Effects of primary prophylaxis of neutropenia on outcomes, utilization and expenditures for elderly breast cancer patients receiving chemotherapy

Systemic chemotherapy is a well-established primary as well as adjuvant therapy for breast cancer, and is highly successful in ensuring recurrence free survival among patients. However, toxicity due to chemotherapy, specifically an early onset hematologic toxicity called neutropenia, restricts the use and therefore the efficacy of chemotherapy in breast cancer patients, especially in the elderly. The prophylactic use of granulocyte-colony stimulating factors (G-CSF), helps prevent neutropenia, improves the tolerance of chemotherapy in the elderly, and improves the prognosis of breast cancer. Nevertheless, evidence supporting the clinical and cost effectiveness of prophylactic G-CSF in the elderly is limited, and thus the American Society of Clinical Oncology (ASCO) guidelines for use of prophylactic G-CSF in the elderly are not explicit. This study aims to assess the effect of primary prophylactic G-CSF on - the occurrence of chemotherapy-induced neutropenia hospitalization and length of stay; Medicare expenditures due to neutropenia management; overall expenditures in the first year after the start of chemotherapy; and successful administration of systemic cancer therapies that are otherwise hindered by the occurrence of neutropenia, in elderly breast cancer patients receiving chemotherapy. The study found that primary prophylactic G-CSF reduced the probability of neutropenia hospitalization and improved the provision of systemic chemotherapy and radiation therapy during the first course of the treatment in elderly breast cancer patients. The study also found that duration of primary prophylactic G-CSF administration was significantly associated with better outcomes, with lower rates of neutropenia hospitalization and better adherence to systemic cancer therapies. These findings have implications for ASCO guidelines and Medicare coverage policies for G-CSF administration and duration of administration in elderly breast cancer patients

NF‐κB, But Not p38 MAP Kinase, is Required for TNF‐α‐Induced Expression of Cell Adhesion Molecules In Endothelial Cells

In response to inflammation stimuli, tumor necrosis factor‐α (TNF‐α) induces expression of cell adhesion molecules (CAMs) in endothelial cells (ECs). Studies have suggested that the nuclear factor‐κB (NF‐κB) and the p38 MAP kinase (p38) signaling pathways play central roles in this process, but conflicting results have been reported. The objective of this study is to determine the relative contributions of the two pathways to the effect of TNF‐α. Our initial data indicated that blockade of p38 activity by chemical inhibitor SB203580 (SB) at 10 µM moderately inhibited TNF‐α‐induced expression of three types of CAMs; ICAM‐1, VCAM‐1 and E‐selectin, indicating that p38 may be involved in the process. However, subsequent analysis revealed that neither 1 µM SB that could completely inhibit p38 nor specific knockdown of p38α and p38β with small interference RNA (siRNA) had an apparent effect, indicating that p38 activity is not essential for TNF‐α‐induced CAMs. The most definitive evidence to support this conclusion was from the experiments using cells differentiated from p38α knockout embryonic stem cells. We could show that deletion of p38α gene did not affect TNF‐α‐induced ICAM‐1 and VCAM‐1 expression when compared with wild‐type cells. We further demonstrated that inhibition of NF‐κB completely blocked TNF‐α‐induced expression of ICAM‐1, VCAM‐1 and E‐selectin. Taken together, our results clearly demonstrate that NF‐κB, but not p38, is critical for TNF‐α‐induced CAM expression. The inhibition of SB at 10 µM on TNF‐α‐induced ICAM‐1, VCAM‐1 and E‐selectin is likely due to the nonspecific effect of SB. J. Cell. Biochem. 105: 477–486, 2008. © 2008 Wiley‐Liss, Inc

Implementation of an Evidence-Based intervention With Safety Net Clinics to Improve Mammography appointment adherence among Underserved Women

The Peace of Mind Program is an evidence-based intervention to improve mammography appointment adherence in underserved women. The aim of this study was to assess effectiveness of the intervention and implementation of the intervention in safety net clinics. The intervention was implemented through a non-randomized stepped wedge cluster hybrid study design with 19 Federally Qualified Health Centers and charity care clinics within the Greater Houston area. A multivariable generalized estimating equation logistic regression was conducted to examine mammography appointment adherence. A survey assessing Consolidated Framework for Implementation Research constructs was also conducted with clinic staff prior to adoption and eight weeks post implementation. One-sided t-tests were conducted to analyze mean score changes between the surveys. A total of 4402 women (baseline period = 2078; intervention period = 2324) were included in the final regression analysis. Women in the intervention period were more likely to attend or reschedule their mammography appointment (OR = 1.30; p \u3c 0.01) than those in the baseline period receiving usual care. Women who completed the intervention were more likely to attend or reschedule their mammography appointment than those who did not complete the intervention (OR = 1.62; p \u3c 0.01). The mammography appointment no-show rates for those in the baseline period, in the intervention period, and who completed the intervention were, respectively, 22%, 19%, and 15%. A total of 15 clinics prior to adoption and eight clinics completed the survey at 8 weeks post implementation A statistically significant mean score decrease was observed in Inner Setting and in two Inner Setting CFIR constructs, Culture-Effort, and Implementation Climate. While the intervention improved mammography appointment adherence, there are opportunities to further integrate Consolidated Framework for Implementation Research constructs. Trial registration: Clinical trials registration number: NCT02296177

Medical Home Implementation and Follow-Up of Cancer-Related Abnormal Test Results in the Veterans Health Administration

IMPORTANCE: Lack of timely follow-up of cancer-related abnormal test results can lead to delayed or missed diagnoses, adverse cancer outcomes, and substantial cost burden for patients. Care delivery models, such as the Veterans Affairs\u27 (VA) Patient-Aligned Care Team (PACT), which aim to improve patient-centered care coordination, could potentially also improve timely follow-up of abnormal test results. PACT was implemented nationally in the VA between 2010 and 2012. OBJECTIVE: To evaluate the long-term association between PACT implementation and timely follow-up of abnormal test results related to the diagnosis of 5 different cancers. DESIGN, SETTING, AND PARTICIPANTS: This multiyear retrospective cohort study used 14 years of VA data (2006-2019), which were analyzed using panel data-based random-effects linear regressions. The setting included all VA clinics and facilities. The participants were adult patients who underwent diagnostic testing related to 5 different cancers and had abnormal test results. Data extraction and statistical analyses were performed from September 2021 to December 2023. EXPOSURE: Calendar years denoting preperiods and postperiods of PACT implementation, and the PACT Implementation Progress Index Score denoting the extent of implementation in each VA clinic and facility. MAIN OUTCOME AND MEASURE: Percentage of potentially missed timely follow-ups of abnormal test results. RESULTS: This study analyzed 6 data sets representing 5 different types of cancers. During the initial years of PACT implementation (2010 to 2013), percentage of potentially missed timely follow-ups decreased between 3 to 7 percentage points for urinalysis suggestive of bladder cancer, 12 to 14 percentage points for mammograms suggestive of breast cancer, 19 to 22 percentage points for fecal tests suggestive of colorectal cancer, and 6 to 13 percentage points for iron deficiency anemia laboratory tests suggestive of colorectal cancer, with no statistically significant changes for α-fetoprotien tests and lung cancer imaging. However, these beneficial reductions were not sustained over time. Better PACT implementation scores were associated with a decrease in potentially missed timely follow-up percentages for urinalysis (0.3-percentage point reduction [95% CI, -0.6 to -0.1] with 1-point increase in the score), and laboratory tests suggestive of iron deficiency anemia (0.5-percentage point reduction [95% CI,-0.8 to -0.2] with 1-point increase in the score). CONCLUSIONS AND RELEVANCE: This cohort study found that implementation of PACT in the VA was associated with a potential short-term improvement in the quality of follow-up for certain test results. Additional multifaceted sustained interventions to reduce missed test results are required to prevent care delays

Comorbidity network analysis using graphical models for electronic health records

ImportanceThe comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality.ObjectiveThe main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation.MethodThis cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients.ResultsOut of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between “poisoning by psychotropic agents” and “accidental poisoning by tranquilizers” (logOR 8.16), and the most connected diagnosis was “disorders of fluid, electrolyte, and acid–base balance” (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, “diagnoses of mitral and aortic valve” and “other rheumatic heart disease” (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, “disorders of fluid, electrolyte, and acid–base balance” was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes.Conclusion and relevanceOur graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses

Geographic Variations and the associated Factors in adherence to and Persistence With adjuvant Hormonal therapy For the Privately insured Women aged 18-64 With Breast Cancer in Texas

The purpose of this study is to examine the geographical patterns of adjuvant hormonal therapy adherence and persistence and the associated factors in insured Texan women aged 18-64 with early breast cancer. A retrospective cohort study was conducted using 5-year claims data for the population insured by the Blue Cross Blue Shield of Texas (BCBSTX). Women diagnosed with early breast cancer who were taking tamoxifen or aromatase inhibitors (AIs) for adjuvant hormonal therapy with at least one prescription claim were identified. Adherence to adjuvant hormonal therapy and persistence with adjuvant hormonal therapy were calculated as outcome measures. Women without a gap between two consecutively dispensed prescriptions of at least 90 days were considered to be persistently taking the medications. Patient-level multivariate logistic regression models with repeated regional-level adjustments and a Cox proportional hazards model with mixed effects were used to determine the geographical variations and patient-, provider-, and area-level factors that were associated with adjuvant hormonal therapy adherence and persistence. Of the 938 women in the cohort, 627 (66.8%) initiated adjuvant hormonal therapy. Most of the smaller HRRs have significantly higher or lower rates of treatment adherence and persistence rates relative to the median regions. The use of AHT varies substantially from one geographical area to another, especially for adherence, with an approximately two-fold difference between the lowest and highest areas, and area-level factors were found to be significantly associated with the compliance of AHT. There are geographical variations in AHT adherence and persistence in Texas. Patient-level and area-level factors have significant associations explaining these patterns

Geographic Variations and the associated Factors in adherence to and Persistence With adjuvant Hormonal therapy For the Privately insured Women aged 18-64 With Breast Cancer in Texas

The purpose of this study is to examine the geographical patterns of adjuvant hormonal therapy adherence and persistence and the associated factors in insured Texan women aged 18-64 with early breast cancer. A retrospective cohort study was conducted using 5-year claims data for the population insured by the Blue Cross Blue Shield of Texas (BCBSTX). Women diagnosed with early breast cancer who were taking tamoxifen or aromatase inhibitors (AIs) for adjuvant hormonal therapy with at least one prescription claim were identified. Adherence to adjuvant hormonal therapy and persistence with adjuvant hormonal therapy were calculated as outcome measures. Women without a gap between two consecutively dispensed prescriptions of at least 90 days were considered to be persistently taking the medications. Patient-level multivariate logistic regression models with repeated regional-level adjustments and a Cox proportional hazards model with mixed effects were used to determine the geographical variations and patient-, provider-, and area-level factors that were associated with adjuvant hormonal therapy adherence and persistence. Of the 938 women in the cohort, 627 (66.8%) initiated adjuvant hormonal therapy. Most of the smaller HRRs have significantly higher or lower rates of treatment adherence and persistence rates relative to the median regions. The use of AHT varies substantially from one geographical area to another, especially for adherence, with an approximately two-fold difference between the lowest and highest areas, and area-level factors were found to be significantly associated with the compliance of AHT. There are geographical variations in AHT adherence and persistence in Texas. Patient-level and area-level factors have significant associations explaining these patterns

Bayesian Regularization to Predict Neuropsychiatric adverse Events in Smoking Cessation With Pharmacotherapy

BACKGROUND: Research on risk factors for neuropsychiatric adverse events (NAEs) in smoking cessation with pharmacotherapy is scarce. We aimed to identify predictors and develop a prediction model for risk of NAEs in smoking cessation with medications using Bayesian regularization. METHODS: Bayesian regularization was implemented by applying two shrinkage priors, Horseshoe and Laplace, to generalized linear mixed models on data from 1203 patients treated with nicotine patch, varenicline or placebo. Two predictor models were considered to separate summary scores and item scores in the psychosocial instruments. The summary score model had 19 predictors or 26 dummy variables and the item score model 51 predictors or 58 dummy variables. A total of 18 models were investigated. RESULTS: An item score model with Horseshoe prior and 7 degrees of freedom was selected as the final model upon model comparison and assessment. At baseline, smokers reporting more abnormal dreams or nightmares had 16% greater odds of experiencing NAEs during treatment (regularized odds ratio (rOR) = 1.16, 95% credible interval (CrI) = 0.95 - 1.56, posterior probability P(rOR \u3e 1) = 0.90) while those with more severe sleep problems had 9% greater odds (rOR = 1.09, 95% CrI = 0.95 - 1.37, P(rOR \u3e 1) = 0.85). The prouder a person felt one week before baseline resulted in 13% smaller odds of having NAEs (rOR = 0.87, 95% CrI = 0.71 - 1.02, P(rOR \u3c 1) = 0.94). Odds of NAEs were comparable across treatment groups. The final model did not perform well in the test set. CONCLUSIONS: Worse sleep-related symptoms reported at baseline resulted in 85%-90% probability of being more likely to experience NAEs during smoking cessation with pharmacotherapy. Treatment for sleep disturbance should be incorporated in smoking cessation program for smokers with sleep disturbance at baseline. Bayesian regularization with Horseshoe prior permits including more predictors in a regression model when there is a low number of events per variable

Comparison of Collaborative Goal Setting With Enhanced Education for Managing Diabetes-Associated Distress and Hemoglobin A1c Levels: A Randomized Clinical Trial

IMPORTANCE: Type 2 diabetes is a prevalent and morbid condition. Poor engagement with self-management can contribute to diabetes-associated distress and hinder diabetes control. OBJECTIVE: To evaluate the implementation and effectiveness of Empowering Patients in Chronic Care (EPICC), an evidence-based intervention to improve diabetes-associated distress and hemoglobin A1c (HbA1c) levels after the intervention and after 6-month maintenance. DESIGN, SETTING, AND PARTICIPANTS: This hybrid (implementation-effectiveness) randomized clinical trial was performed in Veterans Affairs clinics across Illinois, Indiana, and Texas from July 1, 2015, to June 30, 2017. Participants included adults with uncontrolled type 2 diabetes (HbA1c level \u3e8.0%) who received primary care during the prior year in participating clinics. Data collection was completed on November 30, 2018, and data analysis was completed on June 30, 2020. All analyses were based on intention to treat. INTERVENTIONS: Participants in EPICC attended 6 group sessions based on a collaborative goal-setting theory led by health care professionals. Clinicians conducted individual motivational interviewing sessions after each group. Usual care was enhanced (EUC) with diabetes education. MAIN OUTCOMES AND MEASURES: The primary outcome consisted of changes in HbA1c levels after the intervention and during maintenance. Secondary outcomes included the Diabetes Distress Scale (DDS), Morisky Medication Adherence Scale, and Lorig Self-efficacy Scale. Secondary implementation outcomes included reach, adoption, and implementation (number of sessions attended per patient). RESULTS: A total of 280 participants with type 2 diabetes (mean [SD] age, 67.2 [8.4] years; 264 men [94.3]; 134 non-Hispanic White individuals [47.9%]) were equally randomized to EPICC or EUC. Participants receiving EPICC had significant postintervention improvements in HbA1c levels (F1, 252 = 9.12, Cohen d = 0.36 [95% CI, 0.12-0.59]; P = .003) and DDS (F1, 245 = 9.06, Cohen d = 0.37 [95% CI, 0.13-0.60]; P = .003) compared with EUC. During maintenance, differences between the EUC and EPICC groups remained significant for DDS score (F1, 245 = 8.94, Cohen d = 0.36 [95% CI, 0.12-0.59]; P = .003) but not for HbA1c levels (F1, 252 = 0.29, Cohen d = 0.06 [95% CI, -0.17 to 0.30]; P = .60). Improvements in DDS scores were modest. There were no differences between EPICC and EUC in improvements after intervention or maintenance for either adherence or self-efficacy. Among all 4002 eligible patients, 280 (7.0%) enrolled in the study (reach). Each clinic conducted all planned EPICC sessions and cohorts (100% adoption). The EPICC group participants attended a mean (SD) of 4.34 (1.98) sessions, with 54 (38.6%) receiving all 6 sessions. CONCLUSIONS AND RELEVANCE: A patient-empowerment approach using longitudinal collaborative goal setting and motivational interviewing is feasible in primary care. Improvements in HbA1c levels after the intervention were not sustained after maintenance. Modest improvements in diabetes-associated distress after the intervention were sustained after maintenance. Innovations to expand reach (eg, telemedicine-enabled shared appointments) and sustainability are needed. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01876485

Machine Learning Automated Detection of Large Vessel Occlusion From Mobile Stroke Unit Computed Tomography Angiography

BACKGROUND: Prehospital automated large vessel occlusion (LVO) detection in Mobile Stroke Units (MSUs) could accelerate identification and treatment of patients with LVO acute ischemic stroke. Here, we evaluate the performance of a machine learning (ML) model on CT angiograms (CTAs) obtained from 2 MSUs to detect LVO. METHODS: Patients evaluated on MSUs in Houston and Los Angeles with out-of-hospital CTAs were identified. Anterior circulation LVO was defined as an occlusion of the intracranial internal carotid artery, middle cerebral artery (M1 or M2), or anterior cerebral artery vessels and determined by an expert human reader. A ML model to detect LVO was trained and tested on independent data sets consisting of in-hospital CTAs and then tested on MSU CTA images. Model performance was determined using area under the receiver-operator curve statistics. RESULTS: Among 68 patients with out-of-hospital MSU CTAs, 40% had an LVO. The most common occlusion location was the middle cerebral artery M1 segment (59%), followed by the internal carotid artery (30%), and middle cerebral artery M2 (11%). Median time from last known well to CTA imaging was 88.0 (interquartile range, 59.5-196.0) minutes. After training on 870 in-hospital CTAs, the ML model performed well in identifying LVO in a separate in-hospital data set of 441 images with area under receiver-operator curve of 0.84 (95% CI, 0.80-0.87). ML algorithm analysis time was under 1 minute. The performance of the ML model on the MSU CTA images was comparable with area under receiver-operator curve 0.80 (95% CI, 0.71-0.89). There was no significant difference in performance between the Houston and Los Angeles MSU CTA cohorts. CONCLUSIONS: In this study of patients evaluated on MSUs in 2 cities, a ML algorithm was able to accurately and rapidly detect LVO using prehospital CTA acquisitions