Preoperative Prediction Model Research Articles

Accurate prediction tools in prostate cancer require consistent assessment of included variables

Objective: The aim of this study was to create a preoperative prediction model predicting extraprostatic tumour growth in men with clinically organ-confined disease from a prospectively collected Swedish cohort. Materials and methods: The study used data from 3386 men in the prospective multi-centre Laparoscopic Prostatectomy Robot Open (LAPPRO) trial, with 14 participating urological departments. External validation was performed using a cohort of 634 men from the largest study centre with patients who underwent surgery before and after the inclusion period of the LAPPRO study. External validation of the updated Partin table was used for comparison. The prediction models were created by multivariable logistic regression. Nomogram prediction performance, internal, internal–external and external validation are presented as the area under the receiver operating characteristic curve (AUC). Results: The nomogram reached a prediction performance with an AUC of 0.741, with internal and external validation of 0.738 and 0.698, respectively. Internal–external validation showed great divergence between centres, with AUCs ranging from 0.476 to 0.892, indicating inconsistencies in pathological staging or one or more of the included variables in the regression model. When including centre as a variable in the multivariable model it was significantly associated with the outcome of pT3 (p < 0.001). AUC for external validation of the Partin table was 0.694. Conclusions: Accurate prediction tools in prostate cancer require consistent assessment of included variables, and local validation is needed before the use of such tools in clinical practice.

Predictors of Hospital Length of Stay in an Enhanced Recovery After Surgery Program for Primary Total Hip Arthroplasty

BackgroundShorter length of stay (LOS) for total hip arthroplasty (THA) is becoming standard, yet variables identifying candidates for a 1-day discharge in an enhanced recovery after surgery program are not well defined. With growing emphasis on cost-efficiency and bundled care for THA, this study looked to identify variables that correlated with LOS. MethodsA retrospective chart review was performed for 273 primary THAs, from April 2014 to January 2015. Clinical measures differentiating a 1-day LOS cohort from that of a LOS longer than 1 day were identified. Direct medical costs were calculated for services billed during hospitalization. ResultsLogistic regression identified the following preoperative patient characteristics to correlate with an LOS >1 day: older age (odds ratio [OR]: 1.06, P < .001), increased body mass index (OR: 1.06, P = .005), female gender (OR: 1.76, P = .031), American Society of Anesthesiologists score 3 or 4 (OR: 1.84, P = .029), and coronary artery disease (OR: 3.90, P = .013). After adjusting for age, body mass index, and gender, the following perioperative variables led to an LOS ≥2 days: general anesthesia (OR: 2.24, P = .007), longer operative time (OR: 1.04, P < .001), and increased blood loss (OR: 1.01, P = .001). Postoperatively, not ambulating on the day of surgery strongly correlated with an LOS ≥2 days (OR: 3.9, P < .001). Hospital costs were approximately $2900 higher for a 2-day LOS. ConclusionWith growing emphasis on cost-efficiency, studying the association of clinical factors with LOS is necessary to develop a preoperative and perioperative predictive risk stratification model that may be used to help optimize discharge protocols for patients in an enhanced recovery after surgery program.

Predictive models for improved prognostication and selection of neoadjuvant and adjuvant systemic chemotherapy in upper tract urothelial cell carcinoma.

456 Background: Chemotherapy is still underutilized in management of upper tract urothelial carcinoma (UTUC). We created pre and post-operative predictive tools combining independent prognostics to guide selection of patients for neoadjuvant and adjuvant chemotherapy. Methods: From the UTUC collaboration database (1,453 patients who underwent radical nephroureterectomy [RNU] at 13 academic institutions); a preoperative predictive model was created using 659 patients in whom all preoperative prognostic variables were available and a post-operative model was created using 586 patients with non-metastatic/ high-grade UTU. After multivariable survival analyses, a backward step-down selection process was applied to create a preoperative nomogram. Internal validation was performed using 200 bootstrap resamples. For the postoperative model, TALL score was created based on the sum of the independent prognostic variables. Results: Preoperative model: Grade, architecture and location of the tumor were independently associated with nonorgan confined disease. A nomogram including these 3 variables achieved 76.6% accuracy in predicting nonorgan confined upper tract urothelial cancer. Postoperative model: TALL score (1-7) was the sum of T ( ≤ T1 = 1, T2 = 2, T3 = 3 and T4 = 4), A (papillary = 0 and sessile = 1), LVI (absent = 0 and present = 1) and L (lymphadenectomy = 0 and no lymphadenectomy = 1). Five-year disease-free survival (DFS) and cancer-specific survival (CSS) were stratified into four risk categories according to the TALL score: low (TALL 0-2; 86 % DFS and 90 % CSS), intermediate (TALL = 3; 71 % DFS and 75 % CSS), high (TALL = 4; 57 % DFS and 58 % CSS) and very high risk (TALL ≥ 5; 34 % DFS and 38 % CSS) using Kaplan-Meier survival analyses. TALL score was externally validated in a single-center cohort of 85 UTUC patients. Conclusions: We developed validated multivariable prognostic tools for prediction of locally advanced UTUC and oncological outcomes after RNU for UTUC. These prediction models can be used for patient counseling, selection for neoadjuvant/adjuvant systemic therapies and design of clinical trials.

Risk of postoperative acute kidney injury in patients undergoing orthopaedic surgery—development and validation of a risk score and effect of acute kidney injury on survival: observational cohort study

Study question What is the predicted risk of acute kidney injury after orthopaedic surgery and does it affect short term and long term survival? Methods The cohort comprised adults resident...

Operative Strategies and Outcomes in Type A Aortic Dissection After the Enactment of a Multidisciplinary Aortic Surgery Team.

The purpose of this study was to compare operative strategies and patient outcomes in acute type A aortic dissection (ATAAD) repairs before and after the implementation of a multidisciplinary aortic surgery team. Between May 2005 and July 2014, 101 patients underwent ATAAD repair at our institution. A dedicated multidisciplinary aortic surgery team (experienced aortic surgeon, perfusionists, cardiac anesthesiologists, nurses, and radiologists) was formed in 2010. We retrospectively compared ATAAD repair outcomes in patients before (2005-2009, N = 39) and after (2010-2014, N = 62) implementation of our program. Expected operative mortality was calculated using the International Registry of Acute Aortic Dissection preoperative predictive model. This study demonstrated a significant reduction in operative mortality after implementation of the aortic surgery program (30.8% vs. 9.7%; P = 0.014). There was also an increase in the complexity of surgical technique and perfusion strategies with fewer postoperative complications related to respiratory (P < 0.0001) and renal failure (P = 0.034). Baseline demographics were similar, and there was no statistically significant difference in International Registry of Acute Aortic Dissection predictive variables between the 2 groups. However, there was a 3.5-fold reduction in the observed-to-expected (O/E) operative mortality ratio. There was a 50% increase in volume with a significant number of patients being admitted directly to our aortic center for ATAAD repair, thus avoiding delay in operation related to transfers from a secondary hospital. Patient outcomes are improved when the surgical treatment of ATAAD is managed by a high-volume multidisciplinary aortic surgery team.

Predictors of Severe Morbidity After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy for Patients With Colorectal Peritoneal Carcinomatosis.

Severe morbidity after cytoreductive surgery (CRS) followed by hyperthermic intraperitoneal chemotherapy (HIPEC) is, besides the obvious short-term consequences, associated with impaired long-term outcomes. The risk factors for severe morbidity in patients with peritoneal carcinomatosis (PC) of colorectal origin are poorly defined. This study aimed to identify risk factors for severe morbidity after CRS + HIPEC in patients with colorectal PC. Patients with colorectal PC who underwent CRS + HIPEC between 2007 and 2015 were categorized and compared between those with and those without severe morbidity. Risk factors were identified using logistic regression analysis. Morbidity was graded according to the Clavien-Dindo classification, with grade 3 or higher indicating severe morbidity. This study included 211 patients, of whom 53 patients (25.1%) experienced morbidity of grade 3 or higher. The identified risk factors for severe morbidity were extensive prior surgery [odds ratio (OR) 4.3], a positive recent smoking history (OR 4.0), a poor physical performance status (OR 2.9), and extensive cytoreduction (OR 1.2 per additional resection). Patients with a greater number of risk factors more often had severe morbidity and higher reoperation, readmission, and mortality rates. Furthermore, an internally validated preoperative prediction model for severe morbidity with an area under the curve of 70% was constructed. The current study identified risk factors for severe morbidity after CRS + HIPEC in patients with colorectal PC. Patients with a combination of risk factors have a substantial risk of severe morbidity and therefore should be carefully selected for CRS + HIPEC. The preoperative decision model can be a valuable additional tool in this process of patient selection.

Development of Validated Computer Based Preoperative Predictive Model for Proximal Junction Failure or Clinically Significant Proximal Junction Kyphosis with 86% Accuracy Based on 510 Adult Spinal Deformity Patients with Two-Year Follow-Up

Development of Validated Computer Based Preoperative Predictive Model for Proximal Junction Failure or Clinically Significant Proximal Junction Kyphosis with 86% Accuracy Based on 510 Adult Spinal Deformity Patients with Two-Year Follow-Up

Parenchymal Volumetric Assessment as a Predictive Tool to Determine Renal Function Benefit of Nephron-Sparing Surgery Compared with Radical Nephrectomy.

To develop a preoperative prediction model using a computer-assisted volumetric assessment of potential spared parenchyma to estimate the probability of chronic kidney disease (CKD, estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m(2)) 6 months from extirpative renal surgery (nephron-sparing surgery [NSS] or radical nephrectomy [RN]). Retrospective analysis of patients who underwent NSS or RN at our institution from January 2000 to June 2013 with a compatible CT scan 6-month renal function follow-up was performed. Primary outcome was defined as the accuracy of 6-month postoperative eGFR compared with actual postoperative eGFR based on root mean square error (RMSE). Models were constructed using renal volumes and externally validated. A clinical tool was developed on the best model after a given surgical procedure using area under the curve (AUC). We identified 130 (51 radical, 79 partial) patients with a median age of 58 years (interquartile range [IQR] 48-67) and preoperative eGFR of 82.1 (IQR 65.9-104.3); postoperative CKD (eGFR <60) developed in 42% (55/130). We performed various linear regression models to predict postoperative eGFR. The Quadratic model was the highest performing model, which relied only on preoperative GFR and the volumetric data for a RMSE of 15.3 on external validation corresponding to a clinical tool with an AUC of 0.89. Volumetric-based assessment provides information to predict postoperative eGFR. A tool based on this equation may assist surgical counseling regarding renal functional outcomes before renal tumor surgical procedures.

Identifying the Most Informative Prediction Tool for Cancer-specific Mortality After Radical Prostatectomy: Comparative Analysis of Three Commonly Used Preoperative Prediction Models

BackgroundThe D’Amico risk stratification, Cancer of the Prostate Risk Assessment (CAPRA) score, and Stephenson nomogram are widely used prediction tools for biochemical recurrence and survival after radical prostatectomy (RP). These models have not been compared with respect to cancer-specific mortality (CSM) prediction. ObjectiveTo validate and compare the prediction tools for 10-yr CSM. Design, setting, and participantsOverall, 2485 prostate cancer patients underwent RP in a European tertiary care center. Outcome measurements and statistical analysisThree preoperative models (D’Amico, CAPRA, and Stephenson) were compared in terms of their ability to predict 10-yr CSM; therefore, accuracy tests (area under the receiver operating characteristic curve [AUC]), calibration plots, and decision curve analysis (DCA) were assessed for each model. Results and limitationsCSM at 10 yr was 3.6%. The AUC was 0.76, 0.77, and 0.80 for the D’Amico, CAPRA, and Stephenson models, respectively. In calibration plots, predicted probabilities were close to the observed probabilities for the D’Amico model but showed underestimation of CSM for the Stephenson nomogram and overestimation of CSM for the CAPRA score. DCA identified a benefit for the CAPRA score. These results apply to patients treated at a European tertiary care center. ConclusionsDespite good discriminatory power, all tested models had some shortcomings in terms of prediction of 10-yr CSM. All three models showed good performance in North American cohorts, but our results suggested a lack of generalizability to European patients. To overcome this issue, local recalibration of the variable weights could be performed. Another possibility is the development of more universal markers that are independent of regional practice differences or, alternatively, the development of better tools to quantify clinical practice differences. Patient summaryPrediction tools can predict cancer survival prior surgery, relying on points for age, prostate-specific antigen levels, aggressiveness, and percentage of cancer at biopsy. These tools are reliable in North American patients but have shortcomings for identifying patients at high risk of prostate cancer death in Europe.

Preoperative predictive model of recovery of urinary continence after radical prostatectomy.

To build a predictive model of urinary continence recovery after radical prostatectomy (RP) that incorporates magnetic resonance imaging (MRI) parameters and clinical data. We conducted a retrospective review of data from 2,849 patients who underwent pelvic staging MRI before RP from November 2001 to June 2010. We used logistic regression to evaluate the association between each MRI variable and continence at 6 or 12 months, adjusting for age, body mass index (BMI) and American Society of Anesthesiologists (ASA) score, and then used multivariable logistic regression to create our model. A nomogram was constructed using the multivariable logistic regression models. In all, 68% (1,742/2,559) and 82% (2,205/2,689) regained function at 6 and 12 months, respectively. In the base model, age, BMI and ASA score were significant predictors of continence at 6 or 12 months on univariate analysis (P < 0.005). Among the preoperative MRI measurements, membranous urethral length, which showed great significance, was incorporated into the base model to create the full model. For continence recovery at 6 months, the addition of membranous urethral length increased the area under the curve (AUC) to 0.664 for the validation set, an increase of 0.064 over the base model. For continence recovery at 12 months, the AUC was 0.674, an increase of 0.085 over the base model. Using our model, the likelihood of continence recovery increases with membranous urethral length and decreases with age, BMI and ASA score. This model could be used for patient counselling and for the identification of patients at high risk for urinary incontinence in whom to study changes in operative technique that improve urinary function after RP.

The cost-effectiveness of selective lymphadenectomy based on a preoperative prediction model in patients with endometrial cancer: Insights from the US and Korean healthcare systems

ObjectiveThe aim of this study was to determine the cost-effectiveness of selective lymphadenectomy using a preoperative prediction model compared to routine lymphadenectomy for patients undergoing surgery for endometrial cancer in the US and Korea. MethodsWe used a modified Markov model to estimate clinical and economic outcomes for newly diagnosed, apparent early-stage endometrial cancer patients under two different strategies: (1) selective lymphadenectomy, where patients classified as low risk based on the preoperative prediction model did not undergo complete surgical staging, and (2) routine lymphadenectomy, where all patients underwent complete surgical staging. Published data were used to estimate the rates of adjuvant therapy and survival. Costs were calculated from the perspective of US or Korean payers. Cost-effectiveness ratios were analyzed separately using data from each country. ResultsBase-case analysis indicated that selective lymphadenectomy was less costly ($6454 vs. $7079 in Korea; $23,995 vs. $26,318 in the US) and more effective (6.91 quality-adjusted life years (QALYs) vs. 6.85 QALYs in Korea; 6.87 QALYs vs. 6.81 QALYs in the US) than routine lymphadenectomy in both countries. This result was robust in a deterministic sensitivity analysis, with the exception of when the utility scores for patients with lymphedema were varied. So long as a modest preference for avoiding lymphedema (disutility of 0.04) was obtained, selective lymphadenectomy remained the dominant strategy. ConclusionsA selective lymphadenectomy strategy based on a preoperative prediction model was shown to be more cost-effective than routine lymphadenectomy for endometrial cancer patients in the US and Korea.

Predictive model for delayed graft function based on easily available pre-renal transplant variables.

Identification of pre-transplant factors influencing delayed graft function (DGF) could have an important clinical impact. This could allow clinicians to early identify dialyzed chronic kidney disease (CKD) patients eligible for special transplant programs, preventive therapeutic strategies and specific post-transplant immunosuppressive treatments. To achieve these objectives, we retrospectively analyzed main demographic and clinical features, follow-up events and outcomes registered in a large dedicated dataset including 2,755 patients compiled collaboratively by four Italian renal/transplant units. The years of transplant ranged from 1984 to 2012. Statistical analysis clearly demonstrated that some recipients' characteristics at the time of transplantation (age and body weight) and dialysis-related variables (modality and duration) were significantly associated with DGF development (p ≤ 0.001). The area under the receiver-operating characteristic (ROC) curve of the final model based on the four identified variables predicting DGF was 0.63 (95 % CI 0.61, 0.65). Additionally, deciles of the score were significantly associated with the incidence of DGF (p value for trend <0.001). Therefore, in conclusion, in our study we identified a pre-operative predictive model for DGF, based on inexpensive and easily available variables, potentially useful in routine clinical practice in most of the Italian and European dialysis units.

Cost-effectiveness of selective lymphadenectomy based on a preoperative prediction model in patients with endometrial cancer

Objectives: The purpose of this study was to compare the myometrial invasion assessed by preoperative MRI and intraoperative examination in endometrial cancer. Methods: Eighty-seven consecutive endometrial cancer patients from a single institution, evaluated between September 2011 and May 2013, were included. MRI was part of the preoperative routine evaluation. All underwent total hysterectomy and bilateral salpingo-oophorectomy, with the intraoperative assessment of depth of myometrium invasion performed by an experienced pathologist. The final pathology report was used as definitive diagnosis. To calculate the positive predictive value (PPV) and negative predictive value (NPV), the reports were divided in 2 categories: ≤50% and N50% myometrial invasion. Results: All patients underwent perioperative examination, and the MRI report was available in 78 patients. MRI failed to predict the depth of myometrial invasion in the final pathology report in 50% of the cases; the perioperative assessment erred in 23%. The PPV and NPP for the MRI were 64.3% and 68%, respectively. The PPV and NPP for the intraoperative evaluation were 80% and 89.5%, respectively. Final results are shown in Table 1.

Development and external validation of new ultrasound‐based mathematical models for preoperative prediction of high‐risk endometrial cancer

To develop and validate strategies, using new ultrasound-based mathematical models, for the prediction of high-risk endometrial cancer and compare them with strategies using previously developed models or the use of preoperative grading only. Women with endometrial cancer were prospectively examined using two-dimensional (2D) and three-dimensional (3D) gray-scale and color Doppler ultrasound imaging. More than 25 ultrasound, demographic and histological variables were analyzed. Two logistic regression models were developed: one 'objective' model using mainly objective variables; and one 'subjective' model including subjective variables (i.e. subjective impression of myometrial and cervical invasion, preoperative grade and demographic variables). The following strategies were validated: a one-step strategy using only preoperative grading and two-step strategies using preoperative grading as the first step and one of the new models, subjective assessment or previously developed models as a second step. One-hundred and twenty-five patients were included in the development set and 211 were included in the validation set. The 'objective' model retained preoperative grade and minimal tumor-free myometrium as variables. The 'subjective' model retained preoperative grade and subjective assessment of myometrial invasion. On external validation, the performance of the new models was similar to that on the development set. Sensitivity for the two-step strategy with the 'objective' model was 78% (95% CI, 69-84%) at a cut-off of 0.50, 82% (95% CI, 74-88%) for the strategy with the 'subjective' model and 83% (95% CI, 75-88%) for that with subjective assessment. Specificity was 68% (95% CI, 58-77%), 72% (95% CI, 62-80%) and 71% (95% CI, 61-79%) respectively. The two-step strategies detected up to twice as many high-risk cases as preoperative grading only. The new models had a significantly higher sensitivity than did previously developed models, at the same specificity. Two-step strategies with 'new' ultrasound-based models predict high-risk endometrial cancers with good accuracy and do this better than do previously developed models.

Preoperative prediction model for conversion of laparoscopic to open cholecystectomy in patient with acute cholecystitis: based on clinical, laboratory, and CT parameters.

To identify preoperative computed tomography (CT) predictors associated with conversion from laparoscopic to open cholecystectomy and to propose the risk scoring model for prediction of conversion by integrating clinical, laboratory, and CT parameters. The institutional review board approved this retrospective study, and informed consent was waived. One hundred eighty-three patients who underwent a laparoscopic cholecystectomy for acute cholecystitis were evaluated for clinical, laboratory, and CT parameters. Associations between conversion and these parameters were assessed by using univariate and multivariate logistic regression analysis. The risk scoring model was devised using a regression coefficient-based scoring method. Conversion to open cholecystectomy was performed in 30 patients (17%). Multivariate analysis identified age older than 60 years, male, and pericholecystic fluid as independent predictors of conversion. The preoperative prediction model to calculate the risk score for conversion showed sensitivity of 83% and specificity of 72%, with an area under the receiver operator curve of 0.83. Pericholecystic fluid collection was the only CT parameter with clinical parameters of age older than 60 years and male in prediction for conversion in acute cholecystitis. The preoperative prediction model using these 3 parameters can be adapted easily in clinical practice with a good discrimination.

Prediction of the probability of breast cancer-related lymphedema after axillary lymph node dissection.

140 Background: Early detection and timely intervention demonstrate the greatest promise of reducing incidence of late-stage lymphedema (LE) in breast cancer patients undergoing axillary lymph node dissection (ALND). A nomogram was developed for predicting the risk of arm LE in this group. This study’s aim was to test the preoperative prediction model for the early diagnosis of clinical and subclinical LE in ALND including patients prospectively monitored by bioimpedance spectroscopy (BIS). Methods: Patients with pre-existing clinical LE were selected from our existing database. In addition, data on subclinical LE was prospectively collected through our LE monitoring program. This program tracks patients from a baseline, using an L-Dex U400 measurement preoperatively or postoperatively (if no clinical LE), and at 3-6 month intervals. The 5 year probability of LE after ALND was calculated using The Cleveland Clinic Risk Calculator. The discrimination of the nomogram was assessed by calculating the area under (AUC) the receiver operating characteristic curve (ROC); AUC values between 0.7–0.8 represent considerable discrimination. Results: A total of 334 patients who underwent ALND were included in this study; 116 patients with previously diagnosed clinical LE and 218 patients at-risk of LE (monitored with BIS since 2010). Since BIS monitoring began, a total of 58 patients were diagnosed with subclinical LE; a total of 174 patients with clinical or subclinical LE (LE group). The mean age was 60.6+11.4 years in LE group and 57.8+12.03 years in No-LE group (p=0.03). BMI was higher in LE group compared with no LE group (29.8 + 7.4 vs.28.3+6.3 kg/m2(p=0.05). The AUC value was calculated from the nomogram (AUC=0.609). Conclusions: The recently created preoperative prediction model for the diagnosis of LE in ALND is not accurate in predicting who will develop clinical or subclinical LE. Periodic monitoring of women at high risk for LE, with modern technology, helps early detection and timely intervention for LE, which can minimize or eliminate more advanced LE among women undergoing ALND.

PREDICTING THE OCCURRENCE OF MAJOR ADVERSE CARDIAC EVENTS WITHIN 30 DAYS AFTER A PATIENT'S VASCULAR SURGERY: AN INDIVIDUAL PATIENT-DATA META-ANALYSIS

IntroductionMajor adverse cardiac events (MACE) – which include cardiac death and non-fatal myocardial infarction – are severe harmful outcomes that commonly arise after elective non-cardiac vascular surgeries. Current preoperative risk...

Recalibration and Validation of a Preoperative Risk Prediction Model for Mortality in Major Colorectal Surgery

In 2009, Barwon Health designed a risk stratification model for mortality in major colorectal surgery with the use of only preoperative risk factors. The Barwon Health 2009 model was shown to predict mortality reliably, and it was comparable to other models, such as the original, POSSUM. However, the Barwon Health 2009 model was never validated with data other than those used to develop the model. The aim of this study was to perform temporal and external validation of the Barwon Health 2009 model and to compare it with other published models. : The temporal validation was a prospective observational study, whereas the external validation was a retrospective observational study. The discrimination and calibration of the models were assessed by using the area under receiver operator characteristic and χ test of Hosmer-Lemeshow goodness-of-fi technique. This is a multi-institutional study. Data were collected from 2008 to 2010. There were 474 major colorectal cases at Geelong Hospital (temporal validation) and 389 cases at Western Hospital (external validation). The overall mortality rate was 5.10% and 1.03%. In the comparison of the 2 demographics, Geelong Hospital had a higher proportion of patients who were older and had higher ASA scores and comorbidity counts, whereas Western Hospital surgeons were operating on a higher number of urgent cases. Despite the differences, the Barwon Health 2009 model was able to discriminate mortality reliably (area under receiver operator characteristic = 0.753) but had poor model calibration (p < 0.001) on temporal validation. Hence, the model was recalibrated to predict mortality accurately(area under receiver operator characteristic = 0.772; p = 0.83), and this was successfully validated at Western Hospital (area under receiver operator characteristic = 0.788; p = 0.24). We have developed a model that can accurately predict mortality after major colorectal surgery by using only data that are available preoperatively. After recalibration, the model was successfully validated in a second hospital.

Preoperative risk assessment model for identification of lymph node metastasis in early cervical cancer.

5600 Background: The aim of this study was to develop a preoperative risk prediction model for lymph node metastasis in patients with early cervical cancer. Methods: The medical records of 504 patients with early cervical cancer who underwent hysterectomy and pelvic/paraaortic lymphadenectomy between 2007and 2012 in our center were retrospectively reviewed. According to the order of surgery performed, data between 2007 and 2010 were allocated to a model development cohort (n=314), and data between 2011 and 2012 were allocated to an external validation cohort (n=190). By using preoperative clinicopathologic data, magnetic resonance imaging (MRI) data, and positron emission/computed tomography (PET/CT) data, a multivariate logistic model was created. Based on this model, predictive nomogram was developed and externally validated. Results: Age, tumor size measured by MRI, and lymph node metastasis on PET/CT were found to be independent risk factors for nodal metastasis. Developed nomogram incorporating these three predictors showed good discrimination and calibration, with a bootstrap-adjusted concordance index of 0.772. Also, the validation set showed good discrimination with a bootstrap-adjusted concordance index of 0.783. Conclusions: We have developed a robust model to predict lymph node metastasis in patients with early cervical cancer. This new tool may be useful to clinicians and patients when deciding lymphadenectomy and maybe useful in designing clinical trials. [Table: see text]

Prostate cancer patients older than 70 years treated by radical prostatectomy have higher biochemical recurrence rate than their matched younger counterpart

Consensus on prostate cancer (PCA) treatment in older men is currently lacking. We evaluated clinicopathological and oncological outcomes in patients >70-year-old treated with radical prostatectomy (RP). Clinicopathological and follow-up (FU) data for >70-year-old RP men (2000-2011) were recorded. Association between preoperative features, extraprostatic extension (EPE) and biochemical failure (bF), and postoperative features and bF, was explored. Patients >70-year-old were matched with younger (50- to 70-year-old) men with similar RP features to analyze the effect of age on bF. Two hundred eighteen RP patients were >70-year-old. Clinical stage (cT) was T1 in 74.1%. Biopsy (Bx) Gleason score (GS) was 6 (35.8%), 7 (45.9%), and ≥8 (18.3%); RP GS was 6 (10.1%), 7 (63.3%), and ≥8 (26.6%). Median PSAD was 0.14 (range: 0.01-1.12). Pathologic stage (pT) was pT3 in 45.9%. bF occurred in 14.0%. Best preoperative predictive model for pT3 disease included D'Amico risk, number of Bx positive cores, PSAD, maximum % of PCA per core (P < 0.0001); cT, PSAD and primary Bx Gleason pattern best predicted bF preoperatively (P = 0.0031). Among postoperative features, high RP GS, positive margins, and pT3 were significantly associated with bF. Margin status and pT best predicted bF. Patients >70-year-old had 85% higher odds of bF compared to younger men (P = 0.036). PCA detected in >70-year-old men shows adverse pathologic features. Failure rate is significantly higher in older than in matched younger patients.