Comparison of accuracy of prediction of postoperative mortality and morbidity between a new, parsimonious risk calculator (SURPAS) and the ACS Surgical Risk Calculator

Major liver resections, perioperative issues and posthepatectomy liver failure: A comprehensive update for the anesthesiologist

Significant advances in surgical techniques and relevant medium- and long-term outcomes over the past two decades have led to a substantial expansion in the indications for major liver resections. To support these outstanding results and to reduce perioperative complications, anesthesiologists must address and master key perioperative issues (preoperative assessment, proactive intraoperative anesthesia strategies, and implementation of the Enhanced Recovery After Surgery approach). Intensive care unit monitoring immediately following liver surgery remains a subject of active and often unresolved debate. Among postoperative complications, posthepatectomy liver failure (PHLF) occurs in different grades of severity (A-C) and frequency (9%-30%), and it is the main cause of 90-d postoperative mortality. PHLF, recently redefined with pragmatic clinical criteria and perioperative scores, can be predicted, prevented, or anticipated. This review highlights: (1) The systemic consequences of surgical manipulations anesthesiologists must respond to or prevent, to positively impact PHLF (a proactive approach); and (2) the maximal intensive treatment of PHLF, including artificial options, mainly based, so far, on Acute Liver Failure treatment(s), to buy time waiting for the recovery of the native liver or, when appropriate and in very selected cases, toward liver transplant. Such a clinical context requires a strong commitment to surgeons, anesthesiologists, and intensivists to work together, for a fruitful collaboration in a mandatory clinical continuum.

Validity of the ACS NSQIP surgical risk calculator as a tool to predict postoperative outcomes in subacute orthopedic trauma diagnoses

Purpose

This retrospective study aimed to validate the ACS NSQIP Surgical Risk Calculator (SCR) to predict 30-day postoperative outcomes in patients with one of the following subacute orthopedic trauma diagnoses; multiple rib fractures, pelvic ring/acetabular fracture, or unilateral femoral fracture.

Methods

Data of patients with these diagnoses treated between January 1, 2015 and September 19, 2020 were extracted from the patients' medical files. Diagnostic performance, discrimination, calibration, and accuracy of the ACS NSQIP SRC to predict specific outcomes developing within 30 days after surgery was determined.

Results

The total cohort of the three diagnoses consisted of 435 patients. ACS NSQIP SRC underestimated the risk for serious complications, especially in patients with multiple rib fractures (8.3% predicted vs 17.2% observed) or pelvic ring/acetabular fracture (6.1% vs 19.8%). Underestimation was more pronounced for the composite outcome 'any complication'. Sensitivity ranged from 16.7% to 100% and specificity from 41.1% to 97.1%. Specificity exceeded sensitivity for pelvic ring/acetabular and femoral fractures. Discrimination was good for predicting death (femoral fracture), fair for readmission (femoral fracture), serious complication (multiple rib fractures), and any complication (multiple rib fractures), but poor in all other outcomes and diagnoses. Calibration and accuracy were adequate for all three diagnoses (p-value for Hosmer-Lemeshow test >0.05 and Brier scores <0.25).

Conclusion

Performance of the ACS NSQIP SRC in the studied cohort was variable for all three diagnoses. Although it underestimated the risk of most outcomes, calibration and accuracy seemed generally adequate. For most outcomes, adequate diagnostic performance and discrimination could not be confirmed.

Development and validation of a multivariable preoperative prediction model for postoperative length of stay in a broad inpatient surgical population

BACKGROUND

Postoperative length of stay is a meaningful patient-centered outcome and an important determinant of healthcare costs. The Surgical Risk Preoperative Assessment System preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict postoperative length of stay has not been assessed. We aimed to determine whether the Surgical Risk Preoperative Assessment System variables could accurately predict postoperative length of stay up to 30 days in a broad inpatient surgical population.

METHODS

This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database from 2012 to 2018. A model using the Surgical Risk Preoperative Assessment System variables and a 28-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables, were fit to the analytical cohort (2012-2018) using multiple linear regression and compared using model performance metrics. Internal chronological validation of the Surgical Risk Preoperative Assessment System model was conducted using training (2012-2017) and test (2018) datasets.

RESULTS

We analyzed 3,295,028 procedures. The adjusted R2 for the Surgical Risk Preoperative Assessment System model fit to this cohort was 93.3% of that for the full model (0.347 vs 0.372). In the internal chronological validation of the Surgical Risk Preoperative Assessment System model, the adjusted R2 for the test dataset was 97.1% of that for the training dataset (0.3389 vs 0.3489).

CONCLUSION

The parsimonious Surgical Risk Preoperative Assessment System model can preoperatively predict postoperative length of stay up to 30 days for inpatient surgical procedures almost as accurately as a model using all 28 American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables and has shown acceptable internal chronological validation.

Preoperative Prediction of Unplanned Reoperation in a Broad Surgical Population

INTRODUCTION

Unplanned reoperation is an undesirable outcome with considerable risks and an increasingly assessed quality of care metric. There are no preoperative prediction models for reoperation after an index surgery in a broad surgical population in the literature. The Surgical Risk Preoperative Assessment System (SURPAS) preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict unplanned reoperation has not been assessed. This study's objective was to determine whether the SURPAS model could accurately predict unplanned reoperation.

METHODS

This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database, 2012-2018. An unplanned reoperation was defined as any unintended operation within 30 d of an initial scheduled operation. The 8-variable SURPAS model and a 29-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program nonlaboratory preoperative variables, were developed using multiple logistic regression and compared using discrimination and calibration metrics: C-indices (C), Hosmer-Lemeshow observed-to-expected plots, and Brier scores (BSs). The internal chronological validation of the SURPAS model was conducted using "training" (2012-2017) and "test" (2018) datasets.

RESULTS

Of 5,777,108 patients, 162,387 (2.81%) underwent an unplanned reoperation. The SURPAS model's C-index of 0.748 was 99.20% of that for the full model (C = 0.754). Hosmer-Lemeshow plots showed good calibration for both models and BSs were similar (BS = 0.0264, full; BS = 0.0265, SURPAS). Internal chronological validation results were similar for the training (C = 0.749, BS = 0.0268) and test (C = 0.748, BS = 0.0250) datasets.

CONCLUSIONS

The SURPAS model accurately predicted unplanned reoperation and was internally validated. Unplanned reoperation can be integrated into the SURPAS tool to provide preoperative risk assessment of this outcome, which could aid patient risk education.

A web-based calculator combining Geriatric Nutritional Risk Index (GNRI) and Tilburg Frailty Indicator (TFI) predicts postoperative complications among young elderly patients with gastric cancer

AIM

Nutritional status and frailty are significant indicators reflecting physiological reserve. We sought to establish and validate a web-based calculator containing the Geriatric Nutritional Risk Index (GNRI) and the Tilburg Frailty Indicator (TFI) together with general clinical information to predict total complications among elderly patients with gastric cancer.

METHODS

This was a prospective cohort study of 582 elderly patients with gastric cancer in a tertiary hospital in China. Nutritional status and frailty were assessed by the GNRI and the TFI, respectively. The nomogram was built and further converted into a web-based calculator. The receiver operating characteristic analysis was performed to evaluate the discrimination of the nomogram. Calibration was assessed using the calibration curve and Hosmer-Lemeshow test via the bootstrap resampling procedure. The decision curve analyses (DCAs) were employed to quantify the net benefits of a certain threshold probability for assessing the clinical values.

RESULTS

The GNRI (odds ratio [OR], 0.921; 95% confidence interval [CI], 0.895-0.949; P < 0.001), the TFI (OR, 1.243; 95% CI, 1.113-1.386; P < 0.001), surgical approach (OR, 1.913; 95% CI, 1.073-3.408; P = 0.028) and comorbidity (OR = 1.599, 95%CI = 1.028-2.486, P = 0.037) were independently associated with total complications. The nomogram demonstrated good discrimination (area under the receiver operating characteristic curve: training cohort, 0.735; validation cohort, 0.777) and calibration (P = 0.135). The DCA curves of the nomogram also showed good positive net benefits.

CONCLUSIONS

The web-based calculator incorporating the GNRI, the TFI, surgical approach, and comorbidity could successfully predict total complications among elderly patients with gastric cancer with good accuracy in a convenient manner. Geriatr Gerontol Int 2023; 23: 205-212.

Results of emergency colectomy in nonagenarians and octogenarians previously labeled as prohibitive surgical risk

PURPOSE

There are no standardized criteria for what constitutes prohibitive risk for emergency abdominal surgery.

METHODS

A retrospective review was performed comparing two groups of patients having emergent colectomy. One group had previously been labeled as being prohibitive surgical risk and the other was a contemporary, non-prohibitive risk group also requiring emergency colectomy. All operations were performed by a single surgeon.

RESULTS

There were 27 prohibitive risk patients and 81 non-prohibitive risk (control group) patients. The average age of the prohibitive risk group was 85 years (range 78-99) compared to the control group mean age of 52 years (18-79, p < 0.00001). Prohibitive risk was due to extremes of age combined with congestive heart failure in 44%, followed by chronic obstructive pulmonary disease combined with heart failure in 19%. The groups were closely matched by the type of colectomy performed. The total complication rate was much higher in the prohibitive risk group compared to the non-prohibitive risk patients (81% versus 48%, p 0.005). But the 30-day mortality rate was similar between groups (7% versus 4%, p 0.6).

CONCLUSION

Patients who are labeled as prohibitive surgical risk may be inaccurately assessed in the majority of cases. Additional research will need to be performed to evaluate the presence of quantifiable high-risk physiological conditions, and not just comorbidities, that place a patient at high risk of death after abdominal surgery. Until then, elderly patients should not be denied colectomy based upon comorbidities alone.

Does Adding a Measure of Social Vulnerability to a Surgical Risk Calculator Improve Its Performance?

BACKGROUND

Emerging literature suggests that measures of social vulnerability should be incorporated into surgical risk calculators. The Social Vulnerability Index (SVI) is a measure designed by the CDC that encompasses 15 socioeconomic and demographic variables at the census tract level. We examined whether adding the SVI into a parsimonious surgical risk calculator would improve model performance.

STUDY DESIGN

The eight-variable Surgical Risk Preoperative Assessment System (SURPAS), developed using the entire American College of Surgeons (ACS) NSQIP database, was applied to local ACS-NSQIP data from 2012 to 2018 to predict 12 postoperative outcomes. Patient addresses were geocoded and used to estimate the SVI, which was then added to the model as a ninth predictor variable. Brier scores and c-indices were compared for the models with and without the SVI.

RESULTS

The analysis included 31,222 patients from five hospitals. Brier scores were identical for eight outcomes and improved by only one to two points in the fourth decimal place for four outcomes with addition of the SVI. Similarly, c-indices were not significantly different (p values ranged from 0.15 to 0.96). Of note, the SVI was associated with most of the eight SURPAS predictor variables, suggesting that SURPAS may already indirectly capture this important risk factor.

CONCLUSION

The eight-variable SURPAS prediction model was not significantly improved by adding the SVI, showing that this parsimonious tool functions well without including a measure of social vulnerability.

Attitudes about use of preoperative risk assessment tools: a survey of surgeons and surgical residents in an academic health system

Background

Formal surgical risk assessment tools have been developed to predict risk of adverse postoperative patient outcomes. Such tools accurately predict common postoperative complications, inform patients and providers of likely perioperative outcomes, guide decision making, and improve patient care. However, these are underutilized. We studied the attitudes towards and techniques of how surgeons preoperatively assess risk.

Methods

Surgeons at a large academic tertiary referral hospital and affiliate community hospitals were emailed a 16-question survey via REDCap (Research Electronic Data Capture) between 8/2019-6/2020. Reminder emails were sent once weekly for three weeks. All completed surveys by surgical residents and attendings were included; incomplete surveys were excluded. Surveys were analyzed using descriptive statistics (frequency distributions and percentages for categorical variables, means, and standard deviations for continuous variables), and Fisher’s exact test and unpaired t-tests comparing responses by surgical attendings vs. residents.

Results

A total of 108 surgical faculty, 95 surgical residents, and 58 affiliate surgeons were emailed the survey. Overall response rates were 50.0% for faculty surgeons, 47.4% for residents, and 36.2% for affiliate surgeons. Only 20.8% of surgeons used risk calculators most or all of the time. Attending surgeons were more likely to use prior experience and current literature while residents used risk calculators more frequently. Risk assessment tools were more likely to be used when predicting major complications and death in older patients with significant risk factors. Greatest barriers for use of risk assessment tools included time, inaccessibility, and trust in accuracy.

Conclusions

A small percentage of surgeons use surgical risk calculators as part of their routine practice. Time, inaccessibility, and trust in accuracy were the most significant barriers to use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13037-022-00320-1.

Surgical risk calculators in veterans following lower extremity amputation

OBJECTIVE

To evaluate the accuracy of multiple risk calculators for 30-day mortality on patients undergoing major lower extremity amputation.

METHODS

The actual 30-day mortality at a single Veterans Affairs institution was compared to the predicted outcome from the following risk calculators: ACS-NSQIP, VASQIP, amputation scoring tool (AST), and POTTER elective.

RESULTS

The overall calculated 30-day mortality was similar to the actual mortality with the VASQIP and POTTER elective risk calculators, while the NSQIP and AST over-estimated the 30-day mortality. The predictive accuracy of the POTTER and NSQIP risk calculators were moderate (AUC >0.7), and fair for the VASQIP and AST.

CONCLUSION

Risk assessment tools can provide adjunctive data on predicted 30-day mortality in patients undergoing major lower extremity amputation. In our study, there were differences in predictability of the risk calculators for lower extremity amputation that should be considered when utilizing a risk assessment tool to improve physician-patient shared decision-making.

Associations between preoperative risks of postoperative complications: Results of an analysis of 4.8 Million ACS-NSQIP patients

BACKGROUND

Surgical Risk Preoperative Assessment System (SURPAS) estimates patient's preoperative risk of 12 postoperative complications, yet little is known about associations between these probabilities- We sought to examine relationships between predicted probabilities.

METHODS

Risk of 12 postoperative complications was calculated using SURPAS and the 2012-2018 ACS-NSQIP database. Pearson correlation coefficients (r) were computed to examine relationships between predicted outcomes. "High-risk" was predicted risk in the 10th decile.

RESULTS

4,777,267 patients were included. 71.1% were not high risk, 10.7% were high risk for 1, and 18.2% were high risk for ≥2 complications. High mortality risk was associated with high risk for pulmonary (r = 0.94), cardiac (r = 0.98), renal (r = 0.93), and stroke (0.96) complications. Patients high-risk for ≥2 complications had the most comorbidities and actual adverse outcomes.

CONCLUSIONS

High preoperative risk for certain postoperative complications had strong correlations. 18.2% of patients were high-risk for ≥2 complications and could be targeted for risk reduction interventions.

Comparison of Preoperative Surgical Risk Estimated by Thoracic Surgeons Versus a Standardized Surgical Risk Prediction Tool

Considerable variability exists between surgeons' assessments of a patient's individual pre-operative surgical risk. Surgical risk calculators are not routinely used despite their validation. We sought to compare thoracic surgeons' prediction of patients' risk of postoperative adverse outcomes versus a surgical risk calculator, the Surgical Risk Preoperative Assessment System (SURPAS). We developed vignettes from 30 randomly selected patients who underwent thoracic surgery in the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) database. Twelve thoracic surgeons estimated patients' preoperative risks of postoperative morbidity and mortality. These were compared to SURPAS estimates of the same vignettes. C-indices and Brier scores were calculated for the surgeons' and SURPAS estimates. Agreement between surgeon estimates was examined using intraclass correlation coefficients (ICCs). Surgeons estimated higher morbidity risk compared to SURPAS for low-risk patients (ASA classes 1-2, 11.5% vs. 5.1%, p=<0.001) and lower morbidity risk compared to SURPAS for high-risk patients (ASA class 5, 37.6% vs. 69.8%, p<0.001). This trend also occurred in high-risk patients for mortality (ASA 5, 11.1% vs. 44.3%, p<0.001). C-indices for SURPAS vs. surgeons were 0.84 vs. 0.76 (p=0.3) for morbidity and 0.98 vs. 0.85 (p=0.001) for mortality. Brier scores for SURPAS vs. surgeons were 0.1579 vs. 0.1986 for morbidity (p=0.03) and 0.0409 vs. 0.0543 for mortality (p=0.006). ICCs showed that surgeons had moderate risk agreement for morbidity (ICC=0.654) and mortality (ICC=0.507). Thoracic surgeons and patients could benefit from using a surgical risk calculator to better estimate patients' surgical risks during the informed consent process.

The Hidden Pandemic: the Cost of Postoperative Complications

Purpose of Review

Population-based increases in ageing and medical co-morbidities are expected to substantially increase the incidence of expensive postoperative complications. This threatens the sustainability of essential surgical care, with negative impacts on patients' health and wellbeing.

Recent Findings

Identification of key high-risk areas, and implementation of proven cost-effective strategies to manage both outcome and cost across the end-to-end journey of the surgical episode of care, is clearly feasible. However, good programme design and formal cost-effectiveness analysis is critical to identify, and implement, true high value change.

Summary

Both outcome and cost need to be a high priority for both fundholders and clinicians in perioperative care, with the focus for both groups on delivering high-quality care, which in itself, is the key to good cost management.

Laparoscopic lymph node sampling: a new concept for patients with high-risk early esophagogastric junction cancer resected endoscopically

BACKGROUND AND AIMS

Endoscopic resection is considered a curative treatment for early upper GI cancers under certain histologic (low-risk) criteria. In tumors not completely fulfilling these criteria but resected R0 endoscopically, esophagectomy is still advised because of an increased risk of lymph node (LN) metastases (LNM). However, the benefit-risk ratio, especially in elderly patients at higher risk for radical surgery, can be debated. We now present the outcome of our case series of laparoscopic LN sampling (LLS) in patients with T1 esophagogastric junction tumors, which had been completely resected by endoscopy but did not fulfill the low-risk criteria (G1/2, m, L0, V0).

METHODS

Retrospective review was done of all patients with T1 cancer undergoing LLS with at least 1 high-risk parameter after endoscopic resection during an 8-year period. Repeated endoscopy with biopsy and abdominothoracic CT had been performed before. The patients were divided into 2 periods: before (n = 8) and after (n = 12) the introduction of an extended LLS protocol (additional resection of the left gastric artery). In cases of positive LN, patients underwent conventional oncologic surgery; if negative, follow-up was performed. The main outcome was the number of harvested LNs by means of LLS and the percentage of positive LNs found.

RESULTS

Twenty patients with cardia (n = 1) and distal esophageal/Barrett's cancer (n = 19) were included. The LN rate with use of the extended LLS technique increased by 12% (period 1: median 12 [range, 5-19; 95% CI, 3.4-15.4] vs period 2: median 17.5 [range, 12-40; 95% CI, 12.8-22.2]; P = .013). There were 2 adverse events: 1 inadvertent chest tube removal and 1 postoperative pneumonia. In 15% of cases, patients had positive LNs. and in 2 cases there was local recurrence at the endoscopic resection site, all necessitating surgery.

CONCLUSIONS

An extended technique of laparoscopic LN sampling appears to provide adequate LN numbers and is a safe approach with short hospital stay only. Only long-term follow-up of larger patient numbers will allow conclusions about miss rate as well as oncologic adequacy of this concept.

Accuracy of the surgical risk preoperative assessment system universal risk calculator in predicting risk for patients undergoing selected operations in 9 specialty areas

BACKGROUND

The universal Surgical Risk Preoperative Assessment System prediction models for postoperative adverse outcomes have good accuracy for estimating risk in broad surgical populations and for surgical specialties. The accuracy in individual operations has not yet been assessed. The objective of this study was to evaluate the Surgical Risk Preoperative Assessment System in predicting adverse outcomes for selected individual operations.

METHODS

The Surgical Risk Preoperative Assessment System models were applied to the top 2 most frequent common procedural terminology codes in 9 surgical specialties and 5 additional common general surgical operations in the 2009 to 2018 database of the American College of Surgeons National Surgical Quality Improvement Program. Goodness of fit statistics were estimated, including c-indices for discrimination, Hosmer-Lemeshow graphs and P values for calibration, overall observed versus expected event rates, and Brier scores.

RESULTS

The total sample size was 2,020,172, which represented 29% of the 6.9 million operations in the American College of Surgeons National Surgical Quality Improvement Program database. Average c-indices across 12 outcomes were acceptable (≥0.70) for 13 (56.5%) of the 23 operations. Overall observed-to-expected rates were similar for mortality and overall morbidity across the 23 operations. Hosmer-Lemeshow graphs over quintiles of risk comparing observed-to-expected rates of mortality and overall morbidity were similar for 52% and 70% of operations, respectively. Model performance was better in less complex operations and those done in patients with lower preoperative risk.

CONCLUSION

Surgical Risk Preoperative Assessment System displayed accuracy in estimating postoperative adverse events for some of the 23 operations studied, but not all. In the procedures where Surgical Risk Preoperative Assessment System was not accurate, developing disease or operation-specific risk models might be appropriate.

The preoperative risk tool SURPAS accurately predicts outcomes in emergency surgery

BACKGROUND

The Surgical Risk Preoperative Assessment System (SURPAS) uses eight variables to accurately predict postoperative complications but has not been sufficiently studied in emergency surgery. We evaluated SURPAS in emergency surgery, comparing it to the Emergency Surgery Score (ESS).

METHODS

SURPAS and ESS estimates of 30-day mortality and overall morbidity were calculated for emergency operations in the 2009-2018 ACS-NSQIP database and compared using observed-to-expected plots and rates, c-indices, and Brier scores. Cases with incomplete data were excluded.

RESULTS

In 205,318 emergency patients, SURPAS underestimated (8.1%; 35.9%) while ESS overestimated (10.1%; 43.8%) observed mortality and morbidity (8.9%; 38.8%). Each showed good calibration on observed-to-expected plots. SURPAS had better c-indices (0.855 vs 0.848 mortality; 0.802 vs 0.755 morbidity), while the Brier score was better for ESS for mortality (0.0666 vs. 0.0684) and for SURPAS for morbidity (0.1772 vs. 0.1950).

CONCLUSIONS

SURPAS accurately predicted mortality and morbidity in emergency surgery using eight predictor variables.

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.

Performance Comparison Between SURPAS and ACS NSQIP Surgical Risk Calculator in Pulmonary Resection

BACKGROUND

Accurate preoperative risk assessment is critical for informed decision making. The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) predict risks of common postoperative complications. This study compares observed and predicted outcomes after pulmonary resection between SURPAS and NSQIP SRC.

METHODS

Between January 2016 and December 2018, 2514 patients underwent pulmonary resection and were included. We entered the requisite patient demographics, preoperative risk factors, and procedural details into the online NSQIP SRC and SURPAS formulas. Performance of the prediction models was assessed by discrimination and calibration.

RESULTS

No statistically significant differences were found between the 2 models in discrimination performance for 30-day mortality, urinary tract infection, readmission, and discharge to a nursing or rehabilitation facility. The ability to discriminate between a patient who will develop a complication and a patient who will not was statistically indistinguishable between NSQIP and SURPAS, except for renal failure. With a C index closer to 1.0, the NSQIP performed significantly better than the SURPAS SRC in discriminating risk of renal failure (C index, 0.798 vs 0.694; P = .003). The calibration curves of predicted and observed risk for each model demonstrate similar performance with a tendency toward overestimation of risk, apart from renal failure.

CONCLUSIONS

Overall, SURPAS and NSQIP SRC performed similarly in predicting outcomes for pulmonary resections in this large, single-center validation study with moderate to good discrimination of outcomes. Notably, SURPAS uses a smaller set of input variables to generate the preoperative risk assessment. The addition of thoracic-specific input variables may improve performance.

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.

Preoperative predictors of postoperative complications after gastric cancer resection

Risk management is becoming an increasingly important healthcare issue. Gastrectomy with lymphadenectomy is still the mainstay of treatment for localized gastric cancer, but it is sometimes associated with postoperative complications that compromise the patient’s quality of life, tolerability of adjuvant treatment, and prognosis. Parameters based exclusively on preoperative factors can identify patients most at risk of postoperative complications, whereby surgeons can provide the patient with precise informed consent information and optimal perioperative management. Ultimately, these predictive tools can also help minimize medical costs. In this context, many studies have identified factors that predict postoperative complications, including indicators based on body constitution, nutrition, inflammation, organ function and hypercoagulation. This review presents our current understanding and discusses some future perspectives of preoperatively identified factors predictive of complications after resection for gastric cancer.

Refining the predictive variables in the "Surgical Risk Preoperative Assessment System" (SURPAS): a descriptive analysis

Background

The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious set of models providing accurate preoperative prediction of common adverse outcomes for individual patients. However, focus groups with surgeons and patients have developed a list of questions about and recommendations for how to further improve SURPAS’s usability and usefulness. Eight issues were systematically evaluated to improve SURPAS.

Methods

The eight issues were divided into three groups: concerns to be addressed through further analysis of the database; addition of features to the SURPAS tool; and the collection of additional outcomes. Standard multiple logistic regression analysis was performed using the 2005–2015 American College of Surgeons National Surgical Quality Improvement Participant Use File (ACS NSQIP PUF) to refine models: substitution of the preoperative sepsis variable with a procedure-related risk variable; testing of an indicator variable for multiple concurrent procedure codes in complex operations; and addition of outcomes to increase clinical applicability. Automated risk documentation in the electronic health record and a patient handout and supporting documentation were developed. Long term functional outcomes were considered.

Results

Model discrimination and calibration improved when preoperative sepsis was replaced with a procedure-related risk variable. Addition of an indicator variable for multiple concurrent procedures did not significantly improve the models. Models were developed for a revised set of eleven adverse postoperative outcomes that separated bleeding/transfusion from the cardiac outcomes, UTI from the other infection outcomes, and added a predictive model for unplanned readmission. Automated documentation of risk assessment in the electronic health record, visual displays of risk for providers and patients and an “About” section describing the development of the tool were developed and implemented. Long term functional outcomes were considered to be beyond the scope of the current SURPAS tool.

Conclusion

Refinements to SURPAS were successful in improving the accuracy of the models, while reducing manual entry to five of the eight variables. Adding a predictor variable to indicate a complex operation with multiple current procedure codes did not improve the accuracy of the models. We developed graphical displays of risk for providers and patients, including a take-home handout and automated documentation of risk in the electronic health record. These improvements should facilitate easier implementation of SURPAS.

Electronic supplementary material

The online version of this article (10.1186/s13037-019-0208-2) contains supplementary material, which is available to authorized users.