Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development

IntroductionChronic postsurgical pain (CPSP) is a condition that affects an estimated 10%–50% of adults, depending on the surgical procedure. CPSP often interferes with activities of daily living and may have a negative impact on quality of life, emotional and physical well-being. Clinical prediction models can help clinicians target preventive strategies towards patients at high-risk of CPSP. Therefore, the objective of this study is to develop a clinically applicable and generalisable prediction model for CPSP in adults.Methods and analysisThis research will be a prospective single-centre observational cohort study in Denmark spanning approximately 1 year or until a predefined number of patients are recruited (n=1526). Adult patients aged 18 years and older scheduled to undergo surgery will be recruited at Aarhus University Hospital. The primary outcome is CPSP 3 months after surgery defined as average pain intensity at rest or on movement ≥3 on numerical rating scale (NRS) within the past week, and/or average pain interference ≥3 on NRS among any of seven short-form Brief Pain Inventory items in the past week (general activity, mood, walking ability, normal work (including housework), relations with other people, sleep and enjoyment of life). Logistic regression will be used to conduct multivariate analysis. Predictive model performance will be evaluated by discrimination, calibration and model classification.Ethics and disseminationThis research has been approved by Central Region Denmark and will be conducted in accordance with the Danish Data Protection Act and Declaration of Helsinki. Study findings will be disseminated through conference presentations and peer-reviewed publication. A CPSP risk calculator (CPSP-RC) will be developed based on predictors retained in the final models. The CPSP-RC will be made available online and as a mobile application to be easily accessible for clinical use and future research including validation and clinical impact assessments.Trial registration numberNCT04866147.

Download Full-text

A prospective cohort register-based study of chronic postsurgical pain and long-term use of pain medication after otorhinolaryngological surgery

Scientific Reports ◽

10.1038/s41598-021-84788-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nina Graf ◽

Katharina Geißler ◽

Winfried Meißner ◽

Orlando Guntinas-Lichius

Keyword(s):

Rating Scale ◽

Numeric Rating Scale ◽

Postsurgical Pain ◽

Average Pain Intensity ◽

Chronic Postsurgical Pain ◽

Average Pain ◽

Patients At Risk ◽

Perioperative Assessment ◽

Numeric Rating

AbstractData on chronic postsurgical pain (CPSP) after otorhinolaryngological surgery are sparse. Adult in-patients treated in 2017 were included into the prospective PAIN OUT registry. Patients’ pain on the first postoperative day (D1), after six months (M6) and 12 months (M12) were evaluated. Determining factor for CPSP was an average pain intensity ≥ 3 (numeric rating scale 0–10) at M6. Risk factors associated with CPSP were evaluated by univariate and multivariate analyses. 10% of 191 included patients (60% male, median age: 52 years; maximal pain at D1: 3.5 ± 2.7), had CPSP. Average pain at M6 was 0.1 ± 0.5 for patients without CPSP and 4.2 ± 1.2 with CPSP. Average pain with CPSP still was 3.7 ± 1.1 at M12. Higher ASA status (Odds ratio [OR] = 4.052; 95% confidence interval [CI] = 1.453–11.189; p = 0.007), and higher minimal pain at D1 (OR = 1.721; CI = 1.189–2.492; p = 0.004) were independent predictors of CPSP at M6. Minimal pain at D1 (OR = 1.443; CI = 1.008–2.064; p = 0.045) and maximal pain at M6 (OR = 1.665; CI = 1.340–2.069; p < 0.001) were independent predictors for CPSP at M12. CPSP is an important issue after otorhinolaryngological surgery. Better instrument for perioperative assessment should be defined to identify patients at risk for CPSP.

Download Full-text

Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model

BMJ Open ◽

10.1136/bmjopen-2020-038649 ◽

2020 ◽

Vol 10 (10) ◽

pp. e038649

Author(s):

Vincent A van Vugt ◽

Martijn W Heymans ◽

Johannes C van der Wouden ◽

Henriëtte E van der Horst ◽

Otto R Maarsingh

Keyword(s):

General Practice ◽

The Netherlands ◽

Prediction Model ◽

Predictive Models ◽

Prediction Models ◽

Short Form ◽

Treatment Success ◽

Vestibular Rehabilitation ◽

Internal Validation ◽

Vestibular Symptoms

ObjectivesTo develop and internally validate prediction models to assess treatment success of both stand-alone and blended online vestibular rehabilitation (VR) in patients with chronic vestibular syndrome.DesignSecondary analysis of a randomised controlled trial.Setting59 general practices in The Netherlands.Participants202 adults, aged 50 years and older with a chronic vestibular syndrome who received either stand-alone VR (98) or blended VR (104). Stand-alone VR consisted of a 6-week, internet-based intervention with weekly online sessions and daily exercises. In blended VR, the same intervention was supplemented with physiotherapy support.Main outcome measuresSuccessful treatment was defined as: clinically relevant improvement of (1) vestibular symptoms (≥3 points improvement Vertigo Symptom Scale—Short Form); (2) vestibular-related disability (>11 points improvement Dizziness Handicap Inventory); and (3) both vestibular symptoms and vestibular-related disability. We assessed performance of the predictive models by applying calibration plots, Hosmer-Lemeshow statistics, area under the receiver operating characteristic curves (AUC) and applied internal validation.ResultsImprovement of vestibular symptoms, vestibular-related disability or both was seen in 121, 81 and 64 participants, respectively. We generated predictive models for each outcome, resulting in different predictors in the final models. Calibration for all models was adequate with non-significant Hosmer-Lemeshow statistics, but the discriminative ability of the final predictive models was poor (AUC 0.54 to 0.61). None of the identified models are therefore suitable for use in daily general practice to predict treatment success of online VR.ConclusionIt is difficult to predict treatment success of internet-based VR and it remains unclear who should be treated with stand-alone VR or blended VR. Because we were unable to develop a useful prediction model, the decision to offer stand-alone or blended VR should for now be based on availability, cost effectiveness and patient preference.Trial registration numberThe Netherlands Trial Register NTR5712.

Download Full-text

Predictive Factors of Chronic Post-Surgical Pain at 6 Months Following Knee Replacement: Influence of Postoperative Pain Trajectory and Genetics

January 2018 - Pain Physician ◽

10.36076/ppj/2019.19.e729 ◽

2016 ◽

Vol 5;19 (5;19) ◽

pp. E729-E741

Author(s):

Dr Célia Lloret-Linares

Keyword(s):

Logistic Regression ◽

Postoperative Pain ◽

Knee Replacement ◽

Predictive Factors ◽

High Intensity ◽

Walking Ability ◽

Single Center ◽

Multivariate Logistic Regression ◽

Postsurgical Pain ◽

Chronic Postsurgical Pain

Background: The frequency of chronic postsurgical pain (CPSP) after knee replacement remains high, but might be decreased by improvements to prevention. Objectives: To identify pre- and postsurgical factors predictive of CPSP 6 months after knee replacement. Study Design: Single-center prospective observational study. Setting: An orthopedic unit in a French hospital. Methods: Consecutive patients referred for total or unicompartmental knee arthroplasty from March to July 2013 were prospectively invited to participate in this study. For each patient, we recorded preoperative pain intensity, anxiety and depression levels, and sensitivity and pain thresholds in response to an electrical stimulus. We analyzed OPRM1 and COMT single-nucleotide polymorphisms. Acute postoperative pain (APOP) in the first 5 days after surgery was modeled by a pain trajectory. Changes in the characteristics and consequences of the pain were monitored 3 and 6 months after surgery. Bivariate analysis and multivariate logistic regression were conducted to identify predictors of CPSP. Results: We prospectively evaluated 104 patients in this study, 74 (28.8%) of whom reported CPSP at 6 months. Three preoperative factors were found to be associated with the presence of CPSP in multivariate logistic regression analysis: high school diploma level (OR = 3.83 [1.20 – 12.20]), consequences of pain in terms of walking ability, as assessed with the Brief Pain Inventory short form “walk” item (OR = 4.06 [1.18 – 13.94]), and a lack of physical activity in adulthood (OR = 4.01 [1.33 – 12.10]). One postoperative factor was associated with the presence of CPSP: a high-intensity APOP trajectory. An association of borderline statistical significance was found with the A allele of the COMT gene (OR = 3.4 [0.93 – 12.51]). Two groups of patients were identified on the basis of their APOP trajectory: high (n = 28, 26%) or low (n = 80, 74%) intensity. Patients with high-intensity APOP trajectory had higher anxiety levels and were less able to walk before surgery (P < 0.05). Limitations: This was a single-center study and the sample may have been too small for the detection of some factors predictive of CPSP or to highlight the role of genetic factors. Conclusion: Our findings suggest that several preoperative and postoperative characteristics could be used to facilitate the identification of patients at high risk of CPSP after knee surgery. All therapeutic strategies decreasing APOP, such as anxiety management or performing knee replacement before the pain has a serious effect on ability to walk, may help to decrease the risk of CPSP. Further prospective studies testing specific management practices, including a training program before surgery, are required. Key words: Chronic postsurgical pain, opioids, arthroplasty, pain trajectory, genetics, COMT, predictive factors

Download Full-text

Real-time neural network based predictor for cov19 virus spread

PLoS ONE ◽

10.1371/journal.pone.0243189 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243189

Author(s):

Michał Wieczorek ◽

Jakub Siłka ◽

Dawid Połap ◽

Marcin Woźniak ◽

Robertas Damaševičius

Keyword(s):

Prediction Model ◽

Real Time ◽

Prediction Models ◽

Numerical Data ◽

Real Data ◽

Future Trend ◽

World Health ◽

Number Of Patients ◽

The World ◽

Health Organization

Since the epidemic outbreak in early months of 2020 the spread of COVID-19 has grown rapidly in most countries and regions across the World. Because of that, SARS-CoV-2 was declared as a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, by The World Health Organization (WHO). That’s why many scientists are working on new methods to reduce further growth of new cases and, by intelligent patients allocation, reduce number of patients per doctor, what can lead to more successful treatments. However to properly manage the COVID-19 spread there is a need for real-time prediction models which can reliably support various decisions both at national and international level. The problem in developing such system is the lack of general knowledge how the virus spreads and what would be the number of cases each day. Therefore prediction model must be able to conclude the situation from past data in the way that results will show a future trend and will possibly closely relate to the real numbers. In our opinion Artificial Intelligence gives a possibility to do it. In this article we present a model which can work as a part of an online system as a real-time predictor to help in estimation of COVID-19 spread. This prediction model is developed using Artificial Neural Networks (ANN) to estimate the future situation by the use of geo-location and numerical data from past 2 weeks. The results of our model are confirmed by comparing them with real data and, during our research the model was correctly predicting the trend and very closely matching the numbers of new cases in each day.

Download Full-text

The Effect of a Perioperative Ketamine Infusion on the Incidence of Chronic Postsurgical Pain—A Pilot Study

Anaesthesia and Intensive Care ◽

10.1177/0310057x1704500408 ◽

2017 ◽

Vol 45 (4) ◽

pp. 459-465 ◽

Cited By ~ 6

Author(s):

P. J. Peyton ◽

C. Wu ◽

T. Jacobson ◽

M. Hogg ◽

F. Zia ◽

...

Keyword(s):

Pilot Study ◽

Rating Scale ◽

Randomised Trial ◽

Numeric Rating Scale ◽

Major Surgery ◽

Control Group ◽

Postsurgical Pain ◽

Quality Of Recovery ◽

Chronic Postsurgical Pain ◽

Average Pain

Chronic postsurgical pain (CPSP) is a common and debilitating complication of major surgery. We undertook a pilot study at three hospitals to assess the feasibility of a proposed large multicentre placebo-controlled randomised trial of intravenous perioperative ketamine to reduce the incidence of CPSP. Ketamine, 0.5 mg/kg pre-incision, 0.25 mg/kg/hour intraoperatively and 0.1 mg/kg/hour for 24 hours, or placebo, was administered to 80 patients, recruited over a 15-month period, undergoing abdominal or thoracic surgery under general anaesthesia. The primary endpoint was CPSP in the area of the surgery reported at six-month telephone follow-up using a structured questionnaire. Fourteen patients (17.5%) reported CPSP (relative risk [95% confidence interval] if received ketamine 1.18 [0.70 to 1.98], P=0.56). Four patients in the treatment group and three in the control group reported ongoing analgesic use to treat CPSP and two patients in each group reported their worst pain in the previous 24 hours at ≥3/10 at six months. There were no significant differences in adverse event rates, quality of recovery scores, or cumulative morphine equivalents consumption in the first 72 hours. Numeric Rating Scale pain scores (median [interquartile range]) for average pain in the previous 24 hours among those patients reporting CPSP were 17.5 [0 to 40] /100 with no difference between treatment groups. A large (n=4,000 to 5,000) adequately powered multicentre trial is feasible using this population and methodology.

Download Full-text

Effects of oral vitamin B12 for the treatment of aromatase inhibitors (AI)-related musculoskeletal symptoms in women with early stage breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.7_suppl.86 ◽

2018 ◽

Vol 36 (7_suppl) ◽

pp. 86-86

Author(s):

Zeina A. Nahleh ◽

Aleli Campbell ◽

Rosalinda Heydarian ◽

Alok Kumar Dwivedi

Keyword(s):

Breast Cancer ◽

Vitamin B12 ◽

Early Stage ◽

Short Form ◽

Well Being ◽

Musculoskeletal Symptoms ◽

High Dose ◽

Walking Ability ◽

Breast Cancer Patients ◽

Post Intervention

86 Background: Breast cancer patients receiving Aromatase Inhibitor (AI) therapy experience many side effects including arthralgias, myalgias and stiffness of joints. The objective of this study was to evaluate the effect of vitamin B12 supplements on pain related symptoms. Methods: In this study, patients taking AIs and experiencing pain at baseline were given 2500 mcg of vitamin B12 sublingually daily for 90 days. The validated Brief Pain Inventory-Short Form (BPI-SF) questionnaire using a 10 scale rating was evaluated prior and post- intervention. The BPI-SF asseses pain level and its interference with other daily life events on a scale from one to ten, one meaning pain does not interfere. Item 9 from the BPI assesses 7 important dimensions that are reported here. Results: 36 patients were recruited and scores were evaluated at baseline and at 90 days after taking vitamin B12 for general activity, mood, walking ability, normal work, relations with people, and for enjoyment of life post interventions as shown in the table. Conclusions: This study suggests that by taking a high dose of vitamin B12, significant improvement was observed in several dimensions related to pain scales in patients with AI -related musculoskeletal symptoms. Preliminary data confirms that vitamin B12 not only help improve pain, but it also aids in improving patient’s mood, well-being and relations with others. Larger randomized studies are warranted to confirm these promising findings. Clinical trial information: NCT03069313. [Table: see text]

Download Full-text

Measuring affective well-being at work using short-form scales: Implications for affective structures and participant instructions

Human Relations ◽

10.1177/0018726717751034 ◽

2018 ◽

Vol 71 (11) ◽

pp. 1478-1507 ◽

Cited By ~ 8

Author(s):

Emma Russell ◽

Kevin Daniels

Keyword(s):

Factor Structure ◽

Convergent Validity ◽

Work Performance ◽

Short Form ◽

Well Being ◽

Organizational Studies ◽

Policy Makers ◽

Organizational Functioning ◽

Discrete Emotion ◽

The Past

Measuring affective well-being in organizational studies has become increasingly widespread, given its association with key work-performance and other markers of organizational functioning. As such, researchers and policy-makers need to be confident that well-being measures are valid, reliable and robust. To reduce the burden on participants in applied settings, short-form measures of affective well-being are proving popular. However, these scales are seldom validated as standalone, comprehensive measures in their own right. In this article, we used a short-form measure of affective well-being with 10 items: the Daniels five-factor measure of affective well-being (D-FAW). In Study 1, across six applied sample groups ( N = 2624), we found that the factor structure of the short-form D-FAW is robust when issued as a standalone measure, and that it should be scored differently depending on the participant instruction used. When participant instructions focus on now or today, then affect is best represented by five discrete emotion factors. When participant instructions focus on the past week, then affect is best represented by two or three mood-based factors. In Study 2 ( N = 39), we found good construct convergent validity of short-form D-FAW with another widely used scale (PANAS). Implications for the measurement and structure of affect are discussed.

Download Full-text

Predicting Persistent Unclear Pain Following Primary Total Knee Arthroplasty

Ortopedia Traumatologia Rehabilitacja ◽

10.5604/15093492.1230507 ◽

2016 ◽

Vol 18 (6) ◽

pp. 527-536 ◽

Cited By ~ 5

Author(s):

Paweł Chodór ◽

Jacek Kruczyński

Keyword(s):

Total Knee Arthroplasty ◽

Knee Pain ◽

Knee Arthroplasty ◽

Primary Total Knee Arthroplasty ◽

Pain Modulation ◽

Postsurgical Pain ◽

Number Of Patients ◽

Chronic Postsurgical Pain ◽

Total Knee ◽

Primary Total Knee

Chronic post-surgical pain can be a considerable issue for patients undergoing primary total knee arthroplasty. According to the literature, persistent knee pain is experienced by up to 44% of patients. Most studies on total knee arthroplasty (TKA) outcomes have mainly investigated the biomechanics or function of the operated knee, but chronic pain has never been a primary issue. In recent years several possible predictors of chronic postsurgical pain have been investigated and eventually identified. A younger age, female gender, psychological distress, preoperative pain duration and intensity were all reported to influence chronic postoperative pain rates after total knee arthroplasty. Recently, it has also been hypothesized that preoperative signs of centrally driven hyperalgesia and distorted pain modulation may predict persistent knee pain in some patients. Despite the considerable number of patients suffering from chronic postsurgical pain after TKA, available data is scarce, and well controlled prospective studies are lacking. Predictors of chronic postsurgical pain after total knee arthroplasty have yet to be identified. Thus, this article is aimed at reviewing current knowledge on persistent pain after knee arthroplasty.

Download Full-text

Prognostic risk prediction models for chronic postsurgical pain in adults

Pain ◽

10.1097/j.pain.0000000000002261 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Nicholas Papadomanolakis-Pakis ◽

Peter Uhrbrand ◽

Simon Haroutounian ◽

Lone Nikolajsen

Keyword(s):

Risk Prediction ◽

Prediction Models ◽

Risk Prediction Models ◽

Postsurgical Pain ◽

Chronic Postsurgical Pain

Download Full-text

Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

10.1101/2021.01.20.21250183 ◽

2021 ◽

Author(s):

Esmee Venema ◽

Benjamin S Wessler ◽

Jessica K Paulus ◽

Rehab Salah ◽

Gowri Raman ◽

...

Keyword(s):

High Risk ◽

Prediction Model ◽

Prediction Models ◽

Assessment Tool ◽

Short Form ◽

Risk Of Bias ◽

Clinical Prediction ◽

Model Risk ◽

Clinical Prediction Models ◽

Bias Assessment

AbstractObjectiveTo assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation.Study Design and SettingWe evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation and assessed the change in discrimination (dAUC) between the derivation and the validation cohorts (n=1,147).ResultsPROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 529/556 CPMs (95%) were classified as high ROB, 20 (4%) low ROB, and 7 (1%) unclear ROB. Median change in discrimination was significantly smaller in low ROB models (dAUC −0.9%, IQR −6.2%–4.2%) compared to high ROB models (dAUC −11.7%, IQR −33.3%–2.6%; p<0.001).ConclusionHigh ROB is pervasive among published CPMs. It is associated with poor performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.What is newHigh risk of bias is pervasive among published clinical prediction modelsHigh risk of bias identified with PROBAST is associated with poorer model performance at validationA subset of questions can distinguish between models with high and low risk of bias

Download Full-text