预测年轻乳腺癌患者生存率列线图的建立和验证

目的:构建预测年轻乳腺癌患者生存情况的列线图，以期帮助临床诊疗。方法:收集SEER数据库中5 525例年轻乳腺癌患者的临床信息，通过单因素Log-rank检验和多因素Cox生存分析筛选出独立预后因素,用于构建预测患者3、5年总生存率(overall survival,OS)和癌症特异性生存率（cancer special survival,CSS)的列线图，将我院就诊的147例年轻乳腺癌患者作为验证集进行外部验证。结果:单因素和多因素分析结果显示，种族、病理类型、组织学分级、T分期、N分期、M分期、ER状态、HER-2状态、手术方式是与患者OS和CSS相关的独立危险因素，将这些因素纳入并建立预测患者OS和CSS的列线图模型。内部和外部验证结果显示模型具有良好的预测性能。基于建立的OS和CSS列线图模型对患者进行了风险分层，能够准确地将年轻乳腺癌患者分成预后有显著差异的三个风险亚组。结论:本研究构建的预测模型能较为准确的预测年轻乳腺癌患者的预后情况，为临床的诊疗提供科学依据。     

Postoperative mortality and morbidity after D2 lymphadenectomy for gastric cancer:A retrospective cohort study

BACKGROUND Surgery for gastric cancer is a complex procedure and lymphadenectomy is often mandatory.Postoperative mortality and morbidity after curative gastric cancer surgery is not insignificant.AIM To evaluate the factors determining mortality and morbidity in a population of patients undergoing R0 resection and D2 lymphadenectomy for gastric cancer.METHODS A retrospective analysis of clinical data and pathological characteristics(age,sex,primary site of the tumor,Lauren histotype,number of positive lymph nodes resected,number of negative lymph nodes resected,and depth of invasion as defined by the standard nomenclature)was conducted in patients with gastric cancer.For each patient we calculated the Kattan’s score.We arbitrarily divided the study population of patients into two groups based on the nomogram score(<100 points or≥100 points).Prespecified subgroups in these analyses were defined according to age(≤65 years or>65 years),and number of lymph nodes retrieved(≤35 lymph nodes or>35 lymph nodes).Uni-and multivariate analysis of clinical and pathological findings were performed to identify the factors affecting postoperative mortality and morbidity.RESULTS One-hundred and eighty-six patients underwent a curative R0 resection with D2 lymphadenectomy.Perioperative mortality rate was 3.8%(7 patients);a higher mortality rate was observed in patients aged>65 years(P=0.002)and in N+patients(P=0.04).Following univariate analysis,mortality was related to a Kattan’s score≥100 points(P=0.04)and the presence of advanced gastric cancer(P=0.03).Morbidity rate was 21.0%(40 patients).Surgical complications were observed in 17 patients(9.1%).A higher incidence of morbidity was observed in patients where more than 35 lymph nodes were harvested(P=0.0005).CONCLUSION Mortality and morbidity rate are higher in N+and advanced gastric cancer patients.The removal of more than 35 lymph nodes does not lead to an increase in mortality.     

ICU患者发生产ESBLs革兰阴性杆菌 感染的危险因素分析及预测模型的建立

[摘要]　目的　探讨ICU患者发生产超广谱β-内酰胺酶（extended spectrum beta-lactamases, ESBLs）革兰阴性杆菌感染的危险因素，并构建相关预测模型。方法　选取2017年5月—2021年4月我院ICU发生大肠埃希菌或肺炎克雷伯菌感染的189例患者作为研究对象，收集患者的临床资料，使用单因素分析、LASSO回归和多因素Logistic回归分析ICU患者30 d内发生产ESBLs革兰阴性杆菌感染的危险因素，并据此建立列线图预测模型。结果　急性生理与慢性健康评分≥16分、留置尿管时长≥7 d、抑酸剂使用时长≥3 d、第三代头孢菌素使用时长≥3 d、抗菌药物联用时长≥3 d和ICU住院时间≥15 d是ICU患者30 d内发生产ESBLs革兰阴性杆菌感染的危险因素（P均＜0.05）。依此建立预测ICU患者30 d内发生产ESBLs革兰阴性杆菌感染的列线图风险模型，模型验证结果显示C-index为0.795，校正曲线趋近于理想曲线，AUC为0.807（95%CI：0.775～0.839），在2%～81%预测范围内，列线图净获益。结论　ICU患者30 d内发生产ESBLs革兰阴性杆菌感染的危险因素包括APACHEⅡ评分≥16分、留置尿管时长≥7 d、抑酸剂使用时长≥3 d、第三代头孢菌素使用时长≥3 d、抗菌药物联用时长≥3 d和ICU住院时间≥15 d，据此构建的列线图模型能有效预测ICU患者30 d内发生产ESBLs革兰阴性杆菌感染的风险概率，具有一定的临床价值。     

新型冠状病毒肺炎流行期间基于列线图预测隔离留观患者的焦虑抑郁情绪

<正>新型冠状病毒肺炎(COVID-19)因其传播速度快、传染性强及人群易感等特点,已被我国列入乙类传染病并按甲类传染病管理[1]。发热门诊隔离留观的患者具有发热等类新冠症状且面对环境的改变对心理可能带来负面影响。故本研究探讨COVID-19流行期间发热门诊隔离留观患者焦虑抑郁情绪及相关因素,以期尽早识别高危患者并进行心理干预治疗。     

Comprehensive analysis of the relationship between competitive endogenous RNA (ceRNA) networks and tumor infiltrating-cells in hepatocellular carcinoma

BackgroundThe innovation of immune checkpoint blockade (ICB) represents a promising shift in the treatment of advanced hepatocellular carcinoma (HCC). However, response to ICB has varied largely due to the high tumor heterogeneity and complex tumor microenvironment (TME). The competitive endogenous RNA (ceRNA) network also plays an important role in tumor occurrence and progression, but its relation with tumor-infiltrating immune cells (TICs) remains largely unexplored in HCC. The overriding objective of our study was thus to construct a prognosis-related risk model and to further evaluate the relationship between ceRNA networks and TICs.MethodsDifferentially expressed gene (DEG) analysis was performed to identify the differentially expressed RNAs. Lasso and multivariable Cox regression analyses were used to construct risk models, which were assessed by the area under the receiver operating characteristic curve (AUC of ROC) and Kaplan-Meier (K-M) curves. Then, a single-sample gene set enrichment analysis (ssGSEA) algorithm was adopted to dissect the TICs in HCC samples. Nomograms were constructed and calibration curves were used to verify the discrimination and accuracy of the nomograms. Finally, integration analysis was performed to validate the correlation of ceRNA and TICs.ResultsIn the study, 7 differentially expressed RNAs [5 messenger RNA s (mRNAs) and 2 micro RNAs (miRNAs)] were incorporated to construct a ceRNA risk model. The AUC of the 1-, 3-, and 5-year overall survival (OS) were 0.784, 0.685, and 0.691 respectively. Likewise, 7 types TICs were in the TICs signature model and the AUC of the 1-, 3-, and 5-year OS were 0.706, 0.731, and 0.721 respectively. The integration analysis showed that 7 pairs of mRNA-TICs and 1 pair of miRNA-TICs had a close relation (all correlation coefficients >0.2, P<0.001).ConclusionsThrough constructing two risk models based on ceRNA network and TICs, we identified the hub RNAs and key TICs in the progression and prognosis of HCC, and further explored the relationship between ceRNA and TME. Importantly, targeting these hub RNAs may facilitate the remodeling of the TME and be a potential therapeutic alternative to enhancing the response to ICB, thus improving the prognosis of HCC patients.     

住院癌症患者衰弱风险预测模型的构建与验证

目的 调查住院癌症患者发生衰弱的危险因素，构建风险预测模型并检验预测效果。方法 选取广州市2所三级甲等医院住院癌症患者570例，分为建模集422例和验证集148例。采用一般资料调查表、Fried衰弱表型、安德森症状评估量表、医院焦虑抑郁量表、中文版失志量表、营养风险筛查2002、Barthel指数量表进行调查，同期收集患者的白细胞介素-6、C-反应蛋白等实验室指标，应用logistic回归分析筛选衰弱的独立危险因素，构建风险预测模型。结果 住院癌症患者衰弱发生率为33.7%,衰弱的影响因素分别为：造口、营养状况、抑郁、神经心理症状群、消化道症状群、D-二聚体、白细胞介素-6。建模集和验证集ROC曲线下面积为0.788、0.735;Brier得分为0.205,校准斜率为0.625,决策曲线分析表明模型具有一定的临床可用性。结论 住院癌症患者衰弱发生率较高，构建的列线图预测模型具有较好的风险预测价值，可有效识别和筛选住院癌症衰弱高危人群。