Health‐Related Quality of Life at Admission Is Associated with Postdischarge Mortality, Functional Decline, and Institutionalization in Acutely Hospitalized Older Medical Patients

Parlevliet, Juliette L.; Vroomen, Janet L. MacNeil; Buurman, Bianca M.; Rooij, Sophia E. de; Bosmans, Judith E.

doi:10.1111/jgs.14050

Cited by 30 publications

(19 citation statements)

References 37 publications

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“…Compared to patients with model prediction scores 0–1, the OR (95% CI) for those with scores 2–5, 6–7 and ≥8 were 1.85 (1.38–2.47, p < 0.001), 2.30 (1.32–4.00, p = 0.003) and 7.20 (3.33–15.50, p < 0.001), respectively. These data were largely corroborated by Parlevliet et al, who found that health-related quality of life (HRQOL, utility based on the EuroQol-5D score) at the time of ICU admission was independently associated with risk of mortality and functional decline in a cohort of patients ≥65 years old admitted to three hospitals in the Netherlands [10], and Zeng et al who evaluated the relationship between a frailty index based on 52 discrete acute and chronic characteristics, and 300-day post-discharge mortality in a cohort of older patients admitted to a single geriatric ICU in China [11]. Finally, Bagshaw and coworkers used a validated “global” measure of frailty to demonstrate the independent effect of impaired preadmission FS on hospital and on one-year post-discharge mortality and functional outcomes [12].…”

Section: Discussionmentioning

confidence: 69%

Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients

Krinsley

Wasser²,

Kang

et al. 2017

Crit Care

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Background: Functional status (FS) before intensive care unit (ICU) admission is associated with short-term and long-term outcomes among critically ill patients. However, measures of FS are generally not integrated into ICU-specific mortality prediction models. Methods: This retrospective cohort study used prospectively collected data from 9638 consecutive patients admitted to a single ICU between 1 October 2005 and 30 September 2015. For each ICU admission, FS was prospectively determined and classified into three discrete categories based on performance of basic daily living activities (FS1 -fully independent; FS2 -partly dependent; FS3 -completely dependent). We prospectively calculated Acute Physiology and Chronic Health Evaluation (APACHE) IV predicted mortality percentage (APIV PM) for each admission and calculated observed-expected mortality ratios (OEMR), stratified by FS category and APIV PM. We calculated area under the receiver operator characteristic curve (AUC) for APIV PM and mortality for the entire cohort and the three FS categories. Results: Patients had a median (IQR) age of 67 (52-80) years and mean (SD) APIV PM was 18.3% (24.3%). Of these, 7714 (80.0%) were classified as FS1, 1728 (17.9%) as FS2 and 196 (2.0%) as FS3. FS1 patients were younger, had less comorbid disease, and lower APIV PM compared to FS2 and FS3. The OEMR were significantly lower for FS1 (0.67) than FS2 (0.93) or FS3 (0.90) (p < 0.0001 for both comparisons). Among patients with APIV PM 0-10%, 10-25%, 25-50% and ≥50% the OEMR for FS1 were 0.33, 0.49, 0.61 and 0.86. The AUC (95% CI) for APIV PM and mortality for FS1,, respectively (p < 0.001 for each comparison). Multivariable analysis demonstrated that FS2 (OR 2.18 (1.84-2.57) (p < 0.0001)) and FS3 ) (p = 0.0006)) were independently associated with increased risk of mortality. Conclusions: Baseline FS prior to critical illness is a strong independent predictor of mortality and impacts the relationship between observed and APIV PM in those with lower illness severity. Future iterations of mortality prediction models should integrate a baseline measure of FS to improve performance.

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Section: Discussionmentioning

confidence: 69%

Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients

Krinsley

Wasser²,

Kang

et al. 2017

Crit Care

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“…For elderly patients hospitalized for acute illness, HRQoL is known to be an important determinant of hospital outcomes (e.g., LOS) [9,[20][21][22][23], although in dispute, other studies reported no relationship [24]. Higher baseline HRQoL is considered to have better functional reserves or ability to recover and could thus predict shorter hospital stays [10].…”

Section: Discussionmentioning

confidence: 99%

“…When used in reference to health care, QoL means health-related quality of life (HRQoL) and can be defined as a functional effect on medical conditions and/or its treatment upon a patient's physical, social, and emotional well-being [7]. In healthy and chronically ill community-dwelling elderly people, higher HRQoL is associated with longer survival and lower admission rates [8][9][10][11][12][13][14]. For the acutely hospitalized patients, HRQoL at admission predicts subsequent risk of mortality and functional decline [9].…”

Section: Introductionmentioning

confidence: 99%

“…In healthy and chronically ill community-dwelling elderly people, higher HRQoL is associated with longer survival and lower admission rates [8][9][10][11][12][13][14]. For the acutely hospitalized patients, HRQoL at admission predicts subsequent risk of mortality and functional decline [9]. HRQoL is also dynamic, and it could change during hospitalization [15].…”

Section: Introductionmentioning

confidence: 99%

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Health-Related Quality of Life Measured by EQ-5D in Relation to Hospital Stay and Readmission in Elderly Patients Hospitalized for Acute Illness

Lin

Huang

et al. 2020

IJERPH

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We evaluated the predictability of self-reported Health-related quality of life (HRQoL) assessed by the 3-level 5-dimensional Euro-Quality of Life tool (EQ-5D-3L) and the EQ-Visual Analog Scale (EQ-VAS) on clinical outcomes of elderly patients who were admitted to an acute geriatric ward. A total of 102 participants (56.9% men) with a median age of 81.0 years (interquartile range or IQR: 76.0–85.3 years) were studied. The age-adjusted Charlson comorbidity index was 5.0 (IQR: 4.0–6.0) with a median length of stay (LOS) of 9.0 days (IQR: 7.0–15.0 days). No death occurred during hospitalization, and within 30 days after discharge, 15 patients were readmitted. During hospitalization, the EQ-5D-3L index was 0.440 at admission and that improved to 0.648 at discharge (p < 0.001). EQ-VAS scores also improved similarly from 60 to 70 (p < 0.001). Physical, cognitive function, frailty parameters (hand grip strength and walking speed), and nutritional status at admission all improved significantly during hospitalization and were related to EQ-5D-3L index or EQ-VAS scores at discharge. After controlling for relevant factors, EQ-5D-3L index at admission was found to be associated with LOS. In addition, EQ-VAS was marginally related to readmission. HRQoL assessment during hospitalization could be useful to guide clinical practice and to improve outcome.

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“…The pragmatic nature of this study of ‘usual care’ with assessments done by the practice nurses adds to its generalisability. The tests used have been well studied and shown to be valid, reliable and responsive across populations, for different diseases and in different clinical settings32 38 49 52 63–72. The unique aspect of this study is using these health status tests to measure multiple domains of health, including HRQoL in a primary care setting and aggregating the results to predict adverse outcomes.…”

Section: Discussionmentioning

confidence: 99%

Using frailty and quality of life measures in clinical care of the elderly in Canada to predict death, nursing home transfer and hospitalisation - the frailty and ageing cohort study

Rosenberg

Montgomery

Hay³

et al. 2019

BMJ Open

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ObjectiveTo assess the value of using frailty measures in primary care for predicting death, nursing home transfer (NHT) and hospital admission.DesignCohort study.Setting and participantsAll 380 people, mean age 88.4, living in the community and receiving home-based primary geriatric care from one practice in Victoria, Canada.Interventions/measurementsA 60 min baseline assessment which included: Clinical Frailty Scale (CFS), EuroQol EQ-5D-5L (EQ-5D), EuroQol Visual Analogue Scale (EQ-VAS) and Gait Speed (Gaitspeed).OutcomesDeath, NHT and hospital admission.ResultsDuring 18 months of follow-up, there were 39 (10.3%) deaths, 48 (12.6%) NHTs and 93 (24.5%) individuals admitted to hospital. All three outcomes were predicted by: CFS Level 6+7/4+5 (HR death 5.92, 95% CI 3.12 to 11.22, NHT 6.00, 95% CI 3.37 to 10.66 and hospital admission 2.92, 95% CI 1.93 to 4.40); EQ-5D Quintile 1/Quintile 5 (death 6.26, 95% CI 2.11 to 18.62; NHT 3.18, 95% CI 1.29 to 7.82 and hospital admission 2.94, 95% CI 1.47 to 5.87); EQ-VAS Q1/Q5 (death 7.0, 95% CI 2.34 to 20.93; NHT 3.38, 95% CI 1.22 to 9.35 and hospital admission 6.69, 95% CI 3.20 to 13.99) and Gaitspeed (death 5.87, 95% CI 1.78 to 19.34; NHT 8.51, 95% CI 3.18 to 22.79 and hospital admission 11.05, 95% CI 5.45 to 22.40). Medical diagnoses, multiple comorbidities and polypharmacy were weaker predictors of these outcomes. Cox regression analyses showed CFS (adjusted HR 2.88, 95% CI 1.23 to 6.68), EQ-VAS (0.96, 95% CI 0.93 to 0.98), estimated glomerular filtration rate (0.97, 95% CI 0.95 to 1.00) and haemoglobin (0.97, 95% CI 0.94 to 0.99) were independently associated with death. Gaitspeed (0.13, 95% CI 0.03 to 0.57), Geriatric Depression Scale (1.39, 95% CI 1.07 to 1.82) and dementia diagnosis (4.61, 95% CI 1.86 to 11.44) were associated with NHT. Only CFS (1.75, 95% CI 1.21 to 2.51) and EQ-VAS (0.98, 95% CI 0.96 to 0.99) were associated with hospital admission. No other diagnoses, polypharmacy nor multiple comorbidities predicted these outcomes.ConclusionsFor elderly people, standardised simple measures of frailty and health status were stronger predictors of death, NHT and hospital admission than medical diagnoses. Consideration should be given to adding these measures into usual medical care for this age group.

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Health‐Related Quality of Life at Admission Is Associated with Postdischarge Mortality, Functional Decline, and Institutionalization in Acutely Hospitalized Older Medical Patients

Cited by 30 publications

References 37 publications

Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients

Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients

Health-Related Quality of Life Measured by EQ-5D in Relation to Hospital Stay and Readmission in Elderly Patients Hospitalized for Acute Illness

Using frailty and quality of life measures in clinical care of the elderly in Canada to predict death, nursing home transfer and hospitalisation - the frailty and ageing cohort study

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