Provider-level hypotheses

The clear difference of the estimates of the operative delay effect on mortality between the methods indicated that there was a need for additional methods to explain such differences. Fortunately, the results pointing out the huge variation between providers in the shares of late surgery patients made it possible to formulate novel hypotheses to address the problem. First, it was assumed that

7 Operative delay as a performance indicator

an acceptable delay in hip fracture surgery corresponds to the clinical decision to postpone the operation and that the clinical decisions remain nearly constant within providers. Under this assumption, the potentially achievable lower limit in the proportion of late surgery patients could also be interpreted as the upper limit for the proportion of acceptable delayed patients. Correspondingly, the expected proportion of unacceptable delayed patients was the proportion of late surgery patients exceeding this upper limit. This inference resulted in a hypothesis stating that the overall mortality of hip fracture patients should increase with the rising share of the late surgery patients given that the longer operative delay would have an adverse effect on mortality. Another novel provider-level hypothesis was that the long-term mortality of the late surgery patients is higher if only the patients who are unfit for surgery are delayed, since the unfit condition for the surgery is also a risk factor for one-year mortality.

In order to test these hypotheses, the provider-level heterogeneity was further examined in terms of simultaneous provider specific shares of the late surgery patients and one-year mortality. The provider-specific overall mortalities were decomposed into mortalities of early and late surgery patients by recognizing the fact that the weighted average of these mortalities gives the overall mortality, i.e.

that

m_overall

(

p

)

=

[

p · m_late

(

p

)]

+

[(

1 – p

)

· m_early

(

p

)]

,

where p corresponds to the proportion of late surgery patients and m

(

p

)

to mortality with proportion p of late surgery patients.

It was straightforward to calculate these three (adjusted) mortalities for each provider, and use a scatter plot between the share of late surgery patients and mortalities to describe the associations. In order to extract trends from these associations, it was, after preliminary examinations, assumed that the trends between the share of late surgery patients and overall as well as early surgery mortalities are linear, i.e. that they have the forms

m_overall

(

p

)

= a + bp, and

m_early

(

p

)

= c + dp,

where p is the proportion of late surgery patients, and a, b, c and d are regression coefficients to be estimated from the observed data. Late surgery mortality could then be calculated deterministically from the weighted average formula given above.

Due to the extensive random variation in mortality figures among small providers, there was a need for hierarchical modeling giving shrunken estimates.

7 Operative delay as a performance indicator

s_i = w_io_i +

(

1 – w_i

)

e_i,

where s_i is the shrunken mortality estimate for provider i, o_i corresponds to observed and e_i to guessed mortality, and w_i is the (probability) weight to be estimated from the data [173]. The guesses were chosen to be the trends extracted from the data by using the formulas given above, and the weights and shrunken estimates were calculated by using Stein estimation in the form of an iterative algorithm [172].

This simultaneous examination of the provider-level proportions of the late surgery patients and one-year mortality illustrated in Figure 16 revealed that there was an almost flat (positive) trend between the greater share of late surgery patients and one-year overall mortality. This confirmed that the actual independent effect of operative delay on mortality is very small. Another result indicated that the smaller proportion of late surgery patients was clearly (nonlinearly) associated with a higher mortality for these patients. In other words, the providers with low proportions of late surgery patients had been able to select the patients with such severity that immediate operation would not have been clinically justified.

0 10 20 30 40 50

Share of patients with late surgery, % 0

Share of patients with late surgery, % 0

Share of patients with late surgery, % 0

Share of patients with late surgery, % 0

Figure 16. A scatter plot of adjusted mortality for the operated hip fracture patients and the share of hip fracture patients with late surgery for different providers. (The triangles describe the overall mortality of the operated hip fracture patients for each provider, and the crosses the mortality of the late surgery patients for each provider. The line with a positive slope is the trend of overall

mortality, and the dotted curve describes the association between the share of late surgery patients and mortality across the providers.)

FIguRE 16. A scatter plot of adjusted mortality for the operated hip fracture patients and the share of hip fracture patients with late surgery for different providers. (The triangles describe the overall mortality of the operated hip fracture patients for each provider, and the crosses the mortality of the late surgery patients for each provider. The line with a positive slope is the trend of overall mortality, and the dotted curve describes the association between the share of late surgery patients and mortality across the providers.)

7 Operative delay as a performance indicator

The provider-level associations were further analyzed for five severity groups of patients by using the same methodology. The severity was measured as predicted one-year mortality for observed patient characteristics, and groups were formed by dividing the patients into five equivalently sized classes on the basis of sorted severity score. Intuitively speaking, the least severe patients were young, coming from home and were without severe medical conditions. Correspondingly the most severe patients were older, with much comorbidity and coming from residential care. For the patients in the least severe group, mortality was higher for the late surgery patients for all providers. Probably this indicated that in this group, the hip-fracture-related mortality was caused by the medical problems which necessitated late surgery and the prolonged surgical delay did not itself increase the mortality.

For the patients in the most severe group, mortality was also higher for the late surgery patients for all providers. However, for this group the late surgery mortality was lower in the providers with a small share of late surgery patients and there were no differences in early surgery mortalities between the providers. It seems that in this group, it was essential to perform early surgery for all patients who could bear it, since the significantly prolonged surgical delay makes the patient’s condition worse and increases mortality. For groups 2–4 the interpretation was more difficult than for extreme patients. Since the mortality of early surgery patients increased significantly and was even higher than the mortality of the late surgery patients for the providers with a large share of late surgery patients, it seems that in these groups an operation too early may have caused more harm than the prolonged waiting time. In summary, these results actually gave reasonable explanations for the mixed results in the existing literature.

7.5 Conclusions on operative delay as a

In document Methodological perspectives for register-based health system performance assessment : Developing a hip fracture monitoring system in Finland (sivua 73-76)