CPR for Cervical Radiculopathy

Original Editor - Ben Lippe

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Clinical Prediction Rules[edit | edit source]

CPR: are mathematical tools that are intended to guide clinicians in their everyday decision making.It consist of a combination of medical signs,symptoms and other clinical findings in predicting the probability of specific disease or outcome are determined.[1]

Purpose[edit | edit source]

To assist in the diagnosis of cervical radiculopathy..

Clinical Prediction Rule[edit | edit source]

The following four criteria are considered predictive of the presence of cervical radiculopathy[2]:positive upper limb tension test A (ULTTa), involved-side cervical rotation range of motion less than 60 degrees, positive distraction test, and positive Spurling's test A

Definitions of positive clinical test results are as follows:

  1. ULTTa: Any one of the following: A) symptom reproduction; B) side-to-side difference >10 degrees in elbow extension; or C) with regard to involved/painful side: ipsilateral neck lateral flexion decreases symptoms and/or contralateral neck lateral flexion increases symptoms.
  2. Distraction test:  symptom reduction.
  3. Spurling's A:  symptom reproduction.

 Diagnostic values of results (95% Confidence Intervals) are as follows[2]:

Number of Positive Criteria Sensitivity Specificity Pos LR Neg LR
Two 0.39 (0.16-0.61) 0.56 (0.43-0.68) 0.88 (1.5-2.5) 1.09
Three 0.39 (0.16-0.61) 0.94 (0.88-1.0) 6.1 (2.0-18.6) 0.65
Four 0.24 (0.05-0.43) 0.99 (0.97-1.0) 30.3 (1.7-538.2) 0.77

Pos LR = positive likelihood ratio.  Neg LR = negative likelihood ratio. 

Evidence[edit | edit source]

 A clinical prediction rule (CPR): has been defined as the process by which combinations of clinical findings that have been statistically demonstrated to be meaningful predictors of a condition or outcome of interest are used to categorize a heterogenous group of patients into subgroups based on a shared likelihood of the presence of that condition or outcome.[3] Clinical prediction rules are developed through a structured process and rigorous research in order to arrive at a valid, reliable, and clinically useful tool. It has been suggested that they be developed through a 3-step process of derivation, validation, and impact analysis.[4] Clinical prediction rules are tools that aid clinicians in clinical decision making; they are not intended as a replacement for clinical judgement. Rather, they are intended to be used in conjunction with clinical judgement, clinical examination, patient history, and other parts of the evaluative process to arrive at a clinically meaningful decision about diagnosis or treatment.

In 2003, Wainner and colleagues: identified a CPR ("test item cluster" or "TIC" in the study) for the presence of cervical radiculopathy.[2] Eighty-two consecutive patients across 4 medical facilities referred to the electrophysiological laboratory with suspected diagnosis of cervical radiculopathy (CR) or carpal tunnel syndrome were enrolled in their study. Various patient report and clinical examination variables were analyzed and compared to a reference criterion of needle EMG and nerve conduction study for diagnosis of CR. Binary logistical regression was used to identify the most accurate TIC for diagnosing CR, described above. Readers are referred to the referenced study for detailed reading of the development of this CPR.

Waldrop 2006[5]: describes a case series (6 patients) in which Wainner's CPR was used in the diagnosis of CR. Patients were treated using postural education, thoracic thrust joint manipulation, exercise, and cervical traction. Patients were treated for an average of 10 visits over an average of 33 days. At discharge, reduction in disability scores ranged from 13% to 88%, 5 of 6 patients demonstrated normal cervical spine motion, and 4 of 6 patients did not test positive for any of the CPR criteria.

References[edit | edit source]

  1. Adams ST, Leveson SH. Clinical prediction rules. Bmj. 2012 Jan 16;344:d8312.
  2. 2.0 2.1 2.2 Wainner RS, Irrgang JJ, Boninger ML, Delitto A, Allison S. Reliability and diagnostic accuracy of the clinical examination and patient self-report measures for cervical radiculopathy. Spine 2003;28(1):52-62.
  3. Randolph et al, cited in: Beattie P and Nelson R. Clinical prediction rules--what are they and what do they tell us? Australian J of Physiotherapy 2006;52:157-163.
  4. Childs JD, Cleland JA. Development and application of clinical prediction rules to improve decision making in physical therapist practice. Phys Ther 2006;86(1):122-131.
  5. Waldrop MA. Diagnosis and treatment of cervical radiculopathy using a clinical prediction rule and a multimodal intervention approach: a case series. J Orth Sports Phys Ther 2006;36(3):152-159.