Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study

BACKGROUND

Life-saving emergency major resection of colorectal cancer (CRC) is a high-risk procedure. Accurate prediction of postoperative mortality for patients undergoing this procedure is essential for both healthcare performance monitoring and preoperative risk assessment. Risk-adjustment models for CRC patients often include patient and tumour characteristics, widely available in cancer registries and audits. The authors investigated to what extent inclusion of additional physiological and surgical measures, available through linkage or additional data collection, improves accuracy of risk models.

METHODS

Linked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed.

RESULTS

Eight hundred forty-six of 10 578 (8.0%) patients died within 90 days of surgery. The selected model included seven preoperative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the 10 patient and tumour characteristics in the basic model (calendar year of surgery, age, sex, ASA grade, TNM T stage, TNM N stage, TNM M stage, cancer site, number of comorbidities, and emergency admission). The selected model had considerably better discrimination compared to the basic model (C-statistic: 0.824 versus 0.783, respectively).

CONCLUSION

Linkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improves the accuracy of the prediction of the mortality risk for CRC patients having emergency surgery. This improvement will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.

Development and validation of a prognostic model for death 30 days after adult emergency laparotomy

The probability of death after emergency laparotomy varies greatly between patients. Accurate pre-operative risk prediction is fundamental to planning care and improving outcomes. We aimed to develop a model limited to a few pre-operative factors that performed well irrespective of surgical indication: obstruction; sepsis; ischaemia; bleeding; and other. We derived a model with data from the National Emergency Laparotomy Audit for patients who had emergency laparotomy between December 2016 and November 2018. We tested the model on patients who underwent emergency laparotomy between December 2018 and November 2019. There were 4077/40,816 (10%) deaths 30 days after surgery in the derivation cohort. The final model had 13 pre-operative variables: surgical indication; age; blood pressure; heart rate; respiratory history; urgency; biochemical markers; anticipated malignancy; anticipated peritoneal soiling; and ASA physical status. The predicted mortality probability deciles ranged from 0.1% to 47%. There were 1888/11,187 deaths in the test cohort. The scaled Brier score, integrated calibration index and concordance for the model were 20%, 0.006 and 0.86, respectively. Model metrics were similar for the five surgical indications. In conclusion, we think that this prognostic model is suitable to support decision-making before emergency laparotomy as well as for risk adjustment for comparing organisations.

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70-98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3-5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS.

IMPACT OF SCREENING ON SHORT-TERM MORTALITY AND MORBIDITY FOLLOWING TREATMENT FOR COLORECTAL CANCER

BACKGROUND AND AIMS

The aim of this study was to describe short-term changes in morbidity and mortality associated with the implementation of screening for colorectal cancer in Denmark.

MATERIALS AND METHODS

Prospective cohort study with inclusion of all patients aged 50-75 years treated for colorectal cancer between 1 March 2014 and 31 December 2015 in Denmark. Adjusted hazard ratios were calculated for 30 and 90 days mortality using Cox Regression. We made two adjusted models-a "basic" adjusted for screening status, sex, age, smoking, alcohol consumption, and cancer type and an "advanced" that also included body mass index and American society of Anesthesiologists score in analyses. Relative risks were calculated for postoperative surgical and medical complications.

RESULTS

In total, 5348 patients were included. In the "basic model," adjusted risk of 30 and 90 days total mortality was reduced in the screen-detected group (p < 0.01, HR = 0.43, CI = 0.24-0.76) and (p < 0.01, HR = 0.45, CI = 0.30-0.69). In the "advanced model," only 90 days total mortality was significantly reduced in the screen-detected group (p = 0.01, HR 0.59, CI = 0.39-0.90). No significant changes were found with regard to surgical and medical complications, respectively, (p = 0.05 (CI = 0.76-1.00) and p = 0.47(CI = 0.74-1.15)).

CONCLUSION

This nationwide study showed that screening for colorectal cancer was associated with a lower 90 days total mortality although no significant improvements were seen with regard to morbidity.

Prediction of 90-day mortality after surgery for colorectal cancer using standardized nationwide quality-assurance data

Background

Personalized risk assessment provides opportunities for tailoring treatment, optimizing healthcare resources and improving outcome. The aim of this study was to develop a 90-day mortality-risk prediction model for identification of high- and low-risk patients undergoing surgery for colorectal cancer.

Methods

This was a nationwide cohort study using records from the Danish Colorectal Cancer Group database that included all patients undergoing surgery for colorectal cancer between 1 January 2004 and 31 December 2015. A least absolute shrinkage and selection operator logistic regression prediction model was developed using 121 pre- and intraoperative variables and internally validated in a hold-out test data set. The accuracy of the model was assessed in terms of discrimination and calibration.

Results

In total, 49 607 patients were registered in the database. After exclusion of 16 680 individuals, 32 927 patients were included in the analysis. Overall, 1754 (5.3 per cent) deaths were recorded. Targeting high-risk individuals, the model identified 5.5 per cent of all patients facing a risk of 90-day mortality exceeding 35 per cent, corresponding to a 6.7 times greater risk than the average population. Targeting low-risk individuals, the model identified 20.9 per cent of patients facing a risk less than 0.3 per cent, corresponding to a 17.7 times lower risk compared with the average population. The model exhibited discriminatory power with an area under the receiver operating characteristics curve of 85.3 per cent (95 per cent c.i. 83.6 to 87.0) and excellent calibration with a Brier score of 0.04 and 32 per cent average precision.

Conclusion

Pre- and intraoperative data, as captured in national health registries, can be used to predict 90-day mortality accurately after colorectal cancer surgery.

One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data

Background

The existing literature does not provide a prediction model for mortality of all colorectal cancer patients using contemporary national hospital data. We developed and validated such a model to predict colorectal cancer death within 90, 180 and 365 days after diagnosis.

Methods

Cohort study using linked national cancer and death records. The development population included 27,480 patients diagnosed in England in 2015. The test populations were diagnosed in England in 2016 (n = 26,411) and Wales in 2015–2016 (n = 3814). Predictors were age, gender, socioeconomic status, referral source, performance status, tumour site, TNM stage and treatment intent. Cox regression models were assessed using Brier scores, c-indices and calibration plots.

Results

In the development population, 7.4, 11.7 and 17.9% of patients died from colorectal cancer within 90, 180 and 365 days after diagnosis. T4 versus T1 tumour stage had the largest adjusted association with the outcome (HR 4.67; 95% CI: 3.59–6.09). C-indices were 0.873–0.890 (England) and 0.856–0.873 (Wales) in the test populations, indicating excellent separation of predicted risks by outcome status. Models were generally well calibrated.

Conclusions

The model was valid for predicting short-term colorectal cancer mortality. It can provide personalised information to support clinical practice and research.

Predictive Model of the Risk of In-Hospital Mortality in Colorectal Cancer Surgery, Based on the Minimum Basic Data Set

BACKGROUND

Various models have been proposed to predict mortality rates for hospital patients undergoing colorectal cancer surgery. However, none have been developed in Spain using clinical administrative databases and none are based exclusively on the variables available upon admission. Our study aim is to detect factors associated with in-hospital mortality in patients undergoing surgery for colorectal cancer and, on this basis, to generate a predictive mortality score.

METHODS

A population cohort for analysis was obtained as all hospital admissions for colorectal cancer during the period 2008-2014, according to the Spanish Minimum Basic Data Set. The main measure was actual and expected mortality after the application of the considered mathematical model. A logistic regression model and a mortality score were created, and internal validation was performed.

RESULTS

115,841 hospitalization episodes were studied. Of these, 80% were included in the training set. The variables associated with in-hospital mortality were age (OR: 1.06, 95%CI: 1.05-1.06), urgent admission (OR: 4.68, 95% CI: 4.36-5.02), pulmonary disease (OR: 1.43, 95%CI: 1.28-1.60), stroke (OR: 1.87, 95%CI: 1.53-2.29) and renal insufficiency (OR: 7.26, 95%CI: 6.65-7.94). The level of discrimination (area under the curve) was 0.83.

CONCLUSIONS

This mortality model is the first to be based on administrative clinical databases and hospitalization episodes. The model achieves a moderate-high level of discrimination.

Role of Emergency Laparoscopic Colectomy for Colorectal Cancer: A Population-based Study in England

OBJECTIVE

To evaluate factors associated with the use of laparoscopic surgery and the associated postoperative outcomes for urgent or emergency resection of colorectal cancer in the English National Health Service.

SUMMARY OF BACKGROUND DATA

Laparoscopy is increasingly used for elective colorectal cancer surgery, but uptake has been limited in the emergency setting.

METHODS

Patients recorded in the National Bowel Cancer Audit who underwent urgent or emergency colorectal cancer resection between April 2010 and March 2016 were included. A multivariable multilevel logistic regression model was used to estimate odds ratios (ORs) of undergoing laparoscopic resection and postoperative outcome according to approach.

RESULTS

There were 15,516 patients included. Laparoscopy use doubled from 15.1% in 2010 to 30.2% in 2016. Laparoscopy was less common in patients with poorer physical status [American Society of Anaesthesiologists (ASA) 4/5 vs 1, OR 0.29 (95% confidence interval, 95% CI 0.23-0.37), P < 0.001] and more advanced T-stage [T4 vs T0-T2, OR 0.28 (0.23-0.34), P < 0.001] and M-stage [M1 vs M0, OR 0.85 (0.75-0.96), P < 0.001]. Age, socioeconomic deprivation, nodal stage, hospital volume, and a dedicated colorectal emergency service were not associated with laparoscopy. Laparoscopic patients had a shorter length of stay [median 8 days (interquartile range (IQR) 5 to 15) vs 12 (IQR 8 to 21), adjusted mean difference -3.67 (-4.60 to 2.74), P < 0.001], and lower 90-day mortality [8.1% vs 13.0%; adjusted OR 0.78 (0.66-0.91), P = 0.004] than patients undergoing open resection. There was no significant difference in rates of readmission or reoperation by approach.

CONCLUSION

The use of laparoscopic approach in the emergency resection of colorectal cancer is linked to a shorter length of hospital stay and reduced postoperative mortality.

Cardiac biomarkers improve prediction performance of the combination of American Society of Anesthesiologists physical status classification and Americal College of Surgeons National Surgical Quality Improvement Program calculator for postoperative mortality in elderly patients: a pilot study

BACKGROUND

Our previous research has shown American Society of Anaesthesiologists physical status classification (ASA) score and Americal College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator to have the most accuracy in the prediction of postoperative mortality.

AIMS

The aim of our research was to define the most reliable combination of cardiac biomarkers with ASA and ACS NSQIP.

METHODS

We have included a total of 78 patients. ASA score has been determined in standard fashion, while we used the available interactive calculator for the ACS NSQIP score. Biomarkers BIRC5, H-FABP, and hsCRP have been measured in specialized laboratories.

RESULTS

All of the deceased patients had survivin (BIRC5) > 4.00 pg/ml, higher values of H-FABP and hsCRP and higher estimated levels of ASA and ACS NSQIP (P = 0.0001). ASA and ACS NSQIP alone had AUC of, respectively, 0.669 and 0.813. The combination of ASA and ACS NSQIP had AUC = 0.841. Combination of hsCRP with the two risk scores had AUC = 0.926 (95% CI 0.853-1.000, P < 0.0001). If we add three cardiac biomarkers to this model, we get AUC as high as 0.941 (95% CI 0.876-1.000, P < 0.0001). The correction of statistical models with comorbidities (CIRS-G score) did not change the accuracy of prediction models that we have provided.

DISCUSSION

Addition of ACS NSQIP and biomarkers adds to the accuracy of ASA score, which has already been proved by other authors.

CONCLUSION

Cardiac biomarker hsCRP can be used as the most reliable cardiac biomarker; however, the "multimarker approach" adds the most to the accuracy of the combination of clinical risk scores.

Thirty-day mortality after emergency surgery for obstructing colon cancer: survey and dedicated score from the French Surgical Association

AIM

The aim was to define risk factors for postoperative mortality in patients undergoing emergency surgery for obstructing colon cancer (OCC) and to propose a dedicated score.

METHOD

From 2000 to 2015, 2325 patients were treated for OCC in French surgical centres by members of the French National Surgical Association. A multivariate analysis was performed for variables with P value ≤ 0.20 in the univariate analysis for 30-day mortality. Predictive performance was assessed by the area under the receiver operating characteristic curve.

RESULTS

A total of 1983 patients were included. Thirty-day postoperative mortality was 7%. Multivariate analysis found five significant independent risk factors: age ≥ 75 (P = 0.013), American Society of Anesthesiologists (ASA) score ≥ III (P = 0.027), pulmonary comorbidity (P = 0.0002), right-sided cancer (P = 0.047) and haemodynamic failure (P < 0.0001). The odds ratio for risk of postoperative death was 3.42 with one factor, 5.80 with two factors, 15.73 with three factors, 29.23 with four factors and 77.25 with five factors. The discriminating capacity in predicting 30-day postoperative mortality was 0.80.

CONCLUSION

Thirty-day postoperative mortality after emergency surgery for OCC is correlated with age, ASA score, pulmonary comorbidity, site of tumour and haemodynamic failure, with a specific score ranging from 0 to 5.

CCR-CARESS score for predicting operative mortality in patients with colorectal cancer

BACKGROUND

The aim of this study was to assess factors associated with outcomes after surgery for colorectal cancer and to design and internally validate a simple score for predicting perioperative mortality.

METHODS

Patients undergoing surgery for primary invasive colorectal cancer in 22 centres in Spain between June 2010 and December 2012 were included. Clinical variables up to 30 days were collected prospectively. Multiple logistic regression techniques were applied and a risk score was developed. The Hosmer-Lemeshow test was applied and the area under the receiver operating characteristic (ROC) curve (AUC, with 95 per cent c.i.) was estimated.

RESULTS

A total of 2749 patients with a median age of 68·5 (range 24-97) years were included; the male : female ratio was approximately 2 : 1. Stage III tumours were diagnosed in 32·6 per cent and stage IV in 9·5 per cent. Open surgery was used in 39·3 per cent, and 3·6 per cent of interventions were urgent. Complications were most commonly infectious or surgical, and 25·5 per cent of patients had a transfusion during the hospital stay. The 30-day postoperative mortality rate was 1·9 (95 per cent c.i. 1·4 to 2·4) per cent. Predictive factors independently associated with mortality were: age 80 years or above (odds ratio (OR) 2·76), chronic obstructive pulmonary disease (COPD) (OR 3·62) and palliative surgery (OR 10·46). According to the categorical risk score, a patient aged 80 years or more, with COPD, and who underwent palliative surgery would have a 23·5 per cent risk of death within 30 days of the intervention.

CONCLUSION

Elderly patients with co-morbidity and palliative intention of surgery have an unacceptably high risk of death.

Effect of public reporting of surgeons' outcomes on patient selection, "gaming," and mortality in colorectal cancer surgery in England: population based cohort study

OBJECTIVE

To determine the effect of surgeon specific outcome reporting in colorectal cancer surgery on risk averse clinical practice, "gaming" of clinical data, and 90 day postoperative mortality.

DESIGN

National cohort study.

SETTING

English National Health Service hospital trusts.

POPULATION

111 431 patients diagnosed as having colorectal cancer from 1 April 2011 to 31 March 2015 included in the National Bowel Cancer Audit.

INTERVENTION

Public reporting of surgeon specific 90 day mortality in elective colorectal cancer surgery in England introduced in June 2013.

MAIN OUTCOME MEASURES

Proportion of patients with colorectal cancer who had an elective major resection, predicted 90 day mortality based on characteristics of patients and tumours, and observed 90 day mortality adjusted for differences in characteristics of patients and tumours, comparing patients who had surgery between April 2011 and June 2013 and between July 2013 and March 2015.

RESULTS

The proportion of patients with colorectal cancer undergoing major resection did not change after the introduction of surgeon specific public outcome reporting (39 792/62 854 (63.3%) before versus 30 706/48 577 (63.2%) after; P=0.8). The proportion of these major resections categorised as elective or scheduled also did not change (33 638/39 792 (84.5%) before versus 25 905/30 706 (84.4%) after; P=0.5). The predicted 90 day mortality remained the same (2.7% v 2.7%; P=0.3), but the observed 90 day mortality fell (952/33 638 (2.8%) v 552/25 905 (2.1%)). Change point analysis showed that this reduction was over and above the existing downward trend in mortality before the introduction of public outcome reporting (P=0.03).

CONCLUSIONS

This study did not find evidence that the introduction of public reporting of surgeon specific 90 day postoperative mortality in elective colorectal cancer surgery has led to risk averse clinical practice behaviour or "gaming" of data. However, its introduction coincided with a significant reduction in 90 day mortality.

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.

Determinants of stoma reversal in rectal cancer patients who had an anterior resection between 2009 and 2012 in the English National Health Service

AIM

The rate of ileostomy reversal was estimated in patients undergoing an elective anterior resection for rectal cancer and factors associated with reversal were identified.

METHOD

The records of 4879 rectal patients who had an ileostomy created during anterior resection between 2009 and 2012 were identified in the National Bowel Cancer Audit database and linked to administrative records of the Hospital Episode Statistics. Patients were followed from surgery. Multivariable proportional hazards regression was used to estimate the impact of patient and cancer characteristics on ileostomy reversal with death as the competing risk.

RESULTS

Within 18 months from anterior resection, 3536 (72.5%) patients had undergone ileostomy reversal. The reversal rate was lower in the following circumstances: older patients [hazard ratio (HR) 0.90; 95% CI 0.84-0.96, aged 80 vs 70 years], male gender (HR 0.90; 0.84-0.97), higher American Society of Anesthesiologists (ASA) grade (HR 0.64; 0.56-0.74, ASA 3+ vs 1), more advanced cancer (HR 0.77; 0.69-0.87, T3 vs T1), socioeconomic deprivation (HR 0.83; 0.74-0.93, most vs least deprived quintile), comorbidity (HR 0.92; 0.84-1.00, one vs no comorbidity) and open surgical procedure (HR 0.90; 0.84-0.97, open vs laparoscopic).

CONCLUSION

Overall, two-thirds of ileostomies were reversed within 18 months. Reversal rates were linked to patient and cancer characteristics (age, sex, fitness and stage), mode of surgical access and socioeconomic deprivation. Observed lower reversal rates in patients from poorer backgrounds may indicate inequity in access.