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Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.

ABSTRACT: Purpose:Variability in patient treatment responses can be a barrier to effective care. Utilization of available patient databases may improve the prediction of treatment responses. We evaluated machine learning methods to predict novel, individual patient responses to pregabalin for painful diabetic peripheral neuropathy, utilizing an agent-based modeling and simulation platform that integrates real-world observational study (OS) data and randomized clinical trial (RCT) data. Patients and methods:The best supervised machine learning methods were selected (through literature review) and combined in a novel way for aligning patients with relevant subgroups that best enable prediction of pregabalin responses. Data were derived from a German OS of pregabalin (N=2642) and nine international RCTs (N=1320). Coarsened exact matching of OS and RCT patients was used and a hierarchical cluster analysis was implemented. We tested which machine learning methods would best align candidate patients with specific clusters that predict their pain scores over time. Cluster alignments would trigger assignments of cluster-specific time-series regressions with lagged variables as inputs in order to simulate "virtual" patients and generate 1000 trajectory variations for given novel patients. Results:Instance-based machine learning methods (k-nearest neighbor, supervised fuzzy c-means) were selected for quantitative analyses. Each method alone correctly classified 56.7% and 39.1% of patients, respectively. An "ensemble method" (combining both methods) correctly classified 98.4% and 95.9% of patients in the training and testing datasets, respectively. Conclusion:An ensemble combination of two instance-based machine learning techniques best accommodated different data types (dichotomous, categorical, continuous) and performed better than either technique alone in assigning novel patients to subgroups for predicting treatment outcomes using microsimulation. Assignment of novel patients to a cluster of similar patients has the potential to improve prediction of patient outcomes for chronic conditions in which initial treatment response can be incorporated using microsimulation. Clinical trial registries:www.clinicaltrials.gov: NCT00156078, NCT00159679, NCT00143156, NCT00553475.

SUBMITTER: Alexander J

PROVIDER: S-EPMC6827520 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.

Alexander Joe J Edwards Roger A RA Manca Luigi L Grugni Roberto R Bonfanti Gianluca G Emir Birol B Whalen Ed E Watt Steve S Brodsky Marina M Parsons Bruce B

Pragmatic and observational research 20191031

<h4>Purpose</h4>Variability in patient treatment responses can be a barrier to effective care. Utilization of available patient databases may improve the prediction of treatment responses. We evaluated machine learning methods to predict novel, individual patient responses to pregabalin for painful diabetic peripheral neuropathy, utilizing an agent-based modeling and simulation platform that integrates real-world observational study (OS) data and randomized clinical trial (RCT) data.<h4>Patients ...[more]

PMID: 31802967

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Project description:BackgroundMore patient-specific medical care is expected as more is learned about variations in patient responses to medical treatments. Analytical tools enable insights by linking treatment responses from different types of studies, such as randomized controlled trials (RCTs) and observational studies. Given the importance of evidence from both types of studies, our goal was to integrate these types of data into a single predictive platform to help predict response to pregabalin in individual patients with painful diabetic peripheral neuropathy (pDPN).MethodsWe utilized three pivotal RCTs of pregabalin (398 North American patients) and the largest observational study of pregabalin (3159 German patients). We implemented a hierarchical cluster analysis to identify patient clusters in the Observational Study to which RCT patients could be matched using the coarsened exact matching (CEM) technique, thereby creating a matched dataset. We then developed autoregressive moving average models (ARMAXs) to estimate weekly pain scores for pregabalin-treated patients in each cluster in the matched dataset using the maximum likelihood method. Finally, we validated ARMAX models using Observational Study patients who had not matched with RCT patients, using t tests between observed and predicted pain scores.ResultsCluster analysis yielded six clusters (287-777 patients each) with the following clustering variables: gender, age, pDPN duration, body mass index, depression history, pregabalin monotherapy, prior gabapentin use, baseline pain score, and baseline sleep interference. CEM yielded 1528 unique patients in the matched dataset. The reduction in global imbalance scores for the clusters after adding the RCT patients (ranging from 6 to 63% depending on the cluster) demonstrated that the process reduced the bias of covariates in five of the six clusters. ARMAX models of pain score performed well (R 2 : 0.85-0.91; root mean square errors: 0.53-0.57). t tests did not show differences between observed and predicted pain scores in the 1955 patients who had not matched with RCT patients.ConclusionThe combination of cluster analyses, CEM, and ARMAX modeling enabled strong predictive capabilities with respect to pain scores. Integrating RCT and Observational Study data using CEM enabled effective use of Observational Study data to predict patient responses.

Project description:IntroductionWith the development of new neuroimaging tools it has become possible to assess neurochemical alterations in patients experiencing chronic pain and to determine how these factors change during pharmacological treatment. The goal of this study was to examine the exact neurochemical mechanism underlying pregabalin treatment, utilizing magnetic resonance spectroscopy (1H-MRS), in a population of patients with painful diabetic polyneuropathy (PDN), with the overall aim to ultimately objectify the clinical effect of pregabalin.MethodsA double blind, randomized, placebo-controlled study was conducted. A total of 27 patients with PDN were enrolled in the study, of whom 13 received placebo treatment (control group) and 14 received pregabalin (intervention group). Pregabalin treatment consisted of stepwise dose escalation over the study period from 75 mg daily ultimately to 600 mg daily. 1H-MRS was performed at 3T on four regions of interest in the brain: the rostral anterior cingulate cortex (rACC), left and right thalamus and prefrontal cortex. The absolute concentrations of N-acetyl aspartate, glutamate, glutamine, gamma-amino-butyric-acid (GABA), glucose (Glc) and myo-inositol (mINS) were determined using LCModel.ResultsThe concentration of most neurometabolites in the placebo and pregabalin group did not significantly differ over time, with only a small significant difference in Glc level in the left thalamus (p = 0.049). Comparison of the effects of the different doses revealed significant differences for mINS in the rACC (baseline 2.42 ± 1.21 vs. 450 mg 1.58 ± 0.94; p = 0.022) and dorsolateral prefrontal cortex (75 mg 2.38 ± 0.89 vs. 450 mg 1.59 ± 0.85; p = 0.042) and also for GABA in the rACC (75 mg 0.53 ± 0.51 vs. 225 mg 0.28 ± 0.19; p = 0.014).ConclusionNo differences were found in metabolite concentrations between the placebo (control) and intervention groups, but some differences, although small, were found between the different doses.Trial registrationThis study is registered at ClinicalTrials.gov (NCT01180608).FundingLyrica Independent Investigator Research Award (LIIRA) 2010 (Pfizer) funded the study.

Project description:IntroductionPrediction of final clinical outcomes based on early weeks of treatment can enable more effective patient care for chronic pain. Our goal was to predict, with at least 90% accuracy, 12- to 13-week outcomes for pregabalin-treated painful diabetic peripheral neuropathy (pDPN) patients based on 4 weeks of pain and pain-related sleep interference data.MethodsWe utilized active treatment data from six placebo-controlled randomized controlled trials (n = 939) designed to evaluate efficacy of pregabalin for reducing pain in patients with pDPN. We implemented a three-step, trajectory-focused analytics approach based upon patient responses collected during the first 4 weeks using monotonicity, path length, frequency domain (FD), and k-nearest neighbor (kNN) methods. The first two steps were based on combinations of baseline pain, pain at 4 weeks, weekly monotonicity and path length during the first 4 weeks, and assignment of patients to one of four responder groups (based on presence/absence of 50% or 30% reduction from baseline pain at 4 and at 12/13 weeks). The third step included agreement between prediction of logistic regression of daily FD amplitudes and assignment made from kNN analyses.ResultsStep 1 correctly assigned 520/939 patients from the six studies to a responder group using a 3-metric combination approach based on unique assignment to a 50% responder group. Step 2 (applied to the remaining 419 patients) predicted an additional 121 patients, using a blend of 50% and 30% responder thresholds. Step 3 (using a combination of FD and kNN analyses) predicted 204 of the remaining 298 patients using the 50% responder threshold. Our approach correctly predicted 90.0% of all patients.ConclusionBy correctly predicting 12- to 13-week responder outcomes with 90% accuracy based on responses from the first month of treatment, we demonstrated the value of trajectory measures in predicting pDPN patient response to pregabalin.Trial registrationwww.clinicaltrials.gov identifiers, NCT00156078/NCT00159679/NCT00143156/NCT00553475.FundingPfizer. Plain language summary available for this article.

Dataset Information

Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.

Publications

Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.

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