Establishment and validation of prognosis model for patients with cerebral contusion

Abstract Background And Objective Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients. Methods A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients. Conclusion This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

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Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.765652 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiao Yu Yu ◽

Jialiang Ren ◽

Yushan Jia ◽

Hui Wu ◽

Guangming Niu ◽

...

Keyword(s):

Risk Factors ◽

Ovarian Cancer ◽

Peritoneal Carcinomatosis ◽

Calibration Curve ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Curve Analysis ◽

Imaging Features ◽

Decision Curve Analysis ◽

Dce Mri

ObjectivesTo evaluate the predictive value of radiomics features based on multiparameter magnetic resonance imaging (MP-MRI) for peritoneal carcinomatosis (PC) in patients with ovarian cancer (OC).MethodsA total of 86 patients with epithelial OC were included in this retrospective study. All patients underwent FS-T2WI, DWI, and DCE-MRI scans, followed by total hysterectomy plus omentectomy. Quantitative imaging features were extracted from preoperative FS-T2WI, DWI, and DCE-MRI images, and feature screening was performed using a minimum redundancy maximum correlation (mRMR) and least absolute shrinkage selection operator (LASSO) methods. Four radiomics models were constructed based on three MRI sequences. Then, combined with radiomics characteristics and clinicopathological risk factors, a multi-factor Logistic regression method was used to construct a radiomics nomogram, and the performance of the radiomics nomogram was evaluated by receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis.ResultsThe radiomics model from the MP-MRI combined sequence showed a higher area under the curve (AUC) than the model from FS-T2WI, DWI, and DCE-MRI alone (0.846 vs. 0.762, 0.830, 0.807, respectively). The radiomics nomogram (AUC=0.902) constructed by combining radiomics characteristics and clinicopathological risk factors showed a better diagnostic effect than the clinical model (AUC=0.858) and the radiomics model (AUC=0.846). The decision curve analysis shows that the radiomics nomogram has good clinical application value, and the calibration curve also proves that it has good stability.ConclusionRadiomics nomogram based on MP-MRI combined sequence showed good predictive accuracy for PC in patients with OC. This tool can be used to identify peritoneal carcinomatosis in OC patients before surgery.

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Multi-Phase CT-Based Radiomics Nomogram for Discrimination Between Pancreatic Serous Cystic Neoplasm From Mucinous Cystic Neoplasm

Frontiers in Oncology ◽

10.3389/fonc.2021.699812 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiahao Gao ◽

Fang Han ◽

Xiaoshuang Wang ◽

Shaofeng Duan ◽

Jiawen Zhang

Keyword(s):

Single Phase ◽

Validation Cohort ◽

Curve Analysis ◽

Ct Scans ◽

Cystic Neoplasm ◽

Arterial Phase ◽

Imaging Features ◽

Cystic Neoplasms ◽

Training Cohort ◽

Multi Phase

PurposeThis study aimed to develop and verify a multi-phase (MP) computed tomography (CT)-based radiomics nomogram to differentiate pancreatic serous cystic neoplasms (SCNs) from mucinous cystic neoplasms (MCNs), and to compare the diagnostic efficacy of radiomics models for different phases of CT scans.Materials and MethodsA total of 170 patients who underwent surgical resection between January 2011 and December 2018, with pathologically confirmed pancreatic cystic neoplasms (SCN=115, MCN=55) were included in this single-center retrospective study. Radiomics features were extracted from plain scan (PS), arterial phase (AP), and venous phase (VP) CT scans. Algorithms were performed to identify the optimal features to build a radiomics signature (Radscore) for each phase. All features from these three phases were analyzed to develop the MP-Radscore. A combined model comprised the MP-Radscore and imaging features from which a nomogram was developed. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration tests, and decision curve analysis.ResultsFor each scan phase, 1218 features were extracted, and the optimal ones were selected to construct the PS-Radscore (11 features), AP-Radscore (11 features), and VP-Radscore (12 features). The MP-Radscore (14 features) achieved better performance based on ROC curve analysis than any single phase did [area under the curve (AUC), training cohort: MP-Radscore 0.89, PS-Radscore 0.78, AP-Radscore 0.83, VP-Radscore 0.85; validation cohort: MP-Radscore 0.88, PS-Radscore 0.77, AP-Radscore 0.83, VP-Radscore 0.84]. The combination nomogram performance was excellent, surpassing those of all other nomograms in both the training cohort (AUC, 0.91) and validation cohort (AUC, 0.90). The nomogram also performed well in the calibration and decision curve analyses.ConclusionsRadiomics for arterial and venous single-phase models outperformed the plain scan model. The combination nomogram that incorporated the MP-Radscore, tumor location, and cystic number had the best discriminatory performance and showed excellent accuracy for differentiating SCN from MCN.

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Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

10.21203/rs.3.rs-596906/v1 ◽

2021 ◽

Author(s):

Qing-Bo Zeng ◽

Long-Ping He ◽

Nian-Qing Zhang ◽

Qing-Wei Lin ◽

Lin-Cui Zhong ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care ◽

Validation Cohort ◽

International Normalized Ratio ◽

Curve Analysis ◽

Individual Risk ◽

Decision Curve Analysis ◽

Training Cohort ◽

Receiver Operating Characteristic Curves ◽

Individual Risk Factors

Abstract Background Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work aimed to define a nomogram for reliably predicting mortality. Methods We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. Results Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

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Predictive nomogram for postoperative pancreatic fistula following pancreaticoduodenectomy: a retrospective study

BMC Cancer ◽

10.1186/s12885-021-08201-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jian Shen ◽

Feng Guo ◽

Yan Sun ◽

Jingyuan Zhao ◽

Jin Hu ◽

...

Keyword(s):

Pancreatic Fistula ◽

Validation Cohort ◽

Characteristic Curve ◽

Postoperative Pancreatic Fistula ◽

Individual Risk ◽

Calibration Plot ◽

Multivariable Logistic Regression Analysis ◽

Lasso Regression ◽

Training Cohort ◽

Significant Difference

Abstract Background Postoperative pancreatic fistula (POPF) represents the most common complication following pancreaticoduodenectomy (PD). Predictive models are needed to select patients with a high risk of POPF. This study was aimed to establish an effective predictive nomogram for POPF following PD. Methods Consecutive patients who had undergone PD between January 2016 and May 2020 at a single institution were analysed retrospectively. A predictive nomogram was established based on a training cohort, and Lasso regression and multivariable logistic regression analysis were used to evaluate predictors. The predictive abilities of the predicting model were assessed for internal validation by the area under the receiver operating characteristic curve (AUC) and calibration plot using bootstrap resampling. The performance of the nomogram was compared with that of the currently used a-FRS model. Results A total of 459 patients were divided into a training cohort (n = 302) and a validation cohort (n = 157). No significant difference was observed between the two groups with respect to clinicopathological characteristics. The POPF rate was 16.56%. The risk factors of POPF POPF were albumin difference, drain amylase value on postoperative day 1, pancreas texture, and BMI, which were all selected into a nomogram. Nomogram application revealed good discrimination (AUC = 0.87, 95% CI: 0.81–0.94, P < 0.001) as well as calibration abilities in the validation cohort. The predictive value of the nomogram was better than that of the a-FRS model (AUC: 0.87 vs 0.62, P < 0.001). Conclusions This predictive nomogram could be used to evaluate the individual risk of POPF in patients following PD, and albumin difference is a new, accessible predictor of POPF after PD. Trial registration This study was registered in the Chinese Clinical Trial Register (ChiCTR2000034435).

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Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices

BioMed Research International ◽

10.1155/2021/1023513 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Qingbo Zeng ◽

Longping He ◽

Nianqing Zhang ◽

Qingwei Lin ◽

Lincui Zhong ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care ◽

Validation Cohort ◽

International Normalized Ratio ◽

Curve Analysis ◽

Individual Risk ◽

Decision Curve Analysis ◽

Training Cohort ◽

Receiver Operating Characteristic Curves ◽

Individual Risk Factors

Background. Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality. Methods. We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis. Results. Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion. This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

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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

10.1101/2020.04.18.20071019 ◽

2020 ◽

Cited By ~ 1

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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Construction and validation of a prognostic nomogram for primary vulvar melanoma: a SEER population-based study

Japanese Journal of Clinical Oncology ◽

10.1093/jjco/hyaa137 ◽

2020 ◽

Vol 50 (12) ◽

pp. 1386-1394

Author(s):

Hongyu Zhou ◽

Xuan Zou ◽

Haoran Li ◽

Lihua Chen ◽

Xi Cheng

Keyword(s):

Overall Survival ◽

Validation Cohort ◽

Risk Group ◽

Curve Analysis ◽

Lymph Node Status ◽

Calibration Plot ◽

Decision Curve Analysis ◽

Training Cohort ◽

Clinical Applicability ◽

Vulvar Melanoma

Abstract Background Primary vulvar melanoma was an aggressive and poorly understood gynecological tumor. Unlike cutaneous melanoma, the incidence of vulvar melanoma was low but the survival was poor. There were no standard staging system and no census on treatment strategies of vulvar melanoma. Therefore, we aimed to conduct and validate a comprehensive prognostic model for predicting overall survival of vulvar melanoma and provide guidance for clinical management. Methods Patients diagnosed with vulvar melanoma between year 2004 and 2015 from Surveillance, Epidemiology, and End Result (SEER) database were randomized to training cohort and validation cohort. Multivariate survival analysis was performed to screen for independent factors of survival. A nomogram was established to predict overall survival of vulvar melanoma. Receiver operating characteristic curve and calibration plot were performed to verify the discrimination and accuracy of the model. The decision curve analysis was performed to verify the clinical applicability of the model. Results Total 737 patients with vulvar melanoma were randomized to the training cohort (n = 517) and the validation cohort (n = 220). Nomogram including age, race, tumor site, depth of tumor invasion, lymph node status, distant metastasis, tumor size, surgery, chemotherapy and radiotherapy was established and validated. The c-indexes for SEER stage, American Joint Committee on Cancer stage and this model were 0.561, 0.635 and 0.826, respectively. The high-risk group scored by this model had worse survival than the low-risk group (P < 0.001). Decision curve analysis revealed this model was superior in predicting survival. Conclusions Our model was deemed to be a useful tool for predicting overall survival of vulvar melanoma with good discrimination and clinical applicability. We hoped this model would assist gynecologists in clinical decision and management of patients diagnosed with vulvar melanoma.

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Development of a hemoptysis risk prediction model for patients following CT-guided transthoracic lung biopsy

BMC Pulmonary Medicine ◽

10.1186/s12890-020-01282-9 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Saibin Wang ◽

Ke Dong ◽

Wei Chen

Keyword(s):

Predictive Model ◽

Goodness Of Fit ◽

Characteristic Curve ◽

Tertiary Hospital ◽

Curve Analysis ◽

Lasso Regression ◽

Transthoracic Needle Biopsy ◽

Ct Guided ◽

Decision Curve Analysis ◽

Preoperative Prediction

Abstract Background Computed tomography-guided transthoracic needle biopsy (CT-TNB) is a widely used method for diagnosis of lung diseases; however, CT-TNB-induced bleeding is usually unexpected and this complication can be life-threatening. The aim of this study was to develop and validate a predictive model for hemoptysis following CT-TNB. Methods A total of 436 consecutive patients who underwent CT-TNB from June 2016 to December 2017 at a tertiary hospital in China were divided into derivation (n = 307) and validation (n = 129) cohorts. We used LASSO regression to reduce the data dimension, select variables and determine which predictors were entered into the model. Multivariate logistic regression was used to develop the predictive model. The discrimination capacity of the model was evaluated by the area under the receiver operating characteristic curve (AUROC), the calibration curve was used to test the goodness-of-fit of the model, and decision curve analysis was conducted to assess its clinical utility. Results Five predictive factors (diagnosis of the lesion, lesion characteristics, lesion diameter, procedure time, and puncture distance) selected by LASSO regression analysis were applied to construct the predictive model. The AUC was 0.850 (95% confidence interval [CI], 0.808–0.893) in the derivation, and 0.767 (95% CI, 0.684–0.851) in the validation. The model showed good calibration consistency (p > 0.05). Moreover, decision curve analysis indicated its clinical usefulness. Conclusion We established a predictive model that incorporates lesion features and puncture parameters, which may facilitate the individualized preoperative prediction of hemoptysis following CT-TNB.

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Prediction of Postoperative Day 1 Hemoglobin Level after Cesarean Delivery

American Journal of Perinatology ◽

10.1055/s-0041-1739414 ◽

2021 ◽

Author(s):

Tetsuya Kawakita ◽

Haleema Saeed ◽

Ariunzaya Amgalan ◽

Alexandra Thomas ◽

Elizabeth Coviello

Keyword(s):

Prediction Model ◽

Cesarean Delivery ◽

Sensitivity And Specificity ◽

Validation Cohort ◽

Characteristic Curve ◽

Hemoglobin Level ◽

Cutoff Point ◽

P Value ◽

Severe Anemia ◽

Training Cohort

Objective To create a prediction model for postoperative hemoglobin levels after cesarean delivery, which could reduce routine use of postoperative laboratory test. Study Design This was a secondary analysis of a retrospective cohort study of all women who underwent cesarean delivery (primary or repeat) at or more than 23 weeks' gestation at a single academic center. The cohort was randomly divided into a training cohort to develop a prediction model and a validation cohort to test the model in a 2:1 ratio. Variables with p-value <0.10 were considered for the mixed multivariable linear regression model in a backward stepwise fashion. We obtained the best cutoff point of the predicted hemoglobin level to detect severe anemia (postoperative hemoglobin level less than 7.0 g/dL) in the training cohort. A receiver operating characteristic curve with the area under a curve was created. We calculated the sensitivity and specificity of the model in the validation cohort using the best cutoff point obtained in the training cohort as well as postoperative hemoglobin of 10.0 g/dL. Results Of 2,930 women, 1,954 (66.6%) and 976 (33.3%) were randomly allocated to training and validation cohorts. The final model included preoperative hemoglobin level, preoperative platelet level, quantitative blood loss, height, weight, magnesium administration, labor, and general anesthesia. The best cutoff to predict severe anemia was predicted hemoglobin level of 8.57 g/dL in the training cohort. Using this cutoff, the sensitivity and specificity in the validation cohort were 77% (95% confidence interval [CI]: 56–91%) and 87% (95% CI: 85–89%), respectively. The use of postpartum hemorrhage yielded the sensitivity of 58% (95% CI: 37–77%) and specificity 79% (95% CI: 76–81%), respectively. Conclusion We developed a validated model to predict the postoperative day 1 hemoglobin levels after cesarean delivery that could assist with identifying women who may not need postoperative laboratory tests. Key Points

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A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC

Frontiers in Oncology ◽

10.3389/fonc.2021.722106 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wufei Chen ◽

Yanqing Hua ◽

Dingbiao Mao ◽

Hao Wu ◽

Mingyu Tan ◽

...

Keyword(s):

Egfr Mutation ◽

Validation Cohort ◽

Characteristic Curve ◽

Integrated Model ◽

Curve Analysis ◽

Support Vector ◽

Training Cohort ◽

Contrast Enhanced Ct ◽

Nsclc Patients ◽

Radiomics Signature

PurposeThis study aims to develop a CT-based radiomics approach for identifying the uncommon epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer (NSCLC).MethodsThis study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1,269 radiomics features were extracted from the non-contrast-enhanced CT images after image segmentation and preprocessing. Support vector machine algorithm was used for feature selection and model construction. Receiver operating characteristic curve analysis was applied to evaluate the performance of the radiomics signature, the clinicopathological model, and the integrated model. A nomogram was developed and evaluated by using the calibration curve and decision curve analysis.ResultsThe radiomics signature demonstrated a good performance for predicting the uncommon EGFR mutation in the training cohort (area under the curve, AUC = 0.802; 95% confidence interval, CI: 0.736–0.858) and was verified in the validation cohort (AUC = 0.791, 95% CI: 0.642–0.899). The integrated model combined radiomics signature with clinicopathological independent predictors exhibited an incremental performance compared with the radiomics signature or the clinicopathological model. A nomogram based on the integrated model was developed and showed good calibration (Hosmer–Lemeshow test, P = 0.92 in the training cohort and 0.608 in the validation cohort) and discrimination capacity (AUC of 0.816 in the training cohort and 0.795 in the validation cohort).ConclusionRadiomics signature combined with the clinicopathological features can predict uncommon EGFR mutation in NSCLC patients.

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