Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset

Predictors of subacute postoperative pain after total knee arthroplasty: A secondary analysis of two randomized trials

BACKGROUND

Methods for identifying high-pain responders undergoing total knee arthroplasty remain important to improve individualized pain management. This study aimed at evaluating pre- and perioperative predictors of pain on Days 2-7 after total knee arthroplasty.

METHODS

This is a secondary analysis of data from 227 patients participating in two randomized trials. Pain outcomes were mean pain during walking on Days 2-7 and on Days 2, 4 and 7. Multivariable linear and logistic regressions were carried out in two steps. First, only preoperative available variables including demographics, comorbidities, pain catastrophizing scale and preoperative pain were evaluated while controlling for trial intervention and recruitment site. In the second step, perioperative variables and pain during walking 24 h postoperatively were added.

RESULTS

The model with only preoperative predictors for mean pain Days 2-7 showed preoperative pain (R-squared 0.097) as the only predictor. In the second model, adding postoperative available variables, only pain 24 h postoperatively (R-squared 0.248) was significant, with a significant main effect of recruitment site. Results for the separate day analysis similarly showed preoperative pain and pain during walking 24 h postoperatively as predictors. The overall best sensitivity (60%) and specificity (74%) for predicting a high-subacute postoperative pain response on Days 2-7 was with cut-off values of VAS 45.5 (out of 100) for pain during walking 24 h postoperatively.

CONCLUSIONS

Postoperative pain during walking at 24 h is predictive of subacute postoperative pain on Days 2-7 after total knee arthroplasty, while preoperative pain was only a weak predictor.

SIGNIFICANCE STATEMENT

This study investigated factors associated with pain after total knee arthroplasty beyond the immediate postoperative period. The analysis revealed significant associations between preoperative pain levels and, particularly, pain 24 h postoperatively, with subsequent subacute pain the following week. These findings can assist in identifying patients who would benefit from enhanced, individualized analgesic interventions to facilitate postoperative recovery.

Prognostic clinical prediction models for acute post-surgical pain in adults: a systematic review

BACKGROUND

Acute post-surgical pain is managed inadequately in many patients undergoing surgery. Several prognostic risk prediction models have been developed to identify patients at high risk of developing moderate to severe acute post-surgical pain. The aim of this systematic review was to describe and evaluate the methodological conduct of these prediction models.

METHODS

We searched MEDLINE, EMBASE and CINAHL for studies of prognostic risk prediction models for acute post-surgical pain using predetermined criteria. Prediction model performance was evaluated according to discrimination and calibration. Adherence to TRIPOD guidelines was assessed. Risk of bias and applicability was independently assessed by two reviewers using the prediction model risk of bias assessment tool.

RESULTS

We included 14 studies reporting on 17 prediction models. The most common predictors identified in final prediction models included age; surgery type; sex or gender; anxiety or fear of surgery; pre-operative pain intensity; pre-operative analgesic use; pain catastrophising; and expected surgical incision size. Discrimination, measured by the area under receiver operating characteristic curves or c-statistic, ranged from 0.61 to 0.83. Calibration was only reported for seven models. The median (IQR [range]) overall adherence rate to TRIPOD items was 62 (53-66 [47-72])%. All prediction models were at high risk of bias.

CONCLUSIONS

Effective prediction models could support the prevention and treatment of acute post-surgical pain; however, existing models are at high risk of bias which may affect their reliability to inform practice. Consideration should be given to the goals, timing of intended use and desired outcomes of a prediction model before development.

Peri-operative pain management in adults: a multidisciplinary consensus statement from the Association of Anaesthetists and the British Pain Society

BACKGROUND

Nearly half of adult patients undergoing surgery experience moderate or severe postoperative pain. Inadequate pain management hampers postoperative recovery and function and may be associated with adverse outcomes. This multidisciplinary consensus statement provides principles that might aid postoperative recovery, and which should be applied throughout the entire peri-operative pathway by healthcare professionals, institutions and patients.

METHODS

We conducted a directed literature review followed by a four-round modified Delphi process to formulate recommendations for organisations and individuals.

RESULTS

We make recommendations for the entire peri-operative period, covering pre-admission; admission; intra-operative; post-anaesthetic care unit; ward; intensive care unit; preparation for discharge; and post-discharge phases of care. We also provide generic principles of peri-operative pain management that clinicians should consider throughout the peri-operative pathway, including: assessing pain to facilitate function; use of multimodal analgesia, including regional anaesthesia; non-pharmacological strategies; safe use of opioids; and use of protocols and training for staff in caring for patients with postoperative pain.

CONCLUSIONS

We hope that with attention to these principles and their implementation, outcomes for adult patients having surgery might be improved.

Postoperative Pain at Discharge From the Post-anesthesia Care Unit: A Case-Control Study

INTRODUCTION

Despite advancements in postoperative pain management, approximately 20% of patients still experience severe pain within the first 24 hours post-surgery. Previous studies utilizing machine learning have shown promise in predicting postoperative pain with various models. This study investigates postoperative pain predictors using a machine learning approach based on physiological indicators and demographic factors in a Mexican cohort.

METHODS

We conducted a retrospective case-control study to assess pain determinants at Post-anesthesia Care Unit (PACU) discharge at Hospital Ángeles Lomas in Mexico City. Data were collected from 550 patients discharged from the PACU, including 292 cases and 258 controls, covering a range of surgical procedures and illnesses. Machine learning techniques were employed to develop a predictive model for postoperative pain. Physiological responses, such as blood pressure, heart rate, respiratory rate, and anesthesia type, were recorded prior to PACU admission.

RESULTS

Significant differences were found between cases and controls, with factors such as sex, anesthesia type, and physiological responses influencing postoperative pain. Visual analog scale (VAS) scores at PACU admission were predictive of pain at discharge.

CONCLUSIONS

Our findings reinforce existing literature by highlighting sex-based disparities in pain experiences and the influence of anesthesia type on pain levels. The logistic regression model developed, incorporating physiological responses and sex, shows potential for refining pain management strategies. Limitations include the lack of detailed surgical data and psychological factors, and validation in a prospective cohort. Future research should focus on more comprehensive predictive models and longitudinal studies to further improve postoperative pain management.

Application of Integrated Medical Care "Cloud-Based Virtual Ward" Management Model on Postoperative Analgesia: Based on Zigbee Technology

PURPOSE

This study aimed to compare acute care postoperative patients monitored by standard care to those monitored through virtual ward technology by pain team to evaluate status in real-time.

DESIGN

Retrospective cohort study.

METHODS

We included 72,240 and 68,424 postoperative patients who underwent the acute pain service model between January 2021 and April 2022 and the "cloud-based virtual ward" management model between May 2022 and September 2023, respectively. Patients were administered patient-controlled intravenous analgesia after surgery, and we collected perioperative data regarding the general condition, operation type, postoperative moderate-to-severe pain, nausea and vomiting, dizziness, hoarseness, and drowsiness of the patients.

RESULTS

The incidences of moderate-to-severe postoperative pain, postoperative nausea and vomiting, dizziness, drowsiness, hoarseness, resting pain, and activity pain were significantly reduced in the "cloud-based virtual ward" management model when compared with the acute pain service model.

CONCLUSIONS

Compared to the acute pain service model, the "cloud-based virtual ward" management model can enhance pain management satisfaction and lower the frequency of moderate-to-severe postoperative pain and adverse effects.

CLINICAL IMPLICATIONS

The "cloud-based virtual ward" management model proposed in this study may improve the care of patients with acute postoperative pain. By reviewing the two pain management models for postoperative patients, we were able to compare the incidence of postoperative adverse reactions and use the standard process of the integrated medical care "cloud-based virtual ward" management model to optimize the management of postoperative patients and promote their health outcomes.

Effect of age on postoperative 24-hour moderate-to-severe pain after radical resection of lung cancer-specific pain in the post-anaesthesia care unit: a single-centre retrospective cohort study

OBJECTIVES

To explore the relationship between age and postoperative 24-hour moderate-to-severe pain after radical resection of lung cancer and the specific effect of moderate-to-severe pain in the post-anaesthesia care unit (PACU) on this relationship.

DESIGN

Retrospective cohort study.

SETTING

Single medical centre.

PARTICIPANTS

Patients ≥18 years having radical resection of lung cancer between 2018 and 2020.

MEASUREMENTS

Postoperative 24-hour moderate-to-severe pain.

RESULTS

A total of 3764 patients were included in the analysis. The incidence of postoperative 24-hour moderate-to-severe pain was 28.3%. Age had a significant effect on the prediction model of postoperative 24-hour moderate-to-severe pain. Among the whole population and those without moderate-to-severe pain in the PACU, those who were younger than 58.5 years were prone to experience moderate-to-severe pain 24 hours after surgery, and in patients with moderate-to-severe pain in the PACU, the age threshold was 62.5 years.

CONCLUSION

For patients who underwent elective radical resection for lung cancer, age was related to postoperative 24-hour moderate-to-severe pain, and moderate-to-severe pain in the PACU had a specific effect on this relationship. Patients among the whole population and those patients without moderate-to-severe pain in the PACU were more likely to experience postoperative 24-hour moderate-to-severe pain when they were younger than 58.5 years old, and in patients with moderate-to-severe pain in the PACU, the age threshold was 62.5 years old.

Acute postoperative pain and catastrophizing in unicompartmental knee arthroplasty: a prospective, observational, single-center, cohort study

BACKGROUND AND OBJECTIVES

Pain catastrophizing is associated with acute pain after total knee arthroplasty. However, the association between pain catastrophizing and acute pain after unicompartmental knee arthroplasty (UKA) remains unclear.

METHODS

We investigated the incidence of predicted high-pain and low-pain responders, based on a preoperative Pain Catastrophizing Scale score >20 or ≤20, respectively, and the acute postoperative pain course in both groups. Patients undergoing UKA were consecutively included in this prospective observational cohort study. Pain at rest and during walking (5 m walk test) was evaluated preoperatively, at 24 hours postoperatively, and on days 2-7 using a pain diary.

RESULTS

125 patients were included, with 101 completing the pain diary. The incidence of predicted high-pain responders was 31% (95% CI 23% to 40%). The incidence of moderate to severe pain during walking at 24 hours postoperatively was 69% (95% CI 52% to 83%) in predicted high-pain responders and 66% (95% CI 55% to 76%) in predicted low-pain responders; OR 1.3 (95% CI 0.5 to 3.1). The incidence of moderate to severe pain at rest 24 hours postoperatively was 49% (95% CI 32% to 65%) in predicted high-pain responders and 28% (95% CI 19% to 39%) in predicted low-pain responders; OR 2.6 (95% CI 1.1 to 6.1; p=0.03). Pain catastrophizing was not associated with increased cumulated pain during walking on days 2-7.

CONCLUSIONS

The incidence of predicted high-pain responders in UKA was slightly lower than reported in total knee arthroplasty. Additionally, preoperative pain catastrophizing was not associated with acute postoperative pain during walking.

Risk Factors for Acute Postsurgical Pain: A Narrative Review

Acute postsurgical pain (APSP) has received growing attention as a surgical outcome. When poorly controlled, APSP can affect short- and long-term outcomes in patients. Despite the steady increase in awareness about postoperative pain and standardization of pain prevention and treatment strategies, moderate-to-severe APSP is frequently reported in clinical practice. This is possibly because pain varies widely among individuals and is influenced by distinct factors, such as demographic, perioperative, psychological, and genetic factors. This review investigates the risk factors for APSP, including gender, age, obesity, smoking history, preoperative pain history, pain sensitivity, preoperative anxiety, depression, pain catastrophizing, expected postoperative pain, surgical fear, and genetic polymorphisms. By identifying patients having an increased risk of moderate-to-severe APSP at an early stage, clinicians can more effectively manage individualized analgesic treatment protocols with a combination of pharmacological and non-pharmacological interventions. This would alleviate the transition from APSP to chronic pain and reduce the severity of APSP-induced chronic physical disability and social psychological distress.

Regional anaesthesia truncal blocks for acute postoperative pain and recovery: a narrative review

Significant acute postoperative pain remains prevalent among patients who undergo truncal surgery and is associated with increased morbidity, prolonged patient recovery, and increased healthcare costs. The provision of high-quality postoperative analgesia is an important component of postoperative care, particularly within enhanced recovery programmes. Regional anaesthetic techniques have become increasingly prevalent within multimodal analgesic regimens and the widespread adoption of ultrasonography has facilitated the development of novel fascial plane blocks. The number of described fascial plane blocks has increased significantly over the past decade, leading to a burgeoning area of clinical investigation. Their applications are increasing, and truncal fascial plane blocks are increasingly recommended as part of procedure-specific guidelines. Some fascial plane blocks have been shown to be more efficacious than others, with favourable side-effect profiles compared with neuraxial analgesia, and are increasingly utilised in breast, thoracic, and other truncal surgery. However, use of these blocks is debated in regional anaesthesia circles because of limitations in our understanding of their mechanisms of action. This narrative review evaluates available evidence for the analgesic efficacy of the most commonly practised fascial plane blocks in breast, thoracic, and abdominal truncal surgery, in particular their efficacy compared with systemic analgesia, alternative blocks, and neuraxial techniques. We also highlight areas where investigations are ongoing and suggest priorities for original investigations.

Development and prospective validation of postoperative pain prediction from preoperative EHR data using attention-based set embeddings

Preoperative knowledge of expected postoperative pain can help guide perioperative pain management and focus interventions on patients with the greatest risk of acute pain. However, current methods for predicting postoperative pain require patient and clinician input or laborious manual chart review and often do not achieve sufficient performance. We use routinely collected electronic health record data from a multicenter dataset of 234,274 adult non-cardiac surgical patients to develop a machine learning method which predicts maximum pain scores on the day of surgery and four subsequent days and validate this method in a prospective cohort. Our method, POPS, is fully automated and relies only on data available prior to surgery, allowing application in all patients scheduled for or considering surgery. Here we report that POPS achieves state-of-the-art performance and outperforms clinician predictions on all postoperative days when predicting maximum pain on the 0-10 NRS in prospective validation, though with degraded calibration. POPS is interpretable, identifying comorbidities that significantly contribute to postoperative pain based on patient-specific context, which can assist clinicians in mitigating cases of acute pain.