Cancer-Related Pain: A Longitudinal Study of Time to Stable Pain Control and Its Clinicodemographic Predictors

Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort

Background

Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials.

Materials and Methods

A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques.

Results

For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models.

Conclusion

These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.

Symptom Clusters in Patients With Advanced Cancer: A Prospective Longitudinal Cohort Study to Examine Their Stability and Prognostic Significance

This study's purpose was to assess symptom cluster (SC) stability during disease progression and determine their strength of association with survival in patients with advanced cancer . Consecutively eligible patients with advanced cancer not receiving cancer-specific treatment and referred to a Tertiary Palliative Care Clinic were enrolled in a prospective cohort study. At first consultation (D0) and in subsequent consultations at day 15 (D15) and day 30 (D30), patients rated 9 symptoms through the Edmonton Symptom Assessment System scale (0-10) and 10 others using a Likert scale (1-5). Principal components factor analysis with varimax rotation was used to determine SCs at each consultation. Of 318 patients with advanced cancer, 301 met eligibility criteria with a median age of 69 years (range 37-94). Three SCs were identified: neuro-psycho-metabolic (NPM), gastrointestinal, and sleep impairment, with some variations in their constitution over time. Exploratory factor analysis accounted for 40% of variance of observed variables in all SCs. Shorter median survival was observed continuously for NPM cluster (D0 23 vs. 58 days, P < .001; D15 41 vs. 104 days, P=.004; D30 46 vs. 114 days, P = .002), although the presence of 2 or more SCs on D0 and D15 also had prognostic significance (D0: 21 vs. 45 days, P = .005; D30: 50 vs. 96 days, P = .040). In a multivariable model, NPM cluster (D0 hazard ratio estimate: HR 1.64; 95%CI, 1.17-2.31; P = .005; D15 HR: 2.51; 95%CI, 1.25-5.05; P = .009; D30 HR: 3.9; 95%CI, 1.54-9.86; P = .004) and hospitalization (D0 HR: 2.27; 95%CI, 1.47-3.51; P < .001; D15 HR: 2.43; 95%CI, 1.18-5.01; P = .016; D30 HR: 3.41; 95%CI, 1.35-8.62; P = .009) were independently and significantly associated with worse survival. Three clinically relevant SCs were identified, and their constitution had small variations, maintaining a stable set of nuclear symptoms through disease progression. Presence of the NPM cluster and hospitalization maintained their prognostic value over time.

Cancer Pain Management in Patients Receiving Inpatient Specialized Palliative Care Services

CONTEXT

Cancer pain is a common complication that is frequently undertreated in patients with cancer.

OBJECTIVES

This study is aimed at assessing the time needed to achieve cancer pain management goals through specialized palliative care (SPC).

METHODS

This was a multicenter, prospective, longitudinal study of inpatients with cancer pain who received SPC. Patients were continuously followed up until they considered cancer pain management successful, and we estimated this duration using the Kaplan-Meier method. We investigated the effectiveness of pain management using multiple patient-reported outcomes (PROs) and quantitative measures, including pain intensity change in the Brief Pain Inventory. A paired-sample t-test was used to compare the pain intensity at the beginning and end of the observation period.

RESULTS

Cancer pain management based on the PROs was achieved in 87.9% (385/438) of all cases. In 94.5% (364/385) of these cases, cancer pain management was achieved within 1 week, and the median time to pain management was 3 days (95% confidence interval [CI], 2-3). The mean worst pain intensity in the last 24 h at the start and end of observation were 6.9 ± 2.2 and 4.0 ± 2.3, respectively, with a difference of -2.9 (95% CI, -3.2 to -2.6; p < 0.01). Overall, 81.6% of the patients reported satisfaction with cancer pain management, and 62 adverse events occurred.

CONCLUSION

SPC achieved cancer pain management over a short period with a high level of patient satisfaction resulting in significant pain reduction and few documented adverse events.

Pain experience of cancer patients receiving care in a multidisciplinary pain management clinic

BACKGROUND

Few longitudinal studies have focused on patients' cancer pain experience when receiving care in a multidisciplinary pain management clinic (MPMC). This study aimed to evaluate the experiences of a cohort of cancer patients newly engaged in a MPMC.

METHODS

This study was based on a longitudinal approach in which data were collected over a 6 months period at the King Hussein Cancer Centre in Jordan. The study adopted the Arabic version of the Brief Pain Inventory to identify the level and prevalence of cancer pain, and to identify the impact of receiving care at the MPMC on patients' pain experience. Data were collected over four time points, and the period between these points ranged from 2 to 3 weeks.

RESULTS

The majority of patients demonstrated improvement in their pain after receiving treatment at the MPMC, while a third still experienced severe pain. Significant improvement was reported at T1, and no further decline in pain was noted after this point. This indicates that exposure to the intervention provided by the MPMC generated, on average, an improvement in patients' pain experience.

CONCLUSION

The MPMC may be an effective pain management strategy in the treatment of cancer pain.

Cancer pain: Results of a prospective study on prognostic indicators of pain intensity including pain syndromes assessment

BACKGROUND

Pain is a prevalent symptom in patients with advanced cancer. Recognition of prognostic factors associated with pain intensity, could help provide better assessment, leading to better pain management.

AIM

identifying prognostic factors which could guide improvements on cancer pain classification.

DESIGN

a prospective observational study on chronic cancer pain, exploring the association between average mean pain intensity during a 28 days study follow-up and patients' clinical and pain-related characteristics, including pain syndromes. To evaluate these associations, a mixed model was built.

SETTING/PARTICIPANTS

Patients attending a Palliative Care and Pain Outpatient Clinic from May 2015 to June 2019 were screened. Patients with moderate to severe cancer pain who were already receiving or needed treatment with third step WHO ladder opioids were enrolled in the study. Data from 342 patients with at least one follow-up visit were analyzed.

RESULTS

Pain intensity decreased significantly for all patients during time (p < 0.001). Age, sex, emotional distress, pain duration and neuropathic pain presence evaluated by the Douleur Neuropathique 4 Questions (DN4) questionnaire were not significantly associated to pain intensity. Breakthrough/episodic pain was associated with higher pain intensity during follow-up (p < 0.001). The diagnosis of pain syndrome was overall significantly associated with mean pain intensity during follow-up (p = 0.016). Particularly, the concurrent presence of visceral and soft (p = 0.026) or soft and nervous tissue pain (p = 0.043) were significantly related to worse outcome, whereas pain due to only soft tissue damage with better outcome (p = 0.032).

CONCLUSIONS

The recognition of specific pain syndromes may help to better classify cancer pain.

Breakthrough cancer pain in 2020

PURPOSE OF REVIEW

An overview on breakthrough cancer pain (BTCP), including inherent limitations of the terminology, assessment, clinical presentation, and treatment options.

RECENT FINDINGS

The estimated prevalence of BTCP is dependent on the defined cutoffs for controlled background pain and the magnitude of the pain flare. In addition, pain flares outside the definition of BTCP are prevalent. In the 11th Revision of the International Classification of Diseases, the temporal characteristics of cancer pain are described as continuous background pain and intermittent episodic pain. BTCP should be assessed by validated methods, and the patient perspective should be included. The pain may be related to neoplastic destruction of bone, viscera, or nerve tissue and is characterized by rapid onset, high intensity, and short duration. Treatment directed towards painful metastases must be considered. Due to pharmacological properties mirroring the pain characteristics, transmucosal fentanyl formulations are important for the treatment of BTCP. Oral immediate release opioids can be used for slow-onset or predictable BTCP. For more difficult pain conditions, parenteral, or even intrathecal pain medication, may be indicated.

SUMMARY

All clinically relevant episodic pains must be adequately treated in accordance with the patient's preferences. Transmucosal fentanyl formulations are effective for BTCP.