Sachin Yende1, Karina Alvarez2, Laura Loehr3, Aaron R Folsom4, Anne B Newman5, Lisa A Weissfeld2, Richard G Wunderink6, Stephen B Kritchevsky7, Kenneth J Mukamal8, Stephanie J London9, Tamara B Harris10, Doug C Bauer11, Derek C Angus12. 1. Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, University of Pittsburgh, Pittsburgh, PA; Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA. Electronic address: yendes@upmc.edu. 2. Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, University of Pittsburgh, Pittsburgh, PA; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA. 3. Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC. 4. School of Public Health, University of Minnesota, Minneapolis, MN. 5. Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA. 6. Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. 7. The Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC. 8. Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA. 9. Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC. 10. Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD. 11. Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA. 12. Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, University of Pittsburgh, Pittsburgh, PA; Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
Abstract
BACKGROUND: Preventing pneumonia requires better understanding of incidence, mortality, and long-term clinical and biologic risk factors, particularly in younger individuals. METHODS: This was a cohort study in three population-based cohorts of community-dwelling individuals. A derivation cohort (n = 16,260) was used to determine incidence and survival and develop a risk prediction model. The prediction model was validated in two cohorts (n = 8,495). The primary outcome was 10-year risk of pneumonia hospitalization. RESULTS: The crude and age-adjusted incidences of pneumonia were 6.71 and 9.43 cases/1,000 person-years (10-year risk was 6.15%). The 30-day and 1-year mortality were 16.5% and 31.5%. Although age was the most important risk factor (range of crude incidence rates, 1.69-39.13 cases/1,000 person-years for each 5-year increment from 45-85 years), 38% of pneumonia cases occurred in adults < 65 years of age. The 30-day and 1-year mortality were 12.5% and 25.7% in those < 65 years of age. Although most comorbidities were associated with higher risk of pneumonia, reduced lung function was the most important risk factor (relative risk = 6.61 for severe reduction based on FEV1 by spirometry). A clinical risk prediction model based on age, smoking, and lung function predicted 10-year risk (area under curve [AUC] = 0.77 and Hosmer-Lemeshow [HL] C statistic = 0.12). Model discrimination and calibration were similar in the internal validation cohort (AUC = 0.77; HL C statistic, 0.65) but lower in the external validation cohort (AUC = 0.62; HL C statistic, 0.45). The model also calibrated well in blacks and younger adults. C-reactive protein and IL-6 were associated with higher pneumonia risk but did not improve model performance. CONCLUSIONS: Pneumonia hospitalization is common and associated with high mortality, even in younger healthy adults. Long-term risk of pneumonia can be predicted in community-dwelling adults with a simple clinical risk prediction model.
BACKGROUND: Preventing pneumonia requires better understanding of incidence, mortality, and long-term clinical and biologic risk factors, particularly in younger individuals. METHODS: This was a cohort study in three population-based cohorts of community-dwelling individuals. A derivation cohort (n = 16,260) was used to determine incidence and survival and develop a risk prediction model. The prediction model was validated in two cohorts (n = 8,495). The primary outcome was 10-year risk of pneumonia hospitalization. RESULTS: The crude and age-adjusted incidences of pneumonia were 6.71 and 9.43 cases/1,000 person-years (10-year risk was 6.15%). The 30-day and 1-year mortality were 16.5% and 31.5%. Although age was the most important risk factor (range of crude incidence rates, 1.69-39.13 cases/1,000 person-years for each 5-year increment from 45-85 years), 38% of pneumonia cases occurred in adults &lt; 65 years of age. The 30-day and 1-year mortality were 12.5% and 25.7% in those &lt; 65 years of age. Although most comorbidities were associated with higher risk of pneumonia, reduced lung function was the most important risk factor (relative risk = 6.61 for severe reduction based on FEV1 by spirometry). A clinical risk prediction model based on age, smoking, and lung function predicted 10-year risk (area under curve [AUC] = 0.77 and Hosmer-Lemeshow [HL] C statistic = 0.12). Model discrimination and calibration were similar in the internal validation cohort (AUC = 0.77; HL C statistic, 0.65) but lower in the external validation cohort (AUC = 0.62; HL C statistic, 0.45). The model also calibrated well in blacks and younger adults. C-reactive protein and IL-6 were associated with higher pneumonia risk but did not improve model performance. CONCLUSIONS:Pneumonia hospitalization is common and associated with high mortality, even in younger healthy adults. Long-term risk of pneumonia can be predicted in community-dwelling adults with a simple clinical risk prediction model.
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