Predicting Patients at Risk for Prolonged Hospital Stays.

BACKGROUND: Patients with prolonged hospitalizations account for 14% of all hospital days in US hospitals. Predicting which medical patients are at risk for prolonged hospitalizations would allow early proactive management to reduce their length of stay.
METHODS: Using the National Inpatient Sample, we examined risk factors for prolonged hospitalizations among adults hospitalized on the medicine service in 2014. We defined prolonged hospitalizations as those lasting 21 days or longer. We divided the sample into derivation and validation sets, and used logistic regression to identify significant risk factors in the derivation set, which were validated in the validation set. We used the estimates from the model to derive a risk score for prolonged hospitalizations.
RESULTS: Our sample included 2,997,249 hospitalizations (median age of 66 y, 53.5% female). 1.2% of hospitalizations were 21 days or longer. Patients with prolonged hospitalizations were younger, and had a greater number of chronic diseases. A prolonged hospitalization risk score, derived from the many significant predictors in our model, performed well in discriminating between prolonged and nonprolonged hospitalizations, with c-statistics of 0.80 in both the derivation and validation sets.
CONCLUSIONS: Our predictive model using readily available administrative data was able to discriminate between prolonged and nonprolonged hospitalizations in a national sample of medical patients, and performed well on internal validation. If prospectively validated, such a tool could be of use to hospitals and researchers interested in targeting development, testing, and/or deployment of programs to reduce length of stay.