Effect of the surgical safety checklist on provider and patient outcomes: a systematic review.

BACKGROUND: Despite being implemented for over a decade, literature describing how the surgical safety checklist (SSC) is completed by operating room (OR) teams and how this relates to its effectiveness is scarce. This systematic review aimed to: (1) quantify how many studies reported SSC completion versus described how the SSC was completed; (2) evaluate the impact of the SSC on provider outcomes (Communication, case Understanding, Safety Culture, CUSC), patient outcomes (complications, mortality rates) and moderators of these relationships. METHODS: A systematic literature search was conducted using Medline, CINAHL, Embase, PsycINFO, PubMed, Scopus and Web of Science on 10 January 2020. We included providers who treat human patients and completed any type of SSC in any OR or simulation centre. Statistical directional findings were extracted for provider and patient outcomes and key factors (eg, attentiveness) were used to determine moderating effects. RESULTS: 300 studies were included in the analysis comprising over 7 302 674 operations and 2 480 748 providers and patients. Thirty-eight per cent of studies provided at least some description of how the SSC was completed. Of the studies that described SSC completion, a clearer positive relationship was observed concerning the SSC's influence on provider outcomes (CUSC) compared with patient outcomes (complications and mortality), as well as related moderators. CONCLUSION: There is a scarcity of research that examines how the SSC is completed and how this influences safety outcomes. Examining how a checklist is completed is critical for understanding why the checklist is successful in some instances and not others.

Predicting Short-Term Risk of Falls in a High-Risk Group With Dementia.

OBJECTIVES: To develop a prognostic model to predict the probability of a short-term fall (within the next 7 to 30 days) in older adults with dementia. DESIGN: Prospective observational study. SETTING AND PARTICIPANTS: Fifty-one individuals with dementia at high risk of falls from a specialized dementia inpatient unit. METHODS: Clinical and demographic measures were collected and a vision-based markerless motion capture was used to record the natural gait of participants over a 2-week baseline. Falls were tracked throughout the length of stay. Cox proportional hazard regression analysis was used to build a prognostic model to determine fall-free survival probabilities at 7 days and at 30 days. The model's discriminative ability was also internally validated. RESULTS: Fall history and gait stability (estimated margin of stability) were statistically significant predictors of time to fall and included in the final prognostic model. The model's predicted survival probabilities were close to observed values at both 7 and 30 days. The area under the receiver operating curve was 0.80 at 7 days, and 0.67 at 30 days and the model had a discrimination performance (the Harrel concordance index) of 0.71. CONCLUSIONS AND IMPLICATIONS: Our short-term falls risk model had fair to good predictive and discrimination ability. Gait stability and recent fall history predicted an imminent fall in our population. This provides some preliminary evidence that the degree of gait instability may be measureable in natural everyday gait to allow dynamic falls risk monitoring. External validation of the model using a separate data set is needed to evaluate model's predictive performance.

Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data.

BACKGROUND: Parkinsonism is common in people with dementia, and is associated with neurodegenerative and vascular changes in the brain, or with exposure to antipsychotic or other dopamine antagonist medications. The detection of parkinsonian changes to gait may provide an opportunity to intervene and address reversible causes. In this study, we investigate the use of a vision-based system as an unobtrusive means to assess severity of parkinsonism in gait. METHODS: Videos of walking bouts of natural gait were collected in a specialized dementia unit using a Microsoft Kinect sensor and onboard color camera, and were processed to extract sixteen 3D and eight 2D gait features. Univariate regression to gait quality, as rated on the Unified Parkinson's Disease Rating Scale (UPDRS) and Simpson-Angus Scale (SAS), was used to identify gait features significantly correlated to these clinical scores for inclusion in multivariate models. Multivariate ordinal logistic regression was subsequently performed and the relative contribution of each gait feature for regression to UPDRS-gait and SAS-gait scores was assessed. RESULTS: Four hundred one walking bouts from 14 older adults with dementia were included in the analysis. Multivariate ordinal logistic regression models incorporating selected 2D or 3D gait features attained similar accuracies: the UPDRS-gait regression models achieved accuracies of 61.4 and 62.1% for 2D and 3D features, respectively. Similarly, the SAS-gait models achieved accuracies of 47.4 and 48.5% with 2D or 3D gait features, respectively. CONCLUSIONS: Gait features extracted from both 2D and 3D videos are correlated to UPDRS-gait and SAS-gait scores of parkinsonism severity in gait. Vision-based systems have the potential to be used as tools for longitudinal monitoring of parkinsonism in residential settings.

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia.

Fall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait assessments based on gait data that is collected frequently and unobtrusively, and analysed using computer vision methods. Recent developments in computer vision have led to the availability of open source human pose estimation algorithms, which automatically estimate the joint locations of a person in an image. In this study, a pre-existing pose estimation model was applied to 1066 walking videos collected of 31 older adults with dementia as they walked naturally in a corridor on a specialized dementia unit over a two week period. Using the tracked pose information, gait features were extracted from video recordings of gait bouts and their association with clinical mobility assessment scores and future falls data was examined. A significant association was found between extracted gait features and a clinical mobility assessment and the number of future falls, providing concurrent and predictive validation of this approach.

Vision-Based Assessment of Gait Features Associated With Falls in People With Dementia.

BACKGROUND: Gait impairments contribute to falls in people with dementia. In this study, we used a vision-based system to record episodes of walking over a 2-week period as participants moved naturally around their environment, and from these calculated spatiotemporal, stability, symmetry, and acceleration gait features. The aim of this study was to determine whether features of gait extracted from a vision-based system are associated with falls, and which of these features are most strongly associated with falling. METHODS: Fifty-two people with dementia admitted to a specialized dementia unit participated in this study. Thirty different features describing baseline gait were extracted from Kinect recordings of natural gait over a 2-week period. Baseline clinical and demographic measures were collected, and falls were tracked throughout the participants' admission. RESULTS: A total of 1,744 gait episodes were recorded (mean 33.5 ± 23.0 per participant) over a 2-week baseline period. There were a total of 78 falls during the study period (range 0-10). In single variable analyses, the estimated lateral margin of stability, step width, and step time variability were significantly associated with the number of falls during admission. In a multivariate model controlling for clinical and demographic variables, the estimated lateral margin of stability (p = .01) was remained associated with number of falls. CONCLUSIONS: Information about gait can be extracted from vision-based recordings of natural walking. In particular, the lateral margin of stability, a measure of lateral gait stability, is an important marker of short-term falls risk.