen for the treatment of multiple myeloma. This phase 2 trial was designed to evaluate the efficacy of this regimen in patients with AL, who have not received any therapy. Patients and methods: Newly diagnosed patients with biopsy proven AL amyloidosis, with organ involvement requiring therapy, were enrolled if they had measurable disease (Serum immunoglobulin free light chain ≥5 mg/dL AND abnormal serum free light chain ratio) and adequate organ function. Patients with severe organ involvement were excluded (Alkaline phosphatase >750 U/L, creatinine clearance <30 mL/min or NT-ProBNP ≥ 7500 ng/dL). Treatment consisted of ixazomib 4 mg days 1, 8, 15; cyclophosphamide 500 mg PO weekly and dexamethasone 40 mg, weekly for twelve 28-day cycles, followed by ixazomib maintenance (days 1, 8, 15) at the last tolerated dose till progression. The primary objective was to determine the hematologic response rate of ixazomib, used in combination with cyclophosphamide and dexamethasone in patients with previously untreated AL. A one-stage binomial design was utilized to test the null hypothesis that the hematologic response rate is at most 30% against the alternative hypothesis that it is at least 50%, with 85% power and 9% type I error. Results: Thirty-five patients were enrolled, median age was 67 (range 38-78) years; 69% were male. Organ involvement included cardiac in 23 (65.7%), renal in 19 (54.3%), and nervous system involvement in 5 (14.3%). At data cutoff 8 patients still remain on study with a median follow up of 4.4 months for those who are alive. Across the trial a median of 4 cycles (range 0-23) of treatment have been completed; the most common reason for going off study was institution of alternate therapy in 17 patients (63%). The overall hematologic response was 57% (20/35) and included amyloid CR in 5 (14%), VGPR in 9 (26%) and a PR in 6 (17%) patients. Confirmed organ responses have been observed in 5 patients so far, 2 each for cardiac and renal and 1 hepatic. The median PFS and OS have not been reached; 4 patients had hematological progression; 6 patients (17%) have died. Across 193 cycles of treatment administered, dose modification was required in 5, 3, and 10 patients, respectively, for ixazomib, cyclophosphamide and dexamethasone. A grade 3 or higher adverse event (AE), at least possibly attributed to the study drugs, was observed in 41% of patients. The figure shows the maximum grade of adverse events for individual patients seen in more than one patient across the study. Conclusions: The all-oral regimen of ixazomib, cyclophosphamide, and dexamethasone is active in patients with previously untreated AL amyloidosis with hematologic responses observed in 57% of patients, including complete responses. Organ response has been observed but will likely need longer follow up for accurate assessment, given the delay in organ responses in this disease. Further evaluation of this combination is warranted. Disclosures Gertz: Alnylam: Consultancy; Ionis/Akcea: Consultancy; Amgen: Consultancy; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Data Safety Monitoring board from Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Advisory Board for Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Advisory Board for Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Consultancy; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding; NCI SPORE MM: Research Funding; Prothena: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy. Kapoor:Celgene: Honoraria; GlaxoSmithKline: Research Funding; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Cellectar: Consultancy. Larsen:Janssen Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Dingli:Apellis: Consultancy; Janssen: Consultancy; Sanofi-Genzyme: Consultancy; Rigel: Consultancy; Bristol Myers Squibb: Research Funding; Karyopharm Therapeutics: Research Funding; Alexion: Consultancy; Millenium: Consultancy. Dispenzieri:Janssen: Research Funding; Intellia: Research Funding; Alnylam: Research Funding; Celgene: Research Funding; Pfizer: Research Funding; Takeda: Research Funding. Kumar:Adaptive Biotechnologies: Consultancy; Carsgen: Other, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Dr. Reddy's Laboratories: Honoraria; Tenebio: Other, Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Sanofi: Research Funding; Novartis: Research Funding; Kite Pharma: Consultancy, Research Funding; Karyopharm: Consultancy; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Merck: Consultancy, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; MedImmune: Research Funding.

BACKGROUNDAlthough posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success.OBJECTIVETo identify components of a successful homebased CTP and patient characteristics that are most predictive of reduced 30‐day readmissions.DESIGNRetrospective cohort.PATIENTSA total of 315 community‐dwelling, hospitalized, older adults (≥60 years) at high risk for readmission (Elder Risk Assessment score ≥16), discharged home over the period of January 1, 2011 to June 30, 2013.SETTINGMidwest primary care practice in an integrated health system.INTERVENTIONEnrollment in a CTP during acute hospitalization.MEASUREMENTSThe primary outcome was all‐cause readmission within 30 days of the first CTP evaluation. Logistic regression was used to examine independent variables, including patient demographics, comorbidities, number of medications, completion, and timing of program fidelity measures, and prior utilization of healthcare.RESULTSThe overall 30‐day readmission rate was 17.1%. The intensity of follow‐up varied among patients, with 17.1% and 50.8% of the patients requiring one and ≥3 home visits, respectively, within 30 days. More than half (54.6%) required visits beyond 30 days. Compared with patients who were not readmitted, readmitted patients were less likely to exhibit cognitive impairment (29.6% vs 46.0%; P = .03) and were more likely to have high medication use (59.3% vs 44.4%; P = .047), more emergency department (ED; 0.8 vs 0.4; P = .03) and primary care visits (4.0 vs 3.0; P = .018), and longer cumulative time in the hospital (4.6 vs 2.5 days; P = .03) within 180 days of the index hospitalization. Multivariable analysis indicated that only cognitive impairment and previous ED visits were important predictors of readmission.CONCLUSIONSNo single CTP component reliably predicted reduced readmission risk. Patients with cognitive impairment and polypharmacy derived the most benefit from the program.

ObjectivesTo evaluate the ability of claims‐based risk adjustment and incremental components of clinical data to identify 90‐day episode costs among lower extremity joint replacement (LEJR) patients according to the Centers for Medicare & Medicaid Services (CMS) Comprehensive Care for Joint Replacement (CJR) program provisions.Data SourcesMedicare fee‐for‐service (FFS) data for qualifying CJR episodes in the United States, and FFS data linked with clinical data from CJR‐qualifying LEJR episodes performed at High Value Healthcare Collaborative (HVHC) and Mayo Clinic in 2013. HVHC and Mayo Clinic populations are subsets of the total FFS population to assess the additive value of additional pieces of clinical data in correctly assigning patients to cost groups.Study DesignMultivariable logistic models identified high‐cost episodes.Data Collection/Extraction MethodsClinical data from participating health care systems merged with Medicare FFS data.Principal FindingsOur three populations consisted of 363 621 patients in the CMS population, 4881 in the HVHC population, and 918 in the Mayo population. When modeling per CJR specifications, we observed low to moderate model performance (CMS C‐Stat = 0.714; HVHC C‐Stat = 0.628; Mayo C‐Stat = 0.587). Adding CMS‐HCC categories improved identification of patients in the top 20% of episode costs (CMS C‐Stat = 0.758, HVHC C‐Stat = 0.692, Mayo C‐Stat = 0.677). Clinical variables, particularly functional status in the population for which this was available (Mayo C‐Stat = 0.783), improved ability to identify patients within cost groups.ConclusionsPolicy makers could use these findings to improve payment adjustments for bundled LEJR procedures and in consideration of new data elements for reimbursement.

Background: Care transitions programs have been shown to reduce hospital readmissions. Objectives: The main objective of this study was to evaluate effects of the Mayo Clinic Care Transitions (MCCTs) Program on potentially preventable and nonpreventable 30-day unplanned readmissions among high-risk elders. Research Design: This was a retrospective cohort study of patients enrolled in MCCT following hospitalization and propensity score-matched controls receiving usual primary care. Subjects: The subjects were primary care patients, who were 60 years or older, at high-risk for readmission, and hospitalized for any cause between January 1, 2011 and June 30, 2013. Measures: Hospital readmission within 30 days. The 3M algorithm was used to identify potentially preventable readmissions. Readmissions for ambulatory care sensitive conditions, a subset of preventable readmissions identified by the 3M algorithm, were also assessed. Results: The study cohort included 365 pairs of MCCT enrollees and propensity score-matched controls. Patients were similar in age (mean 83 y) and other baseline demographic and clinical characteristics, including reason for index hospitalization. MCCT enrollees had a significantly lower all-cause readmission rate [12.4% (95% confidence interval: CI, 8.9–15.7) vs. 20.1% (15.8–24.1); P=0.004] resulting from a decrease in potentially preventable readmissions [8.4% (95% CI, 5.5–11.3) vs. 14.3% (95% CI, 10.5–17.9); P=0.01]. Few potentially preventable readmissions were for ambulatory care sensitive conditions (6.7% vs. 12.0%). The rates of nonpotentially preventable readmissions were similar [4.3% (95% CI, 2.2–6.5) vs. 6.7% (95% CI, 4.0–9.4); P=0.16]. Potentially preventable readmissions were reduced by 44% (hazard ratio, 0.56; 95% CI, 0.36–0.88; P=0.01) with no change in other readmissions. Conclusions: The MCCT significantly reduces preventable readmissions, suggesting that access to multidisciplinary care can reduce readmissions and improve outcomes for high-risk elders.

Objectives To assess the degree to which self‐reported symptoms predict unplanned readmission or emergency department ( ED ) care within 30 days of high‐risk, elderly adults enrolled in a posthospitalization care transition program ( CTP ). Design Retrospective cohort study. Setting Posthospitalization CTP at Mayo Clinic, Rochester, Minnesota, from January 1, 2013, through March 3, 2015. Participants Frail, elderly adults (N = 230; mean age 83.5 ± 8.3, 46.5% male). Measurements Charlson Comorbidity Index ( CCI ) and self‐reported symptoms, measured using the Edmonton Symptom Assessment System ( ESAS ), were ascertained upon CTP enrollment. Results Mean CCI was 3.9 ± 2.3. Of 51 participants returning to the hospital within 30 days of discharge, 13 had ED visits, and 38 were readmitted. Age, sex, and CCI were not significantly different between returning and nonreturning participants, but returning participants were significantly more likely to report shortness of breath ( P = .004), anxiety ( P = .02), depression ( P = .02), and drowsiness ( P = .01). Overall ESAS score was also a significant predictor of hospital return ( P = .01). Conclusion Four self‐reported symptoms and overall ESAS score, but not CCI , ascertained after hospital discharge were strong predictors of hospital return within 30 days. Including symptoms in risk stratification of high‐risk elderly adults may help target interventions and reduce readmissions.

Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurologic disorder with predictable challenges regarding disease progression and end-of-life care. These include need for respiratory and nutritional support. Little is known about how such choices impact end-of-life health service utilization for these patients. Using OptumLabs Data Warehouse, a large administrative claims database with more than 150 million privately insured, geographically diverse enrollees, we sought to explore outcomes associated with the use of enteral nutrition (EN). Patients were of age ≥18 years, with first ALS diagnosis during calendar years 2006-2012, and 6 months of continuous health plan coverage before first diagnosis. EN use was identified using procedure codes. Data were summarized descriptively. Among 1974 patients with ALS, mean age was 60.0 ± 12.5 years, 41.8% were women, and 9.7% demonstrated use of EN. Median time from ALS diagnosis to evidence of EN was 211 days (interquartile range [IQR]: 70-426). Those receiving EN had greater aggregate comorbidity (47% with Charlson-Deyo Comorbidity Index ≥ 3 vs only 27% in non-EN subset). In total, 38.1% of patients had at least 1 hospitalization, with median time to hospitalization of 162 days. Unfortunately, the EN group ended coverage a median of 155 days after EN started (IQR: 63.5-388), thereby limiting ability to capture outcomes. Although many ALS patients were identified, EN use was lower than expected, due to being earlier in disease trajectory and lost to follow-up with transition from private insurance. As such, databases exclusively including privately insured patients may be suboptimal for detecting late complications of protracted illnesses.

ObjectiveAssess algorithms for linking patients across de‐identified databases without compromising confidentiality.Data Sources/Study SettingHospital discharges from 11 Mayo Clinic hospitals during January 2008–September 2012 (assessment and validation data). Minnesota death certificates and hospital discharges from 2009 to 2012 for entire state (application data).Study DesignCross‐sectional assessment of sensitivity and positive predictive value (PPV) for four linking algorithms tested by identifying readmissions and posthospital mortality on the assessment data with application to statewide data.Data Collection/Extraction MethodsDe‐identified claims included patient gender, birthdate, and zip code. Assessment records were matched with institutional sources containing unique identifiers and the last four digits of Social Security number (SSNL4).Principal FindingsGender, birthdate, and five‐digit zip code identified readmissions with a sensitivity of 98.0 percent and a PPV of 97.7 percent and identified postdischarge mortality with 84.4 percent sensitivity and 98.9 percent PPV. Inclusion of SSNL4 produced nearly perfect identification of readmissions and deaths. When applied statewide, regions bordering states with unavailable hospital discharge data had lower rates.ConclusionAddition of SSNL4 to administrative data, accompanied by appropriate data use and data release policies, can enable trusted repositories to link data with nearly perfect accuracy without compromising patient confidentiality. States maintaining centralized de‐identified databases should add SSNL4 to data specifications.