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. 2023 Sep 16;23(1):1808.
doi: 10.1186/s12889-023-16738-w.

Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China

Wenyuan Zhang #  1   2   3   4 , Jisong Yan #  2   3   4   5 , Hong Luo  2   3   4   5 , Xianguang Wang  6   7   8   9 , Lianguo Ruan  10   11   12   13
Affiliations

Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China

Wenyuan Zhang et al. BMC Public Health. .

Abstract

Objective: This study aimed to build and validate a nomogram model to predict the risk of incomplete immune reconstitution in people living with HIV (PLWH).

Methods: Totally 3783 individuals with a confirmed diagnosis of HIV/AIDS were included. A predictive model was developed based on a retrospective set (N = 2678) and was validated using the remaining cases (N = 1105). Univariate and multivariate logistic regression analyses were performed to determine valuable predictors among the collected clinical and laboratory variables. The predictive model is presented in the form of a nomogram, which is internally and externally validated with two independent datasets. The discrimination of nomograms was assessed by calculating the area under the curve (AUC). Besides, calibration curve and decision curve (DCA) analyses were performed in the training and validation sets.

Results: The final model comprised 5 predictors, including baseline CD4, age at ART initiation, BMI, HZ and TBIL. The AUC of the nomogram model was 0.902, 0.926, 0.851 in the training cohort, internal validation and external cohorts. The calibration accuracy and diagnostic performance were satisfactory in both the training and validation sets.

Conclusions: This predictive model based on a retrospective study was externally validated using 5 readily available clinical indicators. It showed high performance in predicting the risk of incomplete immune reconstitution in people living with HIV.

Keywords: HIV/AIDS; Immune reconstitution; Nomogram; Predictive model.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Proportion and distribution pattern of missing values in training set. Abbreviations: BMI, body mass index; CD4: CD4+ T lymphocyte; VL: viral load; WBC: white blood cell; PLT: platelet; Hb: hemoglobin; Scr: serum creatinine; TG: triglyceride; TC: total cholesterol; FBG: fasting blood-glucose; ALT: alanine aminotransferase; AST: aspartate aminotransferase; TBIL: total bilirubin
Fig. 2
Fig. 2
Nomogram of predictors for predicting incomplete immune reconstitution of PLHIV. Abbreviations: BMI, body mass index; CD4: CD4+ T lymphocyte; HZ: Herpes zoster; TBIL: total bilirubin
Fig. 3
Fig. 3
ROC curves of age, baseline CD4, BMI, HZ, TBIL and nomogram in the training set (a), the internal validation set (b) and the external validation set (c). Abbreviations: BMI, body mass index; CD4: CD4+ T lymphocyte; HZ: Herpes zoster; TBIL: total bilirubin
Fig. 4
Fig. 4
Calibration curves for predicting incomplete immune reconstitution of PLHIV in the training set (a), the internal validation set (b) and the external validation set (c). Abbreviations: BMI, body mass index; CD4: CD4+ T lymphocyte; HZ: Herpes zoster; TBIL: total bilirubin
Fig. 5
Fig. 5
The DCA curves of age, baseline CD4, BMI, HZ, TBIL and nomogram in the training set (a), the internal validation set (b) and the external validation set (c). Abbreviations: BMI, body mass index; CD4: CD4+ T lymphocyte; HZ: Herpes zoster; TBIL: total bilirubin
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