Dataset Information

Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules.

ABSTRACT:

Importance

Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients to ionizing radiation. The Pediatric Emergency Care Applied Research Network (PECARN) rules for identifying children with minor head trauma who are at very low risk of clinically important traumatic brain injury (ciTBI) are widely used to triage CT imaging.

Objective

To examine whether optimal classification trees (OCTs), which are novel machine-learning classifiers, improve on PECARN rules' predictive accuracy.

Design, setting, and participants

A secondary analysis of prospective, publicly available data on emergency department visits for head trauma used by the PECARN group to develop their tool was conducted to derive OCT-based prediction rules for ciTBI in a development cohort and compare their predictive performance vs the PECARN rules in a validation cohort among children who were younger than 2 years and 2 years or older. Data on 42 412 children with head trauma and without severely altered mental status who were examined between June 1, 2004, and September 30, 2006, were gathered from 25 emergency departments in North America participating in PECARN. Data analysis was conducted from September 15, 2016, to December 18, 2018.

Main outcomes and measures

The outcome was ciTBI, with predictive performance measured by estimating the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for the OCT and the PECARN rules. The OCT and PECARN rules' performance was compared by estimating ratios for each measure.

Results

Of the 42 412 children (15 996 [37.7%] girls) included in the analysis, 10 718 were younger than 2 years (25.3%; mean [SD] age, 11.6 [0.6] months) and 31 694 were 2 years or older (74.7%; age, 9.1 [4.9] years). Compared with PECARN rules, OCTs misclassified 0 vs 1 child with ciTBI in the younger and 10 vs 9 children with ciTBI in the older cohort, and correctly identified more children with very low risk of ciTBI in the younger (7605 vs 5701) and older (20 594 vs 18 134) cohorts. In the validation cohorts, compared with the PECARN rules, the OCTs had statistically significantly better specificity (in the younger cohort: 69.3%; 95% CI, 67.4%-71.2% vs 52.8%; 95% CI, 50.8%-54.9%; in the older cohort: 65.6%; 95% CI, 64.5%-66.8% vs 57.6%; 95% CI, 56.4%-58.8%), positive predictive value (odds ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively), and positive likelihood ratio (risk ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively). There were no statistically significant differences in the sensitivity, negative predictive value, and negative likelihood ratio between the 2 sets of rules.

Conclusions and relevance

If implemented, OCTs may help reduce the number of unnecessary CT scans, without missing more patients with ciTBI than the PECARN rules.

SUBMITTER: Bertsimas D

PROVIDER: S-EPMC6515573 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Study objectiveTo improve the efficiency and appropriateness of computed tomography (CT) use in children with minor head trauma, clinical prediction rules were derived and validated by the Pediatric Emergency Care Applied Research Network (PECARN). The objective of this study was to conduct a cost-effectiveness analysis comparing the PECARN traumatic brain injury prediction rules to usual care for selective CT use.MethodsWe used decision analytic modeling to project the outcomes, costs, and cost-effectiveness of applying the PECARN rules compared with usual care in a hypothetical cohort of 1,000 children with minor blunt head trauma. Clinical management was directed by level of risk as specified by the presence or absence of variables in the PECARN traumatic brain injury prediction rules. Immediate costs of care (diagnostic testing, treatment [not including clinician time], and hospital stay) were derived on single-center data. Quality-adjusted life-year losses related to the sequelae of clinically important traumatic brain injuries and to radiation-induced cancers, number of CT scans, number of radiation-induced cancers, number of missed clinically important traumatic brain injury, and total costs were evaluated.ResultsCompared with the usual care strategy, the PECARN strategy was projected to miss slightly more children with clinically important traumatic brain injuries (0.26 versus 0.02 per 1,000 children) but used fewer cranial CT scans (274 versus 353), resulted in fewer radiation-induced cancers (0.34 versus 0.45), cost less ($904,940 versus $954,420), and had lower net quality-adjusted life-year loss (-4.64 versus -5.79). Because the PECARN strategy was more effective (less quality-adjusted life-year loss) and less costly, it dominated the usual care strategy. Results were robust under sensitivity analyses.ConclusionApplication of the PECARN traumatic brain injury prediction rules for children with minor head trauma would lead to beneficial outcomes and more cost-effective care.