Anti-amyloid treatments for early symptomatic Alzheimer disease have recently become clinically available in some countries, which has greatly increased the need for biomarker confirmation of amyloid pathology. Blood biomarker (BBM) tests for amyloid pathology are more acceptable, accessible and scalable than amyloid PET or cerebrospinal fluid (CSF) tests, but have highly variable levels of performance. The Global CEO Initiative on Alzheimer’s Disease convened a BBM Workgroup to consider the minimum acceptable performance of BBM tests for clinical use. Amyloid PET status was identified as the reference standard. For use as a triaging test before subsequent confirmatory tests such as amyloid PET or CSF tests, the BBM Workgroup recommends that a BBM test has a sensitivity of ≥90% with a specificity of ≥85% in primary care and ≥75–85% in secondary care depending on the availability of follow-up testing. For use as a confirmatory test without follow-up tests, a BBM test should have performance equivalent to that of CSF tests — a sensitivity and specificity of ~90%. Importantly, the predictive values of all biomarker tests vary according to the pre-test probability of amyloid pathology and must be interpreted in the complete clinical context. Use of BBM tests that meet these performance standards could enable more people to receive an accurate and timely Alzheimer disease diagnosis and potentially benefit from new treatments.

Background: With the emergence of disease-modifying Alzheimer’s treatments, timely detection of early-stage disease is more important than ever, as the treatment will not be indicated for later stages. Contemporary population-level data for detection rates of mild cognitive impairment (MCI), the stage at which treatment would ideally start, are lacking, and detection rates for dementia are only available for subsets of the Medicare population. We sought to compare documented diagnosis rates of MCI and dementia in the full Medicare population with expected rates based on a predictive model. Methods: We performed an observational analysis of Medicare beneficiaries aged 65 and older with a near-continuous enrollment over a 3-year observation window or until death using 100% of the Medicare fee-for-service or Medicare Advantage Plans beneficiaries from 2015 to 2019. Actual diagnoses for MCI and dementia were derived from ICD-10 codes documented in those data. We used the 2000–2016 data of the Health and Retirement Study to develop a prediction model for expected diagnoses for the included population. The ratios between actually diagnosed cases of MCI and dementia over number of cases expected, the observed over expected ratio, reflects the detection rate. Results: Although detection rates for MCI cases increased from 2015 to 2019 (0.062 to 0.079), the results mean that 7.4 of 8 million (92%) expected MCI cases remained undiagnosed. The detection rate for MCI was 0.039 and 0.048 in Black and Hispanic beneficiaries, respectively, compared with 0.098 in non-Hispanic White beneficiaries. Individuals dually eligible for Medicare and Medicaid had lower estimated detection rates than their Medicare-only counterparts for MCI (0.056 vs 0.085). Dementia was diagnosed more frequently than expected (1.086 to 1.104) from 2015 to 2019, mostly in non-Hispanic White beneficiaries (1.367) compared with 0.696 in Black beneficiaries and 0.758 in Hispanic beneficiaries. Conclusions: These results highlight the need to increase the overall detection rates of MCI and of dementia particularly in socioeconomically disadvantaged groups.

Objective: Chronic wounds represent a highly prevalent but little recognized condition with substantial implications for patients and payers. While better wound care products and treatment modalities are known to improve healing rates, they are inconsistently used in real-world practice. Predicting healing rates of chronic wounds and comparing to actual rates could be used to detect and reward better quality of care. We developed a prediction model for chronic wound healing. Approach: We analyzed electronic medical records (EMRs) for 620,356 chronic wounds of various etiologies in 261,398 patients from 532 wound care clinics in the United States. Patient-level and wound-level parameters influencing wound healing were identified from prior research and clinician input. Logistic regression and classification tree models to predict the probability of wound healing within 12 weeks were developed using a random sample of 70% of the wounds and validated in the remaining data. Results: A total of 365,659 (58.9%) wounds were healed by week 12. The logistic and classification tree models predicted healing with an area under the curve of 0.712 and 0.717, respectively. Wound-level characteristics, such as location, area, depth, and etiology, were more powerful predictors than patient demographics and comorbidities. Innovation: The probability of wound healing can be predicted with reasonable accuracy in real-world data from EMRs. Conclusion: The resulting severity adjustment model can become the basis for applications like quality measure development, research into clinical practice and performance-based payment.

Introduction Concerns have been raised about the limited health system capacity for identification of patients who are eligible for a disease‐modifying Alzheimer's treatment (DMT). Blood‐based biomarker (BBBM) tests are a promising tool to improve triaging at the primary care level. We projected their impact on cost of and wait times during the diagnostic process. Methods We compare four scenarios for triaging patients at the primary care level from the perspective of the U.S. health care system: (1) cognitive test only (Mini Mental State Examination [MMSE]), (2) BBBM test only, (3) MMSE followed by BBBM if positive, and (4) BBBM followed by MMSE if positive. Results Referring patients to dementa specialists based on MMSE or BBBM results alone would continuously require more specialist appointments than projected to be available until 2050. Combining MMSE and BBBM would eliminate wait lists after the first 3 years and reduce average annual cost by $400 to 700 million, while increasing correctly identified cases by about 120,000 per year. Discussion The combination BBBM with MMSE is projected to increase the efficiency and value of the triage process for DMT eligibility.