Development and validation of a prognostic nomogram to predict 30-day all-cause mortality in patients with CRO infection treated with colistin sulfate. Frontiers in pharmacology Background:Carbapenem-resistant Gram-negative organism (CRO) infection is a critical clinical disease with high mortality rates. The 30-day mortality rate following antibiotic treatment serves as a benchmark for assessing the quality of care. Colistin sulfate is currently considered the last resort therapy against infections caused by CRO. Nevertheless, there is a scarcity of reliable tools for personalized prognosis of CRO infections. This study aimed to develop and validate a nomogram to predict the 30-day all-cause mortality in patients with CRO infection who underwent colistin sulfate treatment. Methods:A prediction model was developed and preliminarily validated using CRO-infected patients treated with colistin sulfate at Tongji Hospital in Wuhan, China, who were hospitalized between May 2018 and May 2023, forming the study cohort. Patients admitted to Xianning Central Hospital in Xianning, China, between May 2018 and May 2023 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of 30-day all-cause mortality. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and the calibration curve were used to evaluate model performance. The decision curve analysis (DCA) was used to assess the model clinical utility. Results:A total of 170 patients in the study cohort and 65 patients in the external validation cohort were included. Factors such as age, duration of combination therapy, nasogastric tube placement, history of previous surgery, presence of polymicrobial infections, and occurrence of septic shock were independently associated with 30-day all-cause mortality and were used to construct the nomogram. The AUC of the nomogram constructed from the above six factors was 0.888 in the training set. The Hosmer-Lemeshow test showed that the model was a good fit ( = 0.944). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram. Conclusion:A nomogram was developed and validated to predict the occurrence of 30-day all-cause mortality in patients with CRO infection treated with colistin sulfate. This nomogram offers healthcare providers a precise and efficient means for early prediction, treatment management, and patient notification in cases of CRO infection treated with colistin sulfate. 10.3389/fphar.2024.1409998

Development and validation of a nomogram for predicting cefoperazone/sulbactam-induced hypoprothrombinaemia in Hospitalized adult patients. PloS one Cefoperazone/sulbactam-induced hypoprothrombinaemia is associated with longer hospital stays and increased risk of death. The aim of this study was to develop and validate a nomogram for predicting the occurrence of cefoperazone/sulbactam-induced hypoprothrombinaemia in hospitalized adult patients. This retrospective cohort study involved hospitalized adult patients at Xi'an Central Hospital from January 2020 to December 2022 based on the Chinese pharmacovigilance system developed and established by the Adverse Drug Reaction Monitoring Center in China. Independent predictors of cefoperazone/sulbactam-induced hypoprothrombinaemia were obtained using multivariate logistic regression and were used to develop and establish the nomogram. According to the same standard, the clinical data of hospitalized patients using cefoperazone/sulbactam at the Third Affiliated Hospital of Xi'an Medical University from January 1, 2023 to June 30, 2023 were collected as the external validation group. The 893 hospitalized patients included 95 who were diagnosed with cefoperazone/sulbactam-induced hypoprothrombinaemia. Our study enrolled 610 patients: 427 in the training group and 183 in the internal validation group. The independent predictors of cefoperazone/sulbactam-induced hypoprothrombinaemia were surgery (odds ratio [OR] = 5.279, 95% confidence interval [CI] = 2.597-10.729), baseline platelet count ≤50×109/L (OR = 2.492, 95% CI = 1.110-5.593), baseline hepatic dysfunction (OR = 12.362, 95% CI = 3.277-46.635), cumulative defined daily doses (OR = 1.162, 95% CI = 1.162-1.221) and nutritional risk (OR = 16.973, 95% CI = 7.339-39.254). The areas under the curve (AUC) of the receiver operating characteristic for the training and internal validation groups were 0.909 (95% CI = 0.875-0.943) and 0.888 (95% CI = 0.832-0.944), respectively. The Hosmer-Lemeshow tests yielded p = 0.475 and p = 0.742 for the training and internal validation groups, respectively, confirming the goodness of fit of the nomogram model. In the external validation group (n = 221), the nomogram was equally robust in cefoperazone/sulbactam-induced hypoprothrombinaemia (AUC = 0.837, 95%CI = 0.736-0.938). The nomogram model constructed in this study had good predictive performance and extrapolation, which can help clinicians to identify patients at high risk of cefoperazone/sulbactam-induced hypoprothrombinaemia early. This will be useful in preventing the occurrence of cefoperazone/sulbactam-induced hypoprothrombinaemia and allowing timely intervention measures to be performed. 10.1371/journal.pone.0291658

A nomogram incorporating linezolid and metabolite concentrations for predicting linezolid induced thrombocytopenia in patients with renal impairment. Scientific reports A nomogram to estimate the risk of linezolid-induced thrombocytopenia in patients with renal impairment is not available. The aim of the study is to develop a nomogram for predicting linezolid-induced thrombocytopenia in patients with renal impairment and to investigate the incremental value of PNU-142300 concentration beyond clinical factors and linezolid trough concentration (C) for risk prediction. Logistic regression was used to identify independent risk factors for linezolid-induced thrombocytopenia in patients with renal impairment and nomograms were established. The performance of the nomograms was assessed in terms of area under the receiver operating characteristic curve (AUROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI) , decision curve analysis (DCA) and calibration. Internal validation and external validation of the nomograms were also performed. Four nomograms were created: nomogram A including total bilirubin, creatinine clearance and concomitant mannitol use; nomogram B containing linezolid C additionally; nomogram C containing total bilirubin, concomitant mannitol use, linezolid C, and PNU142300 concentration; nomogram D including total bilirubin, concomitant mannitol use, and PNU142300 concentration. Nomogram C improved the prediction performance than nomogram A (AUROC 0.881 vs. 0.749; NRI 0.290; IDI 0.226) and nomogram B (AUROC 0.881 vs. 0.812; NRI 0.152; IDI 0.130) in the training cohort. DCA analysis showed that nomogram C yielded a greater net benefit. Compared with nomogram A and nomogram B, nomogram C also showed superior discriminatory efficacy, good calibration and clinical usefulness in the external validation cohort. The nomogram containing PNU-142300 concentration and linezolid C had better predictive capability than that containing linezolid C for predicting linezolid-induced thrombocytopenia in patients with renal impairment. 10.1038/s41598-024-77768-x

Nomogram for predicting bleeding events in nonvalvular atrial fibrillation patients receiving rivaroxaban: A retrospective study. Health science reports Background and Aims:To construct a bleeding events prediction model of nonvalvular atrial fibrillation (NVAF) patients receiving rivaroxaban. Methods:We conducted a retrospective cohort study in patients with NVAF who received rivaroxaban from June 2017 to March 2019. Demographic information and clinical characteristics were obtained from the electronic medical system. Univariate analysis was used to find the primary predictive factors of bleeding events in patients receiving rivaroxaban. Multiple analysis was conducted to screen the primary independent predictive factors selected from the univariate analysis. Finally, the independent influencing factors were applied to build a prediction model by using R software; then, a nomogram was established according to the selected variables visually, and the sensitivity and specificity of the model was evaluated. Results:Twelve primary predictive factors were selected by univariate analysis from 46 variables, and multivariate analysis showed that older age, higher prothrombin time (PT) values, history of heart failure and stroke were independent risk factors of bleeding events. The area under curve (AUC) for this novel nomogram model was 0.828 (95% CI: 0.763-0.894). The mean AUC over 10-fold stratified cross-validation was 0.787, and subgroup analysis validation also showed a satisfied AUC. In addition, the decision curve analysis showed that the PT in combination with CHA2DS2-VASc and HASBLED was more practical and accurate for predicting bleeding events than using CHA2DS2-VASc and HASBLED alone. Conclusions:PT in combination with CHA2DS2-VASc and HASBLED could be considered as a more practical and accurate method for predicting bleeding events in patients taking rivaroxaban. 10.1002/hsr2.1792

Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model. Frontiers in pediatrics Background:Pneumonia remains the leading cause of death among children aged 1-59 months. The early prediction of poor outcomes (PO) is of critical concern. This study aimed to explore the risk factors relating to PO in severe community-acquired pneumonia (SCAP) and build a PO-predictive nomogram model for children with SCAP. Methods:We retrospectively identified 300 Chinese pediatric patients diagnosed with SCAP who were hospitalized in the Affiliated Hospital of Southwest Medical University from August 1, 2018, to October 31, 2021. Children were divided into the PO and the non-PO groups. The occurrence of PO was designated as the dependent variable. Univariate and multivariate logistic regression analyses were used to identify the risk factors of PO. A nomogram model was constructed from the multivariate logistic regression analysis and internally validated for model discrimination and calibration. The performance of the nomogram was estimated using the concordance index (C-index). Results:According to the efficacy evaluation criteria, 56 of 300 children demonstrated PO. The multivariate logistic regression analysis resulted in the following independent risk factors for PO: co-morbidity (OR: 8.032, 95% CI: 3.556-18.140, < 0.0001), requiring invasive mechanical ventilation (IMV) (OR: 7.081, 95% CI: 2.250-22.282, = 0.001), and ALB< 35 g/L (OR: 3.203 95% CI: 1.151-8.912, = 0.026). Results of the internal validation confirmed that the model provided good discrimination (concordance index [C-index], 0.876 [95% CI: 0.828-0.925]). The calibration plots in the nomogram model were of high quality. Conclusion:The nomogram facilitated accurate prediction of PO in children diagnosed with SCAP and could be helpful for clinical decision-making. 10.3389/fped.2023.1194186