A patient decision aid for prescribing pain medication: Results from a pilot test in two emergency departments

A Meta-analysis Exploring the Efficacy of Neuropathic Pain Medication for Low Back Pain or Spine-Related Leg Pain: Is Efficacy Dependent on the Presence of Neuropathic Pain?

BACKGROUND AND OBJECTIVE

Highly variable pain mechanisms in people with low back pain or spine-related leg pain might contribute to inefficacy of neuropathic pain medication. This meta-analysis aimed to determine how neuropathic pain is identified in clinical trials for people taking neuropathic pain medication for low back pain or spine-related leg pain and whether subgrouping based on the presence of neuropathic pain influences efficacy.

METHODS

EMBASE, MEDLINE, Cochrane Central, CINAHL [EBSCO], APA PsycINFO, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry were searched from inception to 14 May, 2024. Randomized and crossover trials comparing first-line neuropathic pain medication for people with low back pain or spine-related leg pain to placebo or usual care were included. Two independent authors extracted data. Random-effects meta-analyses of all studies combined, and pre-planned subgroup meta-analyses based on the certainty of neuropathic pain (according to the neuropathic pain Special Interest Group [NeuPSIG] neuropathic pain grading criteria) were completed. Certainty of evidence was judged using the grading of recommendations assessment development and evaluation [GRADE] framework.

RESULTS

Twenty-seven included studies reported on 3619 participants. Overall, 33% of studies were judged unlikely to include people with neuropathic pain, 26% remained unclear. Only 41% identified people with possible, probable, or definite neuropathic pain. For pain, general analyses revealed only small effects at short term (mean difference [MD] - 9.30 [95% confidence interval [CI] - 13.71, - 4.88], I2 = 87%) and medium term (MD - 5.49 [95% CI - 7.24, - 3.74], I2 = 0%). Subgrouping at short term revealed studies including people with definite or probable neuropathic pain showed larger effects on pain (definite; MD - 16.65 [95% CI - 35.95, 2.65], I2 = 84%; probable; MD - 10.45 [95% CI - 14.79, - 6.12], I2 = 20%) than studies including people with possible (MD - 5.50 [95% CI - 20.52, 9.52], I2 = 78%), unlikely (MD - 6.67 [95% CI - 10.58, 2.76], I2 = 0%), or unclear neuropathic pain (MD - 8.93 [95% CI - 20.57, 2.71], I2 = 96%). Similarly, general analyses revealed negligible effects on disability at short term (MD - 3.35 [95% CI - 9.00, 2.29], I2 = 93%) and medium term (MD - 4.06 [95% CI - 5.63, - 2.48], I2 = 0%). Sub-grouping at short term revealed larger effects in studies including people with definite/probable neuropathic pain (MD - 9.25 [95% CI - 12.59, - 5.90], I2 = 2%) compared with those with possible/unclear/unlikely neuropathic pain (MD -1.57 [95% CI - 8.96, 5.82] I2 = 95%). Medium-term outcomes showed a similar trend, but were limited by low numbers of studies. Certainty of evidence was low to very low for all outcomes.

CONCLUSIONS

Most studies using neuropathic pain medication for low back pain or spine-related leg pain fail to adequately consider the presence of neuropathic pain. Meta-analyses suggest neuropathic pain medication may be most effective in people with low back pain or spine-related leg pain with a definite/probable neuropathic pain component. However, the low to very low certainty of evidence and poor identification of neuropathic pain in most studies prevent firm recommendations.

Systematic review of barriers, facilitators, and tools to promote shared decision making in the emergency department

OBJECTIVE

The objective was to systematically review all studies focusing on barriers, facilitators, and tools currently available for shared decision making (SDM) in emergency departments (EDs).

BACKGROUND

Implementing SDM in EDs seems particularly challenging, considering the fast-paced environment and sometimes life-threatening situations. Over 10 years ago, a previous review revealed only a few patient decision aids (PtDAs) available for EDs.

METHODS

Literature searches were conducted in MEDLINE, Embase, and Cochrane library, up to November 2023. Observational and interventional studies were included to address barriers or facilitators for SDM or to investigate effects of PtDAs on the level of SDM for patients visiting an ED.

RESULTS

We screened 1946 studies for eligibility, of which 33 were included. PtDAs studied in EDs address chest pain, syncope, analgesics usage, lumbar puncture, ureterolithiasis, vascular access, concussion/brain bleeding, head-CT choice, coaching for elderly people, and activation of patients with appendicitis. Only the primary outcome was meta-analyzed, showing that PtDAs significantly increased the level of SDM (18.8 on the 100-point OPTION scale; 95% CI 12.5-25.0). PtDAs also tended to increase patient knowledge, decrease decisional conflict and decrease health care services usage, with no obvious effect on overall patient satisfaction. Barriers and facilitators were identified on three levels: (1) patient level-emotions, health literacy, and their own proactivity; (2) clinician level-fear of medicolegal consequences, lack of SDM skills or knowledge, and their ideas about treatment superiority; and (3) system level-time constraints, institutional guidelines, and availability of PtDAs.

CONCLUSIONS

Circumstances in EDs are generally less favorable for SDM. However, PtDAs for conditions seen in EDs are helpful in overcoming barriers to SDM and are welcomed by patients. Even in EDs, SDM is feasible and supported by an increasing number of tools for patients and physicians.

Decision aids for people facing health treatment or screening decisions

BACKGROUND

Patient decision aids are interventions designed to support people making health decisions. At a minimum, patient decision aids make the decision explicit, provide evidence-based information about the options and associated benefits/harms, and help clarify personal values for features of options. This is an update of a Cochrane review that was first published in 2003 and last updated in 2017.

OBJECTIVES

To assess the effects of patient decision aids in adults considering treatment or screening decisions using an integrated knowledge translation approach.

SEARCH METHODS

We conducted the updated search for the period of 2015 (last search date) to March 2022 in CENTRAL, MEDLINE, Embase, PsycINFO, EBSCO, and grey literature. The cumulative search covers database origins to March 2022.

SELECTION CRITERIA

We included published randomized controlled trials comparing patient decision aids to usual care. Usual care was defined as general information, risk assessment, clinical practice guideline summaries for health consumers, placebo intervention (e.g. information on another topic), or no intervention.

DATA COLLECTION AND ANALYSIS

Two authors independently screened citations for inclusion, extracted intervention and outcome data, and assessed risk of bias using the Cochrane risk of bias tool. Primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were attributes related to the choice made (informed values-based choice congruence) and the decision-making process, such as knowledge, accurate risk perceptions, feeling informed, clear values, participation in decision-making, and adverse events. Secondary outcomes were choice, confidence in decision-making, adherence to the chosen option, preference-linked health outcomes, and impact on the healthcare system (e.g. consultation length). We pooled results using mean differences (MDs) and risk ratios (RRs) with 95% confidence intervals (CIs), applying a random-effects model. We conducted a subgroup analysis of 105 studies that were included in the previous review version compared to those published since that update (n = 104 studies). We used Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to assess the certainty of the evidence.

MAIN RESULTS

This update added 104 new studies for a total of 209 studies involving 107,698 participants. The patient decision aids focused on 71 different decisions. The most common decisions were about cardiovascular treatments (n = 22 studies), cancer screening (n = 17 studies colorectal, 15 prostate, 12 breast), cancer treatments (e.g. 15 breast, 11 prostate), mental health treatments (n = 10 studies), and joint replacement surgery (n = 9 studies). When assessing risk of bias in the included studies, we rated two items as mostly unclear (selective reporting: 100 studies; blinding of participants/personnel: 161 studies), due to inadequate reporting. Of the 209 included studies, 34 had at least one item rated as high risk of bias. There was moderate-certainty evidence that patient decision aids probably increase the congruence between informed values and care choices compared to usual care (RR 1.75, 95% CI 1.44 to 2.13; 21 studies, 9377 participants). Regarding attributes related to the decision-making process and compared to usual care, there was high-certainty evidence that patient decision aids result in improved participants' knowledge (MD 11.90/100, 95% CI 10.60 to 13.19; 107 studies, 25,492 participants), accuracy of risk perceptions (RR 1.94, 95% CI 1.61 to 2.34; 25 studies, 7796 participants), and decreased decisional conflict related to feeling uninformed (MD -10.02, 95% CI -12.31 to -7.74; 58 studies, 12,104 participants), indecision about personal values (MD -7.86, 95% CI -9.69 to -6.02; 55 studies, 11,880 participants), and proportion of people who were passive in decision-making (clinician-controlled) (RR 0.72, 95% CI 0.59 to 0.88; 21 studies, 4348 participants). For adverse outcomes, there was high-certainty evidence that there was no difference in decision regret between the patient decision aid and usual care groups (MD -1.23, 95% CI -3.05 to 0.59; 22 studies, 3707 participants). Of note, there was no difference in the length of consultation when patient decision aids were used in preparation for the consultation (MD -2.97 minutes, 95% CI -7.84 to 1.90; 5 studies, 420 participants). When patient decision aids were used during the consultation with the clinician, the length of consultation was 1.5 minutes longer (MD 1.50 minutes, 95% CI 0.79 to 2.20; 8 studies, 2702 participants). We found the same direction of effect when we compared results for patient decision aid studies reported in the previous update compared to studies conducted since 2015.

AUTHORS' CONCLUSIONS

Compared to usual care, across a wide variety of decisions, patient decision aids probably helped more adults reach informed values-congruent choices. They led to large increases in knowledge, accurate risk perceptions, and an active role in decision-making. Our updated review also found that patient decision aids increased patients' feeling informed and clear about their personal values. There was no difference in decision regret between people using decision aids versus those receiving usual care. Further studies are needed to assess the impact of patient decision aids on adherence and downstream effects on cost and resource use.

A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial

BACKGROUND

Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant.

OBJECTIVE

The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior.

METHODS

This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907).

RESULTS

The recruitment process started in May 2023 and is expected to conclude on December 2023.

CONCLUSIONS

The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48852.

Shared Decision-Making in Acute Pain Services

PURPOSE OF REVIEW

The implementation of shared decision-making (SDM) in acute pain services (APS) is still in its infancies especially when compared to other medical fields.

RECENT FINDINGS

Emerging evidence fosters the value of SDM in various acute care settings. We provide an overview of general SDM practices and possible advantages of incorporating such concepts in APS, point out barriers to SDM in this setting, present common patient decisions aids developed for APS and discuss opportunities for further development. Especially in the APS setting, patient-centred care is a key component for optimal patient outcome. SDM could be included into everyday clinical practice by using structured approaches such as the "seek, help, assess, reach, evaluate" (SHARE) approach, the 3 "MAking Good decisions In Collaboration"(MAGIC) questions, the "Benefits, Risks, Alternatives and doing Nothing"(BRAN) tool or the "the multifocal approach to sharing in shared decision-making"(MAPPIN'SDM) as guidance for participatory decision-making. Such tools aid in the development of a patient-clinician relationship beyond discharge after immediate relief of acute pain has been accomplished. Research addressing patient decision aids and their impact on patient-reported outcomes regarding shared decision-making, organizational barriers and new developments such as remote shared decision-making is needed to advance participatory decision-making in acute pain services.

“Usability testing of a new digital integrated health ecosystem PainRELife for the clinical man-agement of chronic pain in early breast cancer patients: Protocol for a Pilot Study” (Preprint)

BACKGROUND

Chronic pain (CP) and its management are critical issues in the care pathway of patients with breast cancer. Considering the complexity of CP experience in cancer, the international scientific community has advocated identifying cutting-edge approaches for CP management. Recent advances in the field of health technology enable the adoption of a novel approach to care management by developing integrated ecosystems and mobile health apps.

OBJECTIVE

The primary end point of this pilot study is to evaluate patients' usability experience at 3 months of a new digital and integrated technological ecosystem, PainRELife, for CP in a sample of patients with breast cancer. The PainRELife ecosystem is composed of 3 main technological assets integrated into a single digital ecosystem: Fast Healthcare Interoperability Resources-based cloud platform (Nu platform) that enables care pathway definition and data collection; a big data infrastructure connected to the Fast Healthcare Interoperability Resources server that analyzes data and implements dynamic dashboards for aggregate data visualization; and an ecosystem of personalized applications for patient-reported outcomes collection, digital delivery of interventions and tailored information, and decision support of patients and caregivers (PainRELife app).

METHODS

This is an observational, prospective pilot study. Twenty patients with early breast cancer and chronic pain will be enrolled at the European Institute of Oncology at the Division of Medical Senology and the Division of Pain Therapy and Palliative Care. Each patient will use the PainRELife mobile app for 3 months, during which data extracted from the questionnaires will be sent to the Nu Platform that health care professionals will manage. This pilot study is nested in a large-scale project named "PainRELife," which aims to develop a cloud technology platform to interoperate with institutional systems and patients' devices to collect integrated health care data. The study received approval from the Ethical Committee of the European Cancer Institute in December 2021 (number R1597/21-IEO 1701).

RESULTS

The recruitment process started in May 2022 and ended in October 2022.

CONCLUSIONS

The new integrated technological ecosystems might be considered an encouraging affordance to enhance a patient-centered approach to managing patients with cancer. This pilot study will inform about which features the health technological ecosystems should have to be used by cancer patients to manage CP.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/41216.

A Multicentered Randomized Controlled Trial Comparing the Effectiveness of Pain Treatment Communication Tools in Emergency Department Patients With Back or Kidney Stone Pain

Objectives. To compare the effectiveness of 3 approaches for communicating opioid risk during an emergency department visit for a common painful condition. Methods. This parallel, multicenter randomized controlled trial was conducted at 6 geographically disparate emergency department sites in the United States. Participants included adult patients between 18 and 70 years of age presenting with kidney stone or musculoskeletal back pain. Participants were randomly assigned to 1 of 3 risk communication strategies: (1) a personalized probabilistic risk visual aid, (2) a visual aid and a video narrative, or 3) general risk information. The primary outcomes were accuracy of risk recall, reported opioid use, and treatment preference at time of discharge. Results. A total of 1301 participants were enrolled between June 2017 and August 2019. There was no difference in risk recall at 14 days between the narrative and probabilistic groups (43.7% vs 38.8%; absolute risk reduction = 4.9%; 95% confidence interval [CI] = -2.98, 12.75). The narrative group had lower rates of preference for opioids at discharge than the general risk information group (25.9% vs 33.0%; difference = 7.1%; 95% CI = 0.64, 0.97). There were no differences in reported opioid use at 14 days between the narrative, probabilistic, and general risk groups (10.5%, 10.3%, and 13.3%, respectively; P = .44). Conclusions. An emergency medicine communication tool incorporating probabilistic risk and patient narratives was more effective than general information in mitigating preferences for opioids in the treatment of pain but was not more effective with respect to opioid use or risk recall. Trial Registration. Clinical Trials.gov identifier: NCT03134092. (Am J Public Health. 2022;112(S1):S45-S55. https://doi.org/10.2105/AJPH.2021.306511).