Nutritional status assessed by the Controlling Nutritional Status (CONUT) score as a predictor of recurrence of urolithiasis

A New Parameter for Calcium Oxalate Stones: Impact of Linear Calculus Density on Non-Contrast Computed Tomography

Background and Objectives: Non-contrast computed tomography (NCCT) is widely used to evaluate urolithiasis. The NCCT attenuation, measured in Hounsfield units (HU), has been evaluated to predict stone characteristics. We propose a novel parameter, linear calculus density (LCD), and analyze variables from NCCT imaging to predict calcium oxalate (CaOx) stones, which are common and challenging to fragment. Materials and Methods: We retrospectively reviewed the medical records of patients with urolithiasis between 2014 and 2017. Among those, 790 patients were included. Based on the NCCT pre-treatment, the maximal stone length (MSL), mean stone density (MSD), and stone heterogeneity index (SHI) were obtained. In addition, the variation coefficient of stone density (VCSD = SHI/MSD × 100) and linear calculus density (LCD = VCSD/MSL) were calculated. In accordance with the stone analysis, the patients were divided into two groups (CaOx and non-CaOx groups). The logistic regression model and receiver operating characteristic (ROC) curve were used for predictive modeling. Results: In the CaOx group, the SHI, VCSD, and LCD were more significant than in the non-CaOx group (all p < 0.001). SHI (OR 1.002, 95% CI 1.001-1.004, p < 0.001), VCSD (OR 1.028, 95% CI 1.016-1.041, p < 0.001), and LCD (OR 1.352, 95% CI 1.270-1.444, p < 0.001) were significant independent factors for CaOx stones in the logistic regression models. The areas under the ROC curve for predicting CaOx stones were 0.586 for SHI, 0.66 for VCSD, and 0.739 for LCD, with a cut-point of 2.25. Conclusions: LCD can be a useful new parameter to provide additional information to help discriminate CaOx stones before treatment.

Prognostic value of nutritional status in patients with human immunodeficiency virus infection-related lymphoma

Objective

To investigate the predictive value of nutritional status on the prognosis of patients with human immunodeficiency virus (HIV) infection-related lymphoma.

Materials and methods

A total of 149 patients with HIV infection-related lymphoma who were admitted to our hospital from August 2012 to May 2022 were selected as research subjects. Based on the patient prognosis, they were divided into a poor prognosis group (n = 30) and a good prognosis group (n = 119). General data from patients in both groups were collected, and the nutritional status of the patients was evaluated using the Controlling Nutritional Status (CONUT) score. Factors affecting the prognosis of HIV infection-related lymphoma were analyzed using univariate and multivariate analyses, and a prediction model was developed based on the analyzed factors. The receiver operating characteristic (ROC) curve was used to analyze the prediction model of the CONUT score alone and included the CONUT score in the prognosis of patients with HIV infection-related lymphoma. The predictive value of the data was assessed, and a survival curve was drawn to compare the survival of patients with different nutritional statuses.

Results

There were significant differences in age, B symptoms, treatment conditions, International Prognostic Index (IPI), pathological stage, Eastern Collaborative Tumor Group physical status score (ECOG PS), CD4+ cell count, β2 microglobulin, and lactate dehydrogenase (LDH) between the poor prognosis group and the good prognosis group (p &lt; 0.05). The CONUT score of the poor prognosis group was higher than that of the good prognosis group, and the difference was statistically significant (p &lt; 0.05). A univariate analysis demonstrated that the age, B symptoms, treatment status, IPI, pathological stage, ECOG PS, CD4+ cell count, β2 microglobulin, LDH, and CONUT score were prognostic factors for patients with HIV infection-related lymphoma (p &lt; 0.05). The results of a multivariate regression analysis demonstrated that the age, B symptoms, treatment status, IPI, pathological stage, ECOG PS, and CONUT score were independent risk factors for the prognosis of patients with HIV infection-related lymphoma (p &lt; 0.05). The prediction model was constructed according to the multivariate Cox regression analysis results. The model formula was as follows: Logit(p) = −10.687 + 1.728 × age + 1.713 × B symptoms + 1.682 × treatment status + 1.810 × IPI + 1.643 × pathological stage + 1.584 × ECOG PS + 1.779 × CONUT score. The ROC curve was used to analyze the predictive value of the CONUT score alone and the predictive model including the CONUT score on the prognosis of patients with HIV infection-related lymphoma. The predictive value of the prognosis of patients with tumors was higher (p &lt; 0.05). According to the results of the ROC curve analysis, the patients were divided into a high CONUT group (CONUT &gt; 6.00 points, n = 31) and a low CONUT group (CONUT ≤ 6.00 points, n = 118) based on the Optimum threshold of the CONUT score. The survival curve showed that the survival rate of the high CONUT group was lower than that of the low CONUT group (p &lt; 0.05).

Conclusion

The poor prognosis of HIV infection-related lymphoma is related to nutritional status, which is an independent risk factor affecting the prognosis of patients and can be used as a practical indicator to predict the prognosis of patients.