ArticlePloS one2023
Medical Admission Prediction Score (MAPS); a simple tool to predict medical admissions in the emergency department.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Predicting ICU transfer for high-risk patients upon medical admission via the medical intensive care prediction score (MICAPS).BMC emergency medicine · 2026Article
- Care Complexity Factors and Discharge Destination in an Emergency Department: A Retrospective Cohort Study.Archives of academic emergency medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOvercrowding in the emergency departments (ED) is linked to adverse clinical outcomes, a negative impact on patient safety, patient satisfaction, and physician efficiency. We aimed to design a medical admission prediction scoring system based on readily available clinical data during ED presentation.
methodsIn this retrospective cross-sectional study, data on ED presentations and medical admissions were extracted from the Emergency and Internal Medicine departments of a tertiary care facility in Qatar. Primary outcome was medical admission.
resultsOf 320299 ED presentations, 218772 were males (68.3%). A total of 11847 (3.7%) medical admissions occurred. Most patients were Asians (53.7%), followed by Arabs (38.7%). Patients who got admitted were older than those who did not (p <0.001). Admitted patients were predominantly males (56.8%), had a higher number of comorbid conditions and a higher frequency of recent discharge (within the last 30 days) (p <0.001). Age > 60 years, female gender, discharge within the last 30 days, and worse vital signs at presentations were independently associated with higher odds of admission (p<0.001). These factors generated the scoring system with a cut-off of >17, area under the curve (AUC) 0.831 (95% CI 0.827-0.836), and a predictive accuracy of 83.3% (95% CI 83.2-83.4). The model had a sensitivity of 69.1% (95% CI 68.2-69.9), specificity was 83.9% (95% CI 83.7-84.0), positive predictive value (PPV) 14.2% (95% CI 13.8-14.4), negative predictive value (NPV) 98.6% (95% CI 98.5-98.7) and positive likelihood ratio (LR+) 4.28% (95% CI 4.27-4.28).
conclusionMedical admission prediction scoring system can be reliably applied to the regional population to predict medical admissions and may have better generalizability to other parts of the world owing to the diverse patient population in Qatar.
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