Risk modelling of outcome after general and trauma surgery (the IRIS score)

Age is just a number: The role of advanced age in predicting complications following ventral hernia repair with component separation

BACKGROUND

While advanced age is often considered a risk factor for complications following abdominal surgery, its impact on outcomes after complex open ventral hernia repair (VHR) with component separation technique (CST) remains unclear.

METHODS

A single-center retrospective review of patients who VHR with CST from November 2008 to January 2022 was performed and cohorts were stratified by presence of advanced age (≥60 years).

RESULTS

Of 219 patients who underwent VHR with CST, 114 patients (52.1 ​%) were aged ≥60 years. Multivariate analysis demonstrated BMI to be an independent predictor for any complication (OR 1.1, p ​= ​0.002) and COPD was positively associated with seroma development (OR 20.1, p ​= ​0.012). Advanced age did not independently predict postoperative outcomes, including hernia recurrence (OR 0.8, p ​= ​0.766).

CONCLUSIONS

VHR with CST is generally safe to perform in patients of advanced age. Every patient's comorbidity profile should be thoroughly assessed preoperatively for risk stratification regardless of age.

Scores for preoperative risk evaluation of postoperative mortality

Preoperative risk evaluation scores are used prior to surgery to predict perioperative risks. They are also a useful tool to help clinicians communicate the risk-benefit balance of the procedure to patients. This review identifies and assesses the existing preoperative risk evaluation scores (also called prediction scores) of postoperative mortality in all types of surgery (emergency or scheduled) in an adult population. We systematically identified studies using the MEDLINE, Ovid EMBASE and Cochrane databases and published studies reporting the development and validation of preoperative predictive scores of postoperative mortality. We assessed usability, the level of evidence of the studies performed for external validation, and the predictive accuracy of the scores identified. We found 26 scores described within 60 different reports. The most suitable scores with the highest validity identified for anaesthesia practice were the Preoperative Score to Predict Postoperative Mortality (POSPOM), the Universal ACS NSQIP surgical risk calculator (ACS-NSQUIP), the Clinical Frailty Scale (CFS) and the American Society of Anesthesiologists Physical Status (ASA-PS) classification system. While other scores identified in this review could also be endorsed, their level of validity and generalizability to the general surgical population should be carefully considered.

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non-cardiac surgery

BACKGROUND

Risk prediction tools can be used in the perioperative setting to identify high-risk patients who may benefit from increased surveillance and monitoring in the postoperative period, to aid shared decision-making, and to benchmark risk-adjusted hospital performance. We evaluated perioperative risk prediction tools relevant to an Australian context.

METHODS

A systematic review of perioperative mortality risk prediction tools used for adults undergoing inpatient noncardiac surgery, published between 2011 and 2019 (following an earlier systematic review). We searched Medline via OVID using medical subject headings consistent with the three main areas of risk, surgery and mortality/morbidity. A similar search was conducted in Embase. Tools predicting morbidity but not mortality were excluded, as were those predicting a composite outcome that did not report predictive performance for mortality separately. Tools were also excluded if they were specifically designed for use in cardiac or other highly specialized surgery, emergency surgery, paediatrics or elderly patients.

RESULTS

Literature search identified 2568 studies for screening, of which 19 studies identified 21 risk prediction tools for inclusion.

CONCLUSION

Four tools are candidates for adapting in the Australian context, including the Surgical Mortality Probability Model (SMPM), Preoperative Score to Predict Postoperative Mortality (POSPOM), Surgical Outcome Risk Tool (SORT) and NZRISK. SORT has similar predictive performance to POSPOM, using only six variables instead of 17, contains all variables of the SMPM, and the original model developed in the UK has already been successfully adapted in New Zealand as NZRISK. Collecting the SORT and NZRISK variables in a national surgical outcomes study in Australia would present an opportunity to simultaneously investigate three of our four shortlisted models and to develop a locally valid perioperative mortality risk prediction model with high predictive performance.

Development and validation of a brachycephalic risk (BRisk) score to predict the risk of complications in dogs presenting for surgical treatment of brachycephalic obstructive airway syndrome

OBJECTIVE

To develop and validate a preoperative brachycephalic risk (BRisk) score that objectively and accurately predicts the risk of major complications or death in dogs undergoing corrective surgery for brachycephalic obstructive airway syndrome (BOAS).

STUDY DESIGN

Retrospective multicenter cohort study.

SAMPLE POPULATION

Score development n = 233 dogs, validation n = 50 dogs.

METHODS

Data were collected on signalment, medical history, reason for presentation, physical examination, and preoperative diagnostic findings. The primary outcome measures included risk of major complications (requirement for postoperative oxygen support for >48 hours or postoperative temporary/permanent tracheostomy) or death within the hospitalization period. The score was developed by using data from two centers and was validated in a third center. The 10-point BRisk score was modeled on breed, history of previous surgery, concurrent procedures, body condition score, airway status, and admission rectal temperature.

RESULTS

The score was associated with negative outcome (P < .0001) and discriminated well in both the construction (area under the receiver operator characteristic [AUROC] = 0.83) and validation groups (AUROC = 0.84). Dogs with scores >3 were 9.1 times more likely to have a negative outcome (95% CI = 3.9-21.2) compared with dogs with scores ≤3.

CONCLUSION

The BRisk score developed from admission data in this study accurately rated the risk of negative outcome of dogs undergoing corrective surgery for BOAS.

CLINICAL SIGNIFICANCE

Preoperative determination of the BRisk score may assist triage, management of owner expectations, decision making regarding intervention selection, and characterization of populations in clinical research.

In-hospital mortality after surgery: a retrospective cohort study in a Japanese university hospital

Background

The rapidly aging population affects Japan’s health system, which is characterized by equity and full health insurance coverage for the entire population. However, the current outcomes after surgery in tertiary hospitals in Japan are not known. We aimed to gain an overview of postoperative mortality and death in a tertiary university hospital.

Methods

Using the administrative database of Kawasaki Medical School Hospital, we investigated the pattern of in-hospital mortality and death for patients who underwent surgery under general or regional anesthesia between January 2010 and December 2011. We used a logistic regression model to find pre-operative risk factors associated with in-hospital mortality in this derivation cohort and tested its results in the validation cohort obtained from surgical patients between January 2012 and April 2014.

Results

Among 8414 admissions for surgery patients aged ≥65 years was 41.0 %, reflecting aged population in Japan. There were 170 deaths in the derivation cohort, resulting in in-hospital mortality of 2.0 %, and in 30-day mortality of 1.0 %, because a half of the death occurred later than 30 days. We identified four independent preoperative risk factors for in-hospital mortality: high-risk surgery [odds ratio (OR) 18.64], moderate-risk surgery (OR 5.00), ASA-PS ≥3 (OR 5.55), and emergency (OR 2.35). A good correlation between actual and calculated mortality based on the derivation cohort was confirmed in the validation cohort.

Conclusions

This retrospective study of a single university hospital in Japan shows that aged patients in their 70 s is the largest group undergoing surgery, and that the overall in-hospital mortality is similar to that of other countries, but the 30-day mortality is less than that. Risk stratification for in-hospital mortality using preoperative factors was validated.

Incidence and Predictors of Cardiovascular Complications and Death after Vascular Surgery

BACKGROUND

Patients undergoing arterial vascular surgery are considered at increased risk for post-operative complications.

OBJECTIVE

To assess the incidence and predictors of complications and death, as well as the performance of two models of risk stratification, in vascular surgery.

METHODS

This study determined the incidence of cardiovascular complications and deaths within 30 days from surgery in adults. Univariate comparison and logistic regression assessed the risk factors associated with the outcomes, and the receiver operating characteristic (ROC) curve assessed the discriminatory capacity of the revised cardiac risk index (RCRI) and vascular study group of New England cardiac risk index (VSG-CRI).

RESULTS

141 patients (mean age, 66 years; 65% men) underwent the following surgeries: carotid (15); lower limbs (65); abdominal aorta (56); and others (5). Cardiovascular complications and death occurred within 30 days in 28 (19.9%) and 20 (14.2%) patients, respectively. The risk predictors were: age, obesity, stroke, poor functional capacity, altered scintigraphy, surgery of the aorta, and troponin change. The scores RCRI and VSG-CRI had area under the curve of 0.635 and 0.639 for early cardiovascular complications, and 0.562 and 0.610 for death in 30 days.

CONCLUSION

In this small and selected group of patients undergoing arterial vascular surgery, the incidence of adverse events was elevated. The risk assessment indices RCRI and VSG-CRI did not perform well for complications within 30 days.

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.

The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery

OBJECTIVE

To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the "point-of-care," and that can be used by surgeons and hospitals to internally audit their quality of care.

BACKGROUND

Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery.

METHODS

Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database.

RESULTS

The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set.

CONCLUSIONS

Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk-reduction strategies, and guiding quality improvement efforts.

The impact of open versus closed format ICU admission practices on the outcome of high risk surgical patients: a cohort analysis

Background

In the year 2000, the organizational structure of the ICU in the Zaandam Medical Centre (ZMC) changed from an open to a closed format ICU. The objective of this study was to evaluate the effect of this organizational change on outcome in high risk surgical patients.

Methods

The medical records of all consecutive high risk surgical patients admitted to the ICU from 1996 to 1998 (open format) and from 2003 to 2005 (closed format), were reviewed. High-risk patients were defined according to the Identification of Risk in Surgical patients (IRIS) score. Parameters studied were: mortality, morbidity, ICU length of stay (LOS) and hospital LOS.

Results

Mortality of ICU patients was 25.7% in the open format group and 15.8% in the closed format group (p = 0.01). Morbidity decreased from 48.6% to 46.1% (p = 0.6). The average length of hospital stay was 17 days in the open format group, and 21 days in the closed format group (p = 0.03).

Conclusions

High risk surgical patients in the ICU are patients that have undergone complex and often extensive surgery. These patients are in need of specialized treatment and careful monitoring for maximum safety and optimal care. Our results suggest that closed format is a more favourable setting than open format to minimize the effects of high risk surgery, and to warrant safe outcome in this patient group.