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Chinese Journal of Experimental and Clinical Infectious Diseases(Electronic Edition) ›› 2023, Vol. 17 ›› Issue (05): 299-306. doi: 10.3877/cma.j.issn.1674-1358.2023.05.003

• Research Article • Previous Articles     Next Articles

Construction and validation of a nomogram model for predicting infectious kidney stones before surgery

Peng Zheng, Saiping Wu, Xiuzhang Xie, Qingfeng Shi()   

  1. Department of Infection Management, Wujin Hospital Affiliated to Jiangsu University (Wujin Clinical College of Xuzhou Medical University), Changzhou 213000, China
    Department of Infection Management, Zhongshan Hospital, Fudan University, Shanghai 200032, China
  • Received:2023-05-05 Online:2023-10-15 Published:2023-12-19
  • Contact: Qingfeng Shi

Abstract:

Objective

To establish a nomogram model for preoperative prediction of infectious kidney stones.

Methods

Total of 350 patients with kidney stones diagnosed in Wujin Hospital Affiliated to Jiangsu University (Wujin Clinical College of Xuzhou Medical University) from February 2020 to February 2023 were summarized, retrospectively, and were randomly divided into a modeling set (245 cases) and a validation set (105 cases) according to 7︰3. The modeling focused on 91 cases with infectious kidney stones and 154 cases with non-infectious kidney stones, and verified 39 cases of concentrated infectious kidney stones and 66 cases of non-infectious kidney stones. The clinical data of patients in infective kidney stone group and non-infective kidney stone group were compared in the modeling set. The minimum absolute convergence and selection operator regression (Lasso) model and multi-factor Logistic regression model were used to screen the risk factors of infective kidney stone. The nomographic model was established and verified by 1 000 self-repeated samples through R software.

Results

Univariate comparison showed that female, recurrent kidney stones and staghorn stones in the infectious kidney stones group were more, the stone area was larger, while the Hounsfield unit (HU) of stones was significantly fewer; positive preoperative bladder urine culture (PBUC), urine white blood cell count (WBC) and bacterial count, urine protein positive, urine nitrite positive, positive urine leukocyte esterase (ULE), urine pH value, and urine turbidity positive were significantly higher, while urine specific gravity was significantly lower; blood uric acid was lower, while blood phosphorus and magnesium were higher (all P < 0.05). Lasso screened 8 most differential indicators, namely female, recurrent kidney stones, stone area ≥ 601 mm2, HU value < 1 000, positive PBUC, positive ULE, urine pH and urine turbidity positive. Logistic regression showed that female (OR = 1.568, 95%CI: 1.231-1.902, P < 0.001), recurrent kidney stones (OR = 3.023, 95%CI: 2.568-3.467, P < 0.001), stone area ≥ 601 mm2 (OR = 2.123, 95%CI: 1.756-2.569, P < 0.001), HU value < 1 000 (OR = 3.856, 95%CI: 3.456-4.325, P < 0.001), positive PBUC (OR = 1.895, 95%CI: 1.623-2.325, P < 0.001), positive ULE (OR = 1.754, 95%CI: 1.326-2.124, P < 0.001), urinary pH > 6.5 (OR = 1.323, 95%CI: 1.102-1.889, P < 0.001) and positive urine turbidity (OR = 1.602, 95%CI: 1.314-1.956, P < 0.001) were the risk factors to infectious kidney stones. R software was used to establishe a nomogram model, with a total score of 220 points. The receiver operating curve (ROC) showed that the area under the curve (AUC) of the nematic model was 0.856 (95%CI: 0.810-0.912, P < 0.001), the sensitivity and specificity were 79.8% and 83.2%, respectively, indicating that the diagnostic areas of the model were better. The calibration curve and decision curve also showed that the model had a good fit and clinical net benefit ratio.

Conclusions

Female, recurrent kidney stones, stone area ≥ 601 mm2, HU value < 1 000, positive PBUC, positive ULE, urine pH and positive urine turbidity could assist in assessing the risk of infectious kidney stones. A nomogram model that has good application potential to guide clinical practice was established.

Key words: Infectious kidney stones, Nomogram model, Preoperative bladder urine culture, Urinary leukocyte esterase

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