Rates and Risk Factors for Recurrent Pneumonia in Patients Hospitalized With Community-Acquired Pneumonia: Population-Based Prospective Cohort Study With 5 Years of Follow-up
Bibliographic record
Abstract
BACKGROUND: The rates and risk factors for developing recurrent pneumonia following hospitalization with community-acquired pneumonia (CAP) are poorly understood. METHODS: We examined a population-based cohort of patients with CAP who survived hospital admission and who were free of pneumonia for at least 3 months. We collected clinical, functional, and medication-related information and pneumonia severity index (PSI). Using linked databases we followed patients for 5 years and captured any clinical episode of pneumonia 90 days or more post-discharge. We used Cox proportional hazards models (adjusted for age, sex, PSI, functional status, medications) to determine rates and independent correlates of recurrent pneumonia. RESULTS: The final cohort included 2709 inpatients; 43% were 75 years or older, 34% were not fully independent, and 56% had severe pneumonia. Over 5 years of follow-up, 245 (9%; 95% confidence interval [CI], 8%-10%) patients developed recurrent pneumonia, and 156 (64%) of these episodes required hospitalization. Rate of recurrence was 3.0/100 person-years and median time to recurrence was 317 days (interquartile range, 177-569); 32 (13%) patients had 2 or more recurrences. In multivariable analyses only age >75 years (adjusted P = .047) and less than fully independent functional status (12% recurrence rate with impaired functional status vs 7% for fully independent; adjusted hazard ratio, 1.7; 95% CI, 1.3-2.2; P < .001) were significantly associated with recurrent pneumonia. CONCLUSIONS: One of 11 patients who survived CAP hospitalization had recurrent pneumonia over 5 years and those with impaired functional status were at particularly high risk. Recurrent pneumonia is common and more attention to preventive strategies at discharge and closer follow-up over the long-term seem warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".