Hădăreanu Călin-Dinu, Hădăreanu Diana-Ruxandra, Stoiculescu Flavia-Mihaela, Berceanu Mihaela-Corina, Donoiu Ionuț, Istrătoaie Octavian, Florescu Cristina, Novac Marius-Bogdan, Raicea Victor-Cornel
Doctoral School, University of Medicine and Pharmacy of Craiova, 2 Petru Rares St., 200349 Craiova, Romania.
Department of Cardiovascular Surgery, Clinical Emergency County Hospital of Craiova, 1 Tabaci St., 200642 Craiova, Romania.
J Clin Med. 2025 Apr 16;14(8):2747. doi: 10.3390/jcm14082747.
Despite advances in surgical techniques and perioperative management, reliable intraoperative predictors of adverse postoperative outcomes in cardiac surgery remain elusive. This study aimed to identify perioperative factors associated with prolonged intensive care unit (ICU) stay and in-hospital mortality while defining actionable thresholds. : A retrospective analysis was conducted on 130 adult cardiac surgery patients (with a median age of 61 years, 66.2% men) from October 2022 to November 2024. Data on preoperative risk factors, intraoperative variables (cardiopulmonary bypass time-CPBT, aortic cross-clamp time-AXCT), and postoperative outcomes (ICU length of stay, in-hospital mortality) were extracted from electronic medical records. : Prolonged ICU stay (≥7 days) occurred in 38.5% of patients, and in-hospital mortality was 10%. AXCT was the sole independent predictor of prolonged ICU stay (OR = 1.046, 95% CI = 1.014-1.080, = 0.005), with a 110-min cut-off (sensitivity 71%, specificity 61%, AUC = 0.729). A Kaplan-Meier analysis showed significantly longer ICU stays above this threshold ( = 0.006). For in-hospital mortality, prolonged CPBT (OR = 1.030, 95% CI = 1.003-1.057, = 0.030), emergency surgery (OR = 0.043, 95% CI = 0.002-0.863, = 0.040), and higher AXCT (OR = 0.965, 95% CI = 0.934-0.997, = 0.034) were the independent predictors. A receiver operating characteristic analysis identified 140 min for AXCT (sensitivity 67%, specificity 70%, AUC = 0.707) and 227 min for CPBT (sensitivity 83%, specificity 69%, AUC = 0.824) as the optimal cut-offs. A combined model (emergency surgery yes/no, AXCT > 140 min, CPBT > 227 min) yielded excellent discrimination (AUC = 0.846). : These findings suggest perioperative benchmarks that may guide surgical teams in refining operative strategies, reducing ICU resource utilization, and improving survival following cardiac surgery.
尽管手术技术和围手术期管理取得了进展，但心脏手术术后不良结局的可靠术中预测指标仍然难以捉摸。本研究旨在确定与重症监护病房（ICU）住院时间延长和院内死亡率相关的围手术期因素，同时确定可操作的阈值。：对2022年10月至2024年11月期间的130例成年心脏手术患者（中位年龄61岁，男性占66.2%）进行了回顾性分析。从电子病历中提取术前危险因素、术中变量（体外循环时间-CPBT、主动脉阻断时间-AXCT）和术后结局（ICU住院时间、院内死亡率）的数据。：38.5%的患者出现ICU住院时间延长（≥7天），院内死亡率为10%。AXCT是ICU住院时间延长的唯一独立预测因素（OR = 1.046，95%CI = 1.014 - 1.080，P = 0.005），截断值为110分钟（敏感性71%，特异性61%，AUC = 0.729）。Kaplan-Meier分析显示，超过该阈值的患者ICU住院时间显著延长（P = 0.006）。对于院内死亡率，CPBT延长（OR = 1.030，95%CI = 1.003 - 1.057，P = 0.030）、急诊手术（OR = 0.043，95%CI = 0.002 - 0.863，P = 0.040）和较高的AXCT（OR = 0.965，95%CI = 0.934 - 0.997，P = 0.034）是独立预测因素。受试者工作特征分析确定AXCT的最佳截断值为140分钟（敏感性67%，特异性70%，AUC = 0.707），CPBT的最佳截断值为227分钟（敏感性83%，特异性69%，AUC = 0.824）。一个联合模型（急诊手术是/否、AXCT > 140分钟、CPBT > 227分钟）具有出色的辨别能力（AUC = 0.846）。：这些发现提示了围手术期基准，可能指导手术团队优化手术策略、减少ICU资源利用并提高心脏手术后的生存率。
