Clinical management of lupus in the United States: A claims-based analysis.

OBJECTIVES: To understand the evaluation and management of patients coded with lupus in the broad clinical community in the United States. METHODS: Claims data for diagnoses, procedures, medications, and physician specialties were evaluated for three lupus cohorts [lupus nephritis (LN), systemic lupus erythematosus excluding LN (SLE), and cutaneous lupus erythematosus excluding SLE and LN (CLE)] using the EVERSANA claims databases. Identification of patients was based upon the occurrence of lupus-specific codes, with the requirement that a single patient receive a lupus-related ICD code twice within a six-month period. RESULTS: Using ICD codes, we were able to identify 28,372 patients coded with LN, 82,744 patients coded with SLE, and 13,920 patients coded with CLE, and subsequently evaluate the journey of patients in each group in the year before and after being coded as having a diagnosis of lupus. For the three lupus cohorts, the basis of diagnosis was not always apparent, as clinical features of lupus were not often obtained, autoantibody testing was not usual, biopsies were uncommon and subspecialty involvement was not routine. In addition, a significant increase in laboratory testing, non-lupus diagnoses, emergency department visits and cost during the year before receiving a lupus code suggested uncertainty in disease recognition. Nevertheless, these patients received two separate lupus coding events within a six-month period, supporting a sustained or repeated diagnosis of lupus by the evaluating clinicians. When compared, the three lupus cohorts differed with regard to frequency of laboratory testing, subspecialty care, skin and renal biopsies, and medication management. Moreover, there was an increase in the cost of care of patients coded with lupus compared to a reference patient population both during the year before and after being coded with a diagnosis of lupus. CONCLUSION: The data present a comprehensive report of the care of patients coded as having a diagnosis of lupus in the United States, including those outside of specialty centers. Despite the unclear basis of diagnosis in some patients, evaluation and management of patients coded as having a diagnosis of lupus in the general care community does not closely follow the recommended guidelines set forth by professional societies.

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus.

PURPOSE OF REVIEW: Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care. RECENT FINDINGS: The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists. SUMMARY: Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.

Augmentation of brain tumor interstitial flow via focused ultrasound promotes brain-penetrating nanoparticle dispersion and transfection.

The delivery of systemically administered gene therapies to brain tumors is exceptionally difficult because of the blood-brain barrier (BBB) and blood-tumor barrier (BTB). In addition, the adhesive and nanoporous tumor extracellular matrix hinders therapeutic dispersion. We first developed the use of magnetic resonance image (MRI)-guided focused ultrasound (FUS) and microbubbles as a platform approach for transfecting brain tumors by targeting the delivery of systemically administered "brain-penetrating" nanoparticle (BPN) gene vectors across the BTB/BBB. Next, using an MRI-based transport analysis, we determined that after FUS-mediated BTB/BBB opening, mean interstitial flow velocity magnitude doubled, with "per voxel" flow directions changing by an average of ~70° to 80°. Last, we observed that FUS-mediated BTB/BBB opening increased the dispersion of directly injected BPNs through tumor tissue by >100%. We conclude that FUS-mediated BTB/BBB opening yields markedly augmented interstitial tumor flow that, in turn, plays a critical role in enhancing BPN transport through tumor tissue.

MRI analysis to map interstitial flow in the brain tumor microenvironment.

Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion in vivo. Here, we develop a technique to noninvasively measure interstitial flow velocities in the glioma microenvironment of mice using dynamic contrast-enhanced magnetic resonance imaging (MRI), a common clinical technique. Using our in vitro model as a phantom "tumor" system and in silico models of velocity vector fields, we show we can measure average velocities and accurately reconstruct velocity directions. With our combined MR and analysis method, we show that velocity magnitudes are similar across four human GBM cell line xenograft models and the direction of fluid flow is heterogeneous within and around the tumors, and not always in the outward direction. These values were not linked to the tumor size. Finally, we compare our flow velocity magnitudes and the direction of flow to a classical marker of vessel leakage and bulk fluid drainage, Evans blue. With these data, we validate its use as a marker of high and low IFF rates and IFF in the outward direction from the tumor border in implanted glioma models. These methods show, for the first time, the nature of interstitial fluid flow in models of glioma using a technique that is translatable to clinical and preclinical models currently using contrast-enhanced MRI.

Convective forces increase CXCR4-dependent glioblastoma cell invasion in GL261 murine model.

Glioblastoma is the most common and malignant form of brain cancer. Its invasive nature limits treatment efficacy and promotes inevitable recurrence. Previous in vitro studies showed that interstitial fluid flow, a factor characteristically increased in cancer, increases glioma cell invasion through CXCR4-CXCL12 signaling. It is currently unknown if these effects translate in vivo. We used the therapeutic technique of convection enhanced delivery (CED) to test if convective flow alters glioma invasion in a syngeneic GL261 mouse model of glioblastoma. The GL261 cell line was flow responsive in vitro, dependent upon CXCR4 and CXCL12. Additionally, transplanting GL261 intracranially increased the populations of CXCR4+ and double positive cells versus 3D culture. We showed that inducing convective flow within implanted tumors indeed increased invasion over untreated controls, and administering the CXCR4 antagonist AMD3100 (5 mg/kg) effectively eliminated this response. These data confirm that glioma invasion is stimulated by convective flow in vivo and depends on CXCR4 signaling. We also showed that expression of CXCR4 and CXCL12 is increased in patients having received standard therapy, when CED might be elected. Hence, targeting flow-stimulated invasion may prove beneficial as a second line of therapy, particularly in patients chosen to receive treatment by convection enhanced delivery.

Interstitial flow differentially increases patient-derived glioblastoma stem cell invasion via CXCR4, CXCL12, and CD44-mediated mechanisms.

Glioblastoma (GBM) prognosis remains dismal due in part to the invasiveness of GBM cells. Interstitial fluid flow (IFF) has been shown to increase invasion of glioma cells in vitro through the CXCR4 receptor interacting with autologous, pericellular gradients of CXCL12 (autologous chemotaxis) or through the CD44 receptor interactions with the extracellular matrix (hyaluronan-mediated mechanotransduction). These mechanisms have not been examined together and thus we hypothesized that both mechanisms contribute to invasion in populations of cancer cells. Therefore, we examined IFF-stimulated CXCR4-, CXCL12-, and CD44-dependent invasion in patient-derived glioblastoma stem cells (GSCs). Using our 3D in vitro assay and correlative in vivo studies we demonstrated GSC lines show increased invasion with flow. This flow-stimulated invasion was reduced by blockade of CXCR4, CXCL12, and/or CD44, revealing that GSC invasion may be mediated simultaneously by both mechanisms. Characterization of CXCR4+, CXCL12+, and CD44+ populations in four GSC lines revealed different percentages of protein positive subpopulations for each line. We developed an agent-based model to identify the contributions of each subpopulation to flow-stimulated invasion and validated the model through comparisons with experimental blocking studies. Clinically relevant radiation therapy increased flow-stimulated invasion in one GSC line. Our agent-based model predicted that IFF-stimulated invasion is driven primarily by CXCR4+CXCL12+ populations, and, indeed our irradiated cells had an increase in this subpopulation. Together, these data indicate that different mechanisms govern the flow response across GSCs, but that within a single patient, there are subpopulations of GSCs that respond to flow via either CD44- or CXCR4-CXCL12 mechanisms.