Frailty in hospitalized adults

The purpose of this cross-sectional, retrospective, descriptive study was to characterize frailty in hospitalized adults 55 years of age and older admitted to medical units at one large academic medical center during a 15-month time frame and determine if level of frailty on admission predicted length of stay (LOS) and 30-day readmission. Frailty is a syndrome characterized by multisystem physiologic dysregulation due to intrinsic and extrinsic stressors resulting in decreased compensatory reserve and ability to effectively respond to destabilizing health events. Stressors associated with hospitalization may increase risk for frailty or accelerate its development. Frailty is a significant concern as it is associated with morbidity, functional decline, long LOS, readmission, institutionalization, and mortality. There is scant research on frailty in acutely-ill hospitalized adults, especially those ¡Ý 65 years of age. Understanding frailty in this population is imperative because frailty is potentially preventable, treatable, and reversible. Frailty was operationalized as 14 evidence-based frailty components defined by 26 indicator variables. Frailty components were Nutrition, Weakness, Fatigue, Chronic Pain, Dyspnea, Falls, Vision, Depression, Cognition, Social Support, low Hemoglobin, low Albumin, high C-reactive protein (CRP) or hs-CRP, and abnormal WBC count. Each frailty component was scored as one point if at least one indicator variable was present on admission, and summed to derive a Frailty Score, where a higher Frailty Score suggests greater level of frailty (range, 0 to 14). Sociodemographic, clinical, and laboratory data were retrieved from the electronic medical record through web-based data query tools and record review (N = 278). Mean age was 70.2 (SD = 1.3; range, 55¨C98), slightly over half were female, 64% were White, one-third were Black. The mean comorbidity count was 13 (SD = 4.56; range. 1¨C26) and medication count was 12 (SD = 5.2; range, 0¨C31). The most prevalent frailty components (> 81%) were Fatigue, Weakness, Nutrition, Hemoglobin, Albumin, and CRP or hs-CRP. The mean Frailty Score was 9.03 (SD = 1.98; range, 2¨C13). Multiple linear regression was performed with 20 predictor variables and the Frailty Score and then with 14 of the 20 predictor variables that were significant in bivariate linear regression with the Frailty Score using the ENTER and STEPWISE method. All multiple regression models yielded seven significant predictor variables. Six predictors were common to all models: comorbidity, acute pain, ADL assistance, urinary incontinence, Braden Scale score, current tobacco use. In multiple regression with 20 predictors, age was a significant predictor however in multiple regression using ENTER and STEPWISE for 14 predictors, female gender was significant but not age. Results from STEPWISE regression yielded seven significant predictors that explained 27% of the variance in the Frailty Score (adj. R2 = .266, df (14, 263), F = 8.163, p = .000). Mean LOS was 9.92 days (SD = 9.58; range, 1¨C72; median, 7; mode, 5). Simple linear regression for the Frailty Score and log10 transformed LOS was statistically significant (adj. R2 = .090, df (1, 276), F = 29.293, p = .000). Twelve percent experienced 30-day readmission. Logistic regression conducted for the Frailty Score and 30-day readmission was not statistically significant (X 2 = 4.121, df (5), p = .532; ¦Â coefficient = .100, df (1), 95% CI = .913¨C1.1337, p = .307). The Frailty Score characterized this hospitalized population as acutely ill with high comorbidity, symptom burden, nutrition deficits and evidence of physiologic vulnerability and inflammation. Study findings have implications for nursing practice, interdisciplinary collaboration, education, research, and public policy

Frailty risk in hospitalised older adults with and without diabetes mellitus

Background: Research indicates that diabetes mellitus (DM) may be a risk factor for frailty and individuals with DM are more likely to be frail than individuals without DM; however, there is limited research in hospitalised older adults.Objectives: To determine the extent of frailty in hospitalised older adults with and without DM using a 16-item Frailty Risk Score (FRS) and assess the role of frailty in predicting 30-day rehospitalisation, discharge to an institution and in-hospital mortality.Methods: The study was a retrospective, cohort, correlational design and secondary analysis of a data set consisting of electronic health record data. The sample was older adults hospitalised on medicine units. Logistic regression was performed for 30-day rehospitalisation and discharge location. Cox proportional hazards regression was used to analyse time to in-hospital death and weighted using propensity scores.Results: Of 278 hospitalised older adults, 49% had DM, and the mean FRS was not significantly different by DM status (9.6 vs. 9.1, p = 0.07). For 30-day rehospitalisation, increased FRS was associated with significantly increased odds of rehospitalisation (AOR = 1.24, 95% CI [1.01, 1.51], p = 0.04). Although 81% were admitted from home, 57% were discharged home and 43% to an institution. An increased FRS was associated with increased odds of discharge to an institution (AOR = 1.48, 95% CI [1.26, 1.74], p Conclusion: Diabetes mellitus and frailty were highly prevalent; the mean FRS was not significantly different by DM status. Although increased frailty was significantly associated with rehospitalisation and discharge to an institution, only DM was significantly associated with in-hospital mortality.Relevance to clinical practice: Frailty assessment may augment clinical assessment and facilitate tailoring care and determining optimal outcomes in patients with and without DM

MDS Coordinator Relationships and Nursing Home Care Processes

The purpose of this study was to describe how Minimum Data Set (MDS) Coordinators' relationship patterns influence nursing home care processes. The MDS Coordinator potentially interacts with staff across the nursing home to coordinate care processes of resident assessment and care planning. We know little about how MDS Coordinators enact this role or to what extent they may influence particular care processes beyond paper compliance. Guided by complexity science and using two nursing home case studies as examples (pseudonyms Sweet Dell and Safe Harbor), we describe MDS Coordinators' relationship patterns by assessing the extent to which they used and fostered the relationship parameters of good connections, new information flow, and cognitive diversity in their work. Sweet Dell MDS Coordinators fostered new information flow, good connections, and cognitive diversity, which positively influenced assessment and care planning. In contrast, Safe Harbor MDS Coordinators did little to foster good connections, information flow, or cognitive diversity with little influence on care processes. This study revealed that MDS Coordinators are an important new source of capacity for the nursing home industry to improve quality of care. Findings suggest ways to enhance this capacity

Staff perceptions of staff-family interactions in nursing homes

Each year thousands of older adults are admitted to nursing homes. Following admission, nursing home staff and family members must interact and communicate with each other. This study examined relationship and communication patterns between nursing home staff members and family members of nursing home residents, as part of a larger multi-method comparative case study. Here, we report on 6- month case studies of two nursing homes where in-depth interviews, shadowing experiences, and direct observations were completed. Staff members from both nursing homes described staff-family interactions as difficult, problematic and time consuming, yet identified strategies that when implemented consistently, influenced the staff-family interaction positively. Findings suggest explanatory processes in staff-family interactions, while pointing toward promising interventions

Barriers to and Facilitators of Clinical Practice Guideline Use in Nursing Homes

To identify barriers to and facilitators of the diffusion of clinical practice guidelines (CPGs) and clinical protocols in nursing homes (NHs)

Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 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 paper, 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 (e.g., 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 the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration

Definitions of Frailty in Qualitative Research: A Qualitative Systematic Review

The purpose of this qualitative systematic review was to examine how frailty was conceptually and operationally defined for participant inclusion in qualitative research focused on the lived experience of frailty in community-living frail older adults. Search of six electronic databases, 1994–2019, yielded 25 studies. Data collection involved extracting the definition of frailty from the study aim, background, literature review, methods, and sampling strategy in each research study. Quality appraisal indicated that 13 studies (52%) demonstrated potential researcher bias based on insufficient information about participant recruitment, sampling, and relationship between the researcher and participant. Content analysis and concept mapping were applied for data synthesis. Although frailty was generally defined as a multidimensional, biopsychosocial construct with loss of resilience and vulnerability to adverse outcomes, most studies defined the study population based on older age and physical impairments derived from subjective assessment by the researcher, a healthcare professional, or a family member. However, 13 studies (52%) used objective or performance-based quantitative measures to classify participant frailty. There was no consistency across studies in standardized measures or objective assessment of frailty. Synthesis of the findings yielded four themes: Time, Vulnerability, Loss, and Relationships. The predominance of older age and physical limitations as defining characteristics of frailty raises questions about whether participants were frail, since many older adults at advanced age and with physical limitations are not frail. Lack of clear criteria to classify frailty and reliance on subjective assessment introduces the risk for bias, threatens the validity and interpretation of findings, and hinders transferability of findings to other contexts. Clear frailty inclusion and exclusion criteria and a standardized approach in the reporting of how frailty is conceptually and operationally defined in study abstracts and the methodology used is necessary to facilitate dissemination and development of metasynthesis studies that aggregate qualitative research findings that can be used to inform future research and applications in clinical practice to improve healthcare

sj-pdf-1-wjn-10.1177_01939459221123162 – Supplemental material for Using EHR Data to Identify Patient Frailty and Risk for ICU Transfer

Supplemental material, sj-pdf-1-wjn-10.1177_01939459221123162 for Using EHR Data to Identify Patient Frailty and Risk for ICU Transfer by Deborah Lekan, Thomas P. McCoy, Marjorie Jenkins, Somya Mohanty and Prashanti Manda in Western Journal of Nursing Research</p