A Two-Step, Trajectory-Focused, Analytics Approach to Attempt Prediction of Analgesic Response in Patients with Moderate-to-Severe Osteoarthritis

INTRODUCTION

We sought to predict analgesic response to daily oral nonsteroidal anti-inflammatory drugs (NSAIDs) or subcutaneous tanezumab 2.5 mg (every 8 weeks) at week 16 in patients with moderate-to-severe osteoarthritis, based on initial treatment response over 8 weeks.

METHODS

Data were derived from three randomized controlled trials of osteoarthritis. A two-step, trajectory-focused, analytics approach was used to predict patients as responders or non-responders at week 16. Step 1 identified patients using a data-element combination method (based on pain score at baseline, pain score at week 8, pain score monotonicity at week 8, pain score path length at week 8, and body site [knee or hip]). Patients who could not be identified in step 1 were predicted in step 2 using a k-nearest neighbor method based on pain score and pain response level at week 8.

RESULTS

Our approach predicted response with high accuracy in NSAID-treated (83.2-90.2%, n = 931) and tanezumab-treated (84.6-91.0%, n = 1430) patients regardless of the efficacy measure used to assess pain, or the threshold used to define response (20%, 30%, or 50% improvement from baseline). Accuracy remained high using 50% or 20% response thresholds, with 50% and 20% yielding generally slightly better negative and positive predictive value, respectively, relative to 30%. Accuracy was slightly better in patients aged ≥ 65 years relative to younger patients across most efficacy measure/response threshold combinations.

CONCLUSIONS

Analyzing initial 8-week analgesic responses using a two-step, trajectory-based approach can predict future response in patients with moderate-to-severe osteoarthritis treated with NSAIDs or 2.5 mg tanezumab. These findings demonstrate that prediction of treatment response based on a single dose of a novel therapeutic is possible and that predicting future outcomes based on initial response offers a way to potentially advance the approach to clinical management of patients with osteoarthritis.

CLINICALTRIALS

GOV IDENTIFIERS

NCT02528188, NCT02709486, NCT02697773.

Bootstrapping search margin-based nearest neighbor method for qualitative spectroscopic analysis

Qualitative spectroscopic analysis depends in one way or another on comparing spectra of the specimens to be identified with spectra of "known" or "standard" samples. The k-nearest neighbor (k-NN) method is one of the oldest and simplest techniques for performing such comparisons. In this study, we present a new k-NN algorithm for qualitative spectroscopic analysis, which we refer to as the bootstrapping search margin-based nearest neighbor (BSMNN) method. This method consists of two phases. In the first phase, we attempt to find a feature space in which samples with different labels produce large margins, such that the classification has high confidence, by maximizing a margin-based objective function using a weighted bootstrap sampling search strategy. In the subsequent classification phase, we compute local Euclidean distances between different samples under the feature space. A new instance x is classified by the label of its nearest neighbor. Six widely used vibrational spectroscopic data sets were used to validate the performance of the BSMNN method. The results showed that, despite its simplicity, BSMNN yields better results compared with commonly used k-NN algorithms including Relief, neighborhood components analysis, neighborhood component feature selection, and large margin nearest neighbor. Furthermore, BSMNN can be used to identify important spectral regions. It is worth mentioning that the margin-based objective function used in BSMNN is proposed for the first time for measuring the quality of features. Although in this study the margin-based objective function is focused on k-NN classification, it also can be used for other distance-based classifiers, such as soft independent modeling of class analogies and least squares support vector machine.