Development and validation of an early death risk score for older patients treated with chemotherapy for cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 12030-12030
Author(s):  
Jaime Feliu Batlle ◽  
Alvaro Pinto ◽  
Laura Basterretxea ◽  
Irene Paredero Pérez ◽  
Elisenda Llabres ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Risk Score ◽  
Older Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Early Death ◽  
Chemotherapy Treatment ◽  
Training Cohort ◽  
Death Risk ◽  
Patients With Cancer

12030 Background: Determining life expectancy in older patients is needed to select the best treatment strategy. We aimed to develop and validate a score to predict early death risk ( < 6 months) in elderly patients with cancer that are planned to initiate chemotherapy treatment. Methods: Patients over 70 years starting new chemotherapy regimens were prospectively included in a multicenter study. A pre-chemotherapy assessment that included sociodemographics, tumor/treatment variables, and geriatric assessment variables, was performed. Association between these factors and early death was examined by using multivariate logistic regression. Score points were assigned to each risk factor based on their b coefficient. We validated the risk score with an external validation cohort of 206 patients. Results: Three hundred forty two patients were included in the training cohort. The independent predictors for early death were metastasic cancers (odds ratio [OR] 4.8, 95% confidence interval [CI], [2.4-9.6]), ECOG performance status (OR 2.3, 95% CI:1.084-5.232), ADL (OR 1.7, 95% CI:1.08-3.5), serum albumin levels (3.3, 95% CI: 1.6-6.6), BMI (OR 2.4, 95% CI:1,2-4.8), serum GGT levels (OR 1.5, 95% CI:1.05-1.8) and hemoglobin levels (OR 2.3, 95% CI:1.2-4.6). With these results, a score was to stratify patients regarding their risk of early death: low (0 to 2 points; 5%), intermediate (3 to 5 points; 19%) or high (6 to 14 points; 50%) (p < 0.001). The area under the curve of the receiver-operating characteristic (ROC) curve was 0.79 for the training cohort (95% CI, 0.74 to 0.85), and 0.70 (95% CI: 0.60-0.80) for the validation cohort (difference between cohorts not statistically different). Conclusions: We developed a highly accurate tool that uses basic clinical and analytical information to predict the probability of early death in elderly patients with cancer that are planned to initiate chemotherapy treatment. This tool can help physicians in decision making for this population of patients.

scholarly journals Development and Validation of an Early Mortality Risk Score for Older Patients Treated with Chemotherapy for Cancer

2021 ◽  
Vol 10 (8) ◽  
pp. 1615
Author(s):  
Jaime Feliu ◽  
Alvaro Pinto ◽  
Laura Basterretxea ◽  
Borja López-San Vicente ◽  
Irene Paredero ◽  
...  
Keyword(s):  
Older Patients ◽  
Prognostic Score ◽  
External Validation ◽  
Early Death ◽  
Early Mortality ◽  
Background Estimation ◽  
Patients With Cancer ◽  
Validation Set ◽  
Ecog Ps ◽  
Development And Validation

Background: Estimation of life expectancy in older patients is relevant to select the best treatment strategy. We aimed to develop and validate a score to predict early mortality in older patients with cancer. Patients and Methods: A total of 749 patients over 70 years starting new chemotherapy regimens were prospectively included. A prechemotherapy assessment that included sociodemographic variables, tumor/treatment variables, and geriatric assessment variables was performed. Association between these factors and early death was examined using multivariable logistic regression. Score points were assigned to each risk factor. External validation was performed on an independent cohort. Results: In the training cohort, the independent predictors of 6-month mortality were metastatic stage (OR 4.8, 95% CI [2.4–9.6]), ECOG-PS 2 (OR 2.3, 95% CI [1.1–5.2]), ADL ≤ 5 (OR 1.7, 95% CI [1.1–3.5]), serum albumin levels ≤ 3.5 g/dL (OR 3.4, 95% CI [1.7–6.6]), BMI < 23 kg/m2 (OR 2.5, 95% CI [1.3–4.9]), and hemoglobin levels < 11 g/dL (OR 2.4, 95% CI (1.2–4.7)). With these results, we built a prognostic score. The area under the ROC curve was 0.78 (95% CI, 0.73 to 0.84), and in the validation set, it was 0.73 (95% CI: 0.67–0.79). Conclusions: This simple and highly accurate tool can help physicians making decisions in elderly patients with cancer who are planned to initiate chemotherapy treatment.

scholarly journals A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

2022 ◽  
Vol 9 ◽  
Author(s):  
Taiyu He ◽  
Tianyao Chen ◽  
Xiaozhu Liu ◽  
Biqiong Zhang ◽  
Song Yue ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Risk Groups ◽  
Cancer Specific Survival ◽  
Web Based ◽  
Training Cohort ◽  
Kaplan Meier ◽  
Early Hcc ◽  
High Consistency

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

scholarly journals Predictive Model for the Assessment of Preoperative Frailty Risk in the Elderly

2021 ◽  
Vol 10 (19) ◽  
pp. 4612
Author(s):  
Sang-Wook Lee ◽  
Jae-Sik Nam ◽  
Ye-Jee Kim ◽  
Min-Ju Kim ◽  
Jeong-Hyun Choi ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Predictive Model ◽  
Risk Score ◽  
Emergency Surgery ◽  
Validation Cohort ◽  
Preoperative Evaluation ◽  
External Validation ◽  
The Elderly ◽  
Diagnostic Codes ◽  
Temporal Validation

Adequate preoperative evaluation of frailty can greatly assist in the efficient allocation of hospital resources and planning treatments. However, most of the previous frailty evaluation methods, which are complicated, time-consuming, and can have inter-evaluator error, are difficult to apply in urgent situations. Thus, the authors aimed to develop and validate a predictive model for pre-operative frailty risk of elderly patients by using diagnostic and operation codes, which can be obtained easily and quickly from electronic records. We extracted the development cohort of 1762 people who were hospitalized for emergency operations at a single institution between 1 January 2012 and 31 December 2016. The temporal validation cohort from 1 January 2017 to 31 December 2018 in the same center was set. External validation was conducted on 6432 patients aged 75 years or older from 2012 to 2015 who had emergency surgery in the Korean national health insurance database. We developed the Operation Frailty Risk Score (OFRS) by assessing the association of Operation Group and Hospital Frailty Risk Score with the 90-day mortality through logistic regression analysis. We validated the OFRS in both the temporal validation cohort and two external validation cohorts. In the temporal validation cohort and the external validation cohort I and II, the c-statistics for OFRS to predict 90-day mortality were 0.728, 0.626, and 0.619, respectively. OFRS from these diagnostic codes and operation codes may help evaluate the peri-operative frailty risk before emergency surgery for elderly patients where history-taking and pre-operative testing cannot be performed.

scholarly journals Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sijia Cui ◽  
Tianyu Tang ◽  
Qiuming Su ◽  
Yajie Wang ◽  
Zhenyu Shu ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Main Pancreatic Duct ◽  
Validation Cohort ◽  
External Validation ◽  
High Grade ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Training Cohort ◽  
Independent Validation ◽  
Medical Centers ◽  
Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

scholarly journals An Immune Risk Score Predicts Survival of Patients with Myelodysplastic Syndrome

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 33-34
Author(s):  
Yang Liang ◽  
Fang Hu ◽  
Yu-Jun Dai ◽  
Yun Wang ◽  
Huan Li
Keyword(s):  
Regression Analysis ◽  
Myelodysplastic Syndrome ◽  
Risk Score ◽  
Prognostic Model ◽  
Validation Cohort ◽  
Gene Set Enrichment Analysis ◽  
Type I ◽  
Immune Gene ◽  
Training Cohort ◽  
Immune Score

Background: Myelodysplastic syndrome (MDS) was characterized as ineffective hematopoiesis, increased transformation to acute myeloid leukemia (AML), and accompanied by immune system dysfunction. However, the immune signature of MDS remains elusive. Methods: The clinical data (age, sex, international prognostic score system (IPSS), hemoglobin, blast, RBC transfusion dependence, and corresponding subject-level survival) as well as expression profiles of MDS (CD34+ cells) were obtained from Gene Expression Omnibus (GEO: GSE 58831; GSE 114922). A robust prognosis model of immune genes was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis. Survival analysis for prognostic model was carried out through the Kaplan-Meier curve and Log-rank test. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the accuracy of prognostic models. Immune score for different subtype were calculated further by single sample gene set enrichment analysis (ssGSEA). Result: A novel robust immune gene prognostic model indicate that subtype with lower risk score were longer overall survival (OS) than subtype with higher risk score in training cohort (Figure1 A, C). The model was further verified by the validation cohort (Figure1 B, D). The multivariate Cox regression analysis demonstrated the model was an independent prognostic factor for OS prediction with hazard ratios of 56.694 (95% CIs: 9.038−355.648), 3.009 (95% CIs: 1.042−8.692) both in train cohort and external validation cohort respectively (Figure1 G, H). The AUC of 5- year were 0.92 (95% CIs: 0.86 - 0.97) and 0.7 (95% CIs: 0.51 - 0.89) for OS respectively in training cohort and validation cohort (Figure1 E, F). Furthermore, ssGSEA showed higher risk score subtype was significantly associated with higher immune score of check point, human leukocyte antigen (HLA), T cell co-inhibition and type I interferon (IFN) response (Figure1 K-N), which indicating that the poor outcome might be caused by tumor-associated immune response dysfunction partly. Conclusion: We constructed a robust immune gene prognostic model, which have a potential prognostic value for MDS patients and may provide evidence for personalized immunotherapy. Figure Disclosures No relevant conflicts of interest to declare.

scholarly journals Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Jin-Yuan Chen ◽  
Yin-Xing Huang ◽  
Jia-Heng Xu ◽  
Wei-Wei Sun ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
Postoperative Recurrence ◽  
Integrated Model ◽  
Atypical Meningioma ◽  
Tumor Diameter ◽  
Roc Curve Analysis ◽  
Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen &gt;2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p &lt; 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter &gt;4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p &lt; 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

Early death in older patients with cancer: risk factors of worse outcomes?

2013 ◽  
Vol 4 ◽  
pp. S85
Author(s):  
R. Boulahssass ◽  
V. Mari ◽  
S. Gonfrier ◽  
F. Auben ◽  
A. Abakar-Mahamat ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cancer Risk ◽  
Older Patients ◽  
Early Death ◽  
Cancer Risk Factors ◽  
Patients With Cancer

A New Risk Score to Predict In-Hospital Mortality in Elderly Patients With Acute Heart Failure: On Behalf of the Journey HF-TR Study Investigators

Angiology ◽  
2020 ◽  
Vol 71 (10) ◽  
pp. 948-954
Author(s):  
Gülay Gök ◽  
Mehmet Karadağ ◽  
Ümit Yaşar Sinan ◽  
Mehdi Zoghi
Keyword(s):  
Heart Failure ◽  
Elderly Patients ◽  
Hospital Mortality ◽  
Risk Score ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Previous History ◽  
Turkish Population ◽  
Patient Journey

We aimed to predict in-hospital mortality of elderly patients with heart failure (HF) by using a risk score model which could be easily applied in routine clinical practice without using an electronic calculator. The study population (n = 1034) recruited from the Journey HF-TR (Patient Journey in Hospital with Heart Failure in Turkish Population) study was divided into a derivation and a validation cohort. The parameters related to in-hospital mortality were first analyzed by univariate analysis, then the variables found to be significant in that analysis were entered into a stepwise multivariate logistic regression (LR) analysis. Patients were classified as low, intermediate, and high risk. A risk score obtained by taking into account the regression coefficients of the significant variables as a result of the LR analysis was tested in the validation cohort using receiver operating characteristic curve analysis. In total, 6 independent variables (age, blood urea nitrogen, previous history of hemodialysis/hemofiltration, inotropic agent use, and length of intensive care stay) associated with in-hospital mortality were included in the analysis. The risk score had a good discrimination in both the derivation and validation cohorts. A new validated risk score to determine the risk of in-hospital mortality of elderly hospitalized patients with HF was developed by including 6 independent predictors.

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