Misdiagnosing Urinary Tract Infections as Delirium in Older Adults: A Systemic Review of Evidence from Ireland and the UK
Bibliographic record
Abstract
Abstract Background Delirium is a common, serious, and often under-recognized condition in older adults. Misdiagnosis as urinary tract infection (UTI) is widespread, particularly in institutional care settings, leading to inappropriate antibiotic use and poorer health outcomes. Methods A systematic review was conducted according to PRISMA guidelines. Literature searches were done using the PubMed, EMBASE, Cochrane, and HSE Library databases, focusing on studies published between 2015 and 2025. The inclusion criteria were studies that assessed the diagnosis of urinary tract infections (UTIs) in adults aged 65 and older with delirium, conducted in Ireland or the UK, and reported clinical outcomes. Data extraction concentrated on study design, sample size, care setting, diagnostic tools use, and misdiagnosis rates. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS). A meta-analysis was carried out using a random-effects model in RevMan 5.4. Results The misdiagnosis rate of UTIs in older adults with delirium was 36% (95% CI: 29–42%), with over one-third of delirium cases wrongly attributed to UTIs. Long-term care facilities had the highest misdiagnosis rate at 42%, followed by emergency departments at 35% and hospital wards at 28%. About 65% of misdiagnosed cases relied solely on positive dipstick results, causing frequent false positives. Only 40% of cases utilized standardized delirium screening tools. Empiric antibiotic therapy was prescribed in 37% of misdiagnosed cases without confirmatory urine cultures, raising concerns about antimicrobial overuse and associated risks such as adverse drug reactions and resistant infections. Conclusion UTI misdiagnosis remains a major barrier to accurate delirium management in older adults. Our findings target urgent policy action to standardize delirium screening, restrict inappropriate antibiotic use, and train healthcare staff. Embedding tools like 4AT in EMR, enhancing clinical governance through audits, and aligning with HSE/NICE antimicrobial guidelines could reduce diagnostic error and improve outcomes in geriatric population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".