race, preoperative risk factors, and death after surgery...5132 8.17 294 4833 previous cardiac...

Article

Race, Preoperative Risk Factors, and Death After SurgeryOguz Akbilgic, PhD, a, b Max Raymond Langham Jr, MD, c Robert Lowell Davis, MD, MPHa

BACKGROUND AND OBJECTIVES: African American children are more than twice as likely to die after surgery compared with white children. In this study, we evaluated whether risk factors for death after surgery differ for African American and white children, and we also assessed whether race-specific risk stratification models perform better than non–race-specific models.METHODS: The National Surgical Quality Improvement Program Pediatric Participant Use Data File contains clinical data on operations performed on children at participating institutions in the United States. Variables predictive of death within 30 days of surgery were analyzed for differences in prevalence and strength of association with death for both African American and white children. Classification tree and network analysis were used.RESULTS: Network analyses revealed that the prevalence of preoperative risk factors associated with death after surgery was significantly higher for African American than for white children. In addition, many of the risk factors associated with death after surgery carried a higher risk when they occurred in African American children. Race-specific risk models provided high accuracy, with a specificity of 94% and a sensitivity of 83% for African American children and a specificity of 96% and a sensitivity of 77% for white children, and yet these 2 models were significantly different from each other.CONCLUSIONS: Race-specific models predict outcomes after surgery more accurately compared with non–race-specific models. Identification of race-specific modifiable risk factors may help reduce racial disparities in surgery outcome.

abstract

aUTHSC-ORNL Center for Biomedical Informatics, and Departments of bPreventive Medicine and cSurgery, Health Science Center, University of Tennessee, Memphis, Tennessee

Dr Akbilgic conceptualized the study, conducted the initial analysis, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Langham provided critical review on the study design and edited and revised the manuscript; Dr Davis conceptualized and designed the study, drafted the initial manuscript, and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

DOI: https:// doi. org/ 10. 1542/ peds. 2017- 2221

Accepted for publication Nov 15, 2017

Address correspondence to Oguz Akbilgic, PhD, Le Bonheur Children's Hospital Research Center, 50 N Dunlap Ave, Suite 495R, Memphis, TN 38103. E-mail: [email protected]

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2018 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

FUNDING: No external funding.

PEDIATRICS Volume 141, number 2, February 2018:e20172221

WhAt’S KNOWN ON thIS SUbjECt: Racial health disparities exist in surgical outcome. African American children are more than twice as likely to die after surgery compared with white children.

WhAt thIS StUDy ADDS: The prevalence of preoperative risk factors and their association with postsurgical mortality were higher for African American than white children. Classification tree analyses on African American and white children separately were more accurate than non–race-specific risk models.

to cite: Akbilgic O, Langham MR, Davis RL. Race, Preoper-ative Risk Factors, and Death After Surgery. Pediatrics. 2018;141(2):e20172221

by guest on June 17, 2021www.aappublications.org/newsDownloaded from

https://doi.org/10.1542/peds.2017-2221

Racial and ethnic health disparities are pervasive in the United States.1, 2 Life expectancy for African Americans in the United States lags behind that for white individuals, and African Americans experience greater mortality from cardiovascular disease, diabetes, cancer, and trauma.3 –5 Racial and ethnic health outcomes disparities also exist among children. African American children are more likely to die after congenital heart surgery6 or neurologic procedures7, 8 and are more likely to have complications related to appendicitis.9 African American children experience delays in getting placed on kidney transplant lists, wait longer periods on transplant lists, 10, 11 and are less likely to receive kidney transplants compared with white children.12, 13

We previously developed a classification tree model for death within 30 days of surgery (D30) in children by using 6 preoperative conditions to stratify patients into subgroups with risks of D30 from 30%.14 This model performed as well as more complex models using hierarchical logistic regression, but the model systematically resulted in an underestimation of the risk of African American children while resulting in an overestimation of the risk for white children.14 This led us to consider whether race-specific models would be more accurate for predicting the risk for death after surgery for African American and white children, respectively. The purpose of this study was to develop race-specific models predictive of risk for D30 and to examine the performance characteristics of these models. This is a fundamentally different goal than that for models used to compare outcomes after surgery among African American compared with white children, adjusted for various comorbidities and other factors that might differ between the groups. With our

study, we did not seek to assign or understand causation; rather, the goal of our research was to see if race-specific models could be used to identify patients at high risk for death after surgery more successfully than models in which all races were grouped together.

MEthODS

Data

We used the National Surgical Quality Improvement Program (NSQIP) Pediatric Participant Use Data File (Pedi-PUF), a data file containing cases submitted to the American College of Surgeons NSQIP Pediatric to investigate and advance quality of care.15 We used Pedi-PUF data covering 2012–2014, which included data from 50 hospitals in 2012, 56 hospitals in 2013, and 64 hospitals in 2014. This data set included outcomes on a total of 183 233 surgical operations in children

PEDIATRICS Volume 141, number 2, February 2018 3

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specifically to visualize the prevalence of risk factors and their association with D30 for children captured in NSQIP Pediatric.

Nodes were risk factors for the outcome, D30, whereas edges represented the type and the strength of relationship between these risk factors. Edges connecting 2 risk factors were the comorbidity of 2 connected risk factors; 2 risk factors were connected if they coexisted for at least 1 case. Four different node sizes represented the prevalence of risk factors (size 1: prevalence

the thickness of the edge connecting inotropic support and hematologic disorder is larger in Fig 1A network, implying that the co-existence of these 2 risk factors has higher association with death and is more prevalent in African American children. Similar results were seen for the joint effects of inotropic support-ventilator dependency, bleeding disorder-transfusion, hematologic disorder-sepsis, neonate-wound infection, and oxygen support-ventilator dependency. Moreover, in general, the prevalence of co-occurrences of risk factors were higher for African American children, as indicated by the thicker edges in the Fig 1A network, compared with those in the Fig 1B network.

Classification tree

To correct for the systematic underestimation of risk of death

after surgery in African American children, we developed 2 separate risk stratification models for African American and white children, shown in Figs 2 and 3, respectively.

The classification trees developed for African American and white children differed substantially from each other, although some risk factors (ventilation, oxygen support, inotropic support, and emergent case) are present in both models. The risk of death associated with each of the terminal nodes are given in Tables 2 and 3 for African American and white children, respectively.

Model Comparison and External Validation

Finally, we used the 2015 Pedi-PUF data set for validation of tree-based classification models and also for comparison of race-specific models against non–race-specific

models. When we compared the specificity and sensitivity of the new classification trees to the original tree, the race-specific models had better sensitivity with equal specificity (Table 4).

DISCUSSION

We developed an improved risk classification model that more accurately reflects risk for death after surgery among African American children and can be used to identify more African American children who are at risk for death after surgery. The separate classification trees built for African American and white children in Figs 2 and 3 reveal better estimates of risk for each terminal-node risk group. Although 4% improvement in sensitivity may seem a modest improvement from a statistical point of view, even a


FIGURE 1Network of risk factors of D30. A, African American children. B, White children. Emergent, emergent case–type surgery; Urgent, urgent case–type surgery; O2, oxygen support; Cardiac, previous cardiac surgery; CVA, cerebrovascular accident or stroke; Wound, wound infection; Inotropic, inotropic support; Bleeding, bleeding disorder; Hematologic, hematologic disorder.


AkBILgIC et al6

FIGURE 2Classification tree for African American children (under the assumption that the terminal node 1 = low risk and the rest of the terminal nodes = high risk and that the specificity = 93.6% and the sensitivity = 82.7%).

FIGURE 3Classification tree for white children (under the assumption that the terminal node 1 = low risk and the rest of the terminal nodes = high risk and that the specificity = 96.1% and the sensitivity = 76.5%).


modest reduction in false-negatives for predicting mortality may have clinical importance. The finding that ventilation indicates that African American children are in the highest risk group is novel and demands further investigation.

We used network analysis to help visualize relationships among variables and how they impact racial disparities in preoperative risk factors for death after surgery. Although the number of white children was much higher than the number of African American children, we have obtained a more dense (larger nodes and thicker edges) network for African American children. This approach led to our second important finding, which was that the prevalence of most important factors identifying children at risk for dying after surgery was,

in general, higher among African American children than among white children. For example, ventilator dependency was present among 4.3% of African American children compared with 2.5% of white children, whereas hematologic disorder was present among 6.2% among African American children and in only 2.6% of white children. Many of these risk factors also had a stronger association with death after surgery among African American children than among white children, and this was the case even for risk factors similar in prevalence between African American and white children. It may be that race-specific models differ by surgery type or surgical severity, and future researchers could address these topics.

There are both strengths and limitations to our study. NSQIP

Pediatric is a high-quality, clinically abstracted database with data on a large number of concurrent procedures done in a variety of hospitals throughout the country. Findings based on this study should therefore be generalizable to similar operations performed on children in the United States. The Pedi-PUF is deidentified. Therefore, we cannot compare institutional completeness of data collection, nor evaluate differences in outcomes between institutions. It is possible that the NSQIP Pediatric member hospitals may not be representative of non–NSQIP Pediatric hospitals. Finally, the NSQIP Pediatric data set does not include information on social risk factors, which may also influence outcomes and differ among racial groups.

CONCLUSIONS

African American children have more preoperative risk factors, and these surgical risk factors are in general more strongly associated with death than those found in white children. A basic tenet of health equity is that African American families should receive accurate information on the surgical risks their children face, not risks based on analysis drawn from predominantly white children. Interventions to mitigate these risks will need to be tested within the context of race-specific risk strata to reduce the higher surgical mortality rate currently found in African American children.

AbbREVIAtIONS

CI: confidence intervalD30: death within 30 days of

surgeryNSQIP: National Surgical Quality

Improvement ProgramPedi-PUF: Pediatric Participant

Use Data File


tAbLE 2 Classification Tree Applied to African American Children

Risk group Risk Factors Risk (%)

1 Inotropic support = no, ventilator = no, oxygen support = no 0.112 Inotropic support = yes, ventilator = no 1.543 Inotropic support = no, ventilator = no, oxygen support = yes 2.014 Inotropic support = no, ventilator = yes, emergent case = no 3.375 Inotropic support = no, ventilator = yes, emergent case = yes 14.206 Inotropic support = yes, ventilator = yes 34.48

tAbLE 3 Classification Tree Applied to White Children

Risk group Risk Factors Risk (%)

1 Ventilator = no, oxygen support = no, DNR = no 0.072 Ventilator = no, oxygen support = yes, malignancy = no 1.313 Ventilator = yes, inotropic support = no, transfusion = no 3.554 Ventilator = no, oxygen support = no, DNR = yes 10.005 Ventilator = no, oxygen support = yes, malignancy = yes 10.006 Ventilator = yes, inotropic support = no, transfusion = yes 14.447 Ventilator = yes, inotropic support = yes, emergent case = no 15.948 Ventilator = yes, inotropic support = yes, emergent case = yes 33.50

DNR, do not resuscitate.

tAbLE 4 Comparison of Classification Models and External Validation

Classification Tree Models Built on Pedi-PUF 2012–2014 Data

Non–Race-Specific CT Model Race-Specific CT Model

Specificity (%)

Sensitivity (%) Specificity (%)

Sensitivity (%)

Pedi-PUF 2015

African American 92.4 76.5 92.3 80.9White 95.2 65.1 95.1 68.7

CT, classification tree.


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AkBILgIC et al8

POtENtIAL CONFLICt OF INtERESt: The authors have indicated they have no potential conflicts of interest to disclose.


DOI: 10.1542/peds.2017-2221 originally published online January 10, 2018; 2018;141;Pediatrics

Oguz Akbilgic, Max Raymond Langham Jr and Robert Lowell DavisRace, Preoperative Risk Factors, and Death After Surgery

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DOI: 10.1542/peds.2017-2221 originally published online January 10, 2018; 2018;141;Pediatrics

Oguz Akbilgic, Max Raymond Langham Jr and Robert Lowell DavisRace, Preoperative Risk Factors, and Death After Surgery

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