目的以《中国医院质量安全管理第4-8部分:医疗管理医院感染管理》对于结肠手术的影响为例,评估《中国医院质量安全管理》团体标准应用对于外科手术服务质量的影响及其实施效果。方法以医院信息系统为依托,分别提取2018年10月1日-2019年9...目的以《中国医院质量安全管理第4-8部分:医疗管理医院感染管理》对于结肠手术的影响为例,评估《中国医院质量安全管理》团体标准应用对于外科手术服务质量的影响及其实施效果。方法以医院信息系统为依托,分别提取2018年10月1日-2019年9月30日(标准实施前)与2019年10月1日-2020年9月30日(标准实施后)结肠手术患者数据,分析与比较患者年龄、共病等基础特征,并采用间断性时间序列分析(ITSA),评估标准实施对于不良事件、住院时长的影响。结果研究共纳入4442例结肠手术患者,其中标准实施前的患者为2936例,实施后的患者为1506例。单因素分析显示,与标准实施前相比,标准实施后的结肠手术患者共病指数较高(2.25±4.95 vs 3.95±6.14,P<0.0001),恶性肿瘤患病率较高(14.03%vs 26.10%,P<0.0001),平均住院日增加(10.95±8.52 vs 13.29±9.50,P<0.0001),但不良事件发生率并未提高(1.12%vs 0.86%,P=0.4164)。ITSA显示,标准实施后,不良事件发生率与住院时长均无显著增加。结论医院质量安全管理标准的实施可在一定程度上应对患者病情的加重对于临床结局造成的负面影响,保障医疗服务的质量安全。展开更多
BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation gr...BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.展开更多
目的探究结直肠息肉内镜下黏膜切除术(EMR)与冷圈套息肉切除术(CSP)治疗肠息肉完整切除率及对炎症因子表达的影响。方法选取夏邑县人民医院2108年12月至2022年4月收治的结直肠息肉患者53例为研究对象,依据手术方式不同分为研究组25例与...目的探究结直肠息肉内镜下黏膜切除术(EMR)与冷圈套息肉切除术(CSP)治疗肠息肉完整切除率及对炎症因子表达的影响。方法选取夏邑县人民医院2108年12月至2022年4月收治的结直肠息肉患者53例为研究对象,依据手术方式不同分为研究组25例与对照组28例。研究组采用EMR,对照组采用CSP,观察2组息肉完整切除率和手术前、手术后1 d 2组患者的血清疼痛因子[(前列腺素E2(PGE2)和物质P(SP)]、儿茶酚胺类代谢产物[肾上腺素(E)、去甲肾上腺素(NE)和皮质醇(Cor)]、血管内皮生长因子(VEGF)和血栓素B2(TXB2)水平。结果研究组完整切除率与对照组比较,差异无统计学意义(96.00%比92.86%,P>0.05);2组术前血清PGE_(2)、SP、E、Cor、NE、VEGF和TXB2水平比较差异均无统计学意义(P>0.05);术后1 d,研究组PGE_(2)、SP、E、Cor、NE、VEGF和TXB2水平均低于对照组(P<0.05)。结论结直肠息肉EMR与CSP治疗结直肠息肉的完整切除率均高,疗效相似,但EMR在改善血清疼痛因子、应激反应产物、血管内皮生长因子和血栓素B2水平方面优于CSP。展开更多
目的探究网状体家族基因1(Reticulon family gene1,RTN1)在结直肠癌(Colorectal cancer,CRC)组织中的表达及意义。方法通过利用TIMER、GEO和UALCAN数据库,对RTN1在结直肠癌及周围正常组织中的表达差异进行分析;应用GEPIA2在线分析工具分...目的探究网状体家族基因1(Reticulon family gene1,RTN1)在结直肠癌(Colorectal cancer,CRC)组织中的表达及意义。方法通过利用TIMER、GEO和UALCAN数据库,对RTN1在结直肠癌及周围正常组织中的表达差异进行分析;应用GEPIA2在线分析工具分析RTN1在CRC中的表达模式和相关基因;借助STRING数据库挖掘与RTN1相互关联的蛋白;通过蛋白免疫印迹试验和免疫组化试验对以上生物信息学分析结果进行验证,以深入探索RTN1在CRC组织及周围正常组织中的差异表达。结果根据TIMER数据库的分析,RTN1在结直肠癌组织中的表达水平明显低于正常组织(均P<0.001)。GEO数据库中的四个数据集也表明,RTN1在CRC中的表达水平显著低于正常组织(均P<0.01)。UALCAN数据库进一步证实了这一结果,无论在哪种临床属性下,RTN1在CRC中的表达都低于正常组织。GEPIA2的分析结果表明,无论是结肠癌还是直肠癌,RTN1的表达均显著低于相邻的正常组织(均P<0.05),并且与RTN1表达呈正相关的四个基因(CSF1R、CD4、SLC9A9和ARHGEF6)在结直肠癌中也表现出类似的表达模式。STRING数据库的挖掘显示,RTN1可能与SPAST、REEP5、ATL3、ATL2、RTN4、ATL1、RTN2、TMEM33、BACE1和MANF等蛋白有交互作用。蛋白免疫印迹和免疫组化试验均证实,RTN1在CRC组织中的表达低于邻近正常组织。结论RTN1在结直肠癌组织中的表达低于癌旁正常组织,这一发现可能对CRC的诊断及治疗具有重要意义。展开更多
文摘目的以《中国医院质量安全管理第4-8部分:医疗管理医院感染管理》对于结肠手术的影响为例,评估《中国医院质量安全管理》团体标准应用对于外科手术服务质量的影响及其实施效果。方法以医院信息系统为依托,分别提取2018年10月1日-2019年9月30日(标准实施前)与2019年10月1日-2020年9月30日(标准实施后)结肠手术患者数据,分析与比较患者年龄、共病等基础特征,并采用间断性时间序列分析(ITSA),评估标准实施对于不良事件、住院时长的影响。结果研究共纳入4442例结肠手术患者,其中标准实施前的患者为2936例,实施后的患者为1506例。单因素分析显示,与标准实施前相比,标准实施后的结肠手术患者共病指数较高(2.25±4.95 vs 3.95±6.14,P<0.0001),恶性肿瘤患病率较高(14.03%vs 26.10%,P<0.0001),平均住院日增加(10.95±8.52 vs 13.29±9.50,P<0.0001),但不良事件发生率并未提高(1.12%vs 0.86%,P=0.4164)。ITSA显示,标准实施后,不良事件发生率与住院时长均无显著增加。结论医院质量安全管理标准的实施可在一定程度上应对患者病情的加重对于临床结局造成的负面影响,保障医疗服务的质量安全。
基金the Fujian Province Clinical Key Specialty Construction Project,No.2022884Quanzhou Science and Technology Plan Project,No.2021N034S+1 种基金The Youth Research Project of Fujian Provincial Health Commission,No.2022QNA067Malignant Tumor Clinical Medicine Research Center,No.2020N090s.
文摘BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.
文摘目的探究结直肠息肉内镜下黏膜切除术(EMR)与冷圈套息肉切除术(CSP)治疗肠息肉完整切除率及对炎症因子表达的影响。方法选取夏邑县人民医院2108年12月至2022年4月收治的结直肠息肉患者53例为研究对象,依据手术方式不同分为研究组25例与对照组28例。研究组采用EMR,对照组采用CSP,观察2组息肉完整切除率和手术前、手术后1 d 2组患者的血清疼痛因子[(前列腺素E2(PGE2)和物质P(SP)]、儿茶酚胺类代谢产物[肾上腺素(E)、去甲肾上腺素(NE)和皮质醇(Cor)]、血管内皮生长因子(VEGF)和血栓素B2(TXB2)水平。结果研究组完整切除率与对照组比较,差异无统计学意义(96.00%比92.86%,P>0.05);2组术前血清PGE_(2)、SP、E、Cor、NE、VEGF和TXB2水平比较差异均无统计学意义(P>0.05);术后1 d,研究组PGE_(2)、SP、E、Cor、NE、VEGF和TXB2水平均低于对照组(P<0.05)。结论结直肠息肉EMR与CSP治疗结直肠息肉的完整切除率均高,疗效相似,但EMR在改善血清疼痛因子、应激反应产物、血管内皮生长因子和血栓素B2水平方面优于CSP。
文摘目的探究网状体家族基因1(Reticulon family gene1,RTN1)在结直肠癌(Colorectal cancer,CRC)组织中的表达及意义。方法通过利用TIMER、GEO和UALCAN数据库,对RTN1在结直肠癌及周围正常组织中的表达差异进行分析;应用GEPIA2在线分析工具分析RTN1在CRC中的表达模式和相关基因;借助STRING数据库挖掘与RTN1相互关联的蛋白;通过蛋白免疫印迹试验和免疫组化试验对以上生物信息学分析结果进行验证,以深入探索RTN1在CRC组织及周围正常组织中的差异表达。结果根据TIMER数据库的分析,RTN1在结直肠癌组织中的表达水平明显低于正常组织(均P<0.001)。GEO数据库中的四个数据集也表明,RTN1在CRC中的表达水平显著低于正常组织(均P<0.01)。UALCAN数据库进一步证实了这一结果,无论在哪种临床属性下,RTN1在CRC中的表达都低于正常组织。GEPIA2的分析结果表明,无论是结肠癌还是直肠癌,RTN1的表达均显著低于相邻的正常组织(均P<0.05),并且与RTN1表达呈正相关的四个基因(CSF1R、CD4、SLC9A9和ARHGEF6)在结直肠癌中也表现出类似的表达模式。STRING数据库的挖掘显示,RTN1可能与SPAST、REEP5、ATL3、ATL2、RTN4、ATL1、RTN2、TMEM33、BACE1和MANF等蛋白有交互作用。蛋白免疫印迹和免疫组化试验均证实,RTN1在CRC组织中的表达低于邻近正常组织。结论RTN1在结直肠癌组织中的表达低于癌旁正常组织,这一发现可能对CRC的诊断及治疗具有重要意义。
