Contact Us

Call us at +1-877-867-0945 or
Chat Now

Chat with Predixion expert now

Contact Us

Have a question or want more information?


Exposing the Shortcomings of LACE as a Valid Tool for Reducing Hospital Readmissions

Sep 24 2012

With the first round of CMS penalties about to come out for hospitals with unacceptable readmission rates, there will be a lot of people suddenly interested in ways to decrease their readmission rates. I’m guessing that a lot of those people will want to use the LACE index, developed in Ontario, Canada, to predict the risk of patients for death or unplanned readmission within 30 days of discharge. LACE is an acronym for Length of Stay, Acuity of admission, Co-morbities (as measured by a Charlson Score) and number of previous ED visits in the last six (6) months preceding this admission.

Like many things in medicine, many will rush willy-nilly, to acquire this tool as the latest and greatest “thing” that will fix a readmission problem without truly understanding how it was derived, how valid it might be and what it really can tell you about your readmit risk. I can’t explain this, but it just seems that how it works in medicine today – if other people are using it, it must be good and I will use their use of it as a surrogate for validity.

I want to ask the question; what does LACE really give you? Assuming that it is valid to use on your population (and that, my friend, is a VERY big assumption, as you would know if you were to actually read the paper[1] ), how do I use this score to intervene in those with increased risk? In other words, does this tool’s score for an actual live patient point me, as a discerning clinician, to something I can actually do to prevent a readmission, in this patient?

Consider what LACE really tells you. It is a score based on Administrative data, as in NOT clinical data. The LACE index generates a score ranging from 0 to 19. The expected probability of death or readmission within 30 days of discharge for each point ranges from 2.0% for a LACE score of 0 to 43.7% for a LACE score of 19. A measure of accuracy of the score, the C-statistic, was 0.7, a moderately discriminative result.

Calculating a LACE score is not possible while the patient is still hospitalized. The “L” in LACE can only be calculated after the patient has been discharged, putting it at a distinct disadvantage if you wanted to use it to prevent a readmission of a patient you’re wondering if you can discharge. NONE of these indicators are what I like to call actionable. This lack of real time and relevant data means that an elevated risk score from LACE doesn’t tell you what to do to keep that patient from readmitting. In other words, there is no data that illustrates the leading influencers for each and every patient at risk for readmission, be it high or low. How can you prevent a readmission unless you know this?

The applicability of LACE across broad demographics is also highly suspect. The tool was developed using participants who were middle-aged, living independently (95%), were free of serious comorbidities, with more than 75% having a Charlson comorbidity index score of zero. Most admissions were emergent (58.1%), and almost half (44.9%) were to a medical service. The most common reasons for hospital admission included acute coronary syndromes, cancer diagnosis and complications, and heart failure. Coronary artery bypass grafting and arthroplasty were the most common procedures. If your hospital demographics match this population, then it’s probably a valid test to produce a risk score at your facility. But even then, you’ll still be left having to determine, for every single admitted patient, their specific key influencer for readmission. So, this begs the following questions; how good is LACE for all those patients you admit that don’t fit the above profile, how does LACE help you to identify the truly high-risk patients and give you data to help prevent a readmission?

Even the authors of the paper that developed LACE acknowledged that it has some major limitations, the most significant of which is that the index cannot be used reliably in patient populations that were not involved in its derivation. The authors warned that “until the LACE index is externally validated with primary data, we recommend that it be used for outcomes research and quality assurance rather than in decision-making for individual patients.” To date, there has been no such validation.

So, what does all this mean? Before you go jumping on the LACE bandwagon as the panacea for all your readmission problems, understand the limitations of the tool and decide if it is applicable to your patient population. Understand that even the authors advise that it does not provide actionable information that could decrease the risk of readmission if applied to individual patients.

Intuitively, we all know that what’s needed is a solution that can provide not only a risk score in real time, but the specific factors for each and every admitted patient that influence that risk. Only then can we act to reduce the risk of and prevent unplanned readmissions in any significant way. I believe that predictive analytics can provide such a solution.

[1] Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. van Walraven C, et. Al; CMAJ. 2010 Apr 6;182(6):551-7.

Total: 0 Comment(s)
Active Blog
Predixion Team
20 0 3/23/2015
Get Predixion Insight™


877.867.0945 or Live Chat Now

Start a conversation with a real human on the other end of the line