Between October 2002 and July 2006, we enrolled 5035 patients from 11 hospitals. We determined outcome status at 30 days for 4812 patients (95.6%). Of the remaining patients, 124 (2.5%) refused participation when contacted for follow-up, 83 (1.6%) were lost to follow-up, and 16 (0.3%) were removed from the study because they were admitted to a nursing home during the first month after discharge from hospital.
The study cohort is described in . Participants were middle-aged, and almost 95% were independent with regard to activities of daily living. Most participants were free of serious comorbidities, with more than 75% having a Charlson comorbidity index score of zero.13
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 (Appendix 1). Coronary artery bypass grafting and arthroplasty were the most common procedures. Patients in the derivation (n
= 2393) and validation (n
= 2419) cohorts were similar.
During the first 30 days after discharge, 385 (8.0%) patients died or were urgently readmitted (death 36 [9.4% of outcomes], unplanned readmission 349 [90.6% of outcomes]). Patients with one of the primary outcomes had more emergency department visits before admission and were more likely to be admitted emergently and for longer durations than patients who did not die (). Most other patient-related and admission-related variables appeared to have little influence on risk of early death or unplanned readmission.
Index derivation and internal validation
Only four variables were independently associated with death or readmission within 30 days after discharge (). These variables were length of stay (“L;” odds ratio [OR] 1.47, 95% CI 1.25–1.73) acuity of the admission (“A;” odds ratio [OR] 1.84, 95% CI 1.29–2.63), patient comorbidity (as measured using the total Charlson comorbidity index score) (“C;” odds ratio [OR] 1.21, 95% CI 1.10–1.33), and emergency department use (measured as the number of visits in the previous six months) (“E;” odds ratio [OR] 1.56, 95% CI 1.27–1.92). Length of stay was modelled as a logarithm and the number of emergency department visits was modelled as a square root term. We found no significant interactions between these or other variables. The final logistic model was moderately discriminative (C statistic 0.700) and was well calibrated (Hosmer–Lemeshow goodness of fit statistic 6.99, 8 degrees of freedom, p = 0.54). None of the other variables listed in met our criteria for inclusion in the model.
Final logistic regression model for risk of death or unplanned readmission within 30 days after discharge (derivation group only, n = 2393)
We modified this logistic model into an index to predict early death or unplanned readmission (). To facilitate recall of the components of the index, we titled the index using a simple mnemonic. The LACE index had a potential score ranging from 0 to 19. The total LACE score in the study population had a normal distribution that was slightly skewed to the right ().
LACE index for the quantification of risk of death or unplanned readmission within 30 days after discharge
Figure 1 Calibration curve for the LACE index, based on data representing patients in the derivation and internal validation groups. Note: bars = number of patients with the same LACE score; black line = expected risk of death or unplanned readmission within 30 (more ...)
The LACE index had moderate discrimination for early death or readmission. The C statistic (95% CI) in the derivation was 0.7114 (0.6736–0.7491). In the validation, it was 0.6935 (0.6548–07321), and in the entire cohort, it was 0.7025 (0.6755–0.7295).
The expected probability of death or readmission within 30 days of discharge for each point ranged from 2.0% for a LACE score of 0 to 43.7% for a LACE score of 19 (). The expected probability of early death or unplanned readmission was within the 95% CIs of the observed rates for all LACE scores in both the derivation and validation cohorts () as well as the entire cohort (). The Hosmer–Lemeshow statistic in the derivation was 18.7 (p = 0.42). In the validation, it was 14.1 (p = 0.59), and in the entire cohort, it was 21.2 (p = 0.27) ().
Expected and observed probability of death or unplanned readmission within 30 days after discharge, by LACE score
The LACE score was strongly associated with each outcome individually. A 1-point increase in the LACE score increased the odds of unplanned readmission by 18% (odds ratio 1.18, 95% CI 1.14–1.21). The LACE index in the entire cohort was moderately discriminative for 30-day unplanned readmission (C statistic 0.679, 95% CI 0.650–0.708) and well calibrated (Hosmer–Lemeshow statistic 11.5, p = 0.18). A one-point increase in the LACE score increased the odds of early death by 29% (odds ratio 1.29, 95% CI 1.20–1.38). The LACE index was very discriminative for early death (C statistic 0.793, 95% CI 0.733–0.854) and well calibrated (Hosmer–Lemeshow statistic 4.7, p = 0.79).
The external validation group contained 1 000 000 randomly selected patients (mean age 59.1, standard deviation [SD] 18.4 years; 48.4% female). Patients had a mean length of stay of 5.1 days (SD 7.7), a mean Charlson comorbidity index score of 0.5 (SD 1.2), and a mean number of emergency department visits of 0.4 (SD 7.9), with 67.6% of the index admissions emergent. Patients had a mean LACE score of 6.0 (SD 3.1) and 7.8% of patients died (1.1%) or were urgently readmitted (7.3%) within 30 days of discharge. Discrimination of the LACE index was the same in this patient group (C statistic 0.684, 95% CI 0.679–0.691). The observed rate of early death or urgent readmission slightly exceeded the expected rates at most LACE scores (). However, the median absolute difference between expected and observed rates was small, at 1.6% (range 0.04%–6.6%).
Figure 2 External validation of the LACE index, as represented by its accuracy for 1 000 000 randomly selected patients discharged from hospital in Ontario between 2004 and 2008. Note: bars = number of patients with the same LACE score; black line = expected risk (more ...)
We have derived and validated an easy-to-use index that is moderately discriminative and very accurate for predicting the risk of early death or unplanned readmission after discharge from hospital to the community. Further research is required to determine whether such quantification changes patient care or outcomes.
We found its simplicity very notable. Although we derived the LACE index in a large cohort of patients using almost 50 factors — each of which could reasonably influence the risk of post-discharge outcomes — we found that four simple factors explained much of the variation in risk of early death or unplanned readmission after discharge from hospital. The LACE index therefore joins other indexes in which seemingly complex outcomes are predicted with a few simple factors.23
The LACE index has several strengths to support its use.24
The outcome predicted by the index is important, clinically relevant and reliably measured. Determination of this outcome for each patient was independent of the LACE score. Each component of the LACE index is readily and reliably determined. The methods we used to derive the LACE index were both valid and transparent. The discrimination of the LACE index was better than that of the widely used Framing-ham score in many populations,25–27
which suggests that the LACE index will be useful when applied at the individual patient level. The calibration of the LACE index was excellent, which suggests that it will also be useful when applied by policy-makers. Finally, the LACE index is easier to use than previous models, because the latter require variables —such as community admission rates28
or area-level socio-economic measures29
— that are usually unavailable to clinicians.
Three main limitations about the LACE index should be noted. First, the index cannot be used reliably in patient populations that were not involved in its derivation. Second, further work is required to identify additional factors that may increase the discrimination or accuracy of the index. Third, clinicians will find it difficult to commit to memory the point system and its expected risks. Therefore, use of the LACE index will usually require a computational aid. 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.
Notwithstanding its limitations, we believe that the LACE index can be used by researchers and administrators to predict the risk of early death or unplanned readmission of cognitively intact medical or surgical patients after discharge from hospital to the community. Further research is required to determine whether quantifying the risk of poor outcomes after discharge actually changes patient care or outcomes.