Moman, Rajat

Background The reported prevalence of chronic pain after spinal cord injury (SCI) varies widely due, in part, to differences in the taxonomy of chronic pain. A widely used classification system is available to describe subcategories of chronic pain in SCI, but the prevalence of chronic pain in SCI based on this system is unknown. Objective The primary objective of this systematic review and meta-analysis is to determine the prevalence of chronic pain after SCI based on the International Spinal Cord Injury Pain (ISCIP) classification system. Evidence review A comprehensive search of databases from January 1980 to August 2019 was conducted. The risk of bias was assessed using a modified tool developed for uncontrolled studies. The Grading of Recommendations, Assessment, Development and Evaluation approach was used to assess certainty in prevalence estimates. Findings A total of 1305 records were screened, and 37 studies met inclusion criteria. The pooled prevalence of overall chronic pain was 68% (95% CI 63% to 73%). The pooled prevalence of neuropathic pain in 13 studies was 58% (95% CI 49% to 68%); the pooled prevalence of musculoskeletal pain in 11 studies was 56% (95% CI 41% to 70%); the pooled prevalence of visceral pain in 8 studies was 20% (95% CI 11% to 29%) and the pooled prevalence of nociceptive pain in 2 studies was 45% (95% CI 13% to 78%). Meta-regression of risk of bias (p=0.20), traumatic versus non-traumatic etiology of injury (p=0.59), and studies where pain was a primary outcome (p=0.32) demonstrated that these factors were not significant moderators of heterogeneity. Certainty in prevalence estimates was judged to be low due to unexplained heterogeneity. Conclusion This systematic review and meta-analysis extends the findings of previous studies by reporting the prevalence of chronic pain after SCI based on the ISCIP classification system, thereby reducing clinical heterogeneity in the reporting of pain prevalence related to SCI.

Abstract Objective Electronic (eHealth) and mobile (mHealth) technologies may be a useful adjunct to clinicians treating patients with chronic pain. The primary aim of this study was to investigate the effects of eHealth and mHealth interventions that do not require clinician contact or feedback on pain-related outcomes recommended by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) guidelines in adults with chronic pain. Methods We searched four databases and included English language randomized controlled trials of ambulatory adults with chronic pain from January, 1 2000, to January 31, 2018, with interventions that are independent of clinician contact or feedback. In the meta-analysis, outcomes were assessed at short- (three months or less), intermediate- (four to six months), and long-term (seven or more months) follow-up. Results Seventeen randomized controlled trials (N = 2,496) were included in the meta-analysis. Both eHealth and mHealth interventions had a significant effect on pain intensity at short- and intermediate-term follow-up. Similarly, a significant but small effect was observed for depression at short- and intermediate-term follow-up and self-efficacy at short-term follow-up. Finally, a significant effect was observed for pain catastrophizing at short-term follow-up. Conclusions eHealth and mHealth interventions had significant effects on multiple short- and intermediate-term outcome measures recommended in the IMMPACT guidelines. Given widespread availability and low cost to patients, clinicians treating patients with chronic pain could consider using eHealth and mHealth interventions as part of a multidisciplinary pain treatment strategy.

AbstractBackgroundThe Choosing Wisely campaign has called for better engagement of palliative and hospice care services for patients in the emergency department (ED). PREDICT is a clinical prediction tool that was derived in an Australian ED cohort. It assesses a patient's risk of mortality at 1 year to select those who would benefit from advanced care planning. Such goals‐of‐care discussion can improve patients’ ability to communicate what they want out of their healthcare and, in cases of end of life, potentially reduce the number of futile interventions. Using a cutoff of 13 points, PREDICT had a reported 95.3% specificity and 53.9% sensitivity for 1‐year mortality. We externally validated PREDICT and derived a simpler modified PREDICT tool to systematically identify high‐risk patients eligible for goals‐of‐care discussions and palliative care consultation in the ED.MethodsThis was an observational cohort study of a random sample of 927 patients aged 55+ seen in the ED in 2014. We identified advance healthcare directives (AHDs) on file. We summarized diagnostic accuracy of the clinical tool to predict 1‐year mortality using sensitivity, specificity, and area under the curve (AUC). We refined PREDICT using multivariable modeling. We followed reporting guidelines including STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) for cohort studies and Standards for Reporting of Diagnostic Accuracy (STARD).ResultsA total of 927 patients were included: 55.0% were male, 63 (7.0%) were nursing home residents, 389 (42.0%) patients had an AHD in their medical record at the time of ED visit, and 245 (26.4%) were deceased at 1 year. Of the 780 patients with PREDICT scores < 13, a total of 164 (21.0%; 95% confidence interval [CI] = 18.3–24.1) were deceased at 1 year, and of the 147 patients with PREDICT scores ≥ 13, a total of 81 (55.1%; 95% CI = 46.7–63.2) were deceased at 1 year. The AUC of the PREDICT score was 0.717 (95% CI = 0.680–0.754), sensitivity was 33.1% (95% CI = 27.3–39.4), and specificity was 90.3% (95% CI = 87.8–92.4) to predict 1‐year mortality. The modified PREDICT tool resulted in an AUC of 0.709 (95% CI = 0.671–0.747). We decided to select this model as the preferred model, as the variable of intensive care unit (ICU) admission with multiorgan failure can be difficult to assess in the ED and may delay advanced care planning. Reweighting the score did not improve fit or the AUC, so points assigned to each variable were not adjusted.ConclusionPREDICT is an easy tool to administer to be able to identify patients who are at high risk of 1‐year mortality and who could benefit from AHDs, goals‐of‐care discussion, and when appropriate in the context of an end‐of‐life setting, palliative medicine consultation. External validation of PREDICT was successful in our population. We simplified PREDICT and derived a new tool, the modified PREDICT minus ICU tool, without significantly altering the sensitivity, specificity, and AUC for death at 1 year. The next steps include external validation of the newly derived rule and prospective implementation.