Psychosocial Factors and 30-Day Hospital Readmission among Individuals Receiving Maintenance Dialysis: A Prospective Study

Background: Thirty-day hospital readmissions are common among maintenance dialysis patients. Prior studies have evaluated easily measurable readmission risk factors such as comorbid conditions, laboratory results, and hospital discharge day. We undertook this prospective study to investigate the associations between hospital-assessed depression, health literacy, social support, and self-rated health (separately) and 30-day hospital readmission among dialysis patients. Methods: Participants were recruited from the University of North Carolina Hospitals, 2014-2016. Validated depression, health literacy, social support, and self-rated health screening instruments were administered during index hospitalizations. Multivariable logistic regression models with 30-day readmission as the dependent outcome were used to examine readmission risk factors. Results: Of the 154 participants, 58 (37.7%) had a 30-day hospital readmission. In unadjusted analyses, individuals with positive screening for depression, lower health literacy, and poorer social support were more likely to have a 30-day readmission (vs. negative screening). Positive depression screening and poorer social support remained significantly associated with 30-day readmission in models adjusted for race, heart failure, admitting service, weekend discharge day, and serum albumin: adjusted OR (95% CI) 2.33 (1.02-5.15) for positive depressive symptoms and 2.57 (1.10-5.91) for poorer social support. The area under the receiver operating characteristic curve (AUC) of the multivariable model adjusted for social support status was significantly greater than the AUC of the multivariable model without social support status (test for equality; p value = 0.04). Conclusion: Poor social support and depressive symptoms identified during hospitalizations may represent targetable readmission risk factors among dialysis patients. Our findings suggest that hospital-based assessments of select psychosocial factors may improve readmission risk prediction.

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Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

10.1101/19012468 ◽

2019 ◽

Author(s):

Sameh N. Saleh ◽

Anil N. Makam ◽

Ethan A. Halm ◽

Oanh Kieu Nguyen

Keyword(s):

Risk Factors ◽

Prediction Model ◽

Risk Prediction ◽

Hospital Readmission ◽

Model Performance ◽

Risk Prediction Model ◽

Day Hospital ◽

Baseline Characteristics ◽

Substantial Change ◽

Readmission Risk

AbstractDespite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day readmission prediction model predicts 7-day readmissions. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We compared model performance and compared differences in strength of model factors between the 7-day model to the 30-day model. While there was no substantial change in model performance between the original 30-day and the re-derived 7-day model, there was significant change in strength of predictors. Characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to the day of discharge.

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Patient-Related Risk Factors for Unplanned 30-Day Hospital Readmission Following Primary and Revision Total Knee Arthroplasty: A Systematic Review and Meta-Analysis

Journal of Clinical Medicine ◽

10.3390/jcm10010134 ◽

2021 ◽

Vol 10 (1) ◽

pp. 134

Author(s):

Daniel Gould ◽

Michelle M Dowsey ◽

Tim Spelman ◽

Olivia Jo ◽

Wassif Kabir ◽

...

Keyword(s):

Risk Factors ◽

Total Knee Arthroplasty ◽

Knee Arthroplasty ◽

Hospital Readmission ◽

Day Hospital ◽

Post Discharge ◽

Comorbidity Burden ◽

Total Knee ◽

The Impact ◽

Readmission Risk

Total knee arthroplasty (TKA) is a highly effective procedure for advanced osteoarthritis of the knee. Thirty-day hospital readmission is an adverse outcome related to complications, which can be mitigated by identifying associated risk factors. We aimed to identify patient-related characteristics associated with unplanned 30-day readmission following TKA, and to determine the effect size of the association between these risk factors and unplanned 30-day readmission. We searched MEDLINE and EMBASE from inception to 8 September 2020 for English language articles. Reference lists of included articles were searched for additional literature. Patients of interest were TKA recipients (primary and revision) compared for 30-day readmission to any institution, due to any cause, based on patient risk factors; case series were excluded. Two reviewers independently extracted data and carried out critical appraisal. In-hospital complications during the index admission were the strongest risk factors for 30-day readmission in both primary and revision TKA patients, suggesting discharge planning to include closer post-discharge monitoring to prevent avoidable readmission may be warranted. Further research could determine whether closer monitoring post-discharge would prevent unplanned but avoidable readmissions. Increased comorbidity burden correlated with increased risk, as did specific comorbidities. Body mass index was not strongly correlated with readmission risk. Demographic risk factors included low socioeconomic status, but the impact of age on readmission risk was less clear. These risk factors can also be included in predictive models for 30-day readmission in TKA patients to identify high-risk patients as part of risk reduction programs.

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Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

JMIR Medical Informatics ◽

10.2196/16306 ◽

2021 ◽

Vol 9 (3) ◽

pp. e16306

Author(s):

Peng Zhao ◽

Illhoi Yoo ◽

Syed H Naqvi

Keyword(s):

Risk Factors ◽

Protective Factors ◽

Hospital Readmission ◽

Inpatient Care ◽

Early Stage ◽

Multivariate Logistic Regression Analysis ◽

Day Hospital ◽

Multivariate Logistic Regression ◽

Data Set ◽

Readmission Risk

Background Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. Objective The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors. Methods We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors. Results We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported. Conclusions The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

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Psychosocial and health-related risk factors for depressive symptom trajectories among midlife women over 15 years: Study of Women's Health Across the Nation (SWAN)

Psychological Medicine ◽

10.1017/s0033291718000703 ◽

2018 ◽

Vol 49 (2) ◽

pp. 250-259 ◽

Cited By ~ 4

Author(s):

Joyce T. Bromberger ◽

Laura L. Schott ◽

Nancy E. Avis ◽

Sybil L. Crawford ◽

Sioban D. Harlow ◽

...

Keyword(s):

Risk Factors ◽

Social Support ◽

Depressive Symptoms ◽

Depressive Symptom ◽

Midlife Women ◽

Role Functioning ◽

Symptom Trajectories ◽

Related Risk ◽

Health Related ◽

Symptom Trajectory

AbstractBackgroundPsychosocial and health-related risk factors for depressive symptoms are known. It is unclear if these are associated with depressive symptom patterns over time. We identified trajectories of depressive symptoms and their risk factors among midlife women followed over 15 years.MethodsParticipants were 3300 multiracial/ethnic women enrolled in a multisite longitudinal menopause and aging study, Study of Women's Health Across the Nation. Biological, psychosocial, and depressive symptom data were collected approximately annually. Group-based trajectory modeling identified women with similar longitudinal patterns of depressive symptoms. Trajectory groups were compared on time-invariant and varying characteristics using multivariable multinomial analyses and pairwise comparisons.ResultsFive symptom trajectories were compared (50% very low; 29% low; 5% increasing; 11% decreasing; 5% high). Relative to whites, blacks were less likely to be in the increasing trajectory and more likely to be in the decreasing symptom trajectory and Hispanics were more likely to have a high symptom trajectory than an increasing trajectory. Psychosocial/health factors varied between groups. A rise in sleep problems was associated with higher odds of having an increasing trajectory and a rise in social support was associated with lower odds. Women with low role functioning for 50% or more visits had three times the odds of being in the increasing symptom group.ConclusionsChanges in psychosocial and health characteristics were related to changing depressive symptom trajectories. Health care providers need to evaluate women's sleep quality, social support, life events, and role functioning repeatedly during midlife to monitor changes in these and depressive symptoms.

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MP64-12 RISK FACTORS FOR 30-DAY HOSPITAL READMISSION OF ROBOTIC PARTIAL NEPHRECTOMY PATIENTS

The Journal of Urology ◽

10.1016/j.juro.2014.02.1929 ◽

2014 ◽

Vol 191 (4S) ◽

Author(s):

Luis Felipe Brandao ◽

Homayoun Zargar ◽

Humberto Laydner ◽

Oktay Akca ◽

Riccardo Autorino ◽

...

Keyword(s):

Risk Factors ◽

Partial Nephrectomy ◽

Hospital Readmission ◽

Robotic Partial Nephrectomy ◽

Day Hospital

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Health Literacy Associated With Blood Pressure but not Other Cardiovascular Disease Risk Factors Among Dialysis Patients

American Journal of Hypertension ◽

10.1038/ajh.2011.252 ◽

2012 ◽

Vol 25 (3) ◽

pp. 348-353 ◽

Cited By ~ 30

Author(s):

G. A. Adeseun ◽

C. C. Bonney ◽

S. E. Rosas

Keyword(s):

Risk Factors ◽

Blood Pressure ◽

Cardiovascular Disease ◽

Health Literacy ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Cardiovascular Disease Risk Factors ◽

Dialysis Patients

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Prevalence and risk factors of depressive symptoms among dialysis patients with end-stage renal disease (ESRD) in Khartoum, Sudan: A cross-sectional study

Journal of Family Medicine and Primary Care ◽

10.4103/jfmpc.jfmpc_1229_19 ◽

2020 ◽

Vol 9 (7) ◽

pp. 3639

Author(s):

MohamedH Ahmed ◽

HababKhalid Elkheir ◽

ArwaSiddig Wagaella ◽

Safaa Badi ◽

Abbas Khalil ◽

...

Keyword(s):

Risk Factors ◽

Depressive Symptoms ◽

Renal Disease ◽

End Stage Renal Disease ◽

Cross Sectional Study ◽

Sectional Study ◽

Dialysis Patients ◽

Cross Sectional ◽

Stage Renal Disease ◽

End Stage

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The Relationship between Functional Health Literacy, Self-Rated Health, and Social Support between Younger and Older Adults in Ghana

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16173188 ◽

2019 ◽

Vol 16 (17) ◽

pp. 3188 ◽

Cited By ~ 4

Author(s):

Padmore Amoah

Keyword(s):

Social Support ◽

Older Adults ◽

Young Adults ◽

Health Status ◽

Health Literacy ◽

Emotional Support ◽

Functional Health ◽

Functional Health Literacy ◽

Self Rated Health ◽

The Impact

It is well established that health literacy positively affects health outcomes, and social support influences this association. What remains unclear is which aspect of social support (instrumental, informational, and emotional support) is responsible for this effect and whether the influence differs from one population group to another. This study addresses these lacunae. It examines the impact each type of support makes on the relation between functional health literacy (FHL) and self-rated health status among younger and older adults in Ghana. Data were pooled from two cross-sectional surveys, together comprising 521 participants in the Ashanti Region. The results indicated that young adults were more likely to possess sufficient FHL and perceive their health more positively than older adults. While FHL was positively associated with health status, the relation was stronger when young adults received a high level of emotional support. Among older persons, informational support substantially moderated the association between FHL and health status. Thus, social support modifies the relations between FHL and health status among younger and older adults in different ways and to different degrees. Therefore, interventions to improve FHL and health amongst younger and older adults should pay due regard to relevant aspects of social support.

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