Detecting scaphoid fractures in wrist injury: a clinical decision rule

Wouter H Mallee, M M J Walenkamp, M A M Mulders, J C Goslings, N W L Schep, Wouter H Mallee, M M J Walenkamp, M A M Mulders, J C Goslings, N W L Schep

Abstract

Introduction: The aim of this study was to develop and validate an easy to use clinical decision rule, applicable in the ED that limits the number of unnecessary cast immobilizations and diagnostic follow-up in suspected scaphoid injury, without increasing the risk of missing fractures.

Methods: A prospective multicenter study was conducted that consisted of three components: (1) derivation of a clinical prediction model for detecting scaphoid fractures in adult patients following wrist trauma; (2) internal validation of the model; (3) design of a clinical decision rule. The predictors used were: sex, age, swelling of the anatomic snuffbox, tenderness in the anatomic snuffbox, scaphoid tubercle tenderness, painful ulnar deviation and painful axial thumb compression. The outcome measure was the presence of a scaphoid fracture, diagnosed on either initial radiographs or during re-evaluation after 1-2 weeks or on additional imaging (radiographs/MRI/CT). After multivariate logistic regression analysis and bootstrapping, the regression coefficient for each significant predictor was calculated. The effect of the rule was determined by calculating the number of missed scaphoid fractures and reduction of suspected fractures that required a cast.

Results: A consecutive series of 893 patients with acute wrist injury was included. Sixty-eight patients (7.6%) were diagnosed with a scaphoid fracture. The final prediction rule incorporated sex, swelling of the anatomic snuffbox, tenderness in the anatomic snuffbox, painful ulnar deviation and painful axial thumb compression. Internal validation of the prediction rule showed a sensitivity of 97% and a specificity of 20%. Using this rule, a 15% reduction in unnecessary immobilization and imaging could be achieved with a 50% decreased risk of missing a fracture compared with current clinical practice.

Conclusions: This dataset provided a simple clinical decision rule for scaphoid fractures following acute wrist injury that limits unnecessary immobilization and imaging with a decreased risk of missing a fracture compared to current clinical practice.

Clinical prediction rule: 1/(1 + EXP (-(0.649662618 × if man) + (0.51353467826 × if swelling anatomic snuffbox) + (-0.79038263985 × if painful palpation anatomic snuffbox) + (0.57681198857 × if painful ulnar deviation) + (0.66499549728 × if painful thumb compression)-1.685).

Trial registration: Trial register NTR 2544, www.trialregister.nl.

Keywords: Clinical evaluation; Decision rule; Diagnosis; Fracture; Predictors; Scaphoid.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Flowchart of patient inclusion and exclusion

References

    1. Bleeker SE-MHA-S, Donders EW, Derksen-Lubsen ART, Grobbee G, Moons DE, K.G. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol. 2003;56:826–832. doi: 10.1016/S0895-4356(03)00207-5.
    1. Cummings P. Missing data and multiple imputation. JAMA Pediatr. 2013;167(7):656–661. doi: 10.1001/jamapediatrics.2013.1329.
    1. Duckworth AD, Buijze GA, Moran M, et al. Predictors of fracture following suspected injury to the scaphoid. J Bone Joint Surg Br. 2012;94(7):961–968. doi: 10.1302/0301-620X.94B7.28704.
    1. Duckworth AD, Ring D, McQueen MM. Assessment of the suspected fracture of the scaphoid. J Bone Joint Surg Br. 2011;93(6):713–719. doi: 10.1302/0301-620X.93B6.26506.
    1. Janssen KJ, Donders AR, Harrell FE, Jr, et al. Missing covariate data in medical research: to impute is better than to ignore. J Clin Epidemiol. 2010;63(7):721–727. doi: 10.1016/j.jclinepi.2009.12.008.
    1. Jenkins PJ, Slade K, Huntley JS, Robinson CM. A comparative analysis of the accuracy, diagnostic uncertainty and cost of imaging modalities in suspected scaphoid fractures. Injury. 2008;39(7):768–774. doi: 10.1016/j.injury.2008.01.003.
    1. Mallee WH, Henny EP, van Dijk CN, Kamminga SP, van Enst WA, Kloen P. Clinical diagnostic evaluation for scaphoid fractures: a systematic review and meta-analysis. J Hand Surg Am. 2014;39(9):1683–1691. doi: 10.1016/j.jhsa.2014.06.004.
    1. Mallee WH, Wang J, Poolman RW, et al. Computed tomography versus magnetic resonance imaging versus bone scintigraphy for clinically suspected scaphoid fractures in patients with negative plain radiographs. Cochrane Database Syst Rev. 2015;6:CD010023.
    1. Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98(9):691–698. doi: 10.1136/heartjnl-2011-301247.
    1. Parvizi J, Wayman J, Kelly P, Moran CG. Combining the clinical signs improves diagnosis of scaphoid fractures. A prospective study with follow-up. J Hand Surg (Edinburgh, Scotland) 1998;23(3):324–327. doi: 10.1016/S0266-7681(98)80050-8.
    1. Rhemrev SJ, Beeres FJ, van Leerdam RH, Hogervorst M, Ring D. Clinical prediction rule for suspected scaphoid fractures: a prospective cohort study. Injury. 2010;41(10):1026–1030. doi: 10.1016/j.injury.2010.03.029.
    1. Silveira PC, Ip IK, Sumption S, Raja AS, Tajmir S, Khorasani R. Impact of a clinical decision support tool on adherence to the Ottawa Ankle Rules. Am J Emerg Med. 2016;34(3):412–418. doi: 10.1016/j.ajem.2015.11.028.
    1. Slaar A, Walenkamp MM, Bentohami A, et al. A clinical decision rule for the use of plain radiography in children after acute wrist injury: development and external validation of the Amsterdam Pediatric Wrist Rules. Pediatr Radiol. 2016;46(1):50–60. doi: 10.1007/s00247-015-3436-3.
    1. Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. doi: 10.1136/bmj.b2393.
    1. Steyerberg E. Evaluation of performance anonymous clinical prediction models, a practical approach to development, validation, and updating. New York: Springer; 2009. pp. 270–279.
    1. Steyerberg E. Overfitting and optimism in prediction models anonymous clinical prediction models, a practical approach to development, validation, and updating. New York: Springer; 2009. pp. 94–96.
    1. Steyerberg E. Study design for prediction models clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009. pp. 50–51.
    1. Steyerberg EW, Harrell FE, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–781. doi: 10.1016/S0895-4356(01)00341-9.
    1. Stiell IG, Bennett C. Implementation of clinical decision rules in the emergency department. Acad Emerg Med. 2007;14(11):955–959. doi: 10.1197/j.aem.2007.06.039.
    1. Walenkamp MM, Bentohami A, Slaar A, et al. The Amsterdam wrist rules: the multicenter prospective derivation and external validation of a clinical decision rule for the use of radiography in acute wrist trauma. BMC Musculoskelet Disord. 2015;16:389. doi: 10.1186/s12891-015-0829-2.

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться