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Wang H, Li S, Wang Z, Wu D, Guo Z, Zhao B, Wan J. Online dynamic nomogram for predicting pain recurrence after microvascular decompression in trigeminal neuralgia. Exp Ther Med 2023; 26:431. [PMID: 37602298 PMCID: PMC10433436 DOI: 10.3892/etm.2023.12130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/05/2023] [Indexed: 08/22/2023] Open
Abstract
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.
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Affiliation(s)
- Hongliang Wang
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230000, P.R. China
- Cerebral Vascular Disease Research Center, Anhui Medical University, Hefei, Anhui 230000, P.R. China
| | - Sai Li
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230000, P.R. China
- Cerebral Vascular Disease Research Center, Anhui Medical University, Hefei, Anhui 230000, P.R. China
| | - Zhiwei Wang
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230000, P.R. China
- Cerebral Vascular Disease Research Center, Anhui Medical University, Hefei, Anhui 230000, P.R. China
| | - Dejun Wu
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230000, P.R. China
- Cerebral Vascular Disease Research Center, Anhui Medical University, Hefei, Anhui 230000, P.R. China
| | - Zhifei Guo
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230000, P.R. China
- Cerebral Vascular Disease Research Center, Anhui Medical University, Hefei, Anhui 230000, P.R. China
| | - Bing Zhao
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230000, P.R. China
- Cerebral Vascular Disease Research Center, Anhui Medical University, Hefei, Anhui 230000, P.R. China
| | - Jinghai Wan
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230000, P.R. China
- Department of Neurosurgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100000, P.R. China
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Li L, He J. Prognostic Role of Geriatric Nutritional Risk Index in Patients with Pancreatic Cancer: A Meta-Analysis. Nutr Cancer 2023:1-10. [PMID: 37162261 DOI: 10.1080/01635581.2023.2209345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
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.
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Affiliation(s)
- Lili Li
- Clinical Laboratory, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Huzhou, Zhejiang, China
| | - Junjun He
- Clinical Laboratory, Traditional Chinese Medical Hospital of Huzhou Affiliated to Zhejiang Chinese Medical University, Huzhou, Zhejiang, China
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Wu D, Sha Z, Fan Y, Yuan J, Jiang W, Liu M, Nie M, Wu C, Liu T, Chen Y, Feng J, Dong S, Li J, Sun J, Pang C, Jiang R. 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. Front Cell Infect Microbiol 2023; 13:1152512. [PMID: 37180447 PMCID: PMC10167012 DOI: 10.3389/fcimb.2023.1152512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
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.
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Affiliation(s)
- Di Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Zhuang Sha
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Yibing Fan
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Jiangyuan Yuan
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Weiwei Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Mingqi Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Meng Nie
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Chenrui Wu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Tao Liu
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Yupeng Chen
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Jiancheng Feng
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Shiying Dong
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Jin Li
- Department of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin, China
| | - Jian Sun
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
| | - Chongjie Pang
- Department of Infectious Diseases, Tianjin Medical University General Hospital, Tianjin, China
| | - Rongcai Jiang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Neurological Institute, Key Laboratory of Post Neuro-Injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education, Tianjin, China
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Song X, He Y, Bai J, Zhang J. A nomogram based on nutritional status and A 2DS 2 score for predicting stroke-associated pneumonia in acute ischemic stroke patients with type 2 diabetes mellitus: A retrospective study. Front Nutr 2022; 9:1009041. [PMID: 36313103 PMCID: PMC9608514 DOI: 10.3389/fnut.2022.1009041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/29/2022] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Xiaodong Song
- Department of Neurology, Peking University People’s Hospital, Beijing, China
| | - Yang He
- Department of Neurology, Peking University People’s Hospital, Beijing, China
| | - Jie Bai
- Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China,*Correspondence: Jie Bai,
| | - Jun Zhang
- Department of Neurology, Peking University People’s Hospital, Beijing, China,Jun Zhang,
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