Perry Whitney A, Chow Jennifer K, Nelson Jason, Kent David M, Snydman David R
Division of Geographic Medicine and Infectious Diseases, Tufts Medical Center, Boston, MA.
Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA.
Transplant Direct. 2023 Nov 2;9(12):e1542. doi: 10.1097/TXD.0000000000001542. eCollection 2023 Dec.
Invasive infection remains a dangerous complication of heart transplantation (HT). No objectively defined set of clinical risk factors has been established to reliably predict infection in HT. The aim of this study was to develop a clinical prediction model for use at 1 mo post-HT to predict serious infection by 1 y.
A retrospective cohort study of HT recipients (2000-2018) was performed. The composite endpoint included cytomegalovirus (CMV), herpes simplex or varicella zoster virus infection, blood stream infection, invasive fungal, or nocardial infection occurring 1 mo to 1 y post-HT. A least absolute shrinkage and selection operator regression model was constructed using 10 candidate variables. A concordance statistic, calibration curve, and mean calibration error were calculated. A scoring system was derived for ease of clinical application.
Three hundred seventy-five patients were analyzed; 93 patients experienced an outcome event. All variables remained in the final model: aged 55 y or above, history of diabetes, need for renal replacement therapy in first month, CMV risk derived from donor and recipient serology, use of induction and/or early lymphodepleting therapy in the first month, use of trimethoprim-sulfamethoxazole prophylaxis at 1 mo, lymphocyte count under 0.75 × 10cells/µL at 1 mo, and inpatient status at 1 mo. Good discrimination (C-index 0.80) and calibration (mean absolute calibration error 3.6%) were demonstrated.
This model synthesizes multiple highly relevant clinical parameters, available at 1 mo post-HT, into a unified, objective, and clinically useful prediction tool for occurrence of serious infection by 1 y post-HT.
侵袭性感染仍然是心脏移植（HT）的一种危险并发症。尚未建立一套客观定义的临床危险因素来可靠地预测HT中的感染。本研究的目的是开发一种用于HT后1个月的临床预测模型，以预测1年内的严重感染。
对HT受者（2000 - 2018年）进行回顾性队列研究。复合终点包括HT后1个月至1年发生的巨细胞病毒（CMV）、单纯疱疹或水痘带状疱疹病毒感染、血流感染、侵袭性真菌或诺卡菌感染。使用10个候选变量构建最小绝对收缩和选择算子回归模型。计算一致性统计量、校准曲线和平均校准误差。得出一个评分系统以便于临床应用。
分析了375例患者；93例患者发生结局事件。所有变量均保留在最终模型中：年龄55岁及以上、糖尿病史、第一个月需要肾脏替代治疗、供体和受体血清学得出的CMV风险、第一个月使用诱导和/或早期淋巴细胞清除治疗、1个月时使用甲氧苄啶 - 磺胺甲恶唑预防、1个月时淋巴细胞计数低于0.75×10⁹细胞/µL以及1个月时的住院状态。显示出良好的区分度（C指数0.80）和校准（平均绝对校准误差3.6%）。
该模型将HT后1个月时可用的多个高度相关的临床参数综合成一个统一、客观且对HT后1年发生严重感染具有临床实用性的预测工具。
