Association between prevalent care process measures and facility-specific mortality rates.
BACKGROUND
Medical communities often develop practice guidelines recommending certain care processes intended to promote better clinical outcome among patients. Conformance with those guidelines by facilities is then monitored to evaluate care quality, presuming that the process is associated with and can be used reliably to predict clinical outcome. Outcome is often monitored as a facility-specific mortality rate (SMR) standardized to the mix of patients treated, also presuming that inferior outcome implies a suboptimal process. The U.S. Health Care Financing Administration monitors three practice guidelines, called Core Indicators, in dialysis facilities to assist management of its end-stage renal disease program: (1) patients' hematocrit values should exceed 30 vol%, (2) the urea reduction ratio (URR) during dialysis should equal or exceed 65%, and (3) patients' serum albumin concentrations should equal or exceed 3.5 g/dL.


METHODS
The associations of a facility-specific SMR were evaluated with the fractions of hemodialysis patients not conforming to (that is, at variance with) the Core Indicators during three successive years (1993 to 1995) in large numbers of facilities (394, 450, and 498) using one-variable and multivariable statistical models. Three related strategies were used. First, the association of the SMR with the fraction of patients not meeting the guideline was evaluated. Second, each facility was classified by whether its SMR exceeded the 80% confidence interval above 1.0 (worse than 1.0, Group 3), was less than the interval below 1.0 (better than 1.0, Group 1), or was within the interval (Group 2). The fraction of those patients who did not meet the Indicator guidelines was then evaluated in each group. Third, the ability of variance from Indicator guidelines to predict into which of the three SMR groups a facility would be categorized was evaluated.


RESULTS
SMR was directly correlated with variance from the Indicator guidelines, but the strengths of the associations were weak particularly for the hematocrit (R(2) = 2.2%, 5.6, and 2.2 for each of the 3 years) and URR Indicators (R(2) = 2.6, 0.6, 3.3). It was stronger for the albumin Indicator (R(2) = 11.6, 20.4, 21.8). The fractions of patients falling outside of the Indicator guidelines tended to be higher in the highest SMR group. The groups were not well separated, however, particularly for the hematocrit and URR Indicators, and there was substantial overlap between them. Finally, although the likelihood that a facility would be a member of the high or low SMR group was associated with fractional variance from Core Indicator guidelines, the strengths of association were weak, and the probability that a facility would be a member of the high or low group could not be easily distinguished from the probability that it would be a member of the middle group.


CONCLUSIONS
While there were statistical associations between SMR and the fraction of patients in facilities who were at variance with these guidelines, they were weak and variances from the guidelines could not be used reliably to predict high or low SMR. Such findings do not imply that measures reflecting anemia, dialysis dose, or medical processes that influence serum albumin concentration are irrelevant to the quality of care. They do suggest, however, that more attention needs be paid to these and other associates and causes of mortality among dialysis patients when developing care process indicator guidelines.
Lowrie
E G
EG
Fresenius Medical Care (NA), Incorporated, 95 Hayden Avenue, Lexington, MA 02173, USA. Edmund.Lowrie@fmc-na.com
Teng
M
M
Lacson
E
E
Lew
N
N
Lazarus
J M
JM
Owen
W F
WF
D006801:Humans; D015999:Multivariate Analysis; D017410:Practice Guidelines as Topic; D011787:Quality of Health Care; D012044:Regression Analysis; D006435:Renal Dialysis / Q000401:mortality*; D012709:Serum Albumin / Q000032:analysis; D014508:Urea / Q000378:metabolism
D012709:Serum Albumin; D014508:Urea