Review
A systematic review of predictive modeling for bronchiolitis

https://doi.org/10.1016/j.ijmedinf.2014.07.005Get rights and content

Highlights

  • Bronchiolitis is prevalent in young children.

  • Large practice variation exists on bronchiolitis management.

  • We provide a systematic review of predictive modeling for bronchiolitis.

  • We identify several open problems in predictive modeling for bronchiolitis.

  • We provide some thoughts on how to address these open problems.

Abstract

Purpose

Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases.

Methods

A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years).

Results

The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain.

Conclusions

Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models.

Introduction

Bronchiolitis is inflammation of the smallest air passages in the lungs (bronchioles) and primarily a disease of children younger than 2 years. Bronchiolitis is the leading cause of and accounts for 16% of all infant hospitalizations [1], [2], [3]. 10% of children are affected by bronchiolitis in their first year of life [4]. By age two, more than one third of children have experienced bronchiolitis [5]. In the U.S., bronchiolitis incurs an annual total hospitalization cost of $543 million [6], and consumes significant emergency department (ED) and hospital resources. Bronchiolitis leads to approximately 238 outpatient visits, 71 hospitalizations, and 77 ED visits per 1000 infant years [7]. About 30% of infants with bronchiolitis evaluated in pediatric EDs are hospitalized [2]. Overall, about 10% of children with bronchiolitis are hospitalized [5]. Between 2% and 6% of all children with bronchiolitis require care in an intensive care unit (ICU) [8].

A variety of therapies, such as bronchodilators, are used in bronchiolitis with little supporting evidence [9], and minimal consensus on their use other than recommending that clinicians individualize care based on course and severity. Perhaps the only exception is the recommendation to use infection control procedures, but even the extent of this intervention is unclear beyond using hand decontamination.

In evaluating and treating bronchiolitis, a key step is an attempt to anticipate the disease course to guide the appropriate management setting and intensity [8]. At present, many bronchiolitis management decisions are made subjectively [2], [10]. This leads to significant practice variation, as is reflected in variable admission rates and use of specific therapies among different hospitals and physicians [1], [4], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]. Observed practice variation is not explained by differences in patient severity and has little impact on outcomes, but has a significant impact on healthcare resource usage [17]. Excessive hospital admission leads to overuse of inpatient resources, exposes patients to unnecessary iatrogenic risks and other infectious diseases in the hospital, and unnecessarily exposes other hospitalized children to these patients’ infectious respiratory pathogens [12], [18], [21]. One study [22] suggests that up to 10% of infants with bronchiolitis experience adverse events during their hospital stay. Alternatively, patients not properly admitted risk inadequate treatment and medical deterioration including death [12]. Thus, it is desirable to develop methodologies to standardize bronchiolitis care, which can help reduce healthcare cost and improve patient safety and outcome [21], [23], [24].

One way to standardize care for bronchiolitis is to develop and use clinical practice guidelines [8], [9]. With proper implementation, clinical practice guidelines for bronchiolitis can reduce healthcare resource usage by up to 77% without negatively impacting clinical outcomes or patient family's satisfaction [23], [25], [26], [27], [28]. However, due to an insufficient level of detail and limited amounts of evidence, existing clinical practice guidelines provide guidance for a limited number of patients and still rely heavily on individualized clinician judgment. More detailed guidelines are difficult to generalize because they cannot answer the many combinations of patient and illness characteristics, such as comorbidities.

Another way to standardize care for bronchiolitis is to develop predictive models [21], [29], [30], [31], [32], [33], [34], [35], [36], [37] and use them to help direct an optimal disease management plan. By using a data-driven approach to summarize useful information accumulated in clinical and administrative data sets, predictive models [38] can manage the level of individualized detail inherent in a clinical setting, complement clinical practice guidelines, and overcome their limitations. Predictive models are often integrated into computerized decision support tools [273]. These tools can support clinicians’ provisional judgment, or lead clinicians to question and reconsider that judgment [31]. This is particularly useful for inexperienced junior physicians and physicians who see children relatively infrequently. In general, human experts usually make better decisions when they are provided with predictive models’ computational results [39, p. 6].

In this paper, we present an overview of existing predictive models for clinical management of bronchiolitis and disease outcomes as well as their limitations. We identify several knowledge gaps and opportunities for improving predictive modeling for bronchiolitis, which can help direct the proper care setting and management for children with bronchiolitis. We discuss how to use machine learning techniques to address some of the gaps and limitations, and hope this paper can stimulate future research on predictive modeling for bronchiolitis. Our paper also covers predictive modeling for respiratory syncytial virus (RSV) infection whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases [40] and has a richer predictive modeling literature base. A list of acronyms used in this paper is provided in Appendix.

Section snippets

Methods

The methodology of our study follows the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline [272]. The study protocol was designed by and iteratively refined with inputs from all study co-authors.

We conducted a systematic review limited to PubMed by developing a search strategy to retrieve the literature on predictive modeling for bronchiolitis through April 25, 2014. We started from an initial straightforward search query: (bronchiolitis or RSV) AND

Results

As shown in Fig. 1, the literature search returned 2312 references in total. 201 appeared to be potentially relevant after review of titles and abstracts, and underwent full-text review. 168 of these were determined to be relevant and are discussed in this paper. The included articles are primarily predictive modeling studies and observational cohort studies regarding identifying predictors and risk factors, and contain only two systematic reviews and one randomized controlled trial

Main findings

Substantial effort has been invested in predictive modeling for bronchiolitis. Although considerable progress has been made, much remains to be done. From an algorithmic perspective, three types of research opportunities exist in predictive modeling for bronchiolitis. First, for many issues related to bronchiolitis, existing predictive models have inadequate accuracy. In this case, the research opportunity lies in improving the prediction accuracy. Second, for certain issues related to

Conclusions

We systematically reviewed the literature on predictive modeling for bronchiolitis. Our results show that many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models.

Authors’ contributions

GL had main responsibility for the manuscript. He conceptualized the presentation approach, conducted literature search and review, and drafted the manuscript. FN, PG, TG, and BS participated in conceptualizing the presentation approach, provided feedback on various medical issues, and revised the manuscript. BS and FN also participated in literature search. All authors read and approved the final manuscript.

Conflict of interest

The authors report no conflicts of interest.

Summary points

What is already known on the topic

  • Bronchiolitis is prevalent in young children. Significant practice variation exists among hospitals and physicians caring for children with bronchiolitis.

  • To help standardize care for bronchiolitis, many predictive models have been proposed in the literature on various issues related to bronchiolitis.

What this study added to our knowledge

  • We provide a comprehensive review of the existing state of the art

Acknowledgments

We thank Katherine Sward, Wendy W. Chapman, and Julio C. Facelli for helpful discussions. This work was supported in part by grant K08 HS018538 from the Agency for Healthcare Research and Quality.

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