Translating the Pneumonia Severity Index into Practice: A Trial to Influence the Admission Decision

Yealy, DM

doi:10.1197/aemj.9.5.361-a

Cited by 3 publications

(2 citation statements)

References 0 publications

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“…There is mounting evidence that PSI scores could support the decision whether to hospitalize patients with pneumonia. 15,22,[25][26][27][28] PSI scores can be generated at or before hospital admission, early enough for use in prescribing proper hospital level of care at the time of hospital admission.…”

Section: Discussionmentioning

confidence: 99%

Severe Sepsis in Community-Acquired Pneumonia

Dremsizov

Clermont

Kellum

et al. 2006

Chest

150

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Section: Discussionmentioning

confidence: 99%

Severe Sepsis in Community-Acquired Pneumonia

Dremsizov

Clermont

Kellum

et al. 2006

Chest

150

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“…Third, derivation of a pneumonia clinical prediction rule using a PHC4 inpatient database similar to ours was successfully validated in a cohort of outpatients and inpatients and after follow-up implementation was found to safely reduce low-risk patient admissions from the ED. [35][36][37] We anticipate a similar pattern of performance for our heart failure clinical prediction rule. It would be premature to consider the rule ready for widespread application until its performance has been validated in a full complement of ED inpatients and outpatients and its safety demonstrated in controlled implementation trials.…”

Section: Limitationsmentioning

confidence: 56%

A Prediction Rule to Identify Low-risk Patients with Heart Failure

Auble

Hsieh

Gardner

et al. 2005

Academic Emergency Medicine

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Objectives: To derive a prediction rule using data available in the emergency department (ED) to identify a group of patients hospitalized for the treatment of heart failure who are at low risk of death and serious complications. Methods: The authors analyzed data for all 33,533 patients with a primary hospital discharge diagnosis of heart failure in 1999 who were admitted from EDs in Pennsylvania. Candidate predictors were demographic and medical history variables and the most abnormal examination or diagnostic test values measured in the ED (vital signs only) or on the first day of hospitalization. The authors constructed classification trees to identify a subgroup of patients with an observed rate of death or serious medical complications before discharge ,2%; the tree that identified the subgroup with the lowest rate of this outcome and an inpatient mortality rate ,1% was chosen. Results: Within the entire cohort, 4.5% of patients died and 6.8% survived to hospital discharge after experiencing a serious medical complication. The prediction rule used 21 prognostic factors to classify 17.2% of patients as low risk; 19 (0.3%) died and 59 (1.0%) survived to hospital discharge after experiencing a serious medical complication. Conclusions: This clinical prediction rule identified a group of patients hospitalized from the ED for the treatment of heart failure who were at low risk of adverse inpatient outcomes. Model performance needs to be examined in a cohort of patients with an ED diagnosis of heart failure and treated as outpatients or hospitalized.

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