The Coronavirus disease 2019 (COVID-19) pandemic exerted a tremendous pressure on the healthcare system, people's social life, mental health and financial status with profound implications for the general population. The exact impact of the pandemic on the overall physical, mental and social wellbeing of COVID-19 infection survivors on the long term has not yet been explored in a thorough way. Based on the reporting of persistent pain, fatigue and dyspnea symptoms by these survivors, it is our hypothesis that their quality of life will be extremely impacted, as is observed in patients with chronic pain. Therefore, the first aim of this study was to perform an in-depth evaluation of the quality of life of post-COVID-19 infected persons. The second aim was to compare the quality of life of these persons with a normative population and with patients with chronic pain. Health-related quality of life, as a measure for a person's overall physical, mental, and social wellbeing, was measured with the 3-level EQ5D in 547 post-COVID-19 infected persons. These data were compared to reference data from normal population records for Belgium and to data from patients with chronic pain after spinal surgery with two-way analyses of variance. In total, 89.58% of the post-COVID-19 infected persons reported pain/discomfort and 82.45% indicated limitations when performing usual activities, when evaluated 287 days (SD: 150) after the infection. Self-care was preserved in most post-COVID-19 persons, whereby only 13.16% indicated problems. The mean EQ5D-3L index score was 0.57 (SD: 0.23) and EQ5D VAS mean score was 56.6 (SD: 18.2). The mean index score for the normative population was significantly higher than for COVID-19 infected persons [mean difference of 0.31 (95% from 0.29 to 0.33), p < 0.01] while the mean score of chronic pain patients was significantly lower than the score of COVID-19 infected persons [mean difference of −0.31 (95% from −0.29 to −0.33), p < 0.01]. Compared to age-and sex adjusted reference data, health-related quality of life of persons with long COVID is severely impacted. In relation to patients with chronic pain after spinal surgery, the quality of life of post-COVID-19 infected persons seemed to be better.Clinical trial registrationhttps://www.clinicaltrials.gov/, identifier: NCT04912778.

Persistent Spinal Pain Syndrome Type 2 (PSPS-T2), (Failed Back Surgery Syndrome), dramatically impacts on patient quality of life, as evidenced by Health-Related Quality of Life (HRQoL) assessment tools. However, the importance of functioning, pain perception and psychological status in HRQoL can substantially vary between subjects. Our goal was to extract patient profiles based on HRQoL dimensions in a sample of PSPS-T2 patients and to identify factors associated with these profiles. Two classes were clearly identified using a mixture of mixed effect models from a clinical data set of 200 patients enrolled in “PREDIBACK”, a multicenter observational prospective study including PSPS-T2 patients with 1-year follow-up. We observed that HRQoL was more impacted by functional disability for first class patients (n=136) and by pain perception for second class patients (n=62). Males that perceive their work as physical were more impacted by disability than pain intensity. Lower education level, lack of adaptive coping strategies and higher pain intensity were significantly associated with HRQoL being more impacted by pain perception. The identification of such classes allows for a better understanding of HRQoL dimensions and opens the gate towards optimized health-related quality of life evaluation and personalized pain management.

Persistent Pain after Spinal Surgery can be successfully addressed by Spinal Cord Stimulation (SCS). International guidelines strongly recommend that a lead trial be performed before any permanent implantation. Recent clinical data highlight some major limitations of this approach. First, it appears that patient outcomes, WITH OR WITHOUT lead trial, are similar. In contrast, during trialing, infection rate drops drastically within time and can compromise the therapy. Using composite pain assessment experience and previous research, we hypothesized that ma-chine learning models could be robust screening tools and reliable predictors of long-term SCS efficacy. We developed several algorithms including logistic regression, Regularized Logistic Regression (RLR), naive Bayes classifier, artificial neural networks, random forest and gradient boosted trees to test this hypothesis and to perform internal and external validations, the objec-tive being to confront model predictions with lead trial results using a 1-year composite out-come from 103 patients. While almost all models have demonstrated superiority on lead trial-ing, the RLR model appears to represent the best compromise between complexity and inter-pretability in prediction of SCS efficacy. These results underscore the need to use AI based-predictive medicine, as a synergistic mathematical approach, aimed at helping implanters to optimize their clinical choices on daily practice.

The multidimensionality of chronic pain forces us to look beyond isolated pain assessment such as pain intensity, which does not consider multiple key parameters, particularly in patients suffering from post-operative Persistent Spinal Pain Syndrome (PSPS-T2). Our ambition was to provide a novel Multi-dimensional Clinical Response Index (MCRI), including not only pain intensity but also functional capacity, anxiety-depression, quality of life and objective quantitative pain mapping assessments, the objective being to capture patient condition instantaneously, using machine learning techniques. Two hundred PSPS-T2 patients were enrolled in a real-life observational prospective PREDIBACK study with 12-month follow-up and received various treatments. From a multitude of questionnaires/scores, specific items were combined using exploratory factor analyses to create an optimally accurate MCRI; as a single composite index, using pairwise correlations between measurements, it appeared to better represent all pain dimensions than any other classical score. It appeared to be the best compromise among all existing indexes, showing the highest sensitivity/specificity related to Patient Global Impression of Change (PGIC). Novel composite indexes could help to refine pain assessment by changing the physician’s perception of patient condition on the basis of objective and holistic metrics, and by providing new insights to therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.

The search towards more objective outcome measurements and consequently surrogate markers for pain started decades ago; however, no generally accepted biomarker for pain has qualified yet. The goal is to explore the value of heart rate variability (HRV) as surrogate marker for pain intensity chronic pain setting. Pain intensity scores and HRV were collected in 366 patients with chronic pain, through a cross-sectional multicenter study. Pain intensity was measured with both the visual analogue scale and numeric rating scale, whereas 16 statistical HRV parameters were derived. Canonical correlation analysis was performed to evaluate the correlation between the dependent pain variables and the HRV parameters. Surrogacy was determined for each HRV parameter with point estimates between 0 and 1 whereby values close to 1 indicate a strong association between the surrogate and the true endpoint at the patient level. Weak correlations were revealed between HRV parameters and pain intensity scores. The highest surrogacy point estimate was found for mean heart rate as marker for average pain intensity on the numeric rating scale with point estimates of 0.0961 (95% confidence interval [CI] 0.0384-0.1537) and 0.0209 (95% CI 0-0.05) for patients without medication use and with medication, respectively. This study indicated that HRV parameters as separate entities are no suitable surrogacy candidates for pain intensity, in a population of chronic pain patients. Further potential surrogate candidates and clinical robust true endpoints should be explored, to find a surrogate measure for the highly individual pain experience.