Persistent Pain After Wrist or Hand Fracture: Development and Validation of a Prognostic Model

Prognostic factors for persistent pain after a distal radius fracture: a systematic review

Introduction

The aim of this systematic review was to synthesize the evidence regarding prognostic factors for persistent pain, including Complex Regional Pain Syndrome (CRPS), after a distal radius fracture (DRF), a common condition after which persistent pain can develop.

Methods

Medline, Pubmed, Embase, Psychinfo, CINAHL, BNI, AMED and the Cochrane Register of Clinical Trials were searched from inception to May 2021 for prospective longitudinal prognostic factor studies investigating persistent pain in adults who had sustained a DRF. The Quality in Prognostic Studies (QUIPS) tool and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework were used to assess the strength of evidence.

Results

A search yielded 440 studies of which 7 studies met full eligibility criteria. From five studies we found low evidence for high baseline pain or an ulnar styloid fracture as prognostic factors for persistent pain, and very low evidence for diabetes or older age. From two studies, investigating an outcome of CRPS, there was low evidence for high baseline pain, slow reaction time, dysynchiria, swelling and catastrophising as prognostic factors, and very low evidence for depression. Sex was found not to be a prognostic factor for CRPS or persistent pain.

Conclusions

The associations between prognostic factors and persistent pain following a DRF are unclear. The small number of factors investigated in more than one study, along with poor reporting and methodological limitations contributed to an assessment of low to very low strength of evidence. Further prospective studies, investigating psychosocial factors as candidate predictors of multidimensional pain outcomes are recommended.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.