Measuring functional outcome in Dupuytren's disease: A systematic review of patient-reported outcome measures

Responsiveness of the patient-specific Canadian occupational performance measure and a fixed-items activity limitations measure in patients with dupuytren disease

BACKGROUND

Patients with Dupuytren disease experience various activity limitations. Treatment aims to reduce finger joint contractures to improve hand function and activity performance. For assessing improvement different patient-centered measures have been used. The Canadian Occupational Performance Measure (COPM) was developed as an interview-based outcome measure to detect changes over time in patients' perception of their performance and satisfaction in self-identified activity issues. The 11-item disabilities of the arm, shoulder and hand (QuickDASH) scale consists of fixed items that ask patients to rate the difficulty in performing specific daily activities. Few studies have compared the responsiveness of these two types of patient-reported measures in Dupuytren disease.

PATIENTS AND METHODS

We included 30 patients with Dupuytren disease enrolled in a prospective cohort study of collagenase injection. We used the COPM (score range 1-10), the QuickDASH (score range 0-100) and measurement of finger joint contracture before and 5 weeks after treatment.

RESULTS

Using the COPM the patients identified 107 activity problems (55 in self-care, 19 in productivity and 33 in leisure). The two most common activity problems were to wash self (21 patients) and to don gloves (19 patients). A clinically important improvement with 3 points or greater from baseline to 5 weeks was seen for performance in 77 activities (72%). The median COPM performance score improved from 4.4 at baseline to 9.0 at 5 weeks and the median QuickDASH score improved from 13.6 to 2.5. Responsiveness (Cohen's d) for the COPM performance was 2.6 (95% CI 1.9-3.3) and for the QuickDASH 0.6 (95% CI 0.1-1.1).

CONCLUSION

The COPM had about 6-fold larger responsiveness than the QuickDASH, which supports use of an individualized measure when assessing treatment effects in Dupuytren disease.

QuickDASH questionnaire items behave as 2 distinct subscales rather than one scale in Dupuytren's disease

STUDY DESIGN

Retrospective cohort BACKGROUND: Exploratory Factor Analysis (EFA) and structural equation modelling (SEM) assess relationships between questionnaire items and the constructs ("factors") measured by a questionnaire. The QuickDASH has not been subjected to these analyses in Dupuytren's disease.

PURPOSE

To undertake EFA and SEM to identify the factors measured by the QuickDASH in patients with Dupuytren's disease.

METHODS

We identified 750 cases of surgery for Dupuytren's disease at a single center with preoperative QuickDASH scores. We performed EFA on QuickDASH responses in R, using established methodology. Based on the EFA results, we conducted SEM in a training sample of 200 participants. A test SEM analysis was performed in a second, independent sample of 200 participants.

RESULTS

EFA suggested a 2-factor model. Items 1-6 measured one factor (we interpreted this as "hand function"), whereas items 9-11 measured a different factor ("hand symptoms"). Items 7 and 8 (social and work activities) did not reflect either of these factors well, and may be influenced by other variables. A structural equation model based on the EFA results, with 2 first-order factors, demonstrated excellent fit in our first SEM sample. This was confirmed with a second independent sample in a test analysis.

CONCLUSIONS

The QuickDASH PROM may measure 2 distinct factors in patients with Dupuytren's disease. This aligns with previous analyses of the full-length DASH PROM. Separation of the QuickDASH PROM into 2 sub-scales with distinct scores to measure "hand function" and "hand symptoms" may improve its structural validity in patients with Dupuytren's disease.

Psychometric properties of the Psychosocial Impact of Assistive Devices Scale (PIADS): A systematic review

BACKGROUND

The Psychosocial Impact of Assistive Devices Scale (PIADS) is commonly used to assess the psychosocial effects of an assistive device. Given its growing use, an appraisal of the evidence regarding its psychometric properties is required.

PURPOSE

To conduct a systematic review using validated critical appraisal scales to analyze both the quality and content of the evidence on the psychometric properties of the PIADS.

METHODS

PubMed/Medline, Embase and CINAHL were systematically searched for identification of studies. Two independent reviewers appraised the retrieved studies using MacDermid and COSMIN-RoB checklists, and extracted data regarding the psychometric measurements reported.

RESULTS

MacDermid scores showed that 8 out of 11 studies were at least of good methodological quality. COSMIN-RoB scores ranged from inadequate to very good. Except criterion and construct validity, which have presented a moderate level of evidence, the other psychometric properties assessed have demonstrated a high level of evidence. Cross-cultural validity, measurement error and responsiveness have not been studied.

CONCLUSION

Few studies have yet evaluated the psychometric properties of the PIADS. However, the quality of the evidence that they provide is mostly adequate. Therefore, this review supports the use of the PIADS which has overall good psychometric properties.

Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks

The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.