Cardiac risk stratification in emergency resection for colonic tumours

A Novel Clinical Nomogram for Predicting Overall Survival in Patients with Emergency Surgery for Colorectal Cancer

BACKGROUND

Long-term survival after emergency colorectal cancer surgery is low, and its estimation is most frequently neglected, with priority given to the immediate prognosis. This study aimed to propose an effective nomogram to predict overall survival in these patients.

MATERIALS AND METHODS

We retrospectively studied 437 patients who underwent emergency surgery for colorectal cancer between 2008 and 2019, in whom we analyzed the clinical, paraclinical, and surgical parameters.

RESULTS

Only 30 patients (6.86%) survived until the end of the study. We identified the risk factors through the univariate Cox regression analysis and a multivariate Cox regression model. The model included the following eight independent prognostic factors: age > 63 years, Charlson score > 4, revised cardiac risk index (RCRI), LMR (lymphocytes/neutrophils ratio), tumor site, macroscopic tumoral invasion, surgery type, and lymph node dissection (p < 0.05 for all), with an AUC (area under the curve) of 0.831, with an ideal agreement between the predicted and observed probabilities. On this basis, we constructed a nomogram for prediction of overall survival.

CONCLUSIONS

The nomogram created, on the basis of a multivariate logistic regression model, has a good individual prediction of overall survival for patients with emergency surgery for colon cancer and may support clinicians when informing patients about prognosis.

The revised cardiac risk index is associated with morbidity and mortality independent of injury severity in elderly patients with rib fractures

BACKGROUND

Risk factors for mortality and in-hospital morbidity among geriatric patients with traumatic rib fractures remain unclear. Such patients are often frail and demonstrate a high comorbidity burden. Moreover, outcomes anticipated by current rubrics may reflect the influence of multisystem injury or surgery, and thus not apply to isolated injuries in geriatric patients. We hypothesized that the Revised Cardiac Risk Index (RCRI) may assist in risk-stratifying geriatric patients following rib fracture.

METHODS

All geriatric patients (age ≥65 years) with a conservatively managed rib fracture owing to an isolated thoracic injury (thorax AIS ≥1), in the 2013-2019 TQIP database were assessed including demographics and outcomes. The association between the RCRI and in-hospital morbidity as well as mortality was analyzed using Poisson regression models while adjusting for potential confounders.

RESULTS

96,750 geriatric patients sustained rib fractures. Compared to those with RCRI 0, patients with an RCRI score of 1 had a 16% increased risk of in-hospital mortality [adjusted incidence rate ratio (adj-IRR), 95% confidence interval (CI): 1.16 (1.02-1.32), p=0.020]. An RCRI score of 2 [adj-IRR (95% CI): 1.72 (1.44-2.06), p<0.001] or ≥3 [adj-IRR (95% CI): 3.07 (2.31-4.09), p<0.001] was associated with an even greater mortality risk. Those with an increased RCRI also exhibited a higher incidence of myocardial infarction, cardiac arrest, stroke, and acute respiratory distress syndrome.

CONCLUSIONS

Geriatric patients with rib fractures and an RCRI ≥1 represent a vulnerable and high-risk group. This index may inform the decision to admit for inpatient care and can also guide patient and family counseling as well as computer-based decision-support.

Mortality risk stratification in isolated severe traumatic brain injury using the revised cardiac risk index

PURPOSE

Traumatic brain injury (TBI) continues to be a significant cause of mortality and morbidity worldwide. As cardiovascular events are among the most common extracranial causes of death after a severe TBI, the Revised Cardiac Risk Index (RCRI) could potentially aid in the risk stratification of this patient population. This investigation aimed to determine the association between the RCRI and in-hospital deaths among isolated severe TBI patients.

METHODS

All adult patients registered in the TQIP database between 2013 and 2017 who suffered an isolated severe TBI, defined as a head AIS ≥ 3 with an AIS ≤ 1 in all other body regions, were included. Patients were excluded if they had a head AIS of 6. The association between different RCRI scores (0, 1, 2, 3, ≥ 4) and in-hospital mortality was analyzed using a Poisson regression model with robust standard errors while adjusting for potential confounders, with RCRI 0 as the reference.

RESULTS

259,399 patients met the study's inclusion criteria. RCRI 2 was associated with a 6% increase in mortality risk [adjusted IRR (95% CI) 1.06 (1.01-1.12), p = 0.027], RCRI 3 was associated with a 17% increased risk of mortality [adjusted IRR (95% CI) 1.17 (1.05-1.31), p = 0.004], and RCRI ≥ 4 was associated with a 46% increased risk of in-hospital mortality [adjusted IRR(95% CI) 1.46 (1.11-1.90), p = 0.006], compared to RCRI 0.

CONCLUSION

An elevated RCRI ≥ 2 is significantly associated with an increased risk of in-hospital mortality among patients with an isolated severe traumatic brain injury. The simplicity and bedside applicability of the index makes it an attractive choice for risk stratification in this patient population.

Predicting 1-Year Mortality after Hip Fracture Surgery: An Evaluation of Multiple Machine Learning Approaches

Postoperative death within 1 year following hip fracture surgery is reported to be up to 27%. In the current study, we benchmarked the predictive precision and accuracy of the algorithms support vector machine (SVM), naïve Bayes classifier (NB), and random forest classifier (RF) against logistic regression (LR) in predicting 1-year postoperative mortality in hip fracture patients as well as assessed the relative importance of the variables included in the LR model. All adult patients who underwent primary emergency hip fracture surgery in Sweden, between 1 January 2008 and 31 December 2017 were included in the study. Patients with pathological fractures and non-operatively managed hip fractures, as well as those who died within 30 days after surgery, were excluded from the analysis. A LR model with an elastic net regularization were fitted and compared to NB, SVM, and RF. The relative importance of the variables in the LR model was then evaluated using the permutation importance. The LR model including all the variables demonstrated an acceptable predictive ability on both the training and test datasets for predicting one-year postoperative mortality (Area under the curve (AUC) = 0.74 and 0.74 respectively). NB, SVM, and RF tended to over-predict the mortality, particularly NB and SVM algorithms. In contrast, LR only over-predicted mortality when the predicted probability of mortality was larger than 0.7. The LR algorithm outperformed the other three algorithms in predicting 1-year postoperative mortality in hip fracture patients. The most important predictors of 1-year mortality were the presence of a metastatic carcinoma, American Society of Anesthesiologists(ASA) classification, sex, Charlson Comorbidity Index (CCI) ≤ 4, age, dementia, congestive heart failure, hypertension, surgery using pins/screws, and chronic kidney disease.