Defining the Intrinsic Cardiac Risks of Operations to Improve Preoperative Cardiac Risk Assessments

What's New in Preoperative Cardiac Testing

More than 300 million surgeries are performed annually worldwide. Patients are progressively aging and often have multiple comorbidities that put them at increased cardiovascular risk in the perioperative period. The United States published latest guidelines regarding preoperative cardiac evaluation and risk stratification for patients undergoing non-cardiac surgery in 2014. There are multiple risk stratification tools available that can help guide management. Furthermore, newer laboratory tests, such as preoperative NT-proBNP and high-sensitivity troponin assays, may aid in preventing and diagnosing perioperative myocardial injury.

Maternal heart rate variability patterns associated with maternal hypotension and non-reassuring fetal heart rate patterns following initiation of combined spinal-epidural labor analgesia: a prospective observational trial

BACKGROUND

We evaluated whether baseline maternal heart rate variability (HRV), including the Analgesia Nociception Index (ANI), is associated with maternal hypotension and fetal heart rate (FHR) abnormalities following combined spinal-epidural (CSE) labor analgesia.

METHODS

Laboring women were enrolled in this prospective observational study. The primary endpoint was maternal hypotension. The secondary endpoint was FHR abnormalities within 30 min following CSE analgesia initiated with intrathecal plain bupivacaine 1.0 mg and fentanyl 20 µg. The maternal ANI, electrocardiogram, blood pressure, heart rate, oxygen saturation, and FHR tracings were recorded 15 min before and 30 min after CSE. Parturients were grouped based on presence of hypotension and FHR abnormalities. Patient demographics and HRV metrics were compared. Receiver operating characteristics (ROC) curves were constructed for the prediction of hypotension and FHR abnormalities.

RESULTS

No significant intergroup differences were detected in patient characteristics. Several baseline HRV metrics and ANI differed significantly between the normotensive (n = 50) and hypotensive (n = 31) groups and between parturients showing FHR abnormalities (n = 19) and those showing reassuring FHR traces (n = 62). The area under the ROC curve (AUC) for predicting hypotension of the baseline low-frequency (LF)/high-frequency (HF) ratio was 0.677 (95% CI 0.55 to 0.80), and that of the ANI was 0.858 (95% CI 0.78 to 0.94). For predicting non-reassuring FHR patterns, the AUC of the LF/HF ratio was 0.77 (95% CI 0.65 to 0.89), and that of the ANI was 0.833 (95% CI 0.72 to 0.94).

CONCLUSIONS

The ANI can predict the propensity for maternal hypotension and non-reassuring FHR patterns following CSE.

Endo-anesthesia: a primer

Gastrointestinal (GI) endoscopy has witnessed a Cambrian explosion of techniques, indications, and expanding target populations. GI endoscopy encompasses traditional domains that include preventive measures, palliation, as alternative therapies in patients with prohibitive risks of more invasive procedures, and indicated primary treatments. But, it has expanded to include therapeutic and diagnostic interventional endosonography, luminal endoscopic resection, third space endotherapy, endohepatology, and endobariatrics. The lines between surgery and endoscopy are blurred on many occasions within this paradigm. Moreover, patients with high degrees of co-morbidity and complex physiology require more nuanced peri-endoscopic management. The rising demand for endoscopy services has resulted in the development of endoscopy referral centers that offer these invasive procedures as directly booked referrals for regional and rural patients. This further necessitates specialized programs to ensure appropriate evaluation, risk stratification, and optimization for safe sedation and general anesthesia if needed. This landscape is conducive to the organic evolution of endo-anesthesia to meet the needs of these focused and evolving practices. In this primer, we delineate important aspects of endo-anesthesia care and provide relevant clinical and logistical considerations pertaining to the breadth of procedures.

Variation in preoperative stress testing by patient, physician and surgical type: a cohort study

OBJECTIVES

To describe variation in and drivers of contemporary preoperative cardiac stress testing.

SETTING

A dedicated preoperative risk assessment and optimisation clinic at a large integrated medical centre from 2008 through 2018.

PARTICIPANTS

A cohort of 118 552 adult patients seen by 104 physicians across 159 795 visits to a preoperative risk assessment and optimisation clinic.

MAIN OUTCOME

Referral for stress testing before major surgery, including nuclear, echocardiographic or electrocardiographic-only stress testing, within 30 days after a clinic visit.

RESULTS

A total of 8303 visits (5.2%) resulted in referral for preoperative stress testing. Key patient factors associated with preoperative stress testing included predicted surgical risk, patient functional status, a previous diagnosis of ischaemic heart disease, tobacco use and body mass index. Patients living in either the most-deprived or least-deprived census block groups were more likely to be tested. Patients were tested more frequently before aortic, peripheral vascular or urologic interventions than before other surgical subcategories. Even after fully adjusting for patient and surgical factors, provider effects remained important: marginal testing rates differed by a factor-of-three in relative terms and around 2.5% in absolute terms between the 5th and 95th percentile physicians. Stress testing frequency decreased over the time period; controlling for patient and physician predictors, a visit in 2008 would have resulted in stress testing approximately 3.5% of the time, while a visit in 2018 would have resulted in stress testing approximately 1.3% of the time.

CONCLUSIONS

In this large cohort of patients seen for preoperative risk assessment at a single health system, decisions to refer patients for preoperative stress testing are influenced by various factors other than estimated perioperative risk and functional status, the key considerations in current guidelines. The frequency of preoperative stress testing has decreased over time, but remains highly provider-dependent.

Risk of cardiovascular complications during non-cardiac surgery and preoperative cardiac evaluation

The preoperative evaluation of candidates to non-cardiac surgery requires a knowledge of factors related both to the type of surgery and to the risk of each patient, in order to predict the potential cardiovascular complications. Over the past several decades, the field of preoperative cardiac evaluation before non-cardiac surgery has evolved substantially on the basis of the current guidelines of international medical societies. The aim of this paper is to summarize available evidence on the risk of non-cardiac surgery, focusing on appropriate cardiovascular assessment prior to surgery.

Complications as a Mediator of the Perioperative Frailty-Mortality Association

BACKGROUND

Preoperative frailty is strongly associated with postoperative complications and mortality. However, the pathways between frailty, postoperative complications, and mortality are poorly described. The authors hypothesized that the occurrence of postoperative complications would mediate a substantial proportion of the total effect of frailty on mortality after elective noncardiac surgery.

METHODS

Following protocol registration, the authors conducted a retrospective cohort study of intermediate- to high-risk elective noncardiac surgery patients (2016) using National Surgical Quality Improvement Program data. The authors conducted Bayesian mediation analysis of the relationship between preoperative frailty (exposure, using the Risk Analysis Index), serious complications (mediator), and 30-day mortality (outcome), comprehensively adjusting for confounders. The authors estimated the total effect of frailty on mortality (composed of the indirect effect mediated by complications and the remaining direct effect of frailty) and estimated the proportion of the frailty-mortality association mediated by complications.

RESULTS

The authors identified 205,051 patients; 1,474 (0.7%) died. Complications occurred in 20,211 (9.9%). A 2 SD increase in frailty score resulted in a total association with mortality equal to an odds ratio of 3.79 (95% credible interval, 2.48 to 5.64), resulting from a direct association (odds ratio, 1.76; 95% credible interval, 1.34 to 2.30) and an indirect association mediated by complications (odds ratio, 2.15; 95% credible interval, 1.58 to 2.96). Complications mediated 57.3% (95% credible interval, 40.8 to 73.8) of the frailty-mortality association. Cardiopulmonary complications were the strongest mediators among complication subtypes.

CONCLUSIONS

Complications mediate more than half of the association between frailty and postoperative mortality in elective noncardiac surgery.

EDITOR’S PERSPECTIVE

A Bayesian Comparison of Frailty Instruments in Noncardiac Surgery: A Cohort Study

BACKGROUND

Frailty-a multidimensional syndrome related to age- and disease-related deficits-is a key risk factor for older surgical patients. However, it is unknown which frailty instrument most accurately predicts postoperative outcomes. Our objectives were to quantify the probability of association and relative predictive performance of 2 frailty instruments (ie, the risk analysis index-administrative [RAI-A] and 5-item modified frailty index [mFI-5]) with postoperative outcomes in National Surgical Quality Improvement Program (NSQIP) data.

METHODS

Retrospective cohort study using Bayesian analysis of NSQIP hospitals. Adults having inpatient small or large bowel surgery 2010-2015 (derivation cohort) or intermediate to high risk mixed noncardiac surgery in 2016 (validation cohort) had preoperative frailty assigned using 2 unique approaches (RAI-A and mFI-5). Probabilities of association were calculated based on posterior distributions and relative predictive performance using posterior predictive distributions and Bayes factors for 30-day mortality (primary outcome) and serious complications (secondary outcome).

RESULTS

Of 50,630 participants, 7630 (14.0%) died and 19,545 (38.6%) had a serious complication. Without adjustment, the RAI-A and mFI-5 had >99% probability being associated with mortality with a ≥2.0 odds ratio (ie, large effect size). After adjustment for NSQIP risk calculator variables, only the RAI-A had ≥95% probability of a nonzero association with mortality. Similar results arose when predicting postoperative complications. The RAI-A provided better predictive accuracy for mortality than the mFI-5 (minimum Bayes factor 3.25 × 1014), and only the RAI-A improved predictive accuracy beyond that of the NSQIP risk calculator (minimum Bayes factor = 4.27 × 1013). Results were consistent in leave-one-out cross-validation.

CONCLUSIONS

Translation of frailty-related findings from research and quality improvement studies to clinical care and surgical planning will be aided by a consistent approach to measuring frailty with a multidimensional instrument like RAI-A, which appears to be superior to the mFI-5 when predicting outcomes for inpatient noncardiac surgery.

Comprehensive Risk Assessment of Morbidity in Pediatric Patients Undergoing Noncardiac Surgery: An Institutional Experience

BACKGROUND

Utilizing the intrinsic surgical risk (ISR) and the patient's chronic and acute conditions, this study aims to develop and validate a comprehensive predictive model of perioperative morbidity in children undergoing noncardiac surgery.

METHODS

Following institutional review board (IRB) approval at a tertiary care children's hospital, data for all noncardiac surgical encounters for a derivation dataset from July 2017 to December 2018 including 16,724 cases and for a validation dataset from January 2019 to December 2019 including 9043 cases were collected retrospectively. The primary outcome was a composite morbidity score defined by unplanned transfer to an intensive care unit (ICU), acute respiratory failure requiring intubation, postoperative need for noninvasive or invasive positive pressure ventilation, or cardiac arrest. Internal model validation was performed using 1000 bootstrap resamples, and external validation was performed using the 2019 validation cohort.

RESULTS

A total of 1519 surgical cases (9.1%) experienced the defined composite morbidity. Using multivariable logistic regression, the Risk Assessment of Morbidity in Pediatric Surgery (RAMPS) score was developed with very good predictive ability in the derivation cohort (area under the curve [AUC] = 0.805; 95% confidence interval [CI], 0.795-0.816), very good internal validity using 1000 bootstrap resamples (bias-corrected Nagelkerke R = 0.21 and Brier score = 0.07), and good external validity (AUC = 0.783; 95% CI, 0.770-0.797). The included variables are age <5 years, critically ill, chronic condition indicator (CCI) ≥3, significant CCI ≥2, and ISR quartile ≥3. The RAMPS score ranges from 0 to 10, with the risk of composite morbidity ranging from 1.8% to 42.7%.

CONCLUSIONS

The RAMPS score provides the ability to identify a high-risk cohort of pediatric patients using a 5-component tool, and it demonstrated good internal and external validity and generalizability. It also provides an opportunity to improve perioperative planning with the intent of improving both individual-patient outcomes and the appropriate allocation of health care resources.

Integration of the Intrinsic Surgical Risk With Patient Comorbidities and Severity of Congenital Cardiac Disease Does Not Improve Risk Stratification in Children Undergoing Noncardiac Surgery

BACKGROUND

The objective of this study is to estimate the surgical risk of noncardiac procedures on the incidence of 30-day mortality in children with congenital heart disease.

METHODS

Children with congenital heart disease undergoing noncardiac surgery from 2012 to 2016 and included in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Pediatric database were included in the derivation cohort, while the 2017 database was used as a validation cohort. Intrinsic surgical risk quartiles were built utilizing 30-day mortality rates for each Current Procedural Terminology code and relative value units to create 2 groups defined as low surgical risk (quartiles 1-3) and high surgical risk procedures (quartile 4). We used multivariable logistic regression to determine the predictors for 30-day mortality including patient comorbidities and intrinsic surgical risk. A partially external validation of the model was performed using the 2017 version of the database.

RESULTS

We included 37,658 children with congenital heart disease undergoing noncardiac surgery with an incidence of overall 30-day mortality of 1.7% in the derivation cohort and 1.5% in the validation cohort (n = 13,129). Intrinsic surgical risk of procedures represented by Current Procedural Terminology procedural codes and relative value units risk quartiles was significantly associated with 30-day mortality (unadjusted P < .001). Predicted probability of 30-day mortality ranges from 0.2% (95% confidence interval [CI], 0.2-0.2) with no comorbidities to 39.6% (95% CI, 23.2-56.0) when all comorbidities were present among high surgical risk procedures and from 0.3% (95% CI, 0.3-0.3) to 54.8% (95% CI, 39.4-70.1) among low surgical risk procedures. An excellent discrimination was reported for the multivariable model with area under the curve (AUC) of 0.86 (95% CI, 0.85-0.88). High surgical risk was not associated with increased odds of 30-day mortality after adjustment for all other predictors (adjusted odds ratio [OR]: 0.75, 95% CI, 0.62-0.91). We also estimated the discriminative ability of a model that does not include the surgical risk (0.86 [95% CI, 0.84-0.88], with P value for the direct comparison of the AUC of the 2 models = 0.831). The multivariable model obtained from an external validation cohort reported an optimism corrected AUC of 0.88 (95% CI, 0.85-0.91).

CONCLUSIONS

Our study demonstrates that integration of intrinsic surgical risk to comorbidities and severity of cardiac disease does not improve prediction of 30-day mortality in children undergoing noncardiac surgery. In children with congenital heart disease, patient comorbidities, and severity of the cardiac lesion are the predominant predictors of 30-day mortality.

Risk Factors for Failure to Rescue in Myocardial Infarction after Noncardiac Surgery: A Cohort Study

BACKGROUND

Compared to other perioperative complications, failure to rescue (i.e., death after suffering a complication) is highest after perioperative myocardial infarction (a myocardial infarction that occurs intraoperatively or within 30 days after surgery). The purpose of this study was to identify patient and surgical risk factors for failure to rescue in patients who have had a perioperative myocardial infarction.

METHODS

Individuals who experienced a perioperative myocardial infarction after noncardiac surgery between 2012 and 2016 were identified from the American College of Surgeons (Chicago, Illinois) National Surgical Quality Improvement Program database. Multivariable logistic regression was used to identify risk factors for failure to rescue. Subgroup and sensitivity analyses evaluated the robustness of primary findings.

RESULTS

The authors identified 1,307,884 individuals who had intermediate to high-risk noncardiac surgery. A total of 8,923 (0.68%) individuals had a perioperative myocardial infarction, of which 1,726 (19.3%) experienced failure to rescue. Strongest associations (adjusted odds ratio greater than 1.5) were age 85 yr or older (2.52 [95% CI, 2.05 to 3.09] vs. age younger than 65 yr), underweight body mass index (1.53 [95% CI, 1.17 to 2.01] vs. normal body mass index), American Society of Anesthesiologists class IV (1.76 [95% CI, 1.33 to 2.31] vs. class I or II) and class V (3.48 [95% CI, 2.20 to 5.48] vs. class I or II), and presence of: ascites (1.81 [95% CI, 1.15 to 2.87]), disseminated cancer (1.54 [95% CI, 1.18 to 2.00]), systemic inflammatory response syndrome (1.55 [95% CI, 1.26 to 1.90]), sepsis (1.75 [95% CI, 1.39 to 2.20]), septic shock (1.79 [95% CI, 1.34 to 2.37]), and dyspnea at rest (1.94 [95% CI, 1.32 to 2.86]). Patients who had emergency surgery, high-risk procedures, and postoperative complications were at higher risk of failure to rescue.

CONCLUSIONS

Routinely identified patient and surgical factors predict risk of failure to rescue after perioperative myocardial infarction.

Multimorbidity and Critical Care Neurosurgery: Minimizing Major Perioperative Cardiopulmonary Complications

With increasing prevalence of chronic diseases, multimorbid patients have become commonplace in the neurosurgical intensive care unit (neuro-ICU), offering unique management challenges. By reducing physiological reserve and interacting with one another, chronic comorbidities pose a greatly enhanced risk of major postoperative medical complications, especially cardiopulmonary complications, which ultimately exert a negative impact on neurosurgical outcomes. These premises underscore the importance of perioperative optimization, in turn requiring a thorough preoperative risk stratification, a basic understanding of a multimorbid patient’s deranged physiology and a proper appreciation of the potential of surgery, anesthesia and neurocritical care interventions to exacerbate comorbid pathophysiologies. This knowledge enables neurosurgeons, neuroanesthesiologists and neurointensivists to function with a heightened level of vigilance in the care of these high-risk patients and can inform the perioperative neuro-ICU management with individualized strategies able to minimize the risk of untoward outcomes. This review highlights potential pitfalls in the intra- and postoperative neuro-ICU period, describes common preoperative risk stratification tools and discusses tailored perioperative ICU management strategies in multimorbid neurosurgical patients, with a special focus on approaches geared toward the minimization of postoperative cardiopulmonary complications and unplanned reintubation.

Does Surgical Intensity Correlate With Opioid Prescribing?: Classifying Common Surgical Procedures

OBJECTIVE

To examine the relationship between aspects of surgical intensity and postoperative opioid prescribing.

SUMMARY OF BACKGROUND DATA

Despite the emergence of postoperative prescribing guidelines, recommendations are lacking for many procedures. Identifying a framework based on surgical intensity to guide prescribing for those procedures in which guidelines may not exist could inform postoperative prescribing.

METHODS

We used clustering analysis with 4 factors of surgical intensity (intrinsic cardiac risk, pain score, median operative time, and work relative value units) to devise a classification system for common surgical procedures. We used IBM MarketScan Research Database (2010-2017) to examine the correlation between this framework with initial opioid prescribing and rates of refill for each cluster of procedures.

RESULTS

We examined 2,407,210 patients who underwent 128 commonly performed surgeries. Cluster analysis revealed 5 ordinal clusters by intensity: low, mid-low, mid, mid-high, and high. We found that as the cluster-order increased, the median amount of opioid prescribed increased: 150 oral morphine equivalents (OME) for low-intensity, 225 OME for mid-intensity, and 300 OME for high-intensity surgeries. Rates of refill increased as surgical intensity also increased, from 17.4% for low, 26.4% for mid, and 48.9% for high-intensity procedures. The odds of refill also increased as cluster-order increased; relative to low-intensity procedures, high-intensity procedures were associated with 4.37 times greater odds of refill.

CONCLUSION

Surgical intensity is correlated with initial opioid prescribing and rates of refill. Aspects of surgical intensity could serve as a guide for procedures in which guidelines based on patient-reported outcomes are not available.

Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning

BACKGROUND

Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures.

METHODS

Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard.

RESULTS

Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text.

CONCLUSIONS

Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes.

Association of Patient-reported Experiences and Surgical Outcomes Among Group Practices: Retrospective Cohort Study

OBJECTIVE

The aim of the study was to determine the association of patient-reported experiences (PREs) and risk-adjusted surgical outcomes among group practices.

BACKGROUND

The Centers for Medicare and Medicaid Services required large group practices to submit PREs data for successful participation in the Physician Quality Reporting System (PQRS) using the Consumer Assessment of Healthcare Providers and Systems for PQRS survey. Whether these PREs data correlate with perioperative outcomes remains ill defined.

METHODS

Operations between January 1, 2014 and December 31, 2016 in the American College of Surgeons' National Surgical Quality Improvement Program registry were merged with 2015 Consumer Assessment of Healthcare Providers and Systems for PQRS survey data. Hierarchical logistic models were constructed to estimate associations between 7 subscales and 1 composite score of PREs and 30-day morbidity, unplanned readmission, and unplanned reoperation, separately, while adjusting for patient- and procedure mix.

RESULTS

Among 328 group practices identified, patients reported their experiences with clinician communication the highest (mean ± standard deviation, 82.66 ± 3.10), and with attention to medication cost the lowest (25.96 ± 5.14). The mean composite score was 61.08 (±6.66). On multivariable analyses, better PREs scores regarding medication cost, between-visit communication, and the composite score of experience were each associated with 4% decreased odds of morbidity [odds ratio (OR) 0.96, 95% confidence interval (CI) 0.92-0.99], readmission (OR 0.96, 95% CI 0.93-0.99), and reoperation (OR 0.96, 95% CI 0.93-0.99), respectively. In sensitivity analyses, better between-visit communication remained significantly associated with fewer readmissions.

CONCLUSIONS

In these data, patients' report of better between-visit communication was associated with fewer readmissions. More sensitive, surgery-specific PRE assessments may reveal additional unique insights for improving the quality of surgical care.

Modified paediatric preoperative risk prediction score to predict postoperative ICU admission in children: a retrospective cohort study

OBJECTIVE

To integrate intrinsic surgical risk into the paediatric preoperative risk prediction score (PRPS) model to construct a more comprehensive risk scoring system (modified PRPS) and improve the prediction accuracy of postoperative intensive care unit (ICU) admission in paediatric patients.

DESIGN

This was a retrospective study conducted between 1 January and 30 December 2016. Data on age, American Society of Anaesthesiology physical status (ASA-PS), oxygen saturation, prematurity, non-fasted status, severity of surgery and immediate transfer to the ICU after surgery were collected. The modified PRPS was developed by logistic regression in the derivation cohort; it was tested and compared with the paediatric PRPS and ASA-PS by the Hosmer-Lemeshow test, the receiver operating characteristic (ROC) curve and Kappa analysis in the validation cohort.

SETTING

Hospital-based study in China.

PARTICIPANTS

Paediatric patients (≤14 years) who underwent surgery under general anaesthesia were included, and those who needed reoperation due to surgical complications or stayed in the ICU preoperatively were excluded.

MAIN OUTCOME MEASURE

ICU admission rate, defined as any patients' direct disposition from the operating room to the ICU immediately after the surgery.

RESULTS

A total of 9261 paediatric patients were included in this study, with 418 patients admitted to the ICU. In the validation cohort, the modified PRPS model fit the test data well (deciles of risk goodness-of-fit χ2=6.84, p=0.077). The area under the ROC curve of the modified PRPS, paediatric PRPS and ASA-PS were 0.963, 0.941 and 0.870, respectively (p<0.05), and the Kappa values were 0.620, 0.286 and 0.267. Analyses in the cohort indicated that the modified PRPS was superior to the paediatric PRPS and ASA-PS.

CONCLUSIONS

The modified PRPS integrating intrinsic surgical risk shows better prediction accuracy than the previous PRPS.

Frontline Science: Low regulatory T cells predict perioperative major adverse cardiovascular and cerebrovascular events after noncardiac surgery

Immune cells drive atherosclerotic lesion progression and plaque destabilization. Coronary heart disease patients undergoing noncardiac surgery are at risk for perioperative major adverse cardiac and cerebrovascular events (MACCE). It is unclear whether differential leukocyte subpopulations contribute to perioperative MACCE and thereby could aid identification of patients prone to perioperative cardiovascular events. First, we performed a hypothesis-generating post hoc analysis of the LeukoCAPE-1 study (n = 38). We analyzed preoperative counts of 6 leukocyte subpopulations in coronary heart disease patients for association with MACCE (composite of cardiac death, myocardial infarction, myocardial ischemia, myocardial injury after noncardiac surgery, thromboembolic stroke) within 30 d after surgery. Regulatory T cells (Tregs) were the only leukocyte subgroup associated with MACCE. We found reduced Tregs in patients experiencing MACCE versus no-MACCE (0.02 [0.01; 0.03] vs. 0.04 [0.03; 0.05] Tregs nl^-1 , P = 0.002). Using Youden index, we derived the optimal threshold value for association with MACCE to be 0.027 Tregs nl^-1 . Subsequently, we recruited 233 coronary heart disease patients for the prospective, observational LeukoCAPE-2 study and independently validated this Treg cutoff for prediction of MACCE within 30 d after noncardiac surgery. After multivariate logistic regression, Tregs < 0.027 cells nl^-1 remained an independent predictor for MACCE (OR = 2.54 [1.22; 5.23], P = 0.012). Tregs improved risk discrimination of the revised cardiac risk index based on ΔAUC (area under the curve; ΔAUC = 0.09, P = 0.02), NRI (0.26), and IDI (0.06). Preoperative Treg levels below 0.027 cells nl^-1 predicted perioperative MACCE and can be measured to increase accuracy of established preoperative cardiac risk stratification in coronary heart disease patients undergoing noncardiac surgery.

Pediatric Risk Stratification Is Improved by Integrating Both Patient Comorbidities and Intrinsic Surgical Risk

Editor’s Perspective

What We Already Know about This Topic

What This Article Tells Us That Is New

Background

Recently developed risk stratification models for perioperative mortality incorporate patient comorbidities as predictors but fail to consider the intrinsic risk of surgical procedures. In this study, the authors used the American College of Surgeons National Surgical Quality Improvement Program Pediatric database to demonstrate the relationship between the intrinsic surgical risk and 30-day mortality and develop and validate an accessible risk stratification model that includes the surgical procedures in addition to the patient comorbidities and physical status.

Methods

A retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program Pediatric database was performed. The incidence of 30-day mortality was the primary outcome. Surgical Current Procedural Terminology codes with at least 25 occurrences were included. Multivariable logistic regression model was used to determine the predictors for mortality including patient comorbidities and intrinsic surgical risk. An internal validation using bootstrap resampling, and an external validation of the model were performed.

Results

The authors analyzed 367,065 surgical cases encompassing 659 unique Current Procedural Terminology codes with an incidence of overall 30-day mortality of 0.34%. Intrinsic risk of surgical procedures represented by Current Procedural Terminology risk quartiles instead of broad categorization was significantly associated with 30-day mortality (P &lt; 0.001). Predicted risk of 30-day mortality ranges from 0% with no comorbidities to 4.7% when all comorbidities are present among low-risk surgical procedures and from 0.07 to 46.7% among high-risk surgical procedures. Using an external validation cohort of 110,474 observations, the multivariable predictive risk model displayed good calibration and excellent discrimination with area under curve (c-index) equals 0.95 (95% CI, 0.94 to 0.96; P &lt; 0.001).

Conclusions

Understanding and accurately estimating perioperative risk by accounting for the intrinsic risk of surgical procedures and patient comorbidities will lead to a more comprehensive discussion between patients, families, and providers and could potentially be used to conduct cost analysis and allocate resources.

Major Cardiac Events in Patients Admitted to Intensive Care After Vascular Noncardiac Surgery: A Retrospective Cohort

Introduction. Patients proposed to vascular noncardiac surgery (VS) have several comorbidities associated with major adverse cardiac events (MACE). We evaluated incidence, predictors, and outcomes, and compared different scores to predict MACE after VS. Methods. We included all patients admitted from 2006 to 2013. Perioperative MACE included cardiac arrhythmias, myocardial infarction (MI), cardiogenic pulmonary edema (CPE), acute heart failure (AHF), and cardiac arrest (CA). Lee Revised Cardiac Risk Index (RCRI), Vascular Quality Initiative (VQI-CRI), Vascular Study Group of New England (VSG-CRI), and South African Vascular Surgical (SAVS-CRI) Cardiac Risk Indexes were calculated and analyzed. We performed multivariate logistic regression to assess independent predictors with calculation of odds ratio (OR) and 95% confidence interval (CI). To reduce overfitting, we used leave-one-out cross-validation approach. The Predictive ability of scores was tested using area under receiver operating characteristic curve (AUROC). Results. A total of 928 patients were included. We observed 81 MACE (28 MI, 22 arrhythmias, 10 CPE, 9 AHF, 12 CA) in 60 patients (6.5%): 3.3% in intermediate-risk surgery and 9.8% in high-risk surgery. Previous history of coronary artery disease (OR = 3.2, CI = 1.8-5.7), atrial fibrillation (OR = 5.1, CI = 2.4-11.0), insulin-treated diabetes mellitus (OR = 3.26, CI = 1.51-7.06), mechanical ventilation (OR = 2.75, CI = 1.41-4.63), and heart rate (OR = 1.02, CI = 1.01-1.03) at admission were considered independent risk factors in multivariate analysis. The AUROC of our model was 0.79, compared with RCRI (0.66), VSG-CRI (0.69), VQI-CRI (0.71), and SAVS-CRI (0.73). Conclusions. Observed MACE were within predicted range (1% to 5% after intermediate-risk surgery and >5% after high-risk surgery). SAVS-CRI and VQI-CRI had slightly better predictive capacity than VSG-CRI or RCRI.

Preoperative Cardiac Evaluation for Noncardiac Surgery

Cardiac risk stratification before surgery informs consent, may advise optimization interventions, and guides intraoperative and postoperative management and monitoring. Published guidelines provide an outline for risk stratification but are only updated every 5 to 10 years; hence, cardiology expert opinion is often needed. Preoperative cardiovascular evaluation starts with an excellent history and physical examination. Accurate assessment of exercise tolerance is paramount in defining risk and determining the need for further testing. Risk/benefit ratio needs to be assessed and reviewed with all stakeholders, which pertains to deciding on cardiac intervention before surgery and bleeding versus thrombosis risk when managing medications.

Trends in perioperative outcomes of hospitals performing major cancer surgery

BACKGROUND AND OBJECTIVES

Cancer surgery outcomes at National Cancer Institute-designated cancer centers (NCI-CCs) have been shown to vary, and have not been uniformly better than outcomes among non-NCI-CCs. We aimed to assess whether NCI-CCs have improved their short-term outcomes over time and whether variation across these centers has changed.

METHODS

Patients who underwent colectomy, esophagectomy, hepatectomy, pancreatectomy, and proctectomy for cancer were identified from the 2010 to 2016 American College of Surgeons' National Surgical Quality Improvement Program registry. Hospital trends in risk-adjusted, smoothed observed-to-expected ratios were assessed to evaluate improvement and variation in perioperative complications, stratified by NCI-CC status.

RESULTS

Complications occurred in 18.8% of 204 732 patients who underwent major cancer operations at 645 hospitals, and complications occurred in 19.9% of 60,903 patients at 54 NCI-CCs studied. More NCI-CCs than non-NCI-CCs improved over the period (85.2% vs 58.4%, P < 0.001; relative risk [RR] 1.46, 95% confidence interval [CI], 1.28-1.66); this remained significant after adjusting for years of participation (RR 1.33, 95% CI, 1.17-1.51). Variation in performance remained unchanged over time.

CONCLUSION

NCI-CCs were detected to have improved over a contemporary seven-year period and to have improved more than non-NCI-CCs. However, NCI-CCs do not uniformly outperform non-NCI-CCs, and variation in perioperative outcomes remains, warranting continued quality improvement efforts targeting cancer-specific operations.