Outcome Prediction Model For Patients Research Articles

Development and Validation of a Risk Prediction Algorithm for Evaluating the Efficacy of Postoperative Adjuvant TACE Therapy for Hepatocellular Carcinoma.

There is a lack of a reliable outcome prediction model for patients evaluating the feasibility of postoperative adjuvant transarterial chemoembolization (PATACE) therapy. Our goal was to develop an easy-to-use tool specifically for these patients. From January 2013 to June 2017, patients with hepatocellular carcinoma from the Liver Center of the First Affiliated Hospital of Chongqing Medical University received postoperative adjuvant Transarterial chemoembolization (TACE) therapy after liver cancer resection. A Cox proportional hazards model was established for these patients, followed by internal validation (enhanced bootstrap resampling technique) to further evaluate the predictive performance and discriminanceevaluate the predictive performance and discriminance, and compare it with other predictive models. The prognostic factors considered included tumour number, maximum tumor diameter, Edmondson-Steiner (ES) grade, Microvascular invasion (MVI) grade, Ki67, age, sex, hepatitis B surface antigen, cirrhosis, Alpha-fetoprotein (AFP), Albumin-bilirubin (ALBI) grade, Childpugh grade, body mass index (BMI), Neutrophil-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR). The endpoint of the study was overall survival. The median overall survival was 36 (95%CI: 34.0-38.0) months, with 1-year, 2-year and 3-year survival rates being 96.3%, 84.0% and 75.3%, respectively. Tumour number, MVI grade, and BMI was incorporated into the model, which had good differentiation and accuracy. Internal validation (enhanced bootstrap) suggested that Harrell's C statistic is 0.72. The model consistently outperforms other currently available models. This model may be an easy-to-use tool for screening patients suitable for PA-TACE treatment and guiding the selection of clinical protocols. But further research and external validation are required.

The Significance of a Cerebrovascular Accident Outcome Prediction Model for Patients, Family and Health Care Professionals: a Qualitative Evaluation (Preprint)

The Significance of a Cerebrovascular Accident Outcome Prediction Model for Patients, Family and Health Care Professionals: a Qualitative Evaluation (Preprint)

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit

BackgroundCurrently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 h of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died. MethodsData from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture. ResultsThe area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p < 0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%).Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients. ConclusionsOur study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.

The LACE+ Index as a Predictor of 30-Day Patient Outcomes in a Plastic Surgery Population: A Coarsened Exact Match Study.

This study used coarsened exact matching to investigate the effectiveness of the LACE+ index (i.e., length of stay, acuity of admission, Charlson Comorbidity Index, and emergency department visits in the past 6 months) predictive tool in patients undergoing plastic surgery. Coarsened exact matching was used to assess the predictive ability of the LACE+ index among plastic surgery patients over a 2-year period (2016 to 2018) at one health system (n = 5744). Subjects were matched on factors not included in the LACE+ index such as duration of surgery, body mass index, and race, among others. Outcomes studied included emergency room visits, hospital readmission, and unplanned return to the operating room. Three hundred sixty-six patients were matched and compared for quarter 1 to quarter 4 (n = 732, a 28.2 percent match rate); 504 patients were matched for quarter 2 to quarter 4 (n = 1008, a 36.7 percent match rate); 615 patients were matched for quarter 3 to quarter 4 (n = 1230, a 44.8 percent match rate). Increased LACE+ score significantly predicted readmission within 30 days for quarter 1 versus quarter 4 (1.09 percent versus 4.37 percent; p = 0.019), quarter 2 versus quarter 4 (3.57 percent versus 7.34 percent; p = 0.008), and quarter 3 versus quarter 4 (5.04 percent versus 8.13 percent; p = 0.028). Higher LACE+ score also significantly predicted 30-day reoperation for quarter 3 versus quarter 4 (1.30 percent versus 3.90 percent; p = 0.003) and emergency room visits within 30 days for quarter 2 versus quarter 4 (3.17 percent versus 6.75 percent; p = 0.008). The results of this study demonstrate that the LACE+ index may be suitable as a prediction model for patient outcomes in a plastic surgery population. Risk, II.

LACE+ index as a predictor of 90-day plastic surgery outcomes.

This study used coarsened exact matching to assess the ability of the LACE+ index to predict adverse outcomes after plastic surgery. Two-year retrospective study (2016-2018). LACE+ scores were retrospectively calculated for all patients undergoing plastic surgery at a multicenter health system (N = 5744). Coarsened exact matching was performed to sort patient data before analysis. Outcomes including unplanned hospital readmission, emergency department visits, and reoperation were compared for patients in different LACE+ score quartiles (Q1, Q2, Q3, Q4). A total of 2970 patient procedures were matched during coarsened exact matching. Increased LACE+ score significantly predicted readmission within 90 days of discharge for Q4 versus Q1 (6.28% vs 1.91%; P = .003), Q4 versus Q2 (12.30% vs 5.56%; P <.001), and Q4 versus Q3 (13.84% vs 7.33%; P <.001). Increased LACE+ score also significantly predicted emergency department visits within 90 days for Q4 versus Q1 (9.29% vs 3.01%; P <.001), Q4 versus Q2 (11.31% vs 3.57%; P <.001), and Q4 versus Q3 (13.70% vs 8.48%; P = .003). Higher LACE+ score also significantly predicted secondary reoperation within 90days for Q4 versus Q1 (3.83% vs 1.37%; P = .035), Q4 versus Q2 (5.95% vs 3.37%; P = .042), and Q4 versus Q3 (7.50% vs 3.26%; P <.001). The results of this study demonstrate that the LACE+ index may be suitable as a prediction model for patient outcomes in a plastic surgery population.

Abstract TP165: Mortality After Large Artery Occlusion Acute Ischemic Stroke

Introduction: Despite randomized trials demonstrating the benefit of endovascular therapy (EVT), large artery occlusion (LAO) acute ischemic stroke (AIS) remains associated with high mortality. Identifying factors associated with mortality for patients presenting with LAO AIS can assist in therapeutic decision-making and prognostication. Hypothesis: Among patients with LAO AIS, factors associated with 90-day mortality include older age, higher presenting NIHSS score, and lower final Thrombolysis in Cerebral Infarction (TICI) score. Methods: From November 2016 to April 2019, we conducted a retrospective analysis from a large healthcare system’s stroke network registry of patients presenting with ICA and/or MCA occlusions. Ninety-day mortality status from registry follow-up was corroborated with the Social Security Death Index. A multivariable logistic regression model was fitted to determine demographic and clinical characteristics associated with 90-day mortality. Results: Among 796 patients with 800 encounters, mean age was 68 years, 52% were women, mean presenting NIHSS was 14, and 97% presented within 24 hours of last known well time. Fifty-one percent were treated with EVT. Mortality rate for the entire cohort was 25%. In a univariate analysis, there were significant differences in age, gender, race, blood glucose, presenting NIHSS, hypertension, atrial fibrillation, CTP core volume, CTP delayed perfusion volume, EVT treatment, number of passes for EVT, final TICI score, and discharge mRS, between patients with and without 90-day mortality. In the multivariable model, increasing age (per 10 years, OR 1.54, 95% CI 1.20, 1.97) and higher discharge mRS (per 1 point, OR 4.47, 95% CI 3.05, 6.55) were associated with 90-day mortality. Female gender (OR 0.57, 95% CI 0.27, 1.18) and final TICI score of 2B or better (OR 0.42, 95% CI 0.18, 1.01) were protective against 90-day mortality. Conclusions: Increasing age and worse discharge functional outcome are associated with 90-day mortality after LAO AIS. Female gender and better revascularization after EVT are associated with lower mortality. Additional studies are required to refine mortality outcome prediction models for patients presenting with LAO AIS.

Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks

ObjectiveTo develop artificial neural network (ANN)-based functional outcome prediction models for patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis based on immediate pretreatment parameters. MethodsThe derived cohort consisted of 196 patients with AIS treated with intravenous thrombolysis between 2009 and 2017 at Shuang Ho Hospital in Taiwan. We evaluated the predictive value of parameters associated with major neurologic improvement (MNI) at 24 h after thrombolysis as well as the 3-month outcome. ANN models were applied for outcome prediction. The generalizability of the model was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), ResultsThe parameters associated with MNI were blood pressure (BP), heart rate, glucose level, consciousness level, National Institutes of Health Stroke Scale (NIHSS) score, and history of diabetes mellitus (DM). The parameters associated with the 3-month outcome were age, consciousness level, BP, glucose level, hemoglobin A1c, history of DM, stroke subtype, and NIHSS score. After adequate training, ANN Model 1 to predict MNI achieved an AUC of 0.944. Accuracy, sensitivity, and specificity were 94.6%, 89.8%, and 95.9%, respectively. ANN Model 2 to predict the 3-month outcome achieved an AUC of 0.933, with accuracy, sensitivity, and specificity of 88.8%, 94.7%, and 86.5%, respectively. ConclusionsThe ANN-based models achieved reliable performance to predict MNI and 3-month outcomes after thrombolysis for AIS. The models proposed have clinical value to assist in decision-making, especially when invasive adjuvant strategies are considered.

Can the emergency department triage category and clinical presentation predict hospitalization of H1N1 patients?

BackgroundHuman H1N1 Influenza A virus was first reported in 2009 when seasonal outbreaks consistently occurred around the world. H1N1 patients present to the emergency departments (ED) with flu-like symptoms extending up to severe respiratory symptoms that require hospital admission. Developing a prediction model for patient outcomes is important to select patients for hospital admission. To date, there is no available data to guide the hospital admission of H1N1 patients based on their initial presentation.ObjectiveThe aim of this study was to investigate the predictors of hospital admission of H1N1 patients presenting in the ED.MethodsWe conducted a retrospective review of all laboratory-confirmed H1N1 cases presenting to the ED of a tertiary university hospital in the Eastern region of Saudi Arabia within the period from November 2015 to January 2016. We retrieved data of the initial triage category, vital signs, and presenting symptoms. Multivariate logistic regression analysis was performed to evaluate risk factors for hospital admission among H1N1patients presented to the ED.ResultsWe identified 333 patients with laboratory-confirmed H1N1. Patients were classified into two groups: admitted group (n=80; 24%) and non-admitted group (n=253; 76%). Sixty patients (75%) were triaged under category IV. Triage category of level III and less were the most predictive for hospital admission. Multivariate regression analysis showed that of all vital signs, tachypnea was a significant risk factor for hospital admission (OR=1.1; 95% CI 1.02 to 1.13, p<0.01). The association between lower triage category and hospital stay was statistically significant (χ2=6.068, p=0.037). Also, patients with dyspnea were 4.5 times more likely to have longer hospital stay (OR=4.5; 95% CI 1.2 to 17.1, p=0.025).ConclusionLower triage category and increased respiratory rate predict the need for hospital admission of H1N1 infected patients; while patients with dyspnea or bronchial asthma are likely to stay longer in the hospital. Further prospective studies are needed to evaluate the accuracy of using the CTAS and other clinical parameters in predicting hospitalization of H1N1 patients during outbreaks.

Re: Axitinib versus Placebo as an Adjuvant Treatment of Renal Cell Carcinoma: Results from the Phase III, Randomized ATLAS Trial.

No AccessJournal of UrologyUrological Survey1 Jul 2019Re: Axitinib versus Placebo as an Adjuvant Treatment of Renal Cell Carcinoma: Results from the Phase III, Randomized ATLAS Trial M. Pilar LagunaMD, PhD M. Pilar LagunaM. Pilar Laguna More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000268AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Re: Axitinib versus Placebo as an Adjuvant Treatment of Renal Cell Carcinoma: Results from the Phase III, Randomized ATLAS Trial." The Journal of Urology, 202(1), pp. 27–28 Suggested Reading : Postoperative surveillance protocol for patients with localized and locally advanced renal cell carcinoma based on a validated prognostic nomogram and risk group stratification system. J Urol 2005; 174: 466. Link, Google Scholar : Clinical characteristics and outcomes of patients with recurrence 5 years after nephrectomy for localized renal cell carcinoma. J Urol 2011; 185: 433. Link, Google Scholar : An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol 2002; 168: 2395. Link, Google Scholar : Independent validation of the 2002 American Joint Committee on Cancer primary tumor classification for renal cell carcinoma using a large, single institution cohort. J Urol 2005; 173: 1889. Link, Google Scholar : A postoperative prognostic nomogram for renal cell carcinoma. J Urol 2001; 166: 63. Link, Google Scholar : A postoperative prognostic nomogram predicting recurrence for patients with conventional clear cell renal cell carcinoma. J Urol 2005; 173: 48. Link, Google Scholar : Validation of an integrated staging system toward improved prognostication of patients with localized renal cell carcinoma in an international population. J Urol 2003; 170: 2221. Link, Google Scholar : Symptoms as well as tumor size provide prognostic information on patients with localized renal tumors. J Urol 2004; 172: 2167. Link, Google Scholar © 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 202Issue 1July 2019Page: 27-28 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information M. Pilar Laguna More articles by this author Expand All Advertisement Loading ...

Predicting Outcome 12 Months after Mild Traumatic Brain Injury in Patients Admitted to a Neurosurgery Service.

Accurate outcome prediction models for patients with mild traumatic brain injury (MTBI) are key for prognostic assessment and clinical decision-making. Using multivariate machine learning, we tested the unique and added predictive value of (1) magnetic resonance imaging (MRI)-based brain morphometric and volumetric characterization at 4-week postinjury and (2) demographic, preinjury, injury-related, and postinjury variables on 12-month outcomes, including global functioning level, postconcussion symptoms, and mental health in patients with MTBI. A prospective, cohort study of patients (n = 147) aged 16-65 years with a 12-month follow-up. T1-weighted 3 T MRI data were processed in FreeSurfer, yielding accurate cortical reconstructions for surface-based analyses of cortical thickness, area, and volume, and brain segmentation for subcortical and global brain volumes. The 12-month outcome was defined as a composite score using a principal component analysis including the Glasgow Outcome Scale Extended, Rivermead Postconcussion Questionnaire, and Patient Health Questionnaire-9. Using leave-one-out cross-validation and permutation testing, we tested and compared three prediction models: (1) MRI model, (2) clinical model, and (3) MRI and clinical combined. We found a strong correlation between observed and predicted outcomes for the clinical model (r = 0.55, p < 0.001). The MRI model performed at the chance level (r = 0.03, p = 0.80) and the combined model (r = 0.45, p < 0.002) were slightly weaker than the clinical model. Univariate correlation analyses revealed the strongest association with outcome for postinjury factors of posttraumatic stress (Posttraumatic Symptom Scale-10, r = 0.61), psychological distress (Hospital Anxiety and Depression Scale, r = 0.52), and widespread pain (r = 0.43) assessed at 8 weeks. We found no added predictive value of MRI-based measures of brain cortical morphometry and subcortical volumes over and above demographic and clinical features.

36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016.

36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016.

Re: Conditional Survival after Radical Cystectomy for Bladder Cancer: Evidence for a Patient Changing Risk Profile over Time

No AccessJournal of UrologyAdult Urology1 Mar 2016Re: Conditional Survival after Radical Cystectomy for Bladder Cancer: Evidence for a Patient Changing Risk Profile over Time Sam S. ChangMD Sam S. ChangSam S. Chang More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.12.003AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Re: Conditional Survival after Radical Cystectomy for Bladder Cancer: Evidence for a Patient Changing Risk Profile over Time." The Journal of Urology, 195(3), pp. 610–611 References 1 : Comparative performance of comorbidity indices for estimating perioperative and 5-year all cause mortality following radical cystectomy for bladder cancer. J Urol2013; 190: 55. Link, Google Scholar 2 : Disease-free survival at 2 or 3 years correlates with 5-year overall survival of patients undergoing radical cystectomy for muscle invasive bladder cancer. J Urol2011; 185: 456. Link, Google Scholar 3 : The SPARC score: a multifactorial outcome prediction model for patients undergoing radical cystectomy for bladder cancer. J Urol2013; 190: 2005. Link, Google Scholar © 2016 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 195Issue 3March 2016Page: 610-611 Advertisement Copyright & Permissions© 2016 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sam S. Chang More articles by this author Expand All Advertisement PDF downloadLoading ...

External validation of the Norwegian survival prediction model in trauma after major trauma in Southern Finland.

The Norwegian Survival Prediction Model in Trauma (NORMIT) is a newly developed outcome prediction model for patients with trauma. We aimed to compare the novel NORMIT to the more commonly used Trauma and Injury Severity Score (TRISS) in Finnish trauma patients. We performed a retrospective open-cohort study, using the trauma registry of Helsinki university hospital's trauma unit, including severely injured patients (new injury severity score > 15) admitted from 2007 to 2011. We used 30-day in-hospital mortality as the primary outcome, and discharge functional outcome as a secondary outcome of interest. Model performance was evaluated by comparing discrimination (by area under the receiver operating characteristic curve [AUC]), using a re-sample bootstrap technique, and by assessing calibration (GiViTI belt). We identified 1111 patients fulfilling the study inclusion criteria. Overall mortality was 13% (n = 147). NORMIT showed slightly better discrimination for mortality prediction (AUC = 0.83, 95% confidence interval [CI] = 0.80-0.86 vs. AUC = 0.79, 95% CI = 0.75-0.83, P = 0.004) and functional outcome prediction (AUC = 0.78, 95% CI = 0.76-0.82 vs. AUC = 0.75, 95% CI = 0.72-0.78, P < 0.001) than TRISS. Calibration testing revealed poor calibration for both NORMIT and TRISS (P < 0.001), by giving too pessimistic predictions (predicted survival significantly lower than actual survival). NORMIT and TRISS showed good discrimination, but poor calibration, in this mixed cohort of severely injured trauma patients from Southern Finland. We found NORMIT to be a feasible alternative to TRISS for trauma patient outcome prediction, but trauma prediction models with improved calibration are needed.

Diabetes Mellitus is Independently Associated with an Increased Risk of Mortality in Patients with Clear Cell Renal Cell Carcinoma

Conflicting data exist on the interaction of diabetes mellitus with outcomes in patients with renal cell carcinoma. We evaluated the association of diabetes mellitus with survival in patients with clear cell renal cell carcinoma treated with nephrectomy. We reviewed the records of 1,964 patients treated surgically for sporadic, unilateral, M0 clear cell renal cell carcinoma between 1990 and 2008. One pathologist re-reviewed all specimens to confirm clear cell renal cell carcinoma. We matched 257 patients with diabetes 1:2 to referent patients without diabetes according to clinicopathological and surgical features. Cancer specific and overall survival was estimated using the Kaplan-Meier method. Cox models were used to evaluate associations with outcomes. A total of 257 patients (13%) had diabetes mellitus. They were significantly older and more likely to be obese, and had higher Charlson scores, renal impairment and smoking rates, and worse performance status at surgery (p<0.001). Pathological features were similar between the groups. Median postoperative followup was 8.7 years. Five-year cancer specific survival was similar in patients with and without diabetes (82% vs 86%, p = 0.1) while 5-year overall survival was significantly worse in those with diabetes (65% vs 74%, p<0.001). On multivariable analysis diabetes mellitus independently predicted cancer specific mortality (HR 1.55, 95% CI 1.08-2.21, p = 0.02) and all-cause mortality (HR 1.32, 95% CI 1.06-1.64, p = 0.01). Our results suggest that diabetes mellitus is independently associated with decreased cancer specific and overall survival in patients with surgically treated clear cell renal cell carcinoma.

Impact of Recurrent Copy Number Alterations and Cancer Gene Mutations on the Predictive Accuracy of Prognostic Models in Clear Cell Renal Cell Carcinoma

Several recently reported recurrent genomic alterations in clear cell renal cell carcinoma are linked to pathological and clinical outcomes. We determined whether any recurrent cancer gene mutations or copy number alterations identified in the TCGA (The Cancer Genome Atlas) clear cell renal cell carcinoma data set could add to the predictive accuracy of current prognostic models. In 413 patients who underwent nephrectomy/partial nephrectomy we investigated whole exome, copy number array analyses and clinical variables. We identified 65 recurrent genomic alterations based on prevalence and combined them into 35 alterations, including 12 cancer gene mutations. Genomic markers were modeled using the elastic net algorithm with preoperative variables (tumor size plus patient age) and in the postoperative setting using the externally validated Mayo Clinic SSIGN (stage, size, grade and necrosis) prognostic scoring system. These models were subjected to internal validation using bootstrap. Median followup in survivors was 45 months. Several markers correlated with adverse cancer specific survival and time to recurrence on univariate analysis. However, most of them lost significance when controlling for tumor size with or without age in the preoperative models or for SSIGN score in the postoperative setting. Adding multiple genomic markers selected by the elastic net algorithm failed to substantially add to the predictive accuracy of any preoperative or postoperative model for cancer specific survival or time to recurrence. While recurrent copy number alterations and cancer gene mutations are biologically significant, they do not appear to improve the predictive accuracy of existing models of clinical cancer specific survival or time to recurrence for clear cell renal cell carcinoma.

Development of a new outcome-prediction model for patients with urothelial carcinoma of the bladder prior to cystectomy.

360 Background: Prognostic models for patients undergoing cystectomy are spares and mostly rely on histopathological parameters. The present study investigated clinical prognostic indicators that are available prior to cystectomy for developing a risk stratification model. Methods: Charts were reviewed from 279 bladder cancer patients that were treated with radical cystectomy and bilateral lymphadenectomy. Of those 194 were eligible for study entry due to availability of necessary data. None of the patients had neoadjuvant therapy. Various preoperative parameters were analyzed and optimum cut-off points were identified using ROC curves. A risk stratification model was developed based on multivariate analysis. Analyzed factors included serum-creatinine, hemoglobin, white blood cells, C - reactive protein (CRP), age, gender, and body mass index. Results: In multivariate analysis, preoperative serum-creatinine (&gt;93 umol/l), white blood cells (&gt;10.2 tsd/ul), CRP (&gt;3 mg/l) and BMI (&lt;18,5 kg/m2 or ≥30 kg/m2) were independent predictor of poor cancer-specific survival (CSS). A new scoring model was developed, consisting of those four parameters. The stratification model showed significant differences based on Kaplan-Meier analysis for the whole cohort as well as for subgroup analysis (pT2-4, Nx, M0) (p&lt;0.001). CSS after 36 (60) months for low, intermediate and high risk group was 82.6% (76.2%), 37.7% (30.9%) and 22.2% (16.7%), respectively (p&lt;0.0001). Conclusions: Our scoring model based on preoperative clinical parameters can discriminate bladder cancer patients prior to cystectomy in respect of CSS. This might help physicians to recommend treatment options such as neoadjuvant therapy. The scoring model needs external validation considering prospective study design and discriminating patients who received neoadjuvant chemotherapy.

Prognostic Impact of Preoperative Neutrophil-to-Lymphocyte Ratio in Localized Nonclear Cell Renal Cell Carcinoma

The preoperative neutrophil-to-lymphocyte ratio was proposed as a prognostic factor for localized clear cell renal cell carcinoma. We evaluated its role in nonclear cell renal cell carcinoma. We queried 2 prospective kidney cancer databases. Patients who underwent full resection of localized (T1-3 N0/+ M0) nonclear cell renal cell carcinoma by radical or partial nephrectomy were included in analysis. Associations of the continuously coded neutrophil-to-lymphocyte ratio with disease-free survival were assessed with univariable and multivariable Cox regression models. Prognostic accuracy was evaluated with the Harrell concordance index. Our final cohort included 281 patients. The 5-year disease-free survival rate was 88.1%. The neutrophil-to-lymphocyte ratio was significantly associated with disease-free survival. With each 1.0 increase in the ratio the risk of recurrence increased by 15% (HR 1.15, p=0.028). On multivariable analysis TNM group (HR 2.84, p=0.025), Fuhrman grade (HR 3.40, p<0.001) and the neutrophil-to-lymphocyte ratio (HR 1.17, p=0.022) were independently associated with disease-free survival. Adding the neutrophil-to-lymphocyte ratio improved the accuracy of a base model to predict disease-free survival from 78.8% to 80.8%. The neutrophil-to-lymphocyte ratio is an independent prognostic factor for disease-free survival after surgery with curative intent for localized nonclear cell renal cell carcinoma. It significantly increases the accuracy of established prognostic factors. The neutrophil-to-lymphocyte ratio may provide a meaningful adjunct for patient counseling and clinical trial design.

The SPARC Score: A Multifactorial Outcome Prediction Model for Patients Undergoing Radical Cystectomy for Bladder Cancer

While multiple independent clinicopathological variables are associated with the outcome of radical cystectomy for bladder cancer, limited prediction tools exist to facilitate individualized risk assessment. We developed the SPARC (Survival Prediction After Radical Cystectomy) score, a prediction model for bladder cancer specific survival after radical cystectomy. We evaluated 2,403 patients who underwent radical cystectomy without neoadjuvant therapy at our institution between 1980 and 2008 with pathological re-review of all specimens. Of these patients 1,776 with nonmetastatic urothelial carcinoma were identified for analysis. A multivariate model was developed using stepwise selection to determine variables associated with cancer specific survival. We created a scoring system based on the β coefficients of this model. Median followup after radical cystectomy in patients alive at last followup was 10.5 years (IQR 7.3, 15.3), during which time 610 had died of bladder cancer. In addition to pathological tumor stage, nodal status, multifocality and lymphovascular invasion, the patient specific factors of Charlson comorbidity index, Eastern Cooperative Oncology Group (ECOG) performance status, current smoking, preoperative hydronephrosis and receipt of adjuvant chemotherapy were significantly associated with the risk of bladder cancer death. We used cumulative scores of these variables to stratify patients into risk groups with 95%, 80%, 60%, 38% and 23% 5-year cancer specific survival from the lowest to the highest risk group, respectively (p<0.0001). The concordance index of this model was 0.75. We present a model to individualize the estimation of cancer specific survival after radical cystectomy. Pending external validation, these data may be used for patient counseling, specifically in regard to recommendations for adjuvant therapy and surveillance frequency, as well as for clinical trial development.

Comparative Performance of Comorbidity Indices for Estimating Perioperative and 5-Year All Cause Mortality Following Radical Cystectomy for Bladder Cancer

Radical cystectomy continues to be associated with a nonnegligible risk of perioperative death and all cause mortality in the years after surgery remains relatively high. We investigated the comparative ability of various comorbidity indices to predict perioperative and 5-year all cause mortality after radical cystectomy. We evaluated 891 patients who underwent radical cystectomy between 1994 and 2005. The associations of American Society of Anesthesiologists (ASA) score, Charlson comorbidity index, Elixhauser index and ECOG (Eastern Cooperative Oncology Group) performance status with outcomes were assessed using Cox regression models. Model performance was compared with area under receiver operating curves. A total of 33 (3.7%) patients died within 90 days of radical cystectomy. On multivariate analysis locally advanced pathological tumor stage (HR 4.86, p = 0.002) as well as Elixhauser index (HR 1.48, p = 0.002), ASA score (HR 3.17, p = 0.001) and ECOG (HR 2.40, p <0.0001) were significantly associated with 90-day perioperative mortality. Median followup after radical cystectomy was 10.1 years, during which time 576 patients died. Charlson comorbidity index (HR 1.23, p <0.0001), Elixhauser index (HR 1.28, p <0.0001), ASA score (HR 1.44, p = 0.007) and ECOG (HR 1.97, p <0.0001) were independent predictors of 5-year all cause mortality. Moreover Charlson comorbidity index (AUC 0.798, p <0.0001), Elixhauser index (AUC 0.770, p = 0.03) and ECOG (AUC 0.769, p = 0.03) significantly enhanced the performance of a base model which did not include comorbidity status (AUC 0.757) to predict 5-year all cause mortality. Comorbidity status is predictive of perioperative death and 5-year all cause mortality after radical cystectomy and, therefore, should be incorporated into patient counseling and risk stratification models. Further prospective studies are warranted to overcome the retrospective limitations in determining the relative prognostic value of various comorbidity indices.

Identification of Genomic Alterations Associated With Metastasis and Cancer Specific Survival in Clear Cell Renal Cell Carcinoma

We identified regions of DNA copy number changes that are significantly associated with metastasis and clinical outcome in patients with clear cell renal cell carcinoma. We analyzed 53 primary clear cell renal cell carcinomas, including 31 metastasized and 22 nonmetastasized tumors, by array comparative genomic hybridization with a median resolution of 1 to 1.5 Mbp. To validate copy number aberrations with potential prognostic value we performed fluorescence in situ hybridization analysis using commercially available fluorescent probes. We identified 5 recurrent chromosomal aberrations that were significantly associated with metastasis, including gains of 1q21.3, 12q13.12, 12q13.3q14.1 and 20q11.21q13.2, and loss of 9p21.3p24.1. The most prominent of them with the highest OR for metastatic risk were loss of 9p21.3p24.1, and gains of 1q21.3 and 20q11.21q13.32. Eight alterations involving chromosomes 7, 9, 12, 16 and 20 significantly correlated with shortened cancer specific survival. The lowest p values on Kaplan-Meier analysis showed losses of 9p21.3p24.1 and 9q32q33.1, and gains of 7q36.3 and 20q11.21q13.32. Fluorescence in situ hybridization done in the same cohort for the 4 select regions 1q21.3, 7q36.3, 9p21.3p24.1 and 20q11.21q13.32 clearly confirmed the results of array comparative genomic hybridization. Data suggest that specific chromosomal alterations in clear cell renal cell carcinoma can be used to predict metastasis and cancer specific survival in patients with clear cell renal cell carcinoma. It seems possible to design a combined fluorescence in situ hybridization assay based on these genetic targets for outcome prediction, which can be used for routine diagnostics.