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J Thorac Cardiovasc Surg 2000;119:347-357
© 2000 Mosby, Inc.
SURGERY FOR CONGENITAL HEART DISEASE |
From the Division of Neurology and the Cardiac Center of the Children’s Hospital of Philadelphia, Philadelphia, Pa.
Performed under contract with the National Institute of Neurological Disorders and Stroke, National Institutes of Health, NS-N01-2315. General Clinical Research Center nursing support was provided by NIH MO1-RR00240. Preoperative risk-of-death prediction—September 7, 1999.
Address for reprints: Robert Clancy, MD, Division of Neurology, The Children’s Hospital of Philadelphia, 324 South 34th St, Philadelphia, PA 19104 (E-mail: Clancy{at}email.chop.edu ).
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Abstract |
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5 vs >5), (3) presence of genetic syndrome, and (4) age at hospital admission for surgery (5 or >5 days). Mortality for two-ventricle repair was 3.2% (4/130). Mortality for single ventricle palliation was 25.5% (48/188) and was significantly influenced by Apgar score, genetic diagnosis, and admission age. The preoperative model had a prediction accuracy of 80%. The operative risk model included duration of deep hypothermic circulatory arrest, which significantly (P = .03) increased risk of death, with a prediction accuracy of 82%. |
Introduction |
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1-3 The factors that influence immediate postoperative mortality are not completely understood. Many previous investigations have focused primarily on specific anatomic entities, the contributions of different surgical techniques, and the conduct of cardiac anesthesia. For many forms of CHD, heart surgery in the neonate necessitates both cardiopulmonary bypass (CPB) and deep hypothermic circulatory arrest (DHCA), which may independently confer time-dependent risks. 4,5 The durations of CPB and DHCA themselves are dependent on the complexity of the defective anatomy, the medical status of the infants, and the skill of the surgeon. Consequently, the risk contributions attributed to CPB and DHCA are difficult to quantify. The immediate modes of death after heart surgery in the neonate are generally known and include cardiac arrhythmias, shock, hemorrhage, and sepsis. However, a comprehensive method of quantifying preoperative mortality risks is not available. This study was conducted to determine whether preoperative characteristics could be identified that significantly affect mortality and to examine their relative contribution to death risk. The final purpose was to formulate a simple, broadly applicable risk-of-death prediction model across the spectrum of CHD.
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Methods |
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Infants were enrolled in the trial and consequently were the target population of the risk model building if they satisfied the following criteria: congenital heart defect necessitating an operation in which DHCA was the chosen perfusion strategy; conceptional age* less than 45 weeks; absence of coincident lethal genetic disorders or severe multiple congenital abnormalities; and informed consent provided. Infants were excluded for the following reasons: neurologic unassessablity (from neuromuscular blockade), seizures or coma, elevated liver function tests, neutropenia, or if enrollment would have occurred less than 16 hours before the operation. The study protocol was approved by the Committee for the Protection of Human Subjects.
William I. Norwood and Marshal L. Jacobs performed the operations during approximately the first half of the trial and Thomas L. Spray and J. William Gaynor performed the remainder. The surgical techniques were not standardized, although the Norwood procedure was performed for patients with hypoplastic left heart syndrome and single ventricle. Cardiac anesthesia was uniformly maintained during the trial, including alpha-stat acid-base management.
Data collection.
A comprehensive database was generated, including clinical observations, laboratory values, imaging examinations, and neurodevelopmental test results. The database was temporally organized into three periods: (1) preoperative, (2) intraoperative, and (3) postoperative.
Preoperative observations.
The general categories of preoperative information examined are provided in Table I. Categories were chosen because of their plausible relationship to survival and neurologic outcome and the reliable quality of the data. Preoperative observations included demographic data, maternal/fetal characteristics of gestation, and labor and delivery. Demographic data included mother’s race, age, parity, socioeconomic status (Hollingshead index 6), and the presence of maternal health conditions. Characteristics of the infants included gestational age, birth weight, sex, Apgar scores, age at admission to The Children’s Hospital of Philadelphia, microcephaly (head circumference 2nd percentile for age), and the presence of a genetic or chromosomal syndrome. Diagnoses were based on a clear constellation of clinical findings to name a specific genetic syndrome (eg, Goldenhar) or abnormal chromosomal analysis (eg, 22q11 deletion). Other infants had definite dysmorphic features (eg, cleft palate) but not a specific, named genetic syndrome. The medical status of the heart, lungs, kidneys, liver, gastrointestinal tract, and bone marrow was also recorded.
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Intraoperative and postoperative observations.
Characteristics of the intraoperative period included the duration of surface cooling, lowest recorded nasopharyngeal temperature, duration of CPB cooling, DHCA time, duration of rewarming on CPB, need for additional CPB, and the use of modified ultrafiltration. Postoperative clinical end points for the neuroprotection trial were recorded during the intensive care unit stay, not to exceed 6 weeks after the operation. Observations included the occurrence of death, seizures, coma, and cardiac events.
Statistical considerations.
The study population is characterized by the number of infants available for each variable, means and standard deviations for continuous data, and frequencies and proportions for categorical data. Exact 95% confidence intervals are presented for some proportions.
Model building was iterative, integrating clinical knowledge and results of statistical analyses, with the intent of creating a broadly applicable parsimonious model. Because there were 52 deaths in the database it was determined a priori that a maximum of 5 prediction variables (10:1 ratio) would be allowed in the final preoperative risk prediction model. The logistic regression model 7 was used for selecting variables and characterizing their strength of associations with death, using the odds ratio and 95% confidence intervals. Logistic regression models the proportion of deaths and how these proportions are influenced by predictor variables. Mean values were substituted for candidate predictor variables with 5% or fewer missing values.
Categorical variables were formatted with the use of customary dummy variables structure. Dichotomous variables were coded as zero and one, with zero being the reference category. For exploration of initial models, ordinal and continuous (interval scaled) scaled data from the entire data set (Table I
) were evaluated as collected or transformed by means of the natural logarithm for continuous data. Sums of binary variables representing maternal and infant infections, ischemic conditions, and cardiac, metabolic, and neurologic conditions (Table I) were evaluated for their association with death, as well as the individual binary variables. Stepwise logistic regression (entry P values of .25, P value to stay of .1) was used to facilitate and verify the final model selection of variables; however, statistical knowledge gained through separate analyses and clinical knowledge guided the selection process. Continuous variables that were strongly considered for model selection were also evaluated on the basis of quartiles. Strong statistical support and/or a clear clinical rationale for inclusion of variables into the final preoperative prediction model were required.
Model fit was determined by means of the Hosmer and Lemeshow 8 method, and the deviance method was used for dispersion correction. The area under the receiver operating characteristic (ROC) curve is a measure of the overall prediction accuracy of the logistic model and, with its associated standard error, 9-11 was used to evaluate different models. The ROC curve is a graphic way of presenting separations between two groups and is a plot of percent of deaths predicted to die versus percent of survivors predicted to die. This curve is generated by creating a frequency distribution of model-generated predicted probabilities and by using each of the observed predicted probabilities as a threshold for classification. For a given threshold value (predicted probability), all cases above the threshold are classified as a death, yielding the percent of those that died who were predicted to die and the percent of survivors predicted to die in survivors. Allowing the threshold to assume all observed predicted probabilities results in the ROC curve. The area under this curve is a measure of the probability that given a randomly selected case from the survivor group (N = 266) and a randomly selected case from the death group (N = 52), the case from the death group will be assigned a higher predicted probability of death. This measure is independent of the prior probability of death and is considered to represent accuracy of the prediction model. The area is provided on the basis of the fitted model using all of the data and using an approximate jackknife 12 procedure, which provides predicted probabilities of death for each case, based on fitting the model with the remaining cases. The jackknife provides a conservative estimate of accuracy when using the same data set to create a model and estimate its accuracy. An exact logistic regression model was also fit to the preoperative variables.
The primary intent of the operative model was to evaluate the independent contribution of the duration of DHCA on death, adjusted for the variables in the preoperative model.
A risk stratification model was developed on the basis of the percent of deaths in each of the 18 (of a possible 24) unique combinations (profiles) of independent predictor variables represented in the preoperative logistic model. This model contains the information presented in the logistic model, but in a more clinically useful manner. The preoperative predictor variables will also be referred to as risk factors.
SAS (SAS Institute, Cary, NC) and LogXact (Cytel Software, Cambridge, Mass) software were used for all statistical analyses. All reported P values are 2-sided.
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Results |
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and IV). The distribution of cardiac defects, their associated mortality, and anatomic classes are shown in Table V.
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) to evaluate the independent contribution of DHCA duration. The 4th-quartile DHCA duration (>62 minutes) was associated with a significant increase in the odds of death (odds ratio [OR] = 2.8; P = .03). The accuracy (area under the ROC curve) increased to 82% (standard error of 2.7%) by including DHCA duration. Fig 2 provides a clinically relevant risk stratification tree with 6 terminal nodes (N1 to N6) into which all 318 study subjects are assigned on the basis of the 4 preoperative variables. The lowest percentage of death (1%; 1/102) was in node N1, characterizing infants with class I anatomy. Class II anatomy defines node N2 with a 10.7% (3/28) mortality. Combined classes III and IV had the highest mortality (25.5%; 48/188). Within this group, a 1-minute Apgar score of 5 or less (node N3) was associated with a significantly increased mortality (55.0%; 11/20) compared with those with higher Apgar scores (22.0%; 37/168). In node N4 the mortality for single ventricle infants with Apgar scores more than 5 and a genetic syndrome (55.6%; 5/9) was significantly higher than for those without a genetic diagnosis (20.1%; 32/159). Terminal nodes N5 and N6 show the effects of "age at admission" on infants with no genetic syndrome and an Apgar score more than 5. "Age at admission more than 5 days" was associated with higher mortality (31.6%; 12/38) than that of patients admitted before 5 days (16.5%; 20/121). Finally, survival of single ventricle infants with favorable Apgar scores, genetics, and admission age was 83.5%.
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Discussion |
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13-20 and none has produced a risk-of-death prediction model. Limperopoulos and associates 13 reported the presence of microcephaly (occipitofrontal circumference < 3rd percentile) in 35.7% of 56 neonates before heart surgery and clinical neurologic abnormalities (eg, poor suck or low tone) in 28 of 50 (56%) infants examined. In the study by Newburger and colleagues, 17 preoperative examinations showed definite clinical neurologic abnormalities in 55 of 152 (36.2%) infants before the operation. In the present study, infants frequently had low 1-minute Apgar scores, delivery room resuscitation, genetic syndromes, dysmorphic features, and microcephaly. Consequently, it is reasonable to seek clues regarding postoperative mortality among preoperative characteristics.
The mortality in this study (Table V) is comparable with that of other published series, ranging from 26% for single ventricle lesions, including hypoplastic left heart syndrome, to 3.2% for all other forms of complex neonatal heart disease. 17,21,22 The proportion of deaths in the eligible, nonenrolled patients (25.2%) was significantly (P = .01) higher than that of enrolled infants (16.4%). This was attributed mostly to the urgent need for surgery and immediate death in over half of the nonenrolled infants (Table II).
Not surprisingly, cardiac anatomy was a powerful preoperative predictor of mortality. There are specific anatomic variants within diagnostic groups that increase mortality risk. Examples are hypoplastic left heart syndrome with intact atrial septum or transposition of the great arteries with intramural coronary arteries. This speculative risk model uses a broad anatomic and physiologic classification scheme that was simple and easy to apply to individuals or classes of infants with CHD.
The presence of a specific named genetic syndrome was the second most potent modulator of risk of death. Abnormalities of chromosome 22, reported with about 5% of CHD, 23 occurred in 3.5% (11/318) of study subjects. "Dysmorphism," present in 18% of other study infants, was also associated with a trend to increased risk of death (OR = 1.963; P = .11). This suggests that increased mortality risk is not confined to a single genetic condition but is associated with any dysmorphic or genetic condition. This agrees with Bove’s observation 24 that mortality in patients with hypoplastic left heart syndrome was significantly increased by noncardiac congenital conditions.
Apgar scores can be lowered by asphyxia, sepsis, shock, CHD, medications, and neurologic disorders. Depressed 5-minute Apgar scores traditionally correlate with morbidity such as cerebral palsy. 25-27 In this study, low 1-minute Apgar scores were present in 10% of the population and were associated with increased mortality, perhaps indicating that the CHD prevented a smooth circulatory transition from intrauterine life or that the CHD had already introduced a neurologic disorder.
Older age at admission was associated with a higher risk of death (OR = 1.9; P = .06 in the preoperative model; OR = 2.0; P = .03 in the operative model). The influence of age at admission on mortality was independent of prostaglandin administration and other preoperative characteristics. The mechanism by which age at admission affects mortality risk is unknown.
This study has several important strengths. It is comprehensive in its scope of CHD, includes a wide range of anatomic lesions, and reflects the experience of four cardiothoracic surgeons over a 5-year period. It is intuitively appealing that cardiac anatomy, Apgar scores, and genetic syndromes could be associated with an increased risk of death. The relative joint contribution of each variable to mortality was quantified in terms of odds ratios and segments the population into subgroups of varying risks. This variation in risk is considerable. For example, survival of patients with single ventricle complex with favorable genetics, Apgar score, and age-at-admission status was 83.5% (Table X, 3a), compared with 44.8% (Table X, 3c ) in high-risk patients. After adjustment for the predictive preoperative variables, the length of DHCA time was associated with mortality risk. Those infants requiring between 44 and 62 minutes of DHCA had an odds ratio of approximately 1.5, and those requiring more than 62 minutes had an odds ratio of 2.8, compared with infants needing less than 44 minutes of DHCA.
The study also has important limitations. The risk model reflects the population from which it was derived—a selected group of neonates who underwent heart surgery with DHCA after fulfilling specific inclusion criteria for the neuroprotection trial. Unknown patient selection biases may exist, and the distribution of CHD lesions in this group is heavily weighted to single ventricle complexes. Although internally consistent, the risk-of-death model is speculative, has not been validated on an independent population, and probably exaggerates its predictive abilities. With the use of the jackknife method of generating the predicted probability of death, the ROC curve was constructed to measure the potential over-optimism of the regression model, and the resulting prediction accuracy fell to 70%. Finally, great care should be exercised when considering the application of this information to an individual infant with CHD. The 95% confidence intervals show a substantial range of mortality risk values, especially in single ventricle lesions (Table X). Such information could be useful for counseling families of neonates awaiting heart surgery and for accommodating risk factors in future therapeutic interventional trials in this patient population.
In conclusion, it is important to acknowledge that the preoperative condition of these infants had a profound effect on survival. Potent predictors of mortality already exist before the operation commences.
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Appendix: Discussion |
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I have two questions. You indicated that 123 patients did not fulfill the criteria of enrollment, and the mortality in that subset of patients was significantly higher than that in the patients you have analyzed. Is it conceivable that by excluding those patients you lost the opportunity to identify other predictors of death?
Second, I understand that the operations were performed by two surgeons doing the first phase of the study and two others doing the second phase of the study and that the results were similar for all those surgeons. Without trying to diminish the role of the surgeons, I would like to ask whether you believe that the system or the institutions in which they work is more important than the individual surgeons in the production of excellence?
Dr Clancy. I thank you for those comments, Mr de Leval. The first question concerned the effect of the nonenrolled patients. The children in the nonenrolled group who died were not predominantly the single ventricle patients but rather those who had something critical about their anatomy that precluded their waiting for 16 hours. The mortality in nonenrolled patients who would have been in classes III and IV was the same as that in the enrolled patients in classes III and IV. The differences really came out in the more critical cases. From that point of view, what would have otherwise been a lower mortality in classes I or II could have been raised. However, with class I used as the reference point, those differences would not have violated the broad categorizations.
Part of the difficulty is whether to be "lumpers" or "splitters." I am sure that surgeons could give me, a neurologist, many examples of what should and should not be included in any one of these groups, but painting with a broad stroke kept this study fairly simple.
The second question concerns the importance of the surgeon versus the institution. We wanted to make a generalizable model, something that would not be institution-specific, predicting the results of a surgeon or a group of surgeons. However, the whole team really produces these results. For example, we have had the same cardiothoracic anesthesiologists at Children’s Hospital for the whole series, and I think that is part of the reason why these numbers are as good as they are. Whether this factor can be applied to another institution, I do not know. It is always tempting to think that one’s expertise is the reason for the good results, but much of what is going to happen to the patient is predetermined by the genetics and the condition of the child on admission to the hospital.
This is a speculative model derived from the data that we generated, so of course it fits our data. The question is whether it will hold up if we test it independently in another set of patients in another institution with different anesthesiologists and different surgeons. Testing this model on an independent data set of children is one of the jobs ahead of us. The reason it is consistent in our study is that it is derived from our own data.
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Acknowledgments |
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Footnotes |
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Read at the Seventy-ninth Annual Meeting of The American Association for Thoracic Surgery, New Orleans, La, April 18-21, 1999.
*Age since conception is determined by adding legal age (time from birth) to estimated gestational age (eg, the conceptional age of a 3-week-old, 37-week estimated gestational age infant is 40 weeks).
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References |
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