Predictors of dislocations after reverse shoulder arthroplasty: a study by the ASES complications of RSA multicenter research group.

BACKGROUND: Instability after reverse shoulder arthroplasty (RSA) is one of the most frequent complications and remains a clinical challenge. Current evidence is limited by small sample size, single-center, or single-implant methodologies that limit generalizability. We sought to determine the incidence and patient-related risk factors for dislocation after RSA, using a large, multicenter cohort with varying implants. METHODS: A retrospective, multicenter study was performed involving 15 institutions and 24 American Shoulder and Elbow Surgeons members across the United States. Inclusion criteria consisted of patients undergoing primary or revision RSA between January 2013 and June 2019 with minimum 3-month follow-up. All definitions, inclusion criteria, and collected variables were determined using the Delphi method, an iterative survey process involving all primary investigators requiring at least 75% consensus to be considered a final component of the methodology for each study element. Dislocations were defined as complete loss of articulation between the humeral component and the glenosphere and required radiographic confirmation. Binary logistic regression was performed to determine patient predictors of postoperative dislocation after RSA. RESULTS: We identified 6621 patients who met inclusion criteria with a mean follow-up of 19.4 months (range: 3-84 months). The study population was 40% male with an average age of 71.0 years (range: 23-101 years). The rate of dislocation was 2.1% (n = 138) for the whole cohort, 1.6% (n = 99) for primary RSAs, and 6.5% (n = 39) for revision RSAs (P < .001). Dislocations occurred at a median of 7.0 weeks (interquartile range: 3.0-36.0 weeks) after surgery with 23.0% (n = 32) after a trauma. Patients with a primary diagnosis of glenohumeral osteoarthritis with an intact rotator cuff had an overall lower rate of dislocation than patients with other diagnoses (0.8% vs. 2.5%; P < .001). Patient-related factors independently predictive of dislocation, in order of the magnitude of effect, were a history of postoperative subluxations before radiographically confirmed dislocation (odds ratio [OR]: 19.52, P < .001), primary diagnosis of fracture nonunion (OR: 6.53, P < .001), revision arthroplasty (OR: 5.61, P < .001), primary diagnosis of rotator cuff disease (OR: 2.64, P < .001), male sex (OR: 2.21, P < .001), and no subscapularis repair at surgery (OR: 1.95, P = .001). CONCLUSION: The strongest patient-related factors associated with dislocation were a history of postoperative subluxations and having a primary diagnosis of fracture nonunion. Notably, RSAs for osteoarthritis showed lower rates of dislocations than RSAs for rotator cuff disease. These data can be used to optimize patient counseling before RSA, particularly in male patients undergoing revision RSA.

Capturing patient mobility levels in the hospital: An examination of nursing charting and behavioural mapping.

AIMS: Our study aimed to (1) validate the accuracy of nursing mobility documentation and (2) identify the most effective timings for behavioural mapping. DESIGN: We monitored the mobility of 55 inpatients using behavioural mapping throughout a nursing day shift, comparing the observed mobility levels with the nursing charting in the electronic health record during the same period. RESULTS: Our results showed a high level of agreement between nursing records and observed mobility, with improved accuracy observed particularly when documentation was at 12 PM or later. Behavioural mapping observations revealed that the most effective timeframe to observe the highest levels of patient mobility was between 10 AM AND 2 PM. CONCLUSION: To truly understand patient mobility, comparing nursing charting with methods like behavioural mapping is beneficial. This comparison helps evaluate how well nursing records reflect actual patient mobility and offers insights into the best times for charting to capture peak mobility. While behavioural mapping is a valuable tool for auditing patient mobility, its high resource demands limit its regular use. Thus, determining the most effective times and durations for observations is key for practical implementation in hospital mobility audits. IMPLICATION FOR THE PROFESSION AND/OR PATIENT CARE: Nurses are pivotal in ensuring patient mobility in hospitals, an essential element of quality care. Their role involves safely mobilizing patients and accurately charting their mobility levels during each shift. For nursing practice, this research underscores that nurse charting can accurately reflect patient mobility, and highlights that recording the patient's highest level of mobility later in the shift offers a more precise representation of their actual mobility. REPORTING METHOD: Strobe. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Instrument adaptation and preliminary validation study of the Laryngeal Hypersensitivity Questionnaire used for assessment of laryngeal symptoms in patients with artificial airways.

BACKGROUND: The Newcastle Laryngeal Hypersensitivity Questionnaire (LHQ) was developed to measure abnormal laryngeal sensation and was originally validated in a patient sample from otolaryngologic and respiratory outpatient clinics. Modification is needed for patients who are mechanically ventilated via an endotracheal tube or a tracheostomy tube. OBJECTIVES: We sought to adapt and preliminarily validate a modified version of the LHQ appropriate for nurses and other clinicians to administer in acute hospital settings called the LHQ-Acute (LHQ-A). METHODS: Internal consistency and construct validity analyses using secondary data from patients at a tertiary teaching hospital who presented with symptoms of laryngeal irritability/hypersensitivity between September 2012 and October 2013 were performed. RESULTS: A total of 131 patients, most complaining of coughing and dysphonia, with a median age of 58 (interquartile range: 48, 66) years and 29 healthy participants with a median age of 62 (interquartile range: 50, 66) years were analysed. The original LHQ was reduced from 14 questions with responses on a 7-point scale to the LHQ-A containing 13 questions with responses on a 4-point scale. Correlations between items of the LHQ and LHQ-A were similar, and internal consistency was excellent and highly comparable, with Cronbach's alpha = 0.906 and 0.902, respectively. CONCLUSIONS: The LHQ-A, which has been adapted for nurses and other clinicians to administer to a critically ill patient population, demonstrated comparable reliability and validity to the original LHQ. Validation of the LHQ-A in independent patient populations from acute settings is necessary to better understand norms and changes during recovery from acute illness.

Participant retention in follow-up studies of intensive care unit survivors - A scoping review.

OBJECTIVE: To synthesize participant retention data and related reporting in studies evaluating post-hospital outcomes of survivors of critical illness after an intensive care unit (ICU) stay. REVIEW METHOD USED: A synthesis of literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist. DATA SOURCES: PubMed, EMBASE, PsycINFO, Cumulative Index of Nursing and Allied Health Literature, and the Cochrane Controlled Trials Registry. Hand searched reference lists and personal files of relevant narrative and systematic review articles. REVIEW METHODS: Articles were screened by pairs of independent reviewers. Similarly, data were abstracted by pairs of data collectors, with conflicts resolved by consensus or by a third reviewer. RESULTS: We included 243 publications, from 225 unique studies of 87,602 participants. Participant retention could not be calculated for any time-points in 13% of studies nor in 22% of all follow-up time-points. Retention ranged from 18-100%. When compared to follow-up before 1-month, retention at each later timepoint was not significantly different. Age and sex were not associated with retention and more recent studies had decreased retention (odds ratio: 0.94 [95% confidence interval: 0.92-0.96; p < 0.001]). Reporting of retention-related study methodology was inconsistent. CONCLUSION: Retention rate could not be calculated for 22% of study follow-up time-points, with retention at the remaining time-points generally being high (≥85%), but with high variability (18% - 100%). ICU survivorship research could be improved via: (i) more detailed guidance on reporting participant retention, and (ii) use of existing resources and best practices to facilitate better study design and to improve participant retention to preserve statistical power and reduce selection bias.

Dynamic Prediction of Post-Acute Care Needs for Hospitalized Medicine Patients.

OBJECTIVES: Use patient demographic and clinical characteristics at admission and time-varying in-hospital measures of patient mobility to predict patient post-acute care (PAC) discharge. DESIGN: Retrospective cohort analysis of electronic medical records. SETTING AND PARTICIPANTS: Patients admitted to the two participating Hospitals from November 2016 through December 2019 with ≥72 hours in a general medicine service. METHODS: Discharge location (PAC vs home) was the primary outcome, and 2 time-varying measures of patient mobility, Activity Measure for Post-Acute Care (AM-PAC) Mobility "6-clicks" and Johns Hopkins Highest Level of Mobility, were the primary predictors. Other predictors included demographic and clinical characteristics. For each day of hospitalization, we predicted discharge to PAC using the demographic and clinical characteristics and most recent mobility data within a random forest (RF) for survival, longitudinal, and multivariate (RF-SLAM) data. A regression tree for the daily predicted probabilities of discharge to PAC was constructed to represent a global summary of the RF. RESULTS: There were 23,090 total patients and compared to PAC, those discharged home were younger (64 vs 71), had shorter length of stay (5 vs 8 days), higher AM-PAC at admission (43 vs 32), and average AM-PAC throughout hospitalization (45 vs 35). AM-PAC was the most important predictor, followed by age, and whether the patient lives alone. The area under the hospital day-specific receiver operating characteristic curve ranged from 0.76 to 0.79 during the first 5 days. The global summary tree explained 75% of the variation in predicted probabilities for PAC from the RF. Sensitivity (75%), specificity (70%), and accuracy (72%) were maximized at a PAC probability threshold of 40%. CONCLUSIONS AND IMPLICATIONS: Daily assessment of patient mobility should be part of routine practice to help inform care planning by hospital teams. Our prediction model could be used as a valuable tool by multidisciplinary teams in the discharge planning process.

Inaccuracy of Initial Clinical Mobility Assessment in Venous Thromboembolism Risk Stratification.

BACKGROUND: Venous thromboembolism risk increases in hospitals due to reduced patient mobility. However, initial mobility evaluations for thromboembolism risk are often subjective and lack standardization, potentially leading to inaccurate risk assessments and insufficient prevention. METHODS: A retrospective study at a quaternary academic hospital analyzed patients using the Padua risk tool, which includes a mobility question, and the Johns Hopkins-Highest Level of Mobility (JH-HLM) scores to objectively measure mobility. Reduced mobility was defined as JH-HLM scores ≤3 over ≥3 consecutive days. The study evaluated the association between reduced mobility and hospital-acquired venous thromboembolism using multivariable logistic regression, comparing admitting health care professional assessments with JH-HLM scores. Symptomatic, hospital-acquired thromboembolisms were diagnosed radiographically by treating providers. RESULTS: Of 1715 patients, 33 (1.9%) developed venous thromboembolism. Reduced mobility, as determined by the JH-HLM scores, showed a significant association with thromboembolic events (adjusted OR: 2.53, 95%CI:1.23-5.22, P = .012). In contrast, the initial Padua assessment of expected reduced mobility at admission did not. The JH-HLM identified 19.1% of patients as having reduced mobility versus 6.5% by admitting health care professionals, suggesting 37 high-risk patients were misclassified as low risk and were not prescribed thrombosis prophylaxis; 4 patients developed thromboembolic events. JH-HLM detected reduced mobility in 36% of thromboembolic cases, compared to 9% by admitting health care professionals. CONCLUSION: Initial mobility evaluations by admitting health care professionals during venous thromboembolism risk assessment may not reflect patient mobility over their hospital stay. This highlights the need for objective measures like JH-HLM in risk assessments to improve accuracy and potentially reduce thromboembolism incidents.