Model For Survival In Patients Research Articles

A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma

Introduction: The objective of our study was to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRCC). Methods: A total of 3,408 GSRCC patients between 1975 and 2017 were screened from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts. Univariate and multivariate Cox analyses were conducted to identify independent prognostic factors for the construction of a nomogram. The performance of the model was then assessed by the concordance index (C-index), calibration plot, and area under the receiver operating characteristic curve (AUC). Then, the novel nomogram was further assessed by 64 GSRCC patients from our hospital as the external cohort. Results: We identified age, tumor lymph node metastasis (TNM) staging system, surgery, and chemotherapy as significant independent elements of prognosis. On this basis, a nomogram was constructed, with a C-index of OS in the training and validation cohorts of 0.763 (95% CI: 0.751–0.774) and 0.766 (95% CI: 0.748–0.784) and a C-index of CSS of 0.765 (95% CI: 0.753–0.777) and 0.773 (95% CI: 0.755–0.791), respectively. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.848 and 0.885, respectively, and those for predicting CSS were 0.854 and 0.899, respectively, demonstrating the excellent predictive value of the constructed nomogram compared to the traditional AJCC staging system. Similar results were also observed in both the internal and external validation sets. Conclusion: The nomogram provided an accurate tool to predict OS and CSS in patients with GSRCC, which can assist clinicians in making predictions about individual patient survival.

Socioeconomic variables in the National Cancer Database: Utilization and impact of income and education in survival models for patients with resected pancreas cancer.

607 Background: The National Cancer Database (NCDB) is commonly used for analyzing survival outcomes in pancreatic cancer and has a robust number of patient-specific socioeconomic variables. Although these aggregated variables are commonly collected, their utility and impact on survival has infrequently been assessed. We aim to assess the distribution of income and education variables on their impact on survival models for patients who underwent resection for pancreatic ductal adenocarcinoma (PDAC). Methods: Patients undergoing definitive surgery for PDAC between 2004-2020 were identified from the NCDB. Variables of interest were income and education according to the patient’s ZIP code of residence. Pearson and Spearman correlation was assessed. Overall survival (OS) was calculated using Kaplan Meier method. The effects of income and education was calculated using Cox proportional Hazard (PH) models. Results: 69,362 patients met inclusion criteria. Median age was 66 (IQR 59-73), with 48.9% female. The correlation coefficient between income and education based on the 2020 American Community Survey data was 0.48 (p<0.01). Median OS from the time of diagnosis was 21 months. The 5-year OS was 19.4 and 22.7 months for groups with lowest and highest education (p<0.01). Group with missing education has a 5-year OS of 22.4 month, comparable to the group with highest education (p=0.82). The 5-year OS based on income quartiles was 18.7 and 22.5 months for the Q1 and Q4 (p<0.01). Group with missing income has a 5-year OS of 22.4 month, similar to the highest income group (p=0.85). When including combinations of income, education and their interaction, the concordance of Cox models was changed by less than 0.002. Conclusions: For patients who underwent definitive surgery for PDAC, the income and education variables are only mildly correlated, and their inclusion do not have significant impact on OS models. Investigators should reconsider the utility of the aggregate NCDB measures, as they many not fully elucidate income and education disparities and impact on survival. [Table: see text]

Prognostic model for survival in patients with neuroendocrine carcinomas of the cervix: SEER database analysis and a single-center retrospective study.

Neuroendocrine carcinoma of the cervix (NECC) is extremely rare in clinical practice. This study aimed to methodologically analyze the clinicopathological factors associated with NECC patients and to develop a validated survival prediction model. A total of 535 patients diagnosed with NECC between 2004 and 2016 were identified from the Surveillance, Epidemiology and End Results (SEER) database, while 122 patients diagnosed with NECC at Yunnan Cancer Hospital (YCH) from 2006 to 2019 were also recruited. Patients from the SEER database were divided into a training cohort (n = 376) and a validation cohort (n = 159) in a 7:3 ratio for the construction and internal validation of the nomogram. External validation was performed in a cohort at YCH. The Kaplan-Meier method was used for survival analysis, the Log-rank method test was used for univariate analysis of prognostic influences, and the Cox regression model was used for multivariate analysis. The 3-year and 5-year overall survival (OS) rates for patients with NECC in SEER were 43.6% and 39.7%, respectively. In the training cohort, multivariate analysis showed independent prognostic factors for NECC patients including race, tumor size, distant metastasis, stage, and chemotherapy (p<0.05). For extended application in other cohorts, a nomogram including four factors without race was subsequently created. The consistency index (C-index) of the nomogram predicting survival was 0.736, which was well-validated in the validation cohorts (0.746 for the internal validation cohort and 0.765 for the external validation cohort). In both the training and validation cohorts, the 3-year survival rates predicted by the nomogram were comparable to the actual ones. We then succeeded in dividing patients with NECC into high- and low-risk groups concerning OS using the nomogram we developed. Besides, univariate analysis showed that chemotherapy ≥4 cycles may improve the OS of patients at YCH with NECC. We successfully constructed a nomogram that precisely predicts the OS for patients with NECC based on the SEER database and a large single-center retrospective cohort. The visualized and practical model can distinguish high-risk patients for recurrence and death who may benefit from clinical trials of boost therapy effectively. We also found that patients who received more than 4 cycles of chemotherapy acquired survival benefits than those who received less than 4 cycles.

Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer.

Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting. Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts. Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model's predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2. The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively.

Editor's Choice – External Validation of a Prognostic Model for Survival of Patients With Abdominal Aortic Aneurysms Treated by Endovascular Aneurysm Repair

Current guidelines recommend diameter monitoring of small and asymptomatic abdominal aortic aneurysms (AAAs) due to the low risk of rupture. Elective AAA repair is recommended for diameters ≥ 5.5 cm in men and ≥ 5.0 cm in women. However, data supporting the efficacy of elective treatment for all patients above these thresholds are diverging. For a subgroup of patients, life expectancy might be very short, and elective AAA repair at the current threshold may not be justified. This study aimed to externally validate a predictive model for survival of patients with asymptomatic AAA treated with endovascular aneurysm repair (EVAR). This was a multicentre international retrospective observational cohort study. Data were collected from four European aortic centres treating patients between 2001 and 2021. The initial model included age, estimated glomerular filtration rate (eGFR), and chronic obstructive pulmonary disease (COPD) as independent predictors for survival. Model performance was measured by discrimination and calibration. The validation cohort included 1 500 patients with a median follow up of 65 months, during which 54.6% of the patients died. The external validation showed slightly decreased discrimination ability and signs of overfitting in model calibration. However, a high risk subgroup of patients with impaired survival rates was identified: octogenarians with eGFR < 60 OR COPD, septuagenarians with eGFR < 30, and septuagenarians with eGFR < 60 and COPD having survival rates of only 55.2% and 15.5% at five and 10 years, respectively. EVAR is a valuable treatment option for AAA, especially for patients unsuitable for open repair. Nonetheless, not all these patients will benefit from EVAR, and an individualised treatment recommendation should include considerations on life expectancy. This study provides a risk stratification to identify patients who may not benefit from EVAR under the present diameter threshold.

External validation of a simplified prognostic model for survival in patients with extrinsic malignant ureteral obstruction treated with tandem ureteral stents – a retrospective cohort study

Introduction Prognostic models of survival can identify patients with extrinsic malignant ureteral obstruction who will benefit from long-term drainage as offered by tandem ureteral stents. The study aims to validate a simplified prognostic model published by Cordeiro et al. and to identify additional prognostic predictors in a cohort of patients drained solely with tandem ureteral stents. Methods Medical records of consecutive patients who underwent drainage of malignant ureteral obstruction with tandem ureteral stents between 2007 and 2020 were reviewed retrospectively; patients with benign ureteral obstruction were excluded. Risk factors for survival included were: [1] the number of malignancy-related events (categorized as ≥4 and <4) and [2] the Eastern Cooperative Oncology Group Index (categorized as ≥2 and <2)]. Patients with ≥1 risk factor were grouped as intermediate-unfavorable risk and those without risk factors as favorable risk. The Kaplan–Meier and log-rank tests were used for survival analysis. Univariable and multivariable Cox regression analyses were used to identify predictors of outcome. Results The study cohort consisted of 65 patients; the median age was 60 years (IQR 51–72). The median follow-up time from diagnosis of hydronephrosis was 51 months (IQR 38–64). Estimated probabilities of survival at 1 month, 6 months 1 year, and 2 years were 100%, 87%, 75% and 57%, respectively in the favorable risk group (n = 40), and in the intermediate-unfavorable risk group (n = 25), 96%, 72%, 52%, and 20%, respectively, (p = .003). On multivariable analysis, the presence of ≥4 malignancy-related events (HR = 2.04, 95% CI [1.07–3.86], p = .03) and lung metastasis (HR = 2.37, 95% CI [1.0–5.6], p = .05) were associated with shorter survival. Conclusions Our findings validate the prognostic model published by Cordeiro et al. The model can be applied when counseling patients being considered for drainage with tandem ureteral stents.

Characterization of Patients With Metastatic Renal Cell Carcinoma Experiencing Complete Response to First-line Therapies: Results From the International Metastatic Renal Cell Carcinoma Database Consortium.

Clinical trials have demonstrated higher complete response rates in the immuno-oncology-based combination arms than in the tyrosine kinase inhibitor arms in patients with metastatic renal cell carcinoma. We aimed to characterize real-world patients who experienced complete response to the contemporary first-line therapies. Using the International Metastatic Renal Cell Carcinoma Database Consortium, response-evaluable patients who received frontline immuno-oncology-based combination therapy or tyrosine kinase inhibitor monotherapy were analyzed. Baseline characteristics of patients and post-landmark overall survival were compared based on best overall response, as per RECIST 1.1. A total of 52 (4.6%) of 1,126 and 223 (3.0%) of 7,557 patients experienced complete response to immuno-oncology-based and tyrosine kinase inhibitor therapies, respectively (P = .005). An adjusted odds ratio for complete response achieved by immuno-oncology-based combination therapy (vs tyrosine kinase inhibitor monotherapy) was 1.56 (95% CI 1.11-2.17; P = .009). Among patients who experienced complete response, the immuno-oncology-based cohort had a higher proportion of non-clear cell histology (15.9% and 4.7%; P = .016), sarcomatoid dedifferentiation (29.8% and 13.5%; P = .014), and multiple sites of metastases (80.4% and 50.0%; P < .001) than the tyrosine kinase inhibitor cohort. Complete response was independently associated with post-landmark overall survival benefit in both the immuno-oncology-based and tyrosine kinase inhibitor cohorts, giving respective adjusted hazard ratios of 0.17 (95% CI 0.04-0.72; P = .016) and 0.28 (95% CI 0.21-0.38; P < .001). The complete response rate was not as high in the real-world population as in the clinical trial population. Among those who experienced complete response, several adverse clinicopathological features were more frequently observed in the immuno-oncology-based cohort than in the tyrosine kinase inhibitor cohort. Complete response was an indicator of favorable overall survival.

A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study.

Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. We retrospectively reviewed the clinicopathology characteristics of patients with geriatric TC in the Surveillance, Epidemiology, and End Results database (SEER) from 2004 to 2018. The risk predictors used to build the nomograms were derived from the Cox proportional risk regression. These nomograms were used to predict 1-, 3-, and 5-year overall survival and cancer-specific survival in elderly patients with TC. The accuracy and discriminability of the new model were evaluated by the consistency index (C-index) and calibration curve. The clinical applicability value of the model was assessed using the decision curve analysis. We used the SEER database to include 16475 patients with geriatric TC diagnosed from 2004 to 2018. The patients from 2004 to 2015 were randomly sorted out on a scale of 7:3. They were classified into a training group (n = 8623) and a validation group (n = 3669). Patients with TC diagnosed in 2016-2018 were classified into external validation groups (n = 4183). The overall survival nomogram consisted of 10 variables (age, gender, marital status, histologic type, grade, TNM stage, surgery status, and tumor size). A cancer-specific survival nomogram consisted of eight factors (age, tumor size, grade, histologic type, surgery, and TNM stage). The C-index values for the training, validation, and external validation groups were 0.775 (95% confidence interval [CI] 0.785-0.765), 0.776 (95% CI 0.792-0.760), and 0.895(95% CI 0.873-0.917), respectively. The overall survival was consistent with a nomogram based on the calibration curve. Besides, the decision curve analysis showed excellent clinical application value of the nomogram. Additionally, we found that surgery could improve the prognosis of patients with geriatric at high-risk (P < 0.001) but not those at low-risk (P = 0.069). This was the first study to construct predictive survival nomograms for patients with geriatric TC. The well-established nomograms and the actual results could guide follow-up management strategies.

Prognostic model for survival of patients with abdominal aortic aneurysms treated with endovascular aneurysm repair

The role of endovascular aneurysm repair (EVAR) in patients with asymptomatic abdominal aortic aneurysm (AAA) who are unfit for open surgical repair has been questioned. The impending risk of aneurysm rupture, the risk of elective repair, and the life expectancy must be balanced when considering elective AAA repair. This retrospective observational cohort study included all consecutive patients treated with standard EVAR for AAA at a referral centre between 2001 and 2020. A previously published predictive model for survival after EVAR in patients treated between 2001 and 2012 was temporally validated using patients treated at the same institution between 2013 and 2020 and updated using the overall cohort. 558 patients (91.2% males, mean age 74.9 years) were included. Older age, lower eGFR, and COPD were independent predictors for impaired survival. A risk score showed good discrimination between four risk groups (Harrel’s C = 0.70). The 5-years survival probabilities were only 40% in “high-risk” patients, 68% in “moderate-to-high-risk” patients, 83% in “low-to-moderate-risk”, and 89% in “low-risk” patients. Low-risk patients with a favourable life expectancy are likely to benefit from EVAR, while high-risk patients with a short life expectancy may not benefit from EVAR at the current diameter threshold.

Development and external validation of a clinical prediction model for survival in patients with IDH wild-type glioblastoma.

Prognostication of glioblastoma survival has become more refined due to the molecular reclassification of these tumors into isocitrate dehydrogenase (IDH) wild-type and IDH mutant. Since this molecular stratification, however, robust clinical prediction models relevant to the entire IDH wild-type glioblastoma patient population are lacking. This study aimed to provide an updated model that predicts individual survival prognosis in patients with IDH wild-type glioblastoma. Databases from Germany and the Netherlands provided data on 1036 newly diagnosed glioblastoma patients treated between 2012 and 2018. A clinical prediction model for all-cause mortality was developed with Cox proportional hazards regression. This model included recent glioblastoma-associated molecular markers in addition to well-known classic prognostic variables, which were updated and refined with additional categories. Model performance was evaluated according to calibration (using calibration plots and calibration slope) and discrimination (using a C-statistic) in a cross-validation procedure by country to assess external validity. The German and Dutch patient cohorts consisted of 710 and 326 patients, respectively, of whom 511 (72%) and 308 (95%) had died. Three models were developed, each with increasing complexity. The final model considering age, sex, preoperative Karnofsky Performance Status, extent of resection, O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and adjuvant therapeutic regimen showed an optimism-corrected C-statistic of 0.73 (95% confidence interval 0.71-0.75). Cross-validation between the national cohorts yielded comparable results. This prediction model reliably predicts individual survival prognosis in patients with newly diagnosed IDH wild-type glioblastoma, although additional validation, especially for long-term survival, may be desired. The nomogram and web application of this model may support shared decision-making if used properly.

Immune-Related Biomarkers Improve Performance of Risk Prediction Models for Survival in Patients With Hepatocellular Carcinoma.

ObjectThe prediction of hepatocellular carcinoma (HCC) prognosis faced great challenge due to tumor heterogeneity. The purpose of this study was to explore the correlation between the immune infiltrate and prognosis. Moreover, we aimed to establish a risk prediction model for survival in HCC patients based on clinicopathological and immune indicators.MethodsIn this study, 316 patients with HCC who underwent radical resection in West China Hospital from 2009 to 2014 were included. Clinicopathological data and pathological specimens were collected. H&E staining and immunohistochemical staining were performed on the pathological tissue sections. The evaluation of tumor-infiltrating lymphocyte (TIL) density was based on H&E slices, and the assessment of the expressions of CD8, CD68, Lymphocyte activation gene-3 (LAG-3), T cell immunoglobulin domain and mucin domain-3 (TIM-3), Programmed Cell Death Protein 1 (PD-1), Programmed Cell Death Ligand 1 (PD-L1), OX40, CD66b, and Tryptase. was performed on the immunohistochemical slices. A risk prediction model for survival in HCC patients was established by integrating immune-related biomarkers and clinicopathological indicators.ResultsThe Barcelona Clinic Liver Cancer (BCLC) stage; the microvascular invasion status; the density of TILs; the expressing levels of CD66b, OX40, and PD-L1 in the immune cell; CD68; and CD8 were the predictors of patients’ overall survival (OS). The BCLC stage; the density of TILs; and the expressions of OX40, CD68, and CD8 were associated with disease-free survival (DFS). The expressions of CD66b, CD68, OX40, and CD8 had a cumulative effect on prognosis. The area under the curve of the prediction model for OS based on clinicopathological features was improved from 0.62 to 0.74 by adding to CD8, OX40, CD68, CD66b, and TILs, whereas it was improved from 0.59 to 0.73 for the DFS prediction model.ConclusionOur results, if confirmed, indicated that immune-related biomarkers should be taken into account or stratified in survival analysis for HCC.

Prediction of Mortality in Coronary Artery Disease: Role of Machine Learning and Maximal Exercise Capacity

ObjectiveTo develop a prediction model for survival of patients with coronary artery disease (CAD) using health conditions beyond cardiovascular risk factors, including maximal exercise capacity, through the application of machine learning (ML) techniques. MethodsAnalysis of data from a retrospective cohort linking clinical, administrative, and vital status databases from 1995 to 2016 was performed. Inclusion criteria were age 18 years or older, diagnosis of CAD, referral to a cardiac rehabilitation program, and available baseline exercise test results. Primary outcome was death from any cause. Feature selection was performed using supervised and unsupervised ML techniques. The final prognostic model used the survival tree (ST) algorithm. ResultsFrom the cohort of 13,362 patients (60±11 years; 2400 [18%] women), 1577 died during a median follow-up of 8 years (interquartile range, 4 to 13 years), with an estimated survival of 67% up to 21 years. Feature selection revealed age and peak metabolic equivalents (METs) as the features with the greatest importance for mortality prediction. Using these 2 features, the ST generated a long-term prediction with a C-index of 0.729 by splitting patients in 8 clusters with different survival probabilities (P<.001). The ST root node was split by peak METs of 6.15 or less or more than 6.15, and each patient’s subgroup was further split by age or other peak METs cut points. ConclusionApplying ML techniques, age and maximal exercise capacity accurately predict mortality in patients with CAD and outperform variables commonly used for decision-making in clinical practice. A novel and simple prognostic model was established, and maximal exercise capacity was further suggested to be one of the most powerful predictors of mortality in CAD.

Survival Analysis and a Novel Nomogram Model for Progression-Free Survival in Patients with Prostate Cancer.

Background This study sought to perform a survival analysis and construct a prognostic nomogram model based on the Gleason grade, total prostate-specific antigen (tPSA), alkaline phosphate (ALP), and TNM stage in patients with prostate cancer (PCa). Methods The progression-free survival (PFS) of 255 PCa patients was analyzed in this study. The prognostic value of tPSA and ALP was evaluated using the Kaplan-Meier survival curves and Cox regression analysis, and a nomogram model based on the Gleason grade, tPSA, ALP, and TNM stage was further established for PFS prediction in PCa patients. Results PCa patients with different Gleason grades, tPSA and ALP levels, and TNM stages presented distinct PFS. The Gleason grade, tPSA, ALP, and TNM stage were four independent prognostic indicators. The C-index of the established nomogram was 0.705 for PFS in the test cohort and 0.687 for the validation cohort, and the calibration curves indicated a good consistency between predicted and actual PFS in PCa patients. Conclusion The data of this study demonstrated that the Gleason grade, tPSA, ALP, and TNM stage of PCa patients are independently correlated with PFS, and a nomogram model based on these indicators may be valuable for the PFS prediction in PCa patient.

A1110 - Validation of a simplified prognostic model for survival in patients with extrinsic malignant ureteral obstruction treated with tandem ureteral stents

A1110 - Validation of a simplified prognostic model for survival in patients with extrinsic malignant ureteral obstruction treated with tandem ureteral stents

Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model.

Background:Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.’s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort.Material and Methods:The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000–June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models.Results:External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077–0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12–0.16).Conclusion:External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort.

Survival and prognostic factors in patients after carotid stenting and coronary revascularization

Aim: To analyze the prognostic factors and create a model for survival in patients after interventional carotid revascularization. Methods: In 329 patients after carotid artery stenting (CAS), the median (MS) and overall survival (OS) were calculated for a follow-up period of 2-101 months. All patients underwent coronary angiography prior to carotid stenting and, if indicated, coronary revascularization. 4 groups of factors were analyzed: carotid disease, coronary artery disease (CAD), underlying cardiac pathology and concomitant diseases. Results: MS in all patients was 86 months, OS at 1, 3, 5, and 9 years was &amp;ndash; 94%, 85%, 73%, and 51% respectively. Event free survival was 85 months. Log Rank- Mantel-Cox analysis demonstrated significantly reduced MS in 21 tested factors, most of them related to CAD. Two-step multifactorial Cox regression analysis defined only 7 of them as independent prognostic factors for the survival of patients after CAS: left main stenosis, complete revascularization, late myocardial infarction (MI), stroke, age over 70 years, valvular disease and carotid score. Conclusion: Survival of patients after CAS is limited mainly by CAD and underlying cardiac pathology. Staged revascularization treatment strategy may improve the prognosis and survival of patients with both carotid and coronary disease.

Pilot Prognostic Model for Survival in r/r DLBCL Patients Receiving Palliative Radiation Therapy

Patients with relapsed/refractory (r/r) diffuse large B-cell lymphoma (DLBCL) often require palliative radiotherapy (pRT) for symptomatic relief, bridging therapy, or salvage treatment (i.e., irradiation of only site of disease). Prognostication remains a challenge, especially with the advent of novel therapies such as chimeric antigen receptor (CAR)-T cells. We aimed to construct a predictive model for survival in r/r DLBCL treated with pRT.Patients with r/r DLBCL who received their first course of pRT between 2009 and 2020 were included. OS was defined as time from pRT consult to death. A multivariable prognostic model was developed using Cox proportional hazard regression with LASSO (Least Absolute Shrinkage and Selection Operator)-regularization, which optimizes variable inclusion based on the model's overall prediction accuracy. Model discrimination performance was summarized using the concordance index after optimism-adjustment with the bootstrap (internal validation).82 patients met the inclusion criteria. Mean age at pRT was 62 years, with the majority of patients being male (59%) and Caucasian (80%). Most patients had an ECOG 0-1 (78%), despite being heavily pretreated (50% ≥3 systemic therapies, 22% transplant, 20% CAR-T cell therapy). pRT was most commonly delivered for symptoms (54%), followed by salvage (34%) and bridging (12%) to another systemic therapy. With a median follow-up of 6.3 months (range, 0.1- 91.2), 57 deaths (70%) were observed with median survival (MS) of 7.8 months. Treatment intent was associated with differences in MS: 4.1 mo (symptomatic), 10.4 mo (bridging), and 11.9 mo (salvage; P = 0.026). The LASSO method selected 5 co-variates for the multivariable OS model (Table 1), which was associated with an optimism-adjusted concordance index of 0.70 (95% CI, 0.61-0.80). Conversely, age, tachycardia, double/triple hit lymphoma, prior transplant or CAR-T cell therapy, and time from diagnosis were not selected by the LASSO for the model.Our pilot prognostic model for survival of patients with r/r DLBCL treated with pRT has good predictive performance (concordance index 0.7), with performance status, treatment intent, and number of prior lines of therapy having the most impact on predictions. Interestingly, types of prior therapies (e.g., CAR-T cell) and pathologic features at diagnosis were not selected for the model. Future work will focus on validating these findings in other cohorts, where larger numbers may permit development and validation of a stronger prognostic model.

Novel metastatic burden-stratified risk model in de novo metastatic hormone-sensitive prostate cancer.

The metastatic burden is a critical factor for decision‐making in the treatment of metastatic hormone‐sensitive prostate cancer (HSPC). This study aimed to develop and validate a novel risk model for survival in patients with de novo low‐ and high‐burden metastatic HSPC. The retrospective observational study included men with de novo metastatic prostate cancer who were treated with primary androgen‐deprivation therapy at 30 institutions across Japan between 2008 and 2017. We created a risk model for overall survival (OS) in the discovery cohort (n = 1449) stratified by the metastatic burden (low vs high) and validated its predictive ability in a separate cohort (n = 951). Based on multivariate analyses, lower hemoglobin levels, higher Gleason grades, and higher clinical T‐stage were associated with poor OS in low‐burden disease. Meanwhile, lower hemoglobin levels, higher Gleason grade group, liver metastasis, and higher extent of disease scores in bone were associated with poor OS in patients with high‐burden disease. In the discovery and validation cohorts, the risk model using the aforementioned parameters exhibited excellent discriminatory ability for progression‐free survival and OS. The predictive ability of this risk model was superior to that of previous risk models. Our novel metastatic burden‐stratified risk model exhibited excellent predictive ability for OS, and it is expected to have several clinical uses, such as precise prognostic estimation.

Validation of a Predictive Model for Survival in Patients with Acute Myeloid Leukemia and Myelodysplastic Syndrome Receiving Hematopoietic Stem Cell Transplantation

▪IntroductionAllogeneic hematopoietic stem-cell transplantation (AHSCT) is an effective treatment for patients with high-risk or relapsed acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Several factors are known to influence survival post-transplant. Our group developed a model to predict survival in a small number of patients with AML/MDS using 3 disease (cytogenetics and disease status at transplant) and patient-related [Hematopoietic stem cell transplant comorbidity index (HCT-CI)] variables (Bachegowda et al.Blood.2017) . This model effectively stratified AML/MDS patients into 3 risk groups with very different survival. Here we aim to validate this model in a large cohort of AML/MDS patients receiving AHSCT with various donor types.MethodsA total of 934 patients with AML/MDS consecutively treated at the University of Texas MD Anderson Cancer Center were included in this analysis. Donors were HLA matched related (n=377, 40%), matched unrelated (n=416, 45%) mismatched unrelated (n=68, 7%) and T-cell replete haploidentical transplants (n=73, 8%). The median age was 53 years (range 18-65 years). Cytogenetics data at diagnosis was available in 920 patients and was favorable, intermediate and adverse-risk in 340 (37%), 517 (56%) and 63 (7%), respectively. Five hundred and forty patients (58%) were in first or second complete remission (CR1/2). The hematopoietic stem cell transplant comorbidity index (HCT-CI) was available in 914 patients and the median score was 2 (range 0-17). The sources of hematopoietic stem cells were peripheral blood for 586 patients (63%) and bone marrow for 348 patients (37%). Seven hundred and eighty patients (84%) received myeloablative-conditioning chemotherapy.ResultsPatients were stratified into 3 risk groups according to the previously published model: 1) low-risk group (patients in CR1/2 with intermediate/favorable risk cytogenetics, irrespective of HCT-CI (HCT-CI did not impact survival in this group) (n=351), 2) intermediate-risk group (patients beyond CR1/2 and/or with adverse-risk cytogenetics and HCT-CI score ≤4) (n=412), and 3) high-risk group (patients with at least one negative prognostic factor - beyond CR1/2 and/or with adverse-risk cytogenetics and a HCTCI score >4) (n=155).Median follow up duration for 436 survivors was 48 months. For the whole group, the 5-year progression-free survival (PFS), treatment-related mortality (TRM) and relapse rate were 39%, 21% and 39%, respectively. TRM at 5 years for low-risk group was 17%, for the intermediate-risk group was 18% (HR [95% CI] 1.12 [0.79-1.59]; P=0.52), and for the high-risk group was 40% (3.24 [2.26-4.64]; P<0.0001), respectively, whereas cumulative incidence of relapse at 5 years was 24% for low-risk, 52% for intermediate-risk (2.86 [2.20-3.71]; P<0.0001) and 38% for the high-risk group (1.88 [1.34-2.63]; P<0.0001), respectively. In multivariable model, transplantation in >CR1/2 (2.84 [2.38-3.40]; P<0.0001), adverse cytogenetic risk (1.54 [1.29-1.83]; P<0.0001), males (1.23 [1.03-1.46]; P=0.022) were significantly associated with worse PFS while higher age-adjusted HCT-CI scores (1.20 [0.99-1.47]; P=0.06) approached significance. Other factors including conditioning intensity (1.02 [0.82- 1.29]; P=0.84, age (1.01 [1.00-1.01]; P=0.17) and donor type (haploidentical (1.2 [0.83-1.72]; P=0.33), matched unrelated (1.06 [0.88-1.28]; P=0.55) and mismatched unrelated donor (1.07 [0.76-1.50]; P=0.70) as compared with matched related donor) did not significantly influence PFS. Using our previously described predictive model, the patients separated in 3 risk groups with significantly different median PFS, which was not reached for the low-risk group, 9.2 months for the intermediate-risk group (2.4 [1.97-2.99]; P<0.0001), and 6.0 months for the high-risk group (3.3 [2.57-4.21]; P<0.0001), respectively. (Figures 1A and 1B)In conclusion, we were able to stratify allogeneic stem cell transplant patients in 3 risk groups based on a combination of disease (cytogenetics and disease status at transplant) and patient-related factors (HCT-CI) with significantly different survival. HCT-CI appears to have a much higher discriminatory impact in patients with higher risk disease (not in CR1/CR2 and/or adverse risk cytogenetics) compared with those in the lower risk group (CR1/CR2 and intermediate/favorable risk cytogenetics). [Display omitted] DisclosuresOran:AROG: Research Funding; Celgene: Research Funding; Astex: Research Funding. Khouri:Novartis: Research Funding.

A Prognostic Model for Survival in Patients with Relapsed/Refractory Philadelphia Chromosome-Negative Acute Lymphoblastic Leukemia on the Combination of Low-Intensity Chemotherapy Plus Inotuzumab Ozogamicin with or without Blinatumomab

A Prognostic Model for Survival in Patients with Relapsed/Refractory Philadelphia Chromosome-Negative Acute Lymphoblastic Leukemia on the Combination of Low-Intensity Chemotherapy Plus Inotuzumab Ozogamicin with or without Blinatumomab