Dataset Information

Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer.

ABSTRACT: PURPOSE:Treatment decisions about localized prostate cancer depend on accurate estimation of the patient's life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient's health. METHODS:We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model's predictive capacity was compared with existing survival models with a metric of explained randomness (?2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS:Our noncancer survival model included 143 covariates and had improved survival prediction (?2 = 0.60) compared with the Charlson comorbidity index (?2 = 0.26) and Elixhauser comorbidity index (?2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (?2 = 0.71) to the Memorial Sloan Kettering prediction model (?2 = 0.68). CONCLUSION:Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.

SUBMITTER: Riviere P

PROVIDER: S-EPMC6873997 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer.

Riviere Paul P Tokeshi Christopher C Hou Jiayi J Nalawade Vinit V Sarkar Reith R Paravati Anthony J AJ Schiaffino Melody M Rose Brent B Xu Ronghui R Murphy James D JD

JCO clinical cancer informatics 20190301

<h4>Purpose</h4>Treatment decisions about localized prostate cancer depend on accurate estimation of the patient's life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures ...[more]

PMID: 30830794

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Project description:BackgroundAbnormal metabolism and perturbations in metabolic pathways play significant roles in the development and progression of prostate cancer; however, comprehensive metabolomic analyses of human data are lacking and needed to elucidate the interrelationships.MethodsWe examined the serum metabolome in relation to prostate cancer survival in a cohort of 1812 cases in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Using an ultrahigh-performance LC-MS/MS platform, we identified 961 known metabolites in prospectively collected serum. Median survival time from diagnosis to prostate cancer-specific death (N=472) was 6.6 years (interquartile range=2.9-11.1 years). Cox proportional hazards regression models estimated hazard ratios and 95% confidence intervals of the associations between the serum metabolites (in quartiles) and prostate cancer death, adjusted for age at baseline and diagnosis, disease stage, and Gleason sum. In order to calculate risk scores, we first randomly divided the metabolomic data into a discovery set (70%) and validated in a replication set (30%).ResultsOverall, 49 metabolites were associated with prostate cancer survival after Bonferroni correction. Notably, higher levels of the phospholipid choline, amino acid glutamate, long-chain polyunsaturated fatty acid (n6) arachidonate (20:4n6), and glutamyl amino acids gamma-glutamylglutamate, gamma-glutamylglycine, and gamma-glutamylleucine were associated with increased risk of prostate cancer-specific mortality (fourth versus first quartile HRs=2.07-2.14; P-values <5.2×10-5). By contrast, the ascorbate/aldarate metabolite oxalate, xenobiotics S-carboxymethyl-L-cysteine, fibrinogen cleavage peptides ADpSGEGDFXAEGGGVR and fibrinopeptide B (1-12) were related to reduced disease-specific mortality (fourth versus first quartile HRs=0.82-0.84; P-value <5.2×10-5). Further adjustment for years from blood collection to cancer diagnosis, body mass index, smoking intensity and duration, and serum total and high-density lipoprotein cholesterol did not alter the results. Participants with a higher metabolic score based on the discovery set had an elevated risk of prostate cancer-specific mortality in the replication set (fourth versus first quartile, HR=3.9, P-value for trend<0.0001).ConclusionsThe metabolic traits identified in this study, including for choline, glutamate, arachidonate, gamma-glutamyl amino acids, fibrinopeptides, and endocannabinoid and redox pathways and their composite risk score, corroborate our previous analysis of fatal prostate cancer and provide novel insights and potential leads regarding the molecular basis of prostate cancer progression and mortality.

Project description:ObjectiveProstate cancer (PC) is the most common non-cutaneous malignancy in men worldwide. Accurate predicting the survival of elderly PC patients can help reduce mortality in patients. We aimed to construct nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in elderly PC patients.MethodsInformation on PC patients aged 65 years and older was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for PC patients. Nomograms were developed to predict the CSS and OS of elderly PC patients based on a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of the nomograms compared with the TNM staging system and D'Amico risk stratification system.Results135183 elderly PC patients in 2010-2018 were included. All patients were randomly assigned to the training set (N=94764) and the validation set (N=40419). Univariate and multivariate Cox regression model analysis revealed that age, race, marriage, histological grade, TNM stage, surgery, chemotherapy, radiotherapy, biopsy Gleason score (GS), and prostate-specific antigen (PSA) were independent risk factors for predicting CSS and OS in elderly patients with PC. The C-index of the training set and the validation set for predicting CSS was 0.883(95%CI:0.877-0.889) and 0.887(95%CI:0.877-0.897), respectively. The C-index of the training set and the validation set for predicting OS was 0.77(95%CI:0.766-0.774)and 0.767(95%CI:0.759-0.775), respectively. It showed that the proposed model has excellent discriminative ability. The AUC and the calibration curves also showed good accuracy and discriminability. The DCA showed that the nomograms for CSS and OS have good clinical potential value.ConclusionsWe developed new nomograms to predict CSS and OS in elderly PC patients. The models have been internally validated with good accuracy and reliability and can help doctors and patients to make better clinical decisions.

Project description:PurposeLife expectancy has become a core consideration in prostate cancer care. While multiple prediction tools exist to support decision making, their discriminative ability remains modest, which hampers usage and utility. We examined whether combining patient reported and claims based health measures into prediction models improves performance.Materials and methodsUsing SEER (Surveillance, Epidemiology, and End Results)-CAHPS (Consumer Assessment of Healthcare Providers and Systems) we identified men 65 years old or older diagnosed with prostate cancer from 2004 to 2013 and extracted 4 types of data, including demographics, cancer information, claims based health measures and patient reported health measures. Next, we compared the performance of 5 nested competing risk regression models for other cause mortality. Additionally, we assessed whether adding new health measures to established prediction models improved discriminative ability.ResultsAmong 3,240 cases 246 (7.6%) died of prostate cancer while 631 (19.5%) died of other causes. The National Cancer Institute Comorbidity Index score was associated but weakly correlated with patient reported overall health (p <0.001, r=0.21). For predicting other cause mortality the 10-year area under the receiver operating characteristic curve improved from 0.721 (demographics only) to 0.755 with cancer information and to 0.777 and 0.812 when adding claims based and patient reported health measures, respectively. The full model generated the highest value of 0.820. Models based on existing tools also improved in their performance with the incorporation of new data types as predictor variables (p <0.001).ConclusionsPrediction models for life expectancy that combine patient reported and claims based health measures outperform models that incorporate these measures separately. However, given the modest degree of improvement, the implementation of life expectancy tools should balance model performance with data availability and fidelity.

Project description:BACKGROUND:Risk of prostate cancer-specific mortality (PCSM) is highly variable for men with adverse pathologic features at radical prostatectomy (RP); a majority will die of other causes. Accurately stratifying PCSM risk can improve therapy decisions. OBJECTIVE:Validate the 22 gene Decipher genomic classifier (GC) to predict PCSM in men with adverse pathologic features after RP. DESIGN, SETTING, AND PARTICIPANTS:Men with adverse pathologic features: pT3, pN1, positive margins, or Gleason score >7 who underwent RP in 1987-2010 at Johns Hopkins, Cleveland Clinic, Mayo Clinic, and Durham Veteran's Affairs Hospital. We also analyzed subgroups at high risk (prostate-specific antigen >20 ng/ml, RP Gleason score 8-10, or stage >pT3b), or very high risk of PCSM (biochemical recurrence in<2 yr [BCR2], or men who developed metastasis after RP [MET]). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Logistic regression evaluated the association of GC with PCSM within 10 yr of RP (PCSM10), adjusted for the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S). GC performance was evaluated with area under the receiver operating characteristic curve (AUC) and decision curves. RESULTS AND LIMITATIONS:Five hundred and sixty-one men (112 with PCSM10), median follow-up 13.0 yr (patients without PCSM10). For high GC score (> 0.6) versus low-intermediate (? 0.6), the odds ratio for PCSM10 adjusted for CAPRA-S was 3.91 (95% confidence interval: 2.43-6.29), with AUC=0.77, an increase of 0.04 compared with CAPRA-S. Subgroup odds ratios were 3.96, 3.06, and 1.95 for high risk, BCR2, or MET, respectively (all p<0.05), with AUCs 0.64-0.72. GC stratified cumulative PCSM10 incidence from 2.8% to 30%. Combined use of case-control and cohort data is a potential limitation. CONCLUSIONS:In a large cohort with the longest follow-up to date, Decipher GC demonstrated clinically important prediction of PCSM at 10 yr, independent of CAPRA-S, in men with adverse pathologic features, BCR2, or MET after RP. PATIENT SUMMARY:Decipher genomic classifier may improve treatment decision-making for men with adverse or high risk pathology after radical prostatectomy.

Dataset Information

Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer.

Publications

Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer.

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