More than half a million people in the United States receive treatment for
kidney failure, mostly through hemodialysis. On average,
‘Women with previous co-morbidities such as liver failure, heart disease and those addicted to drugs were more likely to get readmitted after hemodialysis.’
In 2017, the Centers for Medicare and Medicaid Services began penalizing
outpatient dialysis units for excessive readmissions. Despite these efforts,
there is a lack of information regarding characteristics and predictors of
To investigate, a team led by Girish Nadkarni, MD, MPH and Lili Chan, MD, MS
(Icahn School of Medicine at Mount Sinai) set out to determine the nationwide
readmission rate in dialysis patients and to examine reasons for initial
admissions and readmissions.
When the researchers analyzed 2013 data from the Nationwide Readmission
Database, they found 390,627 initial hospitalizations of hemodialysis patients,
and 22% of these initial hospitalizations were followed by an
unplanned readmission within 30 days
. Readmission rates were similar
across the top 10 initial admission diagnoses, and only 20% of readmission's
were for the same diagnosis as the initial admission.
"Regardless of what patients initially were admitted for, they had
similar readmission rates. This along with the low concordance suggests that we
need to focus on the patient as a whole rather than their admission
diagnoses," said Dr. Chan.
The investigators also found that patient characteristics that were
associated with a high likelihood of readmission included female gender,
younger age, depression, liver disease, congestive heart failure, and drug
abuse. Importantly, only a small proportion (2%) of all patients accounted for
20% of all readmissions.
"To reduce readmissions in dialysis patients, perhaps a good starting
place would be to institute interventions targeted at high utilizers and create
a validated risk score incorporating likely risk factors," said Dr.
In an accompanying editorial, Magdalene Assimon, PharmD, MS and Jennifer Flythe,
MD, MPH (UNC School of Medicine) noted that there is surprisingly little
published data evaluating interventions designed to reduce readmissions and
stressed "the need for innovative, integrative data analytics in
readmission risk modeling and a greater emphasis on testing and refining readmission