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. 2019 Jun 13;4(3):e166.
doi: 10.1097/pq9.0000000000000166. eCollection 2019 May-Jun.

Improving Quality of Chest Computed Tomography for Evaluation of Pediatric Malignancies

Affiliations

Improving Quality of Chest Computed Tomography for Evaluation of Pediatric Malignancies

Sara A Mansfield et al. Pediatr Qual Saf. .

Abstract

Introduction: Atelectasis is a problem in sedated pediatric patients undergoing cross-sectional imaging, impairing the ability to accurately interpret chest computed tomography (CT) imaging for the presence of malignancy, often leading to additional maneuvers and/or repeat imaging with additional radiation exposure.

Methods: A quality improvement team established a best-practice protocol to improve the quality of thoracic CT imaging in young patients with suspected primary or metastatic pulmonary malignancy. The specific aim was to increase the percentage of chest CT scans obtained for the evaluation of pulmonary nodules with acceptable atelectasis scores (0-1) in patients aged 0-5 years with malignancy, from a baseline of 45% to a goal of 75%.

Results: A retrospective cohort consisted of 94 patients undergoing chest CT between February 2014 and January 2015 before protocol implementation. The prospective cohort included 195 patients imaged between February 2015 and April 2018. The baseline percentage of CT scans that were scored 0 or 1 on the atelectasis scale was 44.7%, which improved to 75% with protocol implementation. The mean atelectasis score improved from 1.79 (±0.14) to 0.7 (±0.09). Sedation incidence decreased substantially from 73.2% to 26.5% during the study period.

Conclusions: Using quality improvement methodology including standardization of care, the percentage of children with atelectasis scores of 0-1 undergoing cross-sectional thoracic imaging improved from 45% to 75%. Also, eliminating the need for sedation in these patients has further improved image quality, potentially allowing for optimal detection of smaller nodules, and minimizing morbidity.

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Figures

Fig. 1.
Fig. 1.
Computed tomography of the chest. A, Atelectasis grade 0. No linear or nodular densities that could be confused for lung metastasis. B, Atelectasis grade 1. Small nodular densities that might be confused with metastasis. Please see the right upper lobe posteriorly (arrow). C, Atelectasis grade 2. Subsegmental atelectasis with linear atelectasis present in the posterior aspects of both lungs. D, Atelectasis grade 3. Larger segmental collapse as shown in the left lower lobe medially. E, Atelectasis grade 4. Lobar or multisegmental obscuring large regions of one or both lungs.
Fig. 2.
Fig. 2.
Key driver diagram. The key driver diagram summarizes specific interventions targeting the specific aim of improving the quality of chest CT imaging in children undergoing evaluation for possible pulmonary metastatic disease.
Fig. 3.
Fig. 3.
Trends in Atelectasis Scoring. The annotated P chart displays the percentage of CT scans with an atelectasis score of 0 or 1 over time in children undergoing chest CT for evaluation of pulmonary malignancy. The time point of protocol implementation and specific interventions are noted.
Fig. 4.
Fig. 4.
Trends in average image quality score. The X-Bar chart displays the average image quality score over time in children undergoing chest CT for evaluation of pulmonary malignancy. *A sqrt(b+ax) transform for right skew was used to determine control limits. Control limits were then reverse transformed to reflect original data metrics.
Fig. 5.
Fig. 5.
Trends in compliance with the anesthesia protocol. The annotated P chart displays the percentage of CT scans obtained with compliance to the anesthesia chest CT protocol over time in children undergoing chest CT for evaluation of pulmonary malignancy.
Fig. 6.
Fig. 6.
Trends in percentage of CT scans performed without sedation. The annotated P chart displays the percentage of CTs performed without sedation over time in children undergoing chest CT for evaluation of pulmonary malignancy. **Control limits are wider than standard because the number of 0%’s (or 100%’s) is sufficient to skew probabilities. Standard limits would yield false special cause flags.

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