Get access

Predicting post-discharge death or readmission: deterioration of model performance in population having multiple admissions per patient

Authors

  • Carl van Walraven MD MSc,

    Senior Scientist, Adjunct Scientist, Associate Professor, Corresponding author
    1. Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
    2. Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
    3. Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
    • Correspondence

      Dr Carl van Walraven

      1053 Carling Avenue, Administrative Services Building, 1st Floor, Room 1-003

      Ottawa, ON

      Canada K1Y 4E9

      E-mail: carlv@ohri.ca

    Search for more papers by this author
  • Jenna Wong MSc,

    Methodologist, Analyst
    1. Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
    2. Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
    Search for more papers by this author
  • Alan J. Forster MD MSc,

    Senior Scientist, Adjunct Scientist, Associate Professor
    1. Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
    2. Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
    3. Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
    Search for more papers by this author
  • Stephen Hawken MSc

    Senior Analyst
    1. Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
    Search for more papers by this author

Abstract

Background

To avoid biased estimates of standard errors in regression models, statisticians commonly limit the analytical dataset to one observation per patient.

Objective

Measure and explain changes in model performance when a model predicting 30-day risk of death or urgent readmission (derived on a dataset having one hospitalization per patient) was applied to all hospitalizations for study patients.

Methods

Using administrative data from Ontario, we identified all hospitalizations of 499 996 patients between 2004 and 2009. We calculated the expected risk for 30-day death or urgent readmission using a validated model. The observed-to-expected ratio was determined after categorizing patients into quintiles of rates for hospitalization, emergent hospitalizations, hospital day and total diagnostic risk score.

Results

Study patients had a total of 858 410 hospitalizations. Compared with a dataset having one hospitalization per patient, model performance declined significantly when applied to all hospitalizations [c-statistic decreased from 0.768 to 0.730; the observed-to-expected ratio increased from 0.998 (95% confidence interval 0.977–0.999) to 1.305 (1.297–1.313)]. Model deterioration was most pronounced in patients with higher hospital utilization, with the observed-to-expected ratio increasing to 1.67 in the highest quintile of emergent hospitalization rates.

Conclusions

The accuracy of predicting 30-day death or urgent readmission decreased significantly when the unit of analysis changed from the patient to the hospitalization. Patients with heavy hospital utilization likely have characteristics, not adequately captured in the model, that increase the risk of death or urgent readmission after discharge from hospital. Adequately capturing the characteristics of such high-end hospital users may improve readmission models.

Ancillary