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Sasha Vaziri, Joseph M. Abbatematteo, Max S. Fleisher, Alexander B. Dru, Dennis T. Lockney, Paul S. Kubilis and Daniel J. Hoh

, congestive heart failure, dyspnea, smoking, chronic obstructive pulmonary disease, dialysis, acute renal failure, and body mass index information to estimate the risk of common surgery-related complications. The risk calculator was originally designed as a tool for surgeons to use in guiding the discussion of risks during the informed consent process, with the goal of providing patients with more accurate and complete information to guide their decision-making. The calculator provides the predicted risk scores (estimated percentage) of a perioperative complication (any

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Jun Hirose, Takuya Taniwaki, Toru Fujimoto, Tatsuya Okada, Takayuki Nakamura, Nobukazu Okamoto, Koichiro Usuku and Hiroshi Mizuta

they used nearly uniform operative techniques. Calculations Underlying the Scoring Systems The POSSUM system consists of a physiological score and an operative severity score; its total score is based on both the physiological score and operative severity score ( Appendix 1 ) . The E-PASS system is composed of a preoperative risk score, a surgical stress score, and a comprehensive risk score that is determined by both the preoperative risk score and surgical stress score ( Appendix 2 ) . The predicted morbidity rate (PMR) is based on E-PASS 13 and POSSUM 5

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Sebastian Lohmann, Tobias Brix, Julian Varghese, Nils Warneke, Michael Schwake, Eric Suero Molina, Markus Holling, Walter Stummer and Stephanie Schipmann

clinically relevant stages: leukopenia (< 3910 leukocytes/μl), leukocytosis (> 10,900 leukocytes/μl), CRP > 1 mg/L, and INR > 1.2 as defined by the laboratory reference ranges. Continuous variables such as age and cutting-suture time (i.e., time from incision to suture) were converted into categorial variables, using the median as the cutoff value. For the development of risk scores, it is considered useful to transfer numeric variables into categorial variables to discriminate between two values and assign the patients to the various risk groups. BMI was stratified

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Keaton Piper, Hanna Algattas, Ian A. DeAndrea-Lazarus, Kristopher T. Kimmell, Yan Michael Li, Kevin A. Walter, Howard J. Silberstein and G. Edward Vates

.facs.org/quality-programs/acs-nsqip ). 20 The NSQIP database records characteristics, operative factors, and postoperative complications of patients who underwent surgery. For this analysis, data from the years 2006–2010 were selected and examined. Risk factors were identified and used to calculate a risk score that could be used in the future to predict a patient's propensity to develop VTE. Methods Data Collection All patient data used in this analysis came from the ACS-NSQIP data set. The NSQIP is an effort by the ACS aimed at the standardized collection of national surgical data with a

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Wolfgang A. Dauch, Gotthard Landau and Dietmar Krex

separately in each group. We then tested whether this distribution was different from the expected incidence (chi-square test, probability of error below 5%). When a parameter was more common in patients developing PILRT than in those not developing PILRT, it was considered associated with an increased risk and was labeled as a “risk factor.” The interdependency of these risk factors was examined in a “matched pairs” trial. From the risk factors, a “risk score” was composed based on tests of various models. Finally, the best-fitting model of Group A was applied to Group B

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Cyrus Elahi, Theresa Williamson, Charis A. Spears, Sarah Williams, Josephine Nambi Najjuma, Catherine A. Staton, João Ricardo Nickenig Vissoci, Anthony Fuller, David Kitya and Michael M. Haglund

unfavorable outcome (death, vegetative state, or severe disability) at 6 months (i.e., chronic outcomes) after surgery for hematoma evacuation. We included the output from the calculator in the vignette and provided a brief written introduction to risk calculators. Other than the risk output and the description of risk calculators, the vignettes were identical between the two groups. A co-investigator (S.W.) randomized the order of clinical vignettes with or without the risk score using a computer program and placed them in envelopes. We kept the survey administrator (C

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Björn Latz, Christine Mordhorst, Thomas Kerz, Annette Schmidt, Astrid Schneider, Gregor Wisser, Christian Werner and Kristin Engelhard

also taken into account by this risk score. Statistical Analysis Binominal tests were performed using SPSS (versions 12.0, SPSS Inc.) to compare the proportion of patients having POV and the proportion predicted to have POV according to their calculated risk. A logistic regression analysis was performed using SPSS to identify covariates associated either with nausea or vomiting. Data examined included individual factors (sex, medical history of PONV or motion sickness, smoking status, age, and BMI), anesthesia-related factors (balanced anesthesia vs total

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Ian G. Dorward, Jingqin Luo, Arie Perry, David H. Gutmann, David B. Mansur, Joshua B. Rubin and Jeffrey R. Leonard

was fitted to assess the association of continuous variables with RFS. Pathological variables with p values < 0.1 were then used as predictors in a further multivariate Cox proportional hazard model analysis. Risk factors that were significant at the 0.1 level in the multivariate model were generally retained for use in a risk stratification model, and a likelihood ratio test was used to compare between nested models. The overall predictive value of the multivariate model was indicated by the C-index of Harrell et al. 19 A recurrence risk score was created by the

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Michael Karsy, Jian Guan and L. Eric Huang

Scoring System When age, KPS, and MPC1 and MPC2 z-scores as well as IDH and 1p19q status were factored in, the cumulative score segregated patients by OS (log-rank test, p = 0.001) but not PFS (log-rank test, p = 0.45). Mean OS of 95.7 ± 0.1 months for scores of 1–2, 136.2 ± 17.7 months for a score of 3, 90.4 ± 9.1 months for a score of 4, and 61.7 ± 7.0 months for a score > 4 were observed ( Tables 4 and 5 , Fig. 6A and B ). We then validated the risk score with an independent glioma data set GSE16011 ( Table 6 ). Demographic features in terms of age, KPS score

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Pedro A. Gómez, Javier de-la-Cruz, David Lora, Luis Jiménez-Roldán, Gregorio Rodríguez-Boto, Rosario Sarabia, Juan Sahuquillo, Roberto Lastra, Jesus Morera, Eglis Lazo, Jaime Dominguez, Javier Ibañez, Marta Brell, Adolfo de-la-Lama, Ramiro D. Lobato and Alfonso Lagares

: low risk of an early death, scores 0–3, corresponding to a risk of early death below 1%; moderate risk, scores 4–8, risk between 1% and 10%; high risk, scores 9–12, risk between 10% and 50%; and very high probability of an early death, scores 13–20, risk greater than 50%. It is estimated that the categorization of the risk score in these 4 probability groups produces 10% of misclassified cases. TABLE 4: Prognostic score chart (range 0–20 points) Predictor Category Score age 76–95 3 56–75 2 36–55 1 15–35 0