Data-driven dynamic treatment planning for chronic diseases

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.ISSN:0377-2217ISSN:1872-686

Longitudinal Healthcare Analytics for Disease Management: Empirical Demonstration for Low Back Pain

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.ISSN:0167-9236ISSN:1873-579

Exploring conceptual preprocessing for developing prognostic models: A case study in low back pain patients

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.ISSN:0895-435

Additional file 1: Table A1. of Latent class analysis derived subgroups of low back pain patients – do they have prognostic capacity?

Presentation of Latent Class Analysis derived domain-specific patient categories for each of the six health domains. Table A2a. Baseline characteristics of Latent Class Analysis derived single-stage subgroups. Table A2b. Baseline characteristics of Latent Class Analysis derived two-stage subgroups. Table A3. Test of differences between responders and non-responders on the follow-up questionnaires. Table A4. Test of differences between compliant responders and non-compliant responders on the SMS questions over 12 months. (DOCX 55 kb

COVID-19: How has a global pandemic changed manual therapy technique education in chiropractic programs around the world?

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

Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity?

Background: 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'). Methods: This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above. Results: The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and from 6.8%-20.3% for disability, and highest at the 2 weeks follow-up. Conclusions: Latent Class-derived subgroups provided additional prognostic information when compared to a range of variables, but the improvements were not substantial enough to warrant further development into a new prognostic tool. Further research could investigate if these novel subgrouping approaches may help to improve existing tools that subgroup low back pain patients