Derivation of age-adjusted LACE index thresholds in the prediction of mortality and frequent hospital readmissions in adults

Predictive Value of LACE Scores for Pediatric Readmissions

INTRODUCTION

Hospital readmissions are recognized as a prevalent, yet potentially preventable, personal and economic burden. Length of stay, Acuity of admission, Comorbidities, and number of Emergency Department visits in the preceding 6 months can be quantified into one score, the LACE score. LACE scores have previously been identified to correlate with hospital readmissions within 30 days of discharge, but research specific to the pediatric population is scant. The objective of the present study was to investigate if LACE scores, in addition to other factors, can be utilized to create a predictive pediatric hospital readmission model that may ultimately be used to decrease readmission rates.

METHODS

This study included 25,616 hospitalizations of patients under the age of 18 years. Data were extracted from a hospital network electronic medical record. Demographics included LACE scores, age, gender, race/ethnicity, median household income, and medical centers. The primary exposure variable was LACE score. The main outcome measures were readmissions within 7, 14, and 30 days. The area under the curve (AUC) was used to assess the predictive capability of the regression model on patient 30-day admission.

RESULTS

LACE scores, age, gender, race/ethnicity, median household income, and medical centers were examined in a multivariable model to assess patient risk of a 30-day readmission. Only age and LACE score were observed to be statistically significant. The AUC for the combined model was 0.69.

DISCUSSION

As only age and LACE score were observed to be statistically significant and the AUC for the combined model was 0.69, this model is considered to have poor predictive capability.

CONCLUSIONS

In this study, LACE scores, as well the other factors, had a poor predictive capability for pediatric readmissions.

Causes for 30-day readmissions and accuracy of the LACE index in regional Victoria, Australia

BACKGROUND

Clinicians and funders continue to search for ways to reduce costs without sacrificing quality of care. Ongoing research should focus on innovative care models that identify patients at high-risk for hospitalisation and thereby reduce healthcare costs.

AIMS AND OBJECTIVES

This study examined readmission rates, comorbidity profiles and the performance of the LACEi (Length of stay, Acuity of admission, Charlson Comorbidity Index, ED admissions in the previous 6 months index) to predict the risk of 30-day readmissions in a regional population. Furthermore, we tested a novel clinician-orientated classification for the causes of 30-day readmissions.

DESIGN

Using a nested case-control design, data were extracted from administrative health records using 30-day readmission status as the outcome. We defined cases as discharges within 30 days before readmission and controls without a discharge within 30 days before admission between 1 July 2020 and 30 June 2022.

SETTING

The study was conducted at South West Healthcare in Victoria, Australia.

PARTICIPANTS

All adult medical patients were discharged alive from the facility. We excluded planned readmissions, surgical and obstetric admissions, dialysis, transfers to alternative facilities and discharges against medical advice.

MAIN OUTCOME MEASURES

Thirty-day readmission rate, comorbidity profile for all admissions, LACEi for all admissions, the performance of the LACEi in our setting and the causes leading to readmission using a clinician-orientated classification tool.

RESULTS

Comorbidity burden, male sex and age > 65 years were associated with increased readmission risk but not length of stay. The LACEi demonstrated modest predictive ability to identify high-risk patients for readmissions (area under the receiver operating characteristic curve = 0.59). Additional variables were needed to increase accuracy. The novel classification identified 42% of readmissions as potentially avoidable.

CONCLUSION

Our study identified comorbidity burden, male sex and age ≥ 65 years as critical indicators for readmission risk. Although the LACEi showed moderate predictive ability, additional variables were needed for increased accuracy. Over 40% of readmissions were potentially avoidable, and nearly two thirds occurred within 14 days of discharge from the hospital.

Factors Affecting the Effectiveness of Discharge Planning Implementation: A Case-Control Cohort Study

BACKGROUND

In many hospitals, a discharge planning team works with the medical team to provide case management to ensure high-quality patient care and improve continuity of care from the hospital to the community. However, a large-scale database analysis of the effectiveness of overall discharge planning efforts is lacking.

PURPOSE

This study was designed to investigate the clinical factors that impact the efficacy of discharge planning in terms of hospital length of stay, readmission rate, and survival status.

METHODS

A retrospective study was conducted based on patient medical records and the discharge plans applied to patients hospitalized in a regional medical center between 2017 and 2018. The medical information system database and the care service management information system maintained by the Ministry of Health and Welfare were used to collect data and explore patients' medical care and follow-up status.

RESULTS

Clinical factors such as activities of daily living ≤ 60, having indwelling catheters, having poor control of chronic diseases, and insufficient caregiver capacity were found to be associated with longer hospitalization stays. In addition, men and those with indwelling catheters were found to have a higher risk of readmission within 30 days of discharge. Moreover, significantly higher mortality was found after discharge in men, those ≥ 75 years old, those with activities of daily living ≤ 60, those with indwelling catheters, those with pressure ulcers or unclean wounds, those with financial problems, those with caregivers with insufficient capacity, and those readmitted 14-30 days after discharge.

CONCLUSIONS

The findings of this study indicate that implementing case management for discharge planning does not substantially reduce the length of hospital stay nor does it affect patients' readmission status or prognosis after discharge. However, age, underlying comorbidities, and specific disease factors decrease the efficacy of discharge planning. Therefore, active discharge planning interventions should be provided to ensure transitional care for high-risk patients.

High LACE index scores are associated with disproportionate excess deaths in hospital amongst patients with COVID-19

Risk factors for COVID-19-related outcomes have been variably reported. We used the standardised LACE index to examine admissions and in-hospital mortality associated with COVID-19. Data were collected in the pre-pandemic period (01-04-2019 to 29-02-2020) from 10,173 patients (47.7% men: mean age ± standard deviation = 68.3 years ± 20.0) and in the pandemic period (01-03-2019 to 31-03-2021) from 12,434 patients. With the latter, 10,982 were without COVID-19 (47.4% men: mean age = 68.3 years ± 19.6) and 1452 with COVID-19 (58.5% men: mean age = 67.0 years ± 18.4). Admissions and mortality were compared between pre-pandemic and pandemic patients, according to LACE index. Admission rates rose disproportionately with higher LACE indices amongst the COVID-19 group. Mortality rates amongst the pre-pandemic, pandemic non-COVID-19 and COVID-19 groups with LACE index scores < 4 were 0.7%, 0.5%, 0%; for scores 4-9 were 5.0%, 3.7%, 8.9%; and for scores ≥ 10 were: 24.2%, 20.4%, 43.4%, respectively. The area under the curve receiver operating characteristic for predicting mortality by LACE index was 76% for COVID-19 and 77% for all non-COVID-19 patients. The risk of age and sex-adjusted mortality did not differ from the pre-pandemic group for COVID-19 patients with LACE index scores < 4. However, risk increased drastically for scores from 4 to 9: odds ratio = 3.74 (95% confidence interval = 2.63-5.32), and for scores ≥ 10: odds ratio = 4.02 (95% confidence interval = 3.38-4.77). In conclusion, patients with LACE index scores ≥ 4 have disproportionally greater risk of COVID-19 hospital admissions and deaths, in support of previous studies in patients without COVID-19. However, of importance, our data also emphasise their increased risk in patients with COVID-19. Because the LACE index has a good predictive power of mortality, it should be considered for routine use to identify high-risk COVID-19 patients.

Evaluation of the association of length of stay in hospital and outcomes

BACKGROUND

There exist wide variations in healthcare quality within the National Health Service (NHS). A shorter hospital length of stay (LOS) has been implicated as premature discharge, that may in turn lead to adverse consequences. We tested the hypothesis that a short LOS might be associated with increased risk of readmissions within 28 days of hospital discharge and also post-discharge mortality.

METHODS

We conducted a single-centred study of 32 270 (46.1% men) consecutive alive-discharge episodes (mean age = 64.0 years, standard deviation = 20.5, range = 18-107 years), collected between 01/04/2017 and 31/03/2019. Associations of LOS tertiles (middle tertile as a reference) with readmissions and mortality were assessed using observed/expected ratios, and logistic and Cox regressions to estimate odds (OR) and hazard ratios (HR) (adjusted for age, sex, patients' severity of underlying health status and index admissions), with 95% confidence intervals (CIs).

RESULTS

The observed numbers of readmissions within 28 days of hospital discharge or post-discharge mortality were lower than expected (observed: expected ratio < 1) in patients in the bottom tertile (<1.2 days) and middle tertile (1.2-4.3 days) of LOS, whilst higher than expected (observed: expected ratio > 1) in patients in the top tertile (>4.3 days), amongst all ages. Patients in the top tertile of LOS had increased risks for one readmission: OR = 2.32 (95% CI = 1.86-2.88) or ≥2 readmissions: OR = 6.17 (95% CI = 5.11-7.45), death within 30 days: OR = 2.87 (95% CI = 2.34-3.51), and within six months of discharge: OR = 2.52 (95% CI = 2.23-2.85), and death over a two-year period: HR = 2.25 (95% CI = 2.05-2.47). The LOS explained 7.4% and 15.9% of the total variance (r2) in one readmission and ≥2 readmissions, and 9.1% and 10.0% of the total variance in mortality with 30 days and within six months of hospital discharge, respectively. Within the bottom, middle and top tertiles of the initial LOS, the median duration from hospital discharge to death progressively shortened from 136, 126 to 80 days, whilst LOS during readmission lengthened from 0.4, 0.9 to 2.8 days, respectively.

CONCLUSION

Short LOS in hospital was associated with favourable post-discharge outcomes such as early readmission and mortality, and with a delay in time interval from discharge to death and shorter LOS in hospital during readmission. These findings indicate that timely discharge from our hospital meets the aims of the NHS-generated national improvement programme, Getting It Right First Time.

Comparison of characteristics and outcomes of patients admitted to hospital with COVID-19 during wave 1 and wave 2 of the current pandemic

In this study of patients admitted with COVID-19, we examined differences between the two waves in patient characteristics and outcomes. Data were collected from the first COVID-19 admission to the end of study (01/03/2020-31/03/2021). Data were adjusted for age and sex and presented as odds ratios (OR) with 95% confidence intervals (CI). Among 12,471 admissions, 1452 (11.6%) patients were diagnosed with COVID-19. On admission, the mean (± SD) age of patients with other causes was 68.3 years (± 19.8) and those with COVID-19 in wave 1 was 69.4 years (± 18.0) and wave 2 was 66.2 years (± 18.4). Corresponding ages at discharge were 67.5 years (± 19.7), 63.9 years (± 18.0) and 62.4 years (± 18.0). The highest proportion of total admissions was among the oldest group (≥ 80 years) in wave 1 (35.0%). When compared with patients admitted with other causes, those admitted with COVID-19 in wave 1 and in wave 2 were more frequent in the 40-59 year band: 20.8, 24.6 and 30.0%; consisted of more male patients: 47.5, 57.6 and 58.8%; and a high LACE (Length of stay, Acuity of admission, Comorbidity and Emergency department visits) index (score ≥ 10): 39.4, 61.3 and 50.3%. Compared to wave-2 patients, those admitted in wave 1 had greater risk of death in hospital: OR = 1.58 (1.18-2.12) and within 30 days of discharge: OR = 2.91 (1.40-6.04). Survivors of COVID-19 in wave 1 stayed longer in hospital (median = 6.5 days; interquartile range = 2.9-12.0) as compared to survivors from wave 2 (4.5 days; interquartile range = 1.9-8.7). Patient characteristics differed significantly between the two waves of COVID-19 pandemic. There was an improvement in outcomes in wave 2, including shorter length of stay in hospital and reduction of mortality.

Early emergency readmission frequency as an indicator of short-, medium- and long-term mortality post-discharge from hospital

Frequent emergency readmissions, an indicator of quality of care, has been rising in England but the underlying reasons remain unclear. We examined the association of early readmissions with subsequent mortality in adults, taking into account the underlying presenting diagnoses and hospital length of stay (LOS). Data of alive-discharge episodes were prospectively collected between 01/04/2017 and 31/03/2019 in an National Health Service hospital, comprising 32,270 patients (46.1% men) aged 18-107 years (mean = 64.0, ± SD = 20.5 years). The associations of readmission frequency within 28 days of discharge and mortality within 30 days and 6 months of hospital discharge, and over a 2-year period were evaluated, adjusted for presenting diagnoses, LOS, age and sex during the first admission. Analysis of all patients 18-107 years (reference: no readmission) showed mortality within 30 days was increased for 1 readmission: event rate = 9.2%, odds ratio (OR) = 3.4 (95% confidence interval (CI) = 2.9-4.0), and ≥ 2 readmissions: event rate = 10.0%, OR = 2.6 (95%CI = 2.0-3.3), and within 6 months for 1 readmission: event rate = 19.6%, OR = 3.0 (95%CI = 2.7-3.4), and ≥ 2 readmissions: event rate = 27.4%, OR = 3.4 (95%CI = 2.9-4.0), and over a 2-year period for 1 readmission: event rate = 25.5%, hazard ratio = 2.2 (95%CI = 2.0-2.4), and ≥ 2 readmissions: event rate = 36.1%, hazard ratio = 2.5 (95%CI = 2.2-2.8). Within the age groups 18-49, 50-59, 60-69, 70-79 and ≥ 80 years, readmissions were also associated with increased risk of mortality within 3 months and 6 months of discharge, and over 2-year period. In conclusion, early hospital readmission predicts short-, medium- and long-term mortality post-discharge from hospital in adults aged 18-107 years, independent of underlying presenting conditions, LOS, age and sex. Further research focussing on safe discharge and follow-up patient care may help reduce preventable readmissions and post-discharge mortality.

Frequent identical admission-readmission episodes are associated with increased mortality

Frequent emergency readmissions may associate with health consequences. We examined the association between readmissions within 28 days of hospital discharge and mortality in 32,270 alive-discharge episodes (18-107 years). Data collected between 1 April 2017 and 31 March 2019 are presented as age- and sex-adjusted hazard ratios (HR) with 95% confidence interval (CI).Compared with no readmission, mortality risk over a 2-year period was increased with one non-identical admission-readmission (AR) episode: HR = 2.4 (2.2-2.7), two or more non-identical AR episodes: HR = 3.0 (2.7-3.4), one identical AR episode: HR = 4.7 (3.6-6.1) and two or more identical AR episodes: HR = 5.0 (3.8-6.7). Eight conditions associated with AR episodes had increased risk of mortality including congestive heart failure: HR = 2.7 (2.2-3.2), chronic pulmonary obstructive disease: HR = 3.0 (2.5-3.6), pneumonia: HR = 2.0 (1.8-2.3), sepsis: HR = 2.2 (1.9-2.5), endocrine disorders: HR = 1.9 (1.6-2.3), urinary tract infection: HR = 1.5 (1.3-1.7), psychiatric disorders: HR = 1.5 (1.1-2.1) and haematological disorders: HR = 1.5 (1.2-1.9). Frequent identical AR episodes, particularly from chronic and age-related conditions, are associated with increased mortality.

LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

Background: Effectively predicting and reducing readmission in long-term home care (LTHC) is challenging. We proposed, validated, and evaluated a risk management tool that stratifies LTHC patients by LACE predictive score for readmission risk, which can further help home care providers intervene with individualized preventive plans. Method: A before-and-after study was conducted by a LTHC unit in Taiwan. Patients with acute hospitalization within 30 days after discharge in the unit were enrolled as two cohorts (Pre-Implement cohort in 2017 and Post-Implement cohort in 2019). LACE score performance was evaluated by calibration and discrimination (AUC, area under receiver operator characteristic (ROC) curve). The clinical utility was evaluated by negative predictive value (NPV). Results: There were 48 patients with 87 acute hospitalizations in Pre-Implement cohort, and 132 patients with 179 hospitalizations in Post-Implement cohort. These LTHC patients were of older age, mostly intubated, and had more comorbidities. There was a significant reduction in readmission rate by 44.7% (readmission rate 25.3% vs. 14.0% in both cohorts). Although LACE score predictive model still has room for improvement (AUC = 0.598), it showed the potential as a useful screening tool (NPV, 87.9%; 95% C.I., 74.2–94.8). The reduction effect is more pronounced in infection-related readmission. Conclusion: As real-world evidence, LACE score-based risk management tool significantly reduced readmission by 44.7% in this LTHC unit. Larger scale studies involving multiple homecare units are needed to assess the generalizability of this study.

Validity of the LACE index for identifying frequent early readmissions after hospital discharge in children

The LACE index scoring tool has been designed to predict hospital readmissions in adults. We aimed to evaluate the ability of the LACE index to identify children at risk of frequent readmissions. We analysed data from alive-discharge episodes (1 April 2017 to 31 March 2019) for 6546 males and 5875 females from birth to 18 years. The LACE index predicted frequent all-cause readmissions within 28 days of hospital discharge with high accuracy: the area under the curve = 86.9% (95% confidence interval = 84.3-89.5%, p < 0.001). Two-graph receiver operating characteristic curve analysis revealed the LACE index cutoff to be 4.3, where sensitivity equals specificity, to predict frequent readmissions. Compared with those with a LACE index score = 0-4 (event rates, 0.3%), those with a score > 4 (event rates, 3.7%) were at increased risk of frequent readmissions: age- and sex-adjusted odds ratio = 12.4 (95% confidence interval = 8.0-19.2, p < 0.001) and death within 30 days of discharge: OR = 5.0 (95% CI = 1.5-16.7). The ORs for frequent readmissions were between 6 and 14 for children of different age categories (neonate, infant, young child and adolescent), except for patients in the child category (6-12 years) where odds ratio was 2.8.Conclusion: The LACE index can be used in healthcare services to identify children at risk of frequent readmissions. Focus should be directed at individuals with a LACE index score above 4 to help reduce risk of readmissions. What is Known: • The LACE index scoring tool has been widely used to predict hospital readmissions in adults. What is New: • Compared with children with a LACE index score of 0-4 (event rates, 0.3%), those with a score > 4 are at increased risk of frequent readmissions by 14-fold. • The cutoff of a LACE index of 4 may be a useful level to identify children at increased risk of frequent readmissions.