A systematic review of risk prediction models for diabetic foot infections

doi:10.3761/j.issn.0254-1769.2024.19.006

Abstract

Abstract:

Objective To systematically evaluate the risk prediction models of diabetic foot infections，and to explore its implications for clinical nursing care. Methods Relevant studies in the databases of CNKI，VIP，Wanfang，CBM，CINAHL，PubMed，Web of Science，Cochrane Library，Embase，ProQuest，and Ovid were searched for the timeframe of database construction to December 2023. Literature was screened independently by 2 investigators and the area under the model curve（AUC） and its 95% confidence interval（CI），calibration method and predictors were extracted，and the quality of the model was evaluated using the Predictive Modeling Study Risk of Bias Assessment Tool. Meta-analysis of the predictive value of model predictors was performed using RevMan 5.4 software. Results 7 studies were included，containing 8 risk prediction models，all of which reported the AUC（0.748-0.922），and 6 models underwent model calibration. The overall fit of the included studies was good，among which 1 model was low fit，and all 7 studies were at high risk of bias，mainly focusing on predictors and analysis. Meta-analysis showed that the area under the curve of the combined models was 0.831（95%CI：0.780~0.883）；the disease duration（OR=2.460，95%CI：1.850~3.270），vascular disease（OR=3.110，95%CI：1.070~9.000），and neuropathy（OR=3.550，95%CI：1.920~6.560） were effective predictors of diabetic foot infection. Conclusion The current diabetic foot infection risk prediction model is still in the developmental stage，and further optimization and external validation of the existing model is recommended for the future.

Key words: Diabetic Foot, Infections, Prediction Model, Systematic Review, Evidence-Based Nursing

摘要：

目的系统评价糖尿病足患者发生感染的风险预测模型，并探讨其对临床护理工作的启示。方法系统检索中国知网、维普数据库、万方数据库、中国生物医学文献数据库、CINAHL、PubMed、Web of Science、Cochrane Library、Embase、ProQuest及Ovid等中英文数据库中的相关研究，检索时限为建库至2023年12月。由2名研究者独立筛选文献，并提取模型曲线下面积及其95%置信区间（confidence interval，CI）、校准方法和预测因子，使用预测模型研究偏倚风险评估工具对模型进行质量评价。使用RevMan 5.4软件对模型预测因子的预测价值进行Meta分析。结果共纳入7篇文献，包含8个风险预测模型，所有模型均报告了曲线下面积（0.748~0.922），6个模型进行了模型校准。纳入文献总体适应性良好，1篇文献模型低适应性，7篇文献均存在高偏倚风险，主要集中在预测因子和分析方面。Meta分析结果显示，合并后的模型曲线下面积为0.831（95%CI：0.780~0.883）。糖尿病病程（OR=2.460，95%CI：1.850~3.270）、血管疾病（OR=3.110，95%CI：1.070~9.000）、神经病变（OR=3.550，95%CI：1.920~6.560）是糖尿病足感染的预测因子。结论当前糖尿病足感染风险预测模型尚处在发展阶段，建议未来对现有模型进一步优化和进行外部验证。

关键词: 糖尿病足, 感染, 预测模型, 系统评价, 循证护理学

ZHU Jingyi, CHEN Xi, ZHU Yanyuan, ZHOU Jiaxin, BAI Jiaojiao. A systematic review of risk prediction models for diabetic foot infections[J]. Chinese Journal of Nursing, 2024, 59(19): 2346-2352.

朱婧怡, 陈曦, 朱彦媛, 周嘉欣, 白姣姣. 糖尿病足感染风险预测模型的系统评价[J]. 中华护理杂志, 2024, 59(19): 2346-2352.

Figures/Tables 7

Figure 1 Literature screening flowchart

Table 1 Basic information of included studies(n=7)

Table 2 Establishment of risk prediction models for diabetic foot infections(n=7)

注:a为实测糖尿病足感染;b为预测糖尿病足感染;c为铜绿假单胞菌;d为金黄色葡萄球菌;IWGDF/IDSA为国际糖尿病足工作组/美国感染学会。

Table 3 Performance and predictors of predictive models for diabetic foot infections (n=7)

Figure 2 Meta analysis forest diagram of AUC

Table 4 Evaluation of risk of bias and applicability of included studies(n=7)

Table 5 Meta analysis results of predictive factors

References 24

[1]	van Netten JJ, Bus SA, Apelqvist J, et al. Definitions and criteria for diabetic foot disease[J]. Diabetes Metab Res Rev, 2020, 36(Suppl 1):e3268.
[2]	Mills JP, Patel P, Broekhuizen E, et al. Diabetic foot infections[M]. Ann Arbor(MI): Michigan Medicine University of Michigan, 2019.
[3]	Hardjo Lugito NP, Nawangsih C, Moksidy JC, et al. Diabetic foot gangrene patient with multi-drug resistant pseudomonas putida infection in Karawaci district,Indonesia[J]. J Glob Infect Dis, 2015, 7(1):37-39.
[4]	Neves JM, Duarte B, Pinto M, et al. Diabetic foot infection:causative pathogens and empiric antibiotherapy considerations-the experience of a tertiary center[J]. Int J Low Extrem Wounds, 2019, 18(2):122-128.
[5]	乐忠宏, 汤晓姣, 崔婷婷, 等. 糖尿病足感染患者病原学特点及耐药性分析[J]. 中华医院感染学杂志, 2017, 27(3):590-592,601.
	Yue ZH, Tang XJ, Cui TT, et al. Pathogenic bacteriological characteristics and drug resistance of patients with diabetic foot[J]. Chin J Nosocomiology, 2017, 27(3):590-592,601.
[6]	王晶, 胡岚, 石君, 等. 糖尿病足患者创面感染的影响因素及其模型构建[J]. 中华医院感染学杂志, 2023, 33(24):3733-3737.
	Wang J, Hu L, Shi J, et al. Influencing factors of wound infection the construction of the model in patients with diabetic foot[J]. Chin J Nosocomiology, 2023, 33(24):3733-3737.
[7]	詹师, 杨召伍, 陈山, 等. 糖尿病足感染病原微生物分布及风险预测模型的建立[J]. 中国现代医学杂志, 2023, 33(10):95-100.
	Zhan S, Yang ZW, Chen S, et al. Distribution of pathogenic microorganisms in diabetic foot infection and construction of risk prediction model[J]. China J Mod Med, 2023, 33(10):95-100.
[8]	李江雁, 毛小芳, 赵利超, 等. 糖尿病足并发溃疡感染病原菌及其预测模型构建[J]. 中华医院感染学杂志, 2023, 33(17):2619-2623.
	Li JY, Mao XF, Zhao LC, et al. Pathogens isolated from diabetic foot patients with ulcer infection and establishment of prediction model[J]. Chin J Nosocomiology, 2023, 33(17):2619-2623.
[9]	何慧琳, 王木, 蒋庆丽, 等. 糖尿病足患者发生伤口严重感染风险预测模型的构建[J]. 当代护士(上旬刊), 2022, 29(12):7-10.
	He HL, Wang M, Jiang QL, et al. Construction of risk prediction model for severe wound infection in patients with diabetic foot[J]. Mod Nurse, 2022, 29(12):7-10.
[10]	Ma YN, Zhang LX, Hu YY, et al. Nomogram model for predicting the risk of multidrug-resistant bacteria infection in diabetic foot patients[J]. Infect Drug Resist, 2021, 14:627-637.
[11]	Farkas A, Lin F, Bui K, et al. Development of predictive nomograms for clinical use to quantify the risk of isolating resistance prone organisms in patients with infected foot ulcers[J]. Epidemiol Infect, 2019, 147:e157. DOI
[12]	郑洪伶. 糖尿病足住院患者多重耐药菌感染风险预测模型的构建与验证[D]. 重庆: 重庆医科大学, 2023.
	Zheng HL. Construction and verification of risk prediction model of multi-drug resistant bacteria infection in diabetic foot inpatients[D]. Chongqing: Chongqing Medical University, 2023.
[13]	Moons KG, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane[J]. Cochrane Database Syst Rev, 2018, 10(10):ED000129.
[14]	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of predic-tion modelling studies:the CHARMS checklist[J]. PLoS Med, 2014, 11(10):e1001744.
[15]	Moons KGM, Wolff RF, Riley RD, et al. PROBAST:a tool to assess risk of bias and applicability of prediction model studies:explanation and elaboration[J]. Ann Intern Med, 2019, 170(1):W1-W33.
[16]	Moons KG, Kengne AP, Woodward M, et al. Risk prediction models:I. Development,internal validation,and assessing the incremental value of a new(bio)marker[J]. Heart, 2012, 98(9):683-690.
[17]	MacCallum RC, Zhang SB, Preacher KJ, et al. On the practice of dichotomization of quantitative variables[J]. Psychol Methods, 2002, 7(1):19-40. DOI PMID
[18]	陈香萍, 张奕, 庄一渝, 等. PROBAST:诊断或预后多因素预测模型研究偏倚风险的评估工具[J]. 中国循证医学杂志, 2020, 20(6):737-744.
	Chen XP, Zhang Y, Zhuang YY, et al. PROBAST:a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models[J]. Chin J Evid Based Med, 2020, 20(6):737-744.
[19]	Hsu L, Li L, Poon LY. Analysis of risk factors of infection in diabetic foot patients[J]. Int Wound J, 2024, 21(1):e14411.
[20]	贾盈盈, 张红燕, 马媛媛, 等. ICU患者压力性损伤风险预测模型的系统评价[J]. 中华护理杂志, 2021, 56(8):1242-1248. DOI URL
	Jia YY, Zhang HY, Ma YY, et al. Systematic review of pressure injuries risk prediction models in ICU patients[J]. Chin J Nurs, 2021, 56(8):1242-1248. DOI URL
[21]	张占林, 孙勇, 妥小青, 等. 随机森林算法对体检人群糖尿病患病风险的预测价值研究[J]. 中国全科医学, 2019, 22(9):1021-1026. DOI
	Zhang ZL, Sun Y, Tuo XQ, et al. Predictive value of random forest algorithms for diabetic risk in people underwent physical examination[J]. Chin Gen Pract, 2019, 22(9):1021-1026.
[22]	谢晓冉, 徐蓉. 糖尿病足发病风险预测模型的系统评价[J]. 中华护理杂志, 2021, 56(1):124-131. DOI URL
	Xie XR, Xu R. A systematic review of risk prediction models for diabetic foot[J]. Chin J Nurs, 2021, 56(1):124-131. DOI URL
[23]	李琳琳, 霍磊, 李品川, 等. 糖尿病足病不同程度感染患者临床特点分析[J]. 中国中西医结合外科杂志, 2020, 26(2):290-295.
	Li LL, Huo L, Li PC, et al. Analysis of clinical characteristics of different degrees of infection in patients with diabetic foot disease[J]. Chin J Surg Integr Tradit West Med, 2020, 26(2):290-295.
[24]	Chastain CA, Klopfenstein N, Serezani CH, et al. A clinical review of diabetic foot infections[J]. Clin Podiatr Med Surg, 2019, 36(3):381-395.