Development and Validation of a Risk Score for Predicting Death after Pneumonectomy

Pneumonectomy is associated with significant postoperative mortality. This study was undertaken to develop and validate a risk model of mortality following pneumonectomy. We reviewed our prospective database and identified 774 pneumonectomies from a total of 7792 consecutive anatomical lung resections in the years 2003 to 2010 (rate of pneumonectomy: 9.9%). Based on data from 542 pneumonectomies between 2003 and 2007 (i.e., the "discovery set"), a penalized multivariable logistic regression analysis was performed to identify preoperative risk factors. A risk model was developed and validated in an independent data set of 232 pneumonectomies that were performed between 2008 and 2010 (i.e., the "validation set"). Of the 542 patients in the discovery set (DS), 35 patients (6.5%) died after pneumonectomy during the same admission. We developed a risk prediction model for in-hospital mortality following pneumonectomy; that model included age, current alcohol use, coronary artery disease, preoperative leukocyte count and palliative indication as possible risk factors. The risk model was subsequently successfully validated in an independent data set (n = 232) in which 18 patients (7.8%) died following pneumonectomy. For the validation set, the sensitivity of the model was 53.3% (DS: 54.3%), the specificity was 88.0% (DS: 87.4%), the positive predictive value was 26.7% (DS: 22.9%) and the negative predictive value was 95.8% (DS: 96.5%). The Brier score was 0.062 (DS: 0.054). The prediction model is statistically valid and clinically relevant.     

One-Year Mortality in Older Patients with Cancer: Development and External Validation of an MNA-Based Prognostic Score

Purpose

The MNA (Mini Nutritional Assessment) is known as a prognosis factor in older population. We analyzed the prognostic value for one-year mortality of MNA items in older patients with cancer treated with chemotherapy as the basis of a simplified prognostic score.

Methods

The prospective derivation cohort included 606 patients older than 70 years with an indication of chemotherapy for cancers. The endpoint to predict was one-year mortality. The 18 items of the Full MNA, age, gender, weight loss, cancer origin, TNM, performance status and lymphocyte count were considered to construct the prognostic model. MNA items were analyzed with a backward step-by-step multivariate logistic regression and other items were added in a forward step-by-step regression. External validation was performed on an independent cohort of 229 patients.

Results

At one year 266 deaths had occurred. Decreased dietary intake (p = 0.0002), decreased protein-rich food intake (p = 0.025), 3 or more prescribed drugs (p = 0.023), calf circumference <31cm (p = 0.0002), tumor origin (p<0.0001), metastatic status (p = 0.0007) and lymphocyte count <1500/mm³ (0.029) were found to be associated with 1-year mortality in the final model and were used to construct a prognostic score. The area under curve (AUC) of the score was 0.793, which was higher than the Full MNA AUC (0.706). The AUC of the score in validation cohort (229 subjects, 137 deaths) was 0.698.

Conclusion

Key predictors of one-year mortality included cancer cachexia clinical features, comorbidities, the origin and the advanced status of the tumor. The prognostic value of this model combining a subset of MNA items and cancer related items was better than the full MNA, thus providing a simple score to predict 1-year mortality in older patients with an indication of chemotherapy.     

Development and Validation of a Novel Tool to Predict Hospital Readmission Risk Among Patients with Diabetes

Objective: To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30-d readmission) among hospitalized patients with diabetes.Methods: A cohort of 44,203 discharges was retrospectively selected from the electronic records of adult patients with diabetes hospitalized at an urban academic medical center. Discharges of 60% of the patients (n = 26,402) were randomly selected as a training sample to develop the index. The remaining 40% (n = 17,801) were selected as a validation sample. Multivariable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRI™).Results: Ten statistically significant predictors were identified: employment status; living within 5 miles of the hospital; preadmission insulin use; burden of macrovascular diabetes complications; admission serum hematocrit, creatinine, and sodium; having a hospital discharge within 90 days before admission; most recent discharge status up to 1 year before admission; and a diagnosis of anemia. Discrimination of the model was acceptable (C statistic 0.70), and calibration was good. Characteristics of the validation and training samples were similar. Performance of the DERRI™ in the validation sample was essentially unchanged (C statistic 0.69). Mean predicted 30-d readmission risks were also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles).Conclusion: The DERRI™ was found to be a valid tool to predict all-cause 30-d readmission risk of individual patients with diabetes. The identification of high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.Abbreviations:DERRI™ = Diabetes Early Readmission Risk IndicatorICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical ModificationGEE = generalized estimating equationsROC = receiver operating characteristic     

The Use of Exome Genotyping to Predict Pathological Gleason Score Upgrade after Radical Prostatectomy in Low-Risk Prostate Cancer Patients

Background

Active surveillance (AS) is a promising option for patients with low-risk prostate cancer (PCa), however current criteria could not select the patients correctly, many patients who fulfilled recent AS criteria experienced pathological Gleason score upgrade (PGU) after radical prostatectomy (RP). In this study, we aimed to develop an accurate model for predicting PGU among low-risk PCa patients by using exome genotyping.

Methods

We genotyped 242,221 single nucleotide polymorphisms (SNP)s on a custom HumanExome BeadChip v1.0 (Illuminam Inc.) in blood DNA from 257 low risk PCa patients (PSA <10 ng/ml, biopsy Gleason score (GS) ≤6 and clinical stage ≤T2a) who underwent radical prostatectomy. Genetic data were analyzed using an unconditional logistic regression to calculate an odds ratio as an estimate of relative risk of PGU, which defined pathologic GS above 7. Among them, we selected persistent SNPs after multiple testing using FDR method, and we compared accuracies from the multivariate logistic model incorporating clinical factors between included and excluded selected SNP information.

Results

After analysis of exome genotyping, 15 SNPs were significant to predict PGU in low risk PCa patients. Among them, one SNP – rs33999879 remained significant after multiple testing. When a multivariate model incorporating factors in Epstein definition – PSA density, biopsy GS, positive core number, tumor per core ratio and age was devised for the prediction of PGU, the predictive accuracy of the multivariate model was 78.4% (95%CI: 0.726–0.834). By addition the factor of rs33999879 in aforementioned multivariate model, the predictive accuracy was 82.9%, which was significantly increased (p = 0.0196).

Conclusion

The rs33999879 SNP is a predictor for PGU. The addition of genetic information from the exome sequencing effectively enhanced the predictive accuracy of the multivariate model to establish suitable active surveillance criteria.     

Development and Validation of a Prediction Score for Complications after Hepatectomy in Hepatitis B-Related Hepatocellular Carcinoma Patients

Objective and Background

The aim of the present study was to develop and validate a prediction score for postoperative complications by severity and guide perioperative management and patient selection in hepatitis B-related hepatocellular carcinoma patients undergoing liver resection.

Methods

A total of 1543 consecutive liver resections cases were included in the study. Randomly selected sample set of 70% of the study cohort was used to develop a score to predict complications III–V and the remaining 30% was used to validate the score. Based on the preoperative and predictable intraoperative parameters, logistic regression analysis was used to identify risk factors and create an integer score for the predicting of complication.

Results

American Society of Anesthesiologists category, portal hypertension, major liver resection (more than 3 segments) and extrahepatic procedures were identified as independent predictors for complications III–V by logistic regression analysis. A score system integrating these 4 factors was stratified into three groups and significantly predicted the risk of complications III–V, with a rate of 1.6%, 11.9% and 65.6% for low, moderate and high risk, respectively. Using the score, the complications risk could be predicted accurately in the validation set, without significant differences between predicted (10.4%) and observed (8.4%) risks for complications III–V (P = 0.466).

Conclusions

Based on four preoperative risk factors, we have developed and validated an integer-based risk score to predict postoperative severe complications after liver resection for hepatitis B-related hepatocellular carcinoma patients in high-volume surgical center. This score may contribute to preoperative risk stratification and clinical decision-making.     

Human Papillomavirus and Cystic Node Metastasis in Oropharyngeal Cancer and Cancer of Unknown Primary Origin

The clinical significance of human papillomavirus (HPV) in neck node metastasis from cancer of unknown primary (CUP) is not well established. We aimed to address the relationship of HPV status between node metastasis and the primary tumor, and also the relevance of HPV status regarding radiographically detected cystic node metastasis in head and neck squamous cell carcinoma (HNSCC) and CUP. HPV DNA was examined in 68 matched pairs of node metastasis and primary tumor, and in node metastasis from 27 CUPs. In surgically treated CUPs, p16 was examined immunohistochemically. When tonsillectomy proved occult tonsillar cancer in CUP, HPV DNA and p16 were also examined in the occult primary. Cystic node metastasis on contrast-enhanced computed tomography scans was correlated with the primary site and HPV status in another series of 255 HNSCCs and CUPs with known HPV status. Node metastasis was HPV-positive in 19/37 (51%) oropharyngeal SCCs (OPSCCs) and 10/27 (37%) CUPs, but not in non-OPSCCs. Fluid was collected from cystic node metastasis using fine needle aspiration in two OPSCCs and one CUP, and all fluid collections were HPV-positive. HPV status, including the presence of HPV DNA, genotype, and physical status, as well as the expression pattern of p16 were consistent between node metastasis and primary or occult primary tumor. Occult tonsillar cancer was found more frequently in p16-positive CUP than in p16-negative CUP (odds ratio (OR), 39.0; 95% confidence interval (CI), 1.4–377.8; P = 0.02). Radiographically, cystic node metastasis was specific to OPSCC and CUP, and was associated with HPV positivity relative to necrotic or solid node metastasis (OR, 6.2; 95% CI, 1.2–45.7; P = 0.03). In conclusion, HPV status remains unchanged after metastasis. The occult primary of HPV-positive CUP is most probably localized in the oropharynx. HPV status determined from fine needle aspirates facilitates the diagnosis of cystic node metastasis.     

External Validation of the A2DS2 Score to Predict Stroke-Associated Pneumonia in a Chinese Population: A Prospective Cohort Study

Background and Purpose

The A²DS² score was recently developed from the Berlin Stroke Registry for predicting in-hospital pneumonia after acute ischemic stroke and performed well in an external validation in the North-west Germany Stroke Registry. It could be a useful tool for risk stratification in clinical practice or stroke trials. We aimed to prospectively validate the predictive value of A²DS² score in a Chinese stroke population.

Methods

The prognostic model was used to predict stroke-associated pneumonia (SAP) from Henan Province Stroke Registry (HNSR) in which data were prospectively collected. The receiver-operating characteristic curves were plotted, and the C statistics were calculated to assess the discrimination ability. The Hosmer–Lemeshow goodness-of-fit test and the plot of observed versus predicted SAP risk were used to assess model calibration.

Results

Among 1142 eligible patients, the overall in-hospital SAP was 18.8%, which ranged from 9.0% in patients with lower A²DS² scores (0–4) to 65.0% in those with higher scores of 5 to 10 (P for trend <0.001). The C statistic was 0.836 (95% confidence interval, 0.803–0.868) through the A²DS² score, suggesting excellent discrimination in the HNSR. The A²DS² score also showed excellent calibration (Cox and Snell R ² = 0.243) in the external validation sample from the HNSR.

Conclusions

The A²DS² score could reliably predict in-hospital SAP in Chinese stroke patients. It might be helpful for the assessment of increased risk monitoring and prophylactic treatment in identified high-risk patients for SAP in clinical routine.     

Identification and Validation of a Multigene Predictor of Recurrence in Primary Laryngeal Cancer

Purpose

Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. This study attempts to identify and independently validate gene expression models, prognostic of disease-free survival (DFS) in operable laryngeal cancer.

Materials and Methods

Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 66 patients with laryngeal cancer treated locally with surgery. We applied Cox regression proportional hazards modeling to identify multigene predictors of recurrence. Gene models were then validated in two independent cohorts of 54 and 187 patients (fresh-frozen and formalin-fixed tissue validation sets, respectively).

Results

We focused on genes univariately associated with DFS (p<0.01) in the training set. Among several models comprising different numbers of genes, a 30-probe set model demonstrated optimal performance in both the training (log-rank, p<0.001) and 1^st validation (p = 0.010) sets. Specifically, in the 1^st validation set, median DFS as predicted by the 30-probe set model, was 34 and 80 months for high- and low-risk patients, respectively. Hazard ratio (HR) for recurrence in the high-risk group was 3.87 (95% CI 1.28–11.73, Wald''s p = 0.017). Testing the expression of selected genes from the above model in the 2^nd validation set, with qPCR, revealed significant associations of single markers, such as ACE2, FLOT1 and PRKD1, with patient DFS. High PRKD1 remained an unfavorable prognostic marker upon multivariate analysis (HR = 2.00, 95% CI 1.28–3.14, p = 0.002) along with positive nodal status.

Conclusions

We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. It seems worthy to prospectively validate PRKD1 expression as a laryngeal cancer prognostic marker, for routine clinical applications.     

Validation of the Memorial Sloan-Kettering Cancer Center Nomogram to Predict Disease-Specific Survival after R0 Resection in a Chinese Gastric Cancer Population

Background

Prediction of disease-specific survival (DSS) for individual patient with gastric cancer after R0 resection remains a clinical concern. Since the clinicopathologic characteristics of gastric cancer vary widely between China and western countries, this study is to evaluate a nomogram from Memorial Sloan-Kettering Cancer Center (MSKCC) for predicting the probability of DSS in patients with gastric cancer from a Chinese cohort.

Methods

From 1998 to 2007, clinical data of 979 patients with gastric cancer who underwent R0 resection were retrospectively collected from Peking University Cancer Hospital & Institute and used for external validation. The performance of the MSKCC nomogram in our population was assessed using concordance index (C-index) and calibration plot.

Results

The C-index for the MSKCC predictive nomogram was 0.74 in the Chinese cohort, compared with 0.69 for American Joint Committee on Cancer (AJCC) staging system (P<0.0001). This suggests that the discriminating value of MSKCC nomogram is superior to AJCC staging system for prognostic prediction in the Chinese population. Calibration plots showed that the actual survival of Chinese patients corresponded closely to the MSKCC nonogram-predicted survival probabilities. Moreover, MSKCC nomogram predictions demonstrated the heterogeneity of survival in stage IIA/IIB/IIIA/IIIB disease of the Chinese patients.

Conclusion

In this study, we externally validated MSKCC nomogram for predicting the probability of 5- and 9-year DSS after R0 resection for gastric cancer in a Chinese population. The MSKCC nomogram performed well with good discrimination and calibration. The MSKCC nomogram improved individualized predictions of survival, and may assist Chinese clinicians and patients in individual follow-up scheduling, and decision making with regard to various treatment options.     

Sequential Organ Failure Assessment Score Can Predict Mortality in Patients with Paraquat Intoxication

Introduction

Paraquat poisoning is characterized by multi-organ failure and pulmonary fibrosis with respiratory failure, resulting in high mortality and morbidity. The objective of this study was to identify predictors of mortality in cases of paraquat poisoning. Furthermore, we sought to determine the association between these parameters.

Methods

A total of 187 patients were referred for management of intentional paraquat ingestion between January 2000 and December 2010. Demographic, clinical, and laboratory data were recorded. Sequential organ failure assessment (SOFA) and acute kidney injury network (AKIN) scores were collected, and predictors of mortality were analyzed.

Results

Overall hospital mortality for the entire population was 54% (101/187). Using a multivariate logistic regression model, it was found that age, time to hospitalization, blood paraquat level, estimated glomerular filtration rate at admission (eGFR _{first day}), and the SOFA_48-h score, but not the AKIN_48-h score, were significant predictors of mortality. For predicting the in-hospital mortality, SOFA_48-h scores displayed a good area under the receiver operating characteristic curve (AUROC) (0.795±0.033, P<0.001). The cumulative survival rate differed significantly between patients with SOFA_48-h scores <3 and those ≥3 (P<0.001). A modified SOFA (mSOFA) score was further developed by using the blood paraquat level, and this new score also demonstrated a better AUROC (0.848±0.029, P<0.001) than the original SOFA score. Finally, the cumulative survival rate also differed significantly between patients with mSOFA scores <4 and ≥4 (P<0.001).

Conclusion

The analytical data demonstrate that SOFA and mSOFA scores, which are based on the extent of organ function or rate of organ failure, help to predict mortality after intentional paraquat poisoning.     

Multimodality Imaging to Predict Response to Systemic Treatment in Patients with Advanced Colorectal Cancer

Aim

Aim of this study was to investigate the potential of ¹⁸F-FDG PET, diffusion weighted imaging (DWI) and susceptibility-weighted (T2*) MRI to predict response to systemic treatment in patients with colorectal liver metastases. The predictive values of pretreatment measurements and of early changes one week after start of therapy, were evaluated.

Methods

Imaging was performed prior to and one week after start of first line chemotherapy in 39 patients with colorectal liver metastases. ¹⁸F-FDG PET scans were performed on a PET/CT scanner and DWI and T2* were performed on a 1.5T MR scanner. The maximum standardized uptake values (SUV), total lesion glycolysis (TLG), apparent diffusion coefficient (ADC) and T2* value were assessed in the same lesions. Up to 5 liver metastases per patient were analyzed. Outcome measures were progression free survival (PFS), overall survival (OS) and size response.

Results

Pretreatment, high SUV_max, high TLG, low ADC and high T2* were associated with a shorter OS. Low pretreatment ADC value was associated with shorter PFS. After 1 week a significant drop in SUV_max and rise in ADC were observed. The drop in SUV was correlated with the rise in ADC (r=-0.58, p=0.002). Neither change in ADC nor in SUV was predictive of PFS or OS. T2* did not significantly change after start of treatment.

Conclusion

Pretreatment SUV_max, TLG, ADC, and T2* values in colorectal liver metastases are predictive of patient outcome. Despite sensitivity of DWI and ¹⁸F-FDG PET for early treatment effects, change in these parameters was not predictive of long term outcome.     

Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma

Background: A significant factor influencing the prognosis of lung adenocarcinoma (LUAD) is tumor metastasis. Studies have shown that abnormal DNA methylation in circulating tumor cells (CTCs) is associated with tumour metastasis. Based on the genes expressed in CTCs that play an important role in DNA methylation, we hope to build a risk model to predict prognosis and provide a therapeutic strategy in LUAD.Methods: The CTC sequencing data for LUAD were obtained from {"type":"entrez-geo","attrs":{"text":"GSE74639","term_id":"74639"}}GSE74639, which contains 10 CTC samples and 6 primary tumour samples. To carefully assess the clinical value, functional status, involvement of the tumor microenvironment (TME) based on the risk model, and genetic variants based on based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), a reliable risk model was successfully built.Results: Three differentially methylated genes (DMGs) of CTCs for LUAD, including mitochondrial ribosomal protein L51 (MRPL51), STE20-like kinase (SLK), and protein regulator of cytokinesis 1(PRC1), were effectively used to construct a risk model. Both the training and validation cohorts'' stability and accuracy of the risk model were evaluated. Each patient in the TCGA-LUAD cohort received a risk score, and based on the median score, they were divided into high- and low-risk groups. The tumors in the high-risk group in this study were classified as "cold" and immunosuppressed, which may be linked to a poor prognosis. The tumors in the low-risk group, however, were deemed "hot" and had immune hyperfunction linked to a positive prognosis. Additionally, patients in the low-risk group showed greater sensitivity to immunotherapy than those in the high-risk group.Conclusions: Based on DMGs of CTCs from LUAD, we successfully developed a predictive risk model and discovered differences in biological function, TME, genetic variation, and clinical outcomes between those at high and low risk group.     

miRNA-197 and miRNA-223 Predict Cardiovascular Death in a Cohort of Patients with Symptomatic Coronary Artery Disease

Background

Circulating microRNAs (miRNAs) have been described as potential diagnostic biomarkers in cardiovascular disease and in particular, coronary artery disease (CAD). Few studies were undertaken to perform analyses with regard to risk stratification of future cardiovascular events. miR-126, miR-197 and miR-223 are involved in endovascular inflammation and platelet activation and have been described as biomarkers in the diagnosis of CAD. They were identified in a prospective study in relation to future myocardial infarction.

Objectives

The aim of our study was to further evaluate the prognostic value of these miRNAs in a large prospective cohort of patients with documented CAD.

Methods

Levels of miR-126, miR-197 and miR-223 were evaluated in serum samples of 873 CAD patients with respect to the endpoint cardiovascular death. miRNA quantification was performed using real time polymerase chain reaction (RT-qPCR).

Results

The median follow-up period was 4 years (IQR 2.78–5.04). The median age of all patients was 64 years (IQR 57–69) with 80.2% males. 38.9% of the patients presented with acute coronary syndrome (ACS), 61.1% were diagnosed with stable angina pectoris (SAP). Elevated levels of miRNA-197 and miRNA-223 reliably predicted future cardiovascular death in the overall group (miRNA-197: hazard ratio (HR) 1.77 per one standard deviation (SD) increase (95% confidence interval (CI) 1.20; 2.60), p = 0.004, C-index 0.78; miRNA-223: HR 2.23 per one SD increase (1.20; 4.14), p = 0.011, C-index 0.80). In ACS patients the prognostic power of both miRNAs was even higher (miRNA-197: HR 2.24 per one SD increase (1.25; 4.01), p = 0.006, C-index 0.89); miRA-223: HR 4.94 per one SD increase (1.42; 17.20), p = 0.012, C-index 0.89).

Conclusion

Serum-derived circulating miRNA-197 and miRNA-223 were identified as predictors for cardiovascular death in a large patient cohort with CAD. These results reinforce the assumption that circulating miRNAs are promising biomarkers with prognostic value with respect to future cardiovascular events.     

Early Sympathy and Social Acceptance Predict the Development of Sharing in Children

Sharing is a fascinating activity of the human species and an important basis for the development of fairness, care, and cooperation in human social interaction. Economic research has proposed that sharing, or the willingness to sacrifice own resources for others, has its roots in social emotions such as sympathy. However, only few cross-sectional experiments have investigated children’s other-regarding preferences, and the question how social-emotional skills influence the willingness to share valuable resources has not been tested. In the present longitudinal-experimental study, a sample of 175 6-year-old children, their primary caregivers, and their teachers is examined over a 3-year period of time. Data are analyzed by means of growth curve modeling. The findings show that sharing valuable resources strongly increases in children from 6 to 9 years of age. Increases in sharing behavior are associated with the early-developing ability to sympathize with anonymous others. Sharing at 7 years of age is predicted by feelings of social acceptance at 6 years of age. These findings hold after controlling for children’s IQ and SES. Girls share more equally than boys at 6 and 7 years of age, however, this gender difference disappears at the age of 9 years. These results indicate that human sharing strongly increases in middle childhood and, that this increase is associated with sympathy towards anonymous others and with feelings of social acceptance. Additionally, sharing develops earlier in girls than in boys. This developmental perspective contributes to new evidence on change in sharing and its social-emotional roots. A better understanding of the factors underlying differences in the development of sharing and pro-social orientations should also provide insights into the development of atypical, anti-social orientations which exhibit social-emotional differences such as aggression and bullying behavior.     

Development and Validation of a Novel Platform-Independent Metastasis Signature in Human Breast Cancer

PurposeThe molecular drivers of metastasis in breast cancer are not well understood. Therefore, we sought to identify the biological processes underlying distant progression and define a prognostic signature for metastatic potential in breast cancer.ResultsWe identified a broad range of metastatic potential that was independent of intrinsic breast cancer subtypes. 146 genes were significantly associated with metastasis progression and were linked to cancer-related biological functions, including cell migration/adhesion, Jak-STAT, TGF-beta, and Wnt signaling. These genes were used to develop a platform-independent gene expression signature (M-Sig), which was trained and subsequently validated on 5 independent cohorts totaling nearly 1800 breast cancer patients with all p-values < 0.005 and hazard ratios ranging from approximately 2.5 to 3. On multivariate analysis accounting for standard clinicopathologic prognostic variables, M-Sig remained the strongest prognostic factor for metastatic progression, with p-values < 0.001 and hazard ratios > 2 in three different cohorts.ConclusionM-Sig is strongly prognostic for metastatic progression, and may provide clinical utility in combination with treatment prediction tools to better guide patient care. In addition, the platform-independent nature of the signature makes it an excellent research tool as it can be directly applied onto existing, and future, datasets.     

Early Standard Electroencephalogram Abnormalities Predict Mortality in Septic Intensive Care Unit Patients

IntroductionSepsis is associated with increased mortality, delirium and long-term cognitive impairment in intensive care unit (ICU) patients. Electroencephalogram (EEG) abnormalities occurring at the acute stage of sepsis may correlate with severity of brain dysfunction. Predictive value of early standard EEG abnormalities for mortality in ICU septic patients remains to be assessed.MethodsIn this prospective, single center, observational study, standard EEG was performed, analyzed and classified according to both Synek and Young EEG scales, in consecutive patients acutely admitted in ICU for sepsis. Delirium, coma and the level of sedation were assessed at the time of EEG recording; and duration of sedation, occurrence of in-ICU delirium or death were assessed during follow-up. Adjusted analyses were carried out using multiple logistic regression.ResultsOne hundred ten patients were included, mean age 63.8 (±18.1) years, median SAPS-II score 38 (29–55). At the time of EEG recording, 46 patients (42%) were sedated and 22 (20%) suffered from delirium. Overall, 54 patients (49%) developed delirium, of which 32 (29%) in the days after EEG recording. 23 (21%) patients died in the ICU. Absence of EEG reactivity was observed in 27 patients (25%), periodic discharges (PDs) in 21 (19%) and electrographic seizures (ESZ) in 17 (15%). ICU mortality was independently associated with a delta-predominant background (OR: 3.36; 95% CI [1.08 to 10.4]), absence of EEG reactivity (OR: 4.44; 95% CI [1.37–14.3], PDs (OR: 3.24; 95% CI [1.03 to 10.2]), Synek grade ≥ 3 (OR: 5.35; 95% CI [1.66–17.2]) and Young grade > 1 (OR: 3.44; 95% CI [1.09–10.8]) after adjustment to Simplified Acute Physiology Score (SAPS-II) at admission and level of sedation. Delirium at the time of EEG was associated with ESZ in non-sedated patients (32% vs 10%, p = 0.037); with Synek grade ≥ 3 (36% vs 7%, p< 0.05) and Young grade > 1 (36% vs 17%, p< 0.001). Occurrence of delirium in the days after EEG was associated with a delta-predominant background (48% vs 15%, p = 0.001); absence of reactivity (39% vs 10%, p = 0.003), Synek grade ≥ 3 (42% vs 17%, p = 0.001) and Young grade >1 (58% vs 17%, p = 0.0001).ConclusionsIn this prospective cohort of 110 septic ICU patients, early standard EEG was significantly disturbed. Absence of EEG reactivity, a delta-predominant background, PDs, Synek grade ≥ 3 and Young grade > 1 at day 1 to 3 following admission were independent predictors of ICU mortality and were associated with occurence of delirium. ESZ and PDs, found in about 20% of our patients. Their prevalence could have been higher, with a still higher predictive value, if they had been diagnosed more thoroughly using continuous EEG.     

An Inflammation Based Score Can Optimize the Selection of Patients with Advanced Cancer Considered for Early Phase Clinical Trials

Background

Adequate organ function and good performance status (PS) are common eligibility criteria for phase I trials. As inflammation is pathogenic and prognostic in cancer we investigated the prognostic performance of inflammation-based indices including the neutrophil (NLR) and platelet to lymphocyte ratio (PLR).

Methods

We studied inflammatory scores in 118 unselected referrals. NLR normalization was recalculated at disease reassessment. Each variable was assessed for progression-free (PFS) and overall survival (OS) on uni- and multivariate analyses and tested for 90 days survival (90DS) prediction using receiving operator curves (ROC).

Results

We included 118 patients with median OS 4.4 months, 23% PS>1. LDH≥450 and NLR≥5 were multivariate predictors of OS (p<0.001). NLR normalization predicted for longer OS (p<0.001) and PFS (p<0.05). PS and NLR ranked as most accurate predictors of both 90DS with area under ROC values of 0.66 and 0.64, and OS with c-score of 0.69 and 0.60. The combination of NLR+PS increased prognostic accuracy to 0.72. The NLR was externally validated in a cohort of 126 subjects.

Conclusions

We identified the NLR as a validated and objective index to improve patient selection for experimental therapies, with its normalization following treatment predicting for a survival benefit of 7 months. Prospective validation of the NLR is warranted.