Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study

BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

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A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

Frontiers in Oncology ◽

10.3389/fonc.2021.687333 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiangming Cai ◽

Junhao Zhu ◽

Jin Yang ◽

Chao Tang ◽

Feng Yuan ◽

...

Keyword(s):

Risk Factors ◽

Calibration Curve ◽

Validation Cohort ◽

Characteristic Curve ◽

Treatment Strategies ◽

Predictive Performance ◽

Maximum Dimension ◽

Ki 67 ◽

Training Cohort ◽

Index Level

BackgroundThe Ki-67 index is an indicator of proliferation and aggressive behavior in pituitary adenomas (PAs). This study aims to develop and validate a predictive nomogram for forecasting Ki-67 index levels preoperatively in PAs.MethodsA total of 439 patients with PAs underwent PA resection at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020; they were enrolled in this retrospective study and were classified randomly into a training cohort (n = 300) and a validation cohort (n = 139). A range of clinical, radiological, and laboratory characteristics were collected. The Ki-67 index was classified into the low Ki-67 index (<3%) and the high Ki-67 index (≥3%). Least absolute shrinkage and selection operator algorithm and uni- and multivariate logistic regression analyses were applied to identify independent risk factors associated with Ki-67. A nomogram was constructed to visualize these risk factors. The receiver operation characteristic curve and calibration curve were computed to evaluate the predictive performance of the nomogram model.ResultsAge, primary-recurrence subtype, maximum dimension, and prolactin were included in the nomogram model. The areas under the curve (AUCs) of the nomogram model were 0.694 in the training cohort and 0.658 in the validation cohort. A well-fitted calibration curve was also generated for the nomogram model. A subgroup analysis revealed stable predictive performance for the nomogram model. A correlation analysis revealed that age (R = −0.23; p < 0.01), maximum dimension (R = 0.17; p < 0.01), and prolactin (R = 0.16; p < 0.01) were all significantly correlated with the Ki-67 index level.ConclusionsAge, primary-recurrence subtype, maximum dimension, and prolactin are independent predictors for the Ki-67 index level. The current study provides a novel and feasible nomogram, which can further assist neurosurgeons to develop better, more individualized treatment strategies for patients with PAs by predicting the Ki-67 index level preoperatively.

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A Nomogram Combing Peripheral Parameters for Estimation of CRSwNP Recurrence

American Journal of Rhinology and Allergy ◽

10.1177/1945892420978957 ◽

2020 ◽

pp. 194589242097895

Author(s):

Kun Du ◽

Ming Zheng ◽

Yan Zhao ◽

Chunyuan Jiao ◽

Wenbin Xu ◽

...

Keyword(s):

Risk Factors ◽

Validation Cohort ◽

Univariate Analysis ◽

Sinus Surgery ◽

Total Serum ◽

Training Cohort ◽

Serum Ige ◽

Preoperative Prediction ◽

Risk Factors For Recurrence ◽

Blood Eosinophils

Background The preoperative prediction of the recurrence of chronic rhinosinusitis with nasal polyps (CRSwNP) remains difficult in clinical practice. Objective We aimed to develop a nomogram that combined peripheral risk factors to clinically predict the recurrence of CRSwNP. Methods Data from 158 CRSwNP patients who underwent endoscopic sinus surgery (ESS) from January 2012 to December 2016 were collected, and the patients were followed up for 3 years. Of these, 96 patients who underwent ESS in an earlier period formed the training cohort for nomogram development, and 62 patients who underwent ESS thereafter formed the validation cohort to confirm the model’s performance. Risk factors for recurrence identified by univariate and multivariate logistic regression were used to create a nomogram. Results The recurrence rate was 29.2% (28/96) for the training cohort and 35.5% (22/62) for the validation cohort. Univariate analysis identified blood eosinophils (Eos), serum IgE level, asthma comorbidity, and the number of previous ESSs as risk factors for recurrence. Among those four parameters, serum IgE level and a previous ESS surgery were identified as two independent risk factors. A nomogram consisting of blood Eos, total serum IgE level, asthma comorbidity, and the number of previous ESSs was constructed, demonstrating a C index of 0.81 (95% CI, 0.79-0.83) and 0.80 (95% CI, 0.77-0.83) for predicting recurrence in the training and validation cohorts, respectively. The nomogram had well-fitted calibration curves. Conclusion The nomogram might be able to preoperatively predict the recurrence of CRSwNP by using currently available and objective parameters. Further studies are required to validate its reliability and effectiveness.

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Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature

Frontiers in Oncology ◽

10.3389/fonc.2021.585942 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jia Ran ◽

Ran Cao ◽

Jiumei Cai ◽

Tao Yu ◽

Dan Zhao ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Deep Learning ◽

Prediction Model ◽

Validation Cohort ◽

External Validation ◽

Training Cohort ◽

Preoperative Prediction ◽

Lemeshow Test ◽

Radiomics Signature

Background and PurposeThe preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature.Materials and MethodsThis retrospective study included a training cohort of 200 patients, an internal validation cohort of 40 patients, and an external validation cohort of 60 patients. Radiomics features were extracted from conventional CT (computed tomography) images. T-test and Extra-trees were performed for feature selection, and the selected features were combined using logistic regression to build the radiomics signature. The features and weights of the last fully connected layer of a CNN (convolutional neural network) were combined to obtain a deep learning signature. By incorporating clinical risk factors, the prediction model was developed using a multivariable logistic regression analysis, based on which the nomogram was developed. The calibration, discrimination and clinical values of the nomogram were evaluated.ResultsMultivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and CT-reported LN status were independent predictors. The prediction model developed by all the independent predictors showed good discrimination (C-index, 0.820; 95% CI, 0.762 to 0.879) and calibration (Hosmer-Lemeshow test, P=0.193) capabilities for the training cohort. Additionally, the model achieved satisfactory discrimination (C-index, 0.861; 95% CI, 0.769 to 0.954) and calibration (Hosmer-Lemeshow test, P=0.775) when applied to the external validation cohort. An analysis of the decision curve showed that the nomogram had potential for clinical application.ConclusionsThis study presents a prediction model based on radiomics signature, deep learning signature, and CT-reported LN status that can be used to predict preoperative LN metastasis in patients with LUAD.

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Cerebrospinal fluid leakage after cranial surgery in the pediatric population—a systematic review and meta-analysis

Child s Nervous System ◽

10.1007/s00381-021-05036-8 ◽

2021 ◽

Author(s):

Emma M. H. Slot ◽

Kirsten M. van Baarsen ◽

Eelco W. Hoving ◽

Nicolaas P. A. Zuithoff ◽

Tristan P. C van Doormaal

Keyword(s):

Risk Factors ◽

Systematic Review ◽

Cerebrospinal Fluid ◽

Pediatric Population ◽

Meta Analysis ◽

Leakage Rate ◽

Csf Leak ◽

Cerebrospinal Fluid Leakage ◽

Cranial Surgery ◽

Csf Leakage

Abstract Background Cerebrospinal fluid (CSF) leakage is a common complication after neurosurgical intervention. It is associated with substantial morbidity and increased healthcare costs. The current systematic review and meta-analysis aim to quantify the incidence of cerebrospinal fluid leakage in the pediatric population and identify its risk factors. Methods The authors followed the PRISMA guidelines. The Embase, PubMed, and Cochrane database were searched for studies reporting CSF leakage after intradural cranial surgery in patients up to 18 years old. Meta-analysis of incidences was performed using a generalized linear mixed model. Results Twenty-six articles were included in this systematic review. Data were retrieved of 2929 patients who underwent a total of 3034 intradural cranial surgeries. Surprisingly, only four of the included articles reported their definition of CSF leakage. The overall CSF leakage rate was 4.4% (95% CI 2.6 to 7.3%). The odds of CSF leakage were significantly greater for craniectomy as opposed to craniotomy (OR 4.7, 95% CI 1.7 to 13.4) and infratentorial as opposed to supratentorial surgery (OR 5.9, 95% CI 1.7 to 20.6). The odds of CSF leakage were significantly lower for duraplasty use versus no duraplasty (OR 0.41 95% CI 0.2 to 0.9). Conclusion The overall CSF leakage rate after intradural cranial surgery in the pediatric population is 4.4%. Risk factors are craniectomy and infratentorial surgery. Duraplasty use is negatively associated with CSF leak. We suggest defining a CSF leak as “leakage of CSF through the skin,” as an unambiguous definition is fundamental for future research.

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Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

Cancer Imaging ◽

10.1186/s40644-021-00395-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sijia Cui ◽

Tianyu Tang ◽

Qiuming Su ◽

Yajie Wang ◽

Zhenyu Shu ◽

...

Keyword(s):

Calibration Curve ◽

Main Pancreatic Duct ◽

Validation Cohort ◽

External Validation ◽

High Grade ◽

Intraductal Papillary Mucinous Neoplasms ◽

Training Cohort ◽

Independent Validation ◽

Medical Centers ◽

Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

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Incidence and Risk Factors for Symptomatic Spinal Epidural Hematoma Following Posterior Thoracic Spinal Surgery in a Single Institute

Global Spine Journal ◽

10.1177/2192568220979141 ◽

2020 ◽

pp. 219256822097914

Author(s):

Longjie Wang ◽

Hui Wang ◽

Zhuoran Sun ◽

Zhongqiang Chen ◽

Chuiguo Sun ◽

...

Keyword(s):

Risk Factors ◽

Cerebrospinal Fluid ◽

Spinal Surgery ◽

Cerebrospinal Fluid Leakage ◽

Sectional Area ◽

Kyphosis Angle ◽

Cross Sectional ◽

Thoracic Spinal ◽

Local Kyphosis ◽

Spinal Epidural

Study Design: Case-control study. Objectives: To investigate the incidence of symptomatic spinal epidural hematoma (SSEH) and recognize its risk factors in a cohort of patients undergoing posterior thoracic surgery in isolation. Methods: From January 2010 to December 2019, patients who developed SSEH after posterior thoracic surgery and underwent hematoma evacuation were enrolled. For each SSEH patient, 2 or 3 controls who did not develop SSEH and underwent the same procedures with similar complexity at the same section of the thoracic spine in the same period were collected. The preoperative and intraoperative factors, blood pressure-related factors and radiographic parameters were collected to identify possible risk factors by comparing between the 2 groups. Results: A total of 24 of 1612 patients (1.49%) were identified as having SSEH after thoracic spinal surgery. Compared to the control group (53 patients), SSEH patients had significant differences in the APTT (p = 0.028), INR (p = 0.009), ratio of previous spinal surgery (p = 0.012), ratio of cerebrospinal fluid leakage (p = 0.004), thoracic kyphosis (p<0.05), local kyphosis angle (p<0.05), epidural fat ratio at T7 (p = 0.003), occupying ratio of the cross-sectional area (p<0.05) and spinal epidural venous plexus grade (p<0.05). Multiple logistic regression analysis revealed 3 risk factors for SSEH: cerebrospinal fluid leakage, the local kyphosis angle (>8.77°) and the occupying ratio of the cross-sectional area (>49.58%). Conclusions: The incidence of SSEH was 1.49% in posterior thoracic spinal surgeries. Large local kyphosis angle (>8.77°), high occupying ratio of cross-sectional area (>49.58%) and cerebrospinal fluid leakage were identified as risk factors for SSEH.

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Analysis of Risk Factors of Intracranial Infection After Traumatic Brain Injury Surgery

Tobacco Regulatory Science ◽

10.18001/trs.7.5.1.88 ◽

2021 ◽

Vol 7 (5) ◽

pp. 3161-3167

Author(s):

JiNan Li ◽

XinLi Zhang ◽

Hang SU ◽

YaNan Qu ◽

MeiXuan Piao

Keyword(s):

Risk Factors ◽

Traumatic Brain Injury ◽

Mechanical Ventilation ◽

Cerebrospinal Fluid ◽

Brain Injury ◽

Operation Time ◽

Cerebrospinal Fluid Leakage ◽

Infection Group ◽

Intracranial Infection ◽

Indwelling Time

Background: Craniocerebral operation is the main method for the treatment of traumatic brain injury. However, it is very easy to be complicated with intracranial infection after operation, which affects the surgical efficacy and patient’s prognosis. It is also the main cause of surgical failure. It may also cause patient’s death for some patients with serious diseases. It is found that the infection after craniocerebral operation is often accompanied with abnormal changes of body-related treatment, in which the changes of serological indicators are more significant. Therefore, it is helpful to provide guidance for the prevention and judgment of patient’s postoperative infection by analyzing the patient’s serological indicators. Objective: To investigate the risk factors of intracranial infection and the levels of serum procalcitonin (PCT) and endothelin-1 (ET-1) in patients after traumatic brain injury. Methods: From January 2018 to January 2021, 58 patients with intracranial infection after traumatic brain injury (infection group) were selected, and 116 patients without intracranial infection after traumatic brain injury (non-infection group) were selected. The difference of clinical data between the two groups was analyzed. Serum PCT and ET-1 levels were measured in the two groups. Results: In the infection group, admission GCS scoring <8 points, operation time ≥4h, indwelling time of drainage tube ≥ 2d, preoperative ALB <35g/ L, mechanical ventilation and cerebrospinal fluid leakage were 63.79%, 72.41%, 43.10%, 68.97%, 32.76% and 68.97% respectively, which were obviously higher than those in the non-infection group (P<0.05). Logistic regression analysis results showed that admission GCS scoring, operation time, indwelling time of drainage tube, preoperative ALB, mechanical ventilation and cerebrospinal fluid leakage were the influencing factors of intracranial infection after traumatic brain injury (OR = 0.712,1.556,1.451,0.641,1.954 and 1.667, P<0.05); serum PCT and ET-1 in the infection group were (0.83 ± 0.20) mg/L and (0.87 ± 0.23) ng/L, respectively, which were significantly higher than those in the non-infection group (P<0.05); serum PCT and ET-1 in patients with different sex, age and pathogen had no significant difference (P>0.05); serum PCT and ET-1 area under ROC curve were 0.828 and 0.751, respectively P<0.05. Conclusion: The intracranial infection of patients with traumatic brain injury are affected by many factors including, admission GCS scoring, operation time, and so on, the levels of serum PCT and ET-1 in patients with intracranial infection are increased, which may be useful in predicting intracranial infection.

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Development and Validation of a Clinical Score for Predicting the Risk of Tympanosclerosis Before Surgery

Ear Nose & Throat Journal ◽

10.1177/0145561320951676 ◽

2020 ◽

pp. 014556132095167

Author(s):

Zhihuai Dong ◽

Mingguang Zhou ◽

Gaofei Ye ◽

Jing Ye ◽

Mang Xiao

Keyword(s):

Otitis Media ◽

Calibration Curve ◽

Validation Cohort ◽

Predictive Accuracy ◽

Strong Predictor ◽

Multivariate Logistic Regression Analysis ◽

Clinical Score ◽

Type I ◽

Discriminative Ability ◽

Training Cohort

Objective: To develop and validate a clinical score to predict the risk of tympanosclerosis before surgery. Methods: A sample of 404 patients who underwent middle ear microsurgery for otitis media was enrolled. These patients were randomly divided into 2 cohorts: the training cohort (n = 243, 60%) and the validation cohort (n = 161, 40%). The preoperative predictors of tympanosclerosis were determined by multivariate logistic regression analysis and implemented using a clinical score tool. The predictive accuracy and discriminative ability of the clinical score were determined by the area under the curve (AUC) and the calibration curve. Results: The multivariate analysis in the training cohort (n = 243, 60%) identified independent factors for tympanosclerosis as the female sex (odds ratio [OR]: 3.83; 95% CI: 1.66-9.37), the frequency-specific air-bone gap at 250 Hz ≥ 45 dB HL (OR: 3.68; 95% CI: 1.68-8.57), aditus ad antrum blockage (OR: 3.29; 95% CI: 1.38-8.43), type I eardrum calcification (OR: 25.37; 95% CI: 8.41-88.91) or type II eardrum calcification (OR: 18.86; 95% CI: 6.89-58.77), and a history of otitis media ≥ 10 years (OR: 4.10; 95% CI: 1.58-11.83), which were all included in the clinical score tool. The AUC of the clinical score for predicting tympanosclerosis was 0.89 (95% CI: 0.85-0.93) in the training cohort and 0.89 (95% CI: 0.84-0.95) in the validation cohort. The calibration curve also showed good agreement between the predicted and observed probability. Conclusions: The clinical score achieved an optimal prediction of tympanosclerosis before surgery. The presence of calcification pearls on the promontorium tympani is a strong predictor of tympanosclerosis with stapes fixation.

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Incidence and risk factors of subdural hematoma after intraoperative cerebrospinal fluid leakage during the transsphenoidal approach

Pituitary ◽

10.1007/s11102-016-0746-x ◽

2016 ◽

Vol 19 (6) ◽

pp. 565-572 ◽

Cited By ~ 2

Author(s):

Kazuhito Takeuchi ◽

Tadashi Watanabe ◽

Tetsuya Nagatani ◽

Yuichi Nagata ◽

Jonsu Chu ◽

...

Keyword(s):

Risk Factors ◽

Cerebrospinal Fluid ◽

Subdural Hematoma ◽

Transsphenoidal Approach ◽

Cerebrospinal Fluid Leakage ◽

Fluid Leakage

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Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

BMC Medical Imaging ◽

10.1186/s12880-020-00513-z ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yixing Yu ◽

Ximing Wang ◽

Min Li ◽

Lan Gu ◽

Zongyu Xie ◽

...

Keyword(s):

Risk Factors ◽

Calibration Curve ◽

Severity Score ◽

Early Stage ◽

Area Under The Curve ◽

Clinical Impact ◽

Imaging Data ◽

Net Benefit ◽

Training Cohort ◽

Independent Risk Factors

Abstract Background To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. Methods The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. Results In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5–9) was significantly higher than that of the mild group (4, IQR,2–5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889–0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867–1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. Conclusion The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

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