scholarly journals Development and Validation of a Nomogram for Predicting Postoperative Pulmonary Infection in Esophageal Cancer

2021 ◽  
Author(s):  
Shuang LI ◽  
Jingwen Su ◽  
Qiyu Sui ◽  
Gongchao Wang
Keyword(s):  
Esophageal Cancer ◽  
Pulmonary Infection ◽  
Predictive Accuracy ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Net Benefit ◽  
Individual Probability ◽  
Wide Range ◽  
The Individual ◽  
Development And Validation

Abstract Objective: The main aim of this study is to construct and validate a nomogram for estimating the risk of POI by investigating how perioperative features contribute to POI. Material and Methods: This cohort study enrolled 637 patients with esophageal cancer. Perioperative information on participants were collected to develop and validate a nomogram for predicting postoperative pulmonary infection in esophageal cancer. Predictive accuracy, discriminatory capability and clinical usefulness were evaluated by calibration curves, concordance index (C-index) and decision curve analysis (DCA). Results: Multivariable logistic regression analysis indicated that length of stay, albumin, intraoperative bleeding, and perioperative blood transfusion were independent predictors of POI. The nomogram for assessing individual risk of POI indicated good predictive accuracy in the primary cohort (C-index, 0.802) and validation cohort (C-index, 0.763). Good consistency between predicted risk and observed actual risk was presented as the calibration curve. The nomogram for estimating POI of esophageal cancer had superior net benefit with a wide range of threshold probabilities (4–81%). Conclusions: The present study provided a nomogram developed with perioperative features to assess the individual probability of infection may conducive to strengthen awareness of infection control and provide appropriate resource to manage patients at high-risk following esophagectomy.

scholarly journals A nomogram for predicting postoperative pulmonary infection in esophageal cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shuang Li ◽  
Jingwen Su ◽  
Qiyu Sui ◽  
Gongchao Wang
Keyword(s):  
Esophageal Cancer ◽  
Pulmonary Infection ◽  
Predictive Accuracy ◽  
Patient Specific ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Net Benefit ◽  
Curative Surgery ◽  
Individual Probability ◽  
Wide Range

Abstract Background Although postoperative pulmonary infection (POI) commonly occurs in patients with esophageal cancer after curative surgery, a patient-specific predictive model is still lacking. The main aim of this study is to construct and validate a nomogram for estimating the risk of POI by investigating how perioperative features contribute to POI. Methods This cohort study enrolled 637 patients with esophageal cancer. Perioperative information on participants was collected to develop and validate a nomogram for predicting postoperative pulmonary infection in esophageal cancer. Predictive accuracy, discriminatory capability, and clinical usefulness were evaluated by calibration curves, concordance index (C-index), and decision curve analysis (DCA). Results Multivariable logistic regression analysis indicated that length of stay, albumin, intraoperative bleeding, and perioperative blood transfusion were independent predictors of POI. The nomogram for assessing individual risk of POI indicated good predictive accuracy in the primary cohort (C-index, 0.802) and validation cohort (C-index, 0.763). Good consistency between predicted risk and observed actual risk was presented as the calibration curve. The nomogram for estimating POI of esophageal cancer had superior net benefit with a wide range of threshold probabilities (4–81%). Conclusions The present study provided a nomogram developed with perioperative features to assess the individual probability of infection may conducive to strengthen awareness of infection control and provide appropriate resources to manage patients at high risk following esophagectomy.

scholarly journals Prospective Validation of Pentraxin-3 as a Novel Serum Biomarker to Predict the Risk of Prostate Cancer in Patients Scheduled for Prostate Biopsy

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1611
Author(s):  
Ugo Giovanni Falagario ◽  
Gian Maria Busetto ◽  
Giuseppe Stefano Netti ◽  
Francesca Sanguedolce ◽  
Oscar Selvaggio ◽  
...  
Keyword(s):  
Prostate Biopsy ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Digital Rectal Examination ◽  
Multivariable Logistic Regression Analysis ◽  
Gleason Grade ◽  
Pentraxin 3 ◽  
Net Benefit ◽  
Multivariable Models

Purpose: To test and internally validate serum Pentraxin-3 (PTX3) levels as a potential PCa biomarker to predict prostate biopsy (PBx) results. Materials and Methods: Serum PSA and serum PTX3 were prospectively assessed in patients scheduled for PBx at our Institution due to increased serum PSA levels or abnormal digital rectal examination. Uni- and multivariable logistic regression analysis, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA), were used to test the accuracy of serum PTX3 in predicting anyPCa and clinically significant PCa (csPCa) defined as Gleason Grade (GG) ≥ 2. Results: Among the 455 eligible patients, PCa was detected in 49% and csPCa in 25%. During univariate analysis, PTX3 outperformed other variables in predicting both anyPCa and csPCa. The addition of PTX3 to multivariable models based on standard clinical variables, significantly increased each model’s predictive accuracy for anyPCa (AUC from 0.73 to 0.82; p < 0.001) and csPCa (AUC from 0.79 to 0.83; p < 0.001). At DCA, PTX3, and PTX3, density showed higher net benefit than PSA and PSA density and increased the net benefit of multivariable models in deciding when to perform PBx. Conclusions: Serum PTX3 levels might be of clinical utility in predicting prostate biopsy results. Should our findings be confirmed, this novel reflex test could be used to reduce the number and burden of unnecessary prostate biopsies.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

RA09.07: SURGICAL MANAGEMENT OF EARLY ESOPHAGEAL CANCER BASED ON LYMPH NODE METASTASIS RISK.

2018 ◽  
Vol 31 (Supplement_1) ◽  
pp. 41-41
Author(s):  
Xiaofeng Duan ◽  
Zhentao Yu
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Lymph Node ◽  
Lymph Node Dissection ◽  
Tumor Location ◽  
Individual Risk ◽  
Node Dissection ◽  
Early Esophageal Cancer ◽  
Node Metastasis ◽  
The Individual

Abstract Background Esophagectomy and lymph node dissection is still the main treatment for esophageal cancer. Endoscopic mucosal resection and submucosal dissection are increasingly becoming a treatment of choice to preserve the integrity of the esophagus and decrease the surgical trauma in early esophageal cancer. However, lymph node metastasos (LNM) risk is still a debate focus for the decision of treatment selection. Our objective was to evaluate the prevalence, pattern and risk factors of LNM in early stage esophageal cancer to improve surgical treatment allocation. Methods We identified patients with pathological T1 stage esophageal cancer who underwent esophagectomy and lymph node dissection. The pattern of LNM was analyzed and the risk factors related to LNM were assessed by univariate and multivariable logistic regression analysis.The nomogram model was used to estimate the individual risk of lymph node metastasis. Results In 143 patients, LNM rates were: all patients 17.5%, T1a 8.0%, and T1b 22.5% for T1b. Depth of tumor infiltration (P < 0.05), tumor size (P < 0.01), tumor location (P < 0.05), and tumor differentiation (P < 0.01) were independent risk factors related to LNM. These four parameters allowed the compilation of a nomogram to estimate the individual risk of LNM. Fig. Nomogram to estimate the individual risk of LNM. Each characteristic of the included parameters scores a specific number of points (points per parameter). The summarized total points score indicates the probability of LNM. For a middle esophageal cancer with middle differentiated (G2), 3 cm tumor (> 2.5cm) that invades the submucosa (pT1b), the calculated total scores is 129.5 = 87.5 + 21 + 0 + 21, hence the corresponding LNM risk is 20%. Conclusion T1 esophageal cancer has a relatively high LNM rate, and the depth of tumor infiltration, tumor size, tumor location and tumor differentiation are correlated with LNM. Nomograms that include factors can be used to predict individual LNM risk. The LNM risk and extent must be considered comprehensively in decision-making of a better surgical treatment and lymph node dissection strategy. Disclosure All authors have declared no conflicts of interest.

Predicting overall death risk in patients with H7N9 infection using a nomogram (Preprint)

2019 ◽  
Author(s):  
Qing Lin Cheng ◽  
Gang Zhao ◽  
Li Xie ◽  
Zhou Sun
Keyword(s):  
Prognostic Model ◽  
Predictive Accuracy ◽  
Binary Logistic Regression Analysis ◽  
Decision Threshold ◽  
Medical Visit ◽  
Net Benefit ◽  
Death Risk ◽  
Risk Of Death ◽  
Wide Range ◽  
H7n9 Infection

BACKGROUND To date, almost all of these studies have identified multiple risk factors but did not offer practical instruments for routine use in predicting death risk in human H7N9 infection cases. Such an instrument could be useful in identifying high-risk H7N9 patients who can benefit from reducing the risk of death. OBJECTIVE We aimed to create a clinical nomogram to predict the overall death (OD) risk of patients with H7N9 virus infection (VI). METHODS We reviewed specific factors and outcomes regarding patients with H7N9 VI to determine relationships and developed a nomogram to calculate individualized patient risk. This model was used to predict each individual patient’s probability of death based on results obtained from the multivariate binary logistic regression analysis. RESULTS We examined 227 patients with H7N9 VI enrolled in our study over a nearly 6-year period. Stepwise selection was applied to the data, which resulted in a final model with 7 independent predictors. The nomogram model was constructed for maximum predictive accuracy. The concordance index of this nomogram was 0.906 and 0.822 for the training and validation sets, respectively, which indicates adequate discriminatory power. The calibration curves for the OD showed optimal agreement between nomogram prediction and actual observation in the training and validation sets, respectively. A decision-curve analysis of the clinical benefit indicates that the prognostic model, including age ≥ 60 years , chronic disease, poor hand hygiene , time from illness onset to the first medical visit, incubation period of ≤ 5 days, peak C-reactive protein ≥120 mg/L, and increased initial neutrophil count factors, resulted in a higher net benefit across a wide range of decision threshold probabilities (i.e., an approximately 6 – 98% risk of death). With the cutoff threshold values of 30% and 20% predicted probabilities, nomogram models showed sensitivities of 85.7% and 80.9% and specificities of 78.3% and 73.1% when applied to the training and validation sets, respectively. CONCLUSIONS We established and validated a novel nomogram that can predict OD for patients with H7N9 VI. This practical prognostic model may help clinicians in decision making, clinical diagnosis, and treatment selection.

scholarly journals Financial Market and Inflation

2020 ◽  
Vol 12 (6) ◽  
pp. 90
Author(s):  
Yuqing Qi
Keyword(s):  
Systemic Risk ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Two Dimensions ◽  
Risk Indicators ◽  
Risk Level ◽  
Individual Risk ◽  
Inflation Risk ◽  
The Future ◽  
The Individual

Based on two dimensions of system risk, this paper studies the changes in the future inflation risk level, and uses the out-of-sample quantile R2 &nbsp;to further evaluate the predictive accuracy of different systemic risk indicators on inflation risk. Firstly, we compute two systemic risk indicators, MES and volatility, with data of Chinese financial institutions. And then we explore the amplification effect of these indicators on future inflation risk, under the framework of quantile regression. We find that systematic risk indicators have a strong predictive ability for the inflation level at various quantiles. MES indicator that reflects individual risk can better predict future deflation risk, while volatility index has a stronger ability to predict inflation risk. We also find that systemic risk indicators of different dimensions have different effects on inflation risk and deflation risk. In general, the MES index, which captures the individual risk of the organization, have a greater impact on the future inflation risk. While indicator that measures volatility in financial markets has more influence on the extreme lower tail of inflation rates. Finally, we predict the distribution of inflation in China from March 2020 to June 2021, and visually show the distribution trend of future inflation with forecast fan charts.

scholarly journals What Regional Living Conditions Affect Individual Smoking of Adults in Russia

2021 ◽  
Vol 66 ◽  
Author(s):  
Sergey A. Maksimov ◽  
Svetlana A. Shalnova ◽  
Yulia A. Balanova ◽  
Vladimir A. Kutsenko ◽  
Svetlana E. Evstifeeva ◽  
...  
Keyword(s):  
Mutual Influence ◽  
Adult Population ◽  
Principal Component ◽  
Estimating Equation ◽  
Individual Level ◽  
Composite Indices ◽  
Individual Probability ◽  
Wide Range ◽  
The Individual ◽  
The Impact

Objectives: Our study evaluated the impact of a wide range of characteristics of large administrative regions on the individual level of cigarette smoking in the Russian adult population.Methods: The pool of participants included 20,303 individuals aged 25–64 years. We applied 64 characteristics of the 12 Russian regions under study for 2010–2014. Using principal component analysis, we deduced five evidence-based composite indices of the regions. We applied the generalized estimating equation to determine associations between the regional indices and the individual level of smoking.Results: The increased Industrial index in the region is associated with the probability of smoking (odds ratio = 1.15; 95% confidence interval = 1.06–1.24). The other indices show associations with smoking only in separate gender and educational groups. Surprisingly, it was found that the Economic index has no associations with the probability of smoking.Conclusion: We evaluated the key associations of the territorial indices with the individual probability of smoking, as well as the mutual influence between the territorial indices and individual factors.

scholarly journals Development and validation of a prognostic nomogram for predicting post-operative pulmonary infection in gastric cancer patients following radical gastrectomy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Haifan Xiao ◽  
Huijun Zhou ◽  
Ke Liu ◽  
Xianzhen Liao ◽  
Shipeng Yan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Pulmonary Infection ◽  
Predictive Accuracy ◽  
Operation Time ◽  
Validation Dataset ◽  
Chronic Obstructive ◽  
Radical Gastrectomy ◽  
Open Operation ◽  
Index Value ◽  
Development And Validation

Abstract The aim of this retrospective study was to develop and validate a nomogram for predicting the risk of post-operative pulmonary infection (POI) in gastric cancer (GC) patients following radical gastrectomy. 2469 GC patients who underwent radical gastrectomy were enrolled, and randomly divided into the development and validation groups. The nomogram was constructed based on prognostic factors using logistic regression analysis, and was internally and crossly validated by bootstrap resampling and the validation dataset, respectively. Concordance index (C-index) value and calibration curve were used for estimating the predictive accuracy and discriminatory capability. Sixty-five (2.63%) patients developed POI within 30 days following surgery, with higher rates of requiring intensive care and longer post-operative hospital stays. The nomogram showed that open operation, chronic obstructive pulmonary disease (COPD), intra-operative blood transfusion, tumor located at upper and/or middle third and longer operation time (≥4 h) in a descending order were significant contributors to POI risk. The C-index value for the model was 0.756 (95% CI: 0.675−0.837), and calibration curves showed good agreement between nomogram predictions and actual observations. In conclusion, a nomogram based on these factors could accurately and simply provide a picture tool to predict the incidence of POI in GC patients undergoing radical gastrectomy.

scholarly journals Development and validation of a risk prediction model for radiotherapy-related esophageal fistula in esophageal cancer

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Yiyue Xu ◽  
Linlin Wang ◽  
Bo He ◽  
Wanlong Li ◽  
Qiang Wen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Risk Prediction ◽  
External Validation ◽  
Individual Risk ◽  
Esophageal Fistula ◽  
Multivariable Analyses ◽  
Independent Risk Factors ◽  
Ecog Ps ◽  
Development And Validation

Abstract Objectives We aimed to identify the risk factors and provide a nomogram for the prediction of radiotherapy-related esophageal fistula in patients with esophageal cancer (EC) using a case-control study. Patients and methods Patients with esophageal fistula who received radiotherapy or chemoradiotherapy between 2003 and 2017 were retrospectively collected in two institutions. In the training cohort (TC), clinical, pathologic, and serum data of 136 patients (cases) who developed esophageal fistula during or after radiotherapy were enrolled and compared with 272 controls (1:2 matched with the diagnosis time of EC, sex, marriage, and race). After the univariable and multivariable logistic regression analyses, the independent risk factors were identified and incorporated into a nomogram. Then the nomogram for the risk prediction was externally validated in the validation cohort (VC; 47 cases and 94 controls) using bootstrap resampling. Results Multivariable analyses demonstrated that ECOG PS, BMI, T4, N2/3 and re-radiotherapy were independent factors for esophageal fistula. Then a nomogram was constructed with the C-index of 0.805 (95% CI, 0.762–0.848) for predicting the risk of developing esophageal fistula in EC patients receiving radiotherapy. Importantly, the C-index maintained 0.764 (95% CI, 0.683–0.845) after the external validation. Conclusions We created and externally validated the first risk nomogram of esophageal fistula associated with radiotherapy. This will aid individual risk stratification of patients with EC developing esophageal fistula.

scholarly journals Reflections on the measurement of implementation constructs

2021 ◽  
Vol 2 ◽  
pp. 263348952110201
Author(s):  
Michel Wensing
Keyword(s):  
Implementation Research ◽  
Measure Development ◽  
Plain Language ◽  
Research And Practice ◽  
Wide Range ◽  
Advance Research ◽  
Measurement Validation ◽  
The Individual ◽  
Development And Validation ◽  
Validation Research

To advance research and practice, it is crucial to build on validated measures. A wide range of measures for implementation research were identified in seven systematic reviews conducted under the auspices of the project, “Advancing Implementation Science through Measure Development and Evaluation,” but many had unclear or limited measurement qualities. In this commentary, I suggest the psychometric paradigm of measurement validation may have to be reconsidered because many determinants and outcomes of interest are defined at higher levels of aggregation than the individual. Nonetheless, the practice of using non-validated measures should be reduced, and measurement validation research should be encouraged. Adaptation of existing measures to different domains, settings, and languages further adds to the need for validation research. Coordination of the development and validation of measures is required to avoid unneeded replication in some domains and lack of measures in others, and to take care that validation research remains instrumental to the purposes of implementation research and practice. Plain language abstract: Many measures for implementation research have limited or unknown qualities. There is thus a need for better measures and targeted research is required to provide those. New studies should use measures of high-quality whenever possible.

Sign in / Sign up

Export Citation Format

Share Document