Validation of Claims-Based Algorithm for Lyme Disease, Massachusetts, USA.

Compared with notifiable disease surveillance, claims-based algorithms estimate higher Lyme disease incidence, but their accuracy is unknown. We applied a previously developed Lyme disease algorithm (diagnosis code plus antimicrobial drug prescription dispensing within 30 days) to an administrative claims database in Massachusetts, USA, to identify a Lyme disease cohort during July 2000-June 2019. Clinicians reviewed and adjudicated medical charts from a cohort subset by using national surveillance case definitions. We calculated positive predictive values (PPVs). We identified 12,229 Lyme disease episodes in the claims database and reviewed and adjudicated 128 medical charts. The algorithm's PPV for confirmed, probable, or suspected cases was 93.8% (95% CI 88.1%-97.3%); the PPV was 66.4% (95% CI 57.5%-74.5%) for confirmed and probable cases only. In a high incidence setting, a claims-based algorithm identified cases with a high PPV, suggesting it can be used to assess Lyme disease burden and supplement traditional surveillance data.

Cluster analysis of COVID-19 recovery center patients at a clinic in Boston, MA 2021-2022: impact on strategies for access and personalized care.

BACKGROUND: There are known disparities in COVID-19 resource utilization that may persist during the recovery period for some patients. We sought to define subpopulations of patients seeking COVID-19 recovery care in terms of symptom reporting and care utilization to better personalize their care and to identify ways to improve access to subspecialty care. METHODS: Prospective study of adult patients with prior COVID-19 infection seen in an ambulatory COVID-19 recovery center (CRC) in Boston, Massachusetts from April 2021 to April 2022. Hierarchical clustering with complete linkage to differentiate subpopulations was done with four sociodemographic variables: sex, race, language, and insurance status. Outcomes included ICU admission, utilization of supplementary care, self-report of symptoms. RESULTS: We included 1285 COVID-19 patients referred to the CRC with a mean age of 47 years, of whom 71% were female and 78% White. We identified 3 unique clusters of patients. Cluster 1 and 3 patients were more likely to have had intensive care unit (ICU) admissions; Cluster 2 were more likely to be White with commercial insurance and a low percentage of ICU admission; Cluster 3 were more likely to be Black/African American or Latino/a and have commercial insurance. Compared to Cluster 2, Cluster 1 patients were more likely to report symptoms (ORs ranging 2.4-3.75) but less likely to use support groups, psychoeducation, or care coordination (all p < 0.05). Cluster 3 patients reported greater symptoms with similar levels of community resource utilization. CONCLUSIONS: Within a COVID-19 recovery center, there are distinct groups of patients with different clinical and socio-demographic profiles, which translates to differential resource utilization. These insights from different subpopulations of patients can inform targeted strategies which are tailored to specific patient needs.

Fast quantitative bone marrow lesion measurement on knee MRI for the assessment of osteoarthritis.

Objective: Knee osteoarthritis (KOA) is a prevalent disease with a high economic and social cost. Magnetic resonance imaging (MRI) can be used to visualize many KOA-related structures including bone marrow lesions (BMLs), which are associated with OA pain. Several semi-automated software methods have been developed to segment BMLs, using manual, labor-intensive methods, which can be costly for large clinical trials and other studies of KOA. The goal of our study was to develop and validate a more efficient method to quantify BML volume on knee MRI scans. Materials and methods: We have applied a deep learning approach using a patch-based convolutional neural network (CNN) which was trained using 673 MRI data sets and the segmented BML masks obtained from a trained reader. Given the location of a BML provided by the reader, the network performed a fully automated segmentation of the BML, removing the need for tedious manual delineation. Accuracy was quantified using the Pearson's correlation coefficient, by a comparison to a second expert reader, and using the Dice Similarity Score (DSC). Results: The Pearson's R2 value was 0.94 and we found similar agreement when comparing two readers (R2 â€‹= â€‹0.85) and each reader versus the DL model (R2 â€‹= â€‹0.95 and R2 â€‹= â€‹0.81). The average DSC was 0.70. Conclusions: We developed and validated a deep learning-based method to segment BMLs on knee MRI data sets. This has the potential to be a valuable tool for future large studies of KOA.

Utility of quantitative micro-computed tomographic analysis in zebrafish to define gene function during skeletogenesis.

The zebrafish is a powerful experimental model to investigate the genetic and morphologic basis of vertebrate development. Analysis of skeletogenesis in this fish is challenging as a result of the small size of the developing and adult zebrafish. Many of the bones of small fishes such as the zebrafish and medaka are quite thin, precluding many standard assays of bone quality and morphometrics commonly used on bones of larger animals. Microcomputed tomography (microCT) is a common imaging technique used for detailed analysis of the skeleton of the zebrafish and determination of mutant phenotypes. However, the utility of this modality for analysis of the zebrafish skeleton, and the effect of inherent variation among individual zebrafish, including variables such as sex, age and strain, is not well understood. Given the increased use and accessibility of microCT, we set out to define the sensitivity of microCT methods in developing and adult zebrafish. We assessed skeletal shape and density measures in the developing vertebrae and parasphenoid of the skull base. We found most skeletal variables are tightly correlated to standard length, but that at later growth stages (>3months) there are age dependent effects on some skeletal measures. Further we find modest strain but not sex differences in skeletal measures. These data suggest that the appropriate control for assessing mutant phenotypes should be age and strain matched, ideally a wild-type sibling. By analyzing two mutants exhibiting skeletal dysplasia, we show that microCT imaging can be a sensitive method to quantify distinct skeletal parameters of adults. Finally, as developing zebrafish skeletons remain difficult to resolve by radiographic means, we define a contrast agent specific for bone that enhances resolution at early stages, permitting detailed morphometric analysis of the forming skeleton. This increased capability for detection extends the use of this imaging modality to leverage the zebrafish model to understand the development causes of skeletal dysplasias.