Predictors of revisit and admission after discharge from an emergency department in acute pyelonephritis

Changwoo Kang, Kyuseok Kim, Jae Hyuk Lee, You Hwan Jo, Jae W. Park, Soo Hoon Lee, Yu Jin Kim, Joong Eui Rhee, Dong Hoon Kim

Research output: Contribution to journalArticle


Introduction: The aim of this study is to construct clinical prediction models to predict emergency department (ED) return visit following initial discharge for acute pyelonephritis (APN) and the need for hospital readmission upon ED return visits. Method: A retrospective analysis included 1250 discharged women with APN. Independent risk factors for revisit and subsequent admission after revisit were determined by multivariate analysis, and a prediction model for revisit and subsequent admission after revisit was constructed. Results: Independent risk factors for revisit were nausea (2 points), age ≥65 years (2 points), C-reactive protein >20 mg/dL (2 points), serum albumin <3.3 g/dL (3 points) and urine white blood cell count ≥30/ high power field (2 points). Re-visit risk scores were categorised to five groups and the re-visit rate was 5.4%, 8.6%, 12.2%, 19.1% and 43.8%, respectively, showing an area under curve (AUC) of 0.62. Risk factors for subsequent admission after revisit were vomiting (1 point), hypertension (2 points), serum creatinine >1.5 mg/dL (2 points), C-reactive protein >20 mg/dL (2 points) and serum albumin <3.3 g/dL (4 points). The subsequent admission after revisit risk scores were classified to three groups and subsequent admission after revisit rate was 3.5%, 15.0%, and 38.2%, respectively. Conclusion: The developed model can identify a group of patients at high risk for a return visit and for requiring subsequent hospital admission, and might be used to improve initial disposition decision and discharge instructions.

Original languageEnglish
Pages (from-to)154-156
Number of pages3
JournalHong Kong Journal of Emergency Medicine
Issue number3
StatePublished - 1 May 2015


  • Albumins
  • C-reactive protein
  • Logistic models
  • Patient readmission
  • Risk assessment

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