Abstract
Rationale: No previous studies have examined the role of prehospital vulnerability in acute respiratory distress syndrome (ARDS) development and mortality in an acutely ill adult population.
Objectives: To describe the association between prehospital vulnerability and 1) the development of ARDS, 2) 28-day mortality, and 3) 1-year mortality.
Methods: This was a longitudinal prospective cohort study nested within the multicenter LIPS-A (Lung Injury Prevention Study-Aspirin) trial. We analyzed 301 participants who completed Vulnerable Elders Survey (VES) at baseline. Multivariable logistic regression and Cox regression analyses were used to describe the association between vulnerability and short-term outcomes (ARDS and 28-day mortality) and long-term outcomes (1-year mortality), respectively.
Results: The VES score ranged from 0 to 10 (median [interquartile range], 2.0 [0–6]); 143 (47.5%) fit criteria for prehospital vulnerability (VES ≥ 3). Vulnerability was not significantly associated with ARDS development (10 [7.0%] vulnerable patients developed ARDS as per LIPS-A study criteria vs. 20 [12.7%] without vulnerability; P = 0.10; adjusted odds ratio [95% confidence interval (CI)], 0.54 [0.24–1.24]; P = 0.15). Nor was vulnerability associated with 28-day mortality (15 [10.5%] vulnerable patients were dead by Day 28 vs. 11 [7.0%] nonvulnerable patients; P = 0.28; adjusted odds ratio [95% CI], 0.95 [0.39–2.26]; P = 0.90). Vulnerability was significantly associated with 1-year mortality in hospital survivors (35 [26.9%] vs. 13 [9.3%]; adjusted hazard ratio [95% CI], 2.20 [1.10–4.37]; P = 0.02).
Conclusions: In a population of adults recruited for their high risk of ARDS, prehospital vulnerability, measured by VES, was highly prevalent and strongly associated with 1-year mortality.
Keywords: frailty, acute respiratory distress syndrome, survivors, outcomes
Acute respiratory distress syndrome (ARDS) is a severe form of acute respiratory failure associated with high mortality and morbidity, for which there are few treatments with an impact on outcomes (1, 2). One approach to improving outcomes in patients with ARDS has been to focus on the accurate and early identification of patients at high risk of ARDS using risk factors that can be assessed on admission to the hospital (3, 4). The Lung Injury Prevention Score (LIPS) is one such tool that uses clinical information routinely available on admission to the hospital (such as obesity, presence of diabetes, high risk of surgical intervention, high-risk traumatic event, presence of sepsis or pneumonia) to predict which patients will be at the highest risk for developing ARDS and death (3–6).
With the increase in adult survivors of critical illness facing physical, cognitive, and neuropsychiatric impairments in the months after hospital discharge (7), several recent studies have highlighted the importance of prehospital frailty or vulnerability, the hallmark of which is a decrease in physiologic reserve, in identifying critically ill adults at increased risk of morbidity and mortality (8–12). Although there are numerous approaches to measuring prehospital vulnerability (13), many of the performance or questionnaire-based tools are often impractical in the inpatient setting because acutely ill patients are often unable to answer questions or complete performance assessments, and questionnaires are rarely validated for surrogate response (14).
The Vulnerable Elders Survey (VES), initially developed to identify older community-living adults at high risk of death or functional decline, has been shown to be a useful risk-stratification tool for predicting hospital complications and mortality in older adults presenting with traumatic injury (15, 16). VES has also emerged as one of the screening tools in geriatric oncology to determine which patients will benefit from a comprehensive geriatric assessment (CGA) before chemotherapy treatment (17). No previous studies have examined the role of prehospital vulnerability in the development of ARDS, nor has the VES been explored as a predictor of other short- and long-term mortality outcomes in an acutely ill adult population defined by their high risk for ARDS development.
To address these gaps in the literature, we sought to describe the association between prehospital vulnerability, as measured by VES, and: 1) ARDS development, 2) 28-day mortality, and 3) 1-year mortality in hospital survivors.
Methods
Study Design and Participants
This is a longitudinal analysis of data from the Lung Injury Prevention Study with Aspirin (LIPS-A), a multicenter clinical trial (NCT01504861), which was designed to test the efficacy of aspirin for the prevention of ARDS among at-risk adult patients (18, 19). As published previously, adults (≥18 yr old) who were admitted to the hospital through the emergency department with an elevated risk for developing ARDS on the basis of a calculated lung injury prediction score (LIPS ≥ 4) were included into the parent trial (19). After confirming no effect modification by treatment allocation, these analyses include all randomized patients who completed the VES as part of their baseline questionnaire (Figure 1). The study was approved by the institutional review boards of all participating institutions, and informed consent was obtained from patients or, where appropriate, the legally appropriate proxies.
Figure 1.
Flow diagram of the selection of study participants and follow-up of the study sample. VES = Vulnerable Elders Survey.
Measuring Prehospital Vulnerability
At enrollment, research coordinators asked patients or, where appropriate, their proxy respondents about their prehospital vulnerability using the VES, which is a function-based screening tool that has been previously validated to identify older adults at high risk of death or functional decline on the basis of four categories: activities of daily living, common physical tasks, self-rated health, and age (see Table E1 in the online supplement for scoring details) (15).
Other Covariates
Data on patient demographics, preexisting cognitive impairment (diagnosis of dementia or reported problems in daily life due to poor memory), preexisting sensory impairment (reported problems in daily life due to visual or hearing impairment) (20), preillness disability status (using the Barthel Index of Activities of Daily Living) (21), and health-related quality of life (using the Short Form-12) (22) were collected by interviewing the patient or, where appropriate, the legally appropriate proxy respondent. The Barthel Index quantifies disability with a score ranging from 0 (completely dependent) to 100 (completely independent) by asking about 10 basic activities of daily living (21). On enrollment, data were abstracted from the medical record to calculate the Sequential Organ Failure Assessment score (23). A comorbidity score ranging from 0 to 4 was developed using the data collected on the presence or absence of eight chronic health conditions (24).
Outcomes
The primary long-term outcome for this study was time to death in hospital survivors right-censored at 1 year. The primary short-term outcomes of interest included 1) the development of ARDS within 7 days of hospital admission, on the basis of modified Berlin criteria that required invasive mechanical ventilation (19); and 2) 28-day mortality. Because the incidence of ARDS was lower than expected in this cohort, in sensitivity analyses, we explored two other approaches to defining ARDS: 1) combining the American European Consensus Conference ARDS definition with the LIPS-A definition, and 2) combining the Berlin criteria with the LIPS-A trial definition (25, 26).
Statistical Analysis
Descriptive statistics including means, frequencies, and proportions were used to examine the characteristics of the patient sample. We examined baseline characteristics and hospital processes by prehospital vulnerability (VES ≥ 3) using chi-square, t tests, or, where appropriate, their nonparametric equivalents. We grouped age into quintiles and explored the relationship between VES by age categories. We used multivariable logistic regression models to estimate the independent association between prehospital vulnerability and 1) ARDS development, and 2) 28-day mortality. We used Cox proportional hazard model to examine the relationship between prehospital vulnerability and 1-year mortality in patients who had survived to hospital discharge. For these primary analyses, we drew directed acyclic graphs (DAGs) with all potential confounders shown to be previously associated with short- and long-term outcomes. In our multivariate models, we adjusted for the minimally sufficient set of variables that would allow for the total effect estimate of vulnerability (for the 1-year mortality outcome these were: age, comorbidity, body mass index [BMI] as a categorical variable, and cognitive impairment; see Figures E1–E3 for the DAGs) (27, 28). Statistical significance was defined as a two-sided P ≤ 0.05, and Stata Version 15 was used for the statistical analysis.
Results
Of the 390 patients included in the original trial, we analyzed the 301 who had completed a baseline VES. The median age (interquartile range [IQR]) in the study population was 56 years (46–68 yr). The most common ARDS risk factors were suspected sepsis (76.5%) and pneumonia (59.6%) (Table 1). We compared the baseline characteristics of the study population with the 89 patients who were missing baseline VES and found no differences in baseline characteristics, so these results are not presented. By 12 months after hospital discharge we had vital status for 289 (96.0%) of the original cohort with baseline VES data: 67 (22.3%) were dead, 222 (73.8%) had some evidence of being alive at either 6- or 12-month telephone follow-up, and 12 (4.0%) were lost to follow-up (Figure 1).
Table 1.
Patient characteristics, clinical processes, and outcomes by prehospital vulnerability
| Characteristics | Total (n = 301) | Prehospital Vulnerability (VES ≥ 3)* (n = 143) | No Prehospital Vulnerability (VES < 3) (n = 158)* |
|---|---|---|---|
| Age, yr, mean (SD) | 56.2 (16.3) | 61.6 (15.7) | 51.4 (15.3) |
| Allocated to aspirin treatment | 150 (49.8) | 70 (49.0) | 80 (50.6) |
| Surrogate completed VES | 78 (25.9) | 47 (32.9) | 31 (19.6) |
| Female | 152 (50.5) | 89 (62.2) | 63 (39.9) |
| Hispanic ethnicity | 33 (11.0) | 18 (12.6) | 15 (9.5) |
| White race | 215 (71.4) | 106 (74.1) | 109 (69.0) |
| Body mass index, mean (SD)† | 29.1 (9.0) | 29.0 (9.9) | 29.2 (8.2) |
| Diabetes | 53 (17.6) | 33 (23.1) | 20 (12.7) |
| Current smoker | 72 (23.9) | 28 (19.6) | 44 (27.9) |
| ARDS risk factor | |||
| Suspected sepsis | 230 (76.4) | 117 (81.8) | 113 (71.5) |
| Noncardiogenic shock | 61 (20.3) | 31 (21.7) | 30 (19.0) |
| Aspiration | 38 (12.6) | 23 (16.1) | 15 (9.5) |
| Pneumonia | 179 (59.5) | 91 (63.6) | 88 (55.7) |
| Trauma | 20 (6.6) | 4 (2.8) | 16 (10.1) |
| Barthel score,† median (IQR) | 100 (80–100) | 80 (40–95) | 100 (100–100) |
| SOFA score,‡ median (IQR) | 3 (2–5) | 3 (2–5) | 3 (1–5) |
| Lung Injury Prevention score, median (IQR) | 6 (5–7) | 6 (5–7.5) | 6 (5–7) |
| VES score, median (IQR) | 2 (0–6) | 6 (3–7) | 0 (0–1) |
| No. comorbidities, median (IQR) | 0 (0–1) | 0 (0–1) | 0 (0–1) |
| Prehospital cognitive impairment§ | 11 (3.7) | 11 (7.7) | 0 (0) |
| Prehospital sensory impairment | 22 (7.3) | 15 (10.5) | 7 (4.4) |
| Mechanical ventilation during hospitalization | 67 (22.3) | 32 (22.4) | 35 (22.1) |
| ARDS by LIPS-A criteria | 30 (10.0) | 100 (7.0) | 20 (12.7) |
| Died in hospital | 19 (6.3) | 10 (7.0) | 9 (5.7) |
| Hospital LOS in hospital survivors, median (IQR) | 6 (3–10) | 7 (4–12) | 5 (3–9) |
| 28-d Mortality | 26 (8.6) | 15 (10.5) | 11 (7.0) |
| 1-yr Mortality | 67 (22.3) | 45 (31.5) | 22 (13.9) |
Definition of abbreviations: ARDS = acute respiratory distress syndrome; IQR = interquartile range; LIPS-A = Lung Injury Prevention Study-Aspirin; LOS = length of stay; SD = standard deviation; SOFA = Sequential Organ Failure Assessment; VES = Vulnerable Elders Survey.
Data presented as n (%) unless otherwise noted.
VES ranged from 0 to 10; median (IQR), 2 (0–6).
Total n = 295.
n = 299.
Reported dementia diagnosis or reported impairment in daily life due to memory problems.
Prehospital Vulnerability
The VES scores ranged from 0 to 10, with a median (IQR) of 2.0 (0–6). One hundred forty-two (47.0%) were classified as vulnerable on the basis of a VES score of 3 or greater, of which only 14 (9.9%) were classified as vulnerable on the basis of age alone (age > 85 yr = 3 points).
Patients who reached the threshold of having prehospital vulnerability were older (median age [IQR], 59 yr [50–74 yr] in those with VES ≥ 3 vs. 52 yr [42–62 yr] in those with VES < 3; P value < 0.0001), more likely to be female (58.2% vs. 41.8%; P < 0.001), and more likely to have prehospital disability (median Barthel score [IQR], 80 [40–95] in patients with prehospital vulnerability vs. 100 [100–100] in patients without; P value < 0.001) or report prehospital dementia or memory problems (7.7% vs. 0.0%; P = 0.001).
Patients of all ages had a wide range of VES scores, although prehospital vulnerability was not limited to older patients: prehospital vulnerability was present in 28.4% of the patients younger than 44 years and 37.0% of patients between 45 and 52 years old.
Prehospital Vulnerability and Short-term Outcomes
Patients with prehospital vulnerability did not appear to have significant differences in their calculated risk of developing ARDS (median LIPS score [IQR], 6 [5–7.5] in patients with VES ≥ 3 vs. 6 [5–7] in patients without vulnerability; P = 0.50). The rate of ARDS development in this cohort was low (30 of 301 [10.0%]), and we did not find any association between prehospital vulnerability and ARDS development in this cohort (10 of 143 [7.0%] in patients with prehospital vulnerability vs. 20 of 158 [12.7%] in those without vulnerability; adjusted odds ratio [95% confidence interval (CI), 0.54 [0.24–1.24]; P = 0.15; see Table 2 and Figure E2 for the DAG). Similarly, 28-day mortality was low in this cohort (26 of 301 [8.6%]) and, in a multivariate model on the basis of a DAG that suggested that adjusting for age and comorbidities was adequate to estimate the total effect of vulnerability on 28-day mortality (Figure E3), prehospital vulnerability was not associated with early mortality (adjusted odds ratio [95% CI], 0.95 [0.39–2.26]; P = 0.90).
Table 2.
Association between vulnerability and short-term outcomes (acute respiratory distress syndrome development and 28-d mortality)
| Vulnerability (n = 143) | No Vulnerability (n = 158) | P Value | |
|---|---|---|---|
| ARDS development | |||
| ARDS by LIPS-A criteria, n (%) | 10 (7.0) | 20 (12.7) | 0.10 |
| LIPS-A ARDS, unadjusted OR (95% CI) | 0.52 (0.23–1.15) | Reference | 0.11 |
| LIPS-A ARDS, adjusted OR* (95% CI) | 0.54 (0.24–1.24) | Reference | 0.15 |
| 28-d Mortality | |||
| Dead at Day 28, n (%) | 15 (10.5) | 11 (7.0) | 0.28 |
| Unadjusted OR (95% CI) | 1.51 (0.67–3.40) | Reference | 0.28 |
| Adjusted OR (95% CI)† | 0.95 (0.39–2.26) | Reference | 0.90 |
Definition of abbreviations: ARDS = acute respiratory distress syndrome; CI = confidence interval; DAG = direct acyclic graph; LIPS-A = Lung Injury Prevention Study-Aspirin; OR = odds ratio.
We used a DAG for these analyses, which suggested that all back-door paths close by controlling for age alone (Figure E2).
DAG suggested that for these analyses all back-door paths close with control of age and comorbidities (Figure E3).
Prehospital Vulnerability and 1-Year Mortality after Discharge
Of the 270 hospital survivors, 48 (17.8%) were dead by 1 year. Prehospital vulnerability was associated with higher mortality (35 of 130 vulnerable patients died after discharge vs. 13 of 140 nonvulnerable; unadjusted hazard ratio [aHR] [95% CI] was 3.1 [1.59–5.70]; P = 0.001) (see Figure 2). When we adjusted for the minimal set of variables for estimating the total effect of vulnerability on mortality (which included age, BMI, comorbidity score, and cognitive impairment), vulnerability remained significantly associated with long-term mortality (aHR, 2.20 [1.20–4.37]; P = 0.03; see Table 3 for the DAG). Every 1-unit increase in baseline VES score was associated with an 18% increase in the hazard of death over time (aHR, 1.18 [1.01–1.31]; P = 0.03)].
Figure 2.
Kaplan-Meier survival curves by prehospital vulnerability.
Table 3.
Association between vulnerability and 1-year mortality in hospital survivors
| Unadjusted Model | Multivariate Model 1* | Multivariate Model 2† | |
|---|---|---|---|
| No. of subjects | 270 | 270 | 268 |
| Hazard ratio (95% CI) | 3.01 (1.59–5.70) | 2.20 (1.10–4.37) | 2.22 (1.12–4.42) |
Definition of abbreviation: CI = confidence interval.
Model 1 is a multivariate model in which we adjusted for the minimal sufficient set of variables that would eliminate all back-door paths between vulnerability and 1-year mortality: age, body mass index as a categorical variable, comorbidity score (>0 vs. 0), prehospital cognitive impairment.
Model 2: multivariate model in which we adjusted for the variables in Model 1 plus total Sequential Organ Failure Assessment score, as a measure of severity of illness, and a binary variable capturing the development of acute respiratory distress syndrome.
Discussion
Review of Significant Findings
In this longitudinal study, we found a high prevalence of prehospital vulnerability in a cohort of adults recruited into the LIPS-A clinical trial on the basis of their high risk of ARDS. However, the ARDS development and 28-day mortality were both lower than expected, and we did not find any suggestion of an association between prehospital vulnerability and these short-term outcomes. Prehospital vulnerability was nevertheless strongly associated with 1-year mortality in hospital survivors. These results have implications for the identification of prehospital vulnerability in acutely ill adults and suggest avenues of investigation to improve risk stratification and care of acutely ill adults across a wide age spectrum.
Relationship to Previous Studies
In community-dwelling older adults, vulnerability factors such as frailty and geriatric syndromes have been well studied as a risk factor for functional decline, falls, mortality, and hospitalizations (29, 30). In light of the increasing recognition of the importance of geriatric principles in the care of adults with various types of acute illness, frailty and prehospital vulnerability factors have been studied as a risk-stratification tool in critical care (8, 11, 12, 20, 31, 32). However, how to operationalize measures of prehospital vulnerability from the outpatient setting to the acute hospital setting has been challenging, because acutely ill patients are often too ill to answer complex questions or perform assessments (8, 9). Few prospective studies have examined the role of questionnaire-based approaches for diagnosing prehospital vulnerability in critically ill adults (20, 33).
Vulnerability and frailty necessarily overlap with the health constructs of disability and comorbidities. As a function-based questionnaire approach to identifying vulnerability, VES is squarely agnostic between the two most common conceptual approaches for defining frailty—the deficit accumulation approach and the phenotypic approach (9, 26). Like in the deficit-accumulation model of understanding frailty, where general disability markers could be included as one of many deficits in the frailty index, patients with severe disability would likely have a high VES score and be classified as vulnerable. In addition, as in the phenotypic approach, in which the diminished physiologic reserve is captured by the signs and symptoms of fatigue, weight loss, weakness, slowness, and low activity level, patients who rated their health low and report having difficulty performing physical activities such as crouching, kneeling, or stooping are likely to be vulnerable via the VES.
The fact that we found no significant relationship between vulnerability and ARDS development and short-term mortality likely reflects type 2 error, given the low incidence of both ARDS and 28-day mortality in our cohort. Because the LIPS-A study used a modified Berlin criterion that required intubation, we also did post hoc sensitivity analyses to confirm that the negative vulnerability effect estimate was robust to more sensitive approaches to defining ARDS. A diagnostic approach that combined American European Consensus Conference and the LIPS-A criteria identified almost double the ARDS than the LIPS-A criteria alone, with a similar effect estimate for vulnerability and ARDS (Table E2). Nevertheless, we are likely underpowered to adequately rule out an association between vulnerability and ARDS development.
Potential Clinical and Research Implications
The pressing question for clinicians or administrators in the intensive care unit setting is: should frailty or vulnerability be more systematically identified in the acute care setting? And if so, for which patients would such early identification of vulnerability inform the pre- or posthospitalization care processes? In our study sample of seriously ill adults at high risk of ARDS, the strong relationship we found between vulnerability and 1-year mortality suggests that vulnerability may be more important for improving long-term outcomes in hospital survivors than for short-term prognostication. The VES is one of several evidence-based screening tools used to identify older adults with cancer who may benefit from a CGA (17), which is a multidisciplinary process that focuses on identifying patients’ medical, functional, and psychosocial needs to develop a care plan that matches the patient’s physiologic reserve, with the goal toward improving survival, quality of life, and function (34). The strong relationship we find in our study sample between vulnerability and 1-year mortality suggests that VES could be a useful screening tool in the critical care setting to determine which patients would benefit from more integration of geriatric principles to improve care and communication (35). Future studies in critical care could pivot toward testing the utility of CGA in improving long-term survivorship outcomes.
In our study sample, we also found high prevalence of vulnerability in the younger patients in the cohort (e.g., 28.4% of the patients between 18 and 44 yr old and 37.1% of the patients between 45 and 52 yr old were vulnerable, respectively). Such a high prevalence of vulnerability in younger acutely ill patients is consistent with previous studies in younger critically ill adults (10) and suggests that the frailty/vulnerability construct has broad relevance to seriously ill adults of all ages.
Limitations and Strengths
Our study has several strengths and limitations. The fact that the study participants were enrolled in a clinical trial may have caused selection bias. Although these analyses were prespecified, the low incidence of ARDS in this cohort makes type 2 error likely in our exploration of the effect of vulnerability and ARDS, so these results should not be used to conclude that prehospital vulnerability is not associated with ARDS development. In this study, completion of the VES was performed by either the patient or proxy respondent, and we did not perform any new validation of VES for use with proxy respondents. Although the VES was designed to ask about the patient’s function just before acute illness, we cannot ascertain how sensitive it was to identifying patients with rapid changes in their vulnerability characteristics just before acute presentation.
Conclusions
In conclusion, in a cohort of adults at high risk of ARDS, prehospital vulnerability was highly prevalent. Prehospital vulnerability was associated with higher 1-year mortality but not with ARDS or short-term mortality. Future studies in critical care could pivot toward using screening tools such as the VES for identifying adults at high risk of long-term adverse outcomes to test pilot interventions geared toward improving long-term outcomes in critically ill adults.
Footnotes
Supported by National Institute of Aging grant R03 AG050927 (A.A.H.); National Heart, Lung, and Blood Institute grants U01 HL122998 and UH3 HL125119 (M.N.G.) and K01 HL140279 (A.A.H.).
Author Contributions: A.A.H. and M.N.G. had full access to all of the data in the study and take responsibility for the integrity of the data. A.A.H. and M.N.G. designed the study. M.N.G., D.A.K., D.S.T., D.J.K., and O.G. were responsible for data acquisition. A.A.H., J.-T.C., and M.N.G. were responsible for data analysis and writing first draft of the manuscript. All authors participated in critical revision of the manuscript and important intellectual content.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.
Author disclosures are available with the text of this article at www.atsjournals.org.
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