The revised ACPGBI model is a simple and accurate predictor of operative mortality after potentially curative resection of colorectal cancer

Medical versus surgical causes of death following colorectal resection: a Queensland Audit of Surgical Mortality (QASM) study

BACKGROUND

The causes of death following colorectal resection remain poorly explored. Few studies have addressed whether early post-operative mortality is predominantly caused by a patient's medical co-morbidities, or from factors pertaining to the presenting surgical disease process itself. This study analyses data from the Queensland audit of surgical mortality (QASM) to report the causes of in-hospital death following colorectal resection, identifies whether these were due to either medical or surgical factors, and determines the patient characteristics associated with a medical cause of death.

METHODS

Through analysis of QASM Surgical Case Forms, the causes of in-hospital death were determined in 750 patients who died in Queensland following colorectal resection between January 2010 and December 2020. Deaths were attributed to a specific medical or surgical cause, with multivariate analysis used to identify independent risk factors associated with a medical cause of death.

RESULTS

In total, 395 patients (52.7%) died due to surgical causes and 355 (47.3%) died due to medical causes. Respiratory co-morbidities (OR 1.832, 95% CI: 1.267-2.650), advanced malignancy (OR 1.814, 95% CI: 1.262-2.607), neurological co-morbidities (OR 1.794, 95% CI: 1.168-2.757) and advanced age (OR 1.430, 95% CI: 1.013-2.017) were independent risk factors associated with increased risk of a medical cause of death.

CONCLUSION

Even in the absence of complicating surgical factors, a significant number of patients died in hospital following colorectal resection due to their underlying co-morbidities. Multi-disciplinary models of care which allow for the early recognition and treatment of medical complications may reduce post-operative mortality in these patients.

Prediction of Postoperative Survival in Young Colorectal Cancer Patients: A Cohort Study Based on the SEER Database

Objective

Our aim is to make accurate and robust predictions of the risk of postoperative death in young colorectal cancer patients (18-44 years old) by combining tumor characteristics with medical and demographic information about the patient.

Materials and Methods

We used the SEER database to retrieve young patients diagnosed with colorectal cancer who had undergone surgery between 2010 and 2015 as the study cohort. After excluding cases with missing information, the study cohort was divided in a 7 : 3 ratio into a training dataset and a validation dataset. To assess the predictive ability of each predictor on the prognosis of colorectal cancer patients, we used two steps of Cox univariate analysis and Cox stepwise regression to screen variables, and the screened variables were included in a multifactorial Cox proportional risk regression model for modeling. The performance of the model was tested using calibration curves, decision curves, and area under the curve (AUC) for receiver operating characteristic (ROC).

Results

After excluding cases with missing information (n = 23,606), a total of 11,803 patients were included in the study with a median follow-up time of 45 months (1-119). In the training set, we determined that ethnicity, marital status, insurance status, median annual household income, degree of tumor differentiation, type of pathology, degree of infiltration, and tumor location had independent effects on prognosis. In the training dataset, taking 1 year, 3 years, and 5 years as the time nodes, the areas under the working characteristic curve of subjects are 0.825, 0.851, and 0.839, respectively, and in the validation dataset, they are 0.834, 0.837, and 0.829, respectively.

Conclusion

We trained and validated a model using a large multicenter cohort of young colorectal cancer patients with stable and excellent performance in both training and validation datasets.

OUP accepted manuscript

Background

As the outcome of modern colorectal cancer (CRC) surgery has significantly improved over the years, however, renewed and adequate risk stratification for mortality is important to identify high-risk patients. This population-based study was conducted to analyse postoperative outcomes in patients with CRC and to create a risk model for 30-day mortality.

Methods

Data from the Dutch Colorectal Audit were used to assess differences in postoperative outcomes (30-day mortality, hospital stay, blood transfusion, postoperative complications) in patients with CRC treated from 2009 to 2017. Time trends were analysed. Clinical variables were retrieved (including stage, age, sex, BMI, ASA grade, tumour location, timing, surgical approach) and a prediction model with multivariable regression was computed for 30-day mortality using data from 2009 to 2014. The predictive performance of the model was tested among a validation cohort of patients treated between 2015 and 2017.

Results

The prediction model was obtained using data from 51 484 patients and the validation cohort consisted of 32 926 patients. Trends of decreased length of postoperative hospital stay and blood transfusions were found over the years. In stage I–III, postoperative complications declined from 34.3 per cent to 29.0 per cent (P < 0.001) over time, whereas in stage IV complications increased from 35.6 per cent to 39.5 per cent (P = 0.010). Mortality decreased in stage I–III from 3.0 per cent to 1.4 per cent (P < 0.001) and in stage IV from 7.6 per cent to 2.9 per cent (P < 0.001). Eight factors, including stage, age, sex, BMI, ASA grade, tumour location, timing, and surgical approach were included in a 30-day mortality prediction model. The results on the validation cohort documented a concordance C statistic of 0.82 (95 per cent c.i. 0.80 to 0.83) for the prediction model, indicating good discriminative ability.

Conclusion

Postoperative outcome improved in all stages of CRC surgery in the Netherlands. The developed model accurately predicts postoperative mortality risk and is clinically valuable for decision-making.

Preoperative assessment and optimisation for pelvic exenteration in locally advanced and recurrent rectal cancer: A review

The pre-operative phase in planning a pelvic exenteration or extended resections is critical to optimising patient outcomes. This review summarises the key components of preoperative assessment and planning in patients with locally advanced rectal cancer (LARC) and locally recurrent rectal cancer (LLRC) being considered for potential curative resection. The preoperative period can be considered in 5 key phases: 1) Multidisciplinary meeting (MDT) review and recommendation for neoadjuvant therapy and surgery, 2) Anaesthetic preoperative assessment of fitness for surgery and quantification of risk, 3) Shared decision making with the patient and the process of informed consent, 4) Prehabilitation and physiological optimisation 5) Technical aspects of surgical planning. This review will focus on patients who have been recommended for surgery by the MDT and have completed neoadjuvant therapy. Other important considerations beyond the scope of this review are the various neoadjuvant strategies employed which in this patient group include Total Neo-adjuvant Therapy and reirradiation. Critical to improving perioperative outcomes is the dual aim of achieving a negative resection margin in a patient fit enough for extended surgery. Advanced, realistic communication is required pre-operatively and should be maintained throughout recovery. Optimising patient's physiological and psychological reserve with a preoperative prehabilitation programme is important, with physiotherapy, psychological and nutritional input. From a surgical perspective, image based technical preoperative planning is important to identify risk points and ensure correct surgical strategy. Careful attention to the entire patient journey through these 5 preoperative phases can optimise outcomes with the accumulation of marginal gains at multiple timepoints.

Prediction of the postoperative 90-day mortality after acute colorectal cancer surgery: development and temporal validation of the ACORCA model

PURPOSE

The aim of this study was to develop and validate a model to predict 90-day mortality after acute colorectal cancer surgery.

METHODS

The model was developed in all patients undergoing acute colorectal cancer surgery in 2014-2016 and validated in a patient group operated in 2017 in Denmark. The outcome was 90-day mortality. Tested predictor variables were age, sex, performance status, BMI, smoking, alcohol, education level, cohabitation status, tumour localization and primary surgical procedure. Variables were selected according to the smallest Akaike information criterion. The model was shrunken by bootstrapping. Discrimination was evaluated with a receiver operated characteristic curve, calibration with a calibration slope and the accuracy with a Brier score.

RESULTS

A total of 1450 patients were included for development of the model and 451 patients for validation. The 90-day mortality rate was 19% and 20%, respectively. Age, performance status, alcohol, smoking and primary surgical procedure were the final variables included in the model. Discrimination (AUC = 0.79), calibration (slope = 1.04, intercept = 0.04) and accuracy (brier score = 0.13) were good in the developed model. In the temporal validation, discrimination (AUC = 0.80) and accuracy (brier score = 0.13) were good, and calibration was acceptable (slope = 1.19, intercept = 0.52).

CONCLUSION

We developed prediction model for 90-day mortality after acute colorectal cancer surgery that may be a promising tool for surgeons to identify patients at risk of postoperative mortality.

Perioperative score for octogenarian patients eligible for rectal cancer surgery

BACKGROUND AND OBJECTIVES

Colorectal cancer incidence increases with age; therefore, rectal cancer treatment in elderly patients is increasingly common. Surgery is a common rectal cancer treatment, and the risks involve complicated surgical decisions. There are currently few surgical outcome data for rectal cancer in elderly patients. The aim of this study is to identify new perioperative risk factors that could be associated with higher 30- and 180-day mortality in elderly patients in good clinical condition considering traditional perioperative risk scores and to develop a risk score.

METHODS

A single-centre, retrospective cohort study was performed by reviewing the medical records of patients from the National Cancer Institute of Brazil aged 80 years or older who electively underwent rectal cancer surgery; several independent variables correlated with death from all causes at 30 and 180 days were studied.

RESULTS

Multivariate analysis found new variables (CEA, albumin and reoperation) that had independent correlations with increased 30- and 180-day mortality. A clinical risk score was developed with survival profiles ranging from 29.3% to 97.2% within 30 days and 2.45% to 91.8% within 180 days.

CONCLUSIONS

This score can aid in deciding whether a patient should undergo rectal cancer surgery.

A Prediction Model for Severe Complications after Elective Colorectal Cancer Surgery in Patients of 70 Years and Older

Introduction Older patients have an increased risk of morbidity and mortality after colorectal cancer (CRC) surgery. Existing CRC surgical prediction models have not incorporated geriatric predictors, limiting applicability for preoperative decision-making. The objective was to develop and internally validate a predictive model based on preoperative predictors, including geriatric characteristics, for severe postoperative complications after elective surgery for stage I-III CRC in patients ≥70 years.

PATIENTS AND METHODS

A prospectively collected database contained 1088 consecutive patients from five Dutch hospitals (2014-2017) with 171 severe complications (16%). The least absolute shrinkage and selection operator (LASSO) method was used for predictor selection and prediction model building. Internal validation was done using bootstrapping.

RESULTS

A geriatric model that included gender, previous DVT or pulmonary embolism, COPD/asthma/emphysema, rectal cancer, the use of a mobility aid, ADL assistance, previous delirium and polypharmacy showed satisfactory discrimination with an AUC of 0.69 (95% CI 0.73-0.64); the AUC for the optimism corrected model was 0.65. Based on these predictors, the eight-item colorectal geriatric model (GerCRC) was developed.

CONCLUSION

The GerCRC is the first prediction model specifically developed for older patients expected to undergo CRC surgery. Combining tumour- and patient-specific predictors, including geriatric predictors, improves outcome prediction in the heterogeneous older population.

Nomograms for morbidity and mortality after oncologic colon resection in the enhanced recovery era: results from a multicentric prospective national study

PURPOSE

Predicting postoperative complications and mortality is important to plan the surgical strategy. Different scores have been proposed before to predict them but none of them have been yet implemented into the routine clinical practice because their difficulties and low accuracy with new surgical strategies and enhanced recovery. The main aim of this study is to identify risk factors for postoperative morbidity and mortality after colonic resection (CR) without protective stomas, in order to develop a comprehensive, up-to-date, simple, reliable, and applicable model for the preoperative assessment of patients with colon cancer.

METHODS

Multivariable analysis was performed to identify risk factors for 60-day morbidity and mortality. Coefficients derived from the regression model were used in the nomograms to predict morbidity and mortality.

RESULTS

Three thousand one hundred ninety-three patients from 52 hospitals were included into the analysis. Sixty-day postoperative complications rate was 28.3% and the mortality rate was 3%. In multivariable analysis the independent risk factors for postoperative complications were age, male gender, liver and pulmonary diseases, obesity, preoperative albumin, anticoagulant treatment, open surgery, intraoperative complications, and urgent surgery. Independent risk factors for mortality were age, preoperative albumin anticoagulant treatment, and intraoperative complications.

CONCLUSIONS

Risk factors for morbidity and mortality after CR for cancer were identified and two easy predictive tools were developed. Both of them could provide important information for preoperative consultation and surgical planning in the time of enhance recovery.

Australasian ACPGBI risk prediction model for 30-day mortality after colorectal cancer surgery

BACKGROUND

Postoperative mortality after colorectal cancer surgery varies across hospitals and countries. The aim of this study was to test the Association of Coloproctologists of Great Britain and Ireland (ACPGBI) models as predictors of 30-day mortality in an Australian cohort.

METHODS

Data from patients who underwent surgery in six hospitals between 1996 and 2015 (CRC data set) were reviewed to test ACPGBI models, and patients from 79 hospitals in the Bi-National Colorectal Cancer Audit between 2007 and 2016 (BCCA data set) were analysed to validate model performance. Recalibrated models based on ACPGBI risk models were developed, tested and validated on a data set of Australasian patients.

RESULTS

Of 18 752 patients observed during the study, 6727 (CRC data set) and 3814 (BCCA data set) were analysed. The 30-day mortality rate was 1·1 and 3·5 per cent in the CRC and BCCA data sets respectively. Both the original and revised ACPGBI models overestimated 30-day mortality for the CRC data set (observed to expected (O/E) ratio 0·17 and 0·21 respectively). Their ability to correctly predict mortality risk was poor (P < 0·001, Hosmer-Lemeshow test); however, the area under the curve for both models was 0·88 (95 per cent c.i. 0·85 to 0·92) showing good discriminatory power to classify 30-day mortality. The recalibrated original model performed well for calibration and discrimination, whereas the recalibrated revised model performed well for discrimination but not for calibration. Risk prediction was good for both recalibrated models. On external validation using the BCCA data set, the recalibrated models underestimated mortality risk (O/E ratio 3·06 and 2·98 respectively), whereas both original and revised ACPGBI models overestimated the risk (O/E ratio 0·48 and 0·69). All models showed similar good discrimination.

CONCLUSION

The original and revised ACPGBI models overpredicted risk of 30-day mortality. The new Australasian calibrated ACPGBI model needs to be tested further in clinical practice.

Risk prediction models for postoperative outcomes of colorectal cancer surgery in the older population - a systematic review

BACKGROUND

An increasing number of patients with Colorectal Cancer (CRC) is 65 years or older. We aimed to systematically review existing clinical prediction models for postoperative outcomes of CRC surgery, study their performance in older patients and assess their potential for preoperative decision making.

METHODS

A systematic search in Pubmed and Embase for original studies of clinical prediction models for outcomes of CRC surgery. Bias and relevance for preoperative decision making with older patients were assessed using the CHARMS guidelines.

RESULTS

26 prediction models from 25 publications were included. The average age of included patients ranged from 61 to 76. Two models were exclusively developed for 65 and older. Common outcomes were mortality (n = 10), anastomotic leakage (n = 7) and surgical site infections (n = 3). No prediction models for quality of life or physical functioning were identified. Age, gender and ASA score were common predictors; 12 studies included intraoperative predictors. For the majority of the models, bias for model development and performance was considered moderate to high.

CONCLUSIONS

Prediction models are available that address mortality and surgical complications after CRC surgery. Most models suffer from methodological limitations, and their performance for older patients is uncertain. Models that contain intraoperative predictors are of limited use for preoperative decision making. Future research should address the predictive value of geriatric characteristics to improve the performance of prediction models for older patients.

Prognostication in Patients Treated with Radical Cystectomy for Urothelial Bladder Carcinoma: A New Simplified Model Incorporating Histological Variants

Purpose

The aim of this study was to evaluate the clinical significance of histological variants (HV) and to develop a new and simple prediction model incorporating variant forms in patients who underwent radical cystectomy for urothelial carcinoma (UC).

Materials and methods

We analyzed the data of 365 patients. We evaluated whether HV were independent predictors of survival. A new scoring model was developed using the regression coefficients from the multivariate Cox proportional hazard model. The patients were divided into three groups on the basis of the score: low-, intermediate- and high-risk. To evaluate the performance of the model, we assessed models in terms of discrimination and calibration.

Results

Patients(median age; 68.5 years) with pure form and squamous differentiation did not differ significantly but statistical analysis of the survival curves of patients with pure form and other variants revealed a statistically significant difference in terms of cancer-specific survival (CSS) (p = 0.005) and overall survival (OS) (p = 0.010). According to the new scoring model HV, the 5-yr CSS rate in each group was 93.3%, 82.9% and 50.5%, respectively. The 5-yr OS rate in each group was 90.0%, 66.3% and 33.2%, respectively. Model discrimination was good for all year models. Calibration was also adequate for the model in all year models.

Conclusions

HV were found to be important independent prognostic factors in urothelial bladder cancer. This paper presents a new prognostic model incorporating variant forms for predicting CSS and OS in these patients.

A model predicting operative mortality in the UK has only limited value in Denmark

PURPOSE

Postoperative mortality from colorectal cancer varies between surgical departments. Several models have been developed to predict the operative risk. This study aims to investigate whether the original and the revised Association of Coloproctology of Great Britain and Ireland (ACPGBI) model can predict 30-day mortality after colorectal cancer surgery in Denmark.

METHODS

Data were collected from the Danish Colorectal Cancer Group database which has > 95% completeness. All patients operated on from January 2007 to December 2013 were included. The individual estimated operative risk was calculated with the original and revised ACPGBI models. Discrimination and calibration were evaluated with a Receiver Operating Characteristic (ROC) curve analysis and a Hosmer-Lemeshow test, respectively.

RESULTS

In total, 22,807 patients underwent open or laparoscopic colorectal cancer surgery. After excluding 1437 patients because of missing data, 21,370 patients were left for the analyses. The observed 30-day mortality was 5.0%. The original and revised ACPGBI models estimated an operative risk of 7.0 and 4.0%, respectively, with a significant difference in observed and estimated mortality in both models. However, in patients with an estimated risk of at least 26%, i.e., high-risk, good calibration was found with the original ACPGBI model. Discrimination was good with an AUC of 0.83 (95% CI 0.82-0.84) in both models.

CONCLUSION

The original and revised ACPGBI models are not suitable prediction models for postoperative mortality in the Danish colorectal cancer population. However, the original model might be applicable in predicting mortality in high-risk patients.

Validity of the CR-POSSUM model in surgery for colorectal cancer in Spain (CCR-CARESS study) and comparison with other models to predict operative mortality

Background

To validate and recalibrate the CR- POSSUM model and compared its discriminatory capacity with other European models such as POSSUM, P-POSSUM, AFC or IRCS to predict operative mortality in surgery for colorectal cancer.

Methods

Prospective multicenter cohort study from 22 hospitals in Spain. We included patients undergoing planned or urgent surgery for primary invasive colorectal cancers between June 2010 and December 2012 (N = 2749). Clinical data were gathered through medical chart review. We validated and recalibrated the predictive models using logistic regression techniques. To calculate the discriminatory power of each model, we estimated the areas under the curve - AUC (95% CI). We also assessed the calibration of the models by applying the Hosmer-Lemeshow test.

Results

In-hospital mortality was 1.5% and 30-day mortality, 1.7%. In the validation process, the discriminatory power of the CR-POSSUM for predicting in-hospital mortality was 73.6%. However, in the recalibration process, the AUCs improved slightly: the CR-POSSUM reached 75.5% (95% CI: 67.3–83.7). The discriminatory power of the CR-POSSUM for predicting 30-day mortality was 74.2% (95% CI: 67.1–81.2) after recalibration; among the other models the POSSUM had the greatest discriminatory power, with an AUC of 77.0% (95% CI: 68.9–85.2). The Hosmer-Lemeshow test showed good fit for all the recalibrated models.

Conclusion

The CR-POSSUM and the other models showed moderate capacity to discriminate the risk of operative mortality in our context, where the actual operative mortality is low. Nevertheless the IRCS might better predict in-hospital mortality, with fewer variables, while the CR-POSSUM could be slightly better for predicting 30-day mortality.

Trail registration

Registered at: ClinicalTrials.gov Identifier: NCT02488161

Electronic supplementary material

The online version of this article (10.1186/s12913-018-2839-x) contains supplementary material, which is available to authorized users.

Complications and risk prediction in treatment of elderly patients with rectal cancer

PURPOSE

The primary aim of this study was to characterise complications, identify predictors of postoperative morbidity and mortality and to evaluate existing risk prediction models in elderly rectal cancer patients.

METHODS

An observational single-centre study of 330 consecutive patients >75 years treated in 1994-2006. Analyses were performed by age group: 75-79 years, 80-85 years and >85 years.

RESULTS

Total observed in-hospital morbidity was 48.7 %. In multivariate analysis, age (OR 1.04, 95 % CI 1.01-1.08, p = 0.04), ASA grade ≥ 3 (p = 0.01), acute presentation (OR 1.67, 95 % CI 1.2-13.2, p = 0.02) and major surgery (APR OR 3.72, 95 % CI 1.37-10.15, p = 0.01, LAR OR 2.98, 95 % CI 1.14-7.79, p = 0.03, Hartmann OR 5.46, 95 % CI 1.60-19.28, p = 0.02) were independent risk factors for postoperative morbidity. The 30-day mortality was 6.3, 6.4 and 14.3 % (p = 0.146) in the three age groups, and the 100-day mortality was 8.7, 10.1 and 22.2 % (p = 0.03), respectively. ASA group 3 (OR 6.21, 95 % CI 4.39-27.69, p = 0.017), ASA group 4 (OR 32.6, 95 % CI 5.12-207.75, p < 0.001) and acute presentation (OR 6.48, 95 % CI 1.62-25.99, p = 0.008) increased the risk of 100-day mortality. The Physiological and Operative Severity Score for enUmeration of Mortality and Morbidity (POSSUM) observed/estimated (O/E) ratio for morbidity was 1.05. For 30-day mortality, the colorectal POSSUM (Cr-POSSUM) O/E ratio was 0.74, Surgical Risk Scale 0.61 and the Association of Coloproctology of Great Britain and Ireland (ACPGBI) mortality model 0.63, and for 100-day mortality, ratios were 1.12, 0.91 and 0.95, respectively.

CONCLUSION

In this series, age increased the risk of in-hospital morbidity and 100-day mortality. Cr-POSSUM, SRS and ACPGBI overestimated 30-day mortality but predicted 100-day mortality with a high degree of accuracy. POSSUM correctly predicted in-hospital morbidity.

Colorectal surgical mortality and morbidity in elderly patients: comparison of POSSUM, P-POSSUM, CR-POSSUM, and CR-BHOM

PURPOSE

This study aims to compare the predictive value of POSSUM, P-POSSUM, CR-POSSUM and CR-BHOM in colorectal surgical mortality and morbidity in patients over 80 years old.

METHODS

This is a retrospective observational longitudinal study. A total of 991 patients who underwent major colorectal surgery between 2008 and 2012 in a secondary hospital in Portugal were screened, and 204 who were over 80 years old were included. Subgroup analysis was performed for malignant/benign disease and emergent/elective surgery. The main outcome measure was 30-day postoperative mortality and morbidity with Clavien-Dindo classification ≥ 2.

RESULTS

Of the 204 patients included in this study, 155 had malignant disease, and 65 underwent emergent procedures. Overall average age was 84.3 ± 3.9 years (range 80-100). Overall surgical mortality and morbidity were 18.6% (n = 38) and 52.4% (n = 87), respectively. Expected mortality followed the order P-POSSUM<CR-POSSUM<CR-BHOM (p < 0.001), and expected morbidity followed the order POSSUM<CR-BHOM (p < 0.001) in all groups. All scores were higher in the emergent surgery group compared with elective surgery (p < 0.05). All scores had sensitivity below 60%. Physiology scores were higher among patients with surgical mortality (p < 0.05), with no differences in operative scores.

CONCLUSIONS

In our population, CR-POSSUM was the best predictor of surgical mortality. POSSUM and P-POSSUM underestimated surgical mortality and morbidity, and CR-BHOM overestimated surgical mortality, being however the best predictor of morbidity. Nevertheless, none of the scores showed sufficient discriminatory power to have clinical application value. Moreover, our results suggest that, in elderly patients, it is the patient's health status and not the type of surgery that is mainly responsible for the surgical outcome.

Model for risk adjustment of postoperative mortality in patients with colorectal cancer

Background

A model was developed for risk adjustment of postoperative mortality in patients with colorectal cancer in order to make fair comparisons between healthcare providers. Previous models were derived in relatively small studies with the use of suboptimal modelling techniques.

Methods

Data from adults included in a national study of major surgery for colorectal cancer were used to develop and validate a logistic regression model for 90-day mortality. The main risk factors were identified from a review of the literature. The association with age was modelled as a curved continuous relationship. Bootstrap resampling was used to select interactions between risk factors.

Results

A model based on data from 62 314 adults was developed that was well calibrated (absolute differences between observed and predicted mortality always smaller than 0·75 per cent in deciles of predicted risk). It discriminated well between low- and high-risk patients (C-index 0·800, 95 per cent c.i. 0·793 to 0·807). An interaction between age and metastatic disease was included as metastatic disease was found to increase postoperative risk in young patients aged 50 years (odds ratio 3·53, 95 per cent c.i. 2·66 to 4·67) far more than in elderly patients aged 80 years (odds ratio 1·48, 1·32 to 1·66).

Conclusion

Use of this model, estimated in the largest number of patients with colorectal cancer to date, is recommended when comparing postoperative mortality of major colorectal cancer surgery between hospitals, clinical teams or individual surgeons.

Predicting postoperative mortality after colorectal surgery: a novel clinical model

AIM

The aim of this study was to develop and externally validate a clinically, practical and discriminative prediction model designed to estimate in-hospital mortality of patients undergoing colorectal surgery.

METHOD

All consecutive patients who underwent elective or emergency colorectal surgery from 1990 to 2005, at the Zaandam Medical Centre, The Netherlands, were included in this study. Multivariate logistic regression analysis was performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) linking the explanatory variables to the outcome variable in-hospital mortality, and a simplified Identification of Risk in Colorectal Surgery (IRCS) score was constructed. The model was validated in a population of patients who underwent colorectal surgery from 2005 to 2011 in Barcelona, Spain. Predictive performance was estimated by calculating the area under the receiver operating characteristic curve.

RESULTS

The strongest predictors of in-hospital mortality were emergency surgery (OR = 6.7, 95% CI 4.7-9.5), tumour stage (OR = 3.2, 95% CI 2.8-4.6), age (OR = 13.1, 95% CI 6.6-26.0), pulmonary failure (OR = 4.9, 95% CI 3.3-7.1) and cardiac failure (OR = 3.7, 95% CI 2.6-5.3). These parameters were included in the prediction model and simplified scoring system. The IRCS model predicted in-hospital mortality and demonstrated a predictive performance of 0.83 (95% CI 0.79-0.87) in the validation population. In this population the predictive performance of the CR-POSSUM score was 0.76 (95% CI 0.71-0.81).

CONCLUSIONS

The results of this study have shown that the IRCS score is a good predictor of in-hospital mortality after colorectal surgery despite the relatively low number of model parameters.