Lei Kou

Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma. An international collaborative study.

BACKGROUND Due to the rarity of adenoid cystic carcinoma (ACC), information on outcome is based upon small retrospective case series. The aim of our study was to create a large multiinstitutional international dataset of patients with ACC in order to design predictive nomograms for outcome. METHODS ACC patients managed at 10 international centers were identified. Patient, tumor, and treatment characteristics were recorded and an international collaborative dataset created. Multivariable competing risk models were then built to predict the 10 year recurrence free probability (RFP), distant recurrence free probability (DRFP), overall survival (OS) and cancer specific mortality (CSM). All predictors of interest were added in the starting full models before selection, including age, gender, tumor site, clinical T stage, perineural invasion, margin status, pathologic N-status, and M-status. Stepdown method was used in model selection to choose predictive variables. An external dataset of 99 patients from 2 other institutions was used to validate the nomograms. FINDINGS Of 438 ACC patients, 27.2% (119/438) died from ACC and 38.8% (170/438) died of other causes. Median follow-up was 56 months (range 1-306). The nomogram for OS had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N-status and M-status) with a concordance index (CI) of 0.71. The nomogram for CSM had the same variables, except margin status, with a concordance index (CI) of 0.70. The nomogram for RFP had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N status and perineural invasion) (CI 0.66). The nomogram for DRFP had 6 variables (gender, clinical T stage, tumor site, pathologic N-status, perineural invasion and margin status) (CI 0.64). Concordance index for the external validation set were 0.76, 0.72, 0.67 and 0.70 respectively. INTERPRETATION Using an international collaborative database we have created the first nomograms which estimate outcome in individual patients with ACC. These predictive nomograms will facilitate patient counseling in terms of prognosis and subsequent clinical follow-up. They will also identify high risk patients who may benefit from clinical trials on new targeted therapies for patients with ACC. FUNDING None.

Individualized Risk Estimation for Postoperative Complications After Surgery for Oral Cavity Cancer

IMPORTANCE Postoperative complications after head and neck surgery carry the potential for significant morbidity. Estimating the risk of complications in an individual patient is challenging. OBJECTIVE To develop a statistical tool capable of predicting an individual patients risk of developing a major complication after surgery for oral cavity squamous cell carcinoma. DESIGN, SETTING, AND PARTICIPANTS Retrospective case series derived from an institutional clinical oncologic database, augmented by medical record abstraction, at an academic tertiary care cancer center. Participants were 506 previously untreated adult patients with biopsy-proven oral cavity squamous cell carcinoma who underwent surgery between January 1, 2007, and December 31, 2012. MAIN OUTCOMES AND MEASURES The primary end point was a major postoperative complication requiring invasive intervention (Clavien-Dindo classification grades III-V). Patients treated between January 1, 2007, and December 31, 2008 (354 of 506 [70.0%]) comprised the modeling cohort and were used to develop a nomogram to predict the risk of developing the primary end point. Univariable analysis and correlation analysis were used to prescreen 36 potential predictors for incorporation in the subsequent multivariable logistic regression analysis. The variables with the highest predictive value were identified with the step-down model reduction method and included in the nomogram. Patients treated between January 1, 2007, and December 31, 2008 (152 of 506 [30.0%]) were used to validate the nomogram. RESULTS Clinical characteristics were similar between the 2 cohorts for most comparisons. Thirty-six patients in the modeling cohort (10.2%) and 16 patients in the validation cohort (10.5%) developed a major postoperative complication. The 6 preoperative variables with the highest individual predictive value were incorporated within the nomogram, including body mass index, comorbidity status, preoperative white blood cell count, preoperative hematocrit, planned neck dissection, and planned tracheotomy. The nomogram predicted a major complication with a validated concordance index of 0.79. Inclusion of surgical operative variables in the nomogram maintained predictive accuracy (concordance index, 0.77). CONCLUSIONS AND RELEVANCE A statistical tool was developed that accurately estimates an individual patients risk of developing a major complication after surgery for oral cavity squamous cell carcinoma.

Incorporation of postoperative CT data into clinical models to predict 5-year overall and recurrence free survival after primary cytoreductive surgery for advanced ovarian cancer

PURPOSE The use of multivariable clinical models to assess postoperative prognosis in ovarian cancer increased. All published models incorporate surgical debulking. However, postoperative CT can detect residual disease (CT-RD) in 40% of optimally resected patients. The aim of our study was to investigate the added value of incorporating CT-RD evaluation into clinical models for assessment of overall survival (OS) and progression free survival (PFS) in patients after primary cytoreductive surgery (PCS). METHODS 212 women with PCS for advanced ovarian cancer between 01/1997 and 12/2011, and a contrast enhanced abdominal CT 1-7 weeks after surgery were included in this IRB approved retrospective study. Two radiologists blinded to clinical data, evaluated all CT for the presence of CT-RD, and Cohens kappa assessed agreement. Cox proportional hazards regression with stepwise selection was used to develop OS and PFS models, with CT-RD incorporated afterwards. Model fit was assessed with bootstrapped Concordance Probability Estimates (CPE), accounting for over-fitting bias by correcting the initial estimate after repeated subsampling. RESULTS Readers agreed on the majority of cases (179/212, k=0.68). For OS and PFS, CT-RD was significant after adjusting for clinical factors with a CPE 0.663 (p=0.0264) and 0.649 (p=0.0008). CT-RD was detected in 37% of patients assessed as optimally debulked (RD<1cm) and increased the risk of death (HR: 1.58, 95% CI: 1.06-2.37%). CONCLUSION CT-RD is a significant predictor after adjusting for clinical factors for both OS and PFS. Incorporating CT-RD into the clinical model improved the prediction of OS and PFS in patients after PCS for advanced ovarian cancer.

Prognostic importance of emerging cardiac, inflammatory, and renal biomarkers in chronic heart failure patients with reduced ejection fraction and anaemia: RED-HF study

To test the prognostic value of emerging biomarkers in the Reduction of Events by Darbepoetin Alfa in Heart Failure (RED‐HF) trial.

A Nomogram Derived by Combination of Demographic and Biomarker Data Improves the Noninvasive Evaluation of Patients at Risk for Bladder Cancer

Background: Improvements in the noninvasive clinical evaluation of patients at risk for bladder cancer would be of benefit both to individuals and to health care systems. We investigated the potential utility of a hybrid nomogram that combined key demographic features with the results of a multiplex urinary biomarker assay in hopes of identifying patients at risk of harboring bladder cancer. Methods: Logistic regression analysis was used to model the probability of bladder cancer burden in a cohort of 686 subjects (394 with bladder cancer) using key demographic features alone, biomarker data alone, and the combination of demographic features and key biomarker data. We examined discrimination, calibration, and decision curve analysis techniques to evaluate prediction model performance. Results: Area under the receiver operating characteristic curve (AUC) analyses revealed that demographic features alone predicted tumor burden with an accuracy of 0.806 [95% confidence interval (CI), 0.76–0.85], while biomarker data had an accuracy of 0.835 (95% CI, 0.80–0.87). The addition of molecular data into the nomogram improved the predictive performance to 0.891 (95% CI, 0.86–0.92). Decision curve analyses showed that the hybrid nomogram performed better than demographic or biomarker data alone. Conclusion: A nomogram construction strategy that combines key demographic features with biomarker data may facilitate the accurate, noninvasive evaluation of patients at risk of harboring bladder cancer. Further research is needed to evaluate the bladder cancer risk nomogram for potential clinical utility. Impact: The application of such a nomogram may better inform the decision to perform invasive diagnostic procedures. Cancer Epidemiol Biomarkers Prev; 25(9); 1361–6. ©2016 AACR.

Validation of a Postoperative Nomogram Predicting Recurrence in Patients with Conventional Clear Cell Renal Cell Carcinoma

BACKGROUND Clear cell renal cell carcinoma (RCC) continues to be the most commonly diagnosed subtype and is associated with more aggressive behavior than papillary and chromophobe RCC. Predicting disease recurrence after surgical extirpation is important for counseling and targeting those at high risk for adjuvant therapy clinical trials. OBJECTIVE To validate a postoperative nomogram predicting 5-yr recurrence-free probability (RFP) for clinically localized clear cell RCC. DESIGN, SETTING, AND PARTICIPANTS We identified all patients who underwent nephrectomy for clinically localized clear cell RCC from 1990 to 2009 at Memorial Sloan Kettering Cancer Center. After excluding patients with bilateral renal masses, familial RCC syndromes, and T3c or T4 tumors due to the limited number, 1642 participants were available for analysis. INTERVENTIONS Partial or radical nephrectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Disease recurrence was defined as any new tumor after nephrectomy or kidney cancer-specific mortality, whichever occurred first. A postoperative nomogram was used to calculate the predicted 5-yr RFP, and these values were compared with the actual 5-yr RFP. Nomogram performance was evaluated by concordance index and calibration plot. RESULTS AND LIMITATIONS Median follow-up was 39 mo (interquartile range: 14-79 mo), and disease recurrence was observed in 50 patients. The nomogram concordance index was 0.81. The calibration curve showed that the nomogram underestimated the actual 5-yr RFP. We updated the nomogram by including the entire patient population, which maintained performance and significantly improved calibration. CONCLUSIONS The updated clear cell RCC postoperative nomogram performed well in the combined cohort. Underestimation of actual 5-yr RFP by the original nomogram may be due to increased surgeon experience and other unknown variables. PATIENT SUMMARY We updated a valuable prediction tool used for assessing the disease recurrence probability after nephrectomy for clear cell renal cell carcinoma.

Pro-gastrin-releasing peptide and outcome in patients with heart failure and anaemia: results from the RED-HF study: ProGRP in heart failure

Neuroendocrine activation is associated with poor outcome in heart failure (HF). The neuropeptide gastrin‐releasing peptide (GRP), derived from the precursor proGRP1‐125 (proGRP), has recently been implicated in inflammation and wound repair. We investigated the predictive value of proGRP on clinical outcomes in HF patients with reduced ejection fraction.

T cell and monocyte/macrophage activation markers associate with adverse outcome, but give limited prognostic value in anemic patients with heart failure: results from RED-HF

BackgroundActivated leukocytes may contribute to the development and progression of heart failure (HF). We investigated the predictive value of circulating levels of stable and readily detectable markers reflecting both monocyte/macrophage and T-cell activity, on clinical outcomes in HF patients with reduced ejection fraction (HFrEF).MethodsThe association between baseline plasma levels of soluble CD163 (sCD163), macrophage migration inhibitory factor (MIF), granulysin, soluble interleukin-2 receptor (sIL-2R), and activated leukocyte cell adhesion molecule (ALCAM) and the primary endpoint of death from any cause or first hospitalization for worsening of HF was evaluated using multivariable Cox proportional hazard models in 1541 patients with systolic HF and mild to moderate anemia, enrolled in the Reduction of Events by darbepoetin alfa in Heart Failure (RED-HF) trial. Modifying effects and interaction with darbepoetin alfa treatment were also assessed.ResultsAll leukocyte markers, except granulysin, were associated with the primary outcome and all-cause death in univariate analysis (all p < 0.01) and remained significantly associated in multivariable analysis adjusting for conventional clinical variables (e.g. age, gender, BMI, NYHA class, creatinine, LVEF, etiology) and CRP. However, after final adjustment for TnT and NT-proBNP no associations were found with outcomes. No interaction with darbepoetin alpha treatment was observed for any marker.ConclusionsLeukocyte activation markers sCD163, MIF, sIL-2R, and ALCAM were associated with adverse outcome in patients with HFrEF, but add little as prognostic markers on top of established biochemical risk markers.Clinical Trial Registrationhttps://clinicaltrials.gov/ct2/show/NCT00358215.