Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning

Figures & data

Figure 1. Flow diagram of patient selection. IMN: idiopathic membranous nephropathy.

Figure 2. The model prediction workflow. IMN, idiopathic membranous nephropathy; Adverse events: relapse, ESRD (estimated glomerular filtration rate <15 mL/(min×1.73 m²) or dialysis or kidney transplantation), or doubling of serum creatinine.

Table 1. Baseline clinical and pathological characteristics of the included patients.

Baseline characteristics	All	Non-adverse events	Adverse events	p Value
No. of patients	418	301	117
Gender (male) (n, %)	295 (70.6)	214 (71.1)	81 (69.2)	0.798
Age (years)	49.4 ± 11.5	49.6 ± 11.3	49.1 ± 11.8	0.695
BMI (kg/m^2)	25.6 [23.4, 27.8]	25.4 [23.4, 27.7]	26.2 [23.5, 28.5]	0.060
Hypertension (n, %)	220 (52.6)	156 (51.8)	64 (54.7)	0.675
Thrombosis related diseases (n, %)	19 (4.5)	13 (4.3)	6 (5.1)	0.924
Diabetes (n, %)	51 (12.2)	32 (10.6)	19 (16.2)	0.160
Disease of the thyroid gland (n, %)	61 (14.6)	38 (12.6)	23 (19.7)	0.094
D-DIMER (ng/mL)	222.5 [126.5, 469.5]	193.0 [106.0, 372.0]	369.0 [186.0, 751.0]	<0.001
AST (IU/L)	20.3 [16.9, 24.7]	20.2 [17.0, 24.5]	20.7 [16.9, 24.9]	0.473
ALB (g/L)	24.0 [20.0, 29.3]	25.6 [21.4, 30.5]	21.0 [17.1, 24.9]	<0.001
FPG (mmol/L)	5.1 ± 1.0	5.1 ± 1.0	5.1 ± 1.1	0.831
SCr (μmol/L)	69.3 [58.1, 79.1]	69.4 [57.3, 78.5]	68.5 [58.8, 82.2]	0.764
TG (mmol/L)	2.3 [1.7, 3.0]	2.2 [1.6, 2.9]	2.5 [1.9, 3.4]	0.005
BMG (mg/L)	2.0 [1.6, 2.4]	1.9 [1.6, 2.3]	2.1 [1.8, 2.6]	<0.001
eGFR (mL/(min × 1.73 m^2))	104.0 [96.7, 111.9]	104.1 [97.0, 112.2]	104.0 [94.2, 110.5]	0.309
Anti-PLA2R (RU/mL)	61.4 [25.5, 138.3]	46.3 [14.3, 103.9]	142.0 [59.6, 342.0]	<0.001
IHC IgG4 (n, %)				0.203
–	1 (0.2)	0 (0.0)	1 (0.9)
±	6 (1.4)	3 (1.0)	3 (2.6)
+	89 (21.3)	62 (20.6)	27(23.1)
++	318 (76.1)	232 (77.1)	86 (73.5)
+++	4 (1.0)	4 (1.3)	0 (0.0)

Adverse events: relapse, ESRD (estimated glomerular filtration rate <15 mL/(min × 1.73 m²) or dialysis or kidney transplantation), or doubling of serum creatinine, or death; BMI: body mass index; D-DIMER: D-dimer; AST: aspartate transaminase; ALB: serum albumin; FPG: fasting plasma glucose; SCr: serum creatinine; TG: triglyceride; BMG: β2-microglobulin; eGFR: estimated glomerular filtration rate; anti-PLA2R: anti-phospholipase A2 receptor; IHC IgG4: immunohistochemistry immunoglobulin G4.

Figure 3. Feature selection results based on the recursive feature elimination.

Figure 4. Evaluation of the predictive models. (a) The average ROC curves from five models in the validation set. Mean AUC values with standard deviations of different prediction models were shown in the box. (b) The average precision–recall (PR) curves, indicating the tradeoff between precision and recall. Mean AP values with standard deviations of different prediction models were shown in the box. AUC: area under the curve; ROC: receiver operating characteristic; AP: average precision; LightGBM: light gradient boosting machine; RF: random forest; XGBoost: eXtreme gradient boosting; SVM: support vector machine; LR: logistic regression.

Table 2. Performance of the prediction models generated by the five machine learning models.

Models	Accuracy	Recall	Precision	F1	AUC	95% CI
LR	0.859 ± 0.025	0.537 ± 0.070	0.914 ± 0.113	0.846 ± 0.027	0.835 ± 0.095	0.739–0.930
SVM	0.854 ± 0.019	0.643 ± 0.043	0.783 ± 0.076	0.850 ± 0.018	0.836 ± 0.116	0.720–0.952
RF	0.897 ± 0.058	0.702 ± 0.187	0.890 ± 0.107	0.890 ± 0.063	0.861 ± 0.093	0.765–0.954
XGBoost	0.883 ± 0.017	0.680 ± 0.077	0.869 ± 0.090	0.878 ± 0.017	0.849 ± 0.084	0.764–0.933
LightGBM	0.909 ± 0.016	0.741 ± 0.092	0.906 ± 0.027	0.905 ± 0.020	0.892 ± 0.052	0.840–0.945

LR: logistic regression; SVM: support vector machine; RF: random forest; XGBoost: eXtreme gradient boosting; LightGBM: light gradient boosting machine.

Figure 5. (a) Importance matrix plot of the LightGBM model. (b) SHAP summary plot of the nine clinical and pathological features of the LightGBM model. There is one dot per patient per feature colored according to an attribution value, where red represents a higher value and blue represents a lower value. anti-PLA2R: anti-phospholipase A2 receptor; IHC IgG4: immunohistochemical immunoglobulin G4; D-DIMER: D-dimer; TG: triglyceride; ALB: significantly lower serum albumin; AST: aspartate transaminase; BMG: β2-microglobulin; SCr: serum creatinine; FPG: fasting plasma glucose.

Figure 6. SHAP dependence plot of the LightGBM model, depicting how a single variable affects the prediction. SHAP value for specific features that exceed zero indicates an increased risk of patients experiencing adverse events. (a) SHAP dependence plot of anti-PLA2R. (b) SHAP dependence plot of IHC IgG4. The value 0 of IHC IgG4 represents (–), 0.5 represents (±), 1 represents (+), 2 represents (++), and 3 represents (+++). anti-PLA2R: anti-phospholipase A2 receptor; IHC IgG4: immunohistochemical immunoglobulin G4.

Figure 7. Predictive effect of LightGBM model using anti-PLA2R and IHC IgG4. 0: non-adverse events; 1: adverse events. The value 0 of IHC IgG4 represents (–), 0.5 represents (±), 1 represents (+), 2 represents (++), and 3 represents (+++). anti-PLA2R: anti-phospholipase A2 receptor; IHC IgG4: immunohistochemical immunoglobulin G4.

Figure 8. SHAP force plot of LightGBM model, explaining combined event risk for two example individuals. (a) A high-risk patient experiencing adverse events. (b) A low-risk patient experiencing adverse events. Variables in the red arrow mean the impact values are positive while blue means negative. The length of the arrow bar represents the absolute value of the impact. The value 0.5 of IHC IgG4 represents (±), 2 represents (++). anti-PLA2R: anti-phospholipase A2 receptor; IHC IgG4: immunohistochemical immunoglobulin G4; D-DIMER: D-dimer; TG: triglyceride; ALB: significantly lower serum albumin; AST: aspartate transaminase; SCr: serum creatinine; FPG: fasting plasma glucose.

Data availability statement

The data supporting the findings of this study cannot be disclosed due to the need to protect patients’ privacy, but are available from the corresponding author, Dr. Zhou [[email protected]].