Estimating the Risk of Chronic Pain: Development and Validation of a Prognostic Model (PICKUP) for Patients with Acute Low Back Pain

Abstract

BackgroundLow back pain (LBP) is a major health problem. Globally it is responsible for the most years lived with disability. The most problematic type of LBP is chronic LBP (pain lasting longer than 3 mo); it has a poor prognosis and is costly, and interventions are only moderately effective. Targeting interventions according to risk profile is a promising approach to prevent the onset of chronic LBP. Developing accurate prognostic models is the first step. No validated prognostic models are available to accurately predict the onset of chronic LBP. The primary aim of this study was to develop and validate a prognostic model to estimate the risk of chronic LBP.Methods and FindingsWe used the PROGRESS framework to specify a priori methods, which we published in a study protocol. Data from 2,758 patients with acute LBP attending primary care in Australia between 5 November 2003 and 15 July 2005 (development sample, n = 1,230) and between 10 November 2009 and 5 February 2013 (external validation sample, n = 1,528) were used to develop and externally validate the model. The primary outcome was chronic LBP (ongoing pain at 3 mo). In all, 30% of the development sample and 19% of the external validation sample developed chronic LBP. In the external validation sample, the primary model (PICKUP) discriminated between those who did and did not develop chronic LBP with acceptable performance (area under the receiver operating characteristic curve 0.66 [95% CI 0.63 to 0.69]). Although model calibration was also acceptable in the external validation sample (intercept = −0.55, slope = 0.89), some miscalibration was observed for high-risk groups. The decision curve analysis estimated that, if decisions to recommend further intervention were based on risk scores, screening could lead to a net reduction of 40 unnecessary interventions for every 100 patients presenting to primary care compared to a “treat all” approach. Limitations of the method include the model being restricted to using prognostic factors measured in existing studies and using stepwise methods to specify the model. Limitations of the model include modest discrimination performance. The model also requires recalibration for local settings.ConclusionsBased on its performance in these cohorts, this five-item prognostic model for patients with acute LBP may be a useful tool for estimating risk of chronic LBP. Further validation is required to determine whether screening with this model leads to a net reduction in unnecessary interventions provided to low-risk patients.

Can screening instruments accurately determine poor outcome risk in adults with recent onset low back pain? A systematic review and meta-analysis

“…If the authors chose to calculate AUC values, we offered further instruction on how to do so. The primary outcome of this study was pain intensity at follow-up; poor outcome was pain ≥ 3 on an 11-point NRS, which was based on Grotle et al [25] and Traeger et al [26], and follows evidence that many people with scores of < 3 consider themselves to be recovered [27]. All study authors who reported obtaining pain NRS scores were requested to dichotomise pain outcomes according to this definition.…”

Primary care management of non‐specific low back pain: key messages from recent clinical guidelines

“…edu.au/pain-self-checks/orebro-musculoskeletal-pain-screeningquestionnaire/) 15 and PICKUP (http:/ /pickuptool.neura.edu.au). 16 These tools are freely available online; at present, it is unclear which is best.…”

“…A newer approach, endorsed in the UK National Institute for Health and Care Excellence 7 and Belgian 9 guidelines, uses simple risk prediction tools to match patients to treatment packages based on their risk of poor clinical outcome: STarT Back (http://www.keele.ac.uk/sbst/startbacktool), 14 Örebro Musculoskeletal Pain Screening Questionnaire (https://painhealth.csse.uwa.edu.au/pain-self-checks/orebro-musculoskeletal-pain-screening-questionnaire/) 15 and PICKUP (http://pickuptool.neura.edu.au). 16 These tools are freely available online; at present, it is unclear which is best.…”

Prevalence, incidence, and risk factors for shoulder and neck dysfunction after neck dissection: A systematic review

“…The prevalence rates for shoulder pain were higher after RND (range, 10-100%) [2][3][4][5][17][18][19][20][21] compared with MRND (range, 0-100%) [2-5, 17-19, 21] and SND (range, 9-25%) [2,5,22]. However, there is some evidence to suggest an uncertain relationship between extent of surgery and pain-related disability of the shoulder [9] that aligns with the known understanding of the multifactorial nature of pain and disability [46][47][48]. The large variation in incidence and prevalence rates likely reflects the differences in the methods, analysis and definitions used between studies rather than true differences in the rates.…”