scholarly journals The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: A retrospective study in the SEER database

2021 ◽  
Author(s):  
Heng Shen ◽  
Gang Deng ◽  
Qianxue Chen ◽  
Jin Qian
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Brain Metastases ◽  
Treatment Strategies ◽  
Early Death ◽  
Gleason Grade ◽  
Related Factors ◽  
Prediction Ability ◽  
Short Period ◽  
Independent Risk Factors

Abstract Background Patients of lung cancer with synchronous brain metastases (LCBM) have a poor prognosis and die within a short period of time. However, little is known about the early mortality and related factors of LCBM patients. Methods Patients with LCBM diagnosed between 2010 and 2016 were enrolled from the surveillance, epidemiology, and end result (SEER). Significant independent prognostic factors were identified by univariate and multivariate logistic regression analyses. Nomograms of overall and cancer-specific early death were constructed using independent risk factors. The prediction ability and clinical application value of the model was verified by receiver operating characteristic (ROC) and decision curve analyses (DCAs). Results A total of 29902 cases of LCBM patients were enrolled in this study. 44.4% had early deaths, of which 38.2% died of lung cancer. Age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis, surgery, radiotherapy, chemotherapy and marital status were significant independent risk factors of overall and cancer-specific early death and was used to construct the nomogram. The areas under the curve (AUC) of the training group were 0.828 (95%CI: 0.822–0.833) and 0.800 (95%CI: 0.794–0.806) in the model of overall and cancer-specific early death, respectively. The DCA analysis showed that the model had good clinical benefits and utility Conclusions We established a comprehensive nomogram to distinguish early death in lung cancer patients with synchronous brain metastases which may help oncologists develop better treatment strategies, such as clinical trials and hospice care.

scholarly journals The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: a retrospective study in the SEER database

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Heng Shen ◽  
Gang Deng ◽  
Qianxue Chen ◽  
Jin Qian
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Brain Metastasis ◽  
Clinical Application ◽  
Roc Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Early Death ◽  
Gleason Grade ◽  
Seer Database ◽  
Prediction Ability

Abstract Background The prognosis of lung cancer with synchronous brain metastasis (LCBM) is very poor, and patients often die within a short time. However, little is known about the early mortality and related factors in patients with LCBM. Methods Patients diagnosed with LCBM between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate logistic regression analysis were used to identify significant independent prognostic factors, which were used to construct nomograms of overall and cancer-specific early death. Then, the prediction ability of the model was verified by receiver operating characteristic (ROC) curve. At last, the clinical application value of the model was tested through decision curve analysis (DCA). Results A total of 29,902 patients with LCBM were enrolled in this study. Among them, 13,275 (44.4%) patients had early death, and 11,425 (38.2%) cases died of lung cancer. The significant independent risk factors for overall and cancer-specific early death included age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status, which were used to construct the nomogram. The ROC curve demonstrated good predictive ability and clinical application value. The areas under the curve (AUC) of the training group was 0.793 (95% CI: 0.788–0.799) and 0.794 (95% CI: 0.788–0.799), in the model of overall and cancer-specific early death respectively. And the AUC of the validation group were 0.803 (95% CI: 0.788–0.818) and 0.806 (95% CI: 0.791–0.821), respectively. The calibration plots of the model showed that the predicted early death is consistent with the actual value. The DCA analysis indicated a good clinical application value of this model. Conclusions We established a comprehensive nomogram to predict early death in lung cancer patients with synchronous brain metastases. Nomograms may help oncologists develop better treatment strategies, such as clinical trials and hospice care.

scholarly journals Peripherally Inserted Central Catheter-Related Vein Thrombosis in Patients With Lung Cancer

2016 ◽  
Vol 23 (2) ◽  
pp. 181-186 ◽  
Author(s):  
Jun Ren Kang ◽  
Li Hai Long ◽  
Sun Wen Yan ◽  
Wang Wei Wei ◽  
Han Zhi Jun ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Advanced Disease ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Related Factors ◽  
Vein Thrombosis ◽  
Patients With Cancer ◽  
Logistic Analysis ◽  
Independent Risk Factors

Background: Peripherally inserted central venous catheters (PICCs) are widely used in patients with cancer. Catheter usage is one of the risk factors for venous thromboembolism. We aimed to scrutinize the incidence and risk factors for PICC-related upper extremity venous thrombosis (UEVT) in patients with lung cancer receiving chemotherapy. Patients and Methods: We performed a retrospective cohort study of patients with lung cancer with PICC insertion undergoing chemotherapy. Symptomatic PICC-UEVT was diagnosed by ultrasound. The relationship between chemotherapeutic agent exposure and PICC-UEVT was evaluated. Patient-, catheter-, and insertion-related factors were analyzed in univariable and multivariable logistic regression to identify significant independent risk factors for PICC-UEVT in patients with lung cancer. Results: A total of 328 patients with lung cancer having PICC undergoing chemotherapy were included, for a total of 34 895 catheter days. Seventeen (5.2%) patients developed PICC-related UEVT, with an incidence of 0.49 per 1000 catheter days. In multivariable logistic analysis, advanced disease was shown to be a significant risk factor for PICC-UEVT (odds ratio [OR]: 4.9; 95% confidence interval [CI]: 1.4-16.7; P = .011). Patients treated with etoposide had a higher risk of PICC-related UEVT (OR: 3.6; 95% CI: 1.1-12.1; P = .042). Patients were followed up after PICC removal for a median duration of 246 days. None of the patients developed pulmonary embolism. Conclusion: Patients with lung cancer harboring an advanced disease or treating with etoposide were at higher risk of PICC-UEVT.

scholarly journals Incidence and risk factors for deep venous thrombosis of lower extremity after surgical treatment of isolated patella fractures

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhanchao Tan ◽  
Hongzhi Hu ◽  
Xiangtian Deng ◽  
Jian Zhu ◽  
Yanbin Zhu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lower Extremity ◽  
Venous Thrombosis ◽  
Deep Venous Thrombosis ◽  
Medical Data ◽  
Limited Information ◽  
Related Factors ◽  
Patella Fractures ◽  
Independent Risk Factors ◽  
Associated Risk Factors

Abstract Background Limited information exists on the incidence of postoperative deep venous thromboembolism (DVT) in patients with isolated patella fractures. The objective of this study was to investigate the postoperative incidence and locations of deep venous thrombosis (DVT) of the lower extremity in patients who underwent isolated patella fractures and identify the associated risk factors. Methods Medical data of 716 hospitalized patients was collected. The patients had acute isolated patella fractures and were admitted at the 3rd Hospital of Hebei Medical University between January 1, 2016, and February 31, 2019. All patients met the inclusion criteria. Medical data was collected using the inpatient record system, which included the patient demographics, patient’s bad hobbies, comorbidities, past medical history, fracture and surgery-related factors, hematological biomarkers, total hospital stay, and preoperative stay. Doppler examination was conducted for the diagnosis of DVT. Univariate analyses and multivariate logistic regression analyses were used to identify the independent risk factors. Results Among the 716 patients, DVT was confirmed in 29 cases, indicating an incidence of 4.1%. DVT involved bilateral limbs (injured and uninjured) in one patient (3.4%). DVT involved superficial femoral common vein in 1 case (3.4%), popliteal vein in 6 cases (20.7%), posterior tibial vein in 11 cases (37.9%), and peroneal vein in 11 cases (37.9%). The median of the interval between surgery and diagnosis of DVT was 4.0 days (range, 1.0-8.0 days). Six variables were identified to be independent risk factors for DVT which included age category (> 65 years old), OR, 4.44 (1.34-14.71); arrhythmia, OR, 4.41 (1.20-16.15); intra-operative blood loss, OR, 1.01 (1.00-1.02); preoperative stay (delay of each day), OR, 1.43 (1.15-1.78); surgical duration, OR, 1.04 (1.03-1.06); LDL-C (> 3.37 mmol/L), OR, 2.98 (1.14-7.76). Conclusion Incidence of postoperative DVT in patients with isolated patella fractures is substantial. More attentions should be paid on postoperative DVT prophylaxis in patients with isolated patella fractures. Identification of associated risk factors can help clinicians recognize the risk population, assess the risk of DVT, and develop personalized prophylaxis strategies.

scholarly journals Chemotherapy-induced nausea and vomiting (CINV) with carboplatin plus pemetrexed or carboplatin plus paclitaxel in patients with lung cancer: a propensity score-matched analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Toshinobu Hayashi ◽  
Mototsugu Shimokawa ◽  
Koichi Matsuo ◽  
Hirotoshi Iihara ◽  
Kei Kawada ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Propensity Score ◽  
Nausea And Vomiting ◽  
Lung Cancer Patients ◽  
Pooled Data ◽  
Female Sex ◽  
Antiemetic Prophylaxis ◽  
Delayed Nausea ◽  
Independent Risk Factors

Abstract Background Patients with lung cancer who are treated with carboplatin-based chemotherapy regimens often experience chemotherapy-induced nausea and vomiting (CINV). However, knowledge on the effect of regimen and cofactors on the risk of CINV is limited. This study aimed to analyze and compare the incidence of CINV between lung cancer patients undergoing carboplatin plus pemetrexed (CBDCA+PEM) and those undergoing carboplatin plus paclitaxel (CBDCA+PTX) chemotherapy. Methods Pooled data of 240 patients from two prospective observational studies were compared using propensity score matching. Separate multivariate logistic regression analyses were used to identify risk factors for nausea and vomiting following chemotherapy. Results Delayed nausea was significantly more common in patients treated with CBDCA+PEM than in those treated with CBDCA+PTX (51.1% vs. 36.2%, P = 0.04), but the incidence of vomiting did not significantly differ between the two groups (23.4% vs. 14.9%, P = 0.14). The occurrence of CINV peaked on day 4 in the CBDCA+PTX group and on day 5 in the CBDCA+PEM group. Multivariate analysis showed that female sex, younger age, and CBDCA+PEM regimen were independent risk factors for delayed nausea, while female sex was an independent risk factor for delayed vomiting. Conclusions The CBDCA + PEM regimen has a higher risk of causing delayed nausea than the CBDCA + PTX regimen, and aggressive antiemetic prophylaxis should be offered to patients treated with CBDCA + PEM.

scholarly journals Clinical Significance of CYFRA21-1, AFP, CA-153, CEA, and CA-199 in the Diagnosis of Lung Cancer Ocular Metastasis in Hypertension Population

2021 ◽  
Vol 8 ◽  
Author(s):  
Jing Tang ◽  
Qian-Min Ge ◽  
Rong Huang ◽  
Hui-Ye Shu ◽  
Ting Su ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Logistic Regression ◽  
Sensitivity And Specificity ◽  
Cancer Metastasis ◽  
Binary Logistic Regression ◽  
Diagnostic Value ◽  
Ca 125 ◽  
Pathological Type ◽  
Independent Risk Factors

Purpose: To detect lung metastases, we conducted a retrospective study to improve patient prognosis.Methods: Hypertension patients with ocular metastases (OM group; n = 58) and without metastases (NM group; n = 1,217) were selected from individuals with lung cancer admitted to our hospital from April 2005 to October 2019. The clinical characteristics were compared by Student's t-test and chi-square test. Independent risk factors were identified by binary logistic regression, and their diagnostic value evaluated by receiver operating characteristic curve analysis.Results: Age and sex did not differ significantly between OM and NM groups; There were significant differences in pathological type and treatment. Adenocarcinoma was the main pathological type in the OM group (67.24%), while squamous cell carcinoma was the largest proportion (46.43%) in the NM group, followed by adenocarcinoma (34.10%). The OM group were treated with chemotherapy (55.17%), while the NM group received both chemotherapy (39.93%) and surgical treatment (37.06%). Significant differences were detected in the concentrations of cancer antigen (CA)−125, CA-199, CA-153, alpha fetoprotein (AFP), carcinoembryonic antigen (CEA), cytokeratin fraction 21-1 (CYFRA21-1), total prostate-specific antigen, alkaline phosphatase, and hemoglobin (Student's t-test). Binary logistic regression analysis indicated that CA-199, CA-153, AFP, CEA, and CYRFA21-1 were independent risk factors for lung cancer metastasis. AFP (98.3%) and CEA (89.3%) exhibited the highest sensitivity and specificity, respectively, while CYRFA21-1 had the highest area under the ROC curve value (0.875), with sensitivity and specificity values of 77.6 and 87.0%, respectively. Hence, CYFRA21-1 had the best diagnostic value.

scholarly journals Clinicopathological Characteristics and Prognosis in Endometrial Cancer With Bone Metastasis: A SEER-Based Study of 584 Women

2021 ◽  
Vol 11 ◽  
Author(s):  
Hejia Hu ◽  
Zhan Wang ◽  
Miaofeng Zhang ◽  
Feng Niu ◽  
Qunfei Yu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Endometrial Cancer ◽  
Bone Metastasis ◽  
Brain Metastases ◽  
Systemic Chemotherapy ◽  
Tumor Grade ◽  
Seer Database ◽  
Tumor Subtype ◽  
Local Surgery ◽  
Independent Risk Factors

PurposeBone metastasis from endometrial cancer (EC) is rare and poorly described. The purpose of the present study was to investigate the correlation between the clinically accessible factors and survival time among EC patients with bone metastasis.Patients and MethodsWe retrospectively identified and reviewed EC patients with bone metastasis from 2010 to 2016, based on the Surveillance, Epidemiology and End Results (SEER) database. Univariable and multivariable Cox regressions were applied to evaluate the effects of clinical variables on survival. Kaplan–Meier plots were used to visually demonstrate the correlation between independent risk factors and survival.ResultsClinical data of 584 EC patients with bone metastasis from the SEER database were analyzed. EC patients with bone metastasis experienced extremely poor survival, with 1-year overall survival (OS) and cancer-specific survival (CSS) rates 33.8 and 35.8%, respectively. Variables associated with OS and CSS in the univariable analysis included race, tumor grade, tumor subtype, tumor size, lung, liver and brain metastases, surgery, radiotherapy, and chemotherapy. In the multivariable analysis, tumor grade, tumor subtype, liver and brain metastases, local surgery, and systemic chemotherapy remained independent risk factors for OS and CSS. However, local radiotherapy was an independent predictor of OS, not CSS.ConclusionsWe identified several factors affect the survival of EC patients with bone metastasis, which is useful for clinicians to assess patients’ outcomes. Our study supports surgery and radiotherapy of primary EC, and systemic chemotherapy for prolonging survival among EC patients with bone metastasis, which lays a solid foundation for defining optimal treatment strategy in this specific cohort.

scholarly journals Prognostic significance of advanced lung cancer inflammation index (ALI) in multiple myeloma patients – a retrospective study

2021 ◽  
Author(s):  
Junxia Huang ◽  
Juanjuan Hu ◽  
Yan Gao ◽  
Fanjun Meng ◽  
Tianlan Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Multiple Myeloma ◽  
Overall Survival ◽  
Progression Free Survival ◽  
Advanced Lung Cancer ◽  
Free Survival ◽  
Factors Influencing ◽  
Independent Risk Factors ◽  
Cancer Inflammation

Abstract Background: Advanced lung cancer inflammation index (ALI) is known to predict the overall survival of patients having some solid tumors or B-cell lymphoma. The study investigates the predictive value of ALI in multiple myeloma (MM) patients and the correlation between ALI and prognosis.Methods: A database of 269 MM consecutive patients who underwent chemotherapy between December 2011 and June 2019 in the Affiliated Hospital of Qingdao University was reviewed. ALI cut-off value calculated before the initial chemotherapy and post 4 courses treatment were identified according to the receiver operating characteristic (ROC) curve, and its association with clinical characteristics, treatment response, overall survival (OS), and progression-free survival (PFS) were assessed.Results: Patients in the low ALI group (n=147) had higher risk of β2 microglobulin elevation, more advanced ISS (International Classification System stage), and TP53 gene mutation, with significantly lower median overall survival (OS; 36.29 vs. 57.92 months, P = 0.010) and progression-free survival (PFS; 30.94 vs. 35.67 months, P = 0.013). Independent risk factors influencing the OS of MM patients were ALI (P = 0.007), extramedullary infiltration (P = 0.001), TP53 (P = 0.020), Plt (P = 0.005), and bone destruction (P = 0.024). ALI (P = 0.005), extramedullary infiltration (P = 0.004), TP53 (P = <0.001), Plt (P = 0.017), and complex chromosome karyotype (P = 0.010) were independent risk factors influencing the PFS of MM patients.Conclusions: ALI is a potential independent risk factor predicting the prognosis of newly diagnosed MM patients.

scholarly journals Risk Scores and Machine Learning to Identify Patients With Acute Periprosthetic Joints Infections That Will Likely Fail Classical Irrigation and Debridement

2021 ◽  
Vol 8 ◽  
Author(s):  
Marjan Wouthuyzen-Bakker ◽  
Noam Shohat ◽  
Javad Parvizi ◽  
Alex Soriano
Keyword(s):  
Machine Learning ◽  
Joint Infection ◽  
Treatment Strategies ◽  
Risk Scores ◽  
Related Factors ◽  
Duration Of Symptoms ◽  
Treatment Regimens ◽  
Irrigation And Debridement ◽  
Individualize Treatment ◽  
Independent Risk Factors

The most preferred treatment for acute periprosthetic joint infection (PJI) is surgical debridement, antibiotics and retention of the implant (DAIR). The reported success of DAIR varies greatly and depends on a complex interplay of several host-related factors, duration of symptoms, the microorganism(s) causing the infection, its susceptibility to antibiotics and many others. Thus, there is a great clinical need to predict failure of the “classical” DAIR procedure so that this surgical option is offered to those most likely to succeed, but also to identify those patients who may benefit from more intensified antibiotic treatment regimens or new and innovative treatment strategies. In this review article, the current recommendations for DAIR will be discussed, a summary of independent risk factors for DAIR failure will be provided and the advantages and limitations of the clinical use of preoperative risk scores in early acute (post-surgical) and late acute (hematogenous) PJIs will be presented. In addition, the potential of implementing machine learning (artificial intelligence) in identifying patients who are at highest risk for failure of DAIR will be addressed. The ultimate goal is to maximally tailor and individualize treatment strategies and to avoid treatment generalization.

scholarly journals Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

2020 ◽  
Author(s):  
Tao Fan ◽  
Bo Hao ◽  
Shuo Yang ◽  
Bo Shen ◽  
Zhixin Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Disease Onset ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Calibration Curves ◽  
Independent Risk Factors ◽  
Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (<i>P</i>&lt;.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; <i>P</i>&lt;.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; <i>P</i>=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; <i>P</i>=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, <i>P</i>&lt;.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; <i>P</i>&lt;.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; <i>P</i>=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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