Prognostic evaluation models for primary thyroid lymphoma, based on the SEER database and an external validation cohort

Abstract Purpose Primary thyroid lymphoma (PTL) is a rare malignancy, and the literature is limited to small case series and case reports. This study aimed to assess the epidemiologic characteristics, survival, and prognostic factors of patients with PTL. Methods We analyzed 2215 PTL patients from the Surveillance, Epidemiology, and End Results database medical records, between 1983 and 2015, as the training cohort. We enrolled 105 patients from the Cancer Hospital, Chinese Academy of Medical Sciences, for the external validation cohort. The nomograms for predicting the 1-, 5-, and 10-year overall survival (OS) and lymphoma-specific survival (LSS) were constructed. Results PTL incidence steadily increased from 1977 to 1994, with an annual percentage change of 3.2% (95% confidence interval [CI]: 1.2–5.2, P < 0.05). The 1-, 5-, and 10-year OS and LSS rates were 84.66%, 71.61%, and 55.95%; and 90.5%, 85.7%, and 82.2%, respectively. Multivariate Cox regression analysis revealed that shorter OS association with age ≥ 60 years (hazard ratio [HR], 3.94; 95% CI 3.31–4.69; P < 0.001), unmarried status (HR, 1.55; 95% CI 1.37–1.75; P < 0.001), Ann Arbor stage III-IV (HR, 1.55; 95% CI 1.37–1.75; P = 0.020), diffuse large B-cell lymphoma (HR, 2.60; 95% CI 1.15–5.87; P = 0.022), and T cell non–Hodgkin lymphoma (HR, 3.53; 95% CI 1.12–11.10; P = 0.031). In the multivariate competing-risk analyzes, age, stages III-IV, year of diagnosis, surgery, radiation, chemotherapy, and histology were strongly predictive of PTL-specific risk of death. To estimate the 1-, 5-, and 10-year LSS and OS rates, respectively, nomograms were built. In the validation cohort, the results also confirmed the utility. Conclusions This study presents the first prognostic model with an external validation that could help clinicians identify patients with high-risk PTL to improve their prognosis.

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Effect of Aging-Related Genes on the Prognosis of Colon Cancer

10.21203/rs.3.rs-984927/v1 ◽

2021 ◽

Author(s):

Yushu Liu ◽

Jiantao Gong ◽

Yanyi Huang ◽

Qunguang Jiang

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Clinical Characteristics ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Predictive Ability ◽

Lasso Regression ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

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Development and Evaluation of Prognostic Nomogram for Patients with Differentiated Thyroid Carcinoma

10.21203/rs.3.rs-60170/v1 ◽

2020 ◽

Author(s):

Guochao Mao ◽

Shuai Lin ◽

Zhangjian Zhou

Keyword(s):

Thyroid Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Predictive Accuracy ◽

External Validation ◽

Survival Rates ◽

Differentiated Thyroid Carcinoma ◽

Cox Regression Analysis ◽

Discrimination Ability ◽

Potential Risk Factors

Abstract Background: Papillary thyroid carcinoma and follicular thyroid carcinoma are both well-differentiated thyroid carcinomas. Here, we aimed to establish and evaluate a nomogram for patients with differentiated thyroid cancer.Methods: Patient records were available from SEER database. We enrolled 17,659 patients in total and randomly separated them into a modeling cohort (n = 12,363, 70%) and a validation cohort (n = 5,296, 30%). Predictive models were established via univariate and multivariate Cox regression analysis of potential risk factors and used to produce a nomogram. Performance of the nomograms in terms of discrimination ability and calibration was evaluated by determining the concordance index (C-index) and by generating calibration plots, respectively, using the internal (modeling cohort) and external (validation cohort) validity.Results: Seven independent prognostic factors (age, race, sex, grade, AJCC T stage, AJCC N stage, and AJCC M stage) were identified and used to develop the nomogram for OS prediction of patients with DTC. The C-index for the modeling cohort was 0.829 (95% CI: 0.807-0.851), and the C-index for the validation cohort was 0.833 (95% CI: 0.803-0.862). Calibration plots of the nomogram indicated acceptable agreement between the predicted 3-, 5-year survival rates and the actual observations in the modeling and validation groups.Conclusions: We have constructed and verified a nomogram containing clinical factors, which showed better prognostic judgment and predictive accuracy for DTC. This will enable clinicians and patients to easily personalize and quantify the probability of DTC during the postoperative period.

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P785Unplanned readmissions after discharge increases risk of death in acute dyspnoea patients: non-cardiac is as severe as cardiac causes

European Heart Journal ◽

10.1093/eurheartj/ehz747.0384 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

K Cerlinskaite ◽

A Mebazaa ◽

R Cinotti ◽

D N Wussler ◽

E Gayat ◽

...

Keyword(s):

Emergency Department ◽

Cox Regression ◽

Validation Cohort ◽

P Value ◽

Unplanned Readmission ◽

Cox Regression Analysis ◽

Derivation Cohort ◽

Risk Of Death ◽

Acute Dyspnoea ◽

Increased Risk

Abstract Introduction Acute dyspnoea is a major reason for admission to the emergency department and has been associated with high rates of readmission and mortality. However, the association of readmission with mortality risk has not been widely studied in patients with acute dyspnoea. Purpose To determine whether unplanned readmission during first 6 months after discharge is associated with greater risk of death within 1 year in patients with acute dyspnoea. Methods Derivation cohort consisted of 1371 patients from the prospective observational study, which enrolled acute dyspnoea patients admitted to emergency departments of two university centres from 2015 to 2017 and discharged alive from the hospital. Cox regression analysis compared 1-year risk of death between readmitted vs. non-readmitted patients in the first 6 months after discharge. In addition, we studied this association in 1986 patients from a multicentre validation cohort, which included acute dyspnoea patients from 2006 to 2014. Sensitivity analysis was done in the subgroups divided by cause of index admission (acute heart failure [AHF] and non-AHF) and cause of the first readmission (cardiovascular [CV] or non-CV). The statistical analyses were performed using R statistical software. P value of <0.05 was considered statistically significant. Results In the derivation cohort 666 (49%) of patients were readmitted at 6 months and 282 (21%) died in 1 year. Readmitted patients died more frequently than non-readmitted patients (211 [32%] vs. 71 [10%], respectively, p<0.001). All-cause 6-month readmission was associated with an increased 1-year risk of death in a multivariate analysis in both the derivation cohort (adjusted hazard ratio (aHR) 3 [95% confidence interval (CI) 2.2–4], p<0.001) and the validation cohort (aHR 1.8 [95% CI 1.4–2.2], p<0.001). Moreover, deleterious effect of readmission on 1-year survival was equally observed in AHF and non-AHF patients, independent of whether the reason of first readmission was cardiovascular or non-CV, in both study cohorts. The results are displayed in Figure 1. Figure 1. Main results of the study Conclusions Our data demonstrates that readmission is associated with a markedly increased risk of death within 1 year in patients presenting to the emergency department with acute dyspnoea. Furthermore, the detrimental relationship between outcomes is similar in non-cardiac and cardiac causes. Acknowledgement/Funding The work was supported by the Research Council of Lithuania, grant Nr. MIP-049/2015 and approved by Lithuanian Bioethics Committee, Nr. L-15-01.

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Predictive Significance of the Prognostic Nutritional Index (PNI) in Patients with Severe COVID-19

Journal of Immunology Research ◽

10.1155/2021/9917302 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Wei Wei ◽

Xingyue Wu ◽

Chaoyuan Jin ◽

Tong Mu ◽

Guorong Gu ◽

...

Keyword(s):

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Prognostic Nutritional Index ◽

Internal Validation ◽

Cox Regression Analysis ◽

Neutrophil Lymphocyte Ratio ◽

Nutritional Index ◽

Risk Of Mortality ◽

Time Of Admission

Background. The prognostic nutritional index (PNI) has been reported to significantly correlate with poor survival and postoperative complications in patients with various diseases, but its relationship with mortality in COVID-19 patients has not been addressed. Method. A multicenter retrospective study involving patients with severe COVID-19 was conducted to investigate whether malnutrition and other clinical characteristics could be used to stratify the patients based on risk. Results. A total of 395 patients were included in our study, with 236 patients in the training cohort, 59 patients in the internal validation cohort, and 100 patients in the external validation cohort. During hospitalization, 63/236 (26.69%) and 14/59 (23.73%) patients died in the training and validation cohorts, respectively. PNI had the strongest relationships with the neutrophil-lymphocyte ratio (NLR) and lactate dehydrogenase (LDH) level but was less strongly correlated with the CURB65, APACHE II, and SOFA scores. The baseline PNI score, platelet (PLT) count, LDH level, and PaO2/FiO2 (P/F) ratio were independent predictors of mortality in COVID-19 patients. A nomogram incorporating these four predictors showed good calibration and discrimination in the derivation and validation cohorts. A PNI score less than 33.405 was associated with a higher risk of mortality in severe COVID-19 patients in the Cox regression analysis. Conclusion. These findings have implications for predicting the risk of mortality in COVID-19 patients at the time of admission and provide the first direct evidence that a lower PNI is related to a worse prognosis in severe COVID-19 patients.

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Identification and Validation of Immune-Related Gene Prognostic Signature for breast cancer

10.21203/rs.3.rs-386385/v1 ◽

2021 ◽

Author(s):

Bing Zhou ◽

Linyi Zhang ◽

Qinyu Wang ◽

Changqin Pu ◽

Sixuan Guo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Enrichment Analysis ◽

Risk Groups ◽

Prognostic Signature ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Background Although the outcome of breast cancer patients has been improved by advances in early detection, diagnosis and treatment. Due to the heterogeneity of the disease, prognostic assessment still faces challenges. The accumulated data indicate that there is a clear correlation between the tumor immune microenvironment and clinical outcomes. Objective Construct immune-related gene pairs to evaluate the prognosis of breast cancer and patient survival rate. Methods From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, the Gene expression profiles and clinical data of breast cancer samples were downloaded. TCGA cohort were further divided into a training set (n = 764) and internal validation sets (n = 325). The GEO cohort was analyzed as an external validation cohort (n = 327). In the training set, differently expressed immune-relevant genes (IRGs) were screened firstly, and they were used to construct immune-relevant gene pairs (IRGPs). Then, the prognostic IRGPs were identified via univariate Cox regression analysis. Finally, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to constituted the IRGP prognostic signature. Kaplan-Meier (KM) survival curves, receiver operating characteristic (ROC) curve analysis, univariate and multivariate Cox regression analysis were used to estimate the predictive value of the IRGP prognostic signature. And the IRGP prognostic signature was validated in the internal validation cohort and external validation cohort. We used gene set enrichment analysis (GSEA) to elucidate the biological functions of the IRGP prognostic signature. Results A total of 474 differently expressed IRGs and 2942 prognostic IRGPs were identified. Finally, we generated a IRGP prognostic signature consisting of 33 IRGPs. Subsequently, the 33 IRGPs grouped BRCA patients into high- and low-risk groups. Kaplan-Meier curves shown a significantly different overall survival in risk groups. Time-dependent ROC curves indicated that the IRGP prognostic signature possessed a high specificity and sensitivity in all the sets. Univariate and multivariate Cox regression analysis showed a statistical significance for the prognostic value of IRGP prognostic signature and the IRGP prognostic signature was a strong independent risk factor. The functional enrichment analysis indicated that low IRGP value was correlated with biological processes related to immune. Immune cell infiltration analysis indicated a significant difference in percentage of M2 macrophages between high- and low-risk groups. Conclusion The 33-IRGPs prognostic signature was developed to provide new insights for the identification of high-risk breast cancer and the evaluation of the possibility of immunotherapy in personalized breast cancer treatment.

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Possible unrecognised liver injury is associated with mortality in critically ill COVID-19 patients

Therapeutic Advances in Gastroenterology ◽

10.1177/17562848211023410 ◽

2021 ◽

Vol 14 ◽

pp. 175628482110234

Author(s):

Mario Romero-Cristóbal ◽

Ana Clemente-Sánchez ◽

Patricia Piñeiro ◽

Jamil Cedeño ◽

Laura Rayón ◽

...

Keyword(s):

Liver Disease ◽

Liver Fibrosis ◽

Liver Injury ◽

Chronic Liver Disease ◽

Critically Ill ◽

Cox Regression ◽

Chronic Liver ◽

Cox Regression Analysis ◽

Risk Of Death ◽

The Impact

Background: Coronavirus disease (COVID-19) with acute respiratory distress syndrome is a life-threatening condition. A previous diagnosis of chronic liver disease is associated with poorer outcomes. Nevertheless, the impact of silent liver injury has not been investigated. We aimed to explore the association of pre-admission liver fibrosis indices with the prognosis of critically ill COVID-19 patients. Methods: The work presented was an observational study in 214 patients with COVID-19 consecutively admitted to the intensive care unit (ICU). Pre-admission liver fibrosis indices were calculated. In-hospital mortality and predictive factors were explored with Kaplan–Meier and Cox regression analysis. Results: The mean age was 59.58 (13.79) years; 16 patients (7.48%) had previously recognised chronic liver disease. Up to 78.84% of patients according to Forns, and 45.76% according to FIB-4, had more than minimal fibrosis. Fibrosis indices were higher in non-survivors [Forns: 6.04 (1.42) versus 4.99 (1.58), p < 0.001; FIB-4: 1.77 (1.17) versus 1.41 (0.91), p = 0.020)], but no differences were found in liver biochemistry parameters. Patients with any degree of fibrosis either by Forns or FIB-4 had a higher mortality, which increased according to the severity of fibrosis ( p < 0.05 for both indexes). Both Forns [HR 1.41 (1.11–1.81); p = 0.006] and FIB-4 [HR 1.31 (0.99–1.72); p = 0.051] were independently related to survival after adjusting for the Charlson comorbidity index, APACHE II, and ferritin. Conclusion: Unrecognised liver fibrosis, assessed by serological tests prior to admission, is independently associated with a higher risk of death in patients with severe COVID-19 admitted to the ICU.

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Fast-Track Programs in Total Hip and Knee Replacement at Swedish Hospitals—Influence on 2-Year Risk of Revision and Mortality

Journal of Clinical Medicine ◽

10.3390/jcm10081680 ◽

2021 ◽

Vol 10 (8) ◽

pp. 1680

Author(s):

Urban Berg ◽

Annette W-Dahl ◽

Anna Nilsdotter ◽

Emma Nauclér ◽

Martin Sundberg ◽

...

Keyword(s):

Fast Track ◽

Cox Regression ◽

Cox Regression Analysis ◽

Total Hip ◽

Total Knee Replacements ◽

Risk Of Death ◽

Kaplan Meier ◽

Increased Risk ◽

Hip And Knee Arthroplasty ◽

Total Knee

Purpose: We aimed to study the influence of fast-track care programs in total hip and total knee replacements (THR and TKR) at Swedish hospitals on the risk of revision and mortality within 2 years after the operation. Methods: Data were collected from the Swedish Hip and Knee Arthroplasty Registers (SHAR and SKAR), including 67,913 THR and 59,268 TKR operations from 2011 to 2015 on patients with osteoarthritis. Operations from 2011 to 2015 Revision and mortality in the fast-track group were compared with non-fast-track using Kaplan–Meier survival analysis and Cox regression analysis with adjustments. Results: The hazard ratio (HR) for revision within 2 years after THR with fast-track was 1.19 (CI: 1.03–1.39), indicating increased risk, whereas no increased risk was found in TKR (HR 0.91; CI: 0.79–1.06). The risk of death within 2 years was estimated with a HR of 0.85 (CI: 0.74–0.97) for TKR and 0.96 (CI: 0.85–1.09) for THR in fast-track hospitals compared to non-fast-track. Conclusions: Fast-track programs at Swedish hospitals were associated with an increased risk of revision in THR but not in TKR, while we found the mortality to be lower (TKR) or similar (THR) as compared to non-fast track.

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AACE2021-A-1033: Primary Thyroid Lymphoma: Case Series of Patients from a Developing Country

Endocrine Practice ◽

10.1016/j.eprac.2021.11.047 ◽

2021 ◽

Vol 27 (12) ◽

pp. S32

Author(s):

Sajjad Ali Khan ◽

Abdul Aziz ◽

Dania Ali ◽

Najmul Islam

Keyword(s):

Developing Country ◽

Case Series ◽

Thyroid Lymphoma ◽

Primary Thyroid Lymphoma

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

Frontiers in Oncology ◽

10.3389/fonc.2021.754937 ◽

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 >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 < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 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.

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Incidence and Prognostic Factors of Primary Thyroid Lymphoma and Construction of Prognostic Models for Post-chemotherapy and Postoperative Patients: A Population-based Study

10.21203/rs.3.rs-114469/v1 ◽

2020 ◽

Author(s):

Nan Xiang ◽

Fangyuan Dong ◽

Xuebing Zhan ◽

Shuhan Wang ◽

Junjie Wang ◽

...

Keyword(s):

Prognostic Factors ◽

Prediction Models ◽

Cox Regression ◽

Prognostic Models ◽

Steady Increase ◽

Thyroid Lymphoma ◽

Annual Percent Change ◽

Cancer Specific Survival ◽

Good Reliability ◽

Primary Thyroid Lymphoma

Abstract Background: Primary thyroid lymphoma (PTL) is a rare thyroid malignancy, there are few large sample studies on PTL and no standardized treatment regimen has been established due to the rarity. Objective: The aims of this study were to explore the incidence and prognostic factors of PTL and construct visual prognostic prediction models for post-chemotherapy and postoperative patients.Methods: The incidence of PTL in 1975-2017 was extracted from the US Surveillance, Epidemiology, and End Results (SEER) database, then assessed using joinpoint regression software. A total of 1,616 eligible PTL patients diagnosed in 1998-2016 were brought into prognostic analysis. Multivariate Cox regression analyses were carried out to reveal independent prognostic elements for overall survival (OS) and cancer-specific survival (CSS).Results: PTL incidence showed a relatively steady increase in 1975-1994, which annual percent change (APC) was 4.0%, and steady decreasing in 1994-2017(APC -2.4%). Age, marital status, lymphoma Ann Arbor stage, histological subtypes, surgery, chemotherapy, and radiation were significantly correlated to OS and CSS. The combination of radiotherapy with chemotherapy or surgery was beneficial to the prognosis of patients. Nomograms were constructed to predict OS and CSS in post-chemotherapy and postoperative PTL patients separately, and were verified to have good reliability.Conclusions: The incidence of PTL increased and subsequently decreased. We revealed the prognostic implications and constructed reliable nomograms for post-chemotherapy and postoperative PTL patients.

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