Developing a clinical and PET/CT volumetric prognostic index for risk assessment and management of NSCLC patients after initial therapy.

BACKGROUND: Currently, individual clinical prognostic variables are used sequentially with risk-stratification after TNM staging in clinical practice for the prognostic assessment of patients with NSCLC, which is not effective for estimating the collective impact of multiple individual variables on patient outcomes. Here, we developed a clinical and PET/CT volumetric prognostic (CPVP) index that integrates the prognostic power of multiple clinical variables and metabolic tumor volume from baseline FDG-PET, for use immediately after definitive therapy. PATIENTS AND METHODS: This retrospective cohort study included 998 NSCLC patients diagnosed between 2004 and 2017, randomly assigned to two cohorts for modeling the CPVP index using Cox regression models examining overall survival (OS) and subsequent validation. RESULTS: The CPVP index generated from the model cohort included pretreatment variables (whole-body metabolic tumor volume [MTVwb], clinical TNM stage, tumor histology, performance status, age, race, gender, smoking history) and treatment type. A clinical variable (CV) index without MTVwb and PET/CT volumetric prognostic (PVP) index without clinical variables were also generated for comparison. In the validation cohort, univariate Cox modeling showed a significant association of the index with overall survival (OS; Hazard Ratio [HR] 3.14; 95% confidence interval [95% CI] = 2.71 to 3.65, p < 0.001). Multivariate Cox regression analysis demonstrated a significant association of the index with OS (HR = 3.13, 95% CI = 2.66 to 3.67, p < 0.001). The index showed greater prognostic power (C-statistic = 0.72) than any of its independent variables including clinical TNM stage (C-statistic ranged from 0.50 to 0.69, all p < 0.003), CV index (C-statistic = 0.68, p < 0.001) and PVP index (C-statistic = 0.70, p = 0.006). CONCLUSIONS: The CPVP index for NSCLC patients has moderately strong prognostic power and is more prognostic than its individual prognostic variables and other indices. It provides a practical tool for quantitative prognostic assessment after initial treatment and therefore may be helpful for the development of individualized treatment and monitoring strategy for NSCLC patients.

The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach.

BACKGROUND: Prognostic modeling in health care has been predominantly statistical, despite a rapid growth of literature on machine-learning approaches in biological data analysis. We aim to assess the relative importance of variables in predicting overall survival among patients with non-small cell lung cancer using a Variable Importance (VIMP) approach in a machine-learning Random Survival Forest (RSF) model for posttreatment planning and follow-up. METHODS: A total of 935 non-small cell lung cancer patients were randomly and equally divided into 2 training and testing cohorts in an RFS model. The prognostic variables included age, sex, race, the TNM Classification of Malignant Tumors (TNM) stage, smoking history, Eastern Cooperative Oncology Group performance status, histologic type, treatment category, maximum standard uptake value of whole-body tumor (SUVmaxWB), whole-body metabolic tumor volume (MTVwb), and Charlson Comorbidity Index. The VIMP was calculated using a permutation method in the RSF model. We further compared the VIMP of the RSF model to that of the standard Cox survival model. We examined the order of VIMP with the differential functional forms of the variables. RESULTS: In both the RSF and the standard Cox models, the most important variables are treatment category, TNM stage, and MTVwb. The order of VIMP is more robust in RSF model than in Cox model regarding the differential functional forms of the variables. CONCLUSIONS: The RSF VIMP approach can be applied alongside with the Cox model to further advance the understanding of the roles of prognostic factors, and improve prognostic precision and care efficiency.

Developing and validating a novel metabolic tumor volume risk stratification system for supplementing non-small cell lung cancer staging.

PURPOSE: We hypothesized that whole-body metabolic tumor volume (MTVwb) could be used to supplement non-small cell lung cancer (NSCLC) staging due to its independent prognostic value. The goal of this study was to develop and validate a novel MTVwb risk stratification system to supplement NSCLC staging. METHODS: We performed an IRB-approved retrospective review of 935 patients with NSCLC and FDG-avid tumor divided into modeling and validation cohorts based on the type of PET/CT scanner used for imaging. In addition, sensitivity analysis was conducted by dividing the patient population into two randomized cohorts. Cox regression and Kaplan-Meier survival analyses were performed to determine the prognostic value of the MTVwb risk stratification system. RESULTS: The cut-off values (10.0, 53.4 and 155.0 mL) between the MTVwb quartiles of the modeling cohort were applied to both the modeling and validation cohorts to determine each patient's MTVwb risk stratum. The survival analyses showed that a lower MTVwb risk stratum was associated with better overall survival (all p < 0.01), independent of TNM stage together with other clinical prognostic factors, and the discriminatory power of the MTVwb risk stratification system, as measured by Gönen and Heller's concordance index, was not significantly different from that of TNM stage in both cohorts. Also, the prognostic value of the MTVwb risk stratum was robust in the two randomized cohorts. The discordance rate between the MTVwb risk stratum and TNM stage or substage was 45.1% in the modeling cohort and 50.3% in the validation cohort. CONCLUSION: This study developed and validated a novel MTVwb risk stratification system, which has prognostic value independent of the TNM stage and other clinical prognostic factors in NSCLC, suggesting that it could be used for further NSCLC pretreatment assessment and for refining treatment decisions in individual patients.

Risk factors for cost-related medication non-adherence among older patients with cancer.

We aimed to assess the risk factors for cost-related medication non-adherence (CRN) among older patients with cancer in the United States. We used data from the 2010 Health and Retirement Study (HRS) to assess risk factors for CRN including age, insurance coverage, nursing home residence, functional limitations, and frequency of hospitalization among old patients with cancer. CRN was self-reported. We conducted a multivariate regression analysis to assess the effect of each risk factor. 293 (9.9%) of 2,953 older patients (50+ years) cancer patients reported CRN. Those who reported CRN were more likely to be younger, women, African American, and Hispanics. Compared to those with Medicare, those with no health insurance coverage were 97% more likely to report CRN. High number in limitation in activities of daily living and hospitalization significantly increased risk for CRN. Sicker cancer patients were more likely to report CRN. Lack of health insurance may have prevented the cancer patients from receiving optimal care. Together, these results suggest that expanding insurance coverage and improving insurance benefit design for cancer patients is likely to decrease CRN and improve outcomes.

Socioeconomic disparities in ectopic pregnancy: predictors of adverse outcomes from Illinois hospital-based care, 2000-2006.

This study aimed to identify the incidence of adverse outcomes from ectopic pregnancy hospital care in Illinois (2000-2006), and assess patient, neighborhood, hospital and time factors associated with these outcomes. Discharge data from Illinois hospitals were retrospectively analyzed and ectopic pregnancies were identified using DRG and ICD-9 diagnosis codes. The primary outcome was any complication identified by ICD-9 procedure codes. Secondary outcomes were length of stay and discharge status. Residential zip codes were linked to 2000 U.S. Census data to identify patients' neighborhood demographics. Logistic regression was used to identify risk factors for adverse outcomes. Independent variables were insurance status, age, co-morbidities, neighborhood demographics, hospital type, hospital ectopic pregnancy service volume, and year of discharge. Of 13,007 ectopic pregnancy hospitalizations, 7.4% involved at least one complication identified by procedure codes. Hospitalizations covered by Medicare (for women with chronic disabilities) were more likely than those with other source or without insurance to result in surgical sterilization (OR 4.7, P = 0.012). Hospitalization longer than 2 days was more likely with Medicaid (OR 1.46, P < 0.0005) or no insurance (OR 1.35, P < 0.0005) versus other payers, and among church-operated versus secular hospitals (OR 1.21, P < 0.0005). Compared to public hospitals, private hospitals had lower rates of complications (OR 0.39, P < 0.0005) and of hospitalization longer than 2 days (OR 0.57, P < 0.0005). With time, hospitalizations became shorter (OR 0.53, P < 0.0005) and complication rates higher (OR 1.33, P = 0.024). Ectopic pregnancy patients with Medicaid, Medicare or no insurance, and those admitted to public or religious hospitals, were more likely to experience adverse outcomes.

Disease-specific patterns of hospice and related healthcare use in an incidence cohort of seriously ill elderly patients.

There appears to be significant heterogeneity across diseases in their patterns of health care use at the end of life. We use a new, nationally representative sample of patients diagnosed in 1993 with 13 serious diseases to demonstrate this variation in rates of inpatient, outpatient, and hospice utilization. The diseases are: cancer of the lung, colon, pancreas, urinary tract, liver or biliary tract, head or neck, or central nervous system, as well as leukemia or lymphoma, stroke, congestive heart failure, hip fracture, or myocardial infarction. We present disease-specific rates of: length of stay, interhospital transfer, outpatient visits in the year before and 3 years after diagnosis, death within 4 years, and gender-specific hospice use rates among decedents. Among decedents with noncancer diagnoses, rates of hospice use vary from 5.9% to 8.7%. Among decedents with cancer diagnoses, rates vary from 15.2% to 35.2%. For the cohort overall, 14.2% of male decedents and 12.4% of female decedents used hospice. Patterns of end-of-life care vary substantially according to diagnosis.

The lack of effect of market structure on hospice use.

OBJECTIVE: To describe the relative importance of health care market structure and county-level demographics in determining rates of hospice use. DATA SOURCES: Medicare claims data for a cohort of elderly patients newly diagnosed with lung cancer, colon cancer, stroke, or heart attack in 1993, followed for up to five years, and linked to Census and Area Resource File data. STUDY DESIGN: Variation between markets in rates of hospice use by patients with serious illness was examined after taking into account differences in individual-level data using hierarchical linear models. The relative explanatory power of market-level structure and local demographic variables was compared. DATA COLLECTION METHODS: The cohort was defined within the Medicare hospital claims data using validated algorithms to detect incident cases of disease with a three-year lookback. Use of hospice was determined by linkage at an individual level to the Standard Analytic Files for Hospice through 1997. Individual-level data was linked to the Area Resource File using county identifiers present in the Medicare claims. PRINCIPAL FINDINGS: There is substantial variation in hospice use across markets. This variation is not explained by differences in the major components of health care infrastructure: the availability of hospital, nursing home, or skilled nursing facilities, nor by the availability of HMOs, doctors, or generalists. CONCLUSIONS: Intercounty heterogeneity in hospice use is substantial, and may not be related to the set-up of the medical care system. The important local factors may be local preferences, differences in the particular mix of services provided by local hospices, or differences in community leadership on end of life-issues; many of these differences may be amenable to educational efforts.