Prognostic Factors of Perceived Disability and Perceived Recovery After Whiplash: A Longitudinal, Prospective Study With One-year Follow-up

OBJECTIVES

The understanding of the role that cognitive and emotional factors play in how an individual recovers from a whiplash injury is important. Hence, we sought to evaluate whether pain-related cognitions (self-efficacy beliefs, expectation of recovery, pain catastrophizing, optimism, and pessimism) and emotions (kinesiophobia) are longitudinally associated with the transition to chronic whiplash-associated disorders in terms of perceived disability and perceived recovery at 6 and 12 months.

METHODS

One hundred sixty-one participants with acute or subacute whiplash-associated disorder were included. The predictors were: self-efficacy beliefs, expectation of recovery, pain catastrophizing, optimism, pessimism, pain intensity, and kinesiophobia. The 2 outcomes were the dichotomized scores of perceived disability and recovery expectations at 6 and 12 months. Stepwise regression with bootstrap resampling was performed to identify the predictors most strongly associated with the outcomes and the stability of such selection.

RESULTS

Baseline perceived disability, pain catastrophizing, and expectation of recovery were the most likely to be statistically significant, with an overage frequency of 87.2%, 84.0%, and 84.0%, respectively.

CONCLUSION

Individuals with higher expectations of recovery and lower levels of pain catastrophizing and perceived disability at baseline have higher perceived recovery and perceived disability at 6 and 12 months. These results have important clinical implications as both factors are modifiable through health education approaches.

Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients

BACKGROUND

CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient response. In the era of personalised oncology, new approaches for developing predictive models of response are needed.

MATERIALS AND METHODS

Data derived from the electronic health records (EHRs) of real-world patients with HR+/HER2-negative advanced breast cancer were used to develop predictive models for early and late progression to first-line treatment. Two machine learning approaches were used: a classic approach using a data set of manually extracted features from reviewed (EHR) patients, and a second approach using natural language processing (NLP) of free-text clinical notes recorded during medical visits.

RESULTS

Of the 610 patients included, there were 473 (77.5%) progressions to first-line treatment, of which 126 (20.6%) occurred within the first 6 months. There were 152 patients (24.9%) who showed no disease progression before 28 months from the onset of first-line treatment. The best predictive model for early progression using the manually extracted dataset achieved an area under the curve (AUC) of 0.734 (95% CI 0.687-0.782). Using the NLP free-text processing approach, the best model obtained an AUC of 0.758 (95% CI 0.714-0.800). The best model to predict long responders using manually extracted data obtained an AUC of 0.669 (95% CI 0.608-0.730). With NLP free-text processing, the best model attained an AUC of 0.752 (95% CI 0.705-0.799).

CONCLUSIONS

Using machine learning methods, we developed predictive models for early and late progression to first-line treatment of HR+/HER2-negative metastatic breast cancer, also finding that NLP-based machine learning models are slightly better than predictive models based on manually obtained data.

Prediction of early progression (EP) to CDKIs first line treatment in ER+/HER2- metastatic breast cancer (MBC) using machine learning

e13040

Background: The treatment of luminal MBC has undergone a substantial change with the use of cyclin dependent kinase 4/6 inhibitors (CDKIs). Nevertheless, there is not a clearly defined subgroup of patients who do not initially respond to CDKIs and show EP. Methods: MBC ER+/HER2- patients who have received at least one line of treatment were eligible. The event of interest was disease progression within 6 months of first line treatment according to the type of therapy administered. The first line treatments were categorized in chemotherapy (CT), hormonal therapy (HT), CT plus maintenance HT and HT plus CDKIs. Free text data from clinical visits registered in our Electronic Health Record were obtained until the date of first treatment in order to generate a feature vector composed of the word frequencies for each visit of every patient. Six different machine learning algorithms were evaluated to predict the event of interest and to obtain the risk of EP for every type of therapy. Area under the ROC curve (AUC), True Positive Rate (TPR) and True Negative Rate (TNR) were assessed using 10-fold cross validation. Results: 610 RE+/HER2- MBC treated between November 1991 and August 2019 were included. Median follow up for metastatic disease was 28 months. 17426 clinical visits were analyzed (per patient: range 1-173; median 30). 119 patients received CT as first line treatment, 311 HT, 117 CT plus maintenance HT and 63 HT plus CDKIs. There were 379 patients with disease progression, from which 126 were within 6 months from first line treatment (54 events with CT, 57 with HT, 4 with CT plus maintenance HT and 11 with HT plus CDKIs). The model that yields the best results was the GLMBoost algorithm: AUC 0.72 (95%CI 0.67-0.77), TPR 70.85% (95%CI 70.63%-71.06%), TNR 66.27% (95% 66.08%-66.46%). Conclusions: Our model based on unstructured data from real-world patients predicts EP and establishes the risk for each of the different types of treatment for MBC ER+/HER2-. Obviously an additional validation is needed, but a tool with these characteristics could help to select the best available treatment when that decision has to be made, avoiding those therapies that are probably not to be effective.

Predicting the risk of VISIT emergency department (ED) in lung cancer patients using machine learning

2042

Background: Lung cancer patients commonly need unplanned visits to ED. Many of these visits could be potentially avoidable if it were possible to identify patients at risk when the previous scheduled visit takes place. At that moment, it would be possible to perform elective actions to manage patients at risk to consult the ED in the near future. Methods: Unplanned visits of patients in active cancer therapy (i.e. chemo or immunotherapy) are attended in our own ED facilities. Our Electronic Health Record (EHR) includes specific modules for first visit, scheduled visits and unplanned visits. Lung cancer patients with at least two visits were eligible. The event of interest was patient visit to ED within 21 or 28 days (d) from previous visit. Free text data collected in the three modules were obtained from EHR in order to generate a feature vector composed of the word frequencies for each visit. We evaluate five different machine learning algorithms to predict the event of interest. Area under the ROC curve (AUC), F1 (harmonic mean of precision and recall), True Positive Rate (TPR) and True Negative Rate (TNR) were assessed using 10-fold cross validation. Results: 2,682 lung cancer patients treated between March 2009 and October 2019 were included from which 819 patients were attended at ED. There were 2,237 first visits, 47,465 scheduled visits (per patient: range 1-174; median 12) and 2,125 unplanned visits (per patient: range 1-20; median 2). Mean age at diagnosis was 64 years. The majority of patients had late stage disease (34.24 % III, 51.56 % IV). The Adaptive Boosting Model yields the best results for both 21 d or 28 d prediction. Conclusions: Using unstructured data from real-world EHR enables the possibility to build an accurate predictive model of unplanned visit to an ED within the 21 or 28 following d after a scheduled visit. Such utility would be very useful in order to prevent ED visits related with cancer symptoms and to improve patients care. [Table: see text]

Combining Strengths for Multi-genome Visual Analytics Comparison

The eclosion of data acquisition technologies has shifted the bottleneck in molecular biology research from data acquisition to data analysis. Such is the case in Comparative Genomics, where sequence analysis has transitioned from genes to genomes of several orders of magnitude larger. This fact has revealed the need to adapt software to work with huge experiments efficiently and to incorporate new data-analysis strategies to manage results from such studies. In previous works, we presented GECKO, a software to compare large sequences; now we address the representation, browsing, data exploration, and post-processing of the massive amount of information derived from such comparisons. GECKO-MGV is a web-based application organized as client-server architecture. It is aimed at visual analysis of the results from both pairwise and multiple sequences comparison studies combining a set of common commands for image exploration with improved state-of-the-art solutions. In addition, GECKO-MGV integrates different visualization analysis tools while exploiting the concept of layers to display multiple genome comparison datasets. Moreover, the software is endowed with capabilities for contacting external-proprietary and third-party services for further data post-processing and also presents a method to display a timeline of large-scale evolutionary events. As proof-of-concept, we present 2 exercises using bacterial and mammalian genomes which depict the capabilities of GECKO-MGV to perform in-depth, customizable analyses on the fly using web technologies. The first exercise is mainly descriptive and is carried out over bacterial genomes, whereas the second one aims to show the ability to deal with large sequence comparisons. In this case, we display results from the comparison of the first Homo sapiens chromosome against the first 5 chromosomes of Mus musculus.