BACKGROUND: Vaso-occlusive crises (VOC) are the hallmark of sickle cell disease (SCD), with higher severity among hospitalized patients. Clustering hospitalizations with similar pain trajectories could identify vulnerable patient subgroups. Aims were to (1) identify clusters of hospitalizations based on pain trajectories; (2) identify factors associated with these clusters; and (3) determine the association between these clusters and 30-day readmissions. METHODS: We retrospectively included 350 VOC hospitalizations from 2013-2016 among 59 patients. Finite mixture modeling identified clusters of hospitalizations from intercepts and slopes of pain trajectories during the hospitalization. Generalized estimating equations for multinomial and logistic models were used to identify factors associated with clusters of hospitalizations based on pain trajectories and 30-day readmissions, respectively, while accounting for multiple hospitalizations per patient. RESULTS: Three clusters of hospitalizations based on pain trajectories were identified: slow (n=99), moderate (n=207), and rapid (n=44) decrease in pain scores. In multivariable analysis, SCD complications, female gender, and affective disorders were associated with clusters with slow or moderate decrease in pain scores (compared to rapid decrease). Although univariate analysis found that the cluster with moderate decrease in pain scores was associated with lower odds of 30-day readmissions compared to the cluster with slow decrease, it was non-significant in multivariable analysis. SCD complications were associated with higher odds of 30-day readmissions and older age was associated with lower odds of 30-day readmissions. CONCLUSIONS: Our results highlight variability in pain trajectories among patients with SCD experiencing VOC and provide a novel approach for identifying subgroups of patients that could benefit from more intensive follow-up.
BACKGROUND: Vaso-occlusive crises (VOC) are the hallmark of sickle cell disease (SCD), with higher severity among hospitalized patients. Clustering hospitalizations with similar pain trajectories could identify vulnerable patient subgroups. Aims were to (1) identify clusters of hospitalizations based on pain trajectories; (2) identify factors associated with these clusters; and (3) determine the association between these clusters and 30-day readmissions. METHODS: We retrospectively included 350 VOC hospitalizations from 2013-2016 among 59 patients. Finite mixture modeling identified clusters of hospitalizations from intercepts and slopes of pain trajectories during the hospitalization. Generalized estimating equations for multinomial and logistic models were used to identify factors associated with clusters of hospitalizations based on pain trajectories and 30-day readmissions, respectively, while accounting for multiple hospitalizations per patient. RESULTS: Three clusters of hospitalizations based on pain trajectories were identified: slow (n=99), moderate (n=207), and rapid (n=44) decrease in pain scores. In multivariable analysis, SCD complications, female gender, and affective disorders were associated with clusters with slow or moderate decrease in pain scores (compared to rapid decrease). Although univariate analysis found that the cluster with moderate decrease in pain scores was associated with lower odds of 30-day readmissions compared to the cluster with slow decrease, it was non-significant in multivariable analysis. SCD complications were associated with higher odds of 30-day readmissions and older age was associated with lower odds of 30-day readmissions. CONCLUSIONS: Our results highlight variability in pain trajectories among patients with SCD experiencing VOC and provide a novel approach for identifying subgroups of patients that could benefit from more intensive follow-up.
Authors: James L Levenson; Donna K McClish; Bassam A Dahman; Viktor E Bovbjerg; Vanessa de A Citero; Lynne T Penberthy; Imoigele P Aisiku; John D Roberts; Susan D Roseff; Wally R Smith Journal: Psychosom Med Date: 2007-12-24 Impact factor: 4.312
Authors: Noah M Ivers; Karen Tu; Jill Francis; Jan Barnsley; Baiju Shah; Ross Upshur; Alex Kiss; Jeremy M Grimshaw; Merrick Zwarenstein Journal: Implement Sci Date: 2010-12-17 Impact factor: 7.327