Disparities in cardiac arrest and failure to rescue after major elective noncardiac operations

The frailty-driven predictive model for failure to rescue among patients who experienced a major complication following cervical decompression and fusion: an ACS-NSQIP analysis of 3,632 cases (2011-2020)

BACKGROUND CONTEXT

Preoperative risk stratification for patients considering cervical decompression and fusion (CDF) relies on established independent risk factors to predict the probability of complications and outcomes in order to help guide pre and perioperative decision-making.

PURPOSE

This study aims to determine frailty's impact on failure to rescue (FTR), or when a mortality occurs within 30 days following a major complication.

STUDY DESIGN/SETTING

Cross-sectional retrospective analysis of retrospective and nationally-representative data.

PATIENT SAMPLE

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all CDF cases from 2011-2020.

OUTCOME MEASURES

CDF patients who experienced a major complication were identified and FTR was calculated as death or hospice disposition within 30 days of a major complication.

METHODS

Frailty was measured by the Risk Analysis Index-Revised (RAI-Rev). Baseline patient demographics and characteristics were compared for all FTR patients. Significant factors were assessed by univariate and multivariable regression for the development of a frailty-driven predictive model for FTR. The discriminative ability of the predictive model was assessed using a receiving operating characteristic (ROC) curve analysis.

RESULTS

There were 3632 CDF patients who suffered a major complication and 7.6% (277 patients) subsequently expired or dispositioned to hospice, the definition of FTR. Independent predictors of FTR were nonelective surgery, frailty, preoperative intubation, thrombosis or embolic complication, unplanned intubation, on ventilator for >48 hours, cardiac arrest, and septic shock. Frailty, and a combination of preoperative and postoperative risk factors in a predictive model for FTR, achieved outstanding discriminatory accuracy (C-statistic = 0.901, CI: 0.883-0.919).

CONCLUSION

Preoperative and postoperative risk factors, combined with frailty, yield a highly accurate predictive model for FTR in CDF patients. Our model may guide surgical management and/or prognostication regarding the likelihood of FTR after a major complication postoperatively with CDF patients. Future studies may determine the predictive ability of this model in other neurosurgical patient populations.

Racial Disparities in Pediatric Mortality Following Transfusion Within 72 Hours of Operation

BACKGROUND

Postoperative bleeding and transfusion are correlated with mortality risk. Furthermore, postoperative bleeding may often initiate the cascade of complications that leads to death. Given that minority children have increased risk of surgical complications, this study aimed to investigate the association of race with pediatric surgical mortality following postoperative transfusion.

METHODS

We used the NSQIP-P PUF to assemble a retrospective cohort of children <18 who underwent inpatient surgery during 2012-2021. We included White, Black, Hispanic, and 'Other' children who received a transfusion within 72 h of surgery. The primary outcome was defined as all-cause mortality within 30 days following the primary surgical procedure. Using logistic regression models, we estimated the risk-adjusted odds ratio (aOR) and 95% confidence intervals (CI) of mortality, comparing each racial/ethnic cohort to White children.

RESULTS

A total of 466,230 children <18 years of age underwent inpatient surgical procedures from 2012 to 2021. Of these, 46,200 required transfusion and were included in our analysis. The majority of patients were non-Hispanic White (64.6%, n = 29,850), while 18.9% (n = 8752) were non-Hispanic Black, 11.7% (n = 5387) were Hispanic, and 4.8% (n = 2211) were 'Other' race. The overall rate of mortality following transfusion was 2.5%. White children had the lowest incidence of mortality (2.0%), compared to children of 'Other' race (2.5%), Hispanic children (3.1%), and Black children (3.6%). After adjusting for sex, age, comorbidities, case status, preoperative transfusion within 48 h, and year of operation, we found that Black children experienced 1.24 times the odds of mortality following a postoperative transfusion compared to a White child (aOR: 1.24; 95%CI, 1.03-1.51; P = 0.025). Hispanic children were also significantly more likely to die following a postoperative transfusion than White children (aOR: 1.19; 95%CI, 1.02-1.39; P = 0.027).

CONCLUSION

We found that minority children who required a postoperative transfusion had a higher odds of death than White children. Future studies should explore adverse events following postoperative transfusion and the differences in their management by race that may contribute to the higher mortality rate for minority children.

LEVEL OF EVIDENCE

Level II.

CLINICAL TRIAL NUMBER AND REGISTRY

Not applicable.

Classification of Adverse Events in Adult Surgery

Successful surgery combines quality (achievement of a positive outcome) with safety (avoidance of a negative outcome). Outcome assessment serves the purpose of quality improvement in health care by establishing performance indicators and allowing the identification of performance gaps. Novel surgical quality metric tools (benchmark cutoffs and textbook outcomes) provide procedure-specific ideal surgical outcomes in a subgroup of well-defined low-risk patients, with the aim of setting realistic and best achievable goals for surgeons and centers, as well as supporting unbiased comparison of surgical quality between centers and periods of time. Validated classification systems have been deployed to grade adverse events during the surgical journey: (1) the ClassIntra classification for the intraoperative period; (2) the Clavien-Dindo classification for the gravity of single adverse events; and the (3) Comprehensive Complication Index (CCI) for the sum of adverse events over a defined postoperative period. The failure to rescue rate refers to the death of a patient following one or more potentially treatable postoperative adverse event(s) and is a reliable proxy of the institutional safety culture and infrastructure. Complication assessment is undergoing digital transformation to decrease resource-intensity and provide surgeons with real-time pre- or intraoperative decision support. Standardized reporting of complications informs patients on their chances to realize favorable postoperative outcomes and assists surgical centers in the prioritization of quality improvement initiatives, multidisciplinary teamwork, surgical education, and ultimately, in the enhancement of clinical standards.

Major elective non-cardiac operations in adults with congenital heart disease

OBJECTIVE

To assess the impact of congenital heart disease (CHD) on resource utilisation and clinical outcomes in patients undergoing major elective non-cardiac operations.

BACKGROUND

Due to advances in congenital cardiac management in recent years, more patients with CHD are living into adulthood and are requiring non-cardiac operations.

METHODS

The 2010-2018 Nationwide Readmissions Database was used to identify all adults undergoing major elective operations (pneumonectomy, hepatectomy, hip replacement, pancreatectomy, abdominal aortic aneurysm repair, colectomy, gastrectomy and oesophagectomy). Multivariable regression models were used to categorise key clinical outcomes.

RESULTS

Of an estimated 4 941 203 adults meeting inclusion criteria, 5234 (0.11%) had a previous diagnosis of CHD. Over the study period, the incidence of CHD increased from 0.06% to 0.17%, p<0.001. CHD patients were on average younger (63.3±14.8 vs 64.4±12.5 years, p=0.004), had a higher Elixhauser Comorbidity Index (3.3±2.2 vs 2.3±1.8, p<0.001) and received operations at high volume centres more frequently (66.6% vs 62.0%, p=0.003). Following risk adjustment, these patients had increased risk of in-hospital mortality (adjusted risk ratio (ARR): 1.76, 95% CI 1.25 to 2.47), experienced longer hospitalisation durations (+1.6 days, 95% CI 1.3 to 2.0) and cost more (+$8370, 95% CI $6686 to $10 055). Furthermore, they were more at risk for in-hospital complications (ARR: 1.24 95% CI 1.17 to 1.31) and endured higher adjusted risk of readmission at 30 days (ARR: 1.32 95% CI 1.13 to 1.54).

CONCLUSIONS

Adults with CHD are more frequently comprising the major elective operative cohort for non-cardiac cases. Due to the inferior clinical and financial outcomes suffered by this population, perioperative risk stratification may benefit from the inclusion of CHD as a factor that portends unfavourable outcomes.

Geospatial analysis of patients' social determinants of health for health systems science and disparity research

Social context matters for health, healthcare processes/quality and patient outcomes. The social status and circumstances we are born into, grow up in and live under, are called social determinants of health; they drive our health, and how we access and experience care; they are the fundamental causes of disease outcomes. Such circumstances are influenced heavily by our location through neighborhood context, which relates to support networks. Geography can influence proximity to resources and is an important dimension of social determinants of health, which also encompass race/ethnicity, language, health literacy, gender identity, social capital, wealth and income. Beginning with an explanation of social determinants, we explore the use of Geospatial Analysis methods and geocoding, including the importance of collaborating with geography experts, the pitfalls of geocoding, and how geographic analysis can help us to understand patient populations within the context of Social Determinants of Health. We then explain mechanisms and methods of geospatial analysis with two examples: (1) Bayesian hierarchical regression with crossed random effects and (2) discontinuity regression i.e., change point analysis. We leveraged the local University of Utah and Yale cohorts of the Multicenter Perioperative Outcomes Group (MPOG.org ), a perioperative electronic health registry; we enriched the Utah cohort with US-census tract level social determinants of health after geocoding patient addresses and extracting social determinants of health from the National Neighborhood Database (NaNDA). We explain how to investigate the impact of US-census tract level community deprivation indices and racial/ethnic composition on (1) individual clinicians’ administration of risk-adjusted perioperative antiemetic prophylaxis, (2) patients’ decisions to defer cataract surgery at the cusp of Medicare eligibility and finally (3) methods to further characterize patient populations at risk through publicly available datasets in the context of public transit access. Our examples are not rigorous analyses, and our preliminary inferences should not be taken at face value, but rather seen as illustration of geospatial analysis processes and methods. Our worked examples show the potential utility of geospatial analysis, and in particular the power of geocoding patient addresses to extract US-census level social determinants of health from publicly available databases to enrich electronic health registries for healthcare disparity research and targeted health system level countermeasures.