Clinical Outcomes and Healthcare Resource Utilization for Patients With Lower-Risk Myelodysplastic Syndromes Treated With Erythropoiesis-Stimulating Agents.

INTRODUCTION: Real-world studies of lower-risk myelodysplastic syndromes (LR-MDS) are limited. We evaluated treatment patterns, clinical outcomes, and healthcare resource utilization (HCRU) among patients with LR-MDS treated with erythropoiesis-stimulating agents (ESAs) in the United States. PATIENTS AND METHODS: This retrospective study included patients with LR-MDS who initiated treatment with ESAs between January 1, 2016 and June 30, 2019. The primary analysis assessed patient demographic and clinical characteristics, treatment patterns, clinical outcomes (hematologic response, transfusion requirements, disease progression), and HCRU (medical encounters, laboratory tests, and medication use). Subgroup analyses of patients repeatedly treated with ESA therapy evaluated selected clinical outcomes and primary ESA failure by SF3B1 mutational status, per recently updated NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines©). RESULTS: A total of 142 patients were included with a median follow-up time of 17 months (interquartile range [IQR], 7-33). Median age at ESA initiation was 79 years (IQR, 73-85). Patients were predominantly male (54%), overweight or obese (32% and 23%, respectively), of White race (96%) and non-Hispanic ethnicity (89%). Overall, 57% patients were initially treated with darbepoetin alfa and 43% with epoetin alfa. Clinical outcomes were poor, and there was a significant burden on both the health system and individual patients treated with ESA therapies. Hematologic improvement- erythroid was only seen in 26% of 142 patients treated with ESAs, and 65% of 82 retreated patients experienced primary ESA failure. CONCLUSION: Our results indicate that primary ESA failure is largely unrecognized and that many patients should be considered for alternative treatments.

Real-World Impact of Comprehensive Genomic Profiling on Biomarker Detection, Receipt of Therapy, and Clinical Outcomes in Advanced Non-Small Cell Lung Cancer.

PURPOSE: Therapeutic decision making for patients with advanced non-small cell lung cancer (aNSCLC) includes a growing number of options for genomic, biomarker-guided, targeted therapies. We compared actionable biomarker detection, targeted therapy receipt, and real-world overall survival (rwOS) in patients with aNSCLC tested with comprehensive genomic profiling (CGP) versus small panel testing (SP) in real-world community health systems. METHODS: Patients older than 18 years diagnosed with aNSCLC between January 1, 2015, and December 31, 2020, who received biomarker testing were followed until death or study end (September 30, 2021), and categorized by most comprehensive testing during follow-up: SP (≤52 genes) or CGP (>52 genes). RESULTS: Among 3,884 patients (median age, 68 years; 50% female; 73% non-Hispanic White), 20% received CGP and 80% SP. The proportion of patients with ≥one actionable biomarker (actionability) was significantly higher in CGP than in SP (32% v 14%; P < .001). Of patients with actionability, 43% (CGP) and 38% (SP) received matched therapies (P = .20). Among treated patients, CGP before first-line treatment was associated with higher likelihood of matched therapy in any line (odds ratio, 3.2 [95% CI, 1.84 to 5.53]). CGP testing (hazard ratio [HR], 0.80 [95% CI, 0.72 to 0.89]) and actionability (HR, 0.84 [95% CI, 0.77 to 0.91]) were associated with reduced risk of mortality. Among treated patients with actionability, matched therapy receipt showed improved median rwOS in months in CGP (34 [95% CI, 21 to 49] matched v 14 [95% CI, 10 to 18] unmatched) and SP (27 [95% CI, 21 to 43] matched v 10 [95% CI, 8 to 14] unmatched). CONCLUSION: Patients who received CGP had improved detection of actionable biomarkers and greater use of matched therapies, both of which were associated with significant increases in survival.

Impact of Pre-operative Opioid Use on Racial Disparities in Adverse Outcomes Post Total Knee and Hip Arthroplasty.

INTRODUCTION: The growing opioid epidemic in the USA has underlying racial disparities dimensions. Also, studies have shown that patients from minority racial groups are at higher risk of adverse events following major orthopedic surgery. The aim of our study was to determine whether pre-operative opioid-use disorders (OUDs) impacted racial disparities in the likelihood of patients experiencing adverse post-operative outcomes following TKA and THA. METHODS: Data about patients undergoing TKA and THA were collected from the 2005-2014 National Inpatient Sample databases. Regression modeling was used to assess the impact of OUDs on odds of adverse outcomes comparing racial groups. The adverse outcomes included any in-hospital post-surgical complications, prolonged length of stay (LOS), and nonhome discharge. RESULTS: In our fully adjusted regression models using White patients as the reference group, we found that OUDs were associated with racial disparities in prolonged LOS and nonhome discharge. In the non-OUD group, Black patients had significantly higher odds of longer LOS (OR: 1.35, 95% CI: 1.26-1.46, p-value: < 0.0001), whereas those with history of OUD had non-significantly lower odds of longer LOS (OR: 0.94, 95% CI: 0.69-1.29, p-value: 0.71). Similarly, for the outcome of nonhome discharges, Black patients in the non-OUD group had significantly higher odds (OR: 1.31, 95% CI: 1.21-1.43, p-value: < 0.0001) and those with a history of OUD had non-significantly lower odds (OR: 0.91, 95% CI: 0.64-1.29, p-value: 0.59). CONCLUSIONS: Significant racial disparities are present in adverse events among patients in the non-OUD group, but those disparities attenuated in the OUD group.

Utilization of machine learning methods for predicting surgical outcomes after total knee arthroplasty.

BACKGROUND: Predictive models could help clinicians identify risk factors that cause adverse events after total knee arthroplasty (TKA), allowing for appropriate preoperative preventive interventions and allocation of resources. METHODS: The National Inpatient Sample datasets from 2010-2014 were used to build Logistic Regression (LR), Gradient Boosting Method (GBM), Random Forest (RF), and Artificial Neural Network (ANN) predictive models for three clinically relevant outcomes after TKA-disposition at discharge, any post-surgical complications, and blood transfusion. Model performance was evaluated using the Brier scores as calibration measures, and area under the ROC curve (AUC) and F1 scores as discrimination measures. RESULTS: GBM-based predictive models were observed to have better calibration and discrimination than the other models; thus, indicating comparatively better overall performance. The Brier scores for GBM models predicting the outcomes under investigation ranged from 0.09-0.14, AUCs ranged from 79-87%, and F1-scores ranged from 41-73%. Variable importance analysis for GBM models revealed that admission month, patient location, and patient's income level were significant predictors for all the outcomes. Additionally, any post-surgical complications and blood transfusions were significantly predicted by deficiency anemias, and discharge disposition by length of stay and age groups. Notably, any post-surgical complications were also significantly predicted by the patient undergoing blood transfusion. CONCLUSIONS: The predictive abilities of the ML models were successfully demonstrated using data from the National Inpatient Sample (NIS), indicating a wide range of clinical applications for obtaining accurate prognoses of complications following orthopedic surgical procedures.

Clinical Theragnostic Relationship between Drug-Resistance Specific miRNA Expressions, Chemotherapeutic Resistance, and Sensitivity in Breast Cancer: A Systematic Review and Meta-Analysis.

Awareness of breast cancer has been increasing due to early detection, but the advanced disease has limited treatment options. There has been growing evidence on the role of miRNAs involved in regulating the resistance in several cancers. We performed a comprehensive systematic review and meta-analysis on the role of miRNAs in influencing the chemoresistance and sensitivity of breast cancer. A bibliographic search was performed in PubMed and Science Direct based on the search strategy, and studies published until December 2018 were retrieved. The eligible studies were included based on the selection criteria, and a detailed systematic review and meta-analysis were performed based on PRISMA guidelines. A random-effects model was utilised to evaluate the combined effect size of the obtained hazard ratio and 95% confidence intervals from the eligible studies. Publication bias was assessed with Cochran's Q test, I2 statistic, Orwin and Classic fail-safe N test, Begg and Mazumdar rank correlation test, Duval and Tweedie trim and fill calculation and the Egger's bias indicator. A total of 4584 potential studies were screened. Of these, 85 articles were eligible for our systematic review and meta-analysis. In the 85 studies, 188 different miRNAs were studied, of which 96 were upregulated, 87 were downregulated and 5 were not involved in regulation. Overall, 24 drugs were used for treatment, with doxorubicin being prominently reported in 15 studies followed by Paclitaxel in 11 studies, and 5 drugs were used in combinations. We found only two significant HR values from the studies (miR-125b and miR-4443) and our meta-analysis results yielded a combined HR value of 0.748 with a 95% confidence interval of 0.508-1.100; p-value of 0.140. In conclusion, our results suggest there are different miRNAs involved in the regulation of chemoresistance through diverse drug genetic targets. These biomarkers play a crucial role in guiding the effective diagnostic and prognostic efficiency of breast cancer. The screening of miRNAs as a theragnostic biomarker must be brought into regular practice for all diseases. We anticipate that our study serves as a reference in framing future studies and clinical trials for utilising miRNAs and their respective drug targets.