Construction and validation of a prognostic nomogram for predicting the survival of HIV/AIDS adults who received antiretroviral therapy: a cohort between 2003 and 2019 in Nanjing

The data used in this study were extracted from patients who received ART between 2003 and 2019 from Nanjing AIDS Prevention and Control Information System (AIDS-PCIS). All patients received a free ART containing at least three antiviral medicines. The follow-up started after ART initiation and the participants were visited every 3 months. The observation end point was December 31, 2019, and the outcome was death. The survival time was defined as the duration from ART initiation to death or December 31, 2019. The inclusion criteria included: (1) living in Nanjing; (2) being visited at least once; (3) being over 18 years old when ART started; and (4) having complete laboratory test data before starting ART. At the end of the follow-up, a total of 4573 patients met the inclusion criteria. Among them, 120 patients died of HIV/AIDS-related diseases and were determined as the cases in the nested case-control study. The flowchart of recruitment participants is shown in the Fig. 1.

Demographic data and clinical information were retrieved from face-to-face surveys at the patients’ enrollment or extracted from their medical records using a structured questionnaire designed specifically for AIDS-PCIS. The information included the date of birth, gender, height, weight, marital status, infection route and WHO clinical stage. The age of the patient was calculated from the date of birth to the date of starting ART. Body mass index (BMI) was calculated using the following formula: BMI = weight (kg) / (height (m) × height (m)).

The laboratory testing data were obtained from the Nanjing Center for Disease Control and Prevention (CDC) or local hospitals. The laboratory testing indicators included CD4, white blood cell (WBC), blood platelets (PLT), HB, serum creatinine (CR), triglycerides (TG), total cholesterol (TC), fasting blood glucose (FBG), aspartate aminotransferase (AST), alanine aminotransferase (ALT) and total bilirubin (TBIL). All these laboratory tests were carried out by the trained technical personnel strictly following clinical guidelines at each visit in the central laboratory of local hospitals or Nanjing CDC.

Routine blood biochemical indexes, such as TG, TC, FBG, CR, AST, ALT, and TBIL, were measured using a Beckman AU5800 automatic biochemical analyzer (Beckman COULTER K., Japan). Other indexes including WBC, HB and PLT were evaluated by Sysmex Xe-2100 automatic blood cell analyzer (Sysmex Corporation, Japan). CD4 was determined by the BD FACSCalibur flow cytometer (Becton Dickinson Corporation, USA).

In this PSM-based nested case-control study, the characteristics of the 600 PLHIV (420 from the training set and 180 from the validation set) revealed that both sets were similar in all variables (Table 2).

In the univariable Cox proportional hazards regression analysis of the training set, infection route, baseline Tuberculosis (TB), continuous diarrhea, continuous or intermittent fever, shingles, WHO clinical stage, CD4, BMI, HB, CR, TC, FBG, AST and ALT were detected to be statistically related to the mortality of PLHIV (Table 3). Variables with p-value less than or equal to 0.2 in the univariable analysis were included in the multivariable Cox proportional hazards regression model. To avoid multicollinearity caused by the strong relationship between WHO clinical stage and CD4, WHO clinical stage was not included in the multivariable Cox proportional hazards regression model. Shingles, CD4, BMI, HB and TC were found linked to HIV/AIDS-related death. In order to establish an optimal prediction model, the individual and combined performance of these factors were then evaluated using ROC analysis and C-Index. As shown in Fig. 3A, the AUCs of Shingles, CD4, BMI, HB and TC in the training set were 0.549, 0.755, 0.729, 0.669 and 0.596, respectively. The AUC of combine 1 (Shingles + CD4 + BMI + HB + TC) was 0.82, and the AUC of combine 2 (CD4 + BMI + HB) was 0.831. To compare the predictive performances of combine 1 and combine 2, their C-Indexes were calculated, and the results were 0.806 (95% CI: 0.766, 0.846) and 0.798 (95% CI: 0.758, 0.839), indicating both models had a prediction accuracy of around 80%. Besides, no statistically significant difference in the C-Indexes between combine model was observed (P = 0.957) (Fig. 4A). The discrimination between the two models was not large, but combine 2 involved fewer variables. Thus, combine 2 model was chosen and the three variables CD4, BMI and HB were preliminarily selected to construct a prediction model of three-year and five-year survival of PLHIV after ART.

To verify the efficacy of the model in predicting the survival of PLHIV, bootstrap resampling was used for internal validation of the model. In the validation set, the AUCs of Shingles, CD4, BMI, HB and TC were 0.509, 0.821, 0.676, 0.77 and 0.654 in the ROC analysis chart (Fig. 3B).

The AUC of combine 1 achieved 0.802, and the AUC of combine 2 (prediction model) was also 0.802. The C-Indexes of combine 1 (0.786; 95% CI: 0.679, 0.893) and combine 2 (0.786; 95% CI: 0.681, 0.892) were similar and the difference was not statistically significant (P = 0.998), which showed that the discrimination of combine 1 and combine 2 (prediction model) was not very large (Fig. 4B). The calibration curve also exhibited a high consistency in predicting the survival of PLHIV (especially in the first 3 years after ART initiation) (Fig. 5).

As shown in Fig. 6, in both the training set and the validation set, the prediction model (combine 2) showed better performance. Overall, the DCA curve demonstrated that the prediction model (combine 2) could make valuable and profitable judgements. In addition, among the detected factors, CD4 was more beneficial than the other routine clinical laboratory indicators in predicting the three-year and five-year survival probabilities of PLHIV. In Fig. 6D, DCA curve showed that the prediction model had no good benefits in predicting five-year survival probabilities of PLHIV in the validation set.

A nomogram was drawn according to the determined prediction model. As seen in Fig. 7, each selected predictor was assigned with a score according to its value in the nomogram based on the established prediction model. Then a vertical line perpendicular to the Point axis was drawn from this point. The intersection point on the Point axis represented the score under the determined value of the predictor. For example, when CD4 was 1200 cells/μL, the score was 0 point; when BMI was 12 kg/m², the score was 63 points. By analogy, the score of each predictor could be determined, and summed up. Similarly, after the total score was calculated, a vertical line was drawn from the point of the patient’s total score on the Total Points axis to the axis of survival probability (such as three-year survival probability or five-year survival probability). The intersection point on the axis of survival probability represented the patient’s three-year or five-year survival probability.