scholarly journals Analysis of Systemic Inflammatory Factors and Survival Outcomes in Endometrial Cancer Patients Staged I-III FIGO and Treated with Postoperative External Radiotherapy

2020 ◽  
Vol 9 (5) ◽  
pp. 1441
Author(s):  
Katarzyna Holub ◽  
Fabio Busato ◽  
Sebastien Gouy ◽  
Roger Sun ◽  
Patricia Pautier ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
External Beam Radiotherapy ◽  
Univariate Analysis ◽  
External Validation ◽  
Inflammatory Factors ◽  
Lymphocyte Ratio ◽  
Risk Of Death ◽  
The Impact

Background: The causal link between elevated systemic inflammation biomarkers and poor survival has been demonstrated in cancer patients. However, the evidence for this correlation in endometrial cancer (EC) is too weak to influence current criteria of risk assessment. Here, we examined the role of inflammatory indicators as a tool to identify EC patients at higher risk of death in a retrospective observational study. Methods: A total of 155 patients surgically diagnosed with EC stage I-III FIGO 2009 and treated with postoperative External Beam Radiotherapy (EBRT) ± brachytherapy and chemotherapy according to ESMO-ESTRO-ESGO recommendation for patients at high risk of recurrence at the Gustave Roussy Institut, France, and Hospital Clínic, Spain, between 2008 and 2017 were evaluated. The impact of pre-treatment Neutrophil-to-Lymphocyte Ratio (NLR ≥ 2.2), Monocyte-to-Lymphocyte Ratio (MLR ≥ 0.18), Systemic Immune-Inflammatory Index (SII ≥ 1100) and lymphopenia (<1.0×109/L) on overall survival (OS), cancer-specific survival and progression-free survival was evaluated. Subsequently, a cohort of 142 patients within high-advanced risk groups according to ESMO-ESGO-ESTRO classification was evaluated. Results: On univariate analysis, NLR (HR = 2.2, IC 95% 1.1–4.7), SII (HR = 2.2, IC 95% 1.1–4.6), MLR (HR = 5.0, IC 95% 1.1–20.8) and lymphopenia (HR = 3.8, IC 95% 1.6–9.0) were associated with decreased OS. On multivariate analysis, NLR, MLR, SII and lymphopenia proved to be independent unfavorable prognostic factors. Conclusions: lymphopenia and lymphocytes-related ratio are associated with poorer outcome in surgically staged I-III FIGO EC patients classified as high risk and treated with adjuvant EBRT and could be considered at cancer diagnosis. External validation in an independent cohort is required before implementation for patients’ stratification.

scholarly journals The Prognostic Value of the New Combined Hemo-Eosinophil Inflammation Index (HEI Index): A Multicenter Analysis of Anal Cancer Patients Treated with Concurrent Chemo-Radiation

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 671
Author(s):  
Margherita Rimini ◽  
Pierfrancesco Franco ◽  
Berardino De Bari ◽  
Maria Giulia Zampino ◽  
Stefano Vagge ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Anal Cancer ◽  
Prognostic Score ◽  
Univariate Analysis ◽  
External Validation ◽  
Risk Groups ◽  
Anal Squamous Cell Carcinoma ◽  
Predictors Of Response ◽  
Risk Patients

Anal squamous cell carcinoma (SCC) is a rare tumor, and bio-humoral predictors of response to chemo-radiation (CT-RT) are lacking. We developed a prognostic score system based on laboratory inflammation parameters. We investigated the correlation between baseline clinical and laboratory variables and disease-free (DFS) and overall (OS) survival in anal SCC patients treated with CT-RT in five institutions. The bio-humoral parameters of significance were included in a new scoring system, which was tested with other significant variables in a Cox’s proportional hazard model. A total of 308 patients was included. We devised a prognostic model by combining baseline hemoglobin level, SII, and eosinophil count: the Hemo-Eosinophils Inflammation (HEI) Index. We stratified patients according to the HEI index into low- and high-risk groups. Median DFS for low-risk patients was not reached, and it was found to be 79.5 months for high-risk cases (Hazard Ratio 3.22; 95% CI: 2.04–5.10; p < 0.0001). Following adjustment for clinical covariates found significant at univariate analysis, multivariate analysis confirmed the HEI index as an independent prognostic factor for DFS and OS. The HEI index was shown to be a prognostic parameter for DFS and OS in anal cancer patients treated with CT-RT. An external validation of the HEI index is mandatory for its use in clinical practice.

scholarly journals Biofilm Production by Carbapenem-Resistant Klebsiella pneumoniae Significantly Increases the Risk of Death in Oncological Patients

2020 ◽  
Vol 10 ◽  
Author(s):  
Enea Gino Di Domenico ◽  
Ilaria Cavallo ◽  
Francesca Sivori ◽  
Francesco Marchesi ◽  
Grazia Prignano ◽  
...  
Keyword(s):  
High Risk ◽  
Klebsiella Pneumoniae ◽  
Univariate Analysis ◽  
Carbapenem Resistance ◽  
Morbidity And Mortality ◽  
Risk Of Death ◽  
Carbapenem Resistant ◽  
Biofilm Production ◽  
Oncological Patients ◽  
The Impact

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a prominent cause of nosocomial infections associated with high rates of morbidity and mortality, particularly in oncological patients. The hypermucoviscous (HMV) phenotype and biofilm production are key factors for CRKP colonization and persistence in the host. This study aims at exploring the impact of CRKP virulence factors on morbidity and mortality in oncological patients. A total of 86 CRKP were collected between January 2015 and December 2019. Carbapenem resistance-associated genes, antibiotic susceptibility, the HMV phenotype, and biofilm production were evaluated. The median age of the patients was 71 years (range 40–96 years). Clinically infected patients were 53 (61.6%), while CRKP colonized individuals were 33 (38.4%). The most common infectious manifestations were sepsis (43.4%) and pneumonia (18.9%), while rectal surveillance swabs were the most common site of CRKP isolation (81.8%) in colonized patients. The leading mechanism of carbapenem resistance was sustained by the KPC gene (96.5%), followed by OXA-48 (2.3%) and VIM (1.2%). Phenotypic CRKP characterization indicated that 55.8% of the isolates were strong biofilm-producers equally distributed between infected (54.2%) and colonized (45.8%) patients. The HMV phenotype was found in 22.1% of the isolates, which showed a significant (P&lt;0.0001) decrease in biofilm production as compared to non-HMV strains. The overall mortality rate calculated on the group of infected patients was 35.8%. In univariate analysis, pneumoniae significantly correlated with death (OR 5.09; CI 95% 1.08–24.02; P=0.04). The non-HMV phenotype (OR 4.67; CI 95% 1.13–19.24; P=0.03) and strong biofilm-producing strains (OR 5.04; CI95% 1.39–18.25; P=0.01) were also associated with increased CRKP infection-related mortality. Notably, the multivariate analysis showed that infection with strong biofilm-producing CRKP was an independent predictor of mortality (OR 6.30; CI 95% 1.392–18.248; P=0.004). CRKP infection presents a high risk of death among oncological patients, particularly when pneumoniae and sepsis are present. In infected patients, the presence of strong biofilm-producing CRKP significantly increases the risk of death. Thus, the assessment of biofilm production may provide a key element in supporting the clinical management of high-risk oncological patients with CRKP infection.

Comprehensive analysis of predictive biomarkers for patients with curative colorectal cancer patients and high risk of recurrence.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 607-607 ◽  
Author(s):  
Koichiro Mori ◽  
Yuji Toiyama ◽  
Susumu Saigusa ◽  
Tomofumi Noguchi ◽  
Shozo Ide ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
T Cells ◽  
High Risk ◽  
Predictive Factors ◽  
Univariate Analysis ◽  
Mononucleotide Repeat ◽  
Lymphocyte Ratio ◽  
Preoperative Serum ◽  
Molecular Phenotypes ◽  
The Impact

607 Background: Preoperative serum systemic inflammatory response (SIR) has been reported in patients with various cancers including colorectal cancer (CRC) as predictive biomarker of early recurrence. Molecular phenotypes of CRC including microsatellite instability (MSI) status and tumor infiltrating lymphocytes (TILs) have also known to be associated with recurrence in curative CRC patients. We comprehensively evaluated SIR status, MSI status and TILs in curative CRC patients and investigated which can be promising as high risk markers of recurrence in these patients. Methods: This retrospective study enrolled 157 CRC patients with stage I-III undergoing curative operation for whom preoperative neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR) and CRP were available as indicators of SIR status. For analysis of molecular phenotypes, we evaluated TILs by counting the number of intra-tumor Foxp3 and CD8 positive T cells by IHC analysis. Next, MSI status was determined in using 5 mononucleotide repeat microsatellite markers. Finally, we investigated the impact of SIR status, TILs and MSI status on the recurrence of these patients. Results: This study included a total of 90 males and 67 females, with an average age of 66.9 years. Twenty-nine patients (18.4%) developed recurrence. Kaplan-Meier analysis using SIR indicators revealed that patients with high CRP, NLR and PLR levels were significantly poorer disease free survival (DFS) than those with low levels. Low infiltrating CD8-positive T cells were significantly predictive factors for poor DFS, but there was no correlation between MSI status and recurrence, In univariate analysis, low infiltrating CD8-positive T cells, high CEA, CRP, NLR and PLR levels in serum were significantly predictive factors for poor DFS, respectively. Multivariate analysis showed that low infiltrating CD8-positive T cells and high serum CRP levels were independent predictive markers for recurrence in curative CRC patients. Conclusions: We demonstrated that evaluation of both CRP levels in preoperative serum and tumor infiltrating CD8 lymphocytes in CRC tissues are useful to predict the curative CRC patients with early relapse.

Renal function improves mortality prediction in acute pulmonary embolism: results of a multicentre cohort study with external validation in the RIETE registry

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
R Chopard ◽  
D Jimenez ◽  
G Serzian ◽  
F Ecarnot ◽  
N Falvo ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Renal Function ◽  
High Risk ◽  
Renal Dysfunction ◽  
External Validation ◽  
Mortality Prediction ◽  
Model Fit ◽  
Funding Source ◽  
Risk Of Death ◽  
Acute Pe

Abstract Background Renal dysfunction may influence outcomes after pulmonary embolism (PE). We determined the incremental value of adding renal function impairment (estimated glomerular filtration rate, eGFR &lt;60 ml/min/1.73m2) on top of the 2019 ESC prognostic model, for the prediction of 30-day all-cause mortality in acute PE patients from a prospective, multicenter cohort. Methods and results We identified which of three eGFR formulae predicted death most accurately. Changes in global model fit, discrimination, calibration and net reclassification index (NRI) were evaluated with addition of eGFR. We prospectively included consecutive adult patients with acute PE diagnosed as per ESC guidelines. Among 1,943 patients, (mean age 67.3±17.1, 50.4% women), 107 (5.5% (95% CI 4.5–6.5%)) died during 30-day follow-up. The eGFRMDRD4 formula was the most accurate for prediction of death. The observed mortality rate was higher for intermediate-low risk (OR 1.8, 95% CI 1.1–3.4) and high-risk PE (OR 10.3, 95% CI 3.6–17.3), and 30-day bleeding was significantly higher (OR 2.1, 95% CI 1.3–3.5) in patients with vs without eGFRMDRD4 &lt;60 ml/min/1.73m2. The addition of eGFRMDRD4 information improved model fit, discriminatory capacity, and calibration of the ESC models. NRI was significantly improved (p&lt;0.001), with 18% reclassification of predicted mortality, specifically in intermediate and high-risk PE. External validation using data from the RIETE registry confirmed our findings (Table). Conclusion Addition of eGFRMDRD4-derived renal dysfunction on top of the ESC prognostic algorithm yields significant reclassification of risk of death in intermediate and high-risk PE. Impact on therapy remains to be determined. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-Pfizer Alliance, Bayer Healthcare

scholarly journals Surgery for primary tumor benefits survival for breast cancer patients with bone metastases: a large cohort retrospective study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhangheng Huang ◽  
Xin Zhou ◽  
Yuexin Tong ◽  
Lujian Zhu ◽  
Ruhan Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Bone Metastases ◽  
Cancer Patients ◽  
Primary Tumor ◽  
Predictive Accuracy ◽  
Rank Test ◽  
Breast Cancer Patients ◽  
Risk Of Death ◽  
The Impact

Abstract Background The role of surgery for the primary tumor in breast cancer patients with bone metastases (BM) remains unclear. The purpose of this study was to determine the impact of surgery for the primary tumor in breast cancer patients with BM and to develop prognostic nomograms to predict the overall survival (OS) of breast cancer patients with BM. Methods A total of 3956 breast cancer patients with BM from the Surveillance, Epidemiology, and End Results database between 2010 and 2016 were included. Propensity score matching (PSM) was used to eliminate the bias between the surgery and non-surgery groups. The Kaplan-Meier analysis and the log-rank test were performed to compare the OS between two groups. Cox proportional risk regression models were used to identify independent prognostic factors. Two nomograms were constructed for predicting the OS of patients in the surgery and non-surgery groups, respectively. In addition, calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the performance of nomograms. Result The survival analysis showed that the surgery of the primary tumor significantly improved the OS for breast cancer patients with BM. Based on independent prognostic factors, separate nomograms were constructed for the surgery and non-surgery groups. The calibration and ROC curves of these nomograms indicated that both two models have high predictive accuracy, with the area under the curve values ≥0.700 on both the training and validation cohorts. Moreover, DCA showed that nomograms have strong clinical utility. Based on the results of the X-tile analysis, all patients were classified in the low-risk-of-death subgroup had a better prognosis. Conclusion The surgery of the primary tumor may provide survival benefits for breast cancer patients with BM. Furthermore, these prognostic nomograms we constructed may be used as a tool to accurately assess the long-term prognosis of patients and help clinicians to develop individualized treatment strategies.

A Case–Control Study Evaluating the Impact of Dedicated Palliative Care Training on Critical Care Interventions at the end of Life

2021 ◽  
pp. 082585972110374
Author(s):  
Jee Y. You ◽  
Lie D. Ligasaputri ◽  
Adarsh Katamreddy ◽  
Kiran Para ◽  
Elizabeth Kavanagh ◽  
...  
Keyword(s):  
Palliative Care ◽  
Critical Care ◽  
High Risk ◽  
End Of Life ◽  
Poor Prognosis ◽  
Medical Interventions ◽  
Risk Of Death ◽  
Before And After ◽  
The Difference ◽  
The Impact

Many patients admitted to intensive care units (ICUs) are at high risk of dying. We hypothesize that focused training sessions for ICU providers by palliative care (PC) certified experts will decrease aggressive medical interventions at the end of life. We designed and implemented a 6-session PC training program in communication skills and goals of care (GOC) meetings for ICU teams, including house staff, critical care fellows, and attendings. We then reviewed charts of ICU patients treated before and after the intervention. Forty-nine of 177 (28%) and 63 of 173 (38%) patients were identified to be at high risk of death in the pre- and postintervention periods, respectively, and were included based on the study criteria. Inpatient mortality (45% vs 33%; P = .24) and need for mechanical ventilation (59% vs 44%, P = .13) were slightly higher in the preintervention population, but the difference was not statistically significant. The proportion of patients in whom the decision not to initiate renal replacement therapy was made because of poor prognosis was significantly higher in the postintervention population (14% vs 67%, P = .05). There was a nonstatistically significant trend toward earlier GOC discussions (median time from ICU admission to GOC 4 vs 3 days) and fewer critical care interventions such as tracheostomies (17% vs 4%, P = .19). Our study demonstrates that directed PC training of ICU teams has a potential to reduce end of life critical care interventions in patients with a poor prognosis.

Implementing clinical decision support for oncology advanced care planning: A systems engineering framework to optimize the usability and utility of a machine learning predictive model in clinical practice.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 330-330
Author(s):  
Teja Ganta ◽  
Stephanie Lehrman ◽  
Rachel Pappalardo ◽  
Madalene Crow ◽  
Meagan Will ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Predictive Model ◽  
Systems Engineering ◽  
Care Planning ◽  
Learning Models ◽  
Predictive Tool ◽  
Risk Of Death ◽  
The Impact ◽  
Machine Learning Models

330 Background: Machine learning models are well-positioned to transform cancer care delivery by providing oncologists with more accurate or accessible information to augment clinical decisions. Many machine learning projects, however, focus on model accuracy without considering the impact of using the model in real-world settings and rarely carry forward to clinical implementation. We present a human-centered systems engineering approach to address clinical problems with workflow interventions utilizing machine learning algorithms. Methods: We aimed to develop a mortality predictive tool, using a Random Forest algorithm, to identify oncology patients at high risk of death within 30 days to move advance care planning (ACP) discussions earlier in the illness trajectory. First, a project sponsor defined the clinical need and requirements of an intervention. The data scientists developed the predictive algorithm using data available in the electronic health record (EHR). A multidisciplinary workgroup was assembled including oncology physicians, advanced practice providers, nurses, social workers, chaplain, clinical informaticists, and data scientists. Meeting bi-monthly, the group utilized human-centered design (HCD) methods to understand clinical workflows and identify points of intervention. The workgroup completed a workflow redesign workshop, a 90-minute facilitated group discussion, to integrate the model in a future state workflow. An EHR (Epic) analyst built the user interface to support the intervention per the group’s requirements. The workflow was piloted in thoracic oncology and bone marrow transplant with plans to scale to other cancer clinics. Results: Our predictive model performance on test data was acceptable (sensitivity 75%, specificity 75%, F-1 score 0.71, AUC 0.82). The workgroup identified a “quality of life coordinator” who: reviews an EHR report of patients scheduled in the upcoming 7 days who have a high risk of 30-day mortality; works with the oncology team to determine ACP clinical appropriateness; documents the need for ACP; identifies potential referrals to supportive oncology, social work, or chaplain; and coordinates the oncology appointment. The oncologist receives a reminder on the day of the patient’s scheduled visit. Conclusions: This workgroup is a viable approach that can be replicated at institutions to address clinical needs and realize the full potential of machine learning models in healthcare. The next steps for this project are to address end-user feedback from the pilot, expand the intervention to other cancer disease groups, and track clinical metrics.

scholarly journals The combination of serum NLR and D-dimer levels at diagnosis predicts overall survival in metastatic pancreatic cancer

2020 ◽  
Author(s):  
Pei Mei ◽  
Qiong Gong ◽  
Yu-Ping Rong ◽  
Jian Chang ◽  
Qi Fang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Group Performance ◽  
Univariate Analysis ◽  
Eastern Cooperative Oncology Group ◽  
Metastatic Pancreatic Cancer ◽  
Inflammatory Factors ◽  
Lymphocyte Ratio ◽  
Prognostic Predictors ◽  
D Dimer

Abstract Background Many studies have confirmed that the systemic inflammatory response and hypercoagulable state of the patient are related to the occurrence and development of various tumors, including pancreatic cancer. The aim of this research was to combine blood inflammatory factors and D-dimer into a new prognostic scoring system.Methods We conducted a retrospective cohort study of 73 patients with metastatic pancreatic cancer between January 2015 and December 2018 at our institution. To identify the prognostic predictors, circulating inflammatory cells and D-dimer were analyzed.Results Univariate analysis showed that the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), CA19-9, Eastern Cooperative Oncology Group performance status (ECOG PS) score and D-dimer levels were significantly associated with overall survival in patients with metastatic pancreatic cancer. Multivariate analysis suggested that only the NLR (p<0.026) and D-dimer level (p<0.012) were independent prognostic predictors. Then, we combined the NLR and D-dimer level to divide the cohort into three “NLRD” groups: “NLRD0”=NLR≤3.38 and D-dimer≤1.47, “NLRD1”=either NLR>3.38 or D-dimer>1.47, “NLRD2”=NLR>3.38 and D-dimer>1.47. Finally, we found that the NLRD2 group had the worst survival, with a median overall survival (OS) of 2 months (95%CI=1.450-2.550), while the NLRD0 group had the best outcome, with a median OS of 7 months (95%CI=5.897-8.121).Conclusions The scoring system combining the blood NLR with D-dimer levels provides important prognostic information for risk stratification in patients with metastatic pancreatic cancer and may help us identify patients who have a poor prognosis so that clinicians can develop personalized treatment strategies for these patients.

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