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Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

ABSTRACT: Prior work applied hierarchical clustering, coarsened exact matching (CEM), time series regressions with lagged variables as inputs, and microsimulation to data from three randomized clinical trials (RCTs) and a large German observational study (OS) to predict pregabalin pain reduction outcomes for patients with painful diabetic peripheral neuropathy. Here, data were added from six RCTs to reduce covariate bias of the same OS and improve accuracy and/or increase the variety of patients for pain response prediction. Using hierarchical cluster analysis and CEM, a matched dataset was created from the OS (N = 2642) and nine total RCTs (N = 1320). Using a maximum likelihood method, we estimated weekly pain scores for pregabalin-treated patients for each cluster (matched dataset); the models were validated with RCT data that did not match with OS data. We predicted novel 'virtual' patient pain scores over time using simulations including instance-based machine learning techniques to assign novel patients to a cluster, then applying cluster-specific regressions to predict pain response trajectories. Six clusters were identified according to baseline variables (gender, age, insulin use, body mass index, depression history, pregabalin monotherapy, prior gabapentin, pain score, and pain-related sleep interference score). CEM yielded 1766 patients (matched dataset) having lower covariate imbalances. Regression models for pain performed well (adjusted R-squared 0.90-0.93; root mean square errors 0.41-0.48). Simulations showed positive predictive values for achieving >50% and >30% change-from-baseline pain score improvements (range 68.6-83.8% and 86.5-93.9%, respectively). Using more RCTs (nine vs. the earlier three) enabled matching of 46.7% more patients in the OS dataset, with substantially reduced global imbalance vs. not matching. This larger RCT pool covered 66.8% of possible patient characteristic combinations (vs. 25.0% with three original RCTs) and made prediction possible for a broader spectrum of patients. Trial Registration: www.clinicaltrials.gov (as applicable): NCT00156078, NCT00159679, NCT00143156, NCT00553475.

SUBMITTER: Alexander J

PROVIDER: S-EPMC6283469 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

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

PloS one 20181206 12

Prior work applied hierarchical clustering, coarsened exact matching (CEM), time series regressions with lagged variables as inputs, and microsimulation to data from three randomized clinical trials (RCTs) and a large German observational study (OS) to predict pregabalin pain reduction outcomes for patients with painful diabetic peripheral neuropathy. Here, data were added from six RCTs to reduce covariate bias of the same OS and improve accuracy and/or increase the variety of patients for pain ...[more]

PMID: 30521533

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Project description:BackgroundDuloxetine hydrochloride is a reuptake inhibitor of 5-hydroxytryptamine and norepinephrine used to treat depression, generalized anxiety disorder, neuropathic pain, and stress incontinence in women. We investigated the efficacy of duloxetine in painful diabetic neuropathy and fibromyalgia to allow comparison with other antidepressants.MethodsWe searched PubMed, EMBASE (via Ovid), and Cochrane CENTRAL up to June 2008 for randomised controlled trials using duloxetine to treat neuropathic pain.ResultsWe identified six trials with 1,696 patients: 1,510 were treated with duloxetine and 706 with placebo. All patients had established baseline pain of at least moderate severity. Trial duration was 12 to 13 weeks. Three trials enrolled patients with painful diabetic neuropathy (PDN) and three enrolled patients with fibromyalgia. The number needed to treat (NNT) for at least 50% pain relief at 12 to 13 weeks with duloxetine 60 mg versus placebo (1,211 patients in the total comparison) was 5.8 (95% CI 4.5 to 8.4), and for duloxetine 120 mg (1,410 patients) was 5.7 (4.5 to 5.7). There was no difference in NNTs between PDN and fibromyalgia. With all doses of duloxetine combined (20/60/120 mg) there were fewer withdrawals for lack of efficacy than with placebo (number needed to treat to prevent one withdrawal 20 (13 to 42)), but more withdrawals due to adverse events (number needed to harm (NNH) 15 (11 to 25)). Nausea, somnolence, constipation, and reduced appetite were all more common with duloxetine than placebo (NNH values 6.3, 11, 11, and 18 respectively). The results for duloxetine are compared with published data for other antidepressants in neuropathic pain.ConclusionDuloxetine is equally effective for the treatment of PDN and fibromyalgia, judged by the outcome of at least 50% pain relief over 12 weeks, and is well tolerated. The NNT of 6 for 50% pain relief suggests that this is likely to be a useful drug in these difficult-to-treat conditions, where typically only a minority of patients respond. Comparing duloxetine with antidepressants for pain relief in DPN shows inadequacies in the evidence for efficacy of antidepressants, which are currently recommended in PDN care pathways.

Project description:Background Diabetic peripheral neuropathy is a common long-term complication of diabetes. Accumulating evidence suggests that vascular impairment plays important roles in the pathogenesis of diabetic peripheral neuropathy, while the mechanism remains unclear. We recently reported that transient receptor potential ankyrin 1 (TRPA1) is sensitized by hypoxia, which can contribute to cold hypersensitivity. In this study, we investigated the involvement of TRPA1 and vascular impairment in painful diabetic peripheral neuropathy using streptozotocin-induced diabetic model mice. Results Streptozotocin-induced diabetic model mice showed mechanical and cold hypersensitivity with a peak at two weeks after the streptozotocin administration, which were likely to be paralleled with the decrease in the skin blood flow of the hindpaw. Streptozotocin-induced cold hypersensitivity was significantly inhibited by an antagonist HC-030031 (100 mg/kg) or deficiency for TRPA1, whereas mechanical hypersensitivity was unaltered. Consistent with these results, the nocifensive behaviors evoked by an intraplantar injection of the TRPA1 agonist allyl isothiocyanate (AITC) were enhanced two weeks after the streptozotocin administration. Both streptozotocin-induced cold hypersensitivity and the enhanced AITC-evoked nocifensive behaviors were significantly inhibited by a vasodilator, tadalafil (10 mg/kg), with recovery of the decreased skin blood flow. Similarly, in a mouse model of hindlimb ischemia induced by the ligation of the external iliac artery, AITC-evoked nocifensive behaviors were significantly enhanced three and seven days after the ischemic operation, whereas mechanical hypersensitivity was unaltered in TRPA1-knockout mice. However, no difference was observed between wild-type and TRPA1-knockout mice in the hyposensitivity for current or mechanical stimulation or the deceased density of intraepidermal nerve fibers eight weeks after the streptozotocin administration. Conclusion These results suggest that TRPA1 sensitization during diabetic vascular impairment causes cold, but not mechanical, hypersensitivity in the early painful phase of diabetic peripheral neuropathy. However, TRPA1 may play little or no role in the progression of diabetic peripheral neuropathy.

Project description:There are no data on physician-patient communication in painful diabetic peripheral neuropathy (pDPN) in the Asia-Pacific region. The objective of this study was to examine patient and physician perceptions of pDPN and clinical practice behaviors in five countries in South-East Asia. Primary care physicians and practitioners, endocrinologists, diabetologists, and patients with pDPN completed separate surveys on pDPN diagnosis, impact, management, and physician-patient interactions in Hong Kong, Malaysia, the Philippines, Taiwan, and Thailand. Data were obtained from 100 physicians and 100 patients in each country. The majority of physicians (range across countries, 30-85%) were primary care physicians and practitioners. Patients were mostly aged 18-55 years and had been diagnosed with diabetes for >5 years. Physicians believed pDPN had a greater impact on quality of life than did patients (ranges 83-92% and 39-72%, respectively), but patients believed pDPN had a greater impact on items such as sleep, anxiety, depression, and work than physicians. Physicians considered the diagnosis and treatment of pDPN a low priority, which may be reflected in the generally low incidence of screening (range 12-65%) and a lack of awareness of pDPN. Barriers to treatment included patients' lack of awareness of pDPN. Both physicians and patients agreed that pain scales and local language descriptions were the most useful tools in helping to describe patients' pain. Most patients were monitored upon diagnosis of pDPN (range 55-97%), but patients reported a shorter duration of monitoring compared with physicians. Both physicians and patients agreed that it was patients who initiated conversations on pDPN. Physicians most commonly referred to guidelines from the American Diabetes Association or local guidelines for the management of pDPN. This study highlights important differences between physician and patient perceptions of pDPN, which may impact on its diagnosis and treatment. For a chronic and debilitating complication like pDPN, the physician-patient dialogue is central to maximizing patient outcomes. Strategies, including education of both groups, need to be developed to improve communication.FundingPfizer.

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Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

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Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

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