Challenges and solutions in prognostic prediction models in spinal disorders

Wingbermühle, Roel W.; Chiarotto, Alessandro; Koes, Bart W.; Heymans, Martijn W.; Trijffel, Emiel van

doi:10.1016/j.jclinepi.2020.12.017

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Only few studies investigated predictors such as blood measures, [59] magnetic resonance imaging, [62] and sensor data [63] . These findings are in line with Wingbermühle et al [66] and Wartenberg et al , [67] which both identified gaps in the investigation of a broad range of possible predictors including biological and physical, as well as psychosocial measures, and especially in the use of directly observable predictors such as imaging, biomarkers, and genetics. In terms of covered ICF components, the integration of body structures and contextual factors in prediction models remains scarce.…”

Section: Discussionsupporting

confidence: 86%

“…Only two identified studies were multi-centre studies and the respective population samples focused on traumatic aetiology and tend to include predominantly men and persons with tetraplegia, which limits the generalizability of the developed prediction models. Due to the complex and multidimensional nature of functioning in SCI, prediction models based on new methods such as machine learning techniques are promising and may allow a dynamic and real time modelling of interactions among a variety of predictors [66] . Beyond the findings of our review, also other methods are deployed in SCI prediction research, such as artificial neural network analysis [68] or individual growth curve models [69] .…”

Section: Discussionmentioning

confidence: 99%

“…However, the applicability and usefulness of these methods needs to be further investigated [70] . To do so, large data sets, ideally designed specifically for prediction research, including a broad variety of predictors and appropriately reflecting the population under study are needed [66] .…”

Section: Discussionmentioning

confidence: 99%

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The potential of prediction models of functioning remains to be fully exploited: A scoping review in the field of spinal cord injury rehabilitation

Hodel

Stucki

Prodinger

2021

Journal of Clinical Epidemiology

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Objective: The study aimed to explore existing prediction models of functioning in spinal cord injury (SCI). Study Design and Setting: The databases PubMed, EBSCOhost CINAHL Complete , and IEEE Xplore were searched for relevant literature. The search strategy included published search filters for prediction model and impact studies, index terms and keywords for SCI, and relevant outcome measures able to assess functioning as reflected in the International Classification of Functioning, Disability and Health (ICF). The search was completed in October 2020.Results: We identified seven prediction model studies reporting twelve prediction models of functioning. The identified prediction models were mainly envisioned to be used for rehabilitation planning, however, also other possible applications were stated. The method predominantly used was regression analysis and the investigated predictors covered mainly the ICF components of body functions and activities and participation , next to characteristics of the health condition and health interventions.Conclusion: Findings suggest that the development of prediction models of functioning for use in clinical practice remains to be fully exploited. By providing a comprehensive overview of what has been done, this review informs future research on prediction models of functioning in SCI and contributes to an efficient use of research evidence.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

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The potential of prediction models of functioning remains to be fully exploited: A scoping review in the field of spinal cord injury rehabilitation

Hodel

Stucki

Prodinger

2021

Journal of Clinical Epidemiology

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“…For optimal adoption in daily practice of such a new prognostic tool, it is conditional that it consists of only a limited number of predictors in order to minimize the burden for patients and clinicians, is integrated within an online clinical decision support system and is easy to interpret [ 24 , 25 ]. The recently introduced artificial intelligence (AI)-based machine learning (ML) techniques have been suggested to be very promising and potentially able to result in a breakthrough in LBP (non-)recovery prediction [ 26 , 27 ]. ML – in comparison to traditional regression analysis – is considered to be more flexible and pragmatic in handling complex datasets with large number of predictors (and their interactions), without strict rules regarding sample sizes and missing values [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Development and internal validation of a machine learning prediction model for low back pain non-recovery in patients with an acute episode consulting a physiotherapist in primary care

Knoop

Lankveld

Beijer

et al. 2022

BMC Musculoskelet Disord

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Background While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling. Methods Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (≤ one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis. Results Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice. Conclusions We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.

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Prognosis of Patients With Neuropathic Low Back-Related Leg Pain: An Exploratory Study Using Prospective Data From UK Primary Care

Harrisson,

Ogollah,

Dunn

et al. 2024

The Journal of Pain

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Challenges and solutions in prognostic prediction models in spinal disorders

Cited by 8 publications

References 35 publications

The potential of prediction models of functioning remains to be fully exploited: A scoping review in the field of spinal cord injury rehabilitation

The potential of prediction models of functioning remains to be fully exploited: A scoping review in the field of spinal cord injury rehabilitation

Development and internal validation of a machine learning prediction model for low back pain non-recovery in patients with an acute episode consulting a physiotherapist in primary care

Prognosis of Patients With Neuropathic Low Back-Related Leg Pain: An Exploratory Study Using Prospective Data From UK Primary Care

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