Development and validation of prediction scores for the outcome associated with persistent inflammation, immunosuppression, and catabolism syndrome among patients with trauma

Author:

Okada KazuhiroORCID,Ohde Sachiko,Yagi Takanori,Hara Yoshiaki,Yokobori Shoji

Abstract

BackgroundPersistent inflammation, immunosuppression, and catabolism syndrome (PICS) has impacted on long-term prognosis of patients with trauma. We aimed to identify patients with trauma at risk of PICS-related complications early in the intensive care unit (ICU) course.MethodsA single-center retrospective cohort study was conducted. All consecutive patients with trauma who had stayed in the ICU for >7 days were included in the study. We developed the prediction score for the incidence of PICS-related outcomes in the derivation cohort for the initial period and then evaluated in the validation cohort for the subsequent period. Other outcomes were also assessed using the score.ResultsIn total, 170 and 133 patients were included in the derivation and validation cohorts, respectively. The prediction score comprised the variables indicating PICS presence, including a maximum value of C-reactive protein >15 mg/dL, minimum value of albumin <2.5 g/dL, and an episode of nosocomial infection for the first 7 days after admission. A score of 1 was assigned to each variable. The area under the receiver operating characteristic curve of the score to predict PICS incidence was 0.74 (95% CI 0.66 to 0.81) and 0.72 (95% CI 0.64 to 0.81) in the derivation and validation cohorts, respectively. The higher score was also significantly associated with a higher Sequential Organ Failure Assessment score at day 14, a longer duration of mechanical ventilation, a longer length of stay in ICU, and experienced multiple episodes of infection. Similar results were obtained in the validation cohort.ConclusionsOur scoring system could predict the outcomes associated with PICS among patients with trauma. Because the score comprised the parameters measured for the first 7 days during the ICU course, it could contribute to identifying patients at a high risk of unfavorable outcome earlier.Level of evidenceMultivariate prediction models; level IV.

Publisher

BMJ

Subject

Critical Care and Intensive Care Medicine,Surgery

Reference28 articles.

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