Ding Qunzhe, Zhang Yi, Zhang Zihao, Huang Peijie, Tian Rui, Zhou Zhigang, Wang Ruilan, Xie Yun
School of Information Management, Wuhan University, Wuhan, Hubei, China.
Department of Rheumatology and Immunology, Changzheng Hospital, Naval Military Medical University, Shanghai, China.
Front Oncol. 2025 Mar 19;15:1520512. doi: 10.3389/fonc.2025.1520512. eCollection 2025.
Pneumonia is a leading cause of morbidity and mortality among patients with cancer, and survival time is a primary concern. Despite their importance, there is a dearth of accurate predictive models in clinical settings. This study aimed to determine the incidence of pneumonia as a cause of death in patients with cancer, analyze trends and risk factors associated with mortality, and develop corresponding predictive models.
We included 26,938 cancer patients in the United States who died from pneumonia between 1973 and 2020, as identified through the Surveillance, Epidemiology, and End Results (SEER) program. Cox regression analysis was used to ascertain the prognostic factors for patients with cancer. The CatBoost model was constructed to predict survival rates via a cross-validation method. Additionally, our model was validated using a cohort of cancer patients from our institution and deployed via a free-access software interface.
The most common cancers resulting in pneumonia-related deaths were prostate (n=7300) and breast (n=5107) cancers, followed by lung and bronchus (n=2839) cancers. The top four cancer systems were digestive (n=5882), endocrine (n=5242), urologic (n=5198), and hematologic (n=3104) systems. The majority of patients were over 70 years old (57.7%), and 54.4% were male. Our CatBoost model demonstrated high precision and accuracy, outperforming other models in predicting the survival of cancer patients with pneumonia (6-month AUC=0.8384,1-year AUC=0.8255,2-year AUC=0.8039, and 3-year AUC=0.7939). The models also revealed robust performance in an external independent dataset (6-month AUC=0.689; 1-year AUC=0.838; 2-year AUC=0.834; and 3-year AUC=0.828). According to the SHAP explanation analysis, the top five factors affecting prognosis were surgery, stage, age, site, and sex; surgery was the most significant factor in both the short-term (6 months and 1 year) and long-term (2 years and 3 years) prognostic models; surgery improved patient prognosis for digestive and endocrine tumor sites with respect to both short- and long-term outcomes but decreased the prognosis of urological and hematologic tumors.
Pneumonia remains a major cause of illness and death in patients with cancer, particularly those with digestive system cancers. The early identification of risk factors and timely intervention may help mitigate the negative impact on patients' quality of life and prognosis, improve outcomes, and prevent early deaths caused by infections, which are often preventable.
肺炎是癌症患者发病和死亡的主要原因之一，生存时间是首要关注点。尽管其重要性，但临床环境中缺乏准确的预测模型。本研究旨在确定肺炎作为癌症患者死因的发生率，分析与死亡率相关的趋势和风险因素，并开发相应的预测模型。
我们纳入了美国26938例在1973年至2020年间死于肺炎的癌症患者，这些患者通过监测、流行病学和最终结果（SEER）计划得以识别。采用Cox回归分析确定癌症患者的预后因素。通过交叉验证方法构建CatBoost模型来预测生存率。此外，我们的模型在来自本机构的一组癌症患者中进行了验证，并通过免费访问的软件界面进行部署。
导致肺炎相关死亡的最常见癌症是前列腺癌（n = 7300）和乳腺癌（n = 5107），其次是肺癌和支气管癌（n = 2839）。前四大癌症系统是消化系统（n = 5882）、内分泌系统（n = 5242）、泌尿系统（n = 5198）和血液系统（n = 3104）。大多数患者年龄超过70岁（57.7%），男性占54.4%。我们的CatBoost模型显示出高精度和准确性，在预测肺炎癌症患者的生存方面优于其他模型（6个月AUC = 0.8384，1年AUC = 0.8255，2年AUC = 0.8039，3年AUC = 0.7939）。该模型在外部独立数据集中也表现出强大的性能（6个月AUC = 0.689；1年AUC = 0.838；2年AUC = 0.834；3年AUC = 0.828）。根据SHAP解释分析，影响预后的前五个因素是手术、分期、年龄、部位和性别；手术在短期（6个月和1年）和长期（2年和3年）预后模型中都是最重要的因素；手术在短期和长期结果方面改善了消化系统和内分泌肿瘤部位患者的预后，但降低了泌尿系统和血液系统肿瘤的预后。
肺炎仍然是癌症患者发病和死亡的主要原因，尤其是消化系统癌症患者。早期识别风险因素并及时干预可能有助于减轻对患者生活质量和预后的负面影响，改善结局，并预防通常可预防的感染导致的早期死亡。
