This is a phase II, multicenter, open-label study of chemotherapy-naïve patients with non-small-cell lung cancer (NSCLC) and age > or = 70 years who were treated with erlotinib and evaluated to determine the median, 1-year, and 2-year survival. The secondary end points include radiographic response rate, time to progression (TTP), toxicity, and symptom improvement.Eligible patients with NSCLC were treated with erlotinib 150 mg/d until disease progression or significant toxicity. Tumor response was assessed every 8 weeks by computed tomography scan using Response Evaluation Criteria in Solid Tumors. Tumor samples were analyzed for the presence of somatic mutations in EGFR and KRAS.Eighty eligible patients initiated erlotinib therapy between March 2003 and May 2005. There were eight partial responses (10%), and an additional 33 patients (41%) had stable disease for 2 months or longer. The median TTP was 3.5 months (95% CI, 2.0 to 5.5 months). The median survival time was 10.9 months (95% CI, 7.8 to 14.6 months). The 1- and 2- year survival rates were 46% and 19%, respectively. The most common toxicities were acneiform rash (79%) and diarrhea (69%). Four patients developed interstitial lung disease of grade 3 or higher, with one treatment-related death. EGFR mutations were detected in nine of 43 patients studied. The presence of an EGFR mutation was strongly correlated with disease control, prolonged TTP, and survival.Erlotinib monotherapy is active and relatively well tolerated in chemotherapy-naïve elderly patients with advanced NSCLC. Erlotinib merits consideration for further investigation as a first-line therapeutic option in elderly patients.
Article Free AccessMichael Rabin Interview: February 22 and March 1, 2009 Share on Author: Dennis Shasha View Profile , Interviewee: Michael Rabin View Profile Authors Info & Claims ACM Oral History interviewsJanuary 2006 Interview No.: 14Pages 1–11https://doi.org/10.1145/1141880.1529269Online:01 January 2006Publication History 0citation591DownloadsMetricsTotal Citations0Total Downloads591Last 12 Months83Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Purpose The Veterans Health Administration (VHA) provides high-quality preventive chronic care and cancer care, but few studies have documented improved patient outcomes that result from this high-quality care. We compared the survival rates of older patients with cancer in the VHA and fee-for-service (FFS) Medicare and examined whether differences in the stage at diagnosis, receipt of guideline-recommended therapies, and unmeasured characteristics explain survival differences. Patients and Methods We used propensity-score methods to compare all-cause and cancer-specific survival rates for men older than age 65 years who were diagnosed or received their first course of treatment for colorectal, lung, lymphoma, or multiple myeloma in VHA hospitals from 2001 to 2004 to similar FFS-Medicare enrollees diagnosed in Surveillance, Epidemiology, and End Results (SEER) areas in the same time frame. We examined the role of unmeasured factors by using sensitivity analyses. Results VHA patients versus similar FFS SEER-Medicare patients had higher survival rates of colon cancer (adjusted hazard ratio [HR], 0.87; 95% CI, 0.82 to 0.93) and non–small-cell lung cancer (NSCLC; HR, 0.91; 95% CI, 0.88 to 0.95) and similar survival rates of rectal cancer (HR, 1.05; 95% CI, 0.95 to 1.16), small-cell lung cancer (HR, 0.99; 95% CI, 0.93 to 1.05), diffuse large–B-cell lymphoma (HR, 1.02; 95% CI, 0.89 to 1.18), and multiple myeloma (HR, 0.92; 95% CI, 0.83 to 1.03). The diagnosis of VHA patients at earlier stages explained much of the survival advantages for colon cancer and NSCLC. Sensitivity analyses suggested that additional adjustment for the severity of comorbid disease or performance status could have substantial effects on estimated differences. Conclusion The survival rate for older men with cancer in the VHA was better than or equivalent to the survival rate for similar FFS-Medicare beneficiaries. The VHA provision of high-quality care, particularly preventive care, can result in improved patient outcomes.
7537 Background: Aggressive EOL cancer care is a health care quality and cost issue. As lung cancer is the leading cause of cancer-related death in the U.S., and NCCN member institutions are considered to offer high-quality, evidence-based care, we examined the aggressiveness of mNSCLC EOL care at NCCN institutions. Methods: The NCCN database was queried to identify all deceased mNSCLC patients (pts) actively treated at 8 NCCN institutions from January 2007-June 2010. Aggressive EOL care was defined as 1) Starting a new CT regimen within 30 days of death (30d New), 2) Receipt of CT within 14 days of death (14d Any), or 3) Any ICU admission within the last 30 days of life (30d ICU). Among pts receiving CT, multivariate logistic regression was used to investigate associations between pt factors and aggressive CT use, controlling for age, NCCN institution, performance status (PS), and comorbidity. Multivariate analysis was not possible for the ICU model due to small sample size. Results: Among 1,092 eligible pts, 18.9% had 1 or more aggressive EOL events: 10.7% 30d New, 11.8% 14d Any, and 3.2% 30d ICU. Forty (34%) of 30d New pts started first line CT. Median age overall was 63 (range 25-91) and was 61 for all pts in the aggressive CT analyses. Initial overall PS was 57% 0-1 and was still predominantly 0-1 (23-38%) at the last CT in all groups. The multivariate results are listed below; an odds ratio > 1 indicating aggressive care more likely. Conclusions: While typical pt factors, such as age and PS, are used to determine fitness for CT receipt in mNSCLC, our analysis suggests that aggressive EOL CT receipt in mNSCLC at NCCN institutions is associated with other pt or clinical factors. Analysis Pt factor Odds ratio 95% CI 30 d New* Liver metastasis 1.82 1.19-2.77 Hospice discussion 2.73 1.24-6.04 14 d Any* Prior radiation 1.80 1.17-2.79 ≥2 prior CT regimens 1.79 1.08-2.94 Prior smoker 0.42 0.24-0.73 Current smoker 0.51 0.28-0.94 *No associations seen with other factors, including age, institution, PS, comorbidity, other metastatic sites, clinical trial participation, time from metastatic diagnosis, gender, race, insurance type, histology, or smoking status at diagnosis.
129 Background: Many factors contribute to long wait times for cancer patients on the day of their infusion. At Dana-Farber Cancer Institute (DFCI), a contributing factor is patient flow between exam and infusion. Order verification affects patient flow and begins when the following two criteria are met: provider signed an order and the patient’s scheduled infusion appointment arrives. Patients often check-in to infusion before their scheduled infusion appointment. Order verification has three sequential steps: nurse verification, pharmacist 1 verify (V1), and pharmacist 2 verify (V2). Methods: A team of pharmacists, nurses, providers, and process improvement leads designed a pilot in which V1 moved before nurse verification, concurrent with patient check-in to infusion. Further, V1 began as soon as an order was signed; the pharmacist did not wait for a patient’s scheduled infusion appointment. Nurse verification and V2 occurred in sequence after V1. Timestamp data were extracted from Epic and analyzed via Tableau to assess reduction in verification throughput, defined as time between infusion check-in and V2. Fourteen providers and one pharmacist joined a 6-week pilot to adopt the redesigned workflow beginning 4/23/18. Results: At baseline, time between check-in and V2 was consistent for pilot and non-pilot orders. During the pilot, time between check-in and V2 was shorter for pilot orders, showing a sustained decrease of approximately 10 minutes. The table below provides time in minutes between infusion check-in and V2 for pilot and non-pilot orders at baseline (3/12/18-4/20/18) and following workflow redesign (4/23/18-6/1/18). Conclusions: Implementing the pilot workflow reduced order verification throughput time and enabled drug preparation to begin sooner. Expanding this workflow to all medication orders can decrease infusion wait time at DFCI.[Table: see text]
<p>Supplementary Methods, Figures S1 - S20. Figure S1. Branched evolution leads to tissue-sampling bias in primary tumor samples. Figure S2. Results of ABSOLUTE on samples from patient 418. Figure S3. 2D Bayesian clustering analysis of point-mutation CCF distributions in case 418. Figure S4. Bayesian clustering of private point-mutation CCF distributions in all sequenced tissue samples from case 418. Figure S5. Genetic alterations supporting phylogeny construction in case 418. Figure S6. Phylogenetic tree for case 418 Figure S7. Evolutionary relationships between primary tumor-samples and brain metastasis samples Figure S8. Detection of homozygous deletion in CDKN2A in the brain metastasis of case 24. Figure S9. Amplification of FGFR1 and MYC detected in the brain metastasis of case 331. Figure S10. Additional alterations under investigation for association with various targeted therapies. Figure S11. Power for paired-detection of somatic mutations. Figure S12. Amplification of CCNE1 detected in the brain metastasis of case 314. Figure S13. Amplification of EGFR detected in the brain metastasis of case 314. Figure S14. Amplification of MYC detected in the brain metastasis of case 308. Figure S15. Amplification of MYC detected in the brain metastasis of case 138. Figure S16. Amplifications of CDK6 and MET detected in the brain metastasis of case 138. Figure S17. Amplifications of CCNE1 and AKT2 detected in the brain metastasis of case 138. Figure S18. Amplification of EGFR detected in a regional lymph node from case 296. Figure S19. Power for somatic mutation detection in 86 matched primary-tumor and brain-metastasis samples. Figure S20. Calling of amplifications in primary-tumor samples and paired brain metastases.</p>
