Dataset Information

Conditional Survival After Resection for Pancreatic Cancer: A Population-Based Study and Prediction Model.

ABSTRACT:

Background

Conditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor characteristics. Such prediction models for patients with resected pancreatic cancer are lacking and therefore conditional survival was assessed and a nomogram predicting 5-year survival at a predefined period after resection of pancreatic cancer was developed.

Methods

This population-based study included patients with resected pancreatic ductal adenocarcinoma from the Netherlands Cancer Registry (2005-2016). Conditional survival was calculated as the median, and the probability of surviving up to 8 years in patients who already survived 0-5 years after resection was calculated using the Kaplan-Meier method. A prediction model was constructed.

Results

Overall, 3082 patients were included, with a median age of 67 years. Median overall survival was 18 months (95% confidence interval 17-18 months), with a 5-year survival of 15%. The 1-year conditional survival (i.e. probability of surviving the next year) increased from 55 to 74 to 86% at 1, 3, and 5 years after surgery, respectively, while the median overall survival increased from 15 to 40 to 64 months at 1, 3, and 5 years after surgery, respectively. The prediction model demonstrated that the probability of achieving 5-year survival at 1 year after surgery varied from 1 to 58% depending on patient and tumor characteristics.

Conclusions

This population-based study showed that 1-year conditional survival was 55% 1 year after resection and 74% 3 years after resection in patients with pancreatic cancer. The prediction model is available via www.pancreascalculator.com to inform patients and caregivers.

SUBMITTER: Latenstein AEJ

PROVIDER: S-EPMC7311496 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Conditional Survival After Resection for Pancreatic Cancer: A Population-Based Study and Prediction Model.

Latenstein Anouk E J AEJ van Roessel Stijn S van der Geest Lydia G M LGM Bonsing Bert A BA Dejong Cornelis H C CHC Groot Koerkamp Bas B de Hingh Ignace H J T IHJT Homs Marjolein Y V MYV Klaase Joost M JM Lemmens Valery V Molenaar I Quintus IQ Steyerberg Ewout W EW Stommel Martijn W J MWJ Busch Olivier R OR van Eijck Casper H J CHJ van Laarhoven Hanneke W M HWM Wilmink Johanna W JW Besselink Marc G MG

Annals of surgical oncology 20200212 7

<h4>Background</h4>Conditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor characteristics. Such prediction models for patients with resected pancreatic cancer are lacking and therefore conditional survival was assessed and a nomogram predicting 5-year survival at a predefined period after resection of pancreatic cancer was developed.<h4>Methods</h4>This population-based study ...[more]

PMID: 32052299

Similar Datasets

Project description:BackgroundThe risk and prognosis of pancreatic cancer with lung metastasis (PCLM) are not well-defined. Thus, this study aimed to identify the risk and prognostic factors for these patients, and establish predictive nomogram models.MethodsPatients diagnosed with PCLM between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors and prognostic factors were identified using logistic regression and Cox regression analyses. Nomograms were constructed to predict the risk and survival of PCLM, and the area under the curve (AUC), C-index, and calibration curve were used to determine the predictive accuracy and discriminability of the established nomogram, while the decision curve analysis was used to confirm the clinical effectiveness.ResultsA total of 11287 cases with complete information were included; 601 (5.3%) patients with PC had lung metastases. Multivariable logistic analysis demonstrated that primary site, histological subtype, and brain, bone, and liver metastases were independent risk factors for lung metastases. We constructed a risk prediction nomogram model for the development of lung metastases among PC patients. The c-index of the established diagnostic nomogram was 0.786 (95%CI 0.726-0.846). Multivariable Cox regression analysis demonstrated that primary site, liver metastases, surgery, and chemotherapy were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS), while bone metastases were independent prognostic factors for CSS. The C-indices for the OS and CSS prediction nomograms were 0.76 (95% CI 0.74-0.78) and 0.76 (95% CI 0.74-0.78), respectively. Based on the AUC of the receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance.ConclusionsThe present study identified the risk and prognostic factors of PCLM and further established nomograms, which can help clinicians effectively identify high-risk patients and predict their clinical outcomes.

Project description:Objective: This study aimed at comprehensively investigating the survival impact of lymphadenectomy during primary surgery in ovarian cancer. Methods: Based on the surveillance, epidemiology, and end results registry (SEER) database, we included ovarian cancer patients with detailed information between 2010 and 2016. Cox regression was performed to select prognostic factors. We conducted propensity score-weighted survival analysis to balance baseline variables, and series of stratified analyses to control main confounding factors. Results: A total of 8,652 patients were ultimately identified. Among 4,360 patients with advanced disease, lymphadenectomy did not show significant survival benefit in general (median overall survival 44 months in non-lymphadenectomy vs. 49 months in lymphadenectomy group, P = 0.055). In subgroup analysis on patients received optimal debulking, lymphadenectomy did not significantly benefit the survival outcome (median overall survival 51, 47, 60, and 58 months in the non-lymphadenectomy, 1-9 lymph nodes, 10-19 lymph nodes, ?20 lymph nodes groups, respectively, P = 0.287). Consistent results were observed in further stratification analyses. In optimal debulking subgroup, lymph node metastasis indicated worse survival. However, when limited the number of removed lymph nodes to more than 15, there was a marginal statistical difference in overall survival (P = 0.0498) while no significant difference presented in cause-specific survival (P = 0.129) between non-lymphadenectomy, pathological negative lymph node group and positive lymph node group. And the regions of lymph metastasis were also not significantly associate with survival (P = 0.123). Among 3,266 (75%) patients with apparent early-stage disease received lymphadenectomy, 7.75% of whom were reported isolated lymph nodes metastasis and have a poorer survival (P < 0.05). Conclusions: In primary debulking for patients with advanced ovarian cancer, lymphadenectomy was not associated with more favorable outcomes when compared to no lymphadenectomy. The value of lymphadenectomy lies more in staging for apparent early disease rather than therapeutic benefit.

Project description:ObjectiveProstate cancer (PC) is the second leading cause of cancer death in men in the United States after lung cancer in global incidence. Elderly male patients over 65 years old account for more than 60% of PC patients, and the impact of surgical treatment on the prognosis of PC patients is controversial. Moreover, there are currently no predictive models that can predict the prognosis of elderly PC patients undergoing surgical treatment. Therefore, we aimed to construct a new nomogram to predict cancer-specific survival (CSS) in elderly PC patients undergoing surgical treatment.MethodsData for surgically treated PC patients aged 65 years and older were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for elderly PC patients undergoing surgical treatment. A nomogram of elderly PC patients undergoing surgical treatment was developed based on the multivariate Cox regression model. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the predictive model. Decision curve analysis (DCA) was used to examine the potential clinical value of this model.ResultsA total of 44,975 elderly PC patients undergoing surgery in 2010-2018 were randomly assigned to the training set (N = 31705) and validation set (N = 13270). the training set was used for nomogram development and the validation set was used for internal validation. Univariate and multivariate Cox regression model analysis showed that age, marriage, TNM stage, surgical style, chemotherapy, radiotherapy, Gleason score(GS), and prostate-specific antigen(PSA) were independent risk factors for CSS in elderly PC patients undergoing surgical treatment. The C index of the training set and validation indices are 0.911(95%CI: 0.899-0.923) and 0.913(95%CI: 0.893-0.933), respectively, indicating that the nomogram has a good discrimination ability. The AUC and the calibration curves also show good accuracy and discriminability.ConclusionsTo our knowledge, our nomogram is the first predictive model for elderly PC patients undergoing surgical treatment, filling the gap in current predictive models for this PC patient population. Our data comes from the SEER database, which is trustworthy and reliable. Moreover, our model has been internally validated in the validation set using the C-index,AUC and the and the calibration curve, showed that the model have good accuracy and reliability, which can help clinicians and patients make better clinical decision-making. Moreover, the DCA results show that our nomogram has a better potential clinical application value than the TNM staging system.

Project description:Hepatic metastases are diagnosed synchronously in 3-14% of patients with gastric cancer, and metachronously in up to 37% of patients following ''curative" gastrectomy. Most patients who have gastric cancer and hepatic metastasis are traditionally treated with palliative chemotherapy. The impact of liver resection is still controversial. We attempted to assess whether liver resection can improve survival in cases of metachronous hepatic metastases from gastric cancer through a nationwide database.We conducted a nationwide cohort study using a claims dataset from Taiwan's National Health Insurance Research Database (NHIRD). We identified all patients with gastric cancer (diagnostic code ICD-9: 151.x) from the Registry for Catastrophic Illness Patient Database (RCIPD) of the NHIRD who received gastrectomy and as well as those with metachronous (≥180 days after gastrectomy) liver metastases (ICD-9 code: 197.7) between 1996/01/01 and 2012/12/31. Patients with other malignancies, with metastasis in the initial admission for gastrectomy and with other metastases were excluded. They were divided into two groups, liver resection group and non-resection group. All patients were followed till 2013/12/31 or withdrawn from the database because of death.653 patients who fullfilled the inclusion criteria were included in the research. They were divided into liver resection group (34 patients) and non-resection group (619 patients). There were no differences between the two groups in gender, Charlson Comorbidity index and major coexisting disease. Kaplan-Meier analysis demostrated the liver resection group had significantly better overall survival than the non-resection group. (1YOS: 73.5% vs. 19.7%, 3YOS: 36.9% vs. 6.6%, 5YOS: 24.5.3% vs. 4.4%, p <0.001). After COX analysis, the liver resection group showed statistical significance for improved patient survival (HR = 0.377, 95%CI: 0.255-0.556. p<0.001).Liver resection in patients presenting with metachronous hepatic metastases as the sole metastases after curative resection of gastric cancer is associated with a significant survival improvement and should be considered a treatment option for such patients.

Dataset Information

Conditional Survival After Resection for Pancreatic Cancer: A Population-Based Study and Prediction Model.

Background

Methods

Results

Conclusions

Publications

Conditional Survival After Resection for Pancreatic Cancer: A Population-Based Study and Prediction Model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets