Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke.

CONTEXT

Rehospitalization following inpatient medical rehabilitation has important health and economic implications for patients who have experienced a stroke.

OBJECTIVE

Compare logistic regression and neural networks in predicting rehospitalization at 3-6-month follow-up for patients with stroke discharged from medical rehabilitation.

DESIGN

The study was retrospective using information from a national database representative of medical rehabilitation patients across the US.

SETTING

Information submitted to the Uniform Data System for Medical Rehabilitation from 1997 and 1998 by 167 hospital and rehabilitation facilities from 40 states was examined.

PARTICIPANTS

9584 patient records were included in the sample. The mean age was 70.74 years (SD = 12.87). The sample included 51.6% females and was 77.6% non-Hispanic White with an average length of stay of 21.47 days (SD = 15.47).

MAIN OUTCOME MEASURES

Hospital readmission from 80 to 180 days following discharge.

RESULTS

Statistically significant variables (P <.05) in the logistic model included sphincter control, self-care ability, age, marital status, ethnicity and length of stay. Area under the ROC curves were 0.68 and 0.74 for logistic regression and neural network analysis, respectively. The Hosmer-Lemeshow goodness-of-fit chi-square was 11.32 (df = 8, P = 0.22) for neural network analysis and 16.33 (df = 8, P = 0.11) for logistic regression. Calibration curves indicated a slightly better fit for the neural network model.

CONCLUSION

There was no statistically significant or practical advantage in predicting hospital readmission using neural network analysis in comparison to logistic regression for persons who experienced a stroke and received medical rehabilitation during the period of the study.