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

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

Acoustic habitat and shellfish mapping and monitoring in shallow coastal water - Sidescan sonar experiences in The Netherlands

Sidescan sonar has been applied in a number of shallow water environments along the Dutch coast to map and monitor shellfish and seabed habitats. The littoral setting of these surveys may hamper data acquisition flying the towfish in zones of turbulence and waves, but also offers valuable opportunities for understanding, interpreting and validating sidescan sonar images because of the ability to ground-truth during low water periods, enabling easy identification and validation. Acoustical images of some of the mussel banks on the tidal flats of the Wadden Sea, recorded at high tide, show a marked resemblance with optical Google Earth images of the same banks. These sonar images may thus serve as 'acoustic type signatures' for the interpretation of sonar patterns recorded in deeper water where ground-truthing is more difficult and more expensive. Similarly, acoustic type signatures of (Japanese) oyster banks were obtained in the estuaries in the southwest of the Netherlands. Automated acoustic pattern recognition of different habitats and acoustical estimation of faunal cover and density are possible applications of sidescan sonar. Both require that the backscattering observed on the sidescan sonar images is directly caused by the biological component of the seafloor. Filtering offers a simple and effective pre-processing technique to separate the faunal signals from linear trends such as emanating from wave ripples or the central tracks of the towfish. Acoustically estimating the faunal density is approached by in-situ counting peaks in backscattering in unit squares. These counts must be calibrated by ground-truthing. Ground-truthing on littoral mussel banks in the Wadden Sea has been carried out by measuring their cover along lines during low tide. Due to its capacity of yielding full-cover, high resolution images of large surfaces, sidescan sonar proves to be an excellent, cost-effective tool for quantitative time-lapse monitoring of habitats. © 2009 Elsevier Ltd. All rights reserved

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

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

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

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