A Prognostic Model to Predict Mortality among Non-Small-Cell Lung Cancer Patients in the U.S. Military Health System

MilCanEpi: Increased Capability for Cancer Care Research in the Department of Defense

The Military Health System (MHS) of the US Department of Defense (DoD) provides comprehensive medical care to over nine million beneficiaries, including active-duty members, reservists, activated National Guard, military retirees, and their family members. The MHS generates an extensive database containing administrative claims and medical encounter data, while the DoD also maintains a cancer registry that collects information about the occurrence of cancer among its beneficiaries who receive care at military treatment facilities. Collating data from the two sources diminishes the limitations of using registry or medical claims data alone for cancer research and extends their usage. To facilitate cancer research using the unique military health resources, a computer interface linking the two databases has been developed, called Military Cancer Epidemiology, or MilCanEpi. The intent of this article is to provide an overview of the MilCanEpi data system, describing its components, structure, potential uses, and limitations.

Clinical impact of histologic type on survival and recurrence in patients with surgically resected stage II and III non-small cell lung cancer

OBJECTIVES

This study aimed to investigate the clinical impact of histologic type on the survival and recurrence outcomes of patients with stage II and III non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A total of 2155 consecutive adult patients who underwent complete resection of stage II and III NSCLC between 2008 and 2018 were enrolled. The primary endpoints were freedom from recurrence (FFR) and overall survival (OS). The secondary endpoint was the time to lung cancer or non-lung cancer death.

RESULTS

Of the 2155 patients, 1436 (66.6 %) had adenocarcinoma (ADC) and 719 (33.4 %) had squamous cell carcinoma (SqCC). Patients with SqCC had better FFR than those with ADC (stage II, p < 0.001; stage III, p < 0.001). Although patients with ADC showed a slightly better OS until 5 years than those with SqCC, the difference was insignificant (stage II, p = 0.292; stage III, p = 0.196). Patients with SqCC had higher rates of non-lung cancer death than patients with ADC (stage II, p < 0.001; stage III, p = 0.039). The time from lung cancer recurrence to death was shorter in patients with SqCC than in those with ADC (stage II, median 13 vs 37 months, p < 0.001; stage III, median 11 vs 26 months, p < 0.001).

CONCLUSIONS

In stage II and III NSCLC, ADC had a higher risk of recurrence than SqCC, with no difference in OS. These results were related to significant differences in non-lung cancer mortality and recurrence-to-death time between the two histologic types.

Chronic Obstructive Pulmonary Disease and Lung Cancer: A Review for Clinicians

Chronic obstructive pulmonary disease (COPD) and lung cancer are common global causes of morbidity and mortality. Because both diseases share several predisposing risks, the 2 diseases may occur concurrently in susceptible individuals. The diagnosis of COPD has important implications for the diagnostic approach and treatment options if lesions concerning for lung cancer are identified during screening. Importantly, the presence of COPD has significant implications on prognosis and management of patients with lung cancer. In this monograph, we review the mechanistic linkage between lung cancer and COPD, the impact of lung cancer screening on patients at risk, and the implications of the presence of COPD on the approach to the diagnosis and treatment of lung cancer. This manuscript succinctly reviews the epidemiology and common pathogenetic factors for the concurrence of COPD and lung cancer. Importantly for the clinician, it summarizes the indications, benefits, and complications of lung cancer screening in patients with COPD, and the assessment of risk factors for patients with COPD undergoing consideration of various treatment options for lung cancer.

Treatment strategy, overall survival and associated risk factors among patients with unresectable stage IIIB/IV non-small cell lung cancer in China (2015-2017): A multicentre prospective study

BACKGROUND

There are limited studies on treatment and survival analysis among patients with unresectable Stage IIIB or IV non-small cell lung cancer (NSCLC) in routine practice in China. To address this gap, we conducted a prospective observational study in a cohort of patients treated at 11 hospitals in China.

METHODS

This was a multicentre, prospective cohort study including patients with newly diagnosed unresectable Stage IIIB or IV NSCLC from June 26th, 2015 to April 28th, 2017. Patient baseline characteristics, disease characteristics, and anti-cancer treatments were obtained by medical chart review. The overall survival (OS) from the initiation of first-line treatment was analysed by the Kaplan-Meier method. Factors associated with survival were analysed by univariate and multivariate Cox regression models.

FINDINGS

Among 1324 patients enrolled with median follow-up duration of 15·0 (range: 0·0-42·1) months, 83·5% (1105/1324) of them received first-line chemotherapy of which platinum-based compounds were the dominated agents. Overall, 30·9% (409/1324) of patients received targeted therapy as 1st-line treatment including 65·0% (266/409) EGFR-TKIs and 5·1% (21/409) ALK-TKIs. Of all eligible patients, gene testing rates were 44·0% (583/1324) for EGFR mutations, 17·0% (225/1324) for EML4-ALK gene fusions, and 8·3% (110/1324) for ROS1 gene fusions. The EGFR-TKIs were administered to 63·9% (179/280) of EGFR mutated patients as first-line treatment. The overall median OS was 23·2 (95%CI 19·5-25·5) months, and patients treated at tier 1 cities had better OS than that of tier 2 cities. Also, the OS in patients with EGFR mutation was longer than those with EGFR wild type. Multivariate Cox regression models suggested that male, education below high school, tier 2 cities, smoking history, and multiple metastases were associated with poor survival.

INTERPRETATION

The gene test coverage was relatively low among the studied population, and over half of EGFR mutated patients received EGFR-TKIs, suggesting that the result of genetic tests in real-world settings may not always indicate the selection of treatment. The OS benefit observed from patients treated in tier 1 cities and those with EGFR mutation may indicate a need for broader gene test coverage, providing NSCLC patients with personalized treatment according to the results of genetic tests.

FUNDING

Roche Holding AG.TRANSLATED ABSTRACT: This translation in Chinese was submitted by the authors and we reproduce it as supplied. It has not been peer reviewed. Our editorial processes have only been applied to the original abstract in English, which should serve as reference for this manuscript.:IIIBIV(NSCLC)., ,, 11.:,, 20156262017428IIIBIVNSCLC.,.Kaplan-Meier(OS), Cox.:1324, 15.0(:0.0-42.1), 83.5%(1105/1324), ., 30.9%(409/1324), 65.0%(266/409)EGFR-TKI5.1%(21/409)ALK-TKI., EGFR,EML4-ALKROS144.0%(583/1324),17.0%(225/1324)8.3%(110/1324).63.9%(179/280)EGFREGFR-TKI.23.2 (95% 19·5-25·5) , ., EGFREGFR.Cox, ,,,.:, EGFREGFR-TKI, , .EGFR, , NSCLC.

Predicting 2-year survival in stage I-III non-small cell lung cancer: the development and validation of a scoring system from an Australian cohort

Background

There are limited data on survival prediction models in contemporary inoperable non-small cell lung cancer (NSCLC) patients. The objective of this study was to develop and validate a survival prediction model in a cohort of inoperable stage I-III NSCLC patients treated with radiotherapy.

Methods

Data from inoperable stage I-III NSCLC patients diagnosed from 1/1/2016 to 31/12/2017 were collected from three radiation oncology clinics. Patient, tumour and treatment-related variables were selected for model inclusion using univariate and multivariate analysis. Cox proportional hazards regression was used to develop a 2-year overall survival prediction model, the South West Sydney Model (SWSM) in one clinic (n = 117) and validated in the other clinics (n = 144). Model performance, assessed internally and on one independent dataset, was expressed as Harrell’s concordance index (c-index).

Results

The SWSM contained five variables: Eastern Cooperative Oncology Group performance status, diffusing capacity of the lung for carbon monoxide, histological diagnosis, tumour lobe and equivalent dose in 2 Gy fractions. The SWSM yielded a c-index of 0.70 on internal validation and 0.72 on external validation. Survival probability could be stratified into three groups using a risk score derived from the model.

Conclusions

A 2-year survival model with good discrimination was developed. The model included tumour lobe as a novel variable and has the potential to guide treatment decisions. Further validation is needed in a larger patient cohort.

Factors associated with early lung cancer mortality: a systematic review

Introduction: Despite recent therapeutic advances, lung cancer remains the primary cause of cancer deaths worldwide, and early lung mortality was poorly studied.Area covered: Early lung-cancer mortality reflects local therapy (surgery or radiotherapy) impact (localized forms), and metastatic disease evolution, comorbidities and healthcare-system accessibility. The definition of early lung cancer mortality is not consensual; thresholds range from 1 to 12 months post-diagnosis. This systematic review was undertaken to identify and analyze factors significantly associated with early lung cancer mortality. Age, male sex, non-adenocarcinoma histology, advanced stage at diagnosis and ECOG performance status are the main clinical factors of early lung cancer mortality. Active/ex-smoking also seems to favor early mortality, despite heterogeneous definitions of smoker status. For radio-chemotherapy treated locally advance disease, the early mortality rate increases according to tumor volume. Less well studied, socioeconomic characteristics (rurality and social deprivation index) yielded contradictory results, partially because definitions vary over studies. However, early lung cancer mortality is significantly higher for lower socioeconomic class patients.Expert opinion: Prospective, observational, general population studies are needed to better evaluate early lung-cancer mortality. International consensus concerning the patient-, disease- or healthcare system-linked factors of interest to be collected would facilitate comparisons among countries.

Structural and functional radiomics for lung cancer

INTRODUCTION

Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.

METHODS

Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer.

CONCLUSION

The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."

Long-Term Survival of Patients with Metastatic Non-Small-Cell Lung Cancer over Five Decades

Objective

Novel therapeutics and supportive care improved outcomes for metastatic non-small-cell lung cancer (mNSCLC) patients. Major advances over the past five decades include the introduction of combination chemotherapy, small molecules targeting mutant proteins, especially EGFR, and more recently immunotherapy. We aim to document real-world long-term survival over the past five decades.

Methods

Survival statistics were extracted from the Survival, Epidemiology, and End Results (SEER) database for mNSCLC patients during 1973-2015. Two- and five-year survival (2yS and 5yS) were analyzed using Kaplan-Meier and proportional hazard models.

Results

The study population consisted of 280,655mNSCLC patients diagnosed during 1973-2015. Longer survival was seen in younger, female, married, Asian/Pacific Islander race, adenocarcinoma, lower grade, more recent diagnosis, higher income, and chemotherapy-treated patients. 2yS increased during the study period from 2.6% to 12.9%, and 5yS increased from 0.7% to 3.2%. 2yS of patients <50 years of age rose from 2.1% to 22.8%, and their 5yS rose from 0.7% to 6.2%. 2yS of adenocarcinoma patients improved from 2.7% to 16.2%, and their improved 5yS from 1.1% to 3.9%.

Conclusions

Between 1973 and 2015, there was a dramatic improvement in long-term survival, with an approximately five-fold increase in both 2yS and 5yS. Nonetheless, absolute numbers of long-term survivors remained low, with less than 4% living 5 years. This provides a baseline to compare long-term outcomes seen in the current generation of clinical trials.

Eurolung risk score is associated with long-term survival after curative resection for lung cancer

Objective

The study objective was to verify whether the Eurolung score was associated with long-term prognosis after lung cancer resection.

Methods

A total of 1359 consecutive patients undergoing anatomic lung resection (1136 lobectomies, 103 pneumonectomies, 120 segmentectomies) (2014-2018) were analyzed. The parsimonious aggregate Eurolung2 score was calculated for each patient. Median follow-up was 802 days. Survival distribution was estimated by the Kaplan–Meier method. Cox proportional hazard regression and competing risk regression analyses were used to assess the independent association of Eurolung with overall and disease-specific survival.

Results

Patients were grouped into 4 classes according to their Eurolung scores (A 0-2.5, B 3-5, C 5.5-6.5, D 7-11.5). Most patients were in class A (52%) and B (33%), 8% were in class C, and 7% were in class D. Five-year overall survival decreased across the categories (A: 75%; B: 52%; C: 29%; D: 27%, log rank P < .0001). The score stratified the 3-year overall survival in patients with pT1 (P < .0001) or pT>1 (P < .0001). In addition, the different classes were associated with incremental risk of long-term overall mortality in patients with pN0 (P < .0001) and positive nodes (P = .0005). Cox proportional hazard regression and competing regression analyses showed that Eurolung aggregate score remained significantly associated with overall (hazard ratio, 1.19; P < .0001) and disease-specific survival after adjusting for pT and pN stage (hazard ratio, 1.09; P = .005).

Conclusions

Eurolung aggregate score was associated with long-term survival after curative resection for cancer. This information may be valuable to inform the shared decision-making process and the multidisciplinary team discussion assisting in the selection of the most appropriate curative treatment in high-risk patients.

Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non-Small-Cell Lung Cancer

PURPOSE

The prediction of clinical outcomes for patients with cancer is central to precision medicine and the design of clinical trials. We developed and validated machine-learning models for three important clinical end points in patients with advanced non-small-cell lung cancer (NSCLC)-objective response (OR), progression-free survival (PFS), and overall survival (OS)-using routinely collected patient and disease variables.

METHODS

We aggregated patient-level data from 17 randomized clinical trials recently submitted to the US Food and Drug Administration evaluating molecularly targeted therapy and immunotherapy in patients with advanced NSCLC. To our knowledge, this is one of the largest studies of NSCLC to consider biomarker and inhibitor therapy as candidate predictive variables. We developed a stochastic tumor growth model to predict tumor response and explored the performance of a range of machine-learning algorithms and survival models. Models were evaluated on out-of-sample data using the standard area under the receiver operating characteristic curve and concordance index (C-index) performance metrics.

RESULTS

Our models achieved promising out-of-sample predictive performances of 0.79 area under the receiver operating characteristic curve (95% CI, 0.77 to 0.81), 0.67 C-index (95% CI, 0.66 to 0.69), and 0.73 C-index (95% CI, 0.72 to 0.74) for OR, PFS, and OS, respectively. The calibration plots for PFS and OS suggested good agreement between actual and predicted survival probabilities. In addition, the Kaplan-Meier survival curves showed that the difference in survival between the low- and high-risk groups was significant (log-rank test P < .001) for both PFS and OS.

CONCLUSION

Biomarker status was the strongest predictor of OR, PFS, and OS in patients with advanced NSCLC treated with immune checkpoint inhibitors and targeted therapies. However, single biomarkers have limited predictive value, especially for programmed death-ligand 1 immunotherapy. To advance beyond the results achieved in this study, more comprehensive data on composite multiomic signatures is required.

Comorbidity and stage at diagnosis among lung cancer patients in the US military health system

PURPOSE

We investigated the association between comorbidities and stage at diagnosis among NSCLC patients in the US Military Health System (MHS), which provides universal health care to its beneficiaries.

METHODS

The linked data from the Department of Defense's Central Cancer Registry (CCR) and the MHS Data Repository (MDR) were used. The study included 4768 patients with histologically confirmed primary NSCLC. Comorbid conditions were extracted from the MDR data. Comorbid conditions were those included in the Charlson Comorbidity Index (CCI) and were defined as a diagnosis during a 3-year time frame prior to the NSCLC diagnosis. Multivariable logistic regression was performed to estimate odds ratios (ORs) and 95% confidence intervals (95% CI) of late stage (stages III and IV) versus early stage (stages I and II) in relation to pre-existing comorbidities.

RESULTS

Compared to patients with no comorbidities, those with prior comorbidities tended to be less likely to have lung cancer diagnosed at late stage. When specific comorbidities were analyzed, decreased odds of being diagnosed at late stage were observed among those with chronic obstructive pulmonary disease (COPD) (adjusted OR 0.78, 95% CI 0.68 to 0.90). In contrast, patients with a congestive heart failure or a liver cirrhosis/chronic hepatitis had an increased likelihood of being diagnosed at late stage (adjusted OR 1.30, 95% CI 1.00 to 1.69 and adjusted OR 1.87, 95% CI 1.24 to 2.82, respectively).

CONCLUSIONS

Among NSCLC patients in an equal access health system, the likelihood of late stage at diagnosis differed by specific comorbid diseases.

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.

Development and validation of nomograms for predicting survival probability of patients with advanced adenocarcinoma in different EGFR mutation status

INTRODUCTION

Molecular markers are important variables in the selection of treatment for cancer patients and highly associated with their survival. Therefore, a nomogram that can predict survival probability by incorporating epidermal growth factor receptor mutation status and treatments for patients with advanced adenocarcinoma would be highly valuable. The aim of the study is to develop and validate a novel nomogram, incorporating epidermal growth factor receptor mutation status and treatments, for predicting 1-year and 2-year survival probability of patients with advanced adenocarcinoma.

MATERIAL AND METHODS

Data on 13,043 patients between June 1, 2011, and December 31, 2014 were collected. Seventy percent of them were randomly assigned to the training cohort for nomogram development, and the remaining 30% assigned to the validation cohort. The most important factors for constructing the nomogram were identified using multivariable Cox regression analysis. The discriminative ability and calibration of the nomograms were tested using C-statistics, calibration plots, and Kaplan-Meier curves.

RESULTS

In the training cohort, 1-year and 2-year OS were 52.8% and 28.5% in EGFR(-) patients, and 73.9% and 44.1% in EGFR(+) patients, respectively. In EGFR(+) group, factors selected were age, gender, congestive heart failure, renal disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, first-line chemotherapy, ECOG performance status, malignant pleural effusion, and smoking. In EGFR(-) group, factors selected were age, gender, myocardial infarction, cerebrovascular disease, chronic pulmonary disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, ECOG performance status, malignant pleural effusion, and a history of smoking. Two nomograms show good accuracy in predicting OS, with a concordance index of 0.83 in EGFR(+) and of 0.88 in EGFR(-).

CONCLUSIONS

The survival prediction models can be used to make individualized predictions with different EGFR mutation status and a useful tool for selecting regimens for treating advanced adenocarcinoma.

External validation of a prognostic model for mortality among patients with non-small-cell lung cancer using the Veterans Precision Oncology Data Commons

BACKGROUND

There is wide interest in developing prognostic models in non-small-cell lung cancer (NSCLC) due to the heterogeneity of the disease. Models developed at other healthcare institutions may not be directly applicable for patients treated at the Department of Veterans Affairs (VA). External validation of a candidate prognostic model among VA patients would be crucial before it can be implemented to aid clinical decision-making.

METHODS

A prognostic model for mortality developed in the Military Health System (MHS) was applied to data from the VA Precision Oncology Data Repository (VA-PODR), which is available to researchers inside and outside the VA at the Veterans Precision Oncology Data Commons (VPODC). Measures of discrimination and calibration were calculated for the MHS model. The MHS model was also refitted in VA-PODR data using the same risk factors to compare the effect of specific factors and predictive performance when the model is developed using VA data.

RESULTS

Time-dependent AUC of the MHS prognostic model was 0.788, 0.806, 0.780, and 0.779 for predicting survival at 1, 2, 3, and 5 years following diagnosis, respectively. Significant discrepancies were found between predicted and observed rates of survival, particularly for later years. When the model is refit in VA-PODR data, it achieved cross-validated AUCs of 0.739, 0.773, 0.769, and 0.807 at the same time points, and discrepancies between predicted and observed survival were reduced.

CONCLUSIONS

Validation of the MHS prognostic model in VA-PODR demonstrates that its discrimination remains strong when applied to VA patients. Nevertheless, further calibration to VA data may be needed to improve its risk estimation performance. This study highlights the utility of VA-PODR and the VPODC as a national resource for developing analytic tools that are well adapted to the Veteran population.

Physician Empathy Interacts with Breaking Bad News in Predicting Lung Cancer and Pleural Mesothelioma Patient Survival: Timing May Be Crucial

This study is the first to examine the prognostic role of physician empathy in interaction with the type of consultation (TC) (TC, bad news versus follow-up consultations) in cancer patient survival. Between January 2015 and March 2016, 179 outpatients with thoracic cancer and a Karnofsky performance status ≥60 assessed their oncologist’s empathy using the CARE questionnaire, which provides a general score and two sub-dimensions: listening/compassion and active/positive empathy. Survival was recorded until April 2018. Usual medical, social and psychological confounders were included in the Cox regression. The median follow-up time was 3.1 years. There was a statistical interaction between listening/compassion empathy and TC (p = 0.016) such that in bad news consultations, higher listening/compassion predicted a higher risk of death (hazard ratio (HR) = 1.13; 95% confidence interval (CI): 1.03–1.23; p = 0.008). In follow-up consultations, listening/compassion did not predict survival (HR = 0.94; 95% CI: 0.85–1.05; p = 0.30). The same results were found with the general score of empathy, but not with active/positive empathy. In bad news consultations, high patient-perceived physician compassion could worry patients by conveying the idea that there is no longer any hope, which could hasten death. Further studies are warranted to confirm these results and find out the determinants of patient perception of physician empathy.

Decision support systems for incurable non-small cell lung cancer: a systematic review

BACKGROUND

Individually tailored cancer treatment is essential to ensure optimal treatment and resource use. Treatments for incurable metastatic non-small cell lung cancer (NSCLC) are evolving rapidly, and decision support systems (DSS) for this patient population have been developed to balance benefits and harms for decision-making. The aim of this systematic review was to inventory DSS for stage IIIB/IV NSCLC patients.

METHODS

A systematic literature search was performed in Pubmed, Embase and the Cochrane Library. DSS were described extensively, including their predictors, model performances (i.e., discriminative ability and calibration), levels of validation and user friendliness.

RESULTS

The systematic search yielded 3531 articles. In total, 67 articles were included after additional reference tracking. The 39 identified DSS aim to predict overall survival and/or progression-free survival, but give no information about toxicity or cost-effectiveness. Various predictors were incorporated, such as performance status, serum and inflammatory markers, and patient and tumor characteristics. Some DSS were developed for the entire incurable NSCLC population, whereas others were specifically for patients with brain or spinal metastases. Few DSS had been validated externally using recent clinical data, and the discrimination and calibration were often poor.

CONCLUSIONS

Many DSS have been developed for incurable NSCLC patients, but DSS are still lacking that are up-to-date with a good model performance, while covering the entire treatment spectrum. Future DSS should incorporate genetic and biological markers based on state-of-the-art evidence, and compare multiple treatment options to estimate survival, toxicity and cost-effectiveness.

Metformin use and survival after non-small cell lung cancer: A cohort study in the US Military health system

Research suggests that metformin may be associated with improved survival in cancer patients with type II diabetes. This study assessed whether metformin use after non-small cell lung cancer (NSCLC) diagnosis is associated with overall survival among type II diabetic patients with NSCLC in the U.S. military health system (MHS). The study included 636 diabetic patients with histologically confirmed NSCLC diagnosed between 2002 and 2007, identified from the linked database from the Department of Defense's Central Cancer Registry (CCR) and the Military Health System Data Repository (MDR). Time-dependent multivariate Cox proportional hazards models were used to assess the association between metformin use and overall survival during follow-up. Among the 636 patients, 411 died during the follow-up. The median follow-up time was 14.6 months. Increased post-diagnosis cumulative use (per 1 year of use) conferred a significant reduction in mortality (adjusted hazard ratio (HR) = 0.76; 95% CI = 0.65-0.88). Further analysis by duration of use revealed that compared to non-users, the lowest risk reduction occurred among patients with the longest duration of use (i.e. use for more than 2 years) (HR = 0.19; 95% CI = 0.09-0.40). Finally, the reduced mortality was particularly observed only among patients who also used metformin before lung cancer diagnosis and among patients at early stage of diagnosis. Prolonged duration of metformin use in the study population was associated with improved survival, especially among early stage patients. Future research with a larger number of patients is warranted.

Metformin use and its effect on survival in diabetic patients with advanced non-small cell lung cancer

Background

Previous population-based studies have demonstrated an association between metformin use and improved survival among diabetic patients with cancer. We sought to analyze the effects of diabetes and its treatment in terms of the survival of patients with lung cancer.

Methods

Overall, 1106 patients with non-small cell lung cancer (94.3 % with stage IV disease) were included. The outcomes were compared between the patients with (n = 186) and without diabetes (n = 920). The characteristics associated with antidiabetic treatment and proper glycemic control (defined as a mean plasma glucose <130 mg/dL) were examined at diagnosis. The overall survivals (OSs) of the different patient populations were analyzed using Kaplan-Meier curves, and a multivariate Cox proportional hazard model was used to determine the influences of the patient and tumor characteristics on survival.

Results

The OS for the entire population was 18.3 months (95 % CI 16.1-20.4). There was no difference in the OSs of the diabetic and non-diabetic patients (18.5 vs 16.4 months, p = 0.62). The diabetic patients taking metformin exhibited a superior OS than did those on other antidiabetic treatments (25.6 vs 13.2 months, p = 0.017). Those with proper glycemic control had a better OS than did those without proper glycemic control and the non-diabetics (40.5 vs 13.2 and 18.5 months, respectively, p < 0.001). Both the use of metformin (HR 0.53, p < 0.0001 and HR 0.57, p = 0.017, respectively) and proper glycemic control (HR 0.49, p < 0.0001 and HR 0.40, p = 0.002, respectively) were significant protective factors in all and only diabetic patients, respectively.

Conclusions

The diabetic patients with proper glycemic control exhibited a better OS than did those without proper glycemic control and even exhibited a better OS than did the patients without diabetes mellitus. Metformin use was independently associated with a better OS.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2658-6) contains supplementary material, which is available to authorized users.