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

2015 ◽  
Vol 15 (1) ◽  
pp. 275-291 ◽  
Author(s):  
Patrick Royston
Keyword(s):  
Cox Model ◽  
External Validation ◽  
Risk Groups ◽  
Survival Curves

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

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.

scholarly journals The Prognostic Value of the New Combined Hemo-Eosinophil Inflammation Index (HEI Index): A Multicenter Analysis of Anal Cancer Patients Treated with Concurrent Chemo-Radiation

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 671
Author(s):  
Margherita Rimini ◽  
Pierfrancesco Franco ◽  
Berardino De Bari ◽  
Maria Giulia Zampino ◽  
Stefano Vagge ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Anal Cancer ◽  
Prognostic Score ◽  
Univariate Analysis ◽  
External Validation ◽  
Risk Groups ◽  
Anal Squamous Cell Carcinoma ◽  
Predictors Of Response ◽  
Risk Patients

Anal squamous cell carcinoma (SCC) is a rare tumor, and bio-humoral predictors of response to chemo-radiation (CT-RT) are lacking. We developed a prognostic score system based on laboratory inflammation parameters. We investigated the correlation between baseline clinical and laboratory variables and disease-free (DFS) and overall (OS) survival in anal SCC patients treated with CT-RT in five institutions. The bio-humoral parameters of significance were included in a new scoring system, which was tested with other significant variables in a Cox’s proportional hazard model. A total of 308 patients was included. We devised a prognostic model by combining baseline hemoglobin level, SII, and eosinophil count: the Hemo-Eosinophils Inflammation (HEI) Index. We stratified patients according to the HEI index into low- and high-risk groups. Median DFS for low-risk patients was not reached, and it was found to be 79.5 months for high-risk cases (Hazard Ratio 3.22; 95% CI: 2.04–5.10; p < 0.0001). Following adjustment for clinical covariates found significant at univariate analysis, multivariate analysis confirmed the HEI index as an independent prognostic factor for DFS and OS. The HEI index was shown to be a prognostic parameter for DFS and OS in anal cancer patients treated with CT-RT. An external validation of the HEI index is mandatory for its use in clinical practice.

scholarly journals U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Janne J. Näppi ◽  
Tomoki Uemura ◽  
Chinatsu Watari ◽  
Toru Hironaka ◽  
Tohru Kamiya ◽  
...  
Keyword(s):  
Risk Groups ◽  
Healthcare Services ◽  
Chest Ct ◽  
Prediction Performance ◽  
Survival Prediction ◽  
Concordance Index ◽  
Survival Curves ◽  
Analysis Methodology ◽  
Kaplan Meier ◽  
Prognostic Prediction

AbstractThe rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan–Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10–14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients.

CDK5RAP3 as a Novel Biomarker Signature Predicting Survival and Adjuvant Chemotherapeutic benefit in Gastric Cancer

2020 ◽  
Author(s):  
Jia-Bin Wang ◽  
You-Xin Gao ◽  
Ning-Zi Lian ◽  
Yu-Bin Ma ◽  
Ping Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Cox Model ◽  
External Validation ◽  
Tumour Tissue ◽  
Tissue Samples ◽  
Significant Difference ◽  
Validation Set ◽  
Gastric Cancer Patients

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.

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

2020 ◽  
Vol 7 (11) ◽  
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 &gt; 0.92) and calibration (P &gt; .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.

scholarly journals MO048MULTICENTRE PROSPECTIVE VALIDATION OF THE URINARY PEPTIDOME-BASED CLASSIFIER CKD273 AS A PREDICTOR OF RENAL FUNCTION DECLINE IN SUBJECTS WITH TYPE 2 DIABETES

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Morten Lindhardt ◽  
Nete Tofte ◽  
Gemma Currie ◽  
Marie Frimodt-Moeller ◽  
Heiko Von der Leyen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Renal Function ◽  
High Risk ◽  
Risk Group ◽  
Cox Model ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Renal Function Decline

Abstract Background and Aims In the PRIORITY study, it was recently demonstrated that the urinary peptidome-based classifier CKD273 was associated with increased risk for progression to microalbuminuria. As a prespecified secondary outcome, we aim to evaluate the classifier CKD273 as a determinant of relative reductions in eGFR (CKD-EPI) of 30% and 40% from baseline, at one timepoint without requirements of confirmation. Method The ‘Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial’ (PRIORITY) is the first prospective observational study to evaluate the early detection of diabetic kidney disease in subjects with type 2 diabetes (T2D) and normoalbuminuria using the CKD273 classifier. Setting 1775 subjects from 15 European sites with a mean follow-up time of 2.6 years (minimum of 7 days and a maximum of 4.3 years). Patients Subjects with T2D, normoalbuminuria and estimated glomerular filtration rate (eGFR) ≥ 45 ml/min/1.73m2. Participants were stratified into high- or low-risk groups based on their CKD273 score in a urine sample at screening (high-risk defined as score &gt; 0.154). Results In total, 12 % (n = 216) of the subjects had a high-risk proteomic pattern. Mean (SD) baseline eGFR was 88 (15) ml/min/1.73m2 in the low-risk group and 81 (17) ml/min/1.73m2 in the high-risk group (p &lt; 0.01). Baseline median (interquartile range) urinary albumin to creatinine ratio (UACR) was 5 (3-8) mg/g and 7 (4-12) mg/g in the low-risk and high-risk groups, respectively (p &lt; 0.01). A 30 % reduction in eGFR from baseline was seen in 42 (19.4 %) subjects in the high-risk group as compared to 62 (3.9 %) in the low-risk group (p &lt; 0.0001). In an unadjusted Cox-model the hazard ratio (HR) for the high-risk group was 5.7, 95 % confidence interval (CI) (3.9 to 8.5; p&lt;0.0001). After adjustment for baseline eGFR and UACR, the HR was 5.2, 95 % CI (3.4 to 7.8; p&lt;0.0001). A 40 % reduction in eGFR was seen in 15 (6.9 %) subjects in the high-risk group whereas 22 (1.4 %) in the low-risk group developed this endpoint (p&lt;0.0001). In an unadjusted Cox-model the HR for the high-risk group was 5.0, 95 % CI (2.6 to 9.6; p&lt;0.0001). After adjustment for baseline eGFR and UACR, the HR was 4.8, 95 % CI (2.4 to 9.7; p&lt;0.0001). Conclusion In normoalbuminuric subjects with T2D, the urinary proteomic classifier CKD273 predicts renal function decline of 30 % and 40 %, independent of baseline eGFR and albuminuria.

scholarly journals Development and external validation of a clinical prediction model for MRSA carriage at hospital admission in Southeast Lower Saxony, Germany

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gabriele Raschpichler ◽  
Heike Raupach-Rosin ◽  
Manas K. Akmatov ◽  
Stefanie Castell ◽  
Nicole Rübsamen ◽  
...  
Keyword(s):  
Hospital Admission ◽  
External Validation ◽  
Model Development ◽  
Cost Effective ◽  
Risk Groups ◽  
Validation Dataset ◽  
Lower Saxony ◽  
Screening Programs ◽  
Lower Specificity ◽  
History Of

Abstract In countries with low endemic Methicillin-resistant Staphylococcus aureus (MRSA) prevalence, identification of risk groups at hospital admission is considered more cost-effective than universal MRSA screening. Predictive statistical models support the selection of suitable stratification factors for effective screening programs. Currently, there are no universal guidelines in Germany for MRSA screening. Instead, a list of criteria is available from the Commission for Hospital Hygiene and Infection Prevention (KRINKO) based on which local strategies should be adopted. We developed and externally validated a model for individual prediction of MRSA carriage at hospital admission in the region of Southeast Lower Saxony based on two prospective studies with universal screening in Braunschweig (n = 2065) and Wolfsburg (n = 461). Logistic regression was used for model development. The final model (simplified to an unweighted score) included history of MRSA carriage, care dependency and cancer treatment. In the external validation dataset, the score showed a sensitivity of 78.4% (95% CI: 64.7–88.7%), and a specificity of 70.3% (95% CI: 65.0–75.2%). Of all admitted patients, 25.4% had to be screened if the score was applied. A model based on KRINKO criteria showed similar sensitivity but lower specificity, leading to a considerably higher proportion of patients to be screened (49.5%).

Nomograms for Predicting Local Recurrence, Distant Metastases, and Overall Survival for Patients With Locally Advanced Rectal Cancer on the Basis of European Randomized Clinical Trials

2011 ◽  
Vol 29 (23) ◽  
pp. 3163-3172 ◽  
Author(s):  
Vincenzo Valentini ◽  
Ruud G.P.M. van Stiphout ◽  
Guido Lammering ◽  
Maria Antonietta Gambacorta ◽  
Maria Cristina Barba ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Adjuvant Chemotherapy ◽  
Local Recurrence ◽  
External Validation ◽  
Locally Advanced ◽  
Distant Metastases ◽  
Risk Groups ◽  
Advanced Rectal Cancer ◽  
Locally Advanced Rectal Cancer

Purpose The purpose of this study was to develop accurate models and nomograms to predict local recurrence, distant metastases, and survival for patients with locally advanced rectal cancer treated with long-course chemoradiotherapy (CRT) followed by surgery and to allow for a selection of patients who may benefit most from postoperative adjuvant chemotherapy and close follow-up. Patients and Methods All data (N = 2,795) from five major European clinical trials for rectal cancer were pooled and used to perform an extensive survival analysis and to develop multivariate nomograms based on Cox regression. Data from one trial was used as an external validation set. The variables used in the analysis were sex, age, clinical tumor stage stage, tumor location, radiotherapy dose, concurrent and adjuvant chemotherapy, surgery procedure, and pTNM stage. Model performance was evaluated by the concordance index (c-index). Risk group stratification was proposed for the nomograms. Results The nomograms are able to predict events with a c-index for external validation of local recurrence (LR; 0.68), distant metastases (DM; 0.73), and overall survival (OS; 0.70). Pathologic staging is essential for accurate prediction of long-term outcome. Both preoperative CRT and adjuvant chemotherapy have an added value when predicting LR, DM, and OS rates. The stratification in risk groups allows significant distinction between Kaplan-Meier curves for outcome. Conclusion The easy-to-use nomograms can predict LR, DM, and OS over a 5-year period after surgery. They may be used as decision support tools in future trials by using the three defined risk groups to select patients for postoperative chemotherapy and close follow-up ( http://www.predictcancer.org ).

scholarly journals Development of a New Risk Stratification System for Patients with Newly Diagnosed Multiple Myeloma Using R-ISS and 18F-FDG PET/CT

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3757-3757
Author(s):  
Hee Jeong Cho ◽  
Juhyung Kim ◽  
Jung Min Lee ◽  
Dong Won Baek ◽  
Sung-Hoon Jung ◽  
...  
Keyword(s):  
Validation Cohort ◽  
External Validation ◽  
Stage Iv ◽  
Risk Groups ◽  
Stage Ii ◽  
Stage Iii ◽  
Pet Ct ◽  
Stratification System ◽  
Fdg Pet Ct ◽  
Risk Stratification System

Abstract Background A high number of focal lesions (FL) detected using PET/CT at diagnosis were found to be associated with adverse prognosis along with Revised International Staging System (R-ISS). In present study, we combined R-ISS with FL using PET/CT to design a reliable and easily applicable risk stratification system in patients with newly diagnosed MM (NDMM). Methods In training cohort, the data of 380 patients with NDMM who underwent 18F-fluorodeoxyglucose (18F-FDG) PET/CT upon diagnosis from 10 hospitals of the Korean Multiple Myeloma Working Party were retrospectively analyzed. All patients were classified by R-ISS and were treated by frontline therapy with proteasome inhibitors (PI) and/or immunomodulatory drugs (IMiD). The K-adaptive partitioning algorithm was adopted to develop the new risk groups with homogeneous survival. Sixty-seven patients in external validation cohort were additionally collected to confirm reproducibility of the new risk groups. Results In the training cohort, 199 patients (52.4%) showed FL &gt; 3 using PET/CT at diagnosis. R-ISS stages I, II, and III were 78 patients (20.5%), 230 (60.5%), and 72 (18.9%), respectively. The combined R-ISS with PET/CT newly allocated NDMM patients into four groups: R-ISS/PET stage I (n=30; R-ISS I with FL≤3), stage II (n=149; R-ISS I with FL&gt;3 and R-ISS II with FL≤3), stage III (n=166; R-ISS II with FL&gt;3 and R-ISS III with FL≤3), and stage IV (n=35; R-ISS III with FL&gt;3). The new R-ISS/PET showed significantly pronounced survival differences according to stages. Two-year overall survival (OS) rates were 96.6%, 89.5%, 75.0%, and 57.9% (p &lt; 0.001), and 2-year progression-free survival (PFS) rates were 86.9%, 65.1%, 41.9%, and 15.2% (p &lt; 0.001) in stages I, II, III, and IV, respectively. The prognostic role of the R-ISS/PET for survival outcomes was also confirmed in different subgroups classified by transplant eligibility and by types of treatments. In the external validation cohort, the new R-ISS/PET was successfully implemented. Two-year OS rates for were 100%, 89.9%, 82.6%, and 42.0% for R-ISS/PET I, II, III, and IV, respectively (p = 0.001). PFS rates at 2 years for each R-ISS/PET were 100%, 74.5%, 57.9%, and 25.6%, respectively (p = 0.004). In the multivariate Cox analysis for survival outcome, R-ISS/PET was a significant factor and could predict long-term outcomes with regard to OS: stage II vs. I (HR 2.50, p = 0.215), (ii) stage III vs. I (HR 5.11, p = 0.025), and (iii) stage IV vs. I (HR 10.3, p = 0.003) and PFS: (i) stage II vs. I (HR 2.21, p = 0.005), (ii) stage III vs. I (HR 4.57, p &lt; 0.001), and (iii) stage IV vs. I (HR 9.48, p &lt; 0.001). Conclusion The new R-ISS/PET had a remarkable prognostic power for estimating the survival outcomes of patients with NDMM. This system helps discriminate patients with a good prognosis from those with a poor prognosis more precisely. Thus, R-ISS/PET is applicable for identifying heterogeneous manifestation of clinical MM. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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