Using the data from two large multicenter cohorts, we developed and externally validated a simple classification tree for hospital admission in acute asthma. Given its internal and external validity, we believe that this classification tree is a potentially useful tool for risk stratification and may aid decision-making in the emergency care of acute asthma.
The classification tree includes three important variables (i.e., hospitalization history, oxygen saturation, and initial PEF) that can be readily assessed at the time of ED presentation, plus one variable (change in PEF) that updates the risk of admission during the ED course. The risk stratification scheme is similar to what is outlined in the asthma guidelines – a brief history-taking, measurements of PEF and oxygen saturation, plus repeated assessments of PEF [19
], but extends the guideline algorithm to an explicit, relatively simple, bed-side tool for risk stratification. For example, CART identified 95% as the cutoff point for room-air oxygen saturation in the classification tree. This finding is of potential importance as the CART algorithm considered this as the optimal cutoff for the continuous predictor of oxygen saturation by an exhaustive search of all possible splits.
The similarity between the guideline and CART algorithms suggests that emergency physicians have followed the guidelines to risk-stratify ED patients with acute asthma; however, the validation process indicates there are modest variations in admission practices over time. For example, compared with the NEDSS physicians, the MARC physicians seemed to put more weight on “non-improved PEF” when making admission decisions, as reflected by higher admission rates in Group 5. This variation also explained the somewhat decreased discriminatory ability of the decision tree in the validation cohort. Nonetheless, this is expected because applying a previously derived model to external data is the true test of a predictive model [28
The CART algorithm has several strengths. First, in a simple 3-step process using 4 variables, the tree-structured decision rule can identify high-risk and low-risk patients, with a variation of more than a 5-fold difference in risk of admission. Compared with multivariable-generated decision rules, the processes and the number of variables involved in obtaining risk estimates are significantly reduced. Second, the CART method uses asymmetric stratification, i.e., different binary splitters at the same level. This feature not only is congruent with physicians’ decision-making processes but also greatly enhances the efficiency of the risk-stratification process.
This study has some potential limitations. First, we did not have information on follow-up outcomes for both cohorts, such as post-ED relapse. Inclusion of follow-up outcomes would have allowed us to examine the need for admission. Rather, we applied the CART method to understand admission practices in large ED samples over time. Second, there is no precise risk threshold that can uniformly dictate which patients should be admitted to the hospital. Thus, the CHOP tree should be used to aid decision making, not to replace physician’s judgment. Third, the areas under the ROC curve in CART analyses are not as high as those seen in multivariable modeling, as CART usually uses fewer variables and generates risk estimates for groups, not individuals [4
]. Fourth, we were not able to derive the percent predicted PEF due to lack of height. Future studies that prospectively measure this item may improve the classification tree. Finally, there are some important variables (e.g., physical findings) that have been shown to predict asthma hospitalization [2
] but are infrequently documented in the medical record. Inclusion of these variables might better risk-stratify patients but would add complexity to the rule.
In summary, we developed and externally validated a novel CHOP classification tree for hospitalization among ED patients with acute asthma. By elaborating on the algorithm outlined in the asthma guidelines [19
], this CART-based algorithm risk-stratifies patients using three variables at ED presentation and one variable after initial management. Use of this explicit risk stratification rule may aid decision-making in the emergency care of acute asthma.