Grading and assessment of clinical predictive tools for paediatric head injury: a new evidence-based approach

Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury

BACKGROUND

Minor head trauma in children is a common reason for emergency department visits, but the risk of traumatic brain injury (TBI) in those children is very low. Therefore, physicians should consider the indication for computed tomography (CT) to avoid unnecessary radiation exposure to children. The purpose of this study was to statistically assess the differences between control and mild TBI (mTBI). In addition, we also investigate the feasibility of machine learning (ML) to predict the necessity of CT scans in children with mTBI.

METHODS AND FINDINGS

The study enrolled 1100 children under the age of 2 years to assess pre-verbal children. Other inclusion and exclusion criteria were per the PECARN study. Data such as demographics, injury details, medical history, and neurological assessment were used for statistical evaluation and creation of the ML algorithm. The number of children with clinically important TBI (ciTBI), mTBI on CT, and controls was 28, 30, and 1042, respectively. Statistical significance between the control group and clinically significant TBI requiring hospitalization (csTBI: ciTBI+mTBI on CT) was demonstrated for all nonparametric predictors except severity of the injury mechanism. The comparison between the three groups also showed significance for all predictors (p<0.05). This study showed that supervised ML for predicting the need for CT scan can be generated with 95% accuracy. It also revealed the significance of each predictor in the decision tree, especially the "days of life."

CONCLUSIONS

These results confirm the role and importance of each of the predictors mentioned in the PECARN study and show that ML could discriminate between children with csTBI and the control group.

The Relationship Between Severe Visual Acuity Loss, Traumatic Brain Injuries, and Ocular Injuries in American Service Members From 2001 to 2015

INTRODUCTION

Although traumatic brain injury (TBI) is known to cause many visual problems, the correlation between the extent of severe visual acuity loss (SVAL) and severity of TBI has not been widely explored. In this retrospective analysis, combined information from Department of Defense (DoD)/Veterans Affairs ocular injury and TBI repositories were used to evaluate the relationship between chronic SVAL, TBI, ocular injuries, and associated ocular sequelae for U.S. service members serving between 2001 and 2015.

MATERIALS AND METHODS

The Defense and Veterans Eye Injury and Vision Registry (DVEIVR) is an initiative led by the DoD and Veterans Affairs that consists of clinical and related data for service members serving in theater since 2001. The Defense and Veterans Brain Injury Center (DVBIC) is the DoD's office for tracking TBI data in the military and maintains data on active-duty service members with a TBI diagnosis since 2000. Longitudinal data from these 2 resources for encounters between February 2001 and October 2015 were analyzed to understand the relation between SVAL, and TBI while adjusting for ocular covariates such as open globe injury (OGI), disorders of the anterior segment and disorders of the posterior segment in a logistic regression model. TBI cases in DVEIVR were identified using DVBIC data and classified according to International Statistical Classification of Diseases criteria established by DVBIC. Head trauma and other open head wounds (OOHW) were also included. SVAL cases in DVEIVR were identified using both International Statistical Classification of Diseases criteria for blindness and low vision as well as visual acuity test data recorded in DVEIVR.

RESULTS

Data for a total of 25,193 unique patients with 88,996 encounters were recorded in DVEIVR from February, 2001 to November, 2015. Of these, 7,217 TBI and 1,367 low vision cases were identified, with 638 patients experiencing both. In a full logistic model, neither UTBI nor differentiated TBI (DTBI, ie, mild, moderate, severe, penetrating, or unclassified) were significant risk factors for SVAL although ocular injuries (disorders of the anterior segment, disorders of the posterior segment, and OGI) and OOHW were significant.

CONCLUSION

Any direct injury to the eye or head risks SVAL but the location and severity will modify that risk. After adjusting for OGIs, OOHW and their sequelae, TBI was found to not be a significant risk factor for SVAL in patients recorded in DVEIVR. Further research is needed to explore whether TBI is associated with more moderate levels of vision acuity loss.

Clinical use and indications for head computed tomography in children presenting with acute medical illness in a low- and middle-income setting

BACKGROUND

Computed tomography (CT) imaging is an indispensable tool in the management of acute paediatric neurological illness providing rapid answers that facilitate timely decisions and interventions that may be lifesaving. While clear guidelines exist for use of CT in trauma to maximise individual benefits against the risk of radiation exposure and the cost to the healthcare system, the same is not the case for medical emergency.

AIMS

The study primarily aimed to retrospectively describe indications for non-trauma head CT and the findings at a tertiary paediatric hospital.

METHODS

Records of children presenting with acute illness to the medical emergency unit of Red Cross War Children's Hospital, Cape Town, over one year (2013) were retrospectively reviewed. Participants were included if they underwent head CT scan within 24 hours of presentation with a non-trauma event. Clinical data and reports of CT findings were extracted.

RESULTS

Inclusion criteria were met by 311 patients; 188 (60.5%) were boys. The median age was 39.2 (IQR 12.6-84.0) months. Most common indications for head CT were seizures (n = 169; 54.3%), reduced level of consciousness (n = 140;45.0%), headache (n = 74;23.8%) and suspected ventriculoperitoneal shunt (VPS) malfunction (n = 61;19.7%). In 217 (69.8%) patients CT showed no abnormal findings. In the 94 (30.2%) with abnormal CT results the predominant findings were hydrocephalus (n = 54;57.4%) and cerebral oedema (n = 29;30.9%). Papilloedema was more common in patients with abnormal CT (3/56; 5.4%) compared with none in those with normal CT; P = 0.015; while long tract signs were found in 42/169 (24.9%) and 23/56 (41.1%) of patients with normal and abnormal CT findings, respectively; P = 0.020. Post-CT surgery was required by 47(15.1%) of which 40 (85.1%) needed a ventricular drainage. A larger proportion of patients with VPS (25/62; 40.3%) required surgery compared to patients without VPS (22/249; 8.8%; P<0.001).

CONCLUSION

A majority of head CT scans in children with medical emergency with acute neurological illness were normal. Patients with VPS constituted the majority of patients with abnormal CT scans that required subsequent neurosurgical intervention. Evidence-based guidelines are required to guide the best use of head CT in the management of children without head trauma.

Evaluating the Impact of the Grading and Assessment of Predictive Tools Framework on Clinicians and Health Care Professionals' Decisions in Selecting Clinical Predictive Tools: Randomized Controlled Trial

BACKGROUND

While selecting predictive tools for implementation in clinical practice or for recommendation in clinical guidelines, clinicians and health care professionals are challenged with an overwhelming number of tools. Many of these tools have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (the GRASP framework). This framework was based on the critical appraisal of the published evidence on such tools.

OBJECTIVE

The aim of the study was to examine the impact of using the GRASP framework on clinicians' and health care professionals' decisions in selecting clinical predictive tools.

METHODS

A controlled experiment was conducted through a web-based survey. Participants were randomized to either review the derivation publications, such as studies describing the development of the predictive tools, on common traumatic brain injury predictive tools (control group) or to review an evidence-based summary, where each tool had been graded and assessed using the GRASP framework (intervention group). Participants in both groups were asked to select the best tool based on the greatest validation or implementation. A wide group of international clinicians and health care professionals were invited to participate in the survey. Task completion time, rate of correct decisions, rate of objective versus subjective decisions, and level of decisional conflict were measured.

RESULTS

We received a total of 194 valid responses. In comparison with not using GRASP, using the framework significantly increased correct decisions by 64%, from 53.7% to 88.1% (88.1/53.7=1.64; t₁₉₃=8.53; P<.001); increased objective decision making by 32%, from 62% (3.11/5) to 82% (4.10/5; t₁₈₉=9.24; P<.001); decreased subjective decision making based on guessing by 20%, from 49% (2.48/5) to 39% (1.98/5; t₁₈₈=-5.47; P<.001); and decreased prior knowledge or experience by 8%, from 71% (3.55/5) to 65% (3.27/5; t₁₈₇=-2.99; P=.003). Using GRASP significantly decreased decisional conflict and increased the confidence and satisfaction of participants with their decisions by 11%, from 71% (3.55/5) to 79% (3.96/5; t₁₈₈=4.27; P<.001), and by 13%, from 70% (3.54/5) to 79% (3.99/5; t₁₈₈=4.89; P<.001), respectively. Using GRASP decreased the task completion time, on the 90th percentile, by 52%, from 12.4 to 6.4 min (t₁₉₃=-0.87; P=.38). The average System Usability Scale of the GRASP framework was very good: 72.5% and 88% (108/122) of the participants found the GRASP useful.

CONCLUSIONS

Using GRASP has positively supported and significantly improved evidence-based decision making. It has increased the accuracy and efficiency of selecting predictive tools. GRASP is not meant to be prescriptive; it represents a high-level approach and an effective, evidence-based, and comprehensive yet simple and feasible method to evaluate, compare, and select clinical predictive tools.