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

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.

Driver gene KRAS aggravates cancer-associated stroke outcomes

The incidence of cancer-associated stroke has increased with the prolonged survival times of cancer patients. Recent genetic studies have led to progress in cancer therapeutics, but relationships between oncogenic mutations and stroke remain elusive. Here, we focused on the driver gene KRAS, which is the predominant RAS isoform mutated in multiple cancer types, in cancer associated stroke study. KRASG13D/- and parental human colorectal carcinoma HCT116 cells were inoculated into mice that were then subjected to a photochemically-induced thrombosis model to establish ischemic stroke. We found that cancer inoculation exacerbated neurological deficits after stroke. Moreover, mice inoculated with KRASG13D/- cells showed worse neurological deficits after stroke compared with mice inoculated with parental cells. Stroke promoted tumor growth, and the KRASG13D/- allele enhanced this growth. Brain RNA sequencing analysis and serum ELISA showed that chemokines and cytokines mediating pro-inflammatory responses were upregulated in mice inoculated with KRASG13D/- cells compared with those inoculated with parental cells. STAT3 phosphorylation was promoted following ischemic stroke in the KRASG13D/- group compared with in the parental group, and STAT3 inhibition significantly ameliorated stroke outcomes by mitigating microglia/macrophage polarization. Finally, we compared the prognosis and mortality of colorectal cancer patients with or without stroke onset between 1 January 2007 and 31 December 2020 using a hospital-based cancer registry and found that colorectal cancer patients with stroke onset within 3 months after cancer diagnosis had a worse prognosis. Our work suggests an interplay between KRAS and ischemic stroke that may offer insight into future treatments for cancer-associated stroke.

Understanding Variation in In-hospital Mortality After Major Surgery in the United States

OBJECTIVES

We aimed to quantify the contributions of patient characteristics (PC), hospital structural characteristics (HC), and hospital operative volumes (HOV) to in-hospital mortality (IHM) after major surgery in the United States (US).

BACKGROUND

The volume-outcome relationship correlates higher HOV with decreased IHM. However, IHM after major surgery is multifactorial, and the relative contribution of PC, HC, and HOV to IHM after major surgery is unknown.

STUDY DESIGN

Patients undergoing major pancreatic, esophageal, lung, bladder, and rectal operations between 2006 and 2011 were identified from the Nationwide Inpatient Sample linked to the American Hospital Association survey. Multilevel logistic regression models were constructed using PC, HC, and HOV to calculate attributable variability in IHM for each.

RESULTS

Eighty thousand nine hundred sixty-nine patients across 1025 hospitals were included. Postoperative IHM ranged from 0.9% for rectal to 3.9% for esophageal surgery. Patient characteristics contributed most of the variability in IHM for esophageal (63%), pancreatic (62.9%), rectal (41.2%), and lung (44.4%) operations. HOV explained < 25% of variability for pancreatic, esophageal, lung, and rectal surgery. HC accounted for 16.9% and 17.4% of the variability in IHM for esophageal and rectal surgery. Unexplained variability in IHM was high in the lung (44.3%), bladder (39.3%), and rectal (33.7%) surgery subgroups.

CONCLUSIONS

Despite recent policy focus on the volume-outcome relationship, HOV was not the most important contributor to IHM for the major organ surgeries studied. PC remains the largest identifiable contributor to hospital mortality. Quality improvement initiatives should emphasize patient optimization and structural improvements, in addition to investigating the yet unexplained sources contributing to IHM.

Estimating postoperative mortality in colorectal surgery- a systematic review of risk prediction models

PURPOSE

Risk prediction models are frequently used to support decision-making in colorectal surgery but can be inaccurate. Machine learning (ML) is becoming increasingly popular, and its application may increase predictive accuracy. We compared conventional risk prediction models for postoperative mortality (based on regression analysis) with ML models to determine the benefit of the latter approach.

METHODS

The study was registered in PROSPERO(CRD42022364753). Following the PRISMA guidelines, a systematic search of three databases (MEDLINE, EMBASE, WoS) was conducted (from 1/1/2000 to 29/09/2022). Studies were included if they reported the development of a risk model to estimate short-term postoperative mortality for patients undergoing colorectal surgery. Discrimination and calibration performance metrics were compared. Studies were evaluated against CHARMS and TRIPOD criteria.

RESULTS

3,052 articles were screened, and 45 studies were included. The total sample size was 1,356,058 patients. Six studies used ML techniques for model development. Most studies (n = 42) reported the area under the receiver operating characteristic curve (AUROC) as a measure of discrimination. There was no significant difference in the mean AUROC values between regression models (0.833 s.d. ± 0.52) and ML (0.846 s.d. ± 0.55), p = 0.539. Calibration statistics, which measure the agreement between predicted estimates and observed outcomes, were less consistent. Risk of bias assessment found most concerns in the data handling and analysis domains of eligible studies.

CONCLUSIONS

Our study showed comparable predictive performance between regression and ML methods in colorectal surgery. Integration of ML in colorectal risk prediction is promising but further refinement of the models is required to support routine clinical adoption.

Validation of the Surgical Outcome Risk Tool (SORT) and SORT v2 for Predicting Postoperative Mortality in Patients with Pancreatic Cancer Undergoing Surgery

Background: Pancreatic cancer surgery is related to significant mortality, thus necessitating the accurate assessment of perioperative risk to enhance treatment decision making. A Surgical Outcome Risk Tool (SORT) and SORT v2 have been developed to provide enhanced risk stratification. Our aim was to validate the accuracy of SORT and SORT v2 in pancreatic cancer surgery. Method: Two hundred and twelve patients were included and underwent pancreatic surgery for cancer. The surgeries were performed by a single surgical team in a single tertiary hospital (2016–2022). We assessed a total of four risk models: SORT, SORT v2, POSSUM (Physiology and Operative Severity Score for the enumeration of Mortality and Morbidity), and P-POSSUM (Portsmouth-POSSUM). The accuracy of the model was evaluated using an observed-to-expected (O:E) ratio and the area under the curve (AUC). Results: The 30-day mortality rate was 3.3% (7 patients). Both SORT and SORT v2 demonstrated excellent discrimination traits (AUC: 0.98 and AUC: 0.98, respectively) and provided the best-performing calibration in the total analysis. However, both tools underestimated the 30-day mortality. Furthermore, both reported a high level of calibration and discrimination in the subgroup of patients undergoing pancreaticoduodenectomy, with previous ERCP, and CA19-9 ≥ 500 U/mL. Conclusions: SORT and SORT v2 are efficient risk-assessment tools that should be adopted in the perioperative pathway, shared decision-making (SDM) process, and counseling of patients with pancreatic cancer undergoing surgery.

Validation of the Surgical Outcome Risk Tool (SORT) for Predicting Postoperative Mortality in Colorectal Cancer Patients Undergoing Surgery and Subgroup Analysis

BACKGROUND

The accurate evaluation of perioperative risk is crucial to facilitate the shared decision-making process. Surgical outcome risk tool (SORT) has been developed to provide enhanced and more feasible identification of high-risk surgical patients. Nonetheless, SORT has not been validated for patients with colorectal cancer undergoing surgery. Our aim was to determine whether SORT can accurately predict mortality after surgery for colorectal cancer and to compare it with traditional risk models.

METHOD

526 patients undergoing surgery performed by a colorectal surgical team in a single Greek tertiary hospital (2011-2019) were included. Five risk models were evaluated: (1) SORT, (2) Physiology and Operative Severity Score for the enumeration of Mortality and Morbidity (POSSUM), (3) Portsmouth POSSUM (P-POSSUM), (4) Colorectal POSSUM (CR-POSSUM), and (5) the Association of Great Britain and Ireland (ACPGBI) score. Model accuracy was assessed by observed to expected (O:E) ratios, and area under Receiver Operating Characteristic curve (AUC).

RESULTS

Ten patients (1.9%) died within 30 days of surgery. SORT was associated with an excellent level of discrimination [AUC:0.81 (95% CI:0.68-0.94); p = 0.001] and provided the best performing calibration of all models in the entire dataset analysis (H-L:2.82; p = 0.83). Nonetheless, SORT underestimated mortality. SORT model demonstrated excellent discrimination and calibration predicting perioperative mortality in patients undergoing (1) open surgery, (2) emergency/acute surgery, and (3) in cases with colon-located cancer.

CONCLUSION

SORT is an easily adopted risk-assessment tool, associated with enhanced accuracy, that could be implemented in the perioperative pathway of patients undergoing surgery for colorectal cancer.