Objective
Statistical methods that measure the independent contribution of individual factors for third-/fourth-degree perineal laceration (TFPL) fall short when the clinician is faced with a combination of factors. Our objective was to demonstrate how a statistical technique, classification and regression trees (CART), can identify high-risk clinical clusters.
Study Design
We performed multivariable logistic regression, and CART analysis on data from 25,150 term vaginal births.
Results
Multivariable analyses found strong associations with the use of episiotomy, forceps, vacuum, nulliparity, and birthweight. CART ranked episiotomy, operative delivery, and birthweight as the more discriminating factors and defined distinct risk groups with TFPL rates that ranged from 0-100%. For example, without episiotomy, the rate of TFPL was 2.2%. In the presence of an episiotomy, forceps, and birthweight of >3634 g, the rate of TFPL was 68.9%.
Conclusion
CART showed that certain combinations held low risk, where as other combinations carried extreme risk, which clarified how choices on delivery options can markedly affect the rate of TFPL for specific mothers.
A considerable challenge that faces all obstetricians involves distilling the myriad of published reports to choose the best tests and treatments for our patients. In addition to an ever-growing number of randomized clinical trials to consider, the increasing prevalence of high-quality observational studies and abundant metaanalyses add to the clinician’s task.
For Editors’ Commentary, see Table of Contents
See related editorial, page 279
See Journal Club, page 366
When the conditions of a certain study are similar to our clinical circumstances, we might expect to obtain similar results over the course of many patients. Although this is important from an overall healthcare perspective, clinicians are often left with 2 problems. Sometimes the particular patient before us is not similar to the average patient and generalizing the study results to her particular situation is unsatisfactory. Furthermore, an odds ratio (OR) for one factor vs another does not communicate the prime piece of information for which our patient asks, namely, what is the actual rate of complication that she may expect to experience. The objective of this report was to demonstrate results from a statistical method that can help with these 2 problems with the use of the well-understood issue of third- and fourth-degree perineal laceration (TFPL).
Many years ago, Koss and Feinstein noted that clinicians in practice often grouped patients with certain signs and symptoms and made decisions based on these groups, rather than relying solely on arithmetic scores representing the whole population. They were among the first in clinical epidemiology to use classification and regression trees (CARTs) to consolidate similar subgroups of patients and provide their specific risks. Since then, CARTs have been used extensively in this area. It should be noted that the acronym CART is used in several contexts with different meanings: sometimes as an abbreviation for the seminal book by Breiman et al, and other times as the name of proprietary software that is based on the same book. We use CART here as an abbreviation for the general tree-growing method.
CARTs, which also are described as recursive partitioning methods, are statistical methods that examine a dataset to find the best variables and associated cutoff points to group the data into those with and without the outcome in question. Factors that are both frequent and discriminating rise in importance and result in groupings that bear resemblance and relevance to everyday clinical practice. From all variables under consideration CART selects the single factor that best separates the groups with and without the problem to form the first branch point or node. Once the first node has been formed, the same procedure is applied to each “child” node, which finds the next most discriminating factor, hence the term recursive . At each junction, CART also searches for the optimal cutoff point if that variable is continuous. Splitting stops when the statistical process determines no further discriminating advantage with any of the remaining factors. Contrary to multivariable logistic regression analysis, where the goal is to isolate the independent effect of specific factors, in CART there is no attempt to identify independence, rather the goal is to define and rank the most predictive clinical groupings.
CART applications are developing in a wide range of clinical situations, such as the prediction of outcomes with obesity, a diagnosis of Alzheimer’s disease, the prediction of cardiovascular disease, and the identification of subgroups with different risks in epidemiologic investigations. Examples of applications in obstetrics include assessment of electronic fetal monitoring tracings, prediction of outcomes of low birthweight babies, or antenatal risk assessment.
In this study, we have examined a well-understood clinical problem, TFPL, first with the use of a standard multivariable logistic regression analysis to assess independent risk factors and then with the use of CART to determine the most discriminating clinical risk groups and their associated risks.
Materials and Methods
This project was deemed to qualify for exempt status by the MedStar Research Institute institutional review board. The retrospective analysis was performed on data from women with vaginal births and live singleton, cephalic-presenting babies at a gestational age of ≥37 weeks and delivering between January 1, 2004, and December 31, 2008, at 1 of 4 acute care, teaching and research hospitals of Medstar Health System, which is a not-for-profit regional health care system. The hospitals with obstetrics services are in the Baltimore-Washington, DC corridor, and consist of 1 university hospital and 2 academic community teaching hospitals with level 3B or C neonatal intensive care units, and 1 academic community teaching hospital with a level 2 neonatal intensive care unit. Three of the 4 hospitals serve as regional referral centers.
Data were extracted from 25,150 records with PeriBirth software (PeriGen, Princeton, NJ), an intelligent electronic medical record and decision support application introduced during this period. We selected 16 study variables that were based on risk factors described in the literature ( Table 1 ).
| Variable | Partially complete dataset a | Records with data present, n (%) | Entirely complete dataset b | P value |
|---|---|---|---|---|
| Maternal age, y c | 26.6 ± 6.2 | 14,426 | 26.3 ± 6.3 | < .001 |
| Maternal height, cm c | 164.0 ± 7.4 | 7449 | 164.0 ± 7.4 | .96 |
| Body mass index, kg/m 2 c | 31.2 ± 6.2 | 7203 | 31.0 ± 6.1 | .09 |
| Birthweight, g c | 3435 ± 348 | 14,115 | 3300 ± 431 | <. 001 |
| Second stage, min c | 62.0 ± 86.3 | 4423 | 63.2 ± 70 | .382 |
| Nulliparity, n (%) | 4906 (33.9) | 14,458 | 5127 (47.9) | < .001 |
| Maternal diabetes mellitus or hypertension or thyroid disease, n (%) | 1475 (10.2) | 14,458 | 1217 (11.4) | .003 |
| Labor induction, n (%) | 5531 (38.3) | 14,458 | 4377 (40.9) | < .001 |
| Labor augmentation with oxytocin, n (%) | 7031 (48.6) | 14,458 | 5700 (53.3) | < .001 |
| Epidural, n (%) | 8605 (59.5) | 14,458 | 8165 (76.4) | < .001 |
| Forceps, n (%) | 73 (0.5) | 14,458 | 79 (0.7) | .020 |
| Vacuum, n (%) | 1203 (8.3) | 14,458 | 1043 (9.8) | < .001 |
| Midwife delivered, n (%) | 325 (2.3) | 14,302 | 314 (2.9) | .001 |
| Episiotomy, n (%) | 1269 (8.8) | 14,458 | 1251 (11.7) | < .001 |
| Fetal heart rate described as “concerning”, n (%) | 1736 (12) | 14,458 | 1149 (10.7) | .002 |
| Third-/fourth-degree perineal laceration, n (%) | 411 (2.8) | 14,458 | 471 (4.4) | < .001 |
The multivariate analysis was confined to the 10,692 records in which study variables were 100% complete in each record.
Although the other 14,458 records were missing data in ≥1 fields, they had a high degree of completeness for all but 3 variables. body mass index, height, and second-stage duration were available in 31-52% of these records. Birthweight, maternal age, and midwifery/physician presence at delivery were complete in 98%. The remaining 10 variables were complete in 100% of these partial records.
CARTs could use some records with partial data because these records contained data on most variables. In addition, CART uses a technique that is based on local multiple imputations to handle missing data and to reduce bias. The number of records that contributed at each CART node is indicated within parentheses in the Figure . The characteristics of the study group with respect to the 16 variables are summarized in Table 1 .
We subjected both the total 25,150 dataset that included partially incomplete records (14,458) and the records with complete data (10,692) to univariate analysis for the 16 variables that are shown in Table 2 . The variable “hospital” was included to provide an opportunity to see whether there were additional unmeasured factors within each institution that were associated with TFPL.
| Univariate analysis relative risk (95% CI) | ||
|---|---|---|
| Variable | 25,150 subjects, including records with incomplete data | 10,692 subjects, including only records with complete data |
| Episiotomy | 7.91 (6.87–9.10) a | 7.16 (6.91–8.69) a |
| Maternal age | 1.028 (1.017–1.039) a | 1.035 (1.02–1.05) a |
| Maternal height | 0.983 (0.973–0.993) a | 0.987 (0.975–0.998) a |
| Body mass index | 0.965 (0.951–0.979) a | 0.971 (0.954–0.987) a |
| Birthweight | 1.001 (1.0007–1.00103) a | 1.001 (1.0007–1.0011) a |
| Second stage | 1.006 (1.0055–1.007) a | 1.008 (1.007–1.009) a |
| Labor augmentation with oxytocin | 1.40 (1.23–1.61) a | 1.19 (0.99–1.44) |
| Labor induction | 1.07 (0.94–1.23) | 0.99 (0.82–1.19) |
| Epidural | 1.27 (1.09–1.47) a | 1.13 (0.90–1.41) |
| Midwife delivered | 0.042 (0.0059–0.298) a | 0 (0–inf) |
| Maternal diabetes mellitus or hypertension or thyroid disease | 1.13 (0.92–1.39) | 1.21 (0.92–1.59) |
| Nulliparity | 6.11 (5.18–7.21) a | 5.84 (5.57–7.47) a |
| Fetal heart rate described as “concerning” | 0.92 (0.74–1.14) | 0.92 (0.67–1.25) |
| Forceps | 17.07 (12.19–23.91) a | 15.78 (9.97–24.98) a |
| Vacuum | 6.25 (5.50–7.23) a | 5.99 (4.89–7.31) a |
| Hospital | ||
| 2 vs 1 | 2.82 (2.31–3.43) a | 3.11 (2.35–4.11) a |
| 3 vs 1 | 1.40 (1.18–1.67) a | 1.57 (1.22–2.01) a |
| 4 vs 1 | 1.84 (1.52–2.23) a | 1.90 (1.41–2.57) a |
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