Cardiac risk stratification in emergency resection for colonic tumours

Bass, Gary Alan; Forssten, Maximilian Peter; Pourlotfi, Arvid; Hulme, Rebecka Ahl; Cao, Yang; Matthiessen, Peter; Mohseni, Shahin

doi:10.1093/bjsopen/zrab057

Cited by 6 publications

(5 citation statements)

References 45 publications

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“…As such, it is important to delineate other factors, which could be modifiable in the perioperative and postoperative period, that could aid in achieving better outcomes beyond the immediate postoperative period. For instance, while the RCRI does not excel at predicting 1-year mortality, it has been found to be predictive of, and associated with, worse short-term outcomes after emergency surgery [ 1 , 8 , 53 , 54 ]. This variable is readily available at the time of admission and for those with an increased cardiac risk, higher levels of care and cardioprotective medication such as beta-blockers are recommended by American Heart Association [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

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

Forssten

Bass

Ismail

et al. 2021

JPM

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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.

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Section: Discussionmentioning

confidence: 99%

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

Forssten

Bass

Ismail

et al. 2021

JPM

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“…As described by previous studies investigating the RCRI, patients were divided into five cohorts: RCRI 0, RCRI 1, RCRI 2, RCRI 3, and RCRI ≥ 4 [19][20][21]24]. Patients' demographics and clinical features were compared between these cohorts.…”

Section: Discussionmentioning

confidence: 99%

“…The RCRI was calculated according to Lindenauer et al, which has previously been used in other surgical subspecialties [19][20][21]24]. This included the variables congestive heart failure, ischemic heart disease, cerebrovascular disease, renal insufficiency, diabetes mellitus, and high-risk surgery [24].…”

Section: Calculation Of the Rcrimentioning

confidence: 99%

“…It makes use of only six independent variables: congestive heart failure, ischemic heart disease, cerebrovascular disease, preoperative serum creatinine level above 177 µmol/L (2 mg/dL), diabetes mellitus requiring insulin therapy, and high-risk surgery [15,16]. Previous studies have demonstrated a clear association between an increase in the RCRI score and excess mortality as well as other adverse outcomes in many non-cardiac surgical procedures [17][18][19][20][21]. Its utility in TBI patients has hitherto not been as thoroughly studied.…”

Section: Introductionmentioning

confidence: 99%

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

Forssten

Bass

Scheufler

et al. 2021

Eur J Trauma Emerg Surg

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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.

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“…Applying RCRI to risk stratification prior to major cancer surgery has been described in limited literature (12,13). Perioperative risk-mitigation strategies, guided by tools like the RCRI, may improve patient outcomes through better resource allocation and individualized perioperative monitoring or rehabilitation.…”

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confidence: 99%

The Relationship Between Revised Cardiac Risk Index and Postoperative Morbidity After Major Abdominal Oncologic Surgery

Kaya

2023

Acad J Health

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Objectives: Major abdominal surgery is associated with postoperative morbidity, including perioperative cardiac ischemic events. Preoperative risk stratification is important for optimal surgical care, which is only possible once the risk has been identified. We aimed to determine a relationship between the Revised Cardiac Risk Index (RCRI) and postoperative morbidity after major abdominal oncologic surgery.Methods: Ethics committee approval was obtained by protocol number 2018-04151. A total of 350 patients, aged over 18 years, undergoing elective major abdominal oncologic surgery and were expected to continue for more than two hours participated in the study. ASA classification, RCRI score, duration of surgery, and postoperative morbidity survey (POMS) on postoperative days 1 and 5 were recorded. We followed the length of hospital stay, hospital admissions after discharge, and postoperative mortality within 30 and 90 days.Results: There was no significant correlation between RCRI and postoperative first-day morbidity (p=0.233). A moderate positive correlation was found between the ASA classification and the RCRI (r=0.443; p<0.001). The patients with high ASA scores had high RCRI scores. The most common morbidities were renal (99,1%), pain (93,7%), and gastrointestinal morbidity (84.3%) on the postoperative first day. As the RCRI score increased, the length of hospital stay was longer; however, this difference was not statistically significant (p=0.180). There was a weak positive correlation between the RCRI score and mortality (r=0.127, p=0.017). Conclusions:Our study showed an insufficient correlation between RCRI as a preliminary assessment tool and postoperative morbidity. We considered a need for different risk-scoring systems that are practical and useful in predicting patients with a high risk of morbidity after major abdominal oncologic surgery.

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Cardiac risk stratification in emergency resection for colonic tumours

Cited by 6 publications

References 45 publications

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

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

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

The Relationship Between Revised Cardiac Risk Index and Postoperative Morbidity After Major Abdominal Oncologic Surgery

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