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A novel risk-adjusted nomogram for rectal cancer surgery outcomes.

ABSTRACT: The circumferential resection margin is the primary determinant of local recurrence and a major factor in survival in rectal cancer. Neither chemotherapy nor chemoradiation compensates for a margin positive for cancer.To identify treatment-related factors associated with hospital margin-positive resection and to develop a tool that could be used by individual hospitals to assess their outcomes based on their unique mix of patient and tumor characteristics.Retrospective review of the National Cancer Data Base, 1998-2007.Community and academic/research hospitals.Individuals with histologically confirmed localized rectal/rectosigmoid adenocarcinoma.All individuals underwent radical resection for rectal cancer with or without neoadjuvant therapy.Rate of margin positivity determined and adjusted for patient- and tumor-related factors to calculate expected margin positivity per hospital. An observed to expected ratio was calculated based on patient- and tumor-related factors to identify treatment-associated variation.The overall margin-positive resection rate was 5.2%. Patients with margins positive for cancer were more likely to be older, male, and African American; not have private insurance; and have their cancer diagnosed later in the study period. Associated tumor-related factors include rectal location, higher American Joint Committee on Cancer stage, signet/mucinous histology, and poor/undifferentiated grade. Among hospitals that were significantly low outliers, 47% were comprehensive community hospitals, and 43.9% were academic/research hospitals; of those that were significantly high outliers, 52.3% were comprehensive community hospitals, and 17.8% were academic/research hospitals. High-volume centers made up 80% of significantly low outlier hospitals and 17% of significantly high outlier hospitals. The rates of chemotherapy and radiation were similar, but low outlier hospitals gave more neoadjuvant radiation (26.3% vs 17%).After adjustment for patient- and tumor-related factors, we identified both low and high outlier hospitals for margin positivity at resection, as well as potentially modifiable risk factors. The nomogram created in this model allows for the evaluation of observed and expected event rates for individual hospitals, providing a hospital self-assessment tool for identifying targets for improvement.

SUBMITTER: Russell MC

PROVIDER: S-EPMC3947615 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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A novel risk-adjusted nomogram for rectal cancer surgery outcomes.

Russell Maria C MC You Y Nancy YN Hu Chung-Yuan CY Cormier Janice N JN Feig Barry W BW Skibber John M JM Rodriguez-Bigas Miguel A MA Nelson Heidi H Chang George J GJ

JAMA surgery 20130801 8

<h4>Importance</h4>The circumferential resection margin is the primary determinant of local recurrence and a major factor in survival in rectal cancer. Neither chemotherapy nor chemoradiation compensates for a margin positive for cancer.<h4>Objective</h4>To identify treatment-related factors associated with hospital margin-positive resection and to develop a tool that could be used by individual hospitals to assess their outcomes based on their unique mix of patient and tumor characteristics.<h4 ...[more]

PMID: 23803722

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Project description:AimTumor deposits (TDs) are an aggressive hallmark of rectal cancer, but their prognostic value has not been addressed in current staging systems. This study aimed to construct and validate a prognostic nomogram for rectal cancer patients with TDs.MethodsA total of 1,388 stage III-IV rectal cancer patients who underwent radical surgical resection from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed to identify the clinical value of TDs. TD-positive rectal cancer patients in the SEER database were used as the training set to construct a prognostic model, which was validated by Fujian Cancer Hospital. Three models were constructed to predict the prognosis of rectal cancer patients with TDs, including the least absolute shrinkage and selection operator regression (LASSO, model 1), backward stepwise regression (BSR, model 2), and LASSO followed by BSR (model 3). A nomogram was established among the three models.ResultsIn the entire cohort, TD was also identified as an independent risk factor for overall survival (OS), even after adjusting for baseline factors, stage, other risk factors, treatments, and all the included variables in this study (all P < 0.05). Among patients with TDs, model 3 exhibited a higher C-index and area under the curves (AUCs) at 3, 4, and 5 years compared with the American Joint Committee on Cancer staging system both in the training and validation sets (all P < 0.05). The nomogram obtained from model 3 showed good consistency based on the calibration curves and excellent clinical applicability by the decision curve analysis curves. In addition, patients were divided into two subgroups with apparently different OS according to the current nomogram (both P < 0.05), and only patients in the high-risk subgroup were found to benefit from postoperative radiotherapy (P < 0.05).ConclusionWe identified a novel nomogram that could not only predict the prognosis of rectal cancer patients with TDs but also provide reliable evidence for clinical decision-making.

Project description:OBJECTIVE:To develop a postoperative mortality case-mix adjustment model to facilitate assessment of cardiac ICU quality of care, and to describe variation in adjusted cardiac ICU mortality across hospitals within the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Multicenter Pediatric Cardiac Critical Care Consortium clinical registry. PARTICIPANTS:All surgical cardiac ICU admissions between August 2014 and May 2016. The analysis included 8,543 admissions from 23 dedicated cardiac ICUs. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We developed a novel case-mix adjustment model to measure postoperative cardiac ICU mortality after congenital heart surgery. Multivariable logistic regression was performed to assess preoperative, intraoperative, and immediate postoperative severity of illness variables as candidate predictors. We used generalized estimating equations to account for clustering of patients within hospital and obtain robust SEs. Bootstrap resampling (1,000 samples) was used to derive bias-corrected 95% CIs around each predictor and validate the model. The final model was used to calculate expected mortality at each hospital. We calculated a standardized mortality ratio (observed-to-expected mortality) for each hospital and derived 95% CIs around the standardized mortality ratio estimate. Hospital standardized mortality ratio was considered a statistically significant outlier if the 95% CI did not include 1. Significant preoperative predictors of mortality in the final model included age, chromosomal abnormality/syndrome, previous cardiac surgeries, preoperative mechanical ventilation, and surgical complexity. Significant early postoperative risk factors included open sternum, mechanical ventilation, maximum vasoactive inotropic score, and extracorporeal membrane oxygenation. The model demonstrated excellent discrimination (C statistic, 0.92) and adequate calibration. Comparison across Pediatric Cardiac Critical Care Consortium hospitals revealed five-fold difference in standardized mortality ratio (0.4-1.9). Two hospitals had significantly better-than-expected and two had significantly worse-than-expected mortality. CONCLUSIONS:For the first time, we have demonstrated that variation in mortality as a quality metric exists across dedicated cardiac ICUs. These findings can guide efforts to reduce mortality after cardiac surgery.

Project description:It is important that actual outcomes of care and not surrogate markers, such as process measures, be used to evaluate the quality of inpatient care. Because of the heterogenous composition of patients, risk-adjustment is essential for the objective evaluation of outcomes following inpatient care. Comparative evaluation of risk-adjusted outcomes can be used to identify suboptimal performance and can provide direction for care improvement initiatives.We studied the risk-adjusted outcomes of 6 medical conditions during the inpatient and 90-day post-discharge period to identify the opportunities for care improvement. The Medicare Limited Dataset for 2012 to 2014 was used to identify acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), pneumonia (PNEU), cerebrovascular accidents (CVA), and gastrointestinal hemorrhage (GIH). Stepwise logistic predictive models were developed for the adverse outcomes (AOs) of inpatient deaths, 3-sigma prolonged length-of-stay outliers, 90-day post-discharge deaths, and 90-day readmissions after unrelated events were excluded. Observed and predicted AOs were determined for each hospital with ?75 cases for each of the 6 medical conditions. Z-scores and risk-adjusted AO rates for each hospital permitted comparative analysis of outcomes after adjusting for covariance among the medical conditions.There were a total of 1,811,749 patients from 973 acute care hospitals with the 6 medical conditions. A total of 41% of all patients had ?1 AO events. One or more readmissions were identified in 29.8% of patients. A total of 64 hospitals (6.4%) were 2 standard deviations better than the mean for risk-adjusted outcomes, and 72 (7.4%) were 2 standard deviations poorer. The best performing decile of hospitals had mean AO rates of 35.1% (odds ratio?=?0.766; 95% confidence interval (CI) CI: 0.762-0.771) and the poorest performing decile a mean AO rate of 48.5% (odds ratio?=?1.357; 95% CI: 1.346-1.369). Volume of qualifying cases ranged from 670 to 9314; no association was identified for increased volume of patients (P?<?.40).Risk-adjusted AO rates demonstrated nearly a 14% opportunity for care improvement between top and suboptimal performing hospitals. Hospitals must be able to benchmark objective measurement of outcomes to inform quality initiatives.

Project description:ObjectiveThe aim of this study was to establish a nomogram model for predicting the risk of short-term recurrence in glioma patients.MethodsThe clinical data of recurrent glioma patients were summarized and analyzed in this study. Univariate and multivariate logistic regression analyses were performed to analyze the correlation between clinical data and the risk of short-term recurrence after operation. A nomogram was established based on the multivariate logistic regression model results.ResultsA total of 175 patients with recurrent glioma were enrolled, with 53 patients in the short-term recurrence (STR) group (recurrent time ≤6 months) and 122 patients in the long-term recurrence (LTR) group (recurrent time ≥36 months). Univariate analysis revealed that age at diagnosis, Karnofsky performance scores (KPSs), tumor location, glioma grade, glioma type, extent of resection (EOR), adjuvant chemotherapy (ad-CT), concurrent chemotherapy (co-CT), and isocitrate dehydrogenase (IDH) status were significantly associated with the short-term glioma recurrence. Multivariate analyses revealed that age at diagnosis, KPS, glioma grade, EOR, and IDH were independent risk factors for short-term glioma recurrence. A risk nomogram for the short-term recurrence of glioma was established, with the concordance index (C-index) of 0.971. The findings of calibration and receiver operating characteristic (ROC) curves showed that our nomogram model had good performance and discrimination to estimate short-term recurrence probability.ConclusionThis nomogram model provides reliable information about the risk of short-term glioma recurrence for oncologists and neurosurgeons. This model can predict the short-term recurrence probability and give assistance to decide the interval of follow-up or formulate individualized treatment strategies based on the predicted results. A free online prediction risk tool for this nomogram is provided: https://rj2021.shinyapps.io/Nomogram_ recurrence-risk/.

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A novel risk-adjusted nomogram for rectal cancer surgery outcomes.

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A novel risk-adjusted nomogram for rectal cancer surgery outcomes.

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