Optimizing Clinical Decision Making with Decision Curve Analysis: Insights for Clinical Investigators

A large number of prediction models are published with the objective of allowing personalized decision making for diagnostic or prognostic purposes. Conventional statistical measures of discrimination, calibration, or other measures of model performance are not well-suited for directly and clearly assessing the clinical value of scores or biomarkers. Decision curve analysis is an increasingly popular technique used to assess the clinical utility of a prognostic or diagnostic score/rule, or even of a biomarker. Clinical utility is expressed as the net benefit, which represents the net balance of patients' benefits and harms and considers, implicitly, the consequences of clinical actions taken in response to a certain prediction score, rule, or biomarker. The net benefit is plotted against a range of possible exchange rates, representing the spectrum of possible patients' and clinicians' preferences. Decision curve analysis is a powerful tool for judging whether newly published or existing scores may truly benefit patients, and represents a significant advancement in improving transparent clinical decision making. This paper is meant to be an introduction to decision curve analysis and its interpretation for clinical investigators. Given the extensive advantages, we advocate applying decision curve analysis to all models intended for use in clinical practice.

Evaluation of the Pattern of Use of a Pediatric Emergency Department in Italy

OBJECTIVES

The aim of this study was to evaluate access to a pediatric emergency department (PED) in a large hospital, in particular to estimate the prevalence of potentially avoidable accesses and the characteristics of return visits.

METHODS

Clinical health records from the PED of San Gerardo Hospital, Monza, Italy, were retrospectively reviewed. The study population was composed of subjects younger than 18 years who attended the PED during the period from October 1, 2017, to November 30, 2017.Accesses were defined nonurgent if characterized by white or green triage codes and patient's discharge as the outcome and were defined potentially avoidable if nonurgent and with no diagnostic/therapeutic procedures performed except a visit by the ED pediatrician.Return visits were defined as accesses that occurred within 72 hours of the first index visit.

RESULTS

A total of 2064 children and adolescents younger than 18 years had at least 1 ED attendance between October and November 2017, for a total of 2364 accesses.The most frequent diagnoses were upper respiratory tract infections (29.5% of accesses), followed by gastroenteritis (7.0%) and abdominal pain (7.0%). In all, 1810 accesses (88%) were classified as "nonurgent," and 1228 (60%) potentially avoidable, 373 of which were probably avoidable because they occurred when the primary care physician was available.The number of return visits was 98 (5% of the accesses): 74 were nonurgent, 31 of which potentially avoidable. On 17 occasions, both index and return visits were potentially avoidable.

CONCLUSIONS

We confirm that most of the accesses to a PED are nonurgent and potentially avoidable. Interventions are needed to improve the appropriateness of use of emergency services.