scholarly journals Development and Validation of a Nomogram for Preoperative Prediction of Localization of Neonatal Gastrointestinal Perforation

2021 ◽  
Vol 9 ◽  
Author(s):  
Yao Huang ◽  
Yuhua Wu ◽  
Dongmei Jin ◽  
Qing Tang ◽  
Peng Yuan ◽  
...  
Keyword(s):  
External Validation ◽  
Multivariate Logistic Regression Analysis ◽  
Predictive Performance ◽  
Gastrointestinal Perforation ◽  
Bloody Stool ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Lemeshow Test ◽  
Key Indicators ◽  
Development And Validation

Background: Information regarding the localization of gastrointestinal perforation is crucial for the following surgical procedure. This study was to determine the key indicators and develop a prediction model for the localization in neonates with gastrointestinal perforation.Methods: A nomogram to predict the location of neonatal gastrointestinal perforation was developed using a cohort of patients who underwent surgery between July 2009 and May 2021. Baseline variables were analyzed using logistics regression and nomogram developed using significant predictors. The predictive performance of the nomogram was assessed by the concordance index (C-index), calibration curve, and area under the receiver operating characteristic (ROC) curve (AUC). The nomogram was further validated in an integrated external cohort.Results: We investigated the data of 201 patients, of which 65 (32.3%) were confirmed with upper gastrointestinal perforation by surgery. Multivariate logistic regression analysis identified the following as independent predictors: preterm [OR: 5.014 (1.492–18.922)], time of onset [OR: 0.705 (0.582–0.829)], preoperative hemoglobin [OR:1.017 (1.001–1.033)], bloody stool: No [OR: 4.860 (1.270–23.588)], shock [OR: 5.790 (1.683–22.455)] and sepsis: No [OR 3.044 (1.124–8.581)]. Furthermore, the nomogram was effective in predicting the perforation site, with an AUC of 0.876 [95% confidence interval (CI): 0.830–0.923]. Internal validation showed that the average AUC was 0.861. Additionally, the model achieved satisfactory discrimination (AUC, 0.900; 95% CI, 0.826–0.974) and calibration (Hosmer-Lemeshow test, P = 0.4802) in external validation.Conclusions: The nomogram based on the six factors revealed good discrimination and calibration, suggesting good clinical utility. The nomogram could help surgeons predict the location of gastrointestinal perforation before surgery to make a surgical plan.

Evaluation of cohort diversity in development and validation studies of hereditary cancer genetic risk assessment models.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10600-10600
Author(s):  
Amanda Gammon ◽  
Ambreen Khan ◽  
Joanne M. Jeter
Keyword(s):  
Risk Assessment ◽  
Validation Studies ◽  
Genetic Risk ◽  
External Validation ◽  
Ashkenazi Jewish ◽  
Genetic Risk Assessment ◽  
Internal Validation ◽  
Risk Assessment Models ◽  
Race Ethnicity ◽  
Development And Validation

10600 Background: Multiple models estimate a person’s chance of harboring a pathogenic variant increasing cancer risk. Some pathogenic variants are more common in individuals from specific ancestries, such as the BRCA1 and BRCA2 founder variants in Ashkenazi Jews. Yet data remains limited on the larger variant spectrum seen among people of different ancestral backgrounds and whether or not the pathogenic variant frequency differs in many populations. Due to this, it is important that genetic risk assessment models be validated in a diverse cohort including Black, Indigenous, People of Color (BIPOC). Methods: A literature search was conducted to identify published development and validation studies for the following genetic risk assessment models: BRCAPRO, MMRPRO, CanRisk/BOADICEA, Tyrer-Cuzick, and PREMM. Validation studies that only evaluated the cancer risk prediction capabilities of the models (and not the genetic variant risk prediction) were excluded. The following participant information was abstracted from each study: total number of participants, gender, race, and ethnicity. Authors were contacted to obtain missing information (if available). Results: 12 development and 12 validation studies of the genetic risk assessment models BRCAPRO, MMRPRO, CanRisk/BOADICEA, Tyrer-Cuzick, and PREMM were abstracted. Of the validation studies, five were internal validation studies conducted by the model developers, and seven were external validation studies. Four external validation studies compared multiple models. 75% (18/24) of papers did not include reporting of participant race or ethnicity information in their published reports. External validation studies (4/7, 57%) more often reported participant race/ethnicity than development (0/12, 0%) or internal validation (2/5, 40%) studies. The external validation studies for BRCAPRO reporting race/ethnicity information involved cohorts that ranged from 50-51% non-Ashkenazi Jewish white, 28% African American, 1% Asian, 2-49% Hispanic, and 19-42% Ashkenazi Jewish. The external validation studies for MMRPRO and PREMM reporting race/ethnicity information involved cohort that ranged from 0-82% white, 4-100% Asian, 7% Black, and 7% Hispanic. Conclusions: Increased reporting of participant ancestry and ethnicity is needed in the development and validation studies of genetic risk assessment models. BRCAPRO’s validation cohorts have included a higher percentage of Hispanic and Black/African American participants, while MMRPRO and PREMM have been validated in a higher percentage of Asian participants. As debate continues about the utility of currently used racial categories in genetics research, it will be important to determine how best to report on participant diversity. These findings highlight the continued need for genetics researchers to engage BIPOC and identify ways to diversify their participant cohorts.

scholarly journals Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data

BMJ ◽  
2019 ◽  
pp. l4293 ◽  
Author(s):  
Mohammed T Hudda ◽  
Mary S Fewtrell ◽  
Dalia Haroun ◽  
Sooky Lum ◽  
Jane E Williams ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Prediction Model ◽  
Fat Mass ◽  
Meta Analysis ◽  
External Validation ◽  
Predictive Performance ◽  
Individual Participant Data ◽  
Internal Validation ◽  
Individual Participant ◽  
Complex Forms

Abstract Objectives To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment. Design Individual participant data meta-analysis. Setting Four population based cross sectional studies and a fifth study for external validation, United Kingdom. Participants A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass. Main outcome measure Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model’s predictive performance within the four development studies; external validation followed using the fifth dataset. Results Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R 2 : 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R 2 : 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was −1.29 kg (95% confidence interval −1.62 to −0.96 kg). Conclusion The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.

scholarly journals Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients of colorectal cancer

2019 ◽  
Vol 50 (3) ◽  
pp. 261-269
Author(s):  
Jieyun Zhang ◽  
Yue Yang ◽  
Xiaojian Fu ◽  
Weijian Guo
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Prognosis ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Development And Validation

Abstract Purpose Nomograms are intuitive tools for individualized cancer prognosis. We sought to develop a clinical nomogram for prediction of overall survival and cancer-specific survival for patients with colorectal cancer. Methods Patients with colorectal cancer diagnosed between 1988 and 2006 and those who underwent surgery were retrieved from the Surveillance, Epidemiology, and End Results database and randomly divided into the training (n = 119 797) and validation (n = 119 797) cohorts. Log-rank and multivariate Cox regression analyses were used in our analysis. To find out death from other cancer causes and non-cancer causes, a competing-risks model was used, based on which we integrated these significant prognostic factors into nomograms and subjected the nomograms to bootstrap internal validation and to external validation. Results The 1-, 3-, 5- and 10-year probabilities of overall survival in patients of colorectal cancer after surgery intervention were 83.04, 65.54, 54.79 and 38.62%, respectively. The 1-, 3-, 5- and 10-year cancer-specific survival was 87.36, 73.44, 66.22 and 59.11%, respectively. Nine independent prognostic factors for overall survival and nine independent prognostic factors for cancer specific survival were included to build the nomograms. Internal and external validation CI indexes of overall survival were 0.722 and 0.721, and those of cancer-specific survival were 0.765 and 0.766, which was satisfactory. Conclusions Nomograms for prediction of overall survival and cancer-specific survival of patients with colorectal cancer. Performance of the model was excellent. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

Nomogram including pretherapeutic parameters for prediction of early response after neoadjuvant treatment in rectal cancer: Results from a prospective randomized study.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 716-716
Author(s):  
Jianwei Zhang ◽  
Yue Cai ◽  
Huabin Hu ◽  
Ping Lan ◽  
Lei Wang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Randomized Study ◽  
External Validation ◽  
Pathologic Complete Response ◽  
Neoadjuvant Treatment ◽  
Predictive Performance ◽  
Complete Response ◽  
Early Response ◽  
Preoperative Treatment ◽  
Internal Validation

716 Background: To establish a clinical nomogram with pretherapeutic parameters for predicting pathologic complete response (pCR) and tumor downstaging after neoadjuvant treatment in patients with rectal cancer. Methods: From Jan 2011 to Feb 2015, complete data was available for 309 patients with rectal cancer who received concurrent chemoradiotherapy or chemotherapy alone enrolled in FOWARC study. All pre-treatment clinical parameters were collected to build a nomogram for pCR and tumor down-staging. The model was subjected to bootstrap internal validation. The predictive performance of the model was assessed with concordance index (c-index) and calibration. Results: Of the 309 patients, 55 (17.8%) had achieved pCR, 138 (44.7%) patients were classified as good down-staging with ypTNM stage 0-I. Basing on the multivariate logistic regression and clinical consideration, 5 factors were identified to be the independent predictors for pCR and good downstaging, respectively (Table 1). The predictive nomograms were developed (fig 1 and 2) to predict the probability of pCR and good down-staging with a C-index of 0.802 (95% CI: 0.736-0.867) and 0.73 (95% CI: 0.672-0.784). Calibration plots showed good performance on internal validation. Conclusions: The nomograms provide individual prediction of response to different preoperative treatment for patients with rectal cancer. This model may help physician in patient selection for optimized treatment. Further external validation is warranted. [Table: see text]

Estimating Bleeding Risk in Patients with Cancer-Associated Thrombosis: Evaluation of Existing Risk Scores and Development of a New Risk Score

2021 ◽  
Author(s):  
Maria A. de Winter ◽  
Jannick A. N. Dorresteijn ◽  
Walter Ageno ◽  
Cihan Ay ◽  
Jan Beyer-Westendorf ◽  
...  
Keyword(s):  
Prediction Model ◽  
External Validation ◽  
Predictive Performance ◽  
Bleeding Risk ◽  
Risk Scores ◽  
Cancer Type ◽  
Clinical Predictors ◽  
Internal Validation ◽  
Patients With Cancer ◽  
Cancer Associated Thrombosis

Abstract Background Bleeding risk is highly relevant for treatment decisions in cancer-associated thrombosis (CAT). Several risk scores exist, but have never been validated in patients with CAT and are not recommended for practice. Objectives To compare methods of estimating clinically relevant (major and clinically relevant nonmajor) bleeding risk in patients with CAT: (1) existing risk scores for bleeding in venous thromboembolism, (2) pragmatic classification based on cancer type, and (3) new prediction model. Methods In a posthoc analysis of the Hokusai VTE Cancer study, a randomized trial comparing edoxaban with dalteparin for treatment of CAT, seven bleeding risk scores were externally validated (ACCP-VTE, HAS-BLED, Hokusai, Kuijer, Martinez, RIETE, and VTE-BLEED). The predictive performance of these scores was compared with a pragmatic classification based on cancer type (gastrointestinal; genitourinary; other) and a newly derived competing risk-adjusted prediction model based on clinical predictors for clinically relevant bleeding within 6 months after CAT diagnosis with nonbleeding-related mortality as the competing event (“CAT-BLEED”). Results Data of 1,046 patients (149 events) were analyzed. Predictive performance of existing risk scores was poor to moderate (C-statistics: 0.50–0.57; poor calibration). Internal validation of the pragmatic classification and “CAT-BLEED” showed moderate performance (respective C-statistics: 0.61; 95% confidence interval [CI]: 0.56–0.66, and 0.63; 95% CI 0.58–0.68; good calibration). Conclusion Existing risk scores for bleeding perform poorly after CAT. Pragmatic classification based on cancer type provides marginally better estimates of clinically relevant bleeding risk. Further improvement may be achieved with “CAT-BLEED,” but this requires external validation in practice-based settings and with other DOACs and its clinical usefulness is yet to be demonstrated.

Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms

2015 ◽  
Vol 75 (4) ◽  
pp. 674-680 ◽  
Author(s):  
E E A Arts ◽  
C D Popa ◽  
A A Den Broeder ◽  
R Donders ◽  
A Sandoo ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Risk Prediction ◽  
Risk Estimation ◽  
External Validation ◽  
Predictive Performance ◽  
Risk Groups ◽  
Cvd Risk ◽  
Risk Algorithm ◽  
Lemeshow Test ◽  
First Time

ObjectivesPredictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm.MethodsData from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated.ResultsOf the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer–Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups.ConclusionsThis study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA.

scholarly journals Development and Validation of a Nomogram for Predicting Survival in Gallbladder Cancer Patients With Recurrence After Surgery

2021 ◽  
Vol 10 ◽  
Author(s):  
Mingyu Chen ◽  
Shijie Li ◽  
Win Topatana ◽  
Xiaozhong Lv ◽  
Jiasheng Cao ◽  
...  
Keyword(s):  
Gallbladder Cancer ◽  
Validation Cohort ◽  
External Validation ◽  
Multivariable Analysis ◽  
Internal Validation ◽  
Time To Recurrence ◽  
Calibration Curves ◽  
Kaplan Meier ◽  
Actual Observation ◽  
Development And Validation

BackgroundThe management of gallbladder cancer (GBC) patients with recurrence who need additional therapy or intensive follow-up remains controversial. Therefore, we aim to develop a nomogram to predict survival in GBC patients with recurrence after surgery.MethodsA total of 313 GBC patients with recurrence from our center was identified as a primary cohort, which were randomly divided into a training cohort (N = 209) and an internal validation cohort (N = 104). In addition, 105 patients from other centers were selected as an external validation cohort. Independent prognostic factors, identified by univariate and multivariable analysis, were used to construct a nomogram. The performance of this nomogram was measured using Harrell’s concordance index (C-index) and calibration curves.ResultsOur nomogram was established by four factors, including time-to-recurrence, site of recurrence, CA19-9 at recurrence, and treatment of recurrence. The C-index of this nomogram in the training, internal and external validation cohort was 0.871, 0.812, and 0.754, respectively. The calibration curves showed an optimal agreement between nomogram prediction and actual observation. Notably, this nomogram could accurately stratify patients into different risk subgroups, which allowed more significant distinction of Kaplan-Meier curves than that of using T category. The 3-year post-recurrence survival (PRS) rates in the low-, medium-, and high-risk subgroups from the external validation cohort were 53.3, 26.2, and 4.1%, respectively.ConclusionThis nomogram provides a tool to predict 1- and 3-year PRS rates in GBC patients with recurrence after surgery.

Prognostic model research

2019 ◽  
pp. 139-187
Author(s):  
Richard D Riley ◽  
Karel GM Moons ◽  
Thomas PA Debray ◽  
Kym IE Snell ◽  
Ewout W Steyerberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Prognostic Model ◽  
Model Updating ◽  
External Validation ◽  
Predictive Performance ◽  
Prognostic Models ◽  
Net Benefit ◽  
Internal Validation ◽  
Model Research ◽  
Future Outcomes

Prognostic models combine multiple prognostic factors to estimate the risk of future outcomes in individuals with a particular disease or health condition. A useful model provides accurate predictions to support decision making by individuals and caregivers. This chapter describes the three phases of prognostic model research development (including internal validation), external validation (including model updating), and impact on decision making and individual health outcomes. Methodology is detailed for each phase, including the need for large representative datasets, methods to avoid or reduce overfitting and optimism, and the use of both discrimination and calibration to assess a model’s predictive performance. TRIPOD reporting guidelines are introduced. Emphasis is also given to the application of models in practice, including linking the model to clinical decisions using risk thresholds, and evaluating this using measures of net benefit, decision curves, cost-effectiveness analyses, and impact studies (such as randomized trials) to evaluate the effectiveness of models in improving outcomes.

scholarly journals Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jiong Li ◽  
Yuntao Chen ◽  
Shujing Chen ◽  
Sihua Wang ◽  
Dingyu Zhang ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Cumulative Incidence ◽  
Prognostic Model ◽  
External Validation ◽  
Predictive Performance ◽  
Prognostic Models ◽  
Internal Validation ◽  
Derivation Cohort ◽  
Proposed Model ◽  
Bootstrap Approach

Abstract Background Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.

scholarly journals Development and validation of a CT radiomics signature for diagnosing COVID-19 pneumonia compared with CO-RADS

2020 ◽  
Author(s):  
Huanhuan Liu ◽  
Hua Ren ◽  
Zengbin Wu ◽  
He Xu ◽  
Shuhai Zhang ◽  
...  
Keyword(s):  
Texture Features ◽  
Predictive Performance ◽  
Viral Pneumonia ◽  
Operating Characteristics ◽  
Internal Validation ◽  
Decision Curve Analysis ◽  
Clinical Model ◽  
Receiver Operating Characteristics Curve ◽  
Development And Validation ◽  
Radiomics Signature

Abstract Objectives: To develop and validate a CT radiomics signature for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS).Methods: This two-center retrospective study enrolled 115 laboratory-confirmed COVID-19 patients with 1127 lesions and 435 non-COVID-19 pneumonia patients with 842 lesions. In study 1, a radiomics signature and a clinical model was developed and validated in the training and internal validation cohorts (patient/lesion [n] = 379/1325, n = 131/505) for identifying COVID-19 pneumonia. In study 2, the developed radiomics signature was tested in another independent cohort including all viral pneumonia (n = 40/139), compared with clinical model and CO-RADS approach. The predictive performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results: Twenty-three texture features were selected to construct the radiomics model. Radiomics model outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the internal validation cohort. Radiomics model also performed better in the testing cohort to distinguish COVID-19 from other viral pneumonia with an AUC of 0.96 compared with 0.75 (P=0.007) for clinical model, and 0.69 (P=0.002) or 0.82 (P=0.04) for two trained radiologists using CO-RADS approach. The sensitivity and specificity of radiomics model can be improved to 0.90 and 1.00. The DCA confirmed the clinical utility of radiomics model. Conclusions: The proposed radiomics signature outperformed clinical model and CO-RADS approach for diagnosing COVID-19, which can facilitate rapid and accurate detection of COVID-19 pneumonia.

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