Survival Prediction Model For Patients Research Articles

Prognostic significance of lab data and performance comparison by validating survival prediction models for patients with spinal metastases after radiotherapy.

Well-performing survival prediction models (SPMs) help patients and healthcare professionals to choose treatment aligning with prognosis. This retrospective study aims to investigate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) for spinal metastases (SM). From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox-proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs. A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8. Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

ObjectiveThis study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC).MethodsFor the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves.ResultsA Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718–0.735) and 0.721 (95% CI, 0.709–0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691–0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system.ConclusionWe developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions.

A LASSO-based survival prediction model for patients with synchronous colorectal carcinomas based on SEER.

BackgroundThe nomogram for postoperative prediction of overall survival (OS) in patients’ synchronous colorectal carcinomas (SCC) was developed and validated by least absolute shrinkage and selection operator (LASSO)-based Cox regression.MethodsThe data was obtained from the SEER database of patients diagnosed with colorectal cancer (CRC) more than one time between 2004 and 2013. Patients who had CRC more than 3 times or multiple metachronous primary carcinomas were excluded. The cut-off points for the continuous variable were identified by the K-adaptive partitioning algorithm and x-tile software. Using LASSO-based Cox regression, a model for predicting the OS of SCC was built, internally and externally validated, and measured through a calibration curve, C-index, Akaike information criterion (AIC), Bayesian information criterion (BIC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), time-dependent receiver operating characteristic (timeROC), time-dependent area under curve (timeAUC), and decision curve analysis (DCA), and results compared to the model developed by the Cox regression.ResultsPatients with SCC were found to be older, more often men, and likely to have a depth of invasion by T3. In addition, there were no significant differences between the model developed by LASSO-based Cox regression and the Cox regression in the C-index (0.712 and 0.710), AIC (33,420 and 33,431), BIC (4.49), IDI (0.002), NRI (–0.009), timeROC, and DCA. Besides, the model developed by LASSO-based Cox regression was found to perform better than the Cox regression in the timeAUC. Moreover, the model developed by LASSO-based Cox regression showed good C-index (0.712, 0.637, and 0.651), AIC (33,420, 34,043, and 33,994), BIC (1,178.76 and 1,098.57), IDI (–0.072 and –0.064), NRI (0.525 and 0.466), timeROC, timeAUC and had a larger net benefit compared to both the first time TNM staging and the combination of two times TNM staging.ConclusionsThis present study indicates that a close follow-up of older patients, male, and T3 should be made. Compared with the traditional Cox regression model, LASSO-based Cox regression decreases the variables of the model, avoids overfitting and collinearity and has clinical significance.

Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm.

Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC.

Survival Risk Prediction of Esophageal Squamous Cell Carcinoma Based on BES-LSSVM.

Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. In this study, ten indicators related to the survival of patients with ESCC are founded using genetic algorithm feature selection. The prognostic index (PI) for ESCC is established using the binary logistic regression. PI is divided into four stages, and each stage can reasonably reflect the survival status of different patients. By plotting the ROC curve, the critical threshold of patients' age could be found, and patients are divided into the high-age groups and the low-age groups. PI and ten survival-related indicators are used as independent variables, based on the bald eagle search (BES) and least-squares support vector machine (LSSVM), and a survival prediction model for patients with ESCC is established. The results show that five-year survival rates of patients are well predicted by the bald eagle search-least-squares support vector machine (BES-LSSVM). BES-LSSVM has higher prediction accuracy than the existing particle swarm optimization-least-squares support vector machine (PSO-LSSVM), grasshopper optimization algorithm-least-squares support vector machine (GOA-LSSVM), differential evolution-least-squares support vector machine (DE-LSSVM), sparrow search algorithm-least-squares support vector machine (SSA-LSSVM), bald eagle search-back propagation neural network (BES-BPNN), and bald eagle search-extreme learning machine (BES-ELM).

Development and validation of a survival prediction model for patients received mechanical ventilation in the intensive care unit: a large sample size cohort from the MIMIC database.

Mechanical ventilation remains one of the primary management measures for critically ill patients in intensive care units (ICUs). However, previous studies on the prognosis prediction of ICU patients received mechanical ventilation were limited. This study was to develop and validate a nomogram for predicting short- and long-term survival among patients who received mechanical ventilation in the ICU. This was a retrospective cohort study with a 3-year follow-up. Demographic, laboratory, clinical data of 16,775 participants aged ≥18 years who received mechanical ventilation in the ICU were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The outcomes of this study were 1-month, 3-month, 1-year, and 3-year survival. All eligible patients were randomly classified into the training and testing groups with a ratio of 7:3. A multivariate Cox regression in the training group was used to explore the predictors and develop the predictive nomogram. Internal and subgroup validations were performed, and the C-index was calculated to estimate the predictive performance of the nomogram. The time-dependent receiver operating characteristic curves were drawn, and corresponding areas under the curve (AUC) were calculated. Totally 6,291 patients died during the follow-up duration. Age, gender, ethnicity, ICU type, comorbidity, days of mechanical ventilation, white blood cell count, blood urea nitrogen, the fraction of inspiration O2, Sequential Organ Failure Assessment scores, and the Glasgow coma score were predictors of the survival of ICU patients who received mechanical ventilation (P<0.05). The C-index of the nomogram was 0.819 and was validated in the testing group at 0.816. The AUCs for the prognostic nomogram for 1-month, 3-month, 1-year, and 3-year survival were 0.889, 0.892, 0.882, and 0.866, respectively. This nomogram showed good predictive performance for short- and long-term survival in ICU patients treated with mechanical ventilation, which may be a useful tool for clinicians to assess the prognosis of patients and to adjust treatment strategies in time.

SOURCE beyond first-line: A survival prediction model for patients with metastatic esophagogastric adenocarcinoma after failure of first-line palliative systemic therapy.

4037 Background: Prediction models for survival may aid shared decision making between physicians and patients. Prior models have been developed that predict survival for patients with potentially curable esophagogastric cancer and patients with metastatic esophageal cancer who start first-line therapy (the SOURCE models). The aim of this study was to develop and internally validate a registry-based clinical prediction model, called SOURCE beyond first-line, for survival of patients with metastatic esophagogastric cancer after failure of first-line palliative systemic therapy. Methods: Patients with unresectable or metastatic (synchronous or metachronous) esophageal or gastric cancer who received first-line systemic therapy (N = 1067) between 2015-2017 were selected from the Netherlands Cancer Registry. Follow-up data were retrieved in 2019. Patient, tumor and treatment characteristics at primary diagnosis and at progression of disease, were used to develop the prediction model. A Cox proportional hazards regression prediction model was developed through forward and backward selection using Akaike’s Information Criterion. The model was internally validated through 10-fold cross-validations to assess performance on unseen data. Model discrimination (C-index) and calibration (slope and intercept) were used to evaluate performance of the complete and cross-validated models. Results: The final model consisted of 10 patient, tumor and treatment characteristics. The C-index was 0.75 (0.74-0.77), the calibration slope 0.99 (0.98-0.99) and the calibration intercept 0.02 (0.01-0.02). Internal cross-validation of the model showed that the model performed adequately on unseen data: C-index 0.79 (0.76-0.80), calibration slope 1.02 (1.00-1.04) and calibration intercept -0.01 (-0.01-0.02). Conclusions: The SOURCE beyond first-line model predicted survival with fair discriminatory ability and good calibration, and is a valuable addition to the existing SOURCE prediction models. In the future this model will be integrated in an online decision support tool that can be used in clinical practice to aid personalized treatment.

Construction and validation of the survival prediction model for patients with cutaneous spindle cell melanoma

Construction and validation of the survival prediction model for patients with cutaneous spindle cell melanoma

Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions.

BackgroundPostoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.MethodsPlasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.ResultsMetabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983–1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969–0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981–0.998).ConclusionA survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required.

Diet- and Lifestyle-Based Prediction Models to Estimate Cancer Recurrence and Death in Patients With Stage III Colon Cancer (CALGB 89803/Alliance).

Current tools in predicting survival outcomes for patients with colon cancer predominantly rely on clinical and pathologic characteristics, but increasing evidence suggests that diet and lifestyle habits are associated with patient outcomes and should be considered to enhance model accuracy. Using an adjuvant chemotherapy trial for stage III colon cancer (CALGB 89803), we developed prediction models of disease-free survival (DFS) and overall survival by additionally incorporating self-reported nine diet and lifestyle factors. Both models were assessed by multivariable Cox proportional hazards regression and externally validated using another trial for stage III colon cancer (CALGB/SWOG 80702), and visual nomograms of prediction models were constructed accordingly. We also proposed three hypothetical scenarios for patients with (1) good-risk, (2) average-risk, and (3) poor-risk clinical and pathologic features, and estimated their predictive survival by considering clinical and pathologic features with or without adding self-reported diet and lifestyle factors. Among 1,024 patients (median age 60.0 years, 43.8% female), we observed 394 DFS events and 311 deaths after median follow-up of 7.3 years. Adding self-reported diet and lifestyle factors to clinical and pathologic characteristics meaningfully improved performance of prediction models (c-index from 0.64 [95% CI, 0.62 to 0.67] to 0.69 [95% CI, 0.67 to 0.72] for DFS, and from 0.67 [95% CI, 0.64 to 0.70] to 0.71 [95% CI, 0.69 to 0.75] for overall survival). External validation also indicated good performance of discrimination and calibration. Adding most self-reported favorable diet and lifestyle exposures to multivariate modeling improved 5-year DFS of all patients and by 6.3% for good-risk, 21.4% for average-risk, and 42.6% for poor-risk clinical and pathologic features. Diet and lifestyle factors further inform current recurrence and survival prediction models for patients with stage III colon cancer.

Development and validation of a dynamic survival prediction model for patients with acute-on-chronic liver failure

Background & AimsAcute-on-chronic liver failure (ACLF) is usually associated with a precipitating event and results in the failure of other organ systems and high short-term mortality. Current prediction models fail to adequately estimate prognosis and need for liver transplantation (LT) in ACLF. This study develops and validates a dynamic prediction model for patients with ACLF that uses both longitudinal and survival data.MethodsAdult patients on the UNOS waitlist for LT between 11.01.2016-31.12.2019 were included. Repeated model for end-stage liver disease-sodium (MELD-Na) measurements were jointly modelled with Cox survival analysis to develop the ACLF joint model (ACLF-JM). Model validation was carried out using separate testing data with area under curve (AUC) and prediction errors. An online ACLF-JM tool was created for clinical application.ResultsIn total, 30,533 patients were included. ACLF grade 1 to 3 was present in 16.4%, 10.4% and 6.2% of patients, respectively. The ACLF-JM predicted survival significantly (p <0.001) better than the MELD-Na score, both at baseline and during follow-up. For 28- and 90-day predictions, ACLF-JM AUCs ranged between 0.840-0.871 and 0.833-875, respectively. Compared to MELD-Na, AUCs and prediction errors were improved by 23.1%-62.0% and 5%-37.6% respectively. Also, the ACLF-JM could have prioritized patients with relatively low MELD-Na scores but with a 4-fold higher rate of waiting list mortality.ConclusionsThe ACLF-JM dynamically predicts outcome based on current and past disease severity. Prediction performance is excellent over time, even in patients with ACLF-3. Therefore, the ACLF-JM could be used as a clinical tool in the evaluation of prognosis and treatment in patients with ACLF.Lay summaryAcute-on-chronic liver failure (ACLF) progresses rapidly and often leads to death. Liver transplantation is used as a treatment and the sickest patients are treated first. In this study, we develop a model that predicts survival in ACLF and we show that the newly developed model performs better than the currently used model for ranking patients on the liver transplant waiting list.

Development and Validation of Artificial Neural Networks for Survival Prediction Model for Patients with Spontaneous Hepatocellular Carcinoma Rupture After Transcatheter Arterial Embolization.

BackgroundSpontaneous rupture bleeding is a fatal hepatocellular carcinoma (HCC) complication and a significant determinant of survival outcomes. This study aimed to develop and validate a novel artificial neural network (ANN)-based survival prediction model for patients with spontaneous HCC rupture after transcatheter arterial embolization (TAE).MethodsPatients with spontaneous HCC rupture bleeding who underwent TAE at our hospital between January 2010 and December 2018 were included in our study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to screen clinical variables related to prognosis. We incorporated the above clinical variables identified by LASSO Cox regression into the ANNs model. Multilayer perceptron ANNs were used to develop the 1-year overall survival (OS) prediction model for patients with spontaneous HCC ruptured bleeding in the training set. The area under the receiver operating characteristic curve and decision curve analysis were used to compare the predictive capability of the ANNs model with that of existing conventional prediction models.ResultsThe median survival time for the whole set was 11.8 months, and the 1-year OS rate was 47.5%. LASSO Cox regression revealed that sex, extrahepatic metastasis, macroscopic vascular invasion, tumor number, hepatitis B surface antigen, hepatitis B e antigen, tumor size, alpha-fetoprotein, fibrinogen, direct bilirubin, red blood cell, and γ-glutamyltransferase were risk factors for OS. An ANNs model with 12 input nodes, seven hidden nodes, and two corresponding prognostic outcomes was constructed. In the training set and the validation set, AUCs for the ability of the ANNs model to predict the 1-year OS of patients with spontaneous HCC rupture bleeding were 0.923 (95% CI, 0.890–0.956) and 0.930 (95% CI, 0.875–0.985), respectively, which were higher than that of the existing conventional models (all P < 0.0001).ConclusionThe ANNs model that we established has better survival prediction performance.

Survival prediction model for patients with mycosis fungoides/Sezary syndrome.

Aim: A nomogram was constructed to forecast the overall survival (OS) of patients with mycosis fungoides/Sezary syndrome. Patients & methods: The clinicopathological information of patients was obtained from the Surveillance, Epidemiologyand End Results (SEER) database. A model was established based on the independent prognostic factors. Predictive ability of the model was evaluated with the concordance index and calibration curves. Risk stratification was conducted for patients with similar tumor node metastasis (TNM) stages. Results: The model included 1997 eligible patients and seven prognostic factors for OS. The concordance index of the nomogram was 0.84 in the training and external validation cohorts, which indicated good predictive ability of the model and reliability of the results. The high agreement between the model predictions and actual observations was identified by calibration curves, which demonstrated the prediction accuracy of the model. Risk stratification displayed significant differences for patients with similar TNM stages, which suggested that the OS of patients with similar TNM stages could be further distinguished. Conclusion: We established a reliable nomogram to predict the OS of patients with mycosis fungoides/Sezary syndrome, which highlighted the advantages of nomograms over the conventional TNM staging system and promoted the application of individualized therapeutic strategies.

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study.

BackgroundRecently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN‐based survival prediction model for patients with gastric cancer.MethodsPatients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time‐dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models.ResultsAn ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5‐year survival prediction, respectively. In the calibration curve analysis, the ANN‐predicted survival had a high consistency with the actual survival. Comparison of the DCA and time‐dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts.ConclusionsThe ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

Natural disease progression and novel survival prediction model for hepatocellular carcinoma with spinal metastases: a 10-year single-center study

BackgroundIndividual prediction of life expectancy in patients with spinal metastases from hepatocellular carcinoma (HCC) is key for optimal treatment selection, especially when identifying potential candidates for surgery. Most reported prognostic tools provide categorical predictions, and only a few include HCC-related factors. This study aimed to investigate the natural progression of the disease and develop a prognostic tool that is capable of providing individualized predictions.MethodsPatients with HCC-derived metastatic spinal disease were identified from a retrospective cohort of patients with spinal metastases who were diagnosed at Chiang Mai University Hospital between 2006 and 2015. Kaplain–Meier methods and log-rank tests were used to statistically evaluate potential factors. Significant predictors from the univariable analysis were included in the flexible parametric survival regression for the development of a prognostic prediction model.ResultsOf the 1143 patients diagnosed with HCC, 69 (6%) had spinal metastases. The median survival time of patients with HCC after spinal metastases was 79 days. In the multivariable analysis, a total of 11 potential clinical predictors were included. After backward elimination, four final predictors remained: patients aged > 60 years, Karnofsky Performance Status, total bilirubin level, and multifocality of HCC. The model showed an acceptable discrimination at C-statistics 0.73 (95% confidence interval 0.68–0.79) and fair calibration.ConclusionFour clinical parameters were used in the development of the individual survival prediction model for patients with HCC-derived spinal metastases of Chiang Mai University or HCC-SM CMU model. Prospective external validation studies in a larger population are required prior to the clinical implication of the model.

Survival prediction models for patients with anal carcinoma receiving definitive chemoradiation: A population-based study.

The present study aimed to develop two nomograms in order to predict cancer-specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C-index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C-indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies.

Development and External Validation of a Nomogram for Predicting Survival in Patients With Stage IA Non-small Cell Lung Cancer ≤2 cm Undergoing Sublobectomy.

Background: Postoperative prognosis of early stage non-small cell lung cancer (NSCLC) undergoing sublobectomy is heterogeneous. Therefore, we sought to construct a novel survival prediction model for stage IA NSCLC ≤2 cm undergoing sublobectomy.Methods: Based on the data from the Surveillance, Epidemiology, and End Results (SEER) program, we successfully determined and incorporated independent prognostic markers to construct the nomogram. Internal validation of the constructed nomogram was conducted through 1,000 bootstrap resamples. The constructed nomogram was further subjected to external validation with an independent cohort of patients from two Chinese institutions. The performance of the survival prediction model was assessed by concordance index, calibration plots, and risk subgroup classification.Results: A total of 3,238 patients from SEER registries (development cohort), as well as 769 patients from two Chinese institutions (validation cohort) was included. Gender, age, size, histologic type, grade, and examined lymph nodes count were identified as significant prognostic parameters. A novel nomogram was developed and externally validated. Concordance index of constructed nomogram was significantly better than that of the current TNM staging system. Calibration plots demonstrated an optimal consistency between the nomogram predicted and actual observed probability of survival. Survival curves of different risk subgroups within respective TNM stage demonstrated significant distinctions.Conclusion: We developed and externally validated a survival prediction model for patients with stage IA NSCLC ≤2 cm undergoing sublobectomy. This novel nomogram outperforms the conventional TNM staging system and could help clinicians in postoperative surveillance and future clinical trial design.

Development and Validation of a Nomogram for Assessing Survival in Patients With Metastatic Lung Cancer Referred for Radiotherapy for Bone Metastases

A survival prediction model for patients with bone metastases arising from lung cancer would be highly valuable. To develop and validate a nomogram for assessing the survival probability of patients with metastatic lung cancer receiving radiotherapy for osseous metastases. In this prognostic study, the putative prognostic indicators for constructing the nomogram were identified using multivariable Cox regression analysis with backward elimination and model selection based on the Akaike information criterion. The nomogram was subjected to internal (bootstrap) and external validation; its calibration and discriminative ability were evaluated with calibration plots and the Uno C statistic, respectively. The training and validation set cohorts were from a tertiary medical center in northern Taiwan and a tertiary institution in southern Taiwan, respectively. The training set comprised 477 patients with metastatic lung cancer who received radiotherapy for osseous metastases between January 2000 and December 2013. The validation set comprised 235 similar patients treated between January 2011 and December 2017. Data analysis was conducted May 2018 to July 2018. The nomogram end points were death within 3, 6, and 12 months. Of 477 patients in the training set, 292 patients (61.2%) were male, and the mean (SD) age was 62.86 (11.66) years. Of 235 patients in the validating set, 113 patients (48.1%) were male, and the mean (SD) age was 62.65 (11.49) years. In the training set, 186 (39%), 291 (61%), and 359 (75%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 4.21 (95% CI, 3.68-4.90) months. In the validating set, 84 (36%), 120 (51%), and 144 (61%) patients died within 3, 6, and 12 months, respectively, and the median overall survival was 5.20 (95% CI, 4.07-7.17) months. Body mass index (18.5 to <25 vs ≥25: hazard ratio [HR], 1.42; 95% CI, 1.14-1.78 and <18.5 vs ≥25: HR, 2.31; 95% CI, 1.56-3.44), histology (non-small cell vs small cell lung cancer: HR, 0.59; 95% CI, 0.41-0.86), epidermal growth factor receptor mutation (positive vs unknown: HR, 0.66; 95% CI, 0.46-0.93 and negative vs unknown: HR, 0.98; 95% CI, 0.66-1.45), smoking status (ever smoker vs never smoker: HR, 1.50; 95% CI, 1.24-1.83), age, and neutrophil to lymphocyte ratio were incorporated. The HRs of age and neutrophil to lymphocyte ratio were modeled nonlinearly with restricted cubic splines (both P < .001). The nomogram's discriminative ability was good in the training set (C statistic, ≥0.77; P < .001) and was validated using both an internal bootstrap method (C statistic, ≥0.76; P < .001) and an external validating set (C statistic, ≥0.75; P < .001). The calibration plots for the end points showed optimal agreement between the nomogram's assessment and actual observations. The nomogram (with web-based tool) can be useful for assessing the probability of survival at 3, 6, and 12 months in patients with metastatic lung cancer referred for radiotherapy to treat bone metastases, and it may guide radiation oncologists in treatment decision making and engaging patients in end-of-life discussions and/or hospice referrals at appropriate times.

A Comparative Analysis of the Usefulness of Survival Prediction Models for Patients with Glioblastoma in the Temozolomide Era: The Importance of Methylguanine Methyltransferase Promoter Methylation, Extent of Resection, and Subventricular Zone Location

Several survival prediction models for patients with glioblastoma have been proposed, but none is widely used. This study aims to identify the predictors of overall survival (OS) and to conduct an independent comparative analysis of 5 prediction models. Multi-institutional data from 159 patients with newly diagnosed glioblastoma who received adjuvant temozolomide concomitant chemoradiotherapy (CCRT) were collected. OS was assessed by Cox proportional hazards regression and adjusted for known prognostic factors. An independent CCRT patient cohort was used to externally validate the 1) RTOG (Radiation Therapy Oncology Group) recursive partitioning analysis (RPA) model, 2) Yang RPA model, and 3) Wee RPA model, Chaichana model, and the RTOG nomogram model. The predictive accuracy for each model at 12-month survival was determined by concordance indices. Calibration plots were performed to ascertain model prediction precision. The median OS for patients who received CCRT was 19.0 months compared with 12.7 months for those who did not (P < 0.001). Independent predictors were:1)subventricular zone II tumors (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.0-2.5); 2) methylguanine methyltransferase promoter methylation (HR, 0.36; 95% CI, 0.2-0.6); and 3) extent of resection of >85% (HR, 0.59; 95% CI, 0.4-0.9). For 12-month OS prediction, the RTOG nomogram model was superior to the RPA models with a c-index of 0.70. Calibration plots for 12-month survival showed that none of the models was precise, but the RTOG nomogram performed relatively better. The RTOG nomogram best predicted 12-month OS. Methylguanine methyltransferase promoter methylation status, subventricular zone tumor location, and volumetric extent of resection should be considered when constructing prediction models.

External validation of a 5-year survival prediction model after elective abdominal aortic aneurysm repair

ObjectiveThe benefit of prophylactic repair of abdominal aortic aneurysms (AAAs) is based on the risk of rupture exceeding the risk of death from other comorbidities. The purpose of this study was to validate a 5-year survival prediction model for patients undergoing elective repair of asymptomatic AAA <6.5 cm to assist in optimal selection of patients. MethodsAll patients undergoing elective repair for asymptomatic AAA <6.5 cm (open or endovascular) from 2002 to 2011 were identified from a single institutional database (validation group). We assessed the ability of a prior published Vascular Study Group of New England (VSGNE) model (derivation group) to predict survival in our cohort. The model was assessed for discrimination (concordance index), calibration (calibration slope and calibration in the large), and goodness of fit (score test). ResultsThe VSGNE derivation group consisted of 2367 patients (70% endovascular). Major factors associated with survival in the derivation group were age, coronary disease, chronic obstructive pulmonary disease, renal function, and antiplatelet and statin medication use. Our validation group consisted of 1038 patients (59% endovascular). The validation group was slightly older (74 vs 72 years; P < .01) and had a higher proportion of men (76% vs 68%; P < .01). In addition, the derivation group had higher rates of advanced cardiac disease, chronic obstructive pulmonary disease, and baseline creatinine concentration (1.2 vs 1.1 mg/dL; P < .01). Despite slight differences in preoperative patient factors, 5-year survival was similar between validation and derivation groups (75% vs 77%; P = .33). The concordance index of the validation group was identical between derivation and validation groups at 0.659 (95% confidence interval, 0.63-0.69). Our validation calibration in the large value was 1.02 (P = .62, closer to 1 indicating better calibration), calibration slope of 0.84 (95% confidence interval, 0.71-0.97), and score test of P = .57 (>.05 indicating goodness of fit). ConclusionsAcross different populations of patients, assessment of age and level of cardiac, pulmonary, and renal disease can accurately predict 5-year survival in patients with AAA <6.5 cm undergoing repair. This risk prediction model is a valid method to assess mortality risk in determining potential overall survival benefit from elective AAA repair.