Patients’ characteristics
Last but the least, 274 patients were recruited in this retrospective investigation, with 146 (53.3%) men and 128 (46.7%) women, and a mean age of 51.8 13.2 years. Few patients (48, 17.5%) received a college education, and most patients (239, 87.2%) were married when they began their initial PD medication. In our CAPD patients, hypertension was the most prevalent comorbidity (238, 86.9%), followed by diabetes (77, 28.1%), and 47 (17.2%) of our participants had a history of CVD. Furthermore, chronic glomerulonephritis (134, 48.9%) was the most prominent leading cause of ESKD, after diabetes (60, 21.9%), hypertension (51, 18.6%), and other or unrecognized causes (29, 10.6%). In Table 1, all patients’ details are shown. Simply put, elderly patients died more frequently and had decreased hemoglobin, platelets, blood albumin, and more substantial hsCRP amounts. In terms of echocardiographic measures, dead patients exhibited higher ARD, LVMI, RAD, e’, and E/e’ ratios and worse LVEF and LVFS (Table 2).
Transthoracic doppler echocardiography score (TTES)
The elements obtained from TTE were used to compute the innovative TTES using univariable and multivariable COX regression analysis. Ultimately, the unique TTES was calculated using ARD, LVEF 55%, LVMI, and E/e’ ratio. As a result, the novel TTES was constructed using the coefficients as follows: ARD (mm) × 0.109 – LVEF (> 55%, yes or no) × 0.976 + 0.010 × LVMI (g/m2) + 0.035 E/e’ ratio (Table 3). All patients were put into two groups predicated on the TTES value (3.7) obtained from the X-tile program (Fig. 2): high TTES (> 3.7) and low TTES (≤ 3.7) groups. Moreover, the TTES for patients treated was shown as a waterfall plot, with significant differences between alive and dead patients (P < 0.001, Fig. 3A–C). TTES levels were substantially connected with parameters relevant to PD treatment as well as other clinical characteristics, as illustrated in Supplemental Fig. 1.
X-tile analyses of TTES to obtain the optimal cutoff value of TTES. X-tile plots for patients with CAPD are shown on the left panels; the black circles indicate the optimal cutoff values, which are also presented in histograms (middle panels). Kaplan-Meier curves are shown in the right panels
The waterfall plots and forest plots of the high-TTES group and low-TTES group for the prognosis of CAPD patients. The waterfall plot of TTES for each patient of all-cause mortality (A), CVD mortality (B), and the subgroup analysis of the TTES for the prognosis of individuals with CAPD patients (C)
During a median follow-up duration of 52 months, 46 patients (16.8%) died from all causes and 32 patients (11.7%) died from CV disorders. In addition, even after correcting for other medical information, patients in the high TTES group had a greater risk of all-cause death (hazard ratio, HR, 3.70, 95% confidence index, 95%CI, 1.45–9.46, P = 0.006) as well as CV mortality (HR, 2.74, 95%CI 1.15–19.17, P = 0.042) (Table 4; Fig. 4A–C, and Supplemental Fig. 2A–C), and the crude HR was 2.03 (95%CI 1.54–2.67, P < 0.001), 2.08 (95%CI 1.50–2.89, P < 0.001), when the TTES value was utilized for continuous covariates.
The TTES was established to detect the overall mortality of patients with CAPD. All patients were distinguished into high and low risk based on the TTES (A), the relationship between survival time and all-cause mortality of patients in the two corresponding groups (B), and the heatmap of other markers between the two groups (C). Receiver operating characteristic (ROC) curve analysis of the TTES for overall mortality (D), Decision curve analysis of the TTES for the overall mortality (E). Kaplan-Meier curves show the overall mortality of groups with different risks (F)
The TTES was also found to have an attractive predictive efficiency for all-cause mortality and CV mortality, with AUCs of 0.762 (95% CI 0.645–0.849, sensitivity, 64.4%, specificity, 83.0%) and 0.746 (95% CI 0.640–0.852, sensitivity, 63.1%, specificity, 81.3%), respectively (Fig. 4D, and Supplemental Fig. 2D). Nonetheless, DCA revealed that TTES was clinically beneficial for all-cause and CV mortality (Fig. 4E, and Supplemental Fig. 2E). The high-TTES group had a worse prognosis for patients with CAPD than the lower-TTES group (Fig. 4F, and Supplemental Fig. 2F, P < 0.0001).
Development and verification of the predictive nomogram
The LASSO COX regression analysis chose 9 variables with nonzero coefficients for all-cause mortality, as shown in Fig. 5A-B. COX regression was also used to further sift predictors because of the small sample size and delivering a portable tool with comparatively high precision for doctors. Age, marital status, CVD, serum albumin, and TTES were eventually enlisted to create the prediction nomogram for all-cause mortality (Fig. 6A), as described in Table 5.
Selection of significant factors associated with all-cause mortality in CAPD patients by LASSO COX regression model
The established nomogram for all-cause mortality in patients with CAPD (A), the 1-year, 3-year, and 5-year calibration curves of the nomogram for the overall survival (B), Decision curve analysis of the nomogram for overall survival (C), and the time-dependent ROC curves for the 1-year, 3-year, and 5-year overall survival in patients with CAPD (D)
Additionally, the forecasting nomogram’s 1-, 3-, and 5-year AUC for all-cause mortality was 0.844, 0.788, and 0.830, respectively (Fig. 6D). The calibration curves likewise showed a high level of agreement between projected and actual mortality (Fig. 6B). Furthermore, DCA indicated that the prediction nomogram was useful for decision-making in CAPD patients for all-cause mortality (Fig. 6C).
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- Source: https://bmcnephrol.biomedcentral.com/articles/10.1186/s12882-024-03493-2