Predictors of Hospitalization Among Newly Admitted Skilled Nursing Facility Residents: Rethinking the Role of Functional Decline

Purpose: Hospital transfer from a skilled nursing facility (SNF) is costly, and many are potentially preventable. This study examines: 1) whether functional decline is a predictor of hospital transfer, and 2) the magnitude of relationships between predictors (functional impairment and chronic medical illness) and hospital transfer from SNFs. Methods: We used Minimum Data Set (MDS) Version 2.0 in the state of Michigan between 2007 and 2009. In total, 196,662 new SNF admissions were observed. Multilevel generalized estimating equations and regression models were performed for each functional and clinical domain while adjusting for demographic variables and change in activities of daily living (ADL). Results: 65% of recently admitted SNF residents experienced functional decline after SNF admission, and 58% were readmitted to a hospital. Residents who needed extensive assistance or were completely dependent in their functional domains had pressure ulcers, deteriorated mood or lower cognitive performance scale scores. These residents experienced higher chances of hospital transfer. However, a deteriorated ADL played a significant role in all multivariate models, indicating that a decline in ADL is a stronger predictor of hospital transfer than other functional or clinical predictors. Conclusion: Although all functional impairments and chronic medical illness can be associated with hospital transfer, functional decline may be the most important predictor of hospital transfer in patients newly admitted to an SNF

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Nursing Home Factors and Their Impact on COVID-19 Cases: A Study of Wisconsin State

COVID-19 has been devastating for Nursing Homes (NHs). The concentration of older adults with underlying chronic conditions inevitably made the setting highly vulnerable leading to high rates of mortality for residents. However, some nursing homes fared better than others. This study examines several quality measures and organizational factors to understand whether these factors are associated with COVID-19 cases in Wisconsin. We combined three datasets from Centers for Medicare & Medicaid Services (CMS) – the Star Rating dataset, Provider Information dataset and COVID-19 Nursing Home dataset. Data used is from the period of Jan 1 – Oct 25, 2020 for the state of Wisconsin. The analysis includes 331 free-standing NHs with no missing values from the data sets. The variables used were self-reported information on nursing home ratings, staff shortage, staff reported hours, occupancy rate, number of beds and ownership. Of the 331 NHs examined, shortages were reported of 25.4%, 31.1%, 3.2% and 15.6% of licensed nurse staff (25.4%), nurse aides (31.1%), clinical staff, (3.2%) and other staff (15.6%) Additionally, there was a significant (p\u3c.05) positive correlation between number of beds and COVID-19 cases, and there was no statistically significant association between occupancy rate and COVID-19 cases. NHs with better star ratings were also found to have less COVID-19 cases. Interestingly, private NHs had significantly higher COVID-19 cases than for-profit and government owned NHs, a finding that is congruent with other studies in this area. Recommendations for practice will be discussed

Predictors of Hospitalization Among Newly Admitted Skilled Nursing Facility Residents: Rethinking the Role of Functional Decline

Purpose: Hospital transfer from a skilled nursing facility (SNF) is costly, and many are potentially preventable. This study examines: 1) whether functional decline is a predictor of hospital transfer, and 2) the magnitude of relationships between predictors (functional impairment and chronic medical illness) and hospital transfer from SNFs. Methods: We used Minimum Data Set (MDS) Version 2.0 in the state of Michigan between 2007 and 2009. In total, 196,662 new SNF admissions were observed. Multilevel generalized estimating equations and regression models were performed for each functional and clinical domain while adjusting for demographic variables and change in activities of daily living (ADL). Results: 65% of recently admitted SNF residents experienced functional decline after SNF admission, and 58% were readmitted to a hospital. Residents who needed extensive assistance or were completely dependent in their functional domains had pressure ulcers, deteriorated mood or lower cognitive performance scale scores. These residents experienced higher chances of hospital transfer. However, a deteriorated ADL played a significant role in all multivariate models, indicating that a decline in ADL is a stronger predictor of hospital transfer than other functional or clinical predictors. Conclusion: Although all functional impairments and chronic medical illness can be associated with hospital transfer, functional decline may be the most important predictor of hospital transfer in patients newly admitted to an SNF

Effects of hospital-based physical therapy on hospital discharge outcomes among hospitalized older adults with community-acquired pneumonia and declining physical function

To examine whether hospital-based physical therapy is associated with functional changes and early hospital readmission among hospitalized older adults with community-acquired pneumonia and declining physical function. Study design was a retrospective observation study. Participants were community-dwelling older adults admitted to medicine floor for community-acquired pneumonia (n = 1,058). Their physical function using Katz activities of daily living (ADL) Index declined between hospital admission and 48 hours since hospital admission (Katz ADL Index 6→5). The intervention group was those receiving physical therapy for ≥ 0.5 hour/day. Outcomes were Katz ADL Index at hospital discharge and all-cause 30-day hospital readmission rate. The intervention and control groups did not differ in the Katz ADL Index at hospital discharge (p = 0.11). All-cause 30-day hospital readmission rate was lower in the intervention than in control groups (OR = 0.65, p = 0.02). Hospital-based physical therapy has the benefits toward reducing 30-day hospital readmission rate of acutely ill older adults with community-acquired pneumonia and declining physical function

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press