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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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Background: Post-hospitalization transition interventions remain a priority in preventing rehospitalization. However, not all patients referred for readmission prevention interventions receive them. We sought to 1) define patient characteristics associated with non-receipt of readmission prevention interventions (among those eligible for them), and 2) determine whether these same patient characteristics are associated with hospital readmission at the state level.Methods: We used state-wide data from the Maryland Health Services Cost Review Commission to determine patient-level factors associated with state-wide readmissions. Concurrently, we conducted a retrospective analysis of discharged patients referred to receive 1 of 3 post-discharge interventions between January 2013 and July 2019-a nurse transition guide, post-discharge phone call, or follow-up appointment in our post-discharge clinic-to determine patient-level factors associated with not receiving the intervention. Multivariable generalized estimating equation logistic regression models were used to calculate the odds of not accepting or not receiving the interventions.Results: Older age, male gender, black race, higher expected readmission rate, and lower socioeconomic status were significantly associated with 30-day readmission in hospitalized Maryland patients. Most of these variables (age, sex, race, payer type [Medicaid or non-Medicaid], and socioeconomic status) were also associated with non-receipt of intervention.Conclusions: We found that many of the same patient-level characteristics associated with the highest readmission risk are also associated with non-receipt of readmission reduction interventions. This highlights the paradox that patients at high risk of readmission are least likely to accept or receive interventions for preventing readmission. Identifying strategies to engage hard-to-reach high-risk patients continues to be an unmet challenge in readmission prevention. [ABSTRACT FROM AUTHOR]