Better prognosis in surgical aortic valve replacement patients with lower red cell distribution width: A MIMIC-IV database study

Author:

Ruan LianchengORCID,Zhu Lingxiao,Su Lang,Hu Sheng,Wang Silin,Guo Qiang,Wan Bingen,Qiu Shengyu,Zhang Yang,Wei YipingORCID

Abstract

Background Surgical aortic valve replacement (SAVR) currently stands as a primary surgical intervention for addressing aortic valve disease in patients. This retrospective study focused on the role of the red blood cell distribution width (RDW) in predicting adverse outcomes among SAVR patients. Methods The subjects for this study were exclusively derived from the Medical Information Mart for Intensive Care database (MIMIC IV 2.0). Kaplan‒Meier (K-M) curves and Cox proportional hazards regression models were employed to assess the correlation between RDW, one-year mortality, and postoperative atrial fibrillation (POAF). The smooth-fitting curves were used to observe the relative risk (RR) of RDW in one-year mortality and POAF. Furthermore, time-dependent receiver operating characteristic (ROC) curves, the continuous-net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed for comprehensive assessment of the prognostic value of RDW. Results Analysis of RDW revealed a distinctive inverted U-shaped relationship with one-year mortality, while its association with POAF appeared nearly linear. Cox multiple regression models showed that RDW > 14.35%, along with preoperative potassium concentration and perioperative red blood cell transfusion, were significantly linked to one-year mortality (K-M curves, log-rank P < 0.01). Additionally, RDW was associated with both POAF and prolonged hospital stays (P < 0.05). There was no significant difference in length of stay in ICU. Notably, the inclusion of RDW in the predictive models substantially enhanced its performance. This was evidenced by the time-dependent ROC curve (AUC = 0.829), NRI (P< 0.05), IDI (P< 0.05), and K-M curves (log-rank P< 0.01). Conclusions RDW serves as a robust prognostic indicator for SAVR patients, offering a novel means of anticipating adverse postoperative events.

Funder

National Natural Science Foundation of China

Key Research and Development Program of Jiangxi Province

Jiangxi Provincial Department of Science and Technology

Publisher

Public Library of Science (PLoS)

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