Disease progress and response to treatment as predictors of survival, disability, cognitive impairment and depression in Parkinson's disease

Abstract

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• The marked between variability in the rate of progression of Parkinson's disease severity assessed with a global functional score (Unified Parkinson' Disease Rating Scale, UPDRS) is recognized but its origin is uncertain and variously attributed to different subtypes of Parkinson's disease, life style, genetic variability and treatment. An increased risk of death in patients treated with selegiline has been reported but this is controversial. WHAT THIS STUDY ADDS• We used a hazard model approach to describe the time to clinical events (death, disability, depression and dementia). The time course of disease status changes was shown to be a key predictor of the risk of these events. Baseline motor subtype was not a predictor of outcome events. Selegiline was associated with an increased risk of death that was independent of its effect on disease status. AIMTo describe the time to clinical events (death, disability, cognitive impairment and depression) in Parkinson's disease using the time course of disease status and treatment as explanatory variables. METHODSDisease status based on the Unified Parkinson's Disease Rating Scale (UPDRS) and the time to clinical outcome events were obtained from 800 patients who initially had early Parkinson's disease. Parametric hazard models were used to describe the time to the events of interest. RESULTSTime course of disease status (severity) was an important predictor of clinical outcome events. There was an increased hazard ratio for death 1.4 (95% CI 1.31, 149), disability 2.75 (95% CI 2.30, 3.28), cognitive impairment 4.35 (95% CI 1.94, 9.74), and depressive state 1.43 (95% CI 1.26, 1.63) with each 10 unit increase of UPDRS. Age at study entry increased the hazard with hazard ratios of 49.1 (95% CI 8.7, 278) for death, 4.76 (95% CI 1.10, 20.6) for disability and 90.0 (95% CI 63.3-128) for cognitive impairment at age 60 years. Selegiline treatment had independent effects as a predictor of death at 8 year follow-up with a hazard ratio of 2.54 (95% CI 1.51, 4.25) but had beneficial effects on disability with a hazard ratio of 0.363 (95% CI 0.132, 0.533) and depression with a hazard ratio of 0.372 (95% CI 0.12, 0.552). CONCLUSIONSOur findings show that the time course of disease status based on UPDRS is a much better predictor of future clinical events than any baseline disease characteristic. Continued selegiline treatment appears to increase the hazard of death.

Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson’s Disease Patients

“…The work was based on an ongoing study which may introduce a bias in the analysis of the data (24). In fact, due to the lack of information we were not able to differentiate the subjects who withdrew from the study, from the censored patients which could introduce a bias in the analysis of the data if the dropout doesn't occur completely at random.…”

Motor subtype in Parkinson's disease: Different disorders or different stages of disease?

“…The baseline UPDRS scores of the DATATOP study of 800 untreated PD patients with a mean duration of disease of 2.1 years was the basis of the initial quantification of T‐D and PIGD‐D motor subtypes . However, when the DATATOP patients were followed for 8 years, there was an attrition of the original T‐D subtype . Furthermore, the total UPDRS score of the PIGD‐D subgroup progressed only 18% faster than did the T‐D subgroup as measured with a nonlinear mixed‐effects model using all of the observations collected during the 8‐year course of the study …”

Application of a hazard-based visual predictive check to evaluate parametric hazard models

“…On the other hand, the time course of drug effect due to fluctuations in concentrations and/or biomarkers influencing the hazard, another growing pharmacometric topic, may not be straightforward to ascertain from differences between log-hazard estimates. Despite the difficulty in evaluating such effects using log-hazard differences, hazard models incorporating time-varying [8]. For example, simulated event times for study subjects using predicted time-varying covariates (e.g., disease progress) can be input into the nonparametric hazard estimator just like when generating KM estimates.…”

“…Therefore, simulation-based graphical methods-i.e., visual predictive checks (VPC) have utility. Currently, VPCs based on nonparametric estimates of the survival function-i.e., the Kaplan-Meier (KM) plots are used widely to evaluate TTE models [8][9][10]. However, even though a survival function is commonly plotted, what is being modeled in the TTE analysis is the hazard function.…”