Characterizing the pain score trajectories of hospitalized adult medical and surgical patients: a retrospective cohort study

Abstract

Pain care for hospitalized patients is often suboptimal. Representing pain scores as a graphical trajectory may provide insights into the understanding and treatment of pain. We describe a 1-year, retrospective, observational study to characterize pain trajectories of hospitalized adults during the first 48 hours after admission at an urban academic medical center. Using a subgroup of patients who presented with significant pain (pain score >4; n = 7762 encounters), we characterized pain trajectories and measured area under the curve, slope of the trajectory for the first 2 hours after admission, and pain intensity at plateau. We used mixed-effects regression to assess the association between pain score and sociodemographics (age, race, and gender), pain medication orders (opioids, nonopioids, and no medications), and medical service (obstetrics, psychiatry, surgery, sickle cell, intensive care unit, and medicine). K-means clustering was used to identify patient subgroups with similar trajectories. Trajectories showed differences based on race, gender, service, and initial pain score. Patients presumed to have dissimilar pain experiences (eg, sickle vs obstetrical) had markedly different pain trajectories. Patients with higher initial pain had a more rapid reduction during their first 2 hours of treatment. Pain reduction achieved in the 48 hours after admission was approximately 50% of the initial pain, regardless of the initial pain. Most patients' pain failed to fully resolve, plateauing at a pain score of 4 or greater. Visualizing pain scores as graphical trajectories illustrates the dynamic variability in pain, highlighting pain responses over a period of observation, and may yield new insights for quality improvement and research.

Figure 1.

Observed data and fitted regression line for N = 7762 encounters with adult inpatients admitted with pain score >4. The solid line represents observed data with geometric smoothing; the dotted line shows the fitted curve from the polynomial regression models. X-axis (“Days”) denotes the time elapsed, in days, since the initial pain measurement. Y-axis (“Pain”) denotes the pain score.

Figure 2.

Observed 48-hour pain trajectories with rolling mean scatter plot smoothing, clockwise from upper left: (A) by gender; (B) by race (African American, White, Hispanic, and Other); and (C) by analgesic medication exposure (no meds, nonopioids only, any opioids). X-axis (“Days”) denotes the time elapsed, in days, since the initial pain measurement. Y-axis (“Pain”) denotes the pain score.

Figure 3.

Observed average pain trajectories for patients with 3 levels of initial pain (5 or 6, 7 or 8, and 9 or 10): V_o is the rate of decrease of pain score during the first 2 hours after initial pain recording and P_plateau is the average pain during the 24 hours (24-48 hours) of pain recording. X-axis (“Days”) denotes the time elapsed, in days, since the initial pain measurement. Y-axis (“Pain”) denotes the pain score.

Figure 4.

Observed and fitted 48-hour pain score trajectories for patients in 4 clusters. The solid lines are the observed data with geometric smoothing. The dotted lines are the fitted curves from the polynomial regression models, using mean empirical Bayes estimates from each cluster. The corresponding area under the curve (AUC) values are also shown. X-axis (“Days”) denotes the time elapsed, in days, since the initial pain measurement. Y-axis (“Pain”) denotes the pain score.