Development and Validation of a Nomogram Based on Geriatric Nutritional Risk Index to Predict Surgical Site Infection Among Gynecologic Oncology Patients

Online dynamic nomogram for predicting pain recurrence after microvascular decompression in trigeminal neuralgia

Trigeminal neuralgia (TN) is one of the most common causes of facial pain. Microvascular decompression (MVD) is the first-choice surgical treatment. The present study aimed to develop a novel practical assessment system based on preoperative clinical and imaging factors for clinicians to predict the likelihood of pain recurrence following MVD in TN. A total of 56 patients with primary unilateral TN who underwent MVD were retrospectively analyzed. Patients were followed up to observe pain recurrence 1 year after MVD. An online dynamic nomogram was constructed for predicting the probability of pain recurrence after MVD in patients with TN based on multivariate logistic model. The concordance index (C-index) and receiver operating characteristic (ROC) were used to measure model discrimination. Bootstrap resampling was used for internal validation of the model and calibration curve was constructed. Decision curve analysis (DCA) was used to assess clinical applicability. Factors such as numeric rating scale (to score pain degree of patients with TN), response to neuroanalgesic drugs and neurovascular contact on magnetic resonance imaging were independent risk factors affecting the pain recurrence rate (all P<0.05). C-index was 0.973 (95%CI, 0.938-1.000) and the area under the ROC was 0.973 (95%CI, 0.938-1.000). Calibration curve with a 1,000 bootstrap resampling showed a good fit between dynamic nomogram prediction and actual observations. The DCA showed that at a threshold probability between 0 and 100%, this model can achieve a greater net benefit than if all patients had surgery or none had surgery. In conclusion, this online dynamic nomogram reliably predicted risk of pain recurrence in patients with TN following MVD.

Prognostic Role of Geriatric Nutritional Risk Index in Patients with Pancreatic Cancer: A Meta-Analysis

Background: The Geriatric Nutritional Risk Index (GNRI) is used to assess the prognosis of patients with pancreatic cancer. Therefore, this meta-analysis was conducted to evaluate the association between the GNRI and prognosis in pancreatic cancer.Methods: We performed a pooled analysis of the hazard ratios (HRs) and 95% confidence intervals (CIs) of the GNRI for survival in pancreatic cancer. Using pooled odds ratios (ORs) and 95% CIs, we investigated the association between the GNRI and clinicopathological characteristics of pancreatic cancer.Results: Six studies were included in this meta-analysis, totaling 1,513 patients. A low GNRI was significantly associated with a poorer overall survival (OS) in the pooled results (HR, 1.95; 95% CI, 1.29-2.94; p = 0.002) in pancreatic cancer. However, GNRI was not significantly associated with progression-free survival (PFS) in pancreatic cancer (HR, 1.57; 95% CI, 0.90-2.73; p = 0.114). The pooled results indicated that a low GNRI was significantly associated with tumor location of pancreas head (OR, 2.18; 95% CI, 1.45-3.29; p < 0.001).Conclusions: This meta-analysis showed that low GNRI was significantly associated with poor OS but not with poor PFS in patients with pancreatic cancer. The GNRI is a novel and effective risk factor and a potential biomarker for the prognosis of pancreatic cancer.

Evaluating the efficiency of a nomogram based on the data of neurosurgical intensive care unit patients to predict pulmonary infection of multidrug-resistant Acinetobacter baumannii

Background

Pulmonary infection caused by multidrug-resistant Acinetobacter baumannii (MDR-AB) is a common and serious complication after brain injury. There are no definitive methods for its prediction and it is usually accompanied by a poor prognosis. This study aimed to construct and evaluate a nomogram based on patient data from the neurosurgical intensive care unit (NSICU) to predict the probability of MDR-AB pulmonary infection.

Methods

In this study, we retrospectively collected patient clinical profiles, early laboratory test results, and doctors' prescriptions (66 variables). Univariate and backward stepwise regression analyses were used to screen the variables to identify predictors, and a nomogram was built in the primary cohort based on the results of a logistic regression model. Discriminatory validity, calibration validity, and clinical utility were evaluated using validation cohort 1 based on receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). For external validation based on predictors, we prospectively collected information from patients as validation cohort 2.

Results

Among 2115 patients admitted to the NSICU between December 1, 2019, and December 31, 2021, 217 were eligible for the study, including 102 patients with MDR-AB infections (102 cases) and 115 patients with other bacterial infections (115 cases). We randomly categorized the patients into the primary cohort (70%, N=152) and validation cohort 1 (30%, N=65). Validation cohort 2 consisted of 24 patients admitted to the NSICU between January 1, 2022, and March 31, 2022, whose clinical information was prospectively collected according to predictors. The nomogram, consisting of only six predictors (age, NSICU stay, Glasgow Coma Scale, meropenem, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio), had significantly high sensitivity and specificity (primary cohort AUC=0.913, validation cohort 1 AUC=0.830, validation cohort 2 AUC=0.889) for early identification of infection and had great calibration (validation cohort 1,2 P=0.3801, 0.6274). DCA confirmed that the nomogram is clinically useful.

Conclusion

Our nomogram could help clinicians make early predictions regarding the onset of pulmonary infection caused by MDR-AB and implement targeted interventions.

A nomogram based on nutritional status and A2DS2 score for predicting stroke-associated pneumonia in acute ischemic stroke patients with type 2 diabetes mellitus: A retrospective study

Background

Stroke-associated pneumonia (SAP) commonly complicates acute ischemic stroke (AIS) and significantly worsens outcomes. Type 2 diabetes mellitus (T2DM) may contribute to malnutrition, impair innate immunity function, and increase the probability of SAP occurrence in AIS patients. We aimed to determine early predictors of SAP in AIS patients with T2DM and to construct a nomogram specifically for predicting SAP in this population by combining the A²DS² score with available nutrition-related parameters.

Methods

A total of 1,330 consecutive AIS patients with T2DM were retrospectively recruited. The patients were randomly allocated to the training (n = 887) and validation groups (n = 443). Univariate and multivariate binary logistic regression analyses were applied to determine the predictors of SAP in the training group. A nomogram was established according to the identified predictors. The areas under the receiver operating characteristic curve (AUROC) and calibration plots were performed to access the predictive values of the nomogram. The decision curve was applied to evaluate the net benefits of the nomogram.

Results

The incidence of SAP was 9% and 9.7% in the training and validation groups, respectively. The results revealed that the A²DS² score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP. A novel nomogram, A²DS²-Nutrition, was constructed based on these five predictors. The AUROC for A²DS²-Nutrition (0.820, 95% CI: 0.794–0.845) was higher than the A²DS² score (0.691, 95% CI: 0.660–0.722) in the training group. Similarly, it showed a better predictive performance than the A²DS² score [AUROC = 0.864 (95% CI: 0.828–0.894) vs. AUROC = 0.763 (95% CI: 0.720–0.801)] in the validation group. These results were well calibrated in the two groups. Moreover, the decision curve revealed that the A²DS²-Nutrition provided an additional net benefit to the AIS patients with T2DM compared to the A²DS² score in both groups.

Conclusion

The A²DS² score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP in AIS patients with T2DM. Thus, the proposed A²DS²-Nutrition may be a simple and reliable prediction model for SAP occurrence in AIS patients with T2DM.