Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty.

BACKGROUND: Practice based research and learning networks (PBRLNs) are groups of learning communities that focus on improving delivery and quality of care. Accurate data from primary care electronic medical records (EMRs) is crucial in forming the backbone for PBRLNs. The purpose of this work is to: (1) report on descriptive findings from recent frailty work, (2) describe strategies for working across PBRLNs in primary care, and (3) provide lessons learned for engaging PBRLNs. METHODS: We carried out a participatory based descriptive study that engaged five different PBRLNs. We collected Clinical Frailty Scale scores from a sample of participating physicians within each PBRLN. Descriptive statistics were used to analyze frailty scores and patients' associated risk factors and demographics. We used the Consolidated Framework for Implementation Research to inform thematic analysis of qualitative data (meeting minutes, notes, and conversations with co-investigators of each network) in recognizing challenges of working across networks. RESULTS: One hundred nine physicians participated in collecting CFS scores across the five provinces (n = 5466). Percentages of frail (11-17%) and not frail (82-91%) patients were similar in all networks, except Ontario who had a higher percentage of frail patients (25%). The majority of frail patients were female (65%) and had a significantly higher prevalence of hypertension, dementia, and depression. Frail patients had more prescribed medications and numbers of healthcare encounters. There were several noteworthy challenges experienced throughout the research process related to differences across provinces in the areas of: numbers of stakeholders/staff involved and thus levels of burden, recruitment strategies, data collection strategies, enhancing engagement, and timelines. DISCUSSION: Lessons learned throughout this multi-jurisdictional work included: the need for continuity in ethics, regular team meetings, enhancing levels of engagement with stakeholders, the need for structural support and recognizing differences in data sharing across provinces. CONCLUSION: The differences noted across CPCSSN networks in our frailty study highlight the challenges of multi-jurisdictional work across provinces and the need for consistent and collaborative healthcare planning efforts.

Machine learning for identification of frailty in Canadian primary care practices.

INTRODUCTION: Frailty is a medical syndrome, commonly affecting people aged 65 years and over and is characterized by a greater risk of adverse outcomes following illness or injury. Electronic medical records contain a large amount of longitudinal data that can be used for primary care research. Machine learning can fully utilize this wide breadth of data for the detection of diseases and syndromes. The creation of a frailty case definition using machine learning may facilitate early intervention, inform advanced screening tests, and allow for surveillance. OBJECTIVES: The objective of this study was to develop a validated case definition of frailty for the primary care context, using machine learning. METHODS: Physicians participating in the Canadian Primary Care Sentinel Surveillance Network across Canada were asked to retrospectively identify the level of frailty present in a sample of their own patients (total n = 5,466), collected from 2015-2019. Frailty levels were dichotomized using a cut-off of 5. Extracted features included previously prescribed medications, billing codes, and other routinely collected primary care data. We used eight supervised machine learning algorithms, with performance assessed using a hold-out test set. A balanced training dataset was also created by oversampling. Sensitivity analyses considered two alternative dichotomization cut-offs. Model performance was evaluated using area under the receiver-operating characteristic curve, F1, accuracy, sensitivity, specificity, negative predictive value and positive predictive value. RESULTS: The prevalence of frailty within our sample was 18.4%. Of the eight models developed to identify frail patients, an XGBoost model achieved the highest sensitivity (78.14%) and specificity (74.41%). The balanced training dataset did not improve classification performance. Sensitivity analyses did not show improved performance for cut-offs other than 5. CONCLUSION: Supervised machine learning was able to create well performing classification models for frailty. Future research is needed to assess frailty inter-rater reliability, and link multiple data sources for frailty identification.

Opposite perceptual and sensorimotor responses to a size-weight illusion.

The perceptual size-weight illusion (SWI) occurs when two different-sized objects with equal mass are lifted in sequence: the smaller object is consistently reported to feel heavier than the larger object even after repeated lifting attempts. Here we explored the relationship between sensorimotor and perceptual responses to a SWI in which the smaller of the two target objects in fact weighed slightly less (2.7 N) than the larger object (3.2 N). For 20 consecutive lifts, participants consistently reported that the small-light object felt heavier than the large-heavy object; however, concurrently measured lifting dynamics showed exactly the opposite pattern: peak grip force, peak grip force rate, peak load force, and peak load force rate were all significantly greater for the large-heavy object versus the small-light object. The difference in peak load rate between the two objects was greatest for the initial lift but decreased significantly beyond that point, suggesting that the sensorimotor system used sensory feedback to correct for initial over- and underestimations of object mass. Despite these adjustments to lifting dynamics over the early trials, the difference between the judged heaviness of the two objects did not change. The findings clearly demonstrate that the sensorimotor and perceptual systems utilize distinctly different mechanisms for determining object mass.