Dataset Information

Combining standard clinical blood values for improving survival prediction in patients with newly diagnosed brain metastases-development and validation of the LabBM score.

ABSTRACT:

Background

We aimed to investigate the potential of standard hematologic and serum biochemical parameters to provide an independent and substantial contribution to the prediction of survival in patients with newly diagnosed brain metastases (BM).

Methods

Hemoglobin, white blood cell count, platelet count, serum albumin, creatinine, lactate dehydrogenase (LDH), and C-reactive protein (CRP) were assessed at diagnosis of BM in a discovery cohort of 1200 cancer patients. A multivariable Cox regression model was used to derive the LabBM score. The LabBM score was externally validated in an independent cohort consisting of 366 patients.

Results

Hemoglobin below lower limit of normal (ULN; HR: 1.51; P < .001), and CRP >ULN (HR: 1.52; P < .001) were associated with survival in a multivariable Cox regression model and were included in the calculation of the LabBM score. Multivariable analysis including the LabBM score and graded prognostic assessment class revealed an independent and significant association of the LabBM score with overall survival (OS) (HR: 1.42; 95% CI: 1.29-1.57; P < .001). The strong and independent association of LabBM score (HR: 1.93; 95% CI: 1.54-2.42) with OS prognosis was confirmed in the validation cohort.

Conclusion

Standard clinical blood parameters, combined in the easy-to-calculate LabBM score, provide strong and independent prognostic information in patients with BM. The LabBM score is an objective, inexpensive, and reproducible tool to plan clinical management strategies in BM patients and to improve patient selection and stratification for clinical trials.

SUBMITTER: Berghoff AS

PROVIDER: S-EPMC5570252 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining standard clinical blood values for improving survival prediction in patients with newly diagnosed brain metastases-development and validation of the LabBM score.

Berghoff Anna S AS Wolpert Fabian F Holland-Letz Tim T Koller Romina R Widhalm Georg G Gatterbauer Brigitte B Dieckmann Karin K Birner Peter P Bartsch Rupert R Zielinski Christoph C CC Weller Michael M Preusser Matthias M

Neuro-oncology 20170901 9

<h4>Background</h4>We aimed to investigate the potential of standard hematologic and serum biochemical parameters to provide an independent and substantial contribution to the prediction of survival in patients with newly diagnosed brain metastases (BM).<h4>Methods</h4>Hemoglobin, white blood cell count, platelet count, serum albumin, creatinine, lactate dehydrogenase (LDH), and C-reactive protein (CRP) were assessed at diagnosis of BM in a discovery cohort of 1200 cancer patients. A multivariab ...[more]

PMID: 28096493

Similar Datasets

Project description:BackgroundMultiple myeloma (MM) is the second most common hematological malignancy, and the treatments markedly elevate the survival rate of the patients in recent years. However, the prevalence of cardiovascular adverse events (CVAEs) in MM had been increasing recently. CVAEs in MM patients are an important problem that we should focus on. Clinical tools for prognostication and risk-stratification are needed.Patients and methodsThis is a retrospective study that included patients who were newly diagnosed with multiple myeloma (NDMM) in Shanghai Changzheng Hospital and Affiliated Jinhua Hospital, Zhejiang University School of Medicine from June 2018 to July 2020. A total of 253 patients from two medical centers were divided into training cohort and validation cohort randomly. Univariable analysis of the baseline factors was performed using CVAEs endpoints. Multivariable analysis identified three factors for a prognostic model that was validated in internal validation cohorts.ResultsFactors independently associated with CVAEs in NDMM were as follows: age>61 years old, high level of baseline office blood pressure, and left ventricular hypertrophy (LVH). Age contributed 2 points, and the other two factors contributed 1 point to a prognostic model. The model distinguished the patients into three groups: 3-4 points, high risk; 2 points, intermediate risk; 0-1 point, low risk. These groups had significant difference in CVAEs during follow-up days in both training cohort (p<0.0001) and validation cohort (p=0.0018). In addition, the model had good calibration. The C-indexes for the prediction of overall survival of CVAEs in the training and validation cohorts were 0.73 (95% CI, 0.67-0.79) and 0.66 (95% CI, 0.51-0.81), respectively. The areas under the receiver operating characteristic curve (AUROCs) of the 1-year CVAEs probability in the training and validation cohorts were 0.738 and 0.673, respectively. The AUROCs of the 2-year CVAE probability in the training and validation cohorts were 0.722 and 0.742, respectively. The decision-curve analysis indicated that the prediction model provided greater net benefit than the default strategies of providing assessment or not providing assessment for all patients.ConclusionA prognostic risk prediction model for predicting CVAEs risk of NDMM patients was developed and internally validated. Patients at increased risk of CVAEs can be identified at treatment initiation and be more focused on cardiovascular protection in the treatment plan.

Project description:Purpose: The purpose of this study was to analyze the frequency and prognosis of pulmonary metastases in newly diagnosed gastric cancer using population-based data from SEER. Methods: Patients with gastric cancer and pulmonary metastases (GCPM) at the time of diagnosis in advanced gastric cancer were identified using the Surveillance, Epidemiology and End Result (SEER) database of the National Cancer Institute from 2010 to 2014. Multivariable logistic regression was performed to identify predictors of the presence of GCPM at diagnosis. Receiver operator characteristics analysis was performed to significant predictors on multivariable logistic regression and was then assessed with Delong's test. Multivariable Cox regression was developed to identify factors associated with all-cause mortality and gastric cancer-specific mortality. Survival curves were obtained according to the Kaplan-Meier method and compared using the log-rank test. Results: We identified 1,104 patients with gastric cancer and pulmonary metastases at the time of diagnosis, representing 6.02% of the entire cohort and 15.19% of the subset with metastatic disease to any distant site. Among the entire cohort, multivariable logistic regression identified six factors (younger, upper 1/3 of stomach, intestinal-type, T4 staging, N1 staging, and presence of more extrapulmonary metastases to liver, bone, and brain) as positive predictors of the presence of pulmonary metastases at diagnosis. The value of AUC for the multivariable logistic regression model was 0.775. Median survival among the entire cohort with GCPM was 3.0 months (interquartile range: 1.0-9.0 mo). Multivariable Cox model in SEER cohort confirmed five factors (diagnosis at previous period, black race, adverse pathology grade, absence of chemotherapy, and presence of more extrapulmonary metastases to liver, bone, and brain) as negative predictors for overall survival. Conclusions: The findings of this study provided population-based estimates of the frequency and prognosis for GCPM at time of diagnosis. The multivariable logistic regression model had an acceptable performance to predict the presence of PM. These findings may provide preventive guidelines for the screening and treatment of PM in GC patients. Patients with high risk factors should be paid more attention before and after diagnosis.

Project description:BackgroundEsophageal cancer is one of the most common cancer types, with its most common distant metastatic site being the lung. Currently, population-based data regarding the proportion and prognosis of patients with esophageal cancer with lung metastases (ECLM) at the time of diagnosis is insufficient. Therefore, we aimed to determine the proportion of patients with ECLM at diagnosis, as well as to investigate the prognostic parameters of ECLM.MethodsThis population-based observational study obtained data from the Surveillance, Epidemiology, and End Results (SEER) database registered between 2010 and 2016. Multivariable logistic regression was performed to identify predictors of the presence of ECLM at diagnosis. Multivariable Cox regression and competing risk analysis were used to assess prognostic factors in patients with ECLM. Median survival was estimated using Kaplan-Meier curves.ResultsOf 10,965 patients diagnosed with esophageal cancer between 2010 and 2016, 713 (6.50%) presented with initial lung metastasis at diagnosis. Lung metastasis represented 27.15% of all cases with metastatic disease to any distant site. Considering all patients with esophageal cancer, multivariable logistic regression indicated that pathology grade, pathology type, T staging, N staging, race, and number of extrapulmonary metastatic sites were predictive factors for the occurrence of lung metastases at diagnosis. The median survival time of patients with ECLM was 4.0 months. Patients receiving chemotherapy or chemoradiotherapy had the longest median overall survival, 7.0 months. Multivariable Cox regression indicated that age, histology type, T2 staging, number of extrapulmonary metastatic sites, and treatment (chemotherapy, radiotherapy, or chemoradiotherapy) were independent predictors for overall survival (OS). Multivariable competing risk analysis determined that age, number of extrapulmonary metastatic sites, and treatment were independent predictors for esophageal cancer-specific survival (CSS).ConclusionThe findings of this study may provide important information for the early diagnosis of ECLM, as well as aid physicians in choosing appropriate treatment regimens for these patients.

Dataset Information

Combining standard clinical blood values for improving survival prediction in patients with newly diagnosed brain metastases-development and validation of the LabBM score.

Background

Methods

Results

Conclusion

Publications

Combining standard clinical blood values for improving survival prediction in patients with newly diagnosed brain metastases-development and validation of the LabBM score.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets