An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non-Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review.

BACKGROUND: Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. OBJECTIVE: The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. METHODS: We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non-small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. RESULTS: For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. CONCLUSIONS: Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.

Outcomes of retreatment with anti-PD-1 monotherapy after response to first course in patients with cutaneous melanoma.

Aim: To determine outcomes of retreatment with anti-PD-1 monotherapy for melanoma. Materials & methods: This retrospective study included adults with unresectable cutaneous melanoma who achieved stable disease (SD) or better after anti-PD-1 monotherapy and were retreated with anti-PD-1 monotherapy after ≥90-day gap. We determined overall survival and real-world tumor response. Results: For 21 eligible patients, from retreatment initiation, median follow-up was 14.4 months (range, 2.6-34.5); median overall survival was 30.0 months (95% CI: 14.4-not reached); 1-year survival was 100% (95% CI: 100-100%); 2-year survival was 83% (48-96%). Of 16 patients with recorded best real-world tumor response, ten (63%) responded (complete/partial response); three achieved SD; three had progressive disease. Conclusion: Patients with advanced melanoma achieving SD/better after first-course anti-PD-1 monotherapy may benefit from retreatment.

Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer.

Lines of therapy (LOT) derived from real-world healthcare data not only depict real-world cancer treatment sequences, but also help define patient phenotypes along the course of disease progression and therapeutic interventions. The sequence of prescribed anticancer therapies can be defined as temporal phenotyping resulting from changes in morphological (tumor staging), biochemical (biomarker testing), physiological (disease progression), and behavioral (physician prescribing and patient adherence) parameters. We introduce a novel methodology that is a two-part approach: 1) create an algorithm to derive patient-level LOT and 2) aggregate LOT information via clustering to derive temporal phenotypes, in conjunction with visualization techniques, within a large insurance claims dataset. We demonstrated the methodology using two examples: metastatic non-small cell lung cancer and metastatic melanoma. First, we generated a longitudinal patient cohort for each cancer type and applied a set of rules to derive patient-level LOT. Then the LOT algorithm outputs for each cancer type were visualized using Sankey plots and K-means clusters based on durations of LOT and of gaps in therapy between LOT. We found differential distribution of temporal phenotypes across clusters. Our approach to identify temporal patient phenotypes can increase the quality and utility of analyses conducted using claims datasets, with the potential for application to multiple oncology disease areas across diverse healthcare data sources. The understanding of LOT as defining patients' temporal phenotypes can contribute to continuous health learning of disease progression and its interaction with different treatment pathways; in addition, this understanding can provide new insights that can be applied by tailoring treatment sequences for the patient phenotypes who will benefit.

Real-world experience with pembrolizumab in patients with advanced melanoma: A large retrospective observational study.

Pembrolizumab has been approved in the United States for treating advanced melanoma for >4 years. We examined real-world pembrolizumab use and associated outcomes in US oncology clinical practices, including patients who would not be eligible for clinical trials.Flatiron Health longitudinal database was used to identify adult patients with advanced melanoma initiating ≥1 dose of pembrolizumab from September 4, 2014, through December 31, 2016, with follow-up through December 31, 2017. Patients in any clinical trial during the study period were excluded. Overall survival (OS) and time on treatment from pembrolizumab initiation were analyzed using the Kaplan-Meier (KM) method. Subgroup analyses were conducted to examine OS for several patient characteristics including Eastern Cooperative Oncology Group (ECOG) performance status >1, brain metastases, and corticosteroids before pembrolizumab initiation.Pembrolizumab was administered to 315 (59%), 152 (29%), and 65 (12%) patients as first-, second-, and third-line/later therapy. Median age at pembrolizumab initiation was 68 years (range, 18-84); most patients were male (66%) and white (94%). Of those with available data, 38% had BRAF-mutant melanoma, 21% had elevated lactate dehydrogenase (LDH) level, and 23% had ECOG >1. Overall, 18% had brain metastases, and 23% were prescribed corticosteroids <3 months before initiating pembrolizumab. Median study follow-up was 12.9 months (range, 0.03-39.6). Median OS was 21.8 months (95% confidence interval [CI] 16.8-29.1); KM 1-year and 2-year survival rates were 61% and 48%, respectively; and median time on pembrolizumab treatment was 4.9 months (95% CI 3.7-5.5). Median OS for first-line pembrolizumab was not reached, and for second-line and third-line/later was 13.9 and 12.5 months, respectively (log-rank P = .0095). Significantly better OS (all P ≤.0014, log-rank test) was evident for patients with ECOG performance status (PS) of 0 to 1 (vs >1), normal (vs elevated) LDH level, and no (vs yes) corticosteroid prescription <3 months before. No difference was recorded in OS by brain metastases (log-rank P = .22) or BRAF mutation status (log-rank P = .90).These findings support effectiveness of pembrolizumab in the real-world clinical setting and provide important insights into patient characteristics and outcomes associated with pembrolizumab therapy for a heterogeneous patient population with advanced melanoma, including patients who would not be eligible for clinical trials.