A model developed to predict 30-day readmissions for heart failure found that having patients self-report their socioeconomic, health status and psychosocial characteristics did not improve the researchers’ ability to determine the readmissions risk.
Harlan M. Krumholz, MD, SM, of the Yale School of Medicine in New Haven, Conn., and colleagues published their results online in the Journal of the American College of Cardiology: Heart Failure on Dec. 2.
“Even with the inclusion of a number of patient-centered variables obtained shortly after admission, there was only minor improvement in the discrimination of a risk model to predict 30-day readmissions after a post-discharge interview following a heart failure hospitalization,” they wrote. “Although our potential predictors were much more extensive than those used in previous studies and our outcomes were validated, we were unable to develop models with high discrimination. Our results reveal that the limitations in predicting readmission do not stem from not having information about the patient’s symptoms, health status, psychosocial characteristics, access to health care, or economic status.”
They noted that previous models have been shown to do a poor job of predicting the risk of readmission. However, those models had not included information obtained from the patient beyond clinical and basic demographic characteristics.
In this study, they analyzed data from the Tele-HF (Telemonitoring to Improve Heart Failure Outcomes) study, which found there was no difference in readmission, death or the combined endpoint of death or readmission whether patients received telemonitoring or usual care. They evaluated patients who were at least 18 years old and had been hospitalized for heart failure in the previous 30 days at 33 U.S. sites.
Patients were excluded if they were not interviewed between 3 and 30 days post-discharge or who were readmitted between discharge and the interview.
The researchers obtained baseline data by reviewing their medical records and interviewing them. The median time from discharge to the interview was 12 days. They obtained readmission data through medical record review, patient interviews at 3 and 6 months after enrolling in the study and contact with the hospitals.
Of the total population of 1,004 patients, the mean age was 62, while 41 percent were women and 40 percent were African-Americans. Most patients had New York Heart Association functional class II or III heart failure when admitted, while approximately three-quarters had hypertension and nearly half had diabetes.
The researchers analyzed 110 variables to evaluate risk factors, and they split the risk factors into two groups: demographic and clinical variables generally available from medical records and socioeconomic, health status and psychosocial variables that are not generally available. Of the 110 variables, 27 were classified as demographic or clinical.
They found that only three variables obtained at admission (blood urea nitrogen level, reported swelling and reported shortness of breath) had a statistically significant independent effect on readmission
Overall, the 30-day mortality rate was 1.7 percent and the 30-day readmission rate was 17.1 percent. Both rates were measured from the time of the interview.
Based on the 3-level risk score researchers derived from restricted medical record variables, the readmission rates were 10.9 percent for patients with a score of 0 (no risk factors) and 32.1 percent for patients with a risk score of 2 (all risk factors). Based on the 5-level risk score researchers derived from restricted medical record variables, the readmission rates were 9.6 percent for patients with a score of 0 (no risk factors) and 55.0 percent for patients with a risk score of 4 (all risk factors).
“In practice, there is a need to recognize that risk-stratification of patients for their risk of readmission is challenging. Even the lowest risk patients have a substantial risk. Clinicians should recognize the limitations of the current readmission models and appreciate that there are likely unmeasured factors that may be providing a strong influence on patient recovery.”