GNRI, PLR and Stroke-Associated Pneumonia: From Association to Development of a Web-Based Dynamic Nomogram.

Objective: Discussing the relationship between geriatric nutritional risk index (GNRI) and platelet-to-lymphocyte ratio (PLR) on stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients, developing and validating a web-based dynamic nomogram. Methods: A total of 996 AIS patients admitted to the Department of General Medicine and Neurology at Xuzhou Medical University Affiliated Hospital were collected. They were divided into Non-SAP group and SAP group based on the occurrence of SAP. The data was randomly divided into training set and validation set in a ratio of 7:3. LASSO regression and multivariable logistic regression analysis were used to screen for independent risk factors and develop a dynamic nomogram. Area under the receiver operating characteristic curve (AUC-ROC), calibration curve, and decision curve analysis (DCA) curve were used to validate the model's discriminative ability, calibration, and clinical value, respectively. Results: Among AIS patients, a total of 221 cases (22.19%) developed SAP. Age, NIHSS score, comorbid atrial fibrillation, dysphagia, PLR, and GNRI were identified as independent factors influencing the occurrence of SAP in AIS patients. A web-based dynamic nomogram was developed based on these six variables. The training set showed an AUC-ROC of 0.864 (95% CI: 0.828-0.892), while the validation set showed an AUC-ROC of 0.825 (95% CI: 0.772-0.882), indicating good predictive ability and discrimination of the model. The calibration curve demonstrated good calibration of the model, and the DCA curve showed its clinical value. This model can be accessed and utilized by anyone on the website (https://moonlittledoctor.shinyapps.io/ANADPG/). Conclusion: PLR and GNRI are independent factors influencing the occurrence of SAP in AIS patients, and a dynamic nomogram was constructed to predict the risk of SAP in AIS patients. It can guide clinical decision-making and improve patient prognosis.

Development and validation of a risk nomogram model for predicting pulmonary hypertension in patients with stage 3-5 chronic kidney disease.

OBJECTIVES: The occurrence of pulmonary arterial hypertension (PAH) can greatly affect the prognosis of patients with chronic kidney disease (CKD). We aimed to construct a nomogram to predict the probability of PAH development in patients with stage 3-5 CKD to guide early intervention and to improve prognosis. METHODS: From August 2018 to December 2021, we collected the data of 1258 patients with stage 3-5 CKD hospitalized at the Affiliated Hospital of Xuzhou Medical University as a training set and 389 patients hospitalized at Zhongda Hospital as a validation set. These patients were divided into PAH and N-PAH groups with pulmonary arterial systolic pressure ≥ 35 mmHg as the cutoff. The results of univariate and multivariate logistic regression analyses were used to establish the nomogram. Then, areas under the receiver operating characteristic curve (AUC-ROCs), a calibration plot, and decision curve analysis (DCA) were used to validate the nomogram. RESULTS: The nomogram included nine variables: age, diabetes mellitus, hemoglobin, platelet count, serum creatinine, left ventricular end-diastolic diameter, left atrial diameter, main pulmonary artery diameter and left ventricular ejection fraction. The AUC-ROCs of the training set and validation set were 0.801 (95% confidence interval (CI) 0.771-0.830) and 0.760 (95% CI 0.699-0.818), respectively, which showed good discriminative ability of the nomogram. The calibration diagram showed good agreement between the predicted and observed results. DCA also demonstrated that the nomogram could be clinically useful. CONCLUSION: The evaluation of the nomogram model for predicting PAH in patients with CKD based on risk factors showed its ideal efficacy. Thus, the nomogram can be used to screen for patients at high risk for PAH and has guiding value for the subsequent formulation of prevention strategies and clinical treatment.