Key Points Question Which prospectively assessed descriptor of the acute pain trajectory in the first 10 days after surgery best estimates the likelihood of remote pain resolution, opioid cessation, and patient-reported complete recovery after surgery? Findings In this secondary analysis of a randomized clinical trial of 422 patients, the worst surgical-site pain intensity over the last 24 hours reported on postoperative day 10 appeared to be the best predictor of remote pain resolution, opioid cessation, and complete recovery after surgery. Meaning A possibly uniform predictor of disparate surgical outcomes long after hospital discharge may be easily assessed.

Background: Reducing hospital-acquired pressure ulcers (PUs) in intensive care units (ICUs) has emerged as an important quality metric for health systems internationally. Limited work has been done to characterize the profile of PUs in the ICU using observational data from the electronic health record (EHR). Consequently, there are limited EHR-based prognostic tools for determining a patient’s risk of PU development, with most institutions relying on nurse-calculated risk scores such as the Braden score to identify high-risk patients. Methods and Results: Using EHR data from 50,851 admissions in a tertiary ICU (MIMIC-III), we show that the prevalence of PUs at stage 2 or above is 7.8 percent. For the 1,690 admissions where a PU was recorded on day 2 or beyond, we evaluated the prognostic value of the Braden score measured within the first 24 hours. A high-risk Braden score (<=12) had precision 0.09 and recall 0.50 for the future development of a PU. We trained a range of machine learning algorithms using demographic parameters, diagnosis codes, laboratory values and vitals available from the EHR within the first 24 hours. A weighted linear regression model showed precision 0.09 and recall 0.71 for future PU development. Classifier performance was not improved by integrating Braden score elements into the model. Conclusion: We demonstrate that an EHR-based model can outperform the Braden score as a screening tool for PUs. This may be a useful tool for automatic risk stratification early in an admission, helping to guide quality protocols in the ICU, including the allocation and timing of prophylactic interventions.

Introduction: Critical for the diagnosis and treatment of chronic pain is the anatomical distribution of pain. Several body maps allow patients to indicate pain areas on paper; however, each has its limitations. Objectives: To provide a comprehensive body map that can be universally applied across pain conditions, we developed the electronic Collaborative Health Outcomes Information Registry (CHOIR) self-report body map by performing an environmental scan and assessing existing body maps. Methods: After initial validation using a Delphi technique, we compared (1) pain location questionnaire responses of 530 participants with chronic pain with (2) their pain endorsements on the CHOIR body map (CBM) graphic. A subset of participants (n 5 278) repeated the survey 1 week later to assess test-retest reliability. Finally, we interviewed a patient cohort from a tertiary pain management clinic (n 5 28) to identify reasons for endorsement discordances. Results: The intraclass correlation coefficient between the total number of body areas endorsed on the survey and those from the body map was 0.86 and improved to 0.93 at follow-up. The intraclass correlation coefficient of the 2 body map graphics separated by 1 week was 0.93. Further examination demonstrated high consistency between the questionnaire and CBM graphic (,10% discordance) in most body areas except for the back and shoulders (15-19% discordance). Participants attributed inconsistencies to misinterpretation of body regions and laterality, the latter of which was addressed by modifying the instructions. Conclusions: Our data suggest that the CBM is a valid and reliable instrument for assessing the distribution of pain.

Chronic pain conditions present in various forms, yet all feature symptomatic impairments in physical, mental, and social domains. Rather than assessing symptoms as manifestations of illness, we used them to develop a chronic pain classification system. A cohort of real-world treatment-seeking patients completed a multidimensional patient-reported registry as part of a routine initial evaluation in a multidisciplinary academic pain clinic. We applied hierarchical clustering on a training subset of 11,448 patients using nine pain-agnostic symptoms. We then validated a three-cluster solution reflecting a graded scale of severity across all symptoms and eight independent pain-specific measures in additional subsets of 3817 and 1273 patients. Negative affect-related factors were key determinants of cluster assignment. The smallest subset included follow-up assessments that were predicted by baseline cluster assignment. Findings provide a cost-effective classification system that promises to improve clinical care and alleviate suffering by providing putative markers for personalized diagnosis and prognosis.

Empirical data on the health impacts of the COVID-19 pandemic remain scarce, especially among patients with chronic pain. We conducted a cross-sectional study matched by season to examine patient-reported health symptoms among patients with chronic pain pre- and post-COVID-19 pandemic onset. Survey responses were analyzed from 7535 patients during their initial visit at a tertiary pain clinic between April 2017–October 2020. Surveys included measures of pain and pain-related physical, emotional, and social function. The post-COVID-19 onset cohort included 1798 initial evaluations, and the control pre-COVID-19 cohort included 5737 initial evaluations. Patients were majority female, White/Caucasian, and middle-aged. The results indicated that pain ratings remained unchanged among patients after the pandemic onset. However, pain catastrophizing scores were elevated when COVID-19 cases peaked in July 2020. Pain interference, physical function, sleep impairment, and emotional support were improved in the post-COVID-19 cohort. Depression, anxiety, anger, and social isolation remained unchanged. Our findings provide evidence of encouraging resilience among patients seeking treatment for pain conditions in the face of the COVID-19 pandemic. However, our findings that pain catastrophizing increased when COVID-19 cases peaked in July 2020 suggests that future monitoring and consideration of the impacts of the pandemic on patients’ pain is warranted.