Tools for Checking Calibration of a Cox Model in External Validation: Approach Based on Individual Event Probabilities

Abstract Background: We previously demonstrated that CDK5RAP3 acts as a tumour suppressor in gastric cancer through negative regulation of the Wnt/β-catenin signalling pathway, but its function in chemotherapeutic responsiveness of gastric cancer has not been investigated. In this study, we aimed to examine the clinical significance of CDK5RAP3 to predict chemotherapeutic responsiveness in gastric cancer.Methods: A collection of 188 pairs of tumour tissue microarray specimens from Fujian Medical University were employed for the discovery set, and 310 tumour tissue samples of gastric cancer patients were employed for the internal validation set. Eight-five tumour tissue samples from Qinghai University Hospital were used as the external validation set 1. Transcriptomic and clinical data of 299 gastric cancer patients from TCGA were used as the external validation set 2. CDK5RAP3 expression, microsatellite instability (MSI) status, and tumour-infiltrating lymphocytes (TIL) were examined with immunohistochemistry. Clinical outcomes of patients were compared with Kaplan-Meier curves and the Cox model.Results: In a multi-centre evaluation, increased CDK5RAP3 indication of better prognosis depends mainly on MSI-L/MSS status or TILhigh. High CDK5RAP3 expression predicts sensitive therapeutic responsiveness to postoperative adjuvant chemotherapy in gastric cancer. In a stratification analysis based on CDK5RAP3 combined with TIL or MSI status, patients with CKD5RAP3low TILlow showed no significant difference in prognosis after receiving chemotherapy, whereas patients with CKD5RAP3low TILhigh, CKD5RAP3high TILlow, and CKD5RAP3high TILhigh had better responsiveness to chemotherapy. In addition, patients with CKD5RAP3high MSI-L/MSS status benefitted the most from adjuvant chemotherapy among all patients evaluated. Conclusions: CKD5RAP3 can be used as an effective marker to evaluate individualized chemotherapy regimens in gastric cancer patients dependent on their TIL and MSI status.

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Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa463 ◽

2020 ◽

Vol 7 (11) ◽

Cited By ~ 2

Author(s):

David N Fisman ◽

Amy L Greer ◽

Michael Hillmer ◽

R Tuite

Keyword(s):

Risk Stratification ◽

Cox Model ◽

External Validation ◽

Sensitivity Analyses ◽

Base Case ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Term Care ◽

Prediction Rules ◽

Validation Set

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada. Methods Data from Ontario’s provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression–based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data. Results Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model–based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables. Conclusions We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials.

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Tools for Checking Calibration of a Cox Model in External Validation: Prediction of Population-Averaged Survival Curves Based on Risk Groups

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x1501500116 ◽

2015 ◽

Vol 15 (1) ◽

pp. 275-291 ◽

Cited By ~ 24

Author(s):

Patrick Royston

Keyword(s):

Cox Model ◽

External Validation ◽

Risk Groups ◽

Survival Curves

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Derivation and Validation of Clinical Prediction Rule for COVID-19 Mortality in Ontario, Canada

10.1101/2020.06.21.20136929 ◽

2020 ◽

Author(s):

David N. Fisman ◽

Amy L. Greer ◽

Ashleigh R. Tuite

Keyword(s):

Risk Stratification ◽

Cox Model ◽

External Validation ◽

Prediction Rule ◽

Sensitivity Analyses ◽

Base Case ◽

Clinical Prediction ◽

Term Care ◽

Prediction Rules ◽

Missing Variables

AbstractBackgroundSARS-CoV-2 is currently causing a high mortality global pandemic. However, the clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to cytokine storm with organ failure and death. Risk stratification of individuals with COVID-19 would be desirable for management, prioritization for trial enrollment, and risk stratification. We sought to develop a prediction rule for mortality due to COVID-19 in individuals with diagnosed infection in Ontario, Canada.MethodsData from Ontario’s provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Both logistic regression-based prediction rules, and a rule derived using a Cox proportional hazards model, were developed in half the study and validated in remaining patients. Sensitivity analyses were performed with varying approaches to missing data.Results21,922 COVID-19 cases were reported. Individuals assigned to the derivation and validation sets were broadly similar. Age and comorbidities (notably diabetes, renal disease and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded as a predictor, long-term care excluded as a predictor, and Cox model based). All rules displayed excellent discrimination (AUC for all rules > 0.92) and calibration (both by graphical inspection and P > 0.50 by Hosmer-Lemeshow test) in the derivation set. All rules performed well in the validation set and were robust to random replacement of missing variables, and to the assumption that missing variables indicated absence of the comorbidity or characteristic in question.ConclusionsWe were able to use a public health case-management data system to derive and internally validate four accurate, well-calibrated and robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be a useful tool for clinical management, risk stratification, and clinical trials.

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Development and validation of a cardiovascular risk score for patients in the community after acute coronary syndrome

Heart ◽

10.1136/heartjnl-2019-315809 ◽

2019 ◽

Vol 106 (7) ◽

pp. 506-511 ◽

Cited By ~ 1

Author(s):

Katrina K Poppe ◽

Rob N Doughty ◽

Susan Wells ◽

Billy Wu ◽

Nikki J Earle ◽

...

Keyword(s):

Risk Management ◽

Acute Coronary Syndrome ◽

Risk Score ◽

Cox Model ◽

External Validation ◽

Laboratory Data ◽

Cvd Risk ◽

Coronary Syndrome ◽

C Statistic ◽

National Mortality

ObjectiveFollowing acute coronary syndrome (ACS), patients are managed long-term in the community, yet few tools are available to guide patient-clinician communication about risk management in that setting. We developed a score for predicting cardiovascular disease (CVD) risk among patients managed in the community after ACS.MethodsAdults aged 30–79 years with prior ACS were identified from a New Zealand primary care CVD risk management database (PREDICT) with linkage to national mortality, hospitalisation, pharmaceutical dispensing and regional laboratory data. A Cox model incorporating clinically relevant factors was developed to estimate the time to a subsequent fatal or non-fatal CVD event and transformed into a 5-year risk score. External validation was performed in patients (Coronary Disease Cohort Study) assessed 4 months post-ACS.ResultsThe PREDICT-ACS cohort included 13 703 patients with prior hospitalisation for ACS (median 1.9 years prior), 69% men, 58% European, median age 63 years, who experienced 3142 CVD events in the subsequent 5 years. Median estimated 5 year CVD risk was 24% (IQR 17%–35%). The validation cohort consisted of 2014 patients, 72% men, 92% European, median age 67 years, with 712 CVD events in the subsequent 5 years. Median estimated 5-year risk was 33% (IQR 24%–51%). The risk score was well calibrated in the derivation and validation cohorts, and Harrell’s c-statistic was 0.69 and 0.68, respectively.ConclusionsThe PREDICT-ACS risk score uses data routinely available in community care to predict the risk of recurrent clinical events. It was derived and validated in real-world contemporary populations and can inform management decisions with patients living in the community after experiencing an ACS.

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Prognosis of hepatocellular carcinoma (HCC): Comparison of four staging systems in two French clinical trials

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.4589 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 4589-4589 ◽

Cited By ~ 2

Author(s):

S. Collette ◽

F. Bonnetain ◽

X. Paoletti ◽

M. Doffoel ◽

O. Bouche ◽

...

Keyword(s):

Predictive Accuracy ◽

Cox Model ◽

External Validation ◽

Selection Procedure ◽

Alcoholic Cirrhosis ◽

Information Criteria ◽

Carcinome Hépatocellulaire ◽

Forward Selection ◽

Prognostic Information ◽

Staging Systems

4589 Background: The aims of our study were to compare performances of 4 staging systems and to explore how to improve prognostic classification among French patients with HCC whose main aetiology is alcoholic cirrhosis. Methods: We have pooled 2 RCTs in palliative condition from Federation Francophone de Cancerologie Digestive (FFCD): - FFCD 9403 comparing tamoxifen vs symptomatic treatment and - FFCD 9402 comparing chemoembolization + tamoxifen vs tamoxifen alone. They had respectively included 416 and 122 patients. Performance of Okuda, Cancer of the Liver Italian Program (CLIP), Barcelona Clinic Liver Cancer group (BCLC) and GRoupe d’Etude et de Traitement du Carcinome Hépatocellulaire scores have been compared using: Akaike information criteria (AIC), discriminatory ability (Harrell’s c and the Royston’s D statistics), monocity of gradients and predictive accuracy (Schemper statistics Vs). To explore how to improve classifications univariate and multivariate Cox model were performed. Variables with univariate p< 0.10 have been retained for multivariate analyses. A forward selection procedure has then been implemented. Bootstraps validation was performed to test the robustness of our results. Analyses were done for each trial and for the pooled database with trial stratification. Results: Median OS was 5,3 months (IC 95%: [4,6; 6,2]), 402 patients had (75%) an alcoholic cirrhosis aetiology . As shown in Table 1 , CLIP staging had the best properties, followed by Okuda and BCLC. Performances of all staging systems were rather disappointing. WHO staging for CLIP or alphafetoprotein for BCLC allowed a significant improvement of prognostic information. Conclusions: Our results suggest that CLIP staging seems to be most adapted to french patients, it could be better by associating WHO PS. An external validation of our result will be performed on another trial in palliative condition. [Table: see text] No significant financial relationships to disclose.

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Outcome Prediction in Primary Resected Retroperitoneal Soft Tissue Sarcoma: Histology-Specific Overall Survival and Disease-Free Survival Nomograms Built on Major Sarcoma Center Data Sets

Journal of Clinical Oncology ◽

10.1200/jco.2012.44.3747 ◽

2013 ◽

Vol 31 (13) ◽

pp. 1649-1655 ◽

Cited By ~ 165

Author(s):

Alessandro Gronchi ◽

Rosalba Miceli ◽

Elizabeth Shurell ◽

Fritz C. Eilber ◽

Frederick R. Eilber ◽

...

Keyword(s):

Overall Survival ◽

Soft Tissue ◽

Cox Model ◽

External Validation ◽

Soft Tissue Sarcomas ◽

Disease Free Survival ◽

Histologic Subtype ◽

Free Survival ◽

Quality Of Surgery ◽

Disease Free

Purpose Integration of numerous prognostic variables not included in the conventional staging of retroperitoneal soft tissue sarcomas (RPS) is essential in providing effective treatment. The purpose of this study was to build a specific nomogram for predicting postoperative overall survival (OS) and disease-free survival (DFS) in patients with primary RPS. Patients and Methods Data registered in three institutional prospective sarcoma databases were used. We included patients with primary localized RPS resected between 1999 and 2009. Univariate (Kaplan and Meier plots) and multivariate (Cox model) analyses were carried out. The a priori chosen prognostic covariates were age, tumor size, grade, histologic subtype, multifocality, quality of surgery, and radiation therapy. External validation was performed by applying the nomograms to the patients of an external cohort. The model's discriminative ability was estimated by means of the bootstrap-corrected Harrell C statistic. Results In all, 523 patients were identified at the three institutions (developing set). At a median follow-up of 45 months (interquartile range, 22 to 72 months), 171 deaths were recorded. Five- and 7-year OS rates were 56.8% (95% CI, 51.4% to 62.6%) and 46.7% (95% CI, 39.9% to 54.6%. Two hundred twenty-one patients had disease recurrence. Five- and 7-year DFS rates were 39.4% (95% CI, 34.5% to 45.0%) and 35.7% (95% CI, 30.3% to 42.1%). The validation set consisted of 135 patients who were identified at the fourth institution for external validation. The bootstrap-corrected Harrell C statistics for OS and DFS were 0.74 and 0.71 in the developing set and 0.68 and 0.69 in the validating set. Conclusion These nomograms accurately predict OS and DFS. They should be used for patient counseling in clinical practice and stratification in clinical trials.

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Prognostic role of pre-treatment magnetic resonance imaging (MRI) radiomic analysis in patients with squamous cell carcinoma of the head and neck (SCCHN).

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.6553 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 6553-6553

Author(s):

Salvatore Alfieri ◽

Rebecca Romanò ◽

Marco Bologna ◽

Giuseppina Calareso ◽

Aurora Mirabile ◽

...

Keyword(s):

Cox Model ◽

External Validation ◽

Prospective Trial ◽

Disease Free Survival ◽

Smoking History ◽

Rank Test ◽

Post Contrast ◽

Risk Class ◽

Pre Treatment ◽

Sensitivity Specificity

6553 Background: Emerging data suggest that radiomics can be used to predict outcomes in SCCHN. At present, only few data are available for pre-treatment MRI. Methods: Study population was retrieved from an ongoing multicenter, randomized, prospective trial (NCT02262221, HETeCo) evaluating health and economic outcomes of two different follow-up (FUP) strategies (intensive vs non-intensive) in effectively cured stage III-IV (VIII TNM ed.) SCCHN. We selected only patients with both pre- and post-contrast enhancement T1 and T2-weighted baseline MRI (b-MRI) and at least 2 years (2y) of FUP. A radiomic model was developed to identify high risk (HR) and low risk (LR) of disease recurrence. Radiomic features (RF) were extracted from the primary tumor in the b-MRI. The best RF combination was selected by Least Absolute Shrinkage and Selection Operator (LASSO). Ten-fold cross-validation was used to compute sensitivity, specificity and area under the curve (AUC) of the classifier. Kaplan-Meier (KM) curves were estimated for HR and LR, for both overall survival (OS) and disease-free survival (DFS) and log rank test was performed. Three years (3y)-DFS and OS were also estimated for the two groups. The radiomic risk class was used as a new variable in a multivariate Cox model including well established prognostic factors in SCCHN (TNM stage, subsite and HPV). Results: Out of 155 enrolled HETeCO patients, 98 baseline imaging were retrieved of which 57 b-MRI. Of these, 51 met the eligibility criteria (25 in intensive and 26 in non-intensive arm). Baseline patients’ characteristics were: median age 66 yr (38-86); sex (M 42; F 9); median smoking history: 30 packs/y (1-100); 25 oral cavity (49%), 18 oropharynx (35%, 14 HPV+), 6 larynx (12%), 2 hypopharynx (4%). At a median FUP of 42 months (25-64), 45 (88%) patients are still alive. The recurrence rate was 20% (10/51, of which 2 distant). In total, 1608 RF were extracted. The sensitivity, specificity and AUC of the classifier were 90%, 76%, and 80%, respectively. The radiomic risk class was found to be an independent prognostic factor for both DFS and OS (p=0.01 and p=0.046, respectively). KM curves for DFS and OS were significantly different between HR and LR groups (p=0.002 and p=0.04, respectively). In HR vs LR, 3-y DFS and OS were: 78% [61-100%] vs 97% [90-100%], and 88% [75-100%] vs 96% [88-100%], respectively. Conclusions: Radiomics of pre-treatment MRI can predict outcomes in SCCHN. External validation of this preliminary radiomics-based model is currently ongoing.

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Identification of the Ferroptosis-Related Long Non-Coding RNAs Signature to Improve the Prognosis Prediction in patients with NSCLC

10.21203/rs.3.rs-598005/v1 ◽

2021 ◽

Author(s):

Meng Li ◽

Yanpeng Zhang ◽

Meng Fan ◽

Hui Ren ◽

Mingwei Chen ◽

...

Keyword(s):

Risk Score ◽

Cox Regression ◽

Cox Model ◽

Expression Profiles ◽

External Validation ◽

Risk Groups ◽

Differentially Expressed ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Kaplan Meier

Abstract Background: Non-small cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with an unfavorable prognosis. Ferroptosis, a novel iron-dependent programmed cell death, is involved in the development of multiple cancers. Of note, the prognostic value of ferroptosis-related lncRNAs in NSCLC remains uncertain. Methods: Gene expression profiles and clinical information of NSCLC were retrieved from the TCGA database. Ferroptosis-related genes (FRGs) were explored in the FerrDb database and ferroptosis-related lncRNAs (FRGs-lncRNAs) were identified by the correlation analysis and the LncTarD database. Next, The differentially expressed FRGs-lncRNAs were screened and FRGs-lncRNAs associated with the prognosis were explored by univariate Cox regression analysis and Kaplan-Meier survival analysis. Then, an FRGs-lncRNAs signature was constructed by the Lasso-penalized Cox model in the training cohort and verified by internal and external validation. Finally, the potential correlation between risk score, immune response, and chemotherapeutic sensitivity was further investigated.Results: 129 lncRNAs with a potential regulatory relationship with 59 differentially expressed FRGs were found in NSCLC and 10 FRGs-lncRNAs associated with the prognosis of NSCLC were identified (P<0.05). 9 prognostic-related FRGs-lncRNAs (AQP4-AS1, DANCR, LINC00460, LINC00892, LINC00996, MED4-AS1, SNHG7, UCA1, and WWC2-AS2) were used to construct the prognostic model and stratify patients with NSCLC into high- and low-risk groups. Kaplan-Meier analysis demonstrated a worse outcome in patients with high risk (P<0.05). Moreover, a good predictive capacity of this signature in predicting NSCLC prognosis was confirmed by the ROC curve analysis. Additionally, 45 immune checkpoint genes and 8 m6A-related genes were found differentially expressed in the two risk groups, and the sensitivity of 28 chemotherapeutics were identified to be correlated with the risk score. Conclusion: A novel FRGs-lncRNAs signature was successfully constructed, which may contribute to improving the management strategies of NSCLC.

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