Characterization of pathogenic microorganisms in diabetic foot infections and development of a risk prediction model

Abstract

This study aimed to investigate the distribution of pathogenic microorganisms in diabetic foot infections (DFIs) and develop a nomogram to predict DFIs. It included 136 diabetic foot (DF) patients hospitalized at Henan University Huaihe Hospital from November 2020 to November 2024, with 86 (63.23%) having confirmed infections. Infections were predominantly caused by Gram-positive cocci (54.65%) and Gram-negative bacilli (43.02%). The nomogram incorporated age, C-reactive protein (CRP), Wagner grade, lower extremity arterial disease (LEAD), and peripheral neuropathy (PN). The predictive model exhibited robust discriminatory capacity, achieving an area under the curve (AUC) of 0.803 (95% confidence interval (CI) 0.735-0.878) with internal cross-validation stability (AUC = 0.804). Goodness-of-fit was confirmed by the Hosmer-Lemeshow test (χ² = 5.014, p = 0.756), with excellent net benefit shown by decision curve analysis. Our findings indicate a high infection rate in DF patients, mainly caused by Gram-positive cocci. The nomogram incorporating age, CRP, Wagner grade, LEAD, and PN parameters enables rapid DFIs screening, facilitating timely antibiotic initiation through early infection detection to enhance clinical management.

Keywords: Diabetic foot infection; Pathogenic microorganism; Risk prediction model.

Figure1

Feature selection using LASSO binary logistic regression model. (a) Log (lambda) value of 18 features in the LASSO model. A coefficient profile plot was produced against a log (lambda) sequence. (b) Parameter selection in the LASSO model uses five-fold cross-validation through minimum criterion. Partial likelihood deviation (binomial deviation) curves and logarithmic (lambda) curves are plotted. Minimum standard and1-SEof the minimum standard are used to draw a vertical dashed line at the optimal value. Optimal lambda produces 5 nonzero coefficients. LASSO, least absolute shrinkage and selection operator.

Fig. 2

Nomogram for predicting the risk of diabetic foot infection. The first line represents the scoring scale. Corresponding scores for each predictor factor are shown in lines 2–6. The score for each predictor is determined by referencing the first line. The total score for the risk evaluation is the sum of each predictor score. To determine the likelihood of diabetic foot infection, the score point is located on the total point line (line 7). Then, the user descends vertically to the risk of complication (line 9).

Fig. 3

(a) ROC curve of the nomogram. The brackets next to the area under the ROC curve (AUC) represent the 95% confidence interval. (b) Calibration curve of the nomogram. The apparent curve represents the relationship between predicted and actual probabilities of clinically significant complications. The bias-corrected curve is plotted by bootstrapping using 1000 resamples. The ideal curve is the 45° line, which indicates perfect prediction. (c) Decision curve analysis of the nomogram. Blue solid lines represent the nomogram, x axis, cutoff probability, and y axis, net benefit. AUC, area under the curve; ROC, receiver operating characteristic.