Citations

Abstract Peripheral blood biomarkers of tumor microenvironment and immune surveillance are independent prognostic factors in multiple myeloma. The timing and prognostic impact of immune reconstitution has been studied after autologous hematopoietic stem cell transplantation, less is known about its significance in newly diagnosed multiple myeloma. We studied absolute lymphocyte (ALC) and absolute monocyte (AMC) counts at the time of treatment initiation and 1 month thereafter in 771 newly diagnosed patients. Two hundred and thirty‐four patients (31%) had evidence of immune dysregulation at baseline (abnormal biomarkers). Eighty‐seven of these patients (37%) recovered normal biomarkers at 1 month (early immune reconstitution). The absence of immune dysregulation at baseline (compared to the presence thereof) was associated with better overall survival (HR 0.77, 95% CI 0.61‐0.97, P = 0.025, n = 771). The absence of immune dysregulation at 1 month (compared to the persistence or development thereof) was associated with better overall survival (HR 0.63, 95% CI 0.50‐0.80, P < 0.001, n = 771). Early immune reconstitution (compared to the persistence or development of immune dysregulation) was associated with better overall survival (HR 0.62, 95% CI 0.43‐0.92, P = 0.016, n = 771). Cytogenetic high‐risk disease was negatively, and treatment with immunomodulators positively, associated with early immune reconstitution. The presence or development of immune dysregulation in newly diagnosed multiple myeloma is an independent risk factor. The favorable impact of early immune reconstitution suggests immune dysregulation to be a potentially modifiable risk factor that may be exploited for therapeutic benefit.

Abstract The plasma cell proliferative index (PCPI), determined by a slide technique or by flow cytometry, detects cells in the S phase of the cell cycle and is a useful prognostic tool in patients with plasma cell disorders such as multiple myeloma and amyloidosis. We conducted a retrospective review analyzing the prognostic effect of PCPI in 306 patients with smoldering multiple myeloma (SMM). Seventy-nine (26%) patients had an elevated PCPI (&gt;0.5). An elevated PCPI predicted an inferior time to progression (median, 3.0 vs 7.1 years for those with a low PCPI; P = .0004). Within 24 months, the progression rate was significantly higher for patients with an elevated PCPI (49% vs. 20%; P &lt; .0001). PCPI is a valuable tool in risk stratifying patients with SMM and identifies patients with earlier progression who may benefit from closer follow-up and consideration of early intervention trials.

Abstract Objective We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata. Materials and Methods This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations. Results The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys. Discussion We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate. Conclusion EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.