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

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.

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

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cerebrospinal Fluid ◽  
Prediction Model ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Laboratory Data ◽  
Pituitary Surgery ◽  
Cerebrospinal Fluid Leakage ◽  
Training Cohort ◽  
Preoperative Prediction

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.

scholarly journals A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Liu ◽  
Xiaoyuan Liang ◽  
Lingxiang Ruan ◽  
Sheng Yan
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Gallbladder Cancer ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Clinical Variables ◽  
Node Metastasis ◽  
Preoperative Prediction

ObjectivesThe aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).MethodsA total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.ResultsThe clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.ConclusionThe findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

scholarly journals Comprehensive Analysis of the Functions, Prognostic and Diagnostic Values of RNA Binding Proteins in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Xueping Ke ◽  
Zhen Fu ◽  
Jingjing Yang ◽  
Shijin Yu ◽  
Tingyuan Yan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Training Cohort

Abstract Background: Increasing evidence has suggested that RNA binding protein (RBP) dysregulation plays an important part in tumorigenesis. Here, we sought to explore the potential molecular functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The Limma package was applied to identify the differently expressed RBPs between HNSCC and normal samples with |log2 fold change (FC)|≥1 and false discovery rate (FDR)<0.05. the immunohistochemistry images from the Human Protein Atlas database The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest and the prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in training cohort and validated in validation cohort. All these analyses were performed using the R software.Results: A total of 84 aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic related hub gene and were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in LASSO-logistic regression analysis and shown high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24 and NR0B1) were identified as prognosis associated hub genes by least absolute shrinkage and selection operator (LASSO) and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The area under the curve of the time-dependent receiver operator characteristic curve of the prognostic model were 0.664 at 3 years and 0.635 at 5 years in training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive effificacy for prognosis in HNSCC.Conclusions: Our results demonstrate the values of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insights to understand potential role of dysregulated RBP in HNSCC.

scholarly journals An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage

2021 ◽  
Vol 12 ◽  
Author(s):  
Ping Hu ◽  
Yang Xu ◽  
Yangfan Liu ◽  
Yuntao Li ◽  
Liguo Ye ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prognostic Factors ◽  
Subarachnoid Hemorrhage ◽  
Validation Cohort ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
External Validation ◽  
Brier Score ◽  
Web Based ◽  
Training Cohort ◽  
Multivariable Logistic Regression

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

scholarly journals A prediction model of Contrast-associated Acute Kidney Injury in Patients with Hypoalbuminemia Undergoing Coronary Angiography

2020 ◽  
Author(s):  
Liwei Liu ◽  
Jin Liu ◽  
Li Lei ◽  
Bo Wang ◽  
Guoli Sun ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Coronary Angiography ◽  
Risk Stratification ◽  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Kidney Injury ◽  
Good Tool ◽  
Lemeshow Test ◽  
Risk Patients

Abstract Background: Risk stratification is recommended as the key step to prevent contrast-associated acute kidney injury (CA-AKI) by allowing for prevention among at-risk patients undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI). Patients with hypoalbuminemia are prone to CA-AKI and do not have their own risk stratification tool. Therefore, we developed and validated a model for predicting CA-AKI in patients with hypoalbuminemia undergoing CAG/PCI.Methods: A total of 1272 consecutive patients with hypoalbuminemia undergoing CAG/PCI were enrolled and randomly assigned (2:1 ratio) to a development cohort (n = 848) and a validation cohort (n = 424). CA-AKI was defined as a serum creatinine (SCr) increase of ≥ 0.3 mg/dL or 50% from baseline within the first 48 to 72 hours following CAG/PCI. A prediction model was established with independent predictors according to multivariate logistic regression and a stepwise approach, showing as a nomogram. The discrimination of the nomogram was assessed by the area under the receiver operating characteristic (ROC) curve and was compared to the classic Mehran CA-AKI score. Calibration was assessed using the Hosmer–Lemeshow test.Results: Overall, 8.4% (71/848) of patients in the development cohort and 11.2% (48/424) of patients in the validation cohort experienced CA-AKI. The simple nomogram included estimated glomerular filtration rate (eGFR), serum albumin (ALB), age and the use of intra-aortic balloon pump (IABP); showed better predictive ability than the Mehran score (C-index 0.756 vs. 0.693, p = 0.02); and had good calibration (Hosmer–Lemeshow test p = 0.187). Conclusions: Our data suggested that the simple model might be a good tool for predicting CA-AKI in high-risk patients with hypoalbuminemia undergoing CAG/PCI, but our findings require further external validation.Trial registration number NCT01400295

P0616REAL-TIME CLINICAL DECISION SUPPORT TO PREDICT AND MANAGE IN-HOSPITAL ACUTE KIDNEY INJURY UTILIZING RECURRENT NEURAL NETWORK

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Hea Eun Kim ◽  
Hyeonsik Yang ◽  
Sejoong Kim ◽  
Kipyo Kim
Keyword(s):  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Vital Signs ◽  
Clinical Decision ◽  
Internal Validation ◽  
Training Cohort ◽  
National University ◽  
Seoul National University ◽  
Dynamic Variables

Abstract Background and Aims Rapidly Increasing electronic health record (EHR) data and recent development of machine learning methods offers the possibilities of improvement in quality of care in clinical practice. Machine learning can incorporate huge amount of features into the model, and enable non-linear algorithms with great performance. Previously published AKI prediction models have simple design without real-time assessment. Major risk factors in in-hospital AKI include use of various nephrotoxins, repeatedly measured laboratory findings, and vital signs, which are dynamic variables rather than static. Given that recurrent neural network (RNN) is a powerful tool to handle the sequential data, using RNN method in the prediction model is a promising approach. Therefore, in the present study, we proposed a RNN-based prediction model with external validation for in-hospital AKI and aimed to provide a framework to link the developed model with clinical decision supports. Method Study populations were all patients aged ≥ 18 years and hospitalized more than a week at Seoul National University Bundang Hospital (SNUBH) from 2013 to 2017 (training cohort) and at Seoul National University Hospital (SNUH) in 2017 (validation cohort). All demographics, laboratory values, vital signs, and clinical conditions were obtained from the EHR of each hospital. A total of 102 variables included in the model. Each variable falls into two categories: static and dynamic variable; static variable was time-invariant values during hospitalization, and dynamic variables were daily-updated values. Baseline creatinine was determined by searching the minimum serum Cr level within 2 weeks before admission. We developed two different models (model 1 and model 2) using RNN algorithms. The outcome for model 1 was the occurrence of AKI within 7 days from the present. In model 2, we constructed the prediction model of the trajectory of Cr values after 24 hours, 48 hours, and 72 hours, using available Cr values from 7 days ago to the present. Internal validation was performed by 5-fold cross validation using the training set (SNUBH), and then external validation was done using test set (SNUH). Results A total of 40,552 patients in training cohort and 4,000 patients in external validation cohort (test cohort) were included in the study. The mean age of participants was 62.2 years in training cohort and 58.7 years in test cohort. Baseline eGFR was 93.8 ± 40.4 ml/min/1.73m2 in training cohort and 88.4 ± 23.2 ml/min/1.73m2 in test cohort. In model 1 for the prediction of AKI occurrence within 7 days, the area under the curve was 0.93 (sensitivity 0.90, specificity 0.96) in internal validation, and 0.83 (sensitivity 0.83, specificity 0.82) in external validation. The model 2 predicted the creatinine trajectory within 3 days accurately; root mean square error was 0.1 in training cohort and 0.3 in test cohort. To support the clinical decision for AKI manage, we estimated the predicted trajectories of future creatinine levels after renal insult removal, such as nephrotoxic drugs, based on the established model 2. Conclusion We developed and validated a real-time AKI prediction model using RNN algorithms. This model showed high performance and can accurately visualize future creatinine trajectories. In addition, the model can provide the information about modifiable factors in patients with high risk of AKI.

scholarly journals Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Yun Bian ◽  
Shiwei Guo ◽  
Hui Jiang ◽  
Suizhi Gao ◽  
Chengwei Shao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Ductal Adenocarcinoma ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Preoperative Prediction ◽  
Multivariable Logistic Regression ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA). Results The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68–0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69–0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful. Conclusions The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC.

scholarly journals CT Radiomics Model for Discriminating the Risk Stratification of Gastrointestinal Stromal Tumors: A Multi-Class Classification and Multi-Center Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhonghua Chen ◽  
Linyi Xu ◽  
Chuanmin Zhang ◽  
Chencui Huang ◽  
Minhong Wang ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Low Risk ◽  
Class Prediction ◽  
Training Cohort ◽  
Stromal Tumors ◽  
Risk Patients ◽  
Multi Class Classification

ObjectiveTo establish and verify a computed tomography (CT)-based multi-class prediction model for discriminating the risk stratification of gastrointestinal stromal tumors (GISTs).Materials and MethodsA total of 381 patients with GISTs were confirmed by surgery and pathology. Information on 213 patients were obtained from one hospital and used as training cohort, whereas the details of 168 patients were collected from two other hospitals and used as independent validation cohort. Regions of interest on CT images of arterial and venous phases were drawn, radiomics features were extracted, and dimensionality reduction processing was performed. Using a one-vs-rest method, a Random Forest-based GISTs risk three-class prediction model was established, and the receiver operating characteristic curve (ROC) was used to evaluate the performance of the multi-class classification model, and the generalization ability was verified using external data.ResultsThe training cohort included 96 very low-risk and low-risk, 60 intermediate-risk and 57 high-risk patients. External validation cohort included 82 very low-risk and low-risk, 48 intermediate-risk and 38 high-risk patients. The GISTs risk three-class radiomics model had a macro/micro average area under the curve (AUC) of 0.84 and an accuracy of 0.78 in the training cohort. It had a stable performance in the external validation cohort, with a macro/micro average AUC of 0.83 and an accuracy of 0.80.ConclusionCT radiomics can discriminate GISTs risk stratification. The performance of the three-class radiomics prediction model is good, and its generalization ability has also been verified in the external validation cohort, indicating its potential to assist stratified and accurate treatment of GISTs in the clinic.

scholarly journals Pretreatment Prediction of Relapse Risk in Patients with Osteosarcoma Using Radiomics Nomogram Based on CT: A Retrospective Multicenter Study

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jin Liu ◽  
Tao Lian ◽  
Haimei Chen ◽  
Xiaohong Wang ◽  
Xianyue Quan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multicenter Study ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Relapse Risk ◽  
Significant Difference ◽  
Prediction Of Relapse ◽  
Radiomics Signature

Objective. To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. Materials and Methods. In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. Results. Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts ( P < 0.001 and P = 0.015 , respectively). The radiomics nomogram achieved good discriminant results in the training cohort ( C -index: 0.779) and the validation cohort ( C -index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram ( P < 0.001 ). Conclusions. This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.

scholarly journals Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

2020 ◽  
Author(s):  
Bangbo Zhao ◽  
Yingxin Wei ◽  
Wenwu Sun ◽  
Cheng Qin ◽  
Xingtong Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Acute Abdomen ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Multivariable Logistic Regression

ABATRACTIMPORTANCEIn the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively.OBJECTIVETo develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19).DESIGNDiagnostic model based on retrospective case series.SETTINGTwo hospitals in Wuhan and Beijing, China.PTRTICIPANTS584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020.METHODSLASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram.RESULTSSix potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p ≤0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale.CONCLUSIONSWe established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

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