Laboratory-wide association study of survival with prostate cancer.

BACKGROUND: Estimates of overall patient health are essential to inform treatment decisions for patients diagnosed with cancer. The authors applied XWAS methods, herein referred to as "laboratory-wide association study (LWAS)", to evaluate associations between routinely collected laboratory tests and survival in veterans with prostate cancer. METHODS: The authors identified 133,878 patients who were diagnosed with prostate cancer between 2000 and 2013 in the Veterans Health Administration using any laboratory tests collected within 6 months of diagnosis (3,345,083 results). Using the LWAS framework, the false-discovery rate was used to test the association between multiple laboratory tests and survival, and these results were validated using training, testing, and validation cohorts. RESULTS: A total of 31 laboratory tests associated with survival met stringent LWAS criteria. LWAS confirmed markers of prostate cancer biology (prostate-specific antigen: hazard ratio [HR], 1.07 [95% confidence interval (95% CI), 1.06-1.08]; and alkaline phosphatase: HR, 1.22 [95% CI, 1.20-1.24]) as well laboratory tests of general health (eg, serum albumin: HR, 0.78 [95% CI, 0.76-0.80]; and creatinine: HR, 1.05 [95% CI, 1.03-1.07]) and inflammation (leukocyte count: HR, 1.23 [95% CI, 1.98-1.26]; and erythrocyte sedimentation rate: HR, 1.33 [95% CI, 1.09-1.61]). In addition, the authors derived and validated separate models for patients with localized and advanced disease, identifying 28 laboratory markers and 15 laboratory markers, respectively, in each cohort. CONCLUSIONS: The authors identified routinely collected laboratory data associated with survival for patients with prostate cancer using LWAS methodologies, including markers of prostate cancer biology, overall health, and inflammation. Broadening consideration of determinants of survival beyond those related to cancer itself could help to inform the design of clinical trials and aid in shared decision making. LAY SUMMARY: This article examined routine laboratory tests associated with survival among veterans with prostate cancer. Using laboratory-wide association studies, the authors identified 31 laboratory tests associated with survival that can be used to inform the design of clinical trials and aid patients in shared decision making.

Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis.

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.

Clinical laboratory tests associated with survival in patients with metastatic renal cell carcinoma: A Laboratory Wide Association Study (LWAS).

BACKGROUND: Prognostic models for patients with metastatic renal cell carcinoma (mRCC) include select laboratory values. These models have important limitations, including reliance on a limited array of laboratory tests, and use of dichotomous ("high-low") cutoffs. We applied a Laboratory-Wide Association Study (LWAS) framework to systematically evaluate common clinical laboratory results associated with survival for patients diagnosed with mRCC. METHODS: We used laboratory data for 3,385 patients diagnosed with mRCC from 2002 to 2017. We developed a LWAS framework, to examine the association with 53 common clinical laboratory tests results (641,712 measurements) and overall survival. We employed false-discovery rate to test the association of multiple laboratory tests with survival, and validated these results using 3 separate cohorts to generate a standardized hazard ratio (sHR), reported for a 1 standard deviation unit change in each laboratory test. RESULTS: The LWAS approach confirmed the association of laboratory values currently used in prognostic models with survival, including calcium (HR 1.35, 95%CI 1.24-1.48), leukocyte count (HR 1.40, 95%CI 1.30-1.51), platelet count (HR 1.36, 95%CI 1.27-1.51), and hemoglobin (HR 0.79, 95%CI 0.72-0.86). Use of these tests as continuous variables improved model performance. LWAS also identified acute phase reactants associated with survival not typically included in prognostic models, including serum albumin (HR 0.66, 95%CI 0.61-0.72), ferritin (HR 1.25, 95%CI 1.08-1.45), alkaline phosphatase (HR 1.31, 95%CI 1.23-1.40), and C-reactive protein (HR 1.70, 95%CI 1.14-2.53). CONCLUSIONS: Routinely measured laboratory tests can refine current prognostic models, facilitate comparisons across clinical trial cohorts, and match patients with specific systemic therapies.

Life expectancy estimates for patients diagnosed with prostate cancer in the Veterans Health Administration.

PURPOSE: Accurate life expectancy estimates are required to inform prostate cancer treatment decisions. However, few models are specific to the population served or easily implemented in a clinical setting. We sought to create life expectancy estimates specific to Veterans diagnosed with prostate cancer. MATERIALS AND METHODS: Using national Veterans Health Administration electronic health records, we identified Veterans diagnosed with prostate cancer between 2000 and 2015. We abstracted demographics, comorbidities, oncologic staging, and treatment information. We fit Cox Proportional Hazards models to determine the impact of age, comorbidity, cancer risk, and race on survival. We stratified life expectancy estimates by age, comorbidity and cancer stage. RESULTS: Our analytic cohort included 145,678 patients. Survival modeling demonstrated the importance of age and comorbidity across all cancer risk categories. Life expectancy estimates generated from age and comorbidity data were predictive of overall survival (C-index 0.676, 95% CI 0.674-0.679) and visualized using Kaplan-Meier plots and heatmaps stratified by age and comorbidity. Separate life expectancy estimates were generated for patients with localized or advanced disease. These life expectancy estimates calibrate well across prostate cancer risk categories. CONCLUSIONS: Life expectancy estimates are essential to providing patient-centered prostate cancer care. We developed accessible life expectancy estimation tools for Veterans diagnosed with prostate cancer that can be used in routine clinical practice to inform medical-decision making.

Hemoglobin A1c Trajectory in Pediatric Patients with Newly Diagnosed Type 1 Diabetes.

Despite advances in diabetes technology and treatment, a majority of children and adolescents with type 1 diabetes (T1D) fail to meet hemoglobin A1c (HbA1c) targets. Among high-income nations, the United States has one of the highest mean HbA1c values. We tracked the HbA1c values of 261 patients diagnosed with T1D in our practice over a 2.5-year period to identify inflection points in the HbA1c trajectory. The HbA1c declined until 5 months postdiagnosis. There was a rise in the HbA1c between the fifth and sixth month postdiagnosis. The HbA1c continued to steadily rise and by 18 months postdiagnosis, the mean HbA1c was 8.2%, which is also our clinic mean. Understanding the HbA1c trajectory early in the course of diabetes has helped to identify opportunities for intensification of diabetes management to flatten the trajectory of HbA1c and improve clinical outcomes.

Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support.

Importance: Pulmonary embolism (PE) is a life-threatening clinical problem, and computed tomographic imaging is the standard for diagnosis. Clinical decision support rules based on PE risk-scoring models have been developed to compute pretest probability but are underused and tend to underperform in practice, leading to persistent overuse of CT imaging for PE. Objective: To develop a machine learning model to generate a patient-specific risk score for PE by analyzing longitudinal clinical data as clinical decision support for patients referred for CT imaging for PE. Design, Setting, and Participants: In this diagnostic study, the proposed workflow for the machine learning model, the Pulmonary Embolism Result Forecast Model (PERFORM), transforms raw electronic medical record (EMR) data into temporal feature vectors and develops a decision analytical model targeted toward adult patients referred for CT imaging for PE. The model was tested on holdout patient EMR data from 2 large, academic medical practices. A total of 3397 annotated CT imaging examinations for PE from 3214 unique patients seen at Stanford University hospitals and clinics were used for training and validation. The models were externally validated on 240 unique patients seen at Duke University Medical Center. The comparison with clinical scoring systems was done on randomly selected 100 outpatient samples from Stanford University hospitals and clinics and 101 outpatient samples from Duke University Medical Center. Main Outcomes and Measures: Prediction performance of diagnosing acute PE was evaluated using ElasticNet, artificial neural networks, and other machine learning approaches on holdout data sets from both institutions, and performance of models was measured by area under the receiver operating characteristic curve (AUROC). Results: Of the 3214 patients included in the study, 1704 (53.0%) were women from Stanford University hospitals and clinics; mean (SD) age was 60.53 (19.43) years. The 240 patients from Duke University Medical Center used for validation included 132 women (55.0%); mean (SD) age was 70.2 (14.2) years. In the samples for clinical scoring system comparisons, the 100 outpatients from Stanford University hospitals and clinics included 67 women (67.0%); mean (SD) age was 57.74 (19.87) years, and the 101 patients from Duke University Medical Center included 59 women (58.4%); mean (SD) age was 73.06 (15.3) years. The best-performing model achieved an AUROC performance of predicting a positive PE study of 0.90 (95% CI, 0.87-0.91) on intrainstitutional holdout data with an AUROC of 0.71 (95% CI, 0.69-0.72) on an external data set from Duke University Medical Center; superior AUROC performance and cross-institutional generalization of the model of 0.81 (95% CI, 0.77-0.87) and 0.81 (95% CI, 0.73-0.82), respectively, were noted on holdout outpatient populations from both intrainstitutional and extrainstitutional data. Conclusions and Relevance: The machine learning model, PERFORM, may consider multitudes of applicable patient-specific risk factors and dependencies to arrive at a PE risk prediction that generalizes to new population distributions. This approach might be used as an automated clinical decision-support tool for patients referred for CT PE imaging to improve CT use.

Macrophage Exclusion after Radiation Therapy (MERT): A First in Human Phase I/II Trial using a CXCR4 Inhibitor in Glioblastoma.

PURPOSE: Preclinical studies have demonstrated that postirradiation tumor revascularization is dependent on a stromal cell-derived factor-1 (SDF-1)/C-X-C chemokine receptor type 4 (CXCR4)-driven process in which myeloid cells are recruited from bone marrow. Blocking this axis results in survival improvement in preclinical models of solid tumors, including glioblastoma (GBM). We conducted a phase I/II study to determine the safety and efficacy of Macrophage Exclusion after Radiation Therapy (MERT) using the reversible CXCR4 inhibitor plerixafor in patients with newly diagnosed glioblastoma. PATIENTS AND METHODS: We enrolled nine patients in the phase I study and an additional 20 patients in phase II using a modified toxicity probability interval (mTPI) design. Plerixafor was continuously infused intravenously via a peripherally inserted central catheter (PICC) line for 4 consecutive weeks beginning at day 35 of conventional treatment with concurrent chemoradiation. Blood serum samples were obtained for pharmacokinetic analysis. Additional studies included relative cerebral blood volume (rCBV) analysis using MRI and histopathology analysis of recurrent tumors. RESULTS: Plerixafor was well tolerated with no drug-attributable grade 3 toxicities observed. At the maximum dose of 400 µg/kg/day, biomarker analysis found suprathreshold plerixafor serum levels and an increase in plasma SDF-1 levels. Median overall survival was 21.3 months [95% confidence interval (CI), 15.9-NA] with a progression-free survival of 14.5 months (95% CI, 11.9-NA). MRI and histopathology support the mechanism of action to inhibit postirradiation tumor revascularization. CONCLUSIONS: Infusion of the CXCR4 inhibitor plerixafor was well tolerated as an adjunct to standard chemoirradiation in patients with newly diagnosed GBM and improves local control of tumor recurrences.

The efficacy and safety of cangrelor in single vessel vs multivessel percutaneous coronary intervention: Insights from CHAMPION PHOENIX.

BACKGROUND: The intravenous, rapidly acting P2Y12 inhibitor cangrelor reduces the rate of ischemic events during PCI with no significant increase in severe bleeding. However, the efficacy and safety of cangrelor compared with clopidogrel in patients treated with single vessel (SV)-percutaneous coronary intervention (PCI) or multivessel (MV)-PCI remains unexplored. METHODS: We studied the modified intention-to-treat population of patients from the CHAMPION PHOENIX trial who were randomized to either cangrelor or clopidogrel. We used logistic regression and propensity score matching to evaluate the effect of cangrelor compared with clopidogrel on the primary efficacy outcome (composite of death, myocardial infarction, ischemia-driven revascularization, or stent thrombosis) at 48 hours. The safety outcome was moderate or severe Global Utilization of Streptokinase and tPA for Occluded Arteries bleeding at 48 hours. HYPOTHESIS: Cangrelor is as efficacious and safe as clopidogrel in both SV and MV PCI. RESULTS: Among 10 854 patients, 9204 (85%) underwent SV- and 1650 (15%) MV-PCI. After adjustment, cangrelor was associated with similar reductions vs clopidogrel in the primary efficacy outcome in patients undergoing SV-PCI (4.5% vs 5.2%; odds ratio [OR] 0.81 [0.66-0.98]) or MV-PCI (6.1% vs 9.8%, OR 0.59 [0.41-0.85]; Pint 0.14). Similar results were observed after propensity score matching (SV-PCI: 5.5% vs 5.9%, OR 0.93 [0.74-1.18]; MV-PCI: 6.2% vs 8.9%, OR 0.67 [0.44-1.01]; Pint 0.17). There was no evidence of heterogeneity in the treatment effect of cangrelor compared with clopidogrel for the safety outcome. CONCLUSIONS: In patients undergoing SV- or MV-PCI, cangrelor was associated with similar relative risk reductions in ischemic complications and no increased risk of significant bleeding compared with clopidogrel, which highlights the expanding repertoire of options for use in complex PCI.