肝癌术后感染性并发症的危险因素分析及构建预测模型
Analysis of the Risk Factors for Postoperative Infectious Complications in Hepatocellular Carcinoma after Hepatectomy and Construction of a Predictive Model
摘要: 目的:分析肝癌切除术后感染性并发症的危险因素,并构建预测模型。方法:回顾性收集江苏省人民医院宿迁医院2017年6月~2023年6月行肝癌切除术患者的临床资料,采用单因素分析及多因素Logistic回归分析筛选出肝癌切除术后感染性并发症的独立因素,基于独立危险因素建立列线图预测模型,并检验其预测效能。结果:共纳入215例患者,42例患者术后发生感染性并发症,发生率为19.5%。共培养出72株病原菌。Logistic回归结果显示,年龄(≥65岁)、合并糖尿病、腹腔引流管留置时间(≥7 d)及Alb (<35 g/L)为肝癌切除术后感染性并发症发生的危险因素(P < 0.05)。基于以上结果构建列线图预测模型,校准曲线显示该列线图模型的预测发生概率和实际发生概率之间一致性较好,Bootstrap法计算出C-index值为0.678,区分度良好,受试者工作特征曲线下面积(AUC)为0.768 (95% CI: 0.688~0.848),决策分析曲线显示价值性高。结论:年龄(≥65岁)、合并糖尿病、腹腔引流管留置时间(≥7 d)及Alb (<35 g/L)为肝癌切除术后感染性并发症发生的危险因素,基于以上危险因素构建的预测模型具有一定预测价值。
Abstract: Objective: To analyze the risk factors of postoperative infectious complications (PIC) in hepatocellular carcinoma (HCC) after hepatectomy and construct a predictive model. Methods: Retrospective collection of clinical data from patients undergoing HCC resection in Jiangsu Province (Suqian) Hospital from June 2017 to June 2023. Single factor analysis and multivariate logistic regression analysis were used to screen for independent risk factors of infectious complications after hepatectomy. Establishing a column chart prediction model and testing its predictive effectiveness based on independent risk factors. Results: A total of 215 patients were included, and 42 patients developed PIC after hepatectomy, with an incidence rate of 19.5%. A total of 72 pathogenic bacteria were cultivated. Logistic regression results showed that age (≥65 years old), diabetes, retention time of peritoneal drainage tube (≥7 days) and Alb (<35 g/L) were risk factors for HCC resection of PIC (P < 0.05). Establishing a column chart prediction model based on the above results, the calibration curve shows good consistency between the predicted probability of occurrence and the actual probability of occurrence of the column graph model. The Bootstrap method calculated a C-index value of 0.678, indicating good discrimination. The area under the Receiver Operating Characteristic was 0.768 (95% CI: 0.688~0.848). The decision analysis curve shows high value. Conclusions: Age (≥65 years old), diabetes, retention time of peritoneal drainage tube (≥7 days) and Alb (<35 g/L) were risk factors for PIC after HCC resection. The prediction model constructed based on the above risk factors has certain predictive value.
文章引用:黄旭, 梁晨曦, 毛学源. 肝癌术后感染性并发症的危险因素分析及构建预测模型[J]. 临床医学进展, 2025, 15(4): 3264-3274. https://doi.org/10.12677/acm.2025.1541295

1. 引言

肝癌是一种来源于肝组织的恶性肿瘤[1]。通常起源于肝细胞,即肝脏内主要负责合成和排泄物质的细胞[1] [2]。肝癌的发生率在近年来逐渐增加,特别是在发展中国家,肝癌的发生率较高,占据了所有癌症病例的约80% [3] [4]

肝癌切除术是一种常见的治疗肝癌的手术方法[5]。对于早期或有限程度的肝癌,肝癌切除术可通过切除潜在的癌细胞和转移风险较高的肝脏部分来减少肿瘤复发和转移的可能性,提供最好的治愈机会[5]。感染性并发症是指在患有某种疾病或进行治疗过程中,机体遭受细菌、病毒、真菌或寄生虫等微生物侵袭,引起局部或全身的感染[6] [7]。严重感染性并发症可能会导致多种症状和并发症,还可能导致疾病恶化、延长住院时间、增加医疗费用,甚至造成生命威胁[7]。据研究表明,肝癌切除术后存在10%至30%之间概率发生感染性并发症,主要包括肺部感染、腹腔内感染、切口感染和尿路感染[7] [8]。肝癌切除术后并发感染会导致切口难以愈合,延长住院时间,增加呼吸窘迫等严重并发症的发生率,导致不良预后发生[8] [9]。因此,早期识别肝癌切除术患者术后感染并发症发生的高危人群,并进行风险预测对于降低术后感染发生率、改善患者预后十分重要。但目前关于肝癌切除术后感染性并发症的预测模型有限。本研究旨在分析肝癌切除术后感染的危险因素,并构建其风险预测模型,为临床疾病术后感染性并发症风险预测提供参考。

2. 资料与方法

2.1. 临床资料

回顾性选取2017年6月~2023年6月在本院行肝癌切除术的患者记录,共计215例。纳入标准:1) 符合原发性肝癌诊断标准,且具备肝切除术手术指征;2) 年龄18~85岁;3) 术前无感染表现;4) 病历资料完整;5) 肝功能Child分级A/B级别。排除标准:1) 肝外转移者;2) 凝血功能异常者;3) 器官移植术、心脏手术史者;4) 合并急性呼吸窘迫综合征、支气管哮喘等疾病者;5) 全身骨代谢疾病;6) 免疫缺陷疾病者;7) 合并其他恶性肿瘤或心脑血管疾病者。

病原菌培养与检测:感染发生后,呼吸道感染患者采集痰液标本,泌尿系感染患者采集尿液标本,切口感染患者收集切口周围脓液标本,腹腔感染者收集腹腔引流液为标本。将收集的标本置于无菌容器中,2 h内送检,并进行细菌培养,病原菌鉴定采用VITEK 2 Compact全自动微生物鉴定及药敏分析系统。质控菌株:大肠埃希菌ATCC25922、金黄色葡萄球菌ATCC29213。样本经两次检测,检出同一菌株则为该病原菌感染,同一患者多次检出同一种菌株,则均按照1株计算。

2.2. 资料收集

检索本院电子病历系统,收集行肝癌切除术患者的性别、年龄、身高、体重、计算身体质量指数(Body Mass Index, BMI)、美国麻醉医师协会分级(American Society of Anesthesiologists, ASA)、肿瘤位置、肿瘤直径、肝功能Child-Pugh分级、吸烟史、合并糖尿病、合并高血压、手术时间、术中失血量、丙氨酸氨基转移酶(Alanine Aminotransferase, ALT)、甲胎蛋白(Alpha Fetoprotein, AFP)、腹腔引流管留置时间、血清白蛋白(Albumin, Alb)、住院时间。

2.3. 统计学分析

研究相关数据采用统计学软件SPSS26.0进行处理,组间计量资料采用t检验,χ2检验用于计数资料的比较,相关因素的分析采用单因素分析及多因素Logistic回归模型分析肝癌切除术后感染性并发症的危险因素并构建预测模型,P < 0.05为差异有统计学意义。列线图预测模型的构建与验证:应用R软件(4.0.2)根据独立危险因素建立列线图预测模型,通过Bootstrap法获得校准曲线,计算C-index,绘制独立危险因素的受试者身体质量指数(Body Mass Index, BMI)、受试者工作特征(Receiver Operating Characteristic, ROC)曲线及计算各曲线下面积(Area Under Curve, AUC),决策曲线分析(Decision Curve Analysis, DCA)评价该风险模型预测效能,以验证列线图预测模型。

3. 结果

3.1. 肝癌术后感染性并发症一般情况及病原菌培养结果

共计215例符合本次纳入标准。其中男性97例,女性118。年龄36~82岁,平均63.60 ± 8.73岁。术后发生感染性并发症患者有42例,发生率为19.5%。其中手术部位感染20例、肝脏周围感染6例、远处部位感染16例。共培养分离出72株病原菌,其中革兰阴性菌39株,革兰阳性菌31株,真菌2株,主要以大肠埃希菌、肺炎克雷伯菌和肺炎链球菌为主(见表1)。

Table 1. The pathogens of causing infection after hepatectomy

1. 肝癌切除术后发生感染的病原菌概况

病原菌

株数(n = 72)

比例(%)

革兰阴性菌

39

54.17

大肠埃希菌

21

29.17

肺炎克雷伯菌

13

18.06

鲍氏不动杆菌

3

4.17

铜绿假单胞菌

2

2.8

革兰阳性菌

31

43.06

肺炎链球菌

20

27.78

金黄色葡萄球菌

8

11.11

表皮葡萄球菌

3

4.17

真菌

2

2.7

3.2. 肝癌术后感染性并发症的危险因素的单因素分析及多因素Logistic回归分析

肝癌术后感染性并发症的危险因素的单因素分析结果显示,两组(感染组与非感染组)患者年龄、合并糖尿病、手术时间、腹腔引流管留置时间、Alb比较,差异有统计学意义(P < 0.05) (见表2)。根据上述单因素分析结果,将有统计学意义的年龄、合并糖尿病、手术时间、腹腔引流管留置时间、Alb为自变量,并进行赋值(见表3)。将组别(1 = 感染组,2 = 非感染组)作为因变量,纳入二元Logistic回归模型,结果显示,年龄(≥65岁)、合并糖尿病、腹腔引流管留置时间(≥7 d)及Alb (<35 g/L)为肝癌切除术后感染性并发症发生的危险因素(P < 0.05),其比值比(Odds Ratio, OR)及95%置信区间(Confidence Interval, CI),分别为:年龄(≥65岁) (OR = 0.474, 95% CI = 0.307~0.732, P < 0.001)、合并糖尿病(OR = 0620, 95% CI = 0.308~0.846, P = 0.009)、腹腔引流管留置时间(≥7 d) (OR = 0.409, 95% CI = 0.167~1.002, P = 0.05)、Alb (<35 g/L) (OR = 0.557, 95% CI = 0.339~0.916, P = 0.021) (见表4)。

Table 2. Univariate analysis of the occurrence of the risk factors for infectious complications in hepatocellular carcinoma after hepatectomy

2. 肝癌术后感染性并发症发生的危险因素单因素分析

Factor

Classify

Infection group

(n = 42)

Non-infected group

(n = 173)

t/χ2

P

Gender

Male

23 (54.76)

74 (42.77)

1.961

0.161

Female

19 (45.24)

99 (57.23)

BMI

<24 kg/m2

24 (57.14)

89 (51.45)

0.440

0.507

≥24 kg/m2

18 (42.86)

84 (48.55)

Age

<65 Years old

15 (35.71)

102 (58.96)

7.362

0.007

≥65 Years old

27 (64.29)

71 (41.04)

ASA classification

Ⅰ~Ⅱ

25 (59.52)

116 (67.05)

0.849

0.357

Ⅲ~Ⅳ

17 (40.48)

57 (32.95)

Tumor location

Left liver

19 (45.24)

77 (44.51)

0.007

0.932

Right liver

23 (54.76)

96 (55.49)

Tumor diameter

<5 cm

20 (47.62)

79 (45.66)

0.052

0.820

≥5 cm

22 (52.38)

94 (54.34)

The Child-Pughclassification of liver function

A

22 (52.38)

116 (67.05)

3.164

0.075

B

20 (47.62)

57 (32.95)

History of smoking

have

17 (40.48)

68 (39.31)

0.019

0.889

not have

25 (59.52)

105 (60.69)

Combined with diabetes

have

16 (38.1)

33 (19.08)

6.948

0.008

not have

26 (61.9)

140 (80.92)

Combined with hypertension

have

11 (26.19)

37 (21.39)

0.45

0.503

not have

31 (73.81)

136 (78.61)

Operation time

<2 h

18 (42.86)

118 (68.21)

9.345

0.002

≥2 h

24 (57.14)

55 (31.79)

Intraoperative blood loss

<400 mL

21 (50)

113 (65.32)

3.377

0.066

≥400 mL

21 (50)

60 (34.68)

ALT

<40 U/L

23 (54.76)

118 (68.21)

2.707

0.100

≥40 U/L

19 (45.24)

55 (31.79)

AFP

<20 µg/L

11 (26.19)

62 (35.84)

1.403

0.236

≥20 µg/L

31 (73.81)

111 (64.16)

Dwelling time of theabdominal drain tube

<7 d

15 (35.71)

109 (63.01)

10.312

0.001

≥7 d

27 (64.29)

64 (36.99)

Alb

<35 g/L

29 (69.05)

71 (41.04)

10.656

0.001

≥35 g/L

13 (30.95)

102 (58.96)

Length of stay

<14 d

24 (57.14)

77 (44.51)

2.166

0.141

≥14 d

18 (42.86)

96 (55.49)

Table 3. The assignments of the independent variables

3. 自变量的赋值

Variable

Assignment

Infectious complications after resection of liver cancer

1 = Infection group; 2 = Non-infected group

age

1 = <65 Years old; 2 = ≥65 Years old

Combined with diabetes

1 = have; 2 = not have

Operation time

1 = <2 h; 2 = ≥2 h

Dwelling time of the abdominal drain tube

1 = <7 d; 2 = ≥7 d

Alb

1 = <35 g/L; 2 = ≥35 g/L

Table 4. The logistic regression analysis the occurrence of the risk factors for infectious complications in hepatocellular carcinoma after hepatectomy

4. 肝癌术后感染性并发症发生危险因素的Logistic回归分析

Variable

B

SE

Wald

P

OR

95% CI

Age (65 years old)

0.950

0.357

7.075

0.008

2.586

1.284~5.208

Complying with diabetes (none)

−0.960

0.372

6.653

0.010

0.383

0.185~0.794

Operation time (2 h)

0.633

0.347

3.319

0.068

1.883

0.953~3.721

Dwelling time of the abdominal drain tube (7 d)

1.120

0.358

9.766

0.002

3.066

1.518~6.189

Alb (≥35 g/L)

−1.165

0.368

10.025

0.002

0.312

0.152~0.642

Constant

0.895

0.220

16.507

<0.001

2.448

3.3. 构建肝癌术后感染性并发症的危险因素列线图预测模型

基于上述分析结果的危险因素,运用R软件4.0.2及其rms程序包,构建肝癌切除术后感染性并发症的列线图模型,绘制列线图(见图1)。通过Hosmer-Lemeshouw法检测评价预测模型的拟合优度,结果显示P = 0.426,说明该列线图预测模型具有良好的拟合度。使用Bootstrap自抽样法及计算预测模型的区分度对此模型进行内部验证。Bootstrap重复抽样1000次,获得该模型的校准曲线(见图2),显示该列线图模型肝癌切除术后感染性并发症的预测发生概率和实际发生概率之间一致性较好。计算的C-index值为0.678,意味着该列线图预测模型的区分能力良好。通过绘制ROC曲线,结果显示列线图预测模型ROC曲线的AUC为0.768 (95% CI: 0.688~0.849),证明该预测模型预测效能好,具有较好的判别能力(见图3)。通过绘制决策曲线分析显示该预测模型价值性高(见图4)。

Figure 1. The alignment diagram of predicting the occurrence of infectious complications in hepatocellular carcinoma after hepatectomy

1. 预测肝癌切除术后感染性并发症发生的列线图

注:Y轴为实际的肝癌切除术后感染性并发症的发生概率,X轴为预测的 肝癌切除术后感染性并发症 发生概率,对角虚线代表了一个理想模型的完美预测,实线代示列线图的性能,其中与对角线虚线越接近则表示预测性能越好。

Figure 2. The calibration curve of alignment diagram prediction model

2. 列线图预测模型的校准曲线

Figure 3. The Receiver operating characteristic curve of alignment diagram prediction model

3. 列线图预测模型的ROC曲线

Figure 4. The decision curve analysis graph of alignment diagram prediction model

4. 列线图预测模型的决策曲线分析图

4. 讨论

本研究结果发生肝癌切除术后感染性并发症的发生率为19.5%,体现了本院在控制患者术后感染风险有待提高。感染是肝切除术后最为常见的并发症,季学闻等[10]在一项纳入148例行肝癌切除术患者的研究中,有26例术后发生感染性并发症,发生率为17.5%。而刘爱祥等[11]在一项纳入789例行肝癌切除术患者的研究中,240例患者术后发生并发症,其中有19例为感染性并发症。Takahashi Y等[12]分析了735例行肝胆胰手术患者,发现术后感染发病率为17.8%,其中肝切除术联合胆道切除术后感染的发病率高达39.1%。本次研究的患者术后感染发病率略高于以上研究,告诫我们应加强围术期管理,应积极分析和控制术后感染的影响因素。

本研究中术后发生感染并发症的患者经病原学培养后的结果显示,主要以大肠埃希菌、肺炎克雷伯菌和肺炎链球菌为主。与Sugawara G等[13]的研究结果相一致。临床可根据病原菌感染类型和药敏试验结果,选择针对性敏感抗菌药物进行治疗,从而促进患者术后康复。

在本次研究中,我们构建了肝癌切除术后感染性并发症的风险预测模型。年龄(≥65岁)、合并糖尿病、腹腔引流管留置时间(≥7 d)及Alb (<35 g/L)为肝癌切除术后感染性并发症发生的危险因素。年龄是我们的首要考虑。主要是由于老年人的免疫系统功能相对较弱,容易受到感染的侵袭。此外,老年人中常见的合并症如肺部疾病、心血管疾病和糖尿病等与术后死亡率与并发症发生率风险相关密切[14] [15]。为预防老年患者肝癌切除术后感染性并发症,对于65岁以上的患者,术前应进行全面的评估,包括评估免疫状态、基础疾病的控制情况等。必要时,可以进行免疫功能的检查和评估[15]。合并糖尿病是肝癌切除术后发生感染性并发症的危险因素,主要原因是合并糖尿病的肝癌患者血糖浓度较高,给细菌生长与繁殖提供适宜条件,致使手术后切口感染发生风险增加[16]。因而预防糖尿病患者肝癌切除术后感染性并发症需要全面考虑糖尿病的控制和术后管理,通过术前评估、糖尿病管理、血糖控制、抗生素使用、定期随访和术后康复等措施,可以降低感染性并发症的风险,提高手术成功率和康复质量[16] [17]。腹腔引流管留置时间是肝癌切除术后发生感染性并发症的影响因素,主要原因为,肝癌手术创面较大,渗出较多,腹腔引流可促进渗出液排出,但长时间的引流管留置会增加感染的机会,同时可能导致腹腔局部免疫功能受损和引流管相关感染的风险增加[10] [18]。预防腹腔引流管留置时间过长导致的感染性并发症需要从适应证判断、规范操作、择期拔除、引流后护理、密切监测和标准化管理等方面综合考虑[18]。血清白蛋白 < 35 g/L是肝癌切除术后发生感染性并发症的危险因素,主要原因是低血清白蛋白水平代表患者的营养状态较差[10] [19]。肝癌患者发生低蛋白血症可致使术后发生组织水肿,进而引发术后部位灌注不足,增加细菌生长繁殖的可能性,且营养状态较差可影响免疫功能,从而增加感染风险[19]

基于上述危险因素的结果,有必要开发更多的预测模型,以更好地控制患者术后发生感染。同时,有一项研究同样构建了肝切除术后感染的列线图模型,该模型包含BMI、术前甲胎蛋白、总胆红素、术中失血量和胆汁漏(bile leakage) [20]。另外,Kong QY等[21]基于Web开发的预测模型,识别出了糖尿病、并发丙型肝炎病毒感染、超出米兰标准的肝细胞癌、肝硬化、术前HBV-DNA水平、白蛋白–胆红素(ALBI)和天冬氨酸转氨酶与血小板比值指数(APRI)等为发生主要并发症的独立预测因子。相比之下,本次研究只识别出了年龄、合并糖尿病、腹腔引流管留置时间及Alb为术后发生感染的影响因素。但这些指标均比较容易获取,模型使用简单。

本研究存在以下局限性:首先,为单中心回顾性研究,样本量较少;其次,不完整的病历和回忆病史的偏倚增加了研究的复杂性,可能容易出现选择偏倚;第三,未分析患者其他并发症,如肝功能不全,腹腔和胸腔积液等;第四,纳入的指标少,特别是实验指标;最后,有必要扩大研究人群的范围,包括受试者的数量及其区域,以改进我们的模型。

5. 结论

综上所述,本研究调查了肝癌术后感染性并发症的危险因素,并使用列线图构建模型,通过年龄、合并糖尿病、腹腔引流管留置时间及Alb风险预测HCC切除后发生PIC发生的风险。这些危险因素对早期筛查和及时预防PIC具有重要意义。

声 明

本研究为回顾性,主要通过查阅患者病历资料获取数据,不涉及对患者的额外干预。在研究过程中,严格遵守《世界医学协会赫尔辛基宣言》及相关伦理准则,保护患者的隐私和个人信息。所有患者的资料均进行匿名化处理,仅用于本研究目的。研究方案提交医院伦理委员会进行审查和批准,确保研究符合伦理要求。

NOTES

*通讯作者。

参考文献

[1] Hafeez, M., Nadeem, M., Ahmed, M. and Rehman, F. (2020) Hepatocellular Carcinoma (HCC), Where Do We Stand? Current Situation. Pakistan Journal of Medical Sciences, 36, 344-348.
https://doi.org/10.12669/pjms.36.3.1594
[2] Li, Y., Chen, Y. and Chen, J. (2020) Diagnostic Value of Serum Biomarkers for Patients Undergoing Curative Resection with Non-B, Non-C Hepatocellular Carcinoma. Journal of the College of Physicians and Surgeons Pakistan, 30, 134-138.
https://doi.org/10.29271/jcpsp.2020.02.134
[3] Manzoor, S., Anwer, M., Soomro, S. and Kumar, D. (2019) Presentation, Diagnosis and Management of Locally Advanced Breast Cancer: Is It Different in Low/middle Income Countries? Pakistan Journal of Medical Sciences, 35, 1554-1557.
https://doi.org/10.12669/pjms.35.6.165
[4] Spiliotis, A.E., Gäbelein, G., Holländer, S., Scherber, P., Glanemann, M. and Patel, B. (2021) Microwave Ablation Compared with Radiofrequency Ablation for the Treatment of Liver Cancer: A Systematic Review and Meta-Analysis. Radiology and Oncology, 55, 247-258.
https://doi.org/10.2478/raon-2021-0030
[5] Orcutt, S.T. and Anaya, D.A. (2018) Liver Resection and Surgical Strategies for Management of Primary Liver Cancer. Cancer Control, 25.
https://doi.org/10.1177/1073274817744621
[6] Demir, D., Doluoglu, O., Yildiz, Y., Bozkurt, S., Ayyildiz, A. and Demirbas, A. (2019) Risk Factors for Infectious Complications in Patients Undergoing Retrograde Intrarenal Surgery. Journal of the College of Physicians and Surgeons Pakistan, 29, 558-562.
https://doi.org/10.29271/jcpsp.2019.06.558
[7] Ahmad, Z., Arshad, S., Idrees, R., Abdul-Ghafar, J. and Din, N. (2019) Fatal Invasive Gastrointestinal Fungal Infection in Three Non-Immunocompromised Patients. Journal of the College of Physicians and Surgeons Pakistan, 29, 181-184.
https://doi.org/10.29271/jcpsp.2019.02.181
[8] Yang, T., Liu, K., Liu, C., Zhong, Q., Zhang, J., Yu, J., et al. (2019) Impact of Postoperative Infective Complications on Long-Term Survival after Liver Resection for Hepatocellular Carcinoma. British Journal of Surgery, 106, 1228-1236.
https://doi.org/10.1002/bjs.11231
[9] Loncar, Y., Tartrat, N., Lastennet, D., Lemoine, L., Vaillant, J.C., Savier, E., et al. (2022) Pulmonary Infection after Hepatic Resection: Associated Factors and Impact on Outcomes. Clinics and Research in Hepatology and Gastroenterology, 46, Article 101733.
https://doi.org/10.1016/j.clinre.2021.101733
[10] Ji, X., Imam, R.R., River, S.R.S. and Elkane, J. (2023) Risk Factors of Imperfect Applications after the Pathomy for Hepatocellular Cancer and the Construction of Its Predictive Model. Chinese Journal of Nosocomology, 33, 542-546.
[11] Liu, A., Wang, H., Bo, W., Feng, X., Tian, L., Zhang, H., et al. (2019) Clinical Efficacy and Prognostic Factors Analysis of Hepatectomy for Hepatocellular Carcinoma. Chinese Journal of Digestive Surgery, 18, 368-374.
https://doi.org/10.3760/cma.j.issn.1673-9752.2019.04.012
[12] Takahashi, Y., Takesue, Y., Fujiwara, M., Tatsumi, S., Ichiki, K., Fujimoto, J., et al. (2018) Risk Factors for Surgical Site Infection after Major Hepatobiliary and Pancreatic Surgery. Journal of Infection and Chemotherapy, 24, 739-743.
https://doi.org/10.1016/j.jiac.2018.05.007
[13] Sugawara, G., Yokoyama, Y., Ebata, T., Igami, T., Yamaguchi, J., Mizuno, T., et al. (2020) Postoperative Infectious Complications Caused by Multidrug-Resistant Pathogens in Patients Undergoing Major Hepatectomy with Extrahepatic Bile Duct Resection. Surgery, 167, 950-956.
https://doi.org/10.1016/j.surg.2020.02.015
[14] Panayi, A.C., Orkaby, A.R., Sakthivel, D., Endo, Y., Varon, D., Roh, D., et al. (2019) Impact of Frailty on Outcomes in Surgical Patients: A Systematic Review and Meta-analysis. The American Journal of Surgery, 218, 393-400.
https://doi.org/10.1016/j.amjsurg.2018.11.020
[15] Zhou, G., Zhou, Y., Chen, R., Wang, D., Zhou, S., Zhong, J., et al. (2022) The Influencing Factors of Infectious Complications after Percutaneous Nephrolithotomy: A Systematic Review and Meta-Analysis. Urolithiasis, 51, Article No. 17.
https://doi.org/10.1007/s00240-022-01376-5
[16] Yilma, M., Saxena, V. and Mehta, N. (2022) Models to Predict Development or Recurence of Hepatocellular Carcinoma (HCC) in Patients with Advanced Hepatic Fibrosis. Current Gastroenterology Reports, 24, 1-9.
https://doi.org/10.1007/s11894-022-00835-8
[17] Mortada, H., Alwadai, A., Bamakhrama, B., Alsinan, T., Hanawi, M.D., Alfaryan, S.M., et al. (2023) The Impact of Diabetes Mellitus on Breast Reconstruction Outcomes and Complications: A Systematic Literature Review and Meta-Analysis. Aesthetic Plastic Surgery, 47, 570-583.
https://doi.org/10.1007/s00266-023-03258-6
[18] Zheng, B., Li, C. and Wang, S. (2023) Duodenal Fistula Caused by Abdominal Drainage Tube: A Rare Postoperative Complication. Asian Journal of Surgery, 46, 3171-3172.
https://doi.org/10.1016/j.asjsur.2023.02.105
[19] Matsuda, T., Umeda, Y., Matsuda, T., Endo, Y., Sato, D., Kojima, T., et al. (2021) Preoperative Prognostic Nutritional Index Predicts Postoperative Infectious Complications and Oncological Outcomes after Hepatectomy in Intrahepatic Cholangiocarcinoma. BMC Cancer, 21, Article No. 708.
https://doi.org/10.1186/s12885-021-08424-0
[20] Ma, Y., Tan, B., Wang, S., Ren, C., Zhang, J. and Gao, Y. (2023) Influencing Factors and Predictive Model of Postoperative Infection in Patients with Primary Hepatic Carcinoma. BMC Gastroenterology, 23, Article No. 123.
https://doi.org/10.1186/s12876-023-02713-7
[21] Kong, Q., Li, C., Wang, M., Sun, L., Pu, J., Chen, Z., et al. (2022) A Web-Based Prediction Model for Estimating the Probability of Post-Hepatectomy Major Complications in Patients with Hepatocellular Carcinoma: A Multicenter Study from a Hepatitis B Virus-Endemic Area. Journal of Gastrointestinal Surgery, 26, 2082-2092.
https://doi.org/10.1007/s11605-022-05435-5