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Deriving benefit of early detection from biomarker-based prognostic models.

ABSTRACT: Many prognostic models for cancer use biomarkers that have utility in early detection. For example, in prostate cancer, models predicting disease-specific survival use serum prostate-specific antigen levels. These models typically show that higher marker levels are associated with poorer prognosis. Consequently, they are often interpreted as indicating that detecting disease at a lower threshold of the biomarker is likely to generate a survival benefit. However, lowering the threshold of the biomarker is tantamount to early detection. For survival benefit to not be simply an artifact of starting the survival clock earlier, we must account for the lead time of early detection. It is not known whether the existing prognostic models imply a survival benefit under early detection once lead time has been accounted for. In this article, we investigate survival benefit implied by prognostic models where the predictor(s) of disease-specific survival are age and/or biomarker level at disease detection. We show that the benefit depends on the rate of biomarker change, the lead time, and the biomarker level at the original date of diagnosis as well as on the parameters of the prognostic model. Even if the prognostic model indicates that lowering the threshold of the biomarker is associated with longer disease-specific survival, this does not necessarily imply that early detection will confer an extension of life expectancy.

SUBMITTER: Inoue LY

PROVIDER: S-EPMC3577108 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Deriving benefit of early detection from biomarker-based prognostic models.

Inoue L Y T LY Gulati R R Yu C C Kattan M W MW Etzioni R R

Biostatistics (Oxford, England) 20120622 1

Many prognostic models for cancer use biomarkers that have utility in early detection. For example, in prostate cancer, models predicting disease-specific survival use serum prostate-specific antigen levels. These models typically show that higher marker levels are associated with poorer prognosis. Consequently, they are often interpreted as indicating that detecting disease at a lower threshold of the biomarker is likely to generate a survival benefit. However, lowering the threshold of the bio ...[more]

PMID: 22730510

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Project description:BackgroundDengue results in a significant public health burden in endemic regions. The World Health Organization (WHO) recommended the use of warning signs (WS) to stratify patients at risk of severe dengue disease in 2009. However, WS is limited in stratifying adult dengue patients at early infection (Day 1-3 post fever), who require close monitoring in hospitals to prevent severe dengue. The aim of this study is to identify and validate prognostic models, built with differentially expressed biomarkers, that enable the early identification of those with early dengue infection that require close clinical monitoring.MethodsRNA microarray and protein assays were performed to identify differentially expressed biomarkers of severity among 92 adult dengue patients recruited at early infection from years 2005-2008. This comprised 47 cases who developed WS after first presentation and required hospitalization (WS+Hosp), as well as 45 controls who did not develop WS after first presentation and did not require hospitalization (Non-WS+Non-Hosp). Independent validation was conducted with 80 adult dengue patients recruited from years 2009-2012. Prognostic models were developed based on forward stepwise and backward elimination estimation, using multiple logistic regressions. Prognostic power was estimated by the area under the receiver operating characteristic curve (AUC).ResultsThe WS+Hosp group had significantly higher viral load (P<0.001), lower platelet (P<0.001) and lymphocytes counts (P = 0.004) at early infection compared to the Non-WS+Non-Hosp group. From the RNA microarray and protein assays, the top single RNA and protein prognostic models at early infection were CCL8 RNA (AUC:0.73) and IP-10 protein (AUC:0.74), respectively. The model with CCL8, VPS13C RNA, uPAR protein, and with CCL8, VPS13C RNA and platelets were the best biomarker models for stratifying adult dengue patients at early infection, with sensitivity and specificity up to 83% and 84%, respectively. These results were tested in the independent validation group, showing sensitivity and specificity up to 96% and 54.6%, respectively.ConclusionsAt early infection, adult dengue patients who later presented WS and require hospitalization have significantly different pathophysiology compared with patients who consistently presented no WS and / or require no hospitalization. The molecular prognostic models developed and validated here based on these pathophysiology differences, could offer earlier and complementary indicators to the clinical WHO 2009 WS guide, in order to triage adult dengue patients at early infection.

Project description:BACKGROUND:Commonly used neuroimaging biomarkers in Parkinson's disease (PD) are useful for diagnosis but poor at predicting outcomes. We explored whether an atrophy pattern from whole-brain structural MRI, measured in the drug-naïve early stage, could predict PD prognosis. METHODS:362 de novo PD patients with T1-weighted MRI (n = 222 for the main analysis, 140 for the validation analysis) were recruited from the Parkinson's Progression Markers Initiative (PPMI). We investigated a previously identified PD-specific network atrophy pattern as a potential biomarker of disease severity and prognosis. Progression trajectories of motor function (MDS-UPDRS-part III), cognition (Montreal Cognitive Assessment (MoCA)), and a global composite outcome measure were compared between atrophy tertiles using mixed effect models. The prognostic value of the MRI atrophy measure was compared with 123I ioflupane single photon emission computed tomography, the postural-instability-gait-disturbance score, and cerebrospinal fluid markers. FINDINGS:After 4.5 years follow-up, PD-specific atrophy network score at baseline significantly predicted change in UPDRS-part III (r = -0.197, p = .003), MoCA (r = 0.253, p = .0002) and global composite outcome (r = -0.249, p = .0002). Compared with the 3rd tertile (i.e. least atrophy), the tertile with the highest baseline atrophy (i.e. the 1st tertile) had a 3-point annual faster progression in UPDRS-part III (p = .012), faster worsening of posture-instability gait scores (+0.21 further annual increase, p < .0001), faster decline in MoCA (-0.74 further annual decline in MoCA, p = .0372) and a + 0.38 (p = .0029) faster annual increase in the global composite z-score. All findings were replicated in a validation analysis using 1.5T MRI. Receiver operating characteristic analysis confirmed the superiority of the MRI biomarker, although it had modest AUC values (0.63). By comparison, the other biomarkers were limited in their ability to predict prognosis either in the main or validation analysis. INTERPRETATION:A PD-specific network atrophy pattern predicts progression of motor, cognitive, and global outcome in PD, and is a better predictor of prognosis than any of the other tested biomarkers. Therefore, it has potential as a prognostic biomarker for clinical trials of early PD.

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Deriving benefit of early detection from biomarker-based prognostic models.

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Deriving benefit of early detection from biomarker-based prognostic models.

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