scholarly journals Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Haisheng You ◽  
Mengmeng Teng ◽  
Chun Xia Gao ◽  
Bo Yang ◽  
Sasa Hu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prognostic Factors ◽  
Elderly Patients ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Validation Cohort ◽  
Discrimination Performance ◽  
Seer Database ◽  
Histologic Subtype ◽  
Training Cohort

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

scholarly journals Construction and validation of a nomogram for predicting cancer-specific survival in hepatocellular carcinoma patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kang Liu ◽  
Gaobo Huang ◽  
Pengkang Chang ◽  
Wei Zhang ◽  
Tao Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Seer Database ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Discrimination Power ◽  
Training Cohort ◽  
Good Agreement

AbstractThe prognosis of patients with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a research hotspot. This study aimed to incorporate important factors obtained from SEER database to construct and validate a nomogram for predicting the cancer-specific survival (CSS) of patients with HCC and ICC. We obtained patient data from SEER database. The nomogram was constructed base on six prognostic factors for predicting CSS rates in HCC patients. The nomogram was validated by concordance index (C-index), the receiver operating characteristic (ROC) curve and calibration curves. A total of 3227 patients diagnosed with HCC (3038) and ICC (189) between 2010 and 2015 were included in this study. The C-index of the nomogram for HCC patients was 0.790 in the training cohort and 0.806 in the validation cohort. The 3- and 5-year AUCs were 0.811 and 0.793 in the training cohort. The calibration plots indicated that there was good agreement between the actual observations and predictions. In conclusion, we constructed and validated a nomogram for predicting the 3- and 5-year CSS in HCC patients. We have confirmed the precise calibration and excellent discrimination power of our nomogram.

scholarly journals Development and Validation of a Nomogram for Predicting the 6-Month Survival Rate in Incident Hemodialysis Patients

2021 ◽  
Author(s):  
Guode Li ◽  
linsen Jiang ◽  
Jiangpeng Li ◽  
huaying shen ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Survival Rate ◽  
High Risk ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Low Risk ◽  
Intradialytic Hypotension ◽  
Training Cohort ◽  
All Cause Mortality

Abstract Background The all-cause mortality in hemodialysis(HD) patients is higher than in the general population and the first 6 months after initiating dialysis is an important transitional period for new HD patients. The aim of this study was to develop and validate a nomogram for predicting the 6-month survival rate among HD patients. Methods We developed a prediction model based on a training cohort of 679 HD patients. Multivariate Cox regression analyses were performed to identify predictive factors, followed by establishment of a nomogram. Next, performance of the nomogram was assessed using the C-index and calibration plots. The nomogram was validated through applying discrimination and calibration to an additional cohort of 173 HD patients. Results During a follow-up period of six months, there were 47 and 12 deaths in the training cohort and validation cohort, respectively, with a mortality rate of 7.3% and 6.9%, respectively. The score included five commonly available predictors: age, temporary dialysis catheter, intradialytic hypotension, use of ACEi or ARB, and use of loop diuretics. The score revealed good discrimination in the training cohort [C-index 0.775(0.693-0.857)] and validation cohort [C-index 0.758(0.677-0.836)], whereas the calibration plots showed good calibration, indicating suitable performance of the nomogram model. The total score point was then divided into two risk classifications: low risk (0-90 points) and high risk (≥ 91 points). Results showed that all-cause mortality was significantly different in HD patients in the high-risk group compared to the low-risk group. Conclusions This nomogram can accurately predict the 6-month survival rate for HD patients, and thus it can be used in clinical decision-making.

Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6046-6046
Author(s):  
Sik-Kwan Chan ◽  
Cheng Lin ◽  
Shao Hui Huang ◽  
Tin Ching Chau ◽  
Qiaojuan Guo ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Bone Metastases ◽  
Validation Cohort ◽  
De Novo ◽  
Multivariable Analysis ◽  
Term Survival ◽  
Training Cohort ◽  
Study Results ◽  
Multivariable Analyses

6046 Background: The eighth edition TNM (TNM-8) classified de novo metastatic (metastatic disease at presentation) nasopharyngeal carcinoma (NPC) as M1 without further subdivision. However, survival heterogeneity exists and long-term survival has been observed in a subset of this population. We hypothesize that certain metastatic characteristics could further segregate survival for de novo M1 NPC. Methods: Patients with previously untreated de novo M1 NPC prospectively treated in two academic institutions (The University of Hong Kong [n = 69] and Provincial Clinical College of Fujian Medical University [n = 114] between 2007 and 2016 were recruited and re-staged based on TNM-8 in this study. They were randomized in 2:1 ratio to generate a training cohort (n = 120) and validation cohort (n = 63) respectively. Univariable and multivariable analyses (MVA) were performed for the training cohort to identify the anatomic prognostic factors of overall survival (OS). We then performed recursive partitioning analysis (RPA) which incorporated the anatomic prognostic factors identified in multivariable analyses and derived a new set of RPA stage groups (Anatomic-RPA groups) which predicted OS in the training cohort. The significance of Anatomic-RPA groups in the training cohort was then validated in the validation cohort. UVA and MVA were performed again on the validation cohorts to identify significant OS prognosticators. Results: The training and the validation cohorts had a median follow-up of 27.2 months and 30.2 months, respectively, with the 3-year OS of 51.6% and 51.1%, respectively. Univariable analysis (UVA) and multivariable analysis (MVA) revealed that co-existing liver and bone metastases was the only factor prognostic of OS. Anatomic-RPA groups based on the anatomic prognostic factors identified in UVA and MVA yielded good segregation (M1a: no co-existing liver and bone metastases and M1b: co-existing both liver and bone metastases; median OS 39.5 and 23.7 months respectively; P =.004). RPA for the validation set also confirmed good segregation with co-existing liver and bone metastases (M1a: no co-existing liver and bone metastases and M1b: co-existing liver and bone metastases), with median OS 47.7 and 16.0 months, respectively; P =.008). It was also the only prognostic factor in UVA and MVA in the validation cohort. Conclusions: Our Anatomic-RPA M1 stage groups with anatomical factors provided better subgroup segregation for de novo M1 NPC. The study results provide a robust justification to refine M1 categories in future editions of TNM staging classification.

Construction and Validation of a Nomogram for Predicting Progression-Free Survival in Patients with Early-Stage Testicular Germ Cell Tumor

2021 ◽  
Vol 16 ◽  
Author(s):  
Jin-Guo Chen ◽  
Jing-Quan Wang ◽  
Tian-Wen Peng ◽  
Zhe-Sheng Chen ◽  
Shan-Chao Zhao
Keyword(s):  
Prognostic Factors ◽  
Validation Cohort ◽  
Early Stage ◽  
Predictive Ability ◽  
Testicular Germ Cell Tumor ◽  
Progression Free Survival ◽  
Cell Tumor ◽  
Testicular Germ Cell ◽  
Free Survival ◽  
Training Cohort

Background: Testicular Germ Cell Tumor (TGCT) is the most common malignant tumor in young men, but there is a lack of prediction model to evaluate prognosis of patients with TGCT. Objective: To explore the prognostic factors for Progression-Free Survival (PFS) and construct a nomogram model for patients with early-stage TGCT after radical orchiectomy. Methods: Patients with TGCT from The Cancer Genome Atlas (TCGA) database were used as the training cohort; univariate and multivariate cox analysis were performed. A nomogram was constructed based on the independent prognostic factors. Patients from the Nanfang Hospital affiliated with Southern Medical University were used as the cohort to validate the predictive ability using the nomogram model. Harrell's concordance index (C-index) and calibration plots were used to evaluate the nomogram. Results: A total of 110 and 62 patients with TGCT were included in training cohort and validation cohort, respectively. Lymphatic Vascular Invasion (LVI), American Joint Committee on Cancer (AJCC) stage and adjuvant therapy were independent prognostic factors in multivariate regression analyses and were included to establish a nomogram. The C-index in the training cohort for 1-, 3-, and 5-year PFS were 0.768, 0.74 and 0.689, respectively. While the C-index for 1-, 3-, and 5-year PFS in the external validation cohort were 0.853, 0.663 and 0.609, respectively. The calibration plots for 1-, 3-, and 5-year PFS in the training and validation cohort showed satisfactory consistency between predicted and actual outcomes. The nomogram revealed a better predictive ability for PFS than AJCC staging system. Conclusion: The nomogram as a simple and visual tool to predict individual PFS in patients with TGCT could guide clinicians and clinical pharmacists in therapeutic strategy.

scholarly journals Prognostic Nutritional Index as an independent prognostic factor in locoregionally advanced squamous cell head and neck cancer

ESMO Open ◽  
2018 ◽  
Vol 3 (6) ◽  
pp. e000425 ◽  
Author(s):  
Gema Bruixola ◽  
Javier Caballero ◽  
Federica Papaccio ◽  
Angelica Petrillo ◽  
Aina Iranzo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Head And Neck ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Prognostic Nutritional Index ◽  
Training Cohort ◽  
Lymphocyte Ratio ◽  
Nutritional Index

BackgroundLocally advanced head and neck squamous cell carcinoma (LAHNSCC) is a heterogeneous disease in which better predictive and prognostic factors are needed. Apart from TNM stage, both systemic inflammation and poor nutritional status have a negative impact on survival.MethodsWe retrospectively analysed two independent cohorts of a total of 145 patients with LAHNSCC treated with induction chemotherapy followed by concurrent chemoradiotherapy at two different academic institutions. Full clinical data, including the Prognostic Nutritional Index (PNI), neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio, were analysed in a training cohort of 50 patients. Receiver operating characteristic curve analysis was used to establish optimal cut-off. Univariate and multivariate analyses of prognostic factors for overall survival (OS) were performed. Independent predictors of OS identified in multivariate analysis were confirmed in a validation cohort of 95 patients.ResultsIn the univariate analysis, low PNI (PNI<45) (p=0.001), large primary tumour (T4) (p=0.044) and advanced lymph node disease (N2b-N3) (p=0.025) were significantly associated with poorer OS in the validation cohort. The independent prognostic factors in the multivariate analysis for OS identified in the training cohort were dRNL (p=0.030) and PNI (p=0.042). In the validation cohort, only the PNI remained as independent prognostic factor (p=0.007).ConclusionsPNI is a readily available, independent prognostic biomarker for OS in LAHNSCC. Adding PNI to tumour staging could improve individual risk stratification of patients with LAHNSCC in future clinical trials.

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.

scholarly journals Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Qian Chen ◽  
Shu Wang ◽  
Jing-He Lang
Keyword(s):  
Overall Survival ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Validation Cohort ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Ovarian Clear Cell Carcinoma ◽  
Cancer Specific Survival ◽  
Training Cohort ◽  
Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

Admission of the very elderly to the intensive care unit: Family members’ perspectives on clinical decision-making from a multicenter cohort study

2015 ◽  
Vol 29 (4) ◽  
pp. 324-335 ◽  
Author(s):  
Daren K Heyland ◽  
Peter Dodek ◽  
Sangeeta Mehta ◽  
Deborah Cook ◽  
Allan Garland ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Decision Making ◽  
Cohort Study ◽  
Intensive Care ◽  
Elderly Patients ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Clinical Decision Making ◽  
Family Members ◽  
Very Elderly

Background: Little is known about the perspectives and experiences of family members of very elderly patients who are admitted to the intensive care unit. Aim: To describe family members’ perspectives about care provided to very elderly critically ill patients. Design: Multicenter, prospective, cohort study. Participants and setting: In total, 535 family members of patients aged 80 years or older admitted to 22 intensive care units for more than 24 h. Results: Family members reported that the “patient be comfortable and suffer as little as possible” was their most important value and “the belief that life should be preserved at all costs” was their least important value considered in making treatment decisions. Most family members (57.9%) preferred that life support be used for their family member, whereas 24.1% preferred comfort measures only, and 14.4% were unsure of their treatment preferences. Only 57.3% reported that a doctor had talked to them about treatment options for the patient. Overall, 29.7% of patients received life-sustaining treatments for more than 7 days and 50.3% of these died in hospital. Families were most satisfied with the skill and competency of nurses and least satisfied with being included and supported in the decision-making process and with their sense of control over the patient’s care. Conclusion: There is incongruity between family values and preferences for end-of-life care and actual care received for very elderly patients who are admitted to the intensive care unit. Deficiencies in communication and decision-making may be associated with prolonged use of life-sustaining treatments in very elderly critically ill patients, many of whom ultimately die.

A novel pretreatment model identifying high-risk of refractoriness after transcatheter arterial chemoembolization in unresectable hepatocellular carcinoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16641-e16641
Author(s):  
Xin Yin ◽  
Ke Shu Hu ◽  
Shen Xin Lu ◽  
Bei Tang ◽  
Zhenggang Ren
Keyword(s):  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Confidence Interval ◽  
Clinical Decision Making ◽  
Transcatheter Arterial Chemoembolization ◽  
Validation Cohort ◽  
Treatment Model ◽  
Training Cohort ◽  
Pre Treatment ◽  
Arterial Chemoembolization

e16641 Background: Refractoriness to transcatheter arterial chemoembolization is common during the therapeutic process of hepatocellular carcinoma, which is an intractable issue and may compromise the prognosis. We aim to establish a pre-treatment model to identify patients with high risks of refractoriness. Methods: From 2010 to 2016, 824 patients who had initially underwent at least two sessions of transcatheter arterial chemoembolization in Zhongshan Hospital, Fudan University were retrospectively enrolled. These patients were randomly allocated into a training cohort and a validation cohort. The pre-treatment scoring model was established based on the clinical and radiological variables using logistic regression and nomogram. The discrimination and calibration of the model were also evaluated. Results: Logistic regression identified vascularization pattern, ALBI grade, serum alpha-fetoprotein level, serum γ- glutamyl transpeptidase level and major tumor size as the key parameters related to refractoriness. The p-TACE model was established using these variables (risk score range: 0-19.5). Patients were divided into six risk subgroups based on their scores ( < 4, ≥4, ≥7, ≥10, ≥13, ≥16). The discriminative ability, as determined by the area under receiver operating characteristic curve was 0.784 (95% confidence interval: 0.741-0.827) in the training cohort and 0.743 (95% confidence interval: 0.696-0.789) in the validation cohort. Moreover, satisfactory calibration was confirmed by Hosmer-Lemeshow test with P values of 0.767 and 0.913 in the training cohort and validation cohort. Conclusions: This study presents a pre-treatment model to identify patients with high risks of refractoriness after transcatheter arterial chemoembolization and shed light on clinical decision making.

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