Are we following the guiding SIGN when managing paediatric head injury?

Guideline adherence in the management of head injury in Australian children: A population-based sample survey

BACKGROUND

Head injuries in children are a common and potentially devastating presentation. The CareTrack Kids (CTK) study assessed care of Australian children aged 0-15 years, in 2012 and 2013, to evaluate the proportion in line with guideline-based indicators for 17 common conditions. Overall adherence to guideline-based recommended practice occurred 59.8% of care encounters (95% CI: 57.5-62.0), and 78.3% (95% CI: 75.1-81.2) for head injury. This paper presents results for head injury, at indicator level.

METHODS

A modified version of the RAND-UCLA method of indicator development was used. Indicators, measurable components of a standard or guideline, were developed from international and national guidelines relating to head injury in children and were ratified by clinical experts using a Delphi process. Paediatric nurses extracted data from medical records from general practitioners (GPs), emergency departments (EDs) and inpatient wards in Queensland, New South Wales and South Australia, for children under 15 years receiving care in 2012-13. Our purpose was to estimate the percentage adherent for each indicator.

RESULTS

The medical records of 629 children with head injury were examined. Fifty-one percent of children were under 5 years old, with more males (61%) than females. Thirty-eight indicators were assessed. Avoidance of nasotracheal airways (100%; 95% CI: 99.4-100) or nasogastric tubes (99.7%; 95% CI: 98.5-100) for children with a head injury had the highest adherence. Indicators relating to primary and secondary assessment of head injuries were mostly adhered to. However, adherence to other indicators was poor (e.g., documentation of the past history of children (e.g., presence or absence of seizures) before the injury; 29.9% (95% CI: 24.5-35.7)), and for others was difficult to estimate with confidence due to small sample sizes (e.g., Children with a head injury who were intubated had PaO2 above 80mm Hg; 56.0% (95% CI: 28.6-80.9)). Indicators guiding clinical decision making regarding the need for CT scan had insufficient data to justify reporting.

CONCLUSION

This study highlights that management of head injury in children mostly follows guidelines, but also flags some specific areas of inconsistency. Individual sites are encouraged to use these results to guide investigation of local practices and inform quality improvement endeavours.

Predictors of 1 year mortality in adult injured patients admitted to the trauma center

BACKGROUND

Traditional approach to predicting trauma-related mortality utilizes scores based on anatomical, physiological, or a combination of both types of criteria. However, several factors are reported in literature to predict mortality independent of severity scores. The objectives of the study were to identify predictors of 1 year mortality and determine their magnitude and significance of association in a resource constrained scenario.

MATERIALS AND METHODS

Prospective observational study enrolled 572 patients. Information regarding factors known to affect mortality was recorded. Other factors which may be important in resource constrained settings were also included. This included referral from a peripheral hospital, number of surgeries performed on the patient, and his socioeconomic status (below poverty line (BPL) card). Patients were followed till death or upto a period of 1year. Logistic regression, actuarial survival analysis, and Cox proportionate hazard model were used to identify predictors of 1year mortality. Limited estimate of external validity of the study was obtained using bootstrapping.

RESULTS

Age of patient, Injury Severity Score (ISS), abnormal activated partial thromboplastin time (APTT), Glasgow Coma Scale (GCS) score at admission, and systolic blood pressure (BP) at admission were found to significantly predict mortality on logistic regression and Cox proportionate hazard models. Abnormal respiratory rate at admission was found to significantly predict mortality in the logistic regression model, but no such association was seen in Cox proportionate hazard model. Bootstrapping of the logistic regression model and Cox proportionate hazard model provide us with a set of factors common to both the models. These were age, ISS, APTT, and GCS score at admission.

CONCLUSION

Multivariate analysis (logistic and Cox proportionate hazard analysis) and subsequent bootstrapping provide us with a set of factors which may be considered as valid predictors universally. However, since bootstrapping only provides limited estimates of external validity, there is a need to test these factors against the well accepted requirements of external validity namely population, ecological, and temporal validity.