Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting—Multicentric Survey (IOPS-MS). A correlation analysis was performed to characterize the NP-BTcP profile about its intensity, number of episodes per day, and type. The multiple correspondence analysis (MCA) determined the identification of four groups (phenotypes). A univariate analysis was performed to assess differences between the four phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (phenotype 1) to the worst (phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the phenotype 1 (37.3%)’ vs. the onset > 10 min in phenotype 4 (25.8%) (p < 0.001). Phenotype 1 was characterized by the gastrointestinal type of cancer (26.4%) with respect to phenotype 4, where the most frequent cancer affected the lung (28.8%) (p < 0.001). Phenotype 4 was mainly managed with rapid-onset opioids, while in phenotype 1, many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p = 0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. Although requiring validation, this strategy can provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management.

Multidimensional Statistical Technique for Interpreting the Spontaneous breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting - Multicentric Survey (IOPS-MS) . A correlation analysis was performed to characterize NP-BTcP profile about its intensity, number of episodes per day, and type. The Multidimensional Correspondence Analysis (MCA) determined the identification of 4 groups (Phenotypes). A univariate analysis was performed to assess differences between the 4 Phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (Phenotype 1) to the worst (Phenotype 4). The univariate analysis found a significant association between the onset time &amp;gt;10 min in the Phenotype 1 (37.3%) vs. the onset &le; 10 min in Phenotype 4 (74.2%) (p&amp;lt;0.001). The Phenotype 1 was characterized by gastrointestinal type of cancer (26.4%) respect to Phenotype 4 where the most frequent cancer affected the lung (28.8%) (p&amp;lt;0.001). Phenotype 4 was mainly managed with rapid onset opioids, while in Phenotype 1 many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p=0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. This strategy can provide many indications for identifying the diagnostic and therapeutic gaps on NP-BTcP management.

Characterizing Breakthrough Cancer Pain Using Ecological Momentary Assessment with a Smartphone App: Feasibility and Clinical Findings

Background: mobile applications (apps) facilitate cancer pain ecological momentary assessment (EMA) and provide more reliable data than retrospective monitoring. The aims of this study are (a) to describe the status of persons with cancer pain when assessed ecologically, (b) to analyze the utility of clinical alarms integrated into the app, and (c) to test the feasibility of implementing an app for daily oncological pain monitoring. Methods: in this feasibility study, 21 patients (mean age = 56.95 years, SD = 10.53, 81.0% men) responded to an app-based evaluation of physical status (baseline and breakthrough cancer pain (BTcP)) and mental health variables (fatigue, mood, and coping) daily during 30 days. Results: cancer pain characterization with the app was similar to data from the literature using retrospective assessments in terms of BTcP duration and perceived medication effectiveness. However, BTcP was less frequent when evaluated ecologically. Pain, fatigue, and mood were comparable in the morning and evening. Passive coping strategies were the most employed daily. Clinical alarms appear to be useful to detect and address adverse events. App implementation was feasible and acceptable. Conclusion: apps reduce recall bias and facilitate a rapid response to adverse events in oncological care. Future efforts should be addressed to integrate EMA and ecological momentary interventions to facilitate pain self-management via apps.

Prediction of the breakthrough cancer pain using deep learning model.

e24085 Background: Breakthrough cancer pain (BTcP), a transitory flare of pain that occurs on a background of relatively well-controlled baseline pain, is a challenging clinical problem in managing cancer pain. We hypothesized that the BTcP could be predictable according to the patients’ previous observed patterns. In this study, we report on the development of a deep learning model that predicts hourly individual-level breakthrough pain for patients with cancer. Methods: We defined the BTcP as the pain with numerical rating scale (NRS) score 4 or above and developed models predicting the onset time of BTcP with the temporal resolution of 1 hour. The datasets which have more than 20 records of NRS score during hospitalization were included in our study. All the pain records were obtained from patients hospitalized on the wards of hematology-oncology in Samsung Medical Center between July 2016 to February 2020. The model used the time windows of 3 days to predict NRS scores over the next 24 hours. To capture irregular pain patterns, we created the sequence of average pain patterns over 24 hours from the previous 3 days and used it for normalization. We trained a Bi-directional long-short term memory (LSTM) based deep learning model. The model was validated using the holdout method with 20% of the datasets. Its performance was assessed with the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AURPC). Results: We included pain log data containing 2,905 admissions from 2,176 patients with solid cancer and 1,755 admissions from 1,082 patients with hematologic cancer in the analysis. The median age was 57 (interquartile range (IQR), 47-64), the most frequent type of cancer was lung cancer (18.0%), and most patients had stage 4 (60.7%). Among the 103,948 hours from patients in whole datasets, 1,091 (4.7%) hours were labeled as the period of BTcP. The patients have the records of NRS score with a median of 3 (IQR, 2.0-4.5) and BTcP with a median of 1.1 (IQR, 0.5-2.0) per day. We allocated approximately 20% of patients (653 patients with 932 admissions) to the holdout test dataset. Our model showed the AUROC 0.719 and AUPRC 0.680 for predicting the BTcP in the test dataset. Conclusions: Our study showed that cancer pain could be predictive by using a deep learning model. Though our exploratory study has a limitation of generalizability, future warranted subgroup analysis and verification research could make our model more applicable in a real-world setting.

The Prevalence and Characteristics of Breakthrough Cancer Pain in Patients Receiving Low Doses of Opioids for Background Pain

The aim of this study was to assess the prevalence and characteristics of breakthrough cancer pain (BTcP) in patients receiving low doses of opioids for background pain. A consecutive sample of advanced cancer patients receiving less than 60 mg/day of oral morphine equivalent (OME) was selected. Epidemiological data, background pain intensity, and current analgesic therapy were recorded. The presence of BTcP was diagnosed according to a standard algorithm. The number of BTcP episodes, intensity of BTcP, its predictability and triggers, onset duration, interference with daily activities, BTcP medications, satisfaction with BTcP medication, and time to meaningful pain relief were collected. A total of 126 patients were screened. The mean intensity of background pain was 2.71 (1.57), and the mean OME was 28.5 mg/day (SD15.8). BTP episodes were recorded in 88 patients (69.8%). The mean number/day of BTP episodes was 4.1 (SD 7.1, range 1–30). In a significant percentage of patients, BTcP was both predictable and unpredictable (23%). The BTcP onset was less than 20 min in the majority of patients. The mean duration of untreated episodes was 47.5 (SD 47.6) minutes. The mean time to meaningful pain relief after taking a BTcP medication was >20 min in 44.5% of patients. The efficacy of BTcP medication was not considered good in more than 63% of patients. Gender (females) (OR = 4.16) and lower Karnofsky (OR = 0.92) were independently associated with BTcP. A higher number of BTcP episodes/day was associated with gender (females) (p = 0.036), short duration of BTcP (p = 0.005), poorer efficacy of BTcP medication (none or mild) (p = 0.001), and late meaningful pain relief (p = 0.024). The poor efficacy of BTcP medication was independently associated with a higher number of episodes/day (OR = 0.22). In patients who were receiving low doses of opioids, BTcP prevalence was 69.8%. Many patients did not achieve a sufficient level of satisfaction with BTcP medications, particularly with oral morphine. Data also suggest that better optimization of background analgesia, though apparently acceptable, may limit the number of BTcP episodes.

Impact of individualized management of breakthrough cancer pain on quality of life in advanced cancer patients: CAVIDIOPAL study

Purpose The main aim of the study was to assess the impact of individualized management of breakthrough cancer pain (BTcP) on quality of life (QoL) of patients with advanced cancer in clinical practice. Methods A prospective, observational, multicenter study was conducted in patients with advanced cancer that were assisted by palliative care units. QoL was assessed with the EORTC QLQ-C30 questionnaire at baseline (V0) and after 28 days (V28) of individualized BTcP therapy. Data on background pain, BTcP, comorbidities, and frailty were also recorded. Results Ninety-three patients completed the study. Intensity, duration, and number of BTcP episodes were reduced (p < 0.001) at V28 with individualized therapy. Transmucosal fentanyl was used in 93.8% of patients, mainly by sublingual route. Fentanyl titration was initiated at low doses (78.3% of patients received doses of 67 μg, 100 μg, or 133 μg) according to physician evaluation. At V28, mean perception of global health status had increased from 31.1 to 53.1 (p < 0.001). All scales of EORTC QLQ-C30 significantly improved (p < 0.001) except physical functioning, diarrhea, and financial difficulties. Pain scale improved from 73.6 ± 22.6 to 35.7 ± 22.3 (p < 0.001). Moreover, 85.9% of patients reported pain improvement. Probability of no ≥ 25% improvement in QoL was significantly higher in patients ≥ 65 years old (OR 1.39; 95% CI 1.001–1.079) and patients hospitalized at baseline (OR 4.126; 95% CI 1.227–13.873). Conclusion Individualized BTcP therapy improved QoL of patients with advanced cancer. Transmucosal fentanyl at low doses was the most used drug. Trial registration This study was registered at ClinicalTrials.gov database (NCT02840500) on July 19, 2016.