Clinical Prediction Rules

Original Editor - Alistair James

Top Contributors -

Peer Review - Erik Thoomes (see Discussion page for peer reviews)

Introduction[edit | edit source]

Clinical prediction rules (CPRs) are mathematical tools that are intended to guide physiotherapists in their everyday clinical decision making.[1] CPRs provide physiotherapists with an evidence-based tool to assist in patient management when determining a particular diagnosis or prognosis, or when predicting a response to a particular intervention. In other words, CPRs are diagnostic, prognostic, or interventional/prescriptive. To date, the large majority of CPRs within the physiotherapy literature are prescriptive in nature.[2]The popularity of CPRs has increased greatly over the past few years.[1]

In many ways much of the art of physiotherapy boils down to playing the percentages and predicting outcomes. For example, when physiotherapists do a subjective assessment with a patient they ask the questions that they think are the most likely to provide them with the information they need to make a diagnosis. They might then order the objective assessment tests that they think are the most likely to support or refute their various differential diagnoses. With each new piece of the puzzle some hypotheses will become more likely and others less likely. At the end of the assessment the physiotherapist will decide which intervention is likely to result in the optimal outcome for the patient, based on the information they have collected.[1]

Given that the above process is the underlying principle of physiotherapy clinical practice, and bearing in mind the ever increasing time constraints imposed on physiotherapists, it is unsurprising that a great deal of work has been done to facilitate physiotherapists and patients to make decisions. This work in referred to by many names: CPRs, prediction rules, probability assessments, prediction models, decision rules, risk scores, etc. All describe the combination of multiple predictors, such as patient characteristics and investigation results, to estimate the probability of certain outcomes or to identify which treatment is most likely to be effective.[1]

Despite the increasing popularity of CPRs, they are not without limitations and should be subjected to the scientific scrutiny of continued methodological sound research. Despite the fact that the majority of CPRs useful to physiotherapists exist in the initial stages of development, in the absence of strong evidence, they are capable of proving useful information to the physiotherapist that may in turn enhance patient outcomes. CPRs should not be constructed as removal of the clinical decision-making process from physiotherapy practice. Instead, they should be used to eliminate some of the uncertainty that occurs with each and every clinical encounter and provide a level of evidence on which physiotherapists can make decisions with adequate confidence. The idea is to stick with the principles of evidence-based practice, and to incorporate the best available evidence (including CPRs) combined with clinical expertise and patient preference to improve the overall quality of care provided to individual patients.[2]

CPR Process[3][4][edit | edit source]

There are 3-step process for developing and testing a CPR prior to widespread implementation of the rule in clinical practice. The purpose of this update is to describe the different steps involved in developing and validating CPRs and illustrate how CPRs can be used to improve decision making in physical therapist practice.[4]

  • The First Step: Creating the Clinical Prediction Rule :Researchers and practitioners may initially brainstorm to develop a list of all possible factors that they believe have some predictive value for identifying the condition of interest. Ultimately, a reasonable list of predictors are selected for consideration based on clinical experience and previous research, which demonstrates that the factor or set of factors has some diagnostic or prognostic accuracy.
  • The Second Step: Validating the Clinical Prediction Rule :Before a CPR can be recommended for use in clinical practice, it is necessary to validate the CPR in a “test set” or “validation set” to ensure that similar results are replicated in a different population of patients or in a different health care setting.
  • The Third Step: Conducting an Impact Analysis:a CPR is useful only to the extent that it can improve clinically relevant outcomes, increase patient satisfaction, and decrease costs once it is implemented into the realities of busy clinical practice. The final step in the development of a CPR, therefore, involves assessing the impact of its implementation on practice patterns, outcomes of care, and costs.

Validity and Applicability[edit | edit source]

There is much debate with regards to their validity and clinical applicability and taking in to consideration results from contemporary research, we should caution clinicians in using them.[5][6][7] The results from the available data do not support the use of clinical prediction rules in the management of non-specific low back pain.[5]  The current body of evidence does not enable confident direct clinical application of any of the identified CPRs.[6]  There is, at present, little evidence that CPRs can be used to predict effects of treatment for musculoskeletal conditions. The principal problem is that most studies use designs that cannot differentiate between predictors of response to treatment and general predictors of outcome.[7]  Currently only 1 CPR, the one classifying patients in a group likely to benefit from spinal manipulation, is at the validation stage of development within an randomised control trial (RCT) designed to predict response to treatment.[8][9][10][11]  All other CPRs are still at a derivation level. Validation of these rules is imperative to allow clinical application.

For now, CPRs are in no way able to replace sound clinical reasoning. Assessment of patients should still rely on a continuous process of testing of (multiple) hypotheses through history taking, physical examination using validated clinimetrical instruments and outcome measures incorporated in clinical expertise.[12][13][14]  The P.I.T. demonstrated in this article is sometimes unjustly used as a specific test to include a potential “instability”; clearly that is not it’s function. It should only be used within the specified CPR.

Last but not least, using CPRs clinician tend to classify patients into just one group, where it is highly unlikely that one would treat patients with low back pain with just one single intervention (manipulation). It is more likely that patients will benefit from multimodal therapy incorporating a combination of interventions. A regime of manual therapy and exercise has been shown to be the more effective treatment in many spinal musculoskeletal problems, such as cervicogenic headache, radiculopathy, hip, ankle and shoulder problems.[15][16][17][18][19][20]

So perhaps using a CPR as “hindsight”, to underpin the hypothesis derived after a sound clinical reasoning process, is a better clinical way forward.

Diagnosis[edit | edit source]

Intervention/Prescriptive[edit | edit source]

Prescriptive CPRs are an exponent of the treatment-based system. In this type of diagnostic classification system, a cluster of signs and symptoms from the patient history and physical examination is used to classify patients into subgroups with specific implications for management. As such, it produces homogenous subgroups where all patients within that group are expected to respond favourably to a matched intervention.[2]

References[edit | edit source]

  1. 1.0 1.1 1.2 1.3 Adams ST, Leveson SH. Clinical prediction rules. BMJ 2012; 344.
  2. 2.0 2.1 2.2 Glynn PE, Weisback PC. Prediction Rules: A Physical Therapy Reference Manual. London: Jones and Bartlett Publishers International, 2011.
  3. Childs JD, Cleland JA. Development and application of clinical prediction rules to improve decision making in physical therapist practice. Physical Therapy. 2006;86(1):122-31.
  4. 4.0 4.1 McGinn TG, Guyatt GH, Wyer PC, Naylor CD, Stiell IG, Richardson WS. Evidence-Based Medicine Working Group. Users' guides to the medical literature: XXII: how to use articles about clinical decision rules. Jama. 2000;284(1):79-84.
  5. 5.0 5.1 Patel S, Friede T, Froud R, Evans DW, Underwood M. Systematic review of randomised controlled trials of clinical prediction rules for physical therapy in low back pain. Spine (Phila Pa 1976). 2013;38(9):762-769.
  6. 6.0 6.1 Haskins R, Rivett DA, Osmotherly PG. Clinical prediction rules in the physiotherapy management of low back pain: a systematic review. Man Ther. 2012;17(1):9-21
  7. 7.0 7.1 Stanton TR, Hancock MJ, Maher CG, Koes BW. Critical appraisal of clinical prediction rules that aim to optimize treatment selection for musculoskeletal conditions. Phys Ther. 2010;90(6):843-54.
  8. Childs J, Fritz J, Flynn T, Irrgang JJ, Johnson KK, Majkowski GR, et al. A clinical prediction rule to identify patients with low back pain most likely to benefit from spinal manipulation: a validation study. Ann Intern Med. 2004;141:920–928
  9. Cleland JA, Fritz JM, Whitman JM,  Childs JD, Palmer JA. The use of a lumbar spine manipulation technique by physical therapists in patients who satisfy a clinical prediction rule: a case series. J Orthop Sports Phys Ther. 2006;36:209–214.
  10. Flynn T, Fritz J, Whitman J,  Wainner R, Magel J, Rendeiro D, et al. A clinical prediction rule for classifying patients with low back pain who demonstrate short-term improvement with spinal manipulation. Spine (Phila Pa 1976). 2002;27:2835–2843
  11. Hancock MJ, Maher CG, Latimer J,  Herbert RD, McAuley JH. Independent evaluation of a clinical prediction rule for spinal manipulative therapy: a randomised controlled trial. Eur Spine J. 2008;17:936–943.
  12. Higgs J, Burn A, Jones M. Integrating clinical reasoning and evidence-based practice. AACN Clin Issues. 2001;12(4):482-490
  13. Nijs J, Roussel N, Paul van Wilgen C, Köke A, Smeets R. Thinking beyond muscles and joints: Therapists' and patients' attitudes and beliefs regarding chronic musculoskeletal pain are key to applying effective treatment. Man Ther. 2012;18(2):96-102.
  14. Sackett DL, Straus SE, Richardson WS, Rosenberg W, Haynes RB. Evidence-based medicine: how to practice and teach EBM, 2nd ed. Edinburgh & New York: Churchill Livingstone, 2000. ISBN 0-443-06240-4
  15. Boyles R, Toy P, Mellon J Jr, Hayes M, Hammer B. Effectiveness of manual physical therapy in the treatment of cervical radiculopathy: a systematic review. J Man Manip Ther. 2011;19(3):135-42
  16. Jull G, Trott P, Potter H, Zito G, Niere K, Shirley D, et al. A randomized controlled trial of exercise and manipulative therapy for cervicogenic headache. Spine (Phila Pa 1976). 2002;27(17):1835-1843.
  17. Cools AM, Struyf F, De Mey K, Maenhout A, Castelein B, Cagnie B. Rehabilitation of scapular dyskinesis: from the office worker to the elite overhead athlete. Br J Sports Med. 2014;48(8):692-7.
  18. Abbott JH, Robertson MC, Chapple C, Pinto D, Wright AA, Leon de la Barra S, et al. Manual therapy, exercise therapy, or both, in addition to usual care, for osteoarthritis of the hip or knee: a randomized controlled trial. 1: clinical effectiveness. Osteoarthritis Cartilage. 2013;21(4):525-34.
  19. French HP, Cusack T, Brennan A, Caffrey A, Conroy R, Cuddy V, et al. Exercise and manual physiotherapy arthritis research trial (EMPART) for osteoarthritis of the hip: a multicenter randomized controlled trial. Arch Phys Med Rehabil. 2013;94(2):302-314.
  20. Cleland JA, Mintken PE, McDevitt A, Bieniek ML, Carpenter KJ, Kulp K, et al. Manual Physical Therapy and Exercise Versus Supervised Home Exercise in the Management of Patients Status Post Inversion Ankle Sprain: A Multi-Center Randomized Clinical Trial. J Orthop Sports Phys Ther. 2013; 43(7):443-55