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Risk Factors of Readmissions in Geriatric Rehabilitation Patients: RESORT

  • Ching S. Wan
    Affiliations
    Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
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  • Esmee M. Reijnierse
    Affiliations
    Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia

    Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands
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  • Andrea B. Maier
    Correspondence
    Corresponding author Andrea B. Maier, MD, PhD, Department of Medicine and Aged Care, @Age, Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, VU University Amsterdam, Amsterdam Movement Sciences, van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands.
    Affiliations
    Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia

    Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands

    Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore

    Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore
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Open AccessPublished:February 16, 2021DOI:https://doi.org/10.1016/j.apmr.2021.01.082

      Abstract

      Objective

      To evaluate the risk factors associated with 30- and 90-day hospital readmissions in geriatric rehabilitation inpatients.

      Design

      Observational, prospective longitudinal inception cohort.

      Setting

      Tertiary hospital in Victoria, Australia.

      Participants

      Geriatric rehabilitation inpatients of the REStORing Health of Acutely Unwell AdulTs (RESORT) cohort evalutated by a comprehensive geriatric assessment including potential readmission risk factors (ie, demographic, social support, lifestyle, functional performance, quality of life, morbidity, length of stay in an acute ward). Of 693 inpatients, 11 died during geriatric rehabilitation. The mean age of the remaining 682 inpatients was 82.2±7.8 years, and 56.7% were women.

      Interventions

      Not applicable.

      Main Outcome Measures

      Thirty- and 90-day readmissions after discharge from geriatric inpatient rehabilitation.

      Results

      The 30- and 90-day unplanned all-cause readmission rates were 11.6% and 25.2%, respectively. Risk factors for 30- and 90-day readmissions were as follows: did not receive tertiary education, lower quality of life, higher Charlson Comorbidity Index and Cumulative Illness Rating Scale (CIRS) scores, and a higher number of medications used in the univariable models. Formal care was associated with increased risk for 90-day readmissions. In multivariable models, CIRS score was a significant risk factor for 30-day readmissions, whereas high fear of falling and CIRS score were significant risk factors for 90-day readmissions.

      Conclusions

      High fear of falling and CIRS score were independent risk factors for readmission in geriatric rehabilitation inpatients. These variables should be included in hospital readmission risk prediction model developments for geriatric rehabilitation inpatients.

      Keywords

      List of abbreviations:

      ADL (activities of daily living), AUC (area under the receiver operating characteristic curve), CCI (Charlson Comorbidity Index), CFS (Clinical Frailty Scale), CGA (comprehensive geriatric assessment), CI (confidence interval), CIRS (Cumulative Illness Rating Scale), IADL (instrumental activities of daily living), IQR (interquartile range), OR (odds ratio), RESORT (REStORing Health of Acutely Unwell AdulTs)
      Unplanned hospital readmissions and mortality rates are quality of care indicators.
      Medicare Payment Advisory Commission
      Measuring quality of care in Medicare 2014.
      Given the steady rise of hospital readmissions by at least 20% in high-income countries over the past decade,
      • Hakim M.A.
      • Garden F.L.
      • Jennings M.D.
      • Dobler C.C.
      Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease.
      as well as the tremendous associated costs,
      Medicare Payment Advisory Commission
      March 2018 report to congress: Medicare payment policy 2018.
      reducing hospital readmissions has become a priority for hospital policymakers.
      • Busby J.
      • Purdy S.
      • Hollingworth W.
      A systematic review of the magnitude and cause of geographic variation in unplanned hospital admission rates and length of stay for ambulatory care sensitive conditions.
      One in 4 readmissions is deemed avoidable.
      • van Walraven C.
      • Jennings A.
      • Forster A.J.
      A meta-analysis of hospital 30-day avoidable readmission rates.
      Hospital readmission risk prediction models, combined with targeted interventions preventing readmissions, help to reallocate public health resources and provide improved clinical outcomes for patients.
      • Walsh C.
      • Hripcsak G.
      The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions.
      ,
      • Kansagara D.
      • Englander H.
      • Salanitro A.
      • et al.
      Risk prediction models for hospital readmission: a systematic review.
      Risk prediction models often use hospital administrative data, which include patient demographics, principal diagnosis, urgency of the previous admission, length of stay, previous admission history, and blood biochemistry results.
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      Is the readmission rate a valid quality indicator? A review of the evidence.
      ,
      • Zhou H.
      • Della P.R.
      • Roberts P.
      • Goh L.
      • Dhaliwal S.S.
      Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.
      However, a prediction model has inconsistent predictive performance between different health care settings.
      • Zhou H.
      • Della P.R.
      • Roberts P.
      • Goh L.
      • Dhaliwal S.S.
      Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.
      In addition, the existing validated prediction models are targeted to either general medical or disease-specific inpatients,
      • Kansagara D.
      • Englander H.
      • Salanitro A.
      • et al.
      Risk prediction models for hospital readmission: a systematic review.
      ,
      • Morgan D.J.
      • Bame B.
      • Zimand P.
      • et al.
      Assessment of machine learning vs standard prediction rules for predicting hospital readmissions.
      and the models have low sensitivity and specificity when applied to geriatric inpatients.
      • Braes T.
      • Moons P.
      • Lipkens P.
      • et al.
      Screening for risk of unplanned readmission in older patients admitted to hospital: predictive accuracy of three instruments.
      Geriatric rehabilitation inpatients have a higher risk of readmission compared with acute inpatients owing to their complex health conditions, a decline in functional capacity, and associated higher health care needs.
      • Davis J.
      • Morgans A.
      • Stewart J.
      Developing an Australian health and aged care research agenda: a systematic review of evidence at the subacute interface.
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      • Mohanty S.
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      • Robinson T.N.
      Association of loss of independence with readmission and death after discharge in older patients after surgical procedures.
      • Hughes J.M.
      • Freiermuth C.E.
      • Shepherd-Banigan M.
      • et al.
      Emergency department interventions for older adults: a systematic review.
      Limited evidence on evaluating and improving geriatric rehabilitation interventions to avoid readmissions is available.
      • Davis J.
      • Morgans A.
      • Stewart J.
      Developing an Australian health and aged care research agenda: a systematic review of evidence at the subacute interface.
      Investigating risk factors of readmissions and identifying high-risk geriatric rehabilitation inpatients upon admission who need tailored case management and transitional care after discharge improve patient-centered care and reduce potentially preventable readmissions.
      • O’Conner K.
      • Meng H.
      • Marino V.
      • Boaz T.L.
      Individual and organizational factors associated with hospital readmission rates: evidence from a US national sample.
      Among geriatric rehabilitation inpatients, malnutrition,
      • Charlton K.
      • Nichols C.
      • Bowden S.
      • et al.
      Poor nutritional status of older subacute patients predicts clinical outcomes and mortality at 18 months of follow-up.
      functional status,
      • Morandi A.
      • Bellelli G.
      • Vasilevskis E.E.
      • et al.
      Predictors of rehospitalization among elderly patients admitted to a rehabilitation hospital: the role of polypharmacy, functional status, and length of stay.
      polypharmacy,
      • Morandi A.
      • Bellelli G.
      • Vasilevskis E.E.
      • et al.
      Predictors of rehospitalization among elderly patients admitted to a rehabilitation hospital: the role of polypharmacy, functional status, and length of stay.
      and multimorbidity
      • Kumar A.
      • Karmarkar A.M.
      • Graham J.E.
      • et al.
      Comorbidity indices versus function as potential predictors of 30-day readmission in older patients following postacute rehabilitation.
      are associated with hospital readmission. However, the association between other patient characteristics, such as social factors, lifestyle, quality of life, and readmissions, is unknown. The identification of aforementioned risk factors for readmissions may provide insights into developing risk prediction models in this population.
      • Kumar A.
      • Karmarkar A.M.
      • Graham J.E.
      • et al.
      Comorbidity indices versus function as potential predictors of 30-day readmission in older patients following postacute rehabilitation.
      This study aimed to identify risk factors associated with the risk of 30- and 90-day hospital readmissions in geriatric rehabilitation inpatients.

      Methods

      Study design

      REStORing Health of Acutely Unwell AdulTs (RESORT) is an ongoing observational, longitudinal inception cohort from October 16, 2017 onwards using a comprehensive geriatric assessment (CGA) to investigate the characteristics and health outcomes of inpatients recruited from geriatric rehabilitation wards at the Royal Melbourne Hospital. Older and frailer adults tending to have multimorbidity who require multidisciplinary rehabilitation care for recovery after acute episodes of ill-health are transferred to geriatric rehabilitation wards. CGA is a multidimensional, interdisciplinary diagnostic process to determine health characteristics and develop relevant coordinated intervention or follow-up.
      • Ellis G.
      • Gardner M.
      • Tsiachristas A.
      • et al.
      Comprehensive geriatric assessment for older adults admitted to hospital.
      The study was approved by the Melbourne Health Human Research Ethics Committee (reference no: HREC/17/MH/103) and followed national and international ethical guidelines according to the Declaration of Helsinki.
      World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.
      Written informed consent was obtained by either the patient or a nominated proxy. Patients were excluded if they were receiving palliative care at admission, incapable of providing informed consent without a nominated proxy, or transferring to acute care prior to consenting to the study.
      This study used data of 693 geriatric rehabilitation inpatients of the first wave from October 16, 2017 until August 31, 2018 after excluding patients (n=152, 15.3%) who met the exclusion criteria. Patients who died during their hospital stay in geriatric rehabilitation wards (n=11) were excluded from the data analysis, leaving 682 patients for the present study. Patients were assessed using the CGA within 48 hours of admission to the geriatric rehabilitation wards by physicians, nurses, physiotherapists, occupational therapists, and dietitians on social characteristics, nutrition status, physical and functional capacity, morbidity, and cognition using standardized assessment tools. The CGA also included patient surveys consisting of brief, primarily closed-ended questions to collect demographics, social support, quality of life, and lifestyle information completed by patients, caregivers, or health professionals. Potential risk factors for readmissions were grouped into 7 domains: demographics, social support, lifestyle, functional performance, quality of life, morbidity (including cognition), and index admission.

      Demographics

      Age and sex were collected from medical records. Country of birth, ethnicity, and highest level of education data were collected from surveys. Patients with tertiary education were defined as those having pursued beyond the secondary school level, including college education.

      Social support

      Whether patients were institutionalized before admission and received services from the council or other organizations (formal care) were collected from surveys. The question on whether patients had caregivers (informal care) in the Brief Abuse Screen for the Elderly
      • Reis M.
      • Nahmiash D.
      Validation of the indicators of abuse (IOA) screen.
      questionnaire was completed by physicians. Caregivers were defined as unremunerated individuals providing needed care regularly.
      • Reis M.
      • Nahmiash D.
      Validation of the indicators of abuse (IOA) screen.

      Lifestyle

      Current smoking status and alcohol consumption over the past year were collected from surveys. Trained nurses completed the Malnutrition Screening Tool.
      • Ferguson M.
      • Capra S.
      • Bauer J.
      • Banks M.
      Development of a valid and reliable malnutrition screening tool for adult acute hospital patients.
      Patients who scored more than 2 were at risk of malnutrition. Body mass index was calculated by anthropometric measurements completed by trained nurses.

      Functional performance

      Patients’ walking ability, history of having at least 1 fall over the past year before hospital admission, and fear of falling 1 month before hospital admission were collected from surveys. Frailty was assessed by physicians using the Clinical Frailty Scale (CFS),
      • Rockwood K.
      • Song X.
      • MacKnight C.
      • et al.
      A global clinical measure of fitness and frailty in elderly people.
      ranging from 1-9, with greater scores indicating a higher level of frailty. Trained occupational therapists assessed functional independence status using activities of daily living (ADL)
      • Katz S.
      • Ford A.B.
      • Moskowitz R.W.
      • Jackson B.A.
      • Jaffe M.W.
      Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function.
      and instrumental activities of daily living (IADL).
      • Lawton M.P.
      • Brody E.M.
      Assessment of older people: self-maintaining and instrumental activities of daily living.
      ADL and IADL scores ranged from 0-6 and 0-8, respectively, with higher scores indicating higher levels of independence for both scales. A physical functioning assessment was performed by trained physiotherapists using the Short Physical Performance Battery.
      • Guralnik J.M.
      • Simonsick E.M.
      • Ferrucci L.
      • et al.
      A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission.
      It included assessments on balance maintenance with eyes open, a timed 4-meter walk, and a timed sit-to-stand test. It ranged from 0-12, with higher scores demonstrating higher levels of lower extremity functioning.

      Quality of life

      Patients were asked to rate their health status from 0 (worst imaginable health) to 100 (best imaginable health) using the EuroQoL
      • Herdman M.
      • Gudex C.
      • Lloyd A.
      • et al.
      Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L).
      visual analog scale in surveys. Patients with visual impairment were asked verbally to rate their health status.

      Morbidity

      Physicians assessed morbidity using the Charlson Comorbidity Index (CCI)
      • Charlson M.
      • Szatrowski T.P.
      • Peterson J.
      • Gold J.
      Validation of a combined comorbidity index.
      and the Cumulative Illness Rating Scale (CIRS).
      • Miller M.D.
      • Paradis C.F.
      • Houck P.R.
      • et al.
      Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale.
      CCI and CIRS scores ranged from 0-37 and 0-56, respectively. CIRS scores at admission were presented as total scores, the total number of organ systems endorsed, and severity index (total score/total number of systems endorsed). The number of medications at admission to geriatric rehabilitation wards was extracted from the medical records.
      Cognitive impairment was defined as a dementia diagnosis captured by the CCI, CIRS, or medical records, a score on the Standardized Mini-Mental State Examination of less than 24 points,
      • Folstein M.
      • Folstein S.
      • McHugh P.
      "Mini-mental state." A practical method for grading the cognitive state of patients for the clinician.
      Montreal Cognitive Assessment less than 26 points,
      • Nasreddine Z.S.
      • Phillips N.A.
      • Bédirian V.
      • et al.
      The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.
      or Rowland Universal Dementia Scale less than 23 points.
      • Storey J.E.
      • Rowland J.T.
      • Conforti D.A.
      • Dickson H.G.
      The Rowland universal dementia assessment scale (RUDAS): a multicultural cognitive assessment scale.
      Cognitive testing was completed by physicians. Hospital Anxiety and Depression Scale
      • Zigmond A.S.
      • Snaith R.P.
      The hospital anxiety and depression scale.
      in surveys was used to assess anxiety (≥11 points indicating anxiety symptoms) and depressive symptoms (≥11 points indicating depression symptoms).

      Index admission

      The length of stay during the acute admission before being admitted to geriatric rehabilitation and the length of stay during geriatric rehabilitation were collected from medical records.

      Readmission

      Information on whether patients had all-cause unplanned 30- and 90-day hospital readmissions to the Royal Melbourne Hospital were obtained from the hospital administrative system. Questions on whether they had hospital readmissions to other hospitals within 90 days after discharged were asked during a follow-up phone call with the patient or caregiver. Planned admissions after discharge were excluded, which included elective admissions for follow-up surgical or medical treatments, such as scheduled dialysis or chemotherapy.

      Statistical analysis

      Descriptive variables were presented as numbers and percentages, means and SDs or as medians and interquartile ranges (IQR). We compared categorical variables using Pearson or Fisher exact tests and continuous variables using Student t tests or Mann-Whitney U tests as appropriate. We performed Levene’s test of homogeneity of variances as part of the continuous variable comparison statistical tests.
      The linearity between continuous variables and readmissions were checked, and univariable logistic regression analyses were performed to identify associations, odds ratios (ORs), and 95% confidence intervals (CIs) between potential risk factors and readmissions. Multivariable logistic regression analyses were performed using the variables with P<.10 from the univariable logistic regression analysis to identify risk factors for readmissions. We checked multicollinearity within each domain using variance inflation factors and chi-square test for significant continuous and categorical variables, respectively.
      • Daoud J.I.
      Multicollinearity and regression analysis.
      Variance inflation factors higher than 3 or a P value below 0.05 in chi-square tests were considered as having multicollinearity. For variables that were found to have multicollinearity, the variable with the lowest P value in univariable analysis was chosen. Given that different sections of the CGA were completed by specific health care professionals at different times, certain sections could have been missed at admission. Multiple imputation was performed in handling missing data before multivariable analysis if data were missing at random.
      • Jakobsen J.C.
      • Gluud C.
      • Wetterslev J.
      • Winkel P.
      When and how should multiple imputation be used for handling missing data in randomised clinical trials–a practical guide with flowcharts.
      Missing value analysis using the Little’s missing completely at random test and missing value patterns graph were used to determine whether the data were missing at random or not. A 2-tailed P value >.05 was considered a statistically significant independent risk factor for readmission in multivariable analysis. Sensitivity analysis was performed comparing independent risk factors of patients with complete data sets and patients with imputed missing data. The performance of the model including significant risk factors in multivariable analysis was assessed using analysis of area under the receiver operating characteristic curve (AUC) statistics. We conducted statistical analysis using the Statistical Package for Social Sciences (SPSS Statistics for Windows, version 25.0a).

      Results

      Patient characteristics

      Table 1 shows the characteristics of 682 geriatric rehabilitation inpatients at admission. The mean age at admission was 82.2±7.8 years, and 56.7% (n=387) were women. Four percent of the patients were institutionalized and 61% had caregivers. Seventy-three percent of the patients were able to walk and 66.2% had experienced at least 1 fall within the year before admission. A median CCI score of 2 (IQR, 1-4) and a median of 6 (IQR, 5-8) systems were affected in CIRS. The median length of stay in acute wards before geriatric rehabilitation ward admission was 7.0 days (IQR, 4.0-11.0). The 30- and 90-day all-cause readmissions rates were 11.6% and 25.2% respectively. Among patients who had 90-day readmissions, 26 (15.1%) were identified outside the Royal Melbourne Hospital.
      Table 1Characteristics of geriatric rehabilitation inpatients at admission
      CharacteristicsNo. of Patients (n)Value
      Demographics
      Age (y), mean ± SD68282.2±7.8
      Women682387 (56.7)
      White677603 (89.1)
      Australian-born679297 (43.7)
      Tertiary education51059 (11.6)
      Social support
      Institutionalized68224 (3.5)
      Informal care
      Informal care indicates that patients had caregivers.
      569348 (61.2)
      Formal care
      Formal care indicates that patients received services from the council or other organizations.
      649360 (55.5)
      Lifestyle
      Current smoking60139 (6.5)
      Alcohol use over the past year546282 (51.6)
      Risk of malnutrition (MST)67283 (12.4)
      BMI (kg/m2), mean ± SD65427.3±6.5
      Functional performance
      Ability to walk with or without a walking aid679494 (72.8)
      Fall history over the past year669443 (66.2)
      High fear of falling 1 mo before admission, n (%)47686 (18.1)
      Clinical Frailty Scale score, median (IQR)6056 (5-6)
      ADL score, median (IQR)6632 (1-3)
      IADL score, median (IQR)6641 (0-1)
      SPPB score, median (IQR)6471 (0-4)
      Quality of life
      EuroQoL-VAS score, median (IQR)41950 (35-70)
      Morbidity
      CCI score, median (IQR)6822 (1-4)
      CIRS score, median (IQR)68211 (8-15)
      CIRS no. of systems affected, median (IQR)6826 (5-8)
      CIRS severity index, median (IQR)6821.9 (1.6-2.2)
      Cognitive impairment682435 (63.8)
      Anxiety, HADS43182 (19.0)
      Depression, HADS444100 (22.5)
      No. of medications, median (IQR)68210 (7-13)
      Index admission
      LOS in acute ward, d, median (IQR)6827.0 (4.0-11.0)
      LOS in rehabilitation ward, d, median (IQR)68220.0 (14.0-30.0)
      Readmission
      30-d readmission68279 (11.6)
      90-d readmission682172 (25.2)
      NOTE. Data in are presented in numbers of patients and percentages unless stated otherwise.
      Abbreviations: BMI, body mass index; EuroQoL-VAS, EuroQol visual analog scale; HADS, Hospital Anxiety and Depression Scale; LOS, length of stay; MST, Malnutrition Screening Tool; SPPB, Short Physical Performance Battery.
      Informal care indicates that patients had caregivers.
      Formal care indicates that patients received services from the council or other organizations.

      Risk factors for 30-day all-cause hospital readmissions

      Table 2 shows the comparison of characteristics between patients with and without readmissions. Patients readmitted within 30 days after discharge were more likely to be non-Australian born, not have received tertiary education, have a lower self-rated quality of life, have higher CCI and CIRS scores, and have a higher number of medications used and longer length of acute hospital stay. Owing to the multicollinearity between CFS, CCI, CIRS, and number of medications (shown in appendix 1), CIRS score was only included in the multivariable analysis. Little’s missing completely at random test and missing value pattern graph showed random arrangement of missing values across variables (P=.541), with more missing data from patient surveys. Multiple imputation was used to handle missing values. Multivariable analysis (table 3) found CIRS to be a significant risk factor for 30-day readmissions (OR, 1.06; 95% CI, 1.01-1.12), achieving an AUC of 0.61 (95% CI, 0.54-0.68). It was also significant in the multivariable analysis using only patients with complete data (appendix 2).
      Table 2Characteristics of geriatric rehabilitation inpatients with and without all-cause unplanned 30- and 90-day readmissions in univariable logistic regression
      Patient Characteristics30-Day Readmission90-Day Readmission
      No. of Patients (n)NoNo. of Patients (n)YesOR (95% CI)No. of Patients (n)NoNo. of Patients (n)YesOR (95% CI)
      Demographics
      Age at admission (y), mean ± SD60382.1±7.97983.0±7.01.02 (0.99- 1.05)51082.2±8.017282.2±7.41.00 (0.98- 1.02)
      Women603341 (56.6)7946 (58.2)1.07 (0.67- 1.72)509290 (57.0)17196 (56.1)0.95 (0.67- 1.35)
      White598535 (89.5)7968 (86.1)0.76 (0.22- 2.66)506453 (89.5)171150 (87.7)0.66 (0.26- 1.67)
      Australian-born600272 (45.3)7925 (31.6)0.56 (0.34- 0.92)
      P<.10.
      P<.05.
      507232 (45.8)17265 (37.8)0.72 (0.51- 1.03)
      P<.10.
      Tertiary education45358 (12.8)571 (1.8)0.12 (0.02- 0.90)
      P<.10.
      P<.05.
      38551 (13.2)1258 (6.4)0.45 (0.21- 0.97)
      P<.10.
      P<.05.
      Social support
      Institutionalized60323 (3.8)791 (1.3)0.32 (0.04- 2.43)51017 (3.3)1727 (4.1)1.23 (0.50- 3.02)
      Informal care
      Informal care indicates that patients had caregivers.
      504307 (60.9)6541 (63.1)1.10 (0.64- 1.87)431257 (59.6)13891 (65.9)1.31 (0.88- 1.96)
      Formal care
      Formal care indicates that patients received services from the council or other organizations.
      573314 (54.8)7646 (60.5)1.32 (0.80- 2.18)486260 (53.5)163100 (61.3)1.39 (1.00-2.06)
      P<.10.
      P<.05.
      Lifestyle
      Smoking52935 (6.6)724 (5.6)0.94 (0.32- 2.80)44928 (6.2)15211 (7.2)1.16 (0.55-2.42)
      Alcohol use over the past year482254 (52.7)6428 (43.8)0.70 (0.41- 1.18)413226 (54.7)13356 (42.1)0.60 (0.41-0.89)
      P<.10.
      P<.05.
      Risk of malnutrition (MST)59368 (11.5)7915 (19.0)1.86 (0.97-3.54)
      P<.10.
      50361 (12.1)16922 (13.0)1.17 (0.68-2.01)
      BMI (kg/m2), mean ± SD57627.2±6.57828.0±6.71.02 (0.98-1.05)48727.2±6.616727.6±6.31.01 (0.98-1.04)
      Functional performance
      Ability to walk601439 (73.0)7855 (70.5)0.88 (0.53-1.48)508366 (72.0)171128 (74.9)1.16 (0.78-1.72)
      Fall history over the past year591393 (66.5)7850 (64.1)0.90 (0.55-1.47)500329 (65.8)169114 (67.5)1.08 (0.74-1.56)
      High fear of falling 1 mo before admission42871 (16.6)4815 (31.3)2.03 (0.97-4.25)
      P<.10.
      36655 (15.0)11031 (28.2)2.51 (1.42-4.46)
      P<.10.
      P<.05.
      Clinical Frailty Scale score, median (IQR)5376 (5-6)686 (5-7)1.21 (0.98-1.50)
      P<.10.
      4586 (5-6)1476 (5-7)1.18 (1.01-1.38)
      P<.10.
      P<.05.
      ADL score, median (IQR)5862 (1-3)772 (1-3)1.09 (0.95-1.26)5002 (1-3)1632 (1-3)1.06 (0.95-1.18)
      IADL score, median (IQR)5871 (0-1)771 (0-1)0.93 (0.76-1.15)5001 (0-1)1641 (0-1)0.93 (0.80-1.08)
      SPPB score, median (IQR)5691 (0-4)782 (0-3)0.99 (0.90-1.08)4851 (0-4)1621 (0-3)0.97 (0.91-1.04)
      Quality of life
      EuroQoL-VAS score, median (IQR)37255 (40-72)4750 (30-70)0.99 (0.98-1.00)
      P<.10.
      P<.05.
      31960 (40-75)10050 (30-70)0.99 (0.98-1.00)
      P<.10.
      P<.05.
      Morbidity
      CCI score, median (IQR)6032 (1-4)793 (2-5)1.18 (1.08-1.28)
      P<.10.
      P<.05.
      5102 (1-4)1723 (1-5)1.14 (1.07-1.22)
      P<.10.
      P<.05.
      CIRS score, median (IQR)60311 (8-14)7913 (10-17)1.08 (1.03-1.13)
      P<.10.
      P<.05.
      51011 (8-14)17212 (9-15)1.05 (1.02-1.09)
      P<.10.
      P<.05.
      CIRS no. of systems affected, median (IQR)6036 (5-8)797 (6-8)1.26 (1.12-1.41)
      P<.10.
      P<.05.
      5106 (4-7)1727 (5-8)1.16 (1.06-1.26)
      P<.10.
      P<.05.
      CIRS severity index, median (IQR)6031.9 (1.6-2.2)791.9 (1.7-2.2)0.83 (0.47-1.44)5101.9 (1.6-2.2)1721.9 (1.7-2.2)0.95 (0.63-1.43)
      Cognitive impairment603385 (63.8)7950 (63.3)0.98 (0.60-1.59)510326 (63.9)172109 (63.4)0.98 (0.68-1.40)
      Anxiety, HADS38576 (19.7)466 (13.0)0.64 (0.26-1.60)33161 (18.4)10021 (21.0)1.21 (0.68-2.13)
      Depression, HADS39690 (22.7)4810 (20.8)0.98 (0.46-2.11)34173 (21.4)10327 (26.2)1.45 (0.85-2.46)
      No. of medications, median (IQR)6039 (7-12)7910 (8-14)1.07 (1.02-1.13)
      P<.10.
      P<.05.
      5109 (7-12)17210 (8-14)1.08 (1.04-1.12)
      P<.10.
      P<.05.
      Index admission
      Acute length of stay, median (IQR), d6037.0 (4.0-11.0)798.0 (3.0-16.0)1.02 (1.00-1.04)
      P<.10.
      P<.05.
      5107.0 (4.0-11.0)1727.0 (3.6-12.9)1.01 (0.99-1.02)
      NOTE. Data are presented in numbers of patients and percentages unless stated otherwise.
      Abbreviations: BMI, body mass index; EuroQoL-VAS, EuroQol visual analog scale; HADS, Hospital Anxiety and Depression Scale; MST, Malnutrition Screening Tool; SPPB, Short Physical Performance Battery.
      P<.10.
      P<.05.
      Informal care indicates that patients had caregivers.
      § Formal care indicates that patients received services from the council or other organizations.
      Table 3Risk factors for 30- and 90-day readmissions in geriatric rehabilitation inpatients in multivariable logistic regression
      Patient Characteristics30-Day Readmission (n=682)90-Day Readmission (n=682)
      OR (95% CI)P ValueOR (95% CI)P Value
      Demographics
      Australian-born0.60 (0.35-1.03).0630.81 (0.55-1.19).277
      Tertiary education0.22 (0.04-1.32).0930.61 (0.29-1.26).174
      Social support
      Formal care
      Formal care indicates that patients received services from the council or other organizations.
      --1.32 (0.91-1.91).147
      Lifestyle
      Alcohol use over the past year--0.75 (0.49-1.13).161
      Risk of malnutrition (MST)1.57 (0.78-3.16).209--
      Functional performance
      High fear of falling 1 mo prior admission1.67 (0.84-3.32).1401.86 (1.11-3.10).018
      P<.05.
      Quality of life
      EuroQoL-VAS, score0.99 (0.98-1.01).6510.99 (0.98-1.01).616
      Morbidity
      CIRS, score1.06 (1.01-1.12).025
      P<.05.
      1.05 (1.01-1.09).012
      P<.05.
      Index admission
      Length of stay in acute ward, d1.02 (0.99-1.04).121--
      Abbreviations: EuroQoL-VAS, EuroQol visual analog scale; MST, Malnutrition Screening Tool.
      Formal care indicates that patients received services from the council or other organizations.
      P<.05.

      Risk factors for 90-day all-cause readmissions

      Not receiving tertiary education; receiving formal care from councils or organizations; nonalcohol consumer; self-reported high fear of falling; lower self-rated quality of life; higher scores in CFS, CCI, and CIRS; and higher number of medications used were risk factors for 90-day readmissions. Significant risk factors for 90-day readmissions were self-reported high fear of falling (OR, 1.86; 95% CI, 1.11-3.10) and CIRS (OR, 1.05; 95% CI, 1.01-1.09) score using multivariable analysis after multiple imputation, achieving an AUC of 0.62 (95% CI, 0.56-0.68). It was similar to multivariable analysis using only patients with complete data (see appendix 2).

      Discussion

      Lower self-rated quality of life and higher CCI, CIRS, and number of medications used were associated with increased risk for 30- and 90-day readmissions in the univariable analysis. Formal care was associated with increased risk for 90-day readmissions. In multivariable analysis, CIRS score was a significant risk factor for both 30- and 90-day readmissions; self-reported high fear of falling was significantly associated with 90-day readmissions.
      Our finding that receiving formal care was a risk factor for 90-day readmissions is consistent with a recently published study among geriatric inpatients demonstrating a positive relationship between receipt of help or home health services postdischarge and 30-day readmissions.
      • Sieck C.
      • Adams W.
      • Burkhart L.
      Validation of the BOOST risk stratification tool as a predictor of unplanned 30-day readmission in elderly patients.
      Requiring a strong social support network can be an indicator for complex health needs and consequent risk of readmissions.
      • Chan B.
      • Goldman L.E.
      • Sarkar U.
      • et al.
      High perceived social support and hospital readmissions in an older multi-ethnic, limited English proficiency, safety-net population.
      ,
      • Brault M.A.
      • Brewster A.L.
      • Bradley E.H.
      • Keene D.
      • Tan A.X.
      • Curry L.A.
      Links between social environment and health care utilization and costs.
      Accessibility to appropriate and timely support services reduces the risk of readmission.
      • Preyde M.
      • Brassard K.
      Evidence-based risk factors for adverse health outcomes in older patients after discharge home and assessment tools: a systematic review.
      ,
      • Gregorevic K.J.
      • Lim W.K.
      • Peel N.M.
      • Martin R.S.
      • Hubbard R.E.
      Are health assets associated with improved outcomes for hospitalised older adults? A systematic review.
      ADL and IADL scores were not associated with readmissions, in contrast to earlier studies among acutely admitted geriatric inpatients.
      • Sieck C.
      • Adams W.
      • Burkhart L.
      Validation of the BOOST risk stratification tool as a predictor of unplanned 30-day readmission in elderly patients.
      ,
      • DePalma G.
      • Xu H.
      • Covinsky K.E.
      • et al.
      Hospital readmission among older adults who return home with unmet need for ADL disability.
      However, fear of falling was a risk factor for 30- and 90-day readmissions. Fear of falling leads to physical inactivity and unmet daily functional needs postdischarge, resulting in the risk of dependence in daily activities
      • Greenberg M.R.
      • Nguyen M.C.
      • Stello B.
      • et al.
      Mechanical falls: are patients willing to discuss their risk with a health care provider?.
      and increased readmission risks.
      • DePalma G.
      • Xu H.
      • Covinsky K.E.
      • et al.
      Hospital readmission among older adults who return home with unmet need for ADL disability.
      Therefore, self-perceived fear of falling assessment is important in identifying patients who are at risk of readmission.
      • Greenberg M.R.
      • Moore E.C.
      • Nguyen M.C.
      • et al.
      Perceived fall risk and functional decline: gender differences in patient's willingness to discuss fall risk, fall history, or to have a home safety evaluation.
      Interventions aiming to reduce fear of falling, which include strategies such as medication reviews, home safety assessment, osteoporosis prevention, regular eye examination, weight-bearing exercise programs, and caregiver-targeted fall prevention education,
      • Greenberg M.R.
      • Nguyen M.C.
      • Stello B.
      • et al.
      Mechanical falls: are patients willing to discuss their risk with a health care provider?.
      ,
      • Ang S.G.M.
      • O’Brien A.P.
      • Wilson A.
      Fall concern about older persons shifts to carers as changing health policy focuses on family, home-based care.
      might enhance self-confidence and self-efficacy in falls prevention.
      Low quality of life was a risk factor for 30- and 90-day readmission, which is in line with previous literature, including geriatric inpatients
      • Andreasen J.
      • Gobbens R.J.
      • Eriksen H.H.
      • Overvad K.
      Health-related quality of life at hospital discharge as a predictor for 6-month unplanned readmission and all-cause mortality of acutely admitted older medical patients.
      and older community-dwelling individuals.
      • Tsai S.-Y.
      • Chi L.-Y.
      • Lee C-h
      • Chou P.
      Health-related quality of life as a predictor of mortality among community-dwelling older persons.
      ,
      • Cavrini G.
      • Broccoli S.
      • Puccini A.
      • Zoli M.
      EQ-5D as a predictor of mortality and hospitalization in elderly people.
      Lower quality of life may indicate living with compromised health due to existing morbidities
      • Karampampa K.
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      • Ahlbom A.
      • Modig K.
      Does a hospital admission in old age denote the beginning of life with a compromised health-related quality of life? A longitudinal study of men and women aged 65 years and above participating in the Stockholm Public Health Cohort.
      and is therefore associated with readmissions.
      The finding that comorbidities and polypharmacy were risk factors for readmissions concurs with existing literature showing an association between the number of comorbidities with medications prescribed and hospital readmissions in geriatric patients after discharge from the hospital.
      • Preyde M.
      • Brassard K.
      Evidence-based risk factors for adverse health outcomes in older patients after discharge home and assessment tools: a systematic review.
      ,
      • Wimmer B.C.
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      Medication regimen complexity and unplanned hospital readmissions in older people.
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      • Miranda A.
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      Comorbidity burden assessment in older people admitted to a Portuguese University Hospital.
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      Thirty-day hospital readmission rate amongst older adults correlates with an increased number of medications, but not with Beers medications.
      • Tanderup A.
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      Disability and morbidity among older patients in the emergency department: a Danish population-based cohort study.
      • Fabbietti P.
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      • Moresi R.
      • et al.
      Impact of potentially inappropriate medications and polypharmacy on 3-month readmission among older patients discharged from acute care hospital: a prospective study.
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      The effect of comorbidities on readmission is linked to polypharmacy.
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      Risk factors for general medicine readmissions and association with mortality.
      Polypharmacy is associated with the increased use of potentially inappropriate medications, increased likelihood of adverse drug reactions, lower adherence to therapeutics, and increased likelihood of making mistakes on complex medication regimens.
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      Comorbidity burden assessment in older people admitted to a Portuguese University Hospital.
      ,
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      Impact of potentially inappropriate medications and polypharmacy on 3-month readmission among older patients discharged from acute care hospital: a prospective study.
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      Study limitations

      This was a single-site study, which might limit generalizability to other hospitals. The prevalence of 30-day readmissions could have been underestimated because it only included readmissions to the Royal Melbourne Hospital. Reasons for subacute ward admission were not available for readmission rate stratification. Furthermore, the sample size of this study was relatively small to detect moderate risk factors. A small proportion of data were randomly missing, which enabled imputation. The data are based on a highly standardized collected comprehensive assessments performed by a trained multidisciplinary team in a highly relevant cohort of geriatric rehabilitation inpatients. Exclusion criteria were limited. Ongoing recruitment within the RESORT cohort will enable validating readmission risk prediction models for geriatric rehabilitation inpatients.

      Conclusions

      In geriatric rehabilitation patients, the risk factors for both 30- and 90-day readmissions included non-Australian born; not receiving a tertiary education; self-reported high fear of falling; self-rated quality of life; CFS, CCI, and CIRS score; and the number of medications used. In multivariable analysis, CIRS score was the significant risk factor for both 30- and 90-day readmissions; self-reported high fear of falling was a risk factor for 90-day readmissions. The inclusion of these risk factors in future readmission risk prediction models among geriatric rehabilitation inpatients is recommended.

      Supplier

      • a.
        SPSS Statistics for Windows, version 25.0; IBM Corp.

      Acknowledgments

      We thank the @AgeMelbourne team for the establishment of the RESORT database and Jade Mitchell (B.A.Sc.) for the discussion about risk factors of readmission in geriatric rehabilitation inpatients. This research was funded by an unrestricted grant of the University of Melbourne received by Prof. Andrea B. Maier and the Medical Research Future Fund (MRFF) provided by the Melbourne Academic Centre for Health (MACH).

      Appendix

      Tabled 1Appendix 1 Multicollinearity analysis for multivariable logistic regression
      Patient CharacteristicsMulticollinearityP Value (Univariate Logistic Regression)
      Variance Inflation FactorP Value (χ2)30-Day Readmission90-Day Readmission
      Demographics
      Australian-born-.176.021
      p<0.05.
      .069
      Tertiary education-.176.014
      p<0.05.
      .038
      p<0.05.
      Morbidity/Functional performance
      Clinical Frailty Scale scoreCCI: 1.368-.083.036
      p<0.05.
      CIRS: 4.209
      CIRS no. of system affected: 3.876
      No. of medications: 1.116
      CCI scoreClinical frailty scale: 1.086-.001
      p<0.05.
      .009
      p<0.05.
      CIRS:4.115
      CIRS no of systems affected: 3.889
      No. of medications: 1.105
      CIRS scoreClinical frailty scale: 1.057-<.0001
      p<0.05.
      <.0001
      p<0.05.
      CCI: 1.301
      CIRS no. of systems affected: 1.328
      No. of medications: 1.104
      CIRS no. of systems affectedClinical Frailty Scale: 1.087-.003
      p<0.05.
      .001
      p<0.05.
      CCI: 1.373
      CIRS: 1.482
      No. of medications: 1.118
      No. of medicationsClinical Frailty Scale: 1.089-.001
      p<0.05.
      .001
      p<0.05.
      CCI: 1.357
      CIRS: 4.288
      CIRS no. of systems affected: 3.893
      p<0.05.
      Tabled 1Appendix 2 Risk factors for 30- and 90-day readmissions in geriatric rehabilitation inpatients with complete data
      Patient Characteristics30-Day Readmission (n=380)90-Day Readmission (n=377)
      OR (95% CI)P ValueOR (95% CI)P Value
      Demographics
      Australian-born0.54 (0.25-1.17).1190.66 (0.38-1.15).143
      Tertiary education0.73 (0.32-3.36).9460.47 (0.17-1.26).132
      Social support
      Formal care--1.06 (0.63-1.78).832
      Lifestyle
      Alcohol use over the past year--0.68 (0.41-1.16).155
      Risk of malnutrition (MST)2.24 (0.83-6.03).109--
      Functional performance
      High fear of falling 1 mo prior to admission2.32 (0.94-5.71).0672.24 (1.13-4.44).020
      P<.05.
      Quality of life
      EuroQoL-VAS score0.99 (0.98-1.01).2350.99 (0.98-1.01).300
      Morbidity
      CIRS score1.07 (0.99-1.15).0681.07 (1.01-1.13).015
      P<.05.
      Index admission
      Length of stay in acute ward, d1.04 (1.01-1.08).020
      P<.05.
      --
      Abbreviation: EuroQoL-VAS, EuroQol visual analog scale.
      P<.05.

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