scholarly journals A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9890
Author(s):  
Xue-ran Kang ◽  
Bin Chen ◽  
Yi-sheng Chen ◽  
Bin Yi ◽  
Xiaojun Yan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Surgical Outcome ◽  
Middle Turbinate ◽  
Multivariate Logistic Regression Analysis ◽  
Preoperative Assessment ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Nasal Septoplasty ◽  
Snot 22

Background To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty, and the likelihood of patient benefiting from the operation. Methods A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change ≥ 9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. Results The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. Conclusions Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

scholarly journals Predicting the risk of sarcopenia in elderly patients with patellar fracture: Development and assessment of a new predictive nomogram

2019 ◽  
Author(s):  
Chen Yisheng ◽  
Tao Jie
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Elderly Patients ◽  
Prediction Model ◽  
Predictive Model ◽  
Body Mass ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Low Body Mass Index ◽  
Patellar Fractures

AbstractPurposeThis study was aimed at developing a risk prediction model for postoperative dysplasia in elderly patients with patellar fractures in China.Patients and methodsWe conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (Lasso analysis). The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We conducted internal sampling methods for qualitative assessment.ResultWe recruited 61 participants (males: 20, mean age: 68.1 years). Various risk factors were assessed, and low body mass index and diabetes mellitus were identified as the most important risk factors (P<0.05). The model showed a good prediction rate (C-index: 0.909; 95% confidence interval: 0.81–1.00) and good correction effect. The C-index remained high (0.828) even after internal sample verification. Decision curve analysis showed that the risk of sarcopenia was 8.3–80.0%, suggesting good clinical practicability.ConclusionOur prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, longer postoperative hospital stay, no higher education, no postoperative rehabilitation, removal of internal fixation, and less outdoor exercise.

scholarly journals Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8793
Author(s):  
Yi-sheng Chen ◽  
Yan-xian Cai ◽  
Xue-ran Kang ◽  
Zi-hui Zhou ◽  
Xin Qi ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Elderly Patients ◽  
Prediction Model ◽  
Predictive Model ◽  
Body Mass ◽  
Curve Analysis ◽  
Advanced Age ◽  
Decision Curve Analysis ◽  
Low Body Mass Index ◽  
Patellar Fractures

Purpose To develop a risk prediction model for postoperative sarcopenia in elderly patients with patellar fractures in China. Patients and methods We conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (lasso analysis) as well as the Support Vector Machine (SVM) algorithm. The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We also conducted internal sampling methods for qualitative assessment. Result We recruited 137 participants (53 male; mean age, 65.7 years). Various risk factors were assessed, and low body mass index and advanced age were identified as the most important risk factor (P < 0.05). The prediction rate of the model was good (C-index: 0.88; 95% CI [0.80552–0.95448]), with a satisfactory correction effect. The C index is 0.97 in the validation queue and 0.894 in the entire cohort. Decision curve analysis suggested good clinical practicability. Conclusion Our prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, less outdoor exercise, no postoperative rehabilitation, different surgical methods, diabetes, open fracture, and removal of internal fixation.

scholarly journals Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: Analysis from BUNMAP Cohort in Ethiopia

2020 ◽  
Vol 9 (5) ◽  
pp. 1587
Author(s):  
Hamid Y. Hassen ◽  
Seifu H. Gebreyesus ◽  
Bilal S. Endris ◽  
Meselech A. Roro ◽  
Jean-Pierre Van Geertruyden
Keyword(s):  
Prediction Model ◽  
Pregnant Women ◽  
Risk Score ◽  
Low Income ◽  
Low Birthweight ◽  
Multivariable Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Maternal Characteristics ◽  
Trained Manpower

At least one ultrasound is recommended to predict fetal growth restriction and low birthweight earlier in pregnancy. However, in low-income countries, imaging equipment and trained manpower are scarce. Hence, we developed and validated a model and risk score to predict low birthweight using maternal characteristics during pregnancy, for use in resource limited settings. We developed the model using a prospective cohort of 379 pregnant women in South Ethiopia. A stepwise multivariable analysis was done to develop the prediction model. To improve the clinical utility, we developed a simplified risk score to classify pregnant women at high- or low-risk of low birthweight. The accuracy of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration plot. All accuracy measures were internally validated using the bootstrapping technique. We evaluated the clinical impact of the model using a decision curve analysis across various threshold probabilities. Age at pregnancy, underweight, anemia, height, gravidity, and presence of comorbidity remained in the final multivariable prediction model. The AUC of the model was 0.83 (95% confidence interval: 0.78 to 0.88). The decision curve analysis indicated the model provides a higher net benefit across ranges of threshold probabilities. In general, this study showed the possibility of predicting low birthweight using maternal characteristics during pregnancy. The model could help to identify pregnant women at higher risk of having a low birthweight baby. This feasible prediction model would offer an opportunity to reduce obstetric-related complications, thus improving the overall maternal and child healthcare in low- and middle-income countries.

scholarly journals Prediction and clinical utility of a contralateral breast cancer risk model

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniele Giardiello ◽  
Ewout W. Steyerberg ◽  
Michael Hauptmann ◽  
Muriel A. Adank ◽  
Delal Akdeniz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Germline Mutation ◽  
Clinical Utility ◽  
Contralateral Breast Cancer ◽  
Contralateral Breast ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Mutation Status

Abstract Background Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52–0.74; at 10 years, 0.53–0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62–1.37), and the calibration slope was 0.90 (95% PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4–10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

scholarly journals Development and validation of a preoperative nomogram for predicting patients with impacted ureteral stone: a retrospective analysis

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chenglu Wang ◽  
Lu Jin ◽  
Xinyang Zhao ◽  
Boxin Xue ◽  
Min Zheng
Keyword(s):  
Calibration Curve ◽  
Ureteral Stone ◽  
Multivariate Logistic Regression Analysis ◽  
Curve Analysis ◽  
Ureteral Stones ◽  
Ureteral Calculus ◽  
Validation Group ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Development And Validation

Abstract Background To develop and validate a practical nomogram for predicting the probability of patients with impacted ureteral stone. Methods Between June 2020 to March 2021, 214 single ureteral stones received ureteroscopy lithotripsy (URSL) were selected in development group. While 82 single ureteral stones received URSL between April 2021 to May 2021 were included in validation group. Independent factors for predicting impacted ureteral stone were screened by univariate and multivariate logistic regression analysis. The relationship between preoperative factors and stone impaction was modeled according to the regression coefficients. Discrimination and calibration were estimated by area under the receiver operating characteristic (AUROC) curve and calibration curve respectively. Clinical usefulness of the nomogram was evaluated by decision curve analysis. Results Age, ipsilateral stone treatment history, hydronephrosis and maximum ureteral wall thickness (UWTmax) at the portion of stone were identified as independent predictors for impacted stone. The AUROC curve of development and validation group were 0.915 and 0.882 respectively. Calibration curve of two groups showed strong concordance between the predicted and actual probabilities. Decision curve analysis showed that the predictive nomogram had a superior net benefit than UWTmax for all examined probabilities. Conclusions We developed and validated an individualized model to predict impacted ureteral stone prior to surgery. Through this prediction model, urologists can select an optimal treatment method and decrease intraoperative and postoperative complications for patients with impacted ureteral calculus.

scholarly journals A Systematic Review of the Literature Demonstrates Some Errors in the Use of Decision Curve Analysis but Generally Correct Interpretation of Findings

2019 ◽  
Vol 39 (5) ◽  
pp. 493-498 ◽  
Author(s):  
Paolo Capogrosso ◽  
Andrew J. Vickers
Keyword(s):  
Systematic Review ◽  
Literature Review ◽  
Predictive Model ◽  
Data Extraction ◽  
Clinical Decision ◽  
Curve Analysis ◽  
Similar Proportion ◽  
Internal Validation ◽  
Decision Curve Analysis ◽  
Almost All

Background. Decision curve analysis (DCA) is a widely used methodology in clinical research studies. Purpose. We performed a literature review to identify common errors in the application of DCA and provide practical suggestions for appropriate use of DCA. Data Sources. We first conducted an informal literature review and identified 6 errors found in some DCAs. We then used Google Scholar to conduct a systematic review of studies applying DCA to evaluate a predictive model, marker, or test. Data Extraction. We used a standard data collection form to collect data for each reviewed article. Data Synthesis. Each article was assessed according to the 6 predefined criteria for a proper analysis, reporting, and interpretation of DCA. Overall, 50 articles were included in the review: 54% did not select an appropriate range of probability thresholds for the x-axis of the DCA, with a similar proportion (50%) failing to present smoothed curves. Among studies with internal validation of a predictive model and correction for overfit, 61% did not clearly report whether the DCA had also been corrected. However, almost all studies correctly interpreted the DCA, used a correct outcome (92% for both), and clearly reported the clinical decision at issue (81%). Limitations. A comprehensive assessment of all DCAs was not performed. However, such a strategy would not influence the main findings. Conclusions. Despite some common errors in the application of DCA, our finding that almost all studies correctly interpreted the DCA results demonstrates that it is a clear and intuitive method to assess clinical utility.

Is My Clinical Prediction Model Clinically Useful? A Primer on Decision Curve Analysis

2021 ◽  
pp. 115-118
Author(s):  
Hendrik-Jan Mijderwijk ◽  
Daan Nieboer
Keyword(s):  
Prediction Model ◽  
Curve Analysis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Decision Curve Analysis

scholarly journals A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-death Hospitalized Patients with COVID-19: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Xiaoli Lei ◽  
Junli Wang ◽  
Lijie Kou ◽  
Zhigang Yang
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Predictive Model ◽  
Goodness Of Fit ◽  
Multivariate Logistic Regression Analysis ◽  
Hospitalized Patients ◽  
Multivariate Logistic Regression ◽  
Data Set

Abstract Background: Because of the lack of compelling evidence for predicting the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA shedding, the purpose of this retrospective study was to establish a predictive model for long-term SARS-CoV-2 RNA shedding in non-death hospitalized patients with coronavirus disease-19 (COVID-19).Methods: 97 non-death hospitalized patients with COVID-19 admitted to two hospitals in Henan province of China from February 3, 2020 to March 31, 2020 were retrospectively enrolled. Multivariate logistic regression was performed to identify the high risk factors associated with long-term SARS-CoV-2 RNA shedding and a predictive model was established and represented by a nomogram. Its performance was assessed with discrimination and calibration.Results: 97 patients were divided into the long-term (>21 days) group (n = 27, 27.8%) and the short-term (≤ 21 days) group (n = 70, 72.2%) based on their viral shedding duration. Multivariate logistic regression analysis showed that time from illness onset to diagnosis (OR 1.224, 95% CI 1.070-1.400, P = 0.003) and interstitial opacity in chest computerized tomography(CT) scan (OR 6.516, 95% CI 2.041-20.798, P = 0.002) were independent risk factors for long-term SARS-CoV-2 RNA shedding. A prediction model, which is presented with a nomogram, was established by incorporating the two risk factors. The goodness-of-fit statistics for the nomogram was not statistically significant (χ2 = 8.292; P = 0.406), and its area under the receiver operator characteristic curve was 0.834 (95% CI 0.731- 0.936; P < 0.001).Conclusion: The established model has a good predictive performance on the long-term viral RNA shedding in non-death hospitalized patients with COVID-19, but it still needs further validation by independent data set of large samples in the future.

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.

scholarly journals A nomogram based on a gene signature for predicting the prognosis of patients with head and neck squamous cell carcinoma

2019 ◽  
Vol 34 (3) ◽  
pp. 309-317
Author(s):  
Bowen Yang ◽  
Lingyu Fu ◽  
Shan Xu ◽  
Jiawen Xiao ◽  
Zhi Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Predictive Model ◽  
Validation Cohort ◽  
Gene Signature ◽  
Curve Analysis ◽  
Decision Curve Analysis

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant tumors. The purpose of this study was to establish and validate a gene-expression-based prognostic signature in non-metastatic patients with HNSCC. Materials and methods: All the patients were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We randomly divided the GSE65858 samples into 70% (training cohort, n = 190) and 30% (internal validation cohort, n = 72). A total of 36 samples collected from the TCGA HNSCC databases were selected as an independent external validation cohort. The oligo package in R was used to normalize the raw data before analysis. Data characteristics were extracted, and a gene signature was built via the least absolute shrinkage and selection operator regression model. The predictive model was developed by multivariable Cox regression analysis. T stage, N stage, human papilloma virus status, and the gene signature were incorporated in this predictive model, which was shown as a nomogram. Calibration and discrimination were performed to assess the performance of the nomogram. The clinical utility of this nomogram was assessed by the decision curve analysis. Results: Overall, 2001 significant messenger RNAs in HNSCC samples were identified compared with normal samples. The gene signature contained seven genes and significantly correlated with overall survival. The gene signature was also significant in subgroup analysis of the primary cohort. The calibration was plotted in the external cohort (C-index 0.90, 95% CI 0.85, 0.95) compared with the training (C-index 0.76, 95% CI 0.73, 0.79) and internal (C-index 0.71, 95% CI 0.66, 0.77) cohorts. In clinic, a decision curve analysis demonstrated that the model including the prognostic gene signature score status was better than that without it. Conclusion: This study developed and validated a predictive model, which can promote the individualized prediction of overall survival in non-metastatic patients with HNSCC.

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