Five-Year Risk of Mechanical Ventilation in Community-Dwelling Adults: The Framingham-Intermountain Anticipating Life Support Study

Clinical risk factors and blood protein biomarkers of 10-year pneumonia risk

BACKGROUND

Chronic inflammation may increase susceptibility to pneumonia.

RESEARCH QUESTION

To explore associations between clinical comorbidities, serum protein immunoassays, and long-term pneumonia risk.

METHODS

Framingham Heart Study Offspring Cohort participants ≥65 years were linked to their Centers for Medicare Services claims data. Clinical data and 88 serum protein immunoassays were evaluated for associations with 10-year incident pneumonia risk using Fine-Gray models for competing risks of death and least absolute shrinkage and selection operators for covariate selection.

RESULTS

We identified 1,370 participants with immunoassays and linkage to Medicare data. During 10 years of follow up, 428 (31%) participants had a pneumonia diagnosis. Chronic pulmonary disease [subdistribution hazard ratio (SHR) 1.87; 95% confidence interval (CI), 1.33-2.61], current smoking (SHR 1.79, CI 1.31-2.45), heart failure (SHR 1.74, CI 1.10-2.74), atrial fibrillation/flutter (SHR 1.43, CI 1.06-1.93), diabetes (SHR 1.36, CI 1.05-1.75), hospitalization within one year (SHR 1.34, CI 1.09-1.65), and age (SHR 1.06 per year, CI 1.04-1.08) were associated with pneumonia. Three baseline serum protein measurements were associated with pneumonia risk independent of measured clinical factors: growth differentiation factor 15 (SHR 1.32; CI 1.02-1.69), C-reactive protein (SHR 1.16, CI 1.06-1.27) and matrix metallopeptidase 8 (SHR 1.14, CI 1.01-1.30). Addition of C-reactive protein to the clinical model improved prediction (Akaike information criterion 4950 from 4960; C-statistic of 0.64 from 0.62).

CONCLUSIONS

Clinical comorbidities and serum immunoassays were predictive of pneumonia risk. C-reactive protein, a routinely-available measure of inflammation, modestly improved pneumonia risk prediction over clinical factors. Our findings support the hypothesis that prior inflammation may increase the risk of pneumonia.

Clinical risk factors and blood protein biomarkers of 10-year pneumonia risk

Background

Chronic inflammation may increase susceptibility to pneumonia.

Research Question

To explore associations between clinical comorbidities, serum protein immunoassays, and long-term pneumonia risk.

Methods

Framingham Heart Study Offspring Cohort participants ≥65 years were linked to their Centers for Medicare Services claims data. Clinical data and 88 serum protein immunoassays were evaluated for associations with 10-year incident pneumonia risk using Fine-Gray models for competing risks of death and least absolute shrinkage and selection operators for covariate selection.

Results

We identified 1,370 participants with immunoassays and linkage to Medicare data. During 10 years of follow up, 428 (31%) participants had a pneumonia diagnosis. Chronic pulmonary disease [subdistribution hazard ratio (SHR) 1.87; 95% confidence interval (CI), 1.33-2.61], current smoking (SHR 1.79, CI 1.31-2.45), heart failure (SHR 1.74, CI 1.10-2.74), atrial fibrillation/flutter (SHR 1.43, CI 1.06-1.93), diabetes (SHR 1.36, CI 1.05-1.75), hospitalization within one year (SHR 1.34, CI 1.09-1.65), and age (SHR 1.06 per year, CI 1.04-1.08) were associated with pneumonia. Three baseline serum protein measurements were associated with pneumonia risk independent of measured clinical factors: growth differentiation factor 15 (SHR 1.32; CI 1.02-1.69), C-reactive protein (SHR 1.16, CI 1.06-1.27) and matrix metallopeptidase 8 (SHR 1.14, CI 1.01-1.30). Addition of C-reactive protein to the clinical model improved prediction (Akaike information criterion 4950 from 4960; C-statistic of 0.64 from 0.62).

Conclusions

Clinical comorbidities and serum immunoassays were predictive of pneumonia risk. C-reactive protein, a routinely-available measure of inflammation, modestly improved pneumonia risk prediction over clinical factors. Our findings support the hypothesis that prior inflammation may increase the risk of pneumonia.

Administrative Data Is Insufficient to Identify Near-Future Critical Illness: A Population-Based Retrospective Cohort Study

Background

Prediction of future critical illness could render it practical to test interventions seeking to avoid or delay the coming event.

Objective

Identify adults having >33% probability of near-future critical illness.

Research Design

Retrospective cohort study, 2013-2015.

Subjects

Community-dwelling residents of Manitoba, Canada, aged 40-89 years.

Measures

The outcome was a near-future critical illness, defined as intensive care unit admission with invasive mechanical ventilation, or non-palliative death occurring 30-180 days after 1 April each year. By dividing the data into training and test cohorts, a Classification and Regression Tree analysis was used to identify subgroups with ≥33% probability of the outcome. We considered 72 predictors including sociodemographics, chronic conditions, frailty, and health care utilization. Sensitivity analysis used logistic regression methods.

Results

Approximately 0.38% of each yearly cohort experienced near-future critical illness. The optimal Tree identified 2,644 mutually exclusive subgroups. Socioeconomic status was the most influential variable, followed by nursing home residency and frailty; age was sixth. In the training data, the model performed well; 41 subgroups containing 493 subjects had ≥33% members who developed the outcome. However, in the test data, those subgroups contained 429 individuals, with 20 (4.7%) experiencing the outcome, which comprised 0.98% of all subjects with the outcome. While logistic regression showed less model overfitting, it likewise failed to achieve the stated objective.

Conclusions

High-fidelity prediction of near-future critical illness among community-dwelling adults was not successful using population-based administrative data. Additional research is needed to ascertain whether the inclusion of additional types of data can achieve this goal.

Examining mechanisms for gender differences in admission to intensive care units

OBJECTIVE

To evaluate whether the male predominance of older people admitted to intensive care units (ICUs) is due to gender differences in the presence of spouses, partners, or children; rates of gender-specific disease; or triage decisions made by health system personnel.

DATA SOURCES AND COLLECTION

Three population-based datasets, 2004-2012, of Canadians ≥65 years: provincial health care data from Manitoba (n = 250 190) and national data of nursing home residents (n = 133 982) and community-based homecare recipients (n = 210 090).

STUDY DESIGN

Retrospective observational study, using multivariable Cox proportional hazards and logistic regression.

PRINCIPAL FINDINGS

Males predominated in ICU admissions: from Manitoba (hazard ratio [HR] = 1.87, 95% CI = 1.80-1.95), nursing homes (HR = 1.47, 1.35-1.60), and homecare (odds ratio = 1.14, 1.11-1.17). Adjustment for spouses, partners, and children did not attenuate this effect. The HR for gender was lower by 13.5 percent, relative, after excluding ICU care for cardiac causes. Male predominance was not present during a second ICU admission among survivors of a first ICU-containing hospitalization (HR = 1.07, 0.96-1.20).

CONCLUSIONS

In three older cohorts, the male predominance of ICU admission was not explained by gender differences in the presence of a spouse, partner, or children, or cardiac disease rates. The third finding suggests that triage bias is unlikely to be responsible for the male predominance.

Accounting for Patient Preferences Regarding Life-Sustaining Treatment in Evaluations of Medical Effectiveness and Quality

The importance of understanding patient preferences for life-sustaining treatment is well described for individual clinical decisions; however, its role in evaluations of healthcare outcomes and quality has received little attention. Decisions to limit life-sustaining therapies are strongly associated with high risks for death in ways that are unaccounted for by routine measures of illness severity. However, this essential information is generally unavailable to researchers, with the potential for spurious inferences. This may lead to "confounding by unmeasured patient preferences" (a type of confounding by indication) and has implications for assessments of treatment effectiveness and healthcare quality, especially in acute and critical care settings in which risk for death and adverse events are high. Through a collection of case studies, we explore the effect of unmeasured patient resuscitation preferences on issues critical for researchers and research consumers to understand. We then propose strategies to more consistently elicit, record, and harmonize documentation of patient preferences that can be used to attenuate confounding by unmeasured patient preferences and provide novel opportunities to improve the patient centeredness of medical care for serious illness.