In order to deliver effective care, health management must consider the distinctive trajectories of chronic diseases. These diseases recurrently undergo acute, unstable, and stable phases, each of which requires a different treatment regimen. However, the correct identification of trajectory phases, and thus treatment regimens, is challenging. In this paper, we propose a data-driven, dynamic approach for identifying trajectory phases of chronic diseases and thus suggesting treatment regimens. Specifically, we develop a novel variable-duration copula hidden Markov model (VDC-HMMX). In our VDC-HMMX, the trajectory is modeled as a series of latent states with acute, stable, and unstable phases, which are eventually recovered. We demonstrate the effectiveness of our VDC-HMMX model on the basis of a longitudinal study with 928 patients suffering from low back pain. A myopic classifier identifies correct treatment regimens with a balanced accuracy of slightly above 70%. In comparison, our VDC-HMMX model is correct with a balanced accuracy of 83.65%. This thus highlights the value of longitudinal monitoring for chronic disease management.

Background Manual therapy is a cornerstone of chiropractic education, whereby students work towards a level of skill and expertise that is regarded as competent to work within the field of chiropractic. Due to the COVID-19 pandemic, chiropractic programs in every region around the world had to make rapid changes to the delivery of manual therapy technique education, however what those changes looked like was unknown. Aims The aims of this study were to describe the immediate actions made by chiropractic programs to deliver education for manual therapy techniques and to summarise the experience of academics who teach manual therapy techniques during the initial outbreak of COVID-19 pandemic. Methods A qualitative descriptive approach was used to describe the immediate actions made by chiropractic programs to deliver manual therapy technique education during the COVID-19 pandemic. Chiropractic programs were identified from the webpages of the Councils on Chiropractic Education International and the Council on Chiropractic Education – USA. Between May and June 2020, a convenience sample of academics who lead or teach in manual therapy technique in those programs were invited via email to participate in an online survey with open-ended questions. Responses were entered into the NVivo software program and analysed using a reflexive thematic analysis by a qualitative researcher independent to the data collection. Results Data from 16 academics in 13 separate chiropractic programs revealed five, interconnected themes: Immediate response; Move to online delivery; Impact on learning and teaching; Additional challenges faced by educators; and Ongoing challenges post lockdown. Conclusion This study used a qualitative descriptive approach to describe how some chiropractic programs immediately responded to the initial outbreak of the COVID-19 pandemic in their teaching of manual therapy techniques. Chiropractic programs around the world provided their students with rapid, innovative learning strategies, in an attempt to maintain high standards of chiropractic education; however, challenges included maintaining student engagement in an online teaching environment, psychomotor skills acquisition and staff workload.

Background Currently, there are no outcome measures assessing the ability of people with non-specific low back pain to self-manage their illness. Inspired by the ‘Patient Enablement Instrument’, we developed the Patient Enablement Instrument for Back Pain (PEI-BP). The aim of this study was to describe the development of the Patient Enablement Instrument for Back Pain (PEI-BP) and investigate content validity, construct validity, internal consistency, test–retest reliability, measurement error, responsiveness and floor and ceiling effects. Methods The PEI-BP consists of 6 items that are rated on a 0–10 Numeric Rating Scale. Measurement properties were evaluated using the COSMIN taxonomy and were based on three cohorts from primary care with low back pain: The content validity cohort (N = 14) which participated in semi-structured interviews, the GLA:D® Back cohort (N = 272) and the test–retest cohort (N = 37) which both completed self-reported questionnaires. For construct validity and responsiveness, enablement was compared to disability (Oswestry Disability Index), back pain beliefs (Brief Illness Perception Questionnaire), fear avoidance (Fear-Avoidance Beliefs Questionnaire—physical activity), mental health (SF-36), educational level and number of previous episodes of low back pain. Results The PEI-BP was found to have acceptable content validity, construct validity, reliability (internal consistency, test–retest reliability and measurement error) and responsiveness. The Smallest Detectable Change was 10.1 points illustrating that a patient would have to change more than 1/6 of the scale range for it to be a true change. A skewed distribution towards the high scores were found at baseline indicating a potentially problematic ceiling effect in the current population. Conclusions The PEI-BP can be considered a valid and reliable tool to measure enablement on people seeking care for non-specific LBP. Further testing of the PEI-BP in populations with more severe LBP is recommended. Trial registration : Not applicable.

Clinician guidelines recommend health management to tailor the form of care to the expected course of diseases. Hence, in order to decide upon a suitable treatment plan, health professionals benefit from decision support, i.e., predictions about how a disease is to evolve. In clinical practice, such a prediction model requires interpretability. Interpretability, however, is often precluded by complex dynamic models that would be capable of capturing the intrapersonal variability of disease trajectories. Therefore, we develop a cross-sectional ARMA model that allows for inference of the expected course of symptoms. Distinct from traditional time series models, it generalizes to cross-sectional settings and thus patient cohorts (i.e., it is estimated to multiple instead of single disease trajectories). Our model is evaluated according to a longitudinal 52-week study involving 928 patients with low back pain. It achieves a favorable prediction performance while maintaining interpretability. In sum, we provide decision support by informing health professionals about whether symptoms will have the tendency to stabilize or continue to be severe.

OBJECTIVES:A conceptually oriented preprocessing of a large number of potential prognostic factors may improve the development of a prognostic model. This study investigated whether various forms of conceptually oriented preprocessing or the preselection of established factors was superior to using all factors as input.STUDY DESIGN AND SETTING:We made use of an existing project that developed two conceptually oriented subgroupings of low back pain patients. Based on the prediction of six outcome variables by seven statistical methods, this type of preprocessing was compared with medical experts' preselection of established factors, as well as using all 112 available baseline factors.RESULTS:Subgrouping of patients was associated with low prognostic capacity. Applying a Lasso-based variable selection to all factors or to domain-specific principal component scores performed best. The preselection of established factors showed a good compromise between model complexity and prognostic capacity.CONCLUSION:The prognostic capacity is hard to improve by means of a conceptually oriented preprocessing when compared to purely statistical approaches. However, a careful selection of already established factors combined in a simple linear model should be considered as an option when constructing a new prognostic rule based on a large number of potential prognostic factors.

Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the ‘comparator variables’).

Background: Heterogeneity in patients with low back pain (LBP) is well recognised and different approaches to subgrouping have been proposed. Latent Class Analysis (LCA) is a statistical technique that is increasingly being used to identify subgroups based on patient characteristics. However, as LBP is a complex multi-domain condition, the optimal approach when using LCA is unknown. Therefore, this paper describes the exploration of two approaches to LCA that may help improve the identification of clinically relevant and interpretable LBP subgroups. Methods: From 928 LBP patients consulting a chiropractor, baseline data were used as input to the statistical subgrouping. In a single-stage LCA, all variables were modelled simultaneously to identify patient subgroups. In a twostage LCA, we used the latent class membership from our previously published LCA within each of six domains of health (activity, contextual factors, pain, participation, physical impairment and psychology) (first stage) as the variables entered into the second stage of the two-stage LCA to identify patient subgroups. The description of the results of the single-stage and two-stage LCA was based on a combination of statistical performance measures, qualitative evaluation of clinical interpretability (face validity) and a subgroup membership comparison. Results: For the single-stage LCA, a model solution with seven patient subgroups was preferred, and for the two-stage LCA, a nine patient subgroup model. Both approaches identified similar, but not identical, patient subgroups characterised by (i) mild intermittent LBP, (ii) recent severe LBP and activity limitations, (iii) very recent severe LBP with both activity and participation limitations, (iv) work-related LBP, (v) LBP and several negative consequences and (vi) LBP with nerve root involvement. Conclusions: Both approaches identified clinically interpretable patient subgroups. The potential importance of these subgroups needs to be investigated by exploring whether they can be identified in other cohorts and by examining their possible association with patient outcomes. This may inform the selection of a preferred LCA approach.

BACKGROUND:Latent class analysis (LCA) is increasingly being used in health research, but optimal approaches to handling complex clinical data are unclear. One issue is that commonly used questionnaires are multidimensional, but expressed as summary scores. Using the example of low back pain (LBP), the aim of this study was to explore and descriptively compare the application of LCA when using questionnaire summary scores and when using single items to subgrouping of patients based on multidimensional data.MATERIALS AND METHODS:Baseline data from 928 LBP patients in an observational study were classified into four health domains (psychology, pain, activity, and participation) using the World Health Organization's International Classification of Functioning, Disability, and Health framework. LCA was performed within each health domain using the strategies of summary-score and single-item analyses. The resulting subgroups were descriptively compared using statistical measures and clinical interpretability.RESULTS:For each health domain, the preferred model solution ranged from five to seven subgroups for the summary-score strategy and seven to eight subgroups for the single-item strategy. There was considerable overlap between the results of the two strategies, indicating that they were reflecting the same underlying data structure. However, in three of the four health domains, the single-item strategy resulted in a more nuanced description, in terms of more subgroups and more distinct clinical characteristics.CONCLUSION:In these data, application of both the summary-score strategy and the single-item strategy in the LCA subgrouping resulted in clinically interpretable subgroups, but the single-item strategy generally revealed more distinguishing characteristics. These results 1) warrant further analyses in other data sets to determine the consistency of this finding, and 2) warrant investigation in longitudinal data to test whether the finer detail provided by the single-item strategy results in improved prediction of outcomes and treatment response.