Objective: To explore the clinical characteristics of nosocomial infection in newly diagnosed multiple myeloma(NDMM) patients, and establish a predictive nomogram model.
Methods: The clinical data of 164 patients with MM who were treated in Shanxi Bethune Hospital from January 2017 to December 2021 were retrospectively analyzed. The clinical characteristics of infection were analyzed. Infections were grouped as microbiologically defined infections and clinically defined infections. Univariate and multivariate regression models were used to analyze the risk factors of infection. A nomogram was established.
Results: 164 patients with NDMM were included in this study, and 122 patients (74.4%) were infected. The incidence of clinically defined infection was the highest (89 cases, 73.0%), followed by microbial infection (33 cases, 27.0%). Among 122 cases of infection, 89 cases (73.0%) had CTCAE grade 3 or above. The most common site of infection was lower respiratory in 52 cases (39.4%), upper respiratory tract in 45 cases (34.1%), and urinary system in 13 cases (9.8%). Bacteria(73.1%) were the main pathogens of infection. Univariate analysis showed that ECOG ≥2, ISS stage Ⅲ, C-reactive protein ≥10 mg/L, serum Creatinine ≥177 μmol/L had higher correlation with nosocomial infection in patients with NDMM. Multivariate regression analysis showed that C-reactive protein ≥10 mg/L (P＜0.001), ECOG ≥2 (P=0.011) and ISS stage Ⅲ （P=0.024） were independent risk factors for infection in patients with NDMM. The nomogram model established based on this has good accuracy and discrimination. The C-index of the nomogram was 0.779（95%CI: 0.682-0.875）. Median follow-up time was 17.5 months, the median OS of the two groups was not reached (P=0.285).
Conclusion: Patients with NDMM are prone to bacterial infection during hospitalization. C-reactive protein ≥10 mg/L, ECOG ≥2 and ISS stage Ⅲ are the risk factors of nosocomial infection in NDMM patients. The nomogram prediction model established based on this has great prediction value.
题目: 新诊断多发性骨髓瘤患者院内感染的临床特征及列线图模型的建立.
目的: 探讨新诊断多发性骨髓瘤（NDMM）患者首次住院治疗院内感染的临床特征，并建立预测感染的列线图模型.
方法: 回顾性分析2017年1月1日至2021年12月31日164例首次入院NDMM患者的临床资料，分析院内感染的临床特征，其中感染被分为微生物感染及临床定义的感染。单因素及多因素回归模型分析感染发生的危险因素，同时建立感染的列线图模型.
结果: 164例NDMM患者纳入本研究，122例(74.4%)出现感染。临床定义的感染发生率最高（89例，73.0%），其次为微生物感染（33例，27.0%）。122例感染患者中89例（73.0%）的感染事件为CTCAE 3级及以上。最常见的感染病种为下呼吸道感染（52例，39.4%），其次为上呼吸道感染（45例，34.1%）和泌尿系统感染（13例，9.8%）。细菌是最常见的病原体（73.1%）。单因素分析结果显示，ECOG≥2分、ISS分期Ⅲ期、C反应蛋白≥10 mg/L、血清肌酐≥177 μmol/L与NDMM患者首次住院期间感染有相关性。多因素回归分析结果显示，C反应蛋白≥10 mg/L（P＜0.001）、ECOG≥2分（P=0.011）及ISS分期Ⅲ期（P=0.024）是NDMM患者院内感染的独立危险因素。据此建立的列线图模型，验证后预测值与实测值相同，一致性指数达到 0.779（95%CI：0.682-0.875），表明，该模型有较好的预测能力、良好的精确度和区分度。中位随访17.5个月，两组患者的中位总生存期（OS）均未达到（P=0.285）.
结论: NDMM患者首次住院期间易合并感染,明确的病原体中以细菌为主。C反应蛋白≥10 mg/L、ECOG≥2分及ISS分期Ⅲ期是NDMM患者发生感染的危险因素，相关列线图模型的建立对临床筛查高危人群具有一定指导意义.
Keywords: infection; multiple myeloma; nomograms; prediction model; risk factors.