Yajun E Zhu

Bio: Yajun E Zhu is an academic researcher from Eli Lilly and Company. The author has contributed to research in topics: Lung cancer & Discontinuation. The author has an hindex of 3, co-authored 5 publications receiving 30 citations.

Factors Associated with Adherence to and Treatment Duration of Erlotinib Among Patients with Non-Small Cell Lung Cancer

Abstract: BACKGROUND: In lung cancer, there is an increasing number of oral agents available for patients; however, little is known about the factors associated with adherence to and treatment duration on oral medications in non-small cell lung cancer (NSCLC). OBJECTIVE: To evaluate the clinical and demographic factors associated with adherence and treatment discontinuation, respectively, to oral oncolytics among patients with NSCLC. METHODS: A retrospective, claims-based analysis of the Humana Research Database supplemented with medical chart review was conducted among patients with NSCLC who started an oral oncolytic between January 1, 2008, and June 30, 2013. Patients were required to be enrolled at least 1 year before the start of oral oncolytics and have no evidence of any oral oncolytic use during this period. Logistic regression models and Cox proportional hazard models were used to identify predictors associated with medication adherence and treatment duration, respectively. RESULTS: Among all oral oncolyti...

Efficacy of immune checkpoint inhibitor therapy in patients with RET fusion-positive non-small-cell lung cancer.

Abstract: Aim: To describe outcomes of patients with rearraned during transfection (RET) fusion-positive non-small-cell lung cancer (NSCLC) who received immune checkpoint inhibitor (ICI)-based treatments in the US. Patients & methods: Using de-identified Flatiron Health-Foundation Medicine NSCLC Clinico-Genomic and Guardant Health databases, treatment patterns and outcomes of 69 patients with advanced/metastatic RET fusion-positive NSCLC who received ICI-based treatment were described. Results: Median real-world progression-free survival and overall survival months were 4.2 (95% CI: 1.4-8.4) and 19.1 (6.9-not reached), respectively, among patients in Clinico-Genomic database (n = 17) receiving first-line ICI-based therapy. In the Guardant Health database, progression-free survival was unavailable, and the median overall survival was not reached (n = 29). Conclusion: Outcomes associated with ICI-based treatments in the first-line setting among patients with RET fusion-positive NSCLC are consistent with unselected populations reported in literature.

Real-World Outcomes and Factors Associated With the Second-Line Treatment of Patients With Gastric, Gastroesophageal Junction, or Esophageal Adenocarcinoma:

Abstract: This retrospective observational study was designed to evaluate overall survival in a real-world patient population and to identify predictive factors associated with receipt of second-line therapy. A retrospective analysis of electronic medical records (Flatiron Health, New York) was conducted among patients initiating first-line therapy from January 1, 2013, through April 30, 2018. Eligible patients were diagnosed with advanced gastric, gastroesophageal junction, or esophageal adenocarcinoma and ≥18 years of age at the time of treatment initiation. Patients alive 45 days after discontinuation of first-line therapy were considered potentially eligible for continued therapy and were categorized into those who received and those who did not receive second-line therapy. Survival analyses were conducted using Kaplan-Meier method and log-rank test without adjusting for any baseline covariates. Factors associated with further treatment were evaluated using logistic regression. A total of 3850 patients met eligibility criteria. Among the 2516 patients available to receive second-line therapy, 1515 (60.2%) received second-line therapy and 1001 (39.8%) did not receive further therapy. Among those potentially eligible to receive second-line therapy, median survival was 15.4 months (95% confidence interval [CI]: 14.6-16.0) from initiation of first-line therapy for those who received second-line therapy and 10.0 months (95% CI: 9.3-10.7) for those who did not. Longer duration of first-line therapy (≥169 vs ≤84 days), HER2-positive tumors, initially diagnosed with stage IV disease, less weight loss during first-line therapy, and younger age were associated with receipt of second-line therapy (all P < .001). Longer survival was associated with multiple lines of therapy; however, these results should be interpreted with caution, and no causal relationship can be inferred.

Challenges of Using ICD-9-CM and ICD-10-CM Codes for Soft-Tissue Sarcoma in Databases for Health Services Research.

Abstract: Objectives Soft-tissue sarcoma (STS) is a heterogeneous group of rare solid tumors that arise from various soft tissues in the body, such as muscle, fat, nerves, and blood vessels. Current International Classification of Diseases (ICD) coding systems include a set of nonspecific codes for malignancies of connective and soft tissue (ICD-9-CM code 171 and ICD-10-CM code C49). The goal of this study was to evaluate the use of these codes for health services research involving patients with a diagnosis of this rare malignancy. Methods Two databases were utilized to explore ICD coding for STS: claims data from Truven MarketScan and electronic medical records (EMRs) from Flatiron Health. Eligible patients from claims data were those with at least two ICD-9-CM codes of 171.x on two different days between July 1, 2004, and March 30, 2014. The treatment patterns of these cases were evaluated for consistency with known therapeutic approaches for STS. Eligible patients from the Flatiron EMR system were those who received olaratumab (a drug indicated only for use in patients diagnosed with STS) after its US Food and Drug Administration approval in October 2016 through the end of the data set (November 2017). ICD-10-CM codes were evaluated for this known STS cohort. Results In claims data, 4,159 patients were eligible for inclusion. Although national treatment guidelines include only a limited number of drugs used to treat STS, 98 unique anticancer drugs were identified as being used to treat patients in a claims data cohort. Only 7.7 percent of patients had claims for doxorubicin-based therapy and 3.8 percent had claims for ifosfamide-based therapy as initial treatment for STS, despite these being a standard of care. In the EMR data, 350 patients were eligible; only 170 patients (48.6 percent) had any evidence in the database of a connective or soft-tissue ICD-10-CM malignancy code within 60 days before or after initiation of olaratumab. Conclusions ICD coding for STS using the "Malignant neoplasm of connective and soft tissue" code is not reliable as a method to identify patients diagnosed with STS. Although codes reflecting the primary site of disease may have clinical relevance, lack of consistency in ICD coding for the diagnosis and treatment of this disease is a limiting factor in the ability to conduct real-world observational research of this rare disease. In the absence of consistent use of this code, an algorithm needs to be developed and validated to accurately identify patients with STS in these databases.

Precision therapy for RET-altered cancers with RET inhibitors.

Abstract: Rearranged during transfection (RET) is involved in the physiological development of some organ systems. Activating RET alterations via either gene fusions or point mutations are potent oncogenic drivers in non-small cell lung cancer, thyroid cancer, and in multiple diverse cancers. RET-altered cancers were initially treated with multikinase inhibitors (MKIs). The efficacy of MKIs was modest at the expense of notable toxicities from their off-target activity. Recently, highly potent and RET-specific inhibitors selpercatinib and pralsetinib were successfully translated to the clinic and FDA approved. We summarize the current state-of-the-art therapeutics with preclinical and clinical insights of these novel RET inhibitors, acquired resistance mechanisms, and future outlooks.

Financial toxicity associated with treatment of localized prostate cancer.

Abstract: Financial toxicity is a broad term to describe the economic consequences and subjective burden resulting from a cancer diagnosis and treatment. As financial toxicity is associated with poor disease outcomes, recognition of this problem and calls for strategies to identify and support those most at risk are increasing. Men with localized prostate cancer face treatment choices including active surveillance, prostatectomy or radiotherapy. The fact that potential patient out-of-pocket costs might influence decision making has rarely been acknowledged and, overall, the risk of financial toxicity for men with localized prostate cancer remains poorly studied. This shortfall requires a work-up in the context of prostate cancer and a multidimensional framework for considering a patient's risk of financial toxicity. The major elements of this framework are direct and indirect costs, patient-specific values, expectations of possible financial burdens, and individual economic circumstances. Current data indicate that total cost patterns probably differ by treatment modality: surgery might have an increased short-term effect, whereas radiotherapy might have an increased long-term risk of financial toxicity. Specific thresholds of patient income levels or out-of-pocket costs that predict risk of financial toxicity are difficult to identify. Compared with other malignancies, prostate cancer might have a lower overall risk of financial toxicity, but persistent post-treatment urinary, bowel or sexual adverse effects are likely to increase this risk.

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

Abstract: Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Time to add screening for financial hardship as a quality measure

Abstract: Cancer treatment is associated with financial hardship for many patients and families. Screening for financial hardship and referrals to appropriate resources for mitigation are not currently part of most clinical practices. In fact, discussions regarding the cost of treatment occur infrequently in clinical practice. As the cost of cancer treatment continues to rise, the need to mitigate adverse consequences of financial hardship grows more urgent. The introduction of quality measurement and reporting has been successful in establishing standards of care, reducing disparities in receipt of care, and improving other aspects of cancer care outcomes within and across providers. The authors propose the development and adoption of financial hardship screening and management as an additional quality metric for oncology practices. They suggest relevant stakeholders, conveners, and approaches for developing, testing, and implementing a screening and management tool and advocate for endorsement by organizations such as the National Quality Forum and professional societies for oncology care clinicians. The confluence of increasingly high-cost care and widening disparities in ability to pay because of underinsurance and lack of health insurance coverage makes a strong argument to take steps to mitigate the financial consequences of cancer.