An Exploratory Study of Long-Term Outcome Measures in Critical Illness Survivors: Construct Validity of Physical Activity, Frailty, and Health-Related Quality of Life Measures*

McNelly, Angela; Rawal, Jai; Shrikrishna, Dinesh; Hopkinson, Nicholas S; Moxham, John; Harridge, Sdr; Hart, Nicholas; Montgomery, Hugh; Puthucheary, Zudin

doi:10.1097/ccm.0000000000001645

Cited by 48 publications

(29 citation statements)

References 43 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The long-term adverse consequences of critical illness are increasingly being recognized as a research priority in critical care [ 19 ]. A growing body of research is now examining the determinants and potential modifiers of post-ICU recovery, including at least one study that made use of a wearable device to track patient movement and activity [ 20 ]. However, post-ICU recovery research currently lacks the richness of data available to researchers focused on the ICU stay itself since post-discharge data collection is limited to infrequent visits to follow-up clinics, or in many cases is nonexistent.…”

Section: Discussionmentioning

confidence: 99%

Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study

et al. 2017

View full text Add to dashboard Cite

BackgroundWearable devices generate signals detecting activity, sleep, and heart rate, all of which could enable detailed and near-continuous characterization of recovery following critical illness.MethodsTo determine the feasibility of using a wrist-worn personal fitness tracker among patients recovering from critical illness, we conducted a prospective observational study of a convenience sample of 50 stable ICU patients. We assessed device wearability, the extent of data capture, sensitivity and specificity for detecting heart rate excursions, and correlations with questionnaire-derived sleep quality measures.Results Wearable devices were worn over a 24-h period, with excellent capture of data. While specificity for the detection of tachycardia was high (98.8%), sensitivity was low to moderate (69.5%). There was a moderate correlation between wearable-derived sleep duration and questionnaire-derived sleep quality (r = 0.33, P = 0.03). Devices were well-tolerated and demonstrated no degradation in quality of data acquisition over time.ConclusionsWe found that wearable devices could be worn by patients recovering from critical illness and could generate useful data for the majority of patients with little adverse effect. Further development and study are needed to better define and enhance the role of wearables in the monitoring of post-ICU recovery.Trial registrationClinicaltrials.gov, NCT02527408 Electronic supplementary materialThe online version of this article (10.1186/s40560-017-0261-9) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionmentioning

confidence: 99%

Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study

et al. 2017

View full text Add to dashboard Cite

show abstract

“…If these factors are not accounted for in a trial design, patient stratification, or analysis, outcome data may be unintentionally skewed. Many of the current outcome assessments for trials in critical care fail to account for these confounders [ 15 , 17 ]. Patient-reported outcome measures are increasingly prioritised as endpoints [ 18 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

Implications for post critical illness trial design: sub-phenotyping trajectories of functional recovery among sepsis survivors

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background Patients who survive critical illness suffer from a significant physical disability. The impact of rehabilitation strategies on health-related quality of life is inconsistent, with population heterogeneity cited as one potential confounder. This secondary analysis aimed to (1) examine trajectories of functional recovery in critically ill patients to delineate sub-phenotypes and (2) to assess differences between these cohorts in both clinical characteristics and clinimetric properties of physical function assessment tools. Methods Two hundred ninety-one adult sepsis survivors were followed-up for 24 months by telephone interviews. Physical function was assessed using the Physical Component Score (PCS) of the Short Form-36 Questionnaire (SF-36) and Activities of Daily Living and the Extra Short Musculoskeletal Function Assessment (XSFMA-F/B). Longitudinal trajectories were clustered by factor analysis. Logistical regression analyses were applied to patient characteristics potentially determining cluster allocation. Responsiveness, floor and ceiling effects and concurrent validity were assessed within clusters. Results One hundred fifty-nine patients completed 24 months of follow-up, presenting overall low PCS scores. Two distinct sub-cohorts were identified, exhibiting complete recovery or persistent impairment. A third sub-cohort could not be classified into either trajectory. Age, education level and number of co-morbidities were independent determinants of poor recovery (AUROC 0.743 ((95%CI 0.659–0.826), p < 0.001). Those with complete recovery trajectories demonstrated high levels of ceiling effects in physical function (PF) (15%), role physical (RP) (45%) and body pain (BP) (57%) domains of the SF-36. Those with persistent impairment demonstrated high levels of floor effects in the same domains: PF (21%), RP (71%) and BP (12%). The PF domain demonstrated high responsiveness between ICU discharge and at 6 months and was predictive of a persistent impairment trajectory (AUROC 0.859 (95%CI 0.804–0.914), p < 0.001). Conclusions Within sepsis survivors, two distinct recovery trajectories of physical recovery were demonstrated. Older patients with more co-morbidities and lower educational achievements were more likely to have a persistent physical impairment trajectory. In regard to trajectory prediction, the PF score of the SF-36 was more responsive than the PCS and could be considered for primary outcomes. Future trials should consider adaptive trial designs that can deal with non-responders or sub-cohort specific outcome measures more effectively.

show abstract

“…Survivors of a critical illness present with a unique pattern of muscle dysfunction that exacerbates immobility-induced changes; this affects their short-and long-term activity levels, something that can be quantified by accelerometer monitoring. [7][8][9] In an intensive care unit (ICU), behavioural mapping suggests that patients rarely ambulate outside their rehabilitation sessions; 10 as a result, therapy sessions are the main contributor to their cumulative daily activity. Because health care staff can overestimate active time and underestimate inactive time, objective activity and inactivity assessments such as accelerometry can provide a more accurate assessment of activity.…”

mentioning

confidence: 99%

Physical Activity of ICU Survivors during Acute Admission: Agreement of the activPAL with Observation

Baldwin

Johnston

Rowlands

et al. 2018

Physiotherapy Canada

View full text Add to dashboard Cite

We estimated the agreement of a thigh-worn accelerometer, the activPAL, used to measure activity and sedentary parameters, with observed mobility assessments of intensive care unit (ICU) survivors. We prospectively compared activPAL measurements with direct observation during assessments at discharge from the ICU or acute hospital in eight participants with a median age of 56 (1st-3rd quartile 48-65) years and an Acute Physiology and Chronic Health Evaluation II score of 23 (1st-3rd quartile 17-24). Frequency of sit-to-stand transitions; time spent standing, stepping, upright (standing and stepping), and sedentary (lying/sitting); and total steps were described; analysis was performed using Bland-Altman plots and calculating the absolute percent error. All sit-to-stand transitions were accurately detected. The mean difference on the Bland-Altman plots suggested an overestimation of standing time with the activPAL of 31 (95% CI: -9, 71) seconds and underestimation of stepping time by 25 (95% CI: -47, -3) seconds. The largest median absolute percent errors were for standing time (21.9%) and stepping time (18.7%); time spent upright (1.7%) or sedentary (0.3%) was more accurately estimated. The activPAL underestimated total steps per session, achieving the largest percent error (70.8%). Because it underestimated step count, the activPAL likely incorrectly recorded stepping time as standing time, so that time spent upright was the measure of activity with the smallest error. Sedentary behaviour, including frequency of transitions, was validly assessed.

show abstract

An Exploratory Study of Long-Term Outcome Measures in Critical Illness Survivors: Construct Validity of Physical Activity, Frailty, and Health-Related Quality of Life Measures*

Cited by 48 publications

References 43 publications

Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study

Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study

Implications for post critical illness trial design: sub-phenotyping trajectories of functional recovery among sepsis survivors

Physical Activity of ICU Survivors during Acute Admission: Agreement of the activPAL with Observation

Contact Info

Product

Resources

About