Acuity Of Admission Research Articles

#1604 LACE index as predictors of 30 day rehospitalisation among patients on maintenance hemodialysis, cohort study

Abstract Background and Aims Over 35% of patients on maintenance dialysis are readmitted to the hospital within 30 days of hospital discharge. Validated risk assessment tools such as the LACE index (Length of stay, Acuity of admission, Co-morbidities, and Emergency department visits) have been developed to identify patients at high risk of hospital readmission in non-dialysis patients but have not been tested in dialysis patients. Method Data was collected from electronic medical records of patients on maintenance hemodialysis in Diaverum outpatient clinics hospitalized during 2023. Data included Age, gender, duration of hospital stay, Charlson comorbidity index (CCS), urgent or planned, and cause of Hospitalization. Multivariable logistic regression models with 30-day readmission for the same reason as the dependent outcome were used to identify readmission risk factors and whether the LACE score validly predicts 30-day rehospitalization among our cohort. Results Among (4829) patients received dialysis in Diaverum Saudi Arabia during 2023. (1487) episodes of hospitalization were reported due to various reasons rate of hospitalization was 0.54 Episodes per patient-years at risk, and 372(25%) of hospitalized patients were re-hospitalized for the same reason within 30 days. The proportion of patients with 30 days of rehospitalization for the same reason was higher in patients with post-initial discharge LACE index ≥10 in comparison to patients with LACE index<10, 348(32.6%) & 24(5.7) respectively P-VALUE<0.001. also significantly higher in patients ≥65years old than those younger 163(27.7%) &209(23.3%) respectively P-VALUE<0.05, those in high risk group (defined as those with higher comorbidity index ≥6 and/or lower Individual Patient Performance Score<75) than those who are not in high risk group 215(35%) & 157(17.9%) respectively P-VALUE<0.001. Multivariate logistic regression including covariates significantly associated with higher 30-day rehospitalization to avoid confounder effect with model including ( age, gender, dialysis duration, body mass index, comorbidity index, diabetes, vascular access, cause of hospitalization and inclusion in high-risk group ) showed significantly higher rehospitalization in patients with post-initial discharge LACE index ≥10 in comparison to patients with LACE index<10 ODDS RATIO (95%CI) 7.95(5.1-12.5) P-VALUE<0.001. independent of other covariates in the model Conclusion LACE index at discharge is an independent predictor of (30) days re-hospitalization in patients on maintenance Hemodialysis. Further analysis needed for a specific model for HD patients taking into consideration the unique characteristics of this cohort of patients.

Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study.

Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%-25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004-2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Utility of the LACE index to assess risk of mortality and readmission in patients with spinal infections

Retrospective cohort study. To assess the utility of the LACE index for predicting death and readmission in patients with spinal infections (SI). SIs are severe conditions, and their incidence has increased in recent years. The LACE (Length of stay, Acuity of admission, Comorbidities, Emergency department visits) index quantifies the risk of mortality or unplanned readmission. It has not yet been validated for SIs. LACE indices were calculated for all adult patients who underwent surgery for spinal infection between 2012 and 2021. Data were collected from a single academic teaching hospital. Outcome measures included the LACE index, mortality, and readmission rate within 30 and 90 days. In total, 164 patients were analyzed. Mean age was 64.6 (± 15.1) years, 73 (45%) were female. Ten (6.1%) patients died within 30 days and 16 (9.8%) died within 90 days after discharge. Mean LACE indices were 13.4 (± 3.6) and 13.8 (± 3.0) for the deceased patients, compared to 11.0 (± 2.8) and 10.8 (± 2.8) for surviving patients (p = 0.01, p < 0.001), respectively. Thirty-seven (22.6%) patients were readmitted ≤ 30 days and 48 (29.3%) were readmitted ≤ 90 days. Readmitted patients had a significantly higher mean LACE index compared to non-readmitted patients (12.9 ± 2.1 vs. 10.6 ± 2.9, < 0.001 and 12.8 ± 2.3 vs. 10.4 ± 2.8, p < 0.001, respectively). ROC analysis for either death or readmission within 30 days estimated a cut-off LACE index of 12.0 points (area under the curve [AUC] 95% CI, 0.757 [0.681–0.833]) with a sensitivity of 70% and specificity of 69%. Patients with SI had high LACE indices that were associated with high mortality and readmission rates. The LACE index can be applied to this patient population to predict the risk of early death or unplanned readmission.

Predictive Value of LACE Scores for Pediatric Readmissions.

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. 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. 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. 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. 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.

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

Chronic care management: An opportunity to drive quality outcomes in oncology care.

444 Background: CMS approved Chronic (CCM) and Principal Care Management (PCM) to improve patient care. These services are typically non-face-to-face; providers can bill ≥ twenty minutes /month. In oncology, this may help with comorbidity and symptom management to reduce the risk and expense of avoidable hospitalizations and ER visits and pay providers for time spent. In 2021, a system of community cancer centers initiated a program with a partner to manage chronic conditions more effectively at home. Approximately three hundred people were enrolled. A review of the data found that patients did not meet the medical criteria for the program, and care was not billable. An internal program was created to improve the enrollment process and financial sustainability. A clinical needs assessment was completed with our oncologists, outlining revised criteria for patient enrollment. Qualified patients were enrolled by their attending oncologist during the initial face-to-face visit and assigned a dedicated RN. Methods: Three hundred unenrolled, actively treating patients were randomly selected from January 2023 to May 2023 by a group of data analysts not associated with the program to be compared to enrolled patients who have been enrolled on average six months. Patients were compared by age, gender, cancer type, and race. The LACE Index for Readmission, a validated tool using the length of stay, acuity of admission, comorbidities, and ER visits to predict the risk of readmission, was done retrospectively by RNs for all patients. Enrolled patients received a comprehensive clinical needs assessment by RNs, and a provider-approved care plan was created. RNs provided patients with education on lifestyle modification and assisted in setting achievable goals. The care plan was monitored with weekly calls, providing the opportunity to respond to patients’ needs proactively. Symptom and medication management were provided, and psychological and nutritional needs were addressed. This coordination of services was documented in the electronic health record, and superbills were created and sent to billing and coding monthly. Results: See table. Conclusions: The purpose of this study was to compare hospital admissions between the groups. Our findings suggest that CCM and PCM may reduce unplanned admissions, with physician engagement pivotal in driving appropriate enrollment. The current program is labor-intensive and further investigation is required to determine the most cost-effective way of scaling the program with the possible use of artificial intelligence, RPM, or other technology.[Table: see text]

Impact of Care Initiation Model on Emergency Department Orders and Operational Metrics: Cohort Study.

Emergency departments (ED) employ many strategies to address crowding and prolonged wait times. They include front-end Care Initiation and clinician-in-triage models that start the diagnostic and therapeutic process while the patient waits for a care space in the ED. The objective of this study was to quantify the impact of a Care Initiation model on resource utilization and operational metrics in the ED. We performed a retrospective analysis of ED visits at our institution during October 2021. Baseline characteristics were compared with Chi-square and quantile regression. We used t-tests to calculate unadjusted difference in outcome measures, including number of laboratory tests ordered and average time patients spent in the waiting room and the ED treatment room, and the time from arrival until ED disposition. We performed propensity score analysis using matching and inverse probability weighting to quantify the direct impact of Care Initiation on outcome measures. There were 2,407 ED patient encounters, 1,191 (49%) of whom arrived during the hours when Care Initiation was active. A total of 811 (68%) of these patients underwent Care Initiation, while the remainder proceeded directly to the main treatment area. Patients were more likely to undergo Care Initiation if they had lower acuity and lower risk of admission, and if the ED was busier as measured by the number of recent arrivals and percentage of occupied ED beds. After adjusting for patient-specific and department-level covariates, Care Initiation did not increase the number of diagnostic laboratory tests ordered. Care Initiation was associated with increased waiting room time by 1.8 hours and longer time from arrival until disposition by 1.3 hours, but with decreased time in the main treatment area by 0.6 hours, which represents a 15% reduction. Care Initiation was associated with a 15% reduction in time spent in the main ED treatment area but longer waiting room time and longer time until ED disposition without significantly increasing the number of laboratory studies ordered. While previous studies produced similar results with Care Initiation models accessing all diagnostic modalities including imaging, our study demonstrates that a more limited Care Initiation model can still result in operational benefits for EDs.

Impact of an Area Agency on Aging pharmacist-led Community Care Transition Initiative

BackgroundRural older adults are at risk of readmissions and medication-related problems after hospital discharge. ObjectivesThis study aimed to compare 30-day hospital readmissions between participants and nonparticipants and describe medication therapy problems (MTPs) and barriers to care, self-management, and social needs among participants. Practice descriptionThe Michigan Region VII Area Agency on Aging (AAA) Community Care Transition Initiative (CCTI) for rural older adults after hospitalization. Practice innovationEligible AAA CCTI participants were identified by an AAA community health worker (CHW) trained as a pharmacy technician. Eligibility criteria were Medicare insurance; diagnoses at risk of readmission; length of stay, acuity of admission, comorbidities, and emergency department visits score more than 4; and discharge to home from January 2018 to December 2019. The AAA CCTI included a CHW home visit, telehealth pharmacist comprehensive medication review (CMR), and follow-up for up to 1 year. Evaluation methodsA retrospective cohort study examined the primary outcomes of 30-day hospital readmissions and MTPs, categorized by the Pharmacy Quality Alliance MTP Framework. Primary care provider (PCP) visit completion, barriers to self-management, health, and social needs were collected. Descriptive statistics, Mann-Whitney U, and chi-square analyses were used. ResultsOf 825 eligible discharges, 477 (57.8%) enrolled in the AAA CCTI; differences in 30-day readmissions between participants and nonparticipants were not statistically significant (11.5% vs. 16.1%, P = 0.07). More than one-third of participants (34.6%) completed their PCP visit within 7 days. MTPs were identified in 76.1% of the pharmacist visits (mean MTP 2.1 [SD 1.4]). Adherence (38.2%) and safety-related (32.0%) MTPs were common. Physical health and financial issues were barriers to self-management. ConclusionAAA CCTI participants did not have lower hospital readmission rates. The AAA CCTI identified and addressed barriers to self-management and MTPs in participants after the care transition home. Community-based, patient-centered strategies to improve medication use and meet rural adults’ health and social needs after care transitions are warranted.

1117: EVALUATION OF PREDICTIVE VALUE OF LACE SCORE FOR 30-DAY READMISSION FOR PNEUMONIA

Introduction: Patients hospitalized with community-acquired pneumonia (CAP) have a 30-day readmission rate of up to 25%, likely secondary to the high comorbidity burden in this patient population. Thirty-day hospital readmission rates are used by the Center for Medicare and Medicaid Services (CMS) as a performance measure to determine hospital reimbursement rates. The LACE score (length of stay, acuity of admission, comorbidities, and emergency department visits within the last six months) was designed to predict the risk of 30-day hospital readmissions to identify patients at risk for 30-day readmission. Herein, we assessed the predictive value of the LACE score, specifically in stratifying patients admitted to the hospital with a diagnosis of CAP for their risk of 30-day readmission. Methods: In this retrospective cohort study, all patients above 18 years of age admitted to our hospital during 2019 with a CAP diagnosis were included. Via chart review, the patient’s clinical characteristics, comorbidity burden, and hospital readmissions within 30-days of discharge were recorded. Logistic regression models were built to assess the predictive value of each variable for 30-day readmission after discharge. Results: A total of 128 patients (median age 65, IQR 54-78, 46.9% females) were included in the study. Fourteen (10.9%) patients required ICU stay, and 6(4.7%) patients died. Within 30 days of discharge from the hospital, 20 (15.6%) of patients were re-evaluated in the emergency department (ED), and 19 (14.8%) were re-admitted. Factors associated with readmission include LACE score on admission (OR 1.21 (1.02-1.47), p-value 0.02), length of stay (OR 1.10 (1.01-1.20), p-value 0.02), and number of ED visits within the last six months (OR 1.70 (1.22-2.49), p-value 0.003). Conclusions: A higher LACE score or behavior of frequent ED visits is associated with a higher chance of hospital readmission in patients with community-acquired pneumonia after discharge. LACE score can be used as a tool by physicians and case managers to identify patients at risk for 30-day readmission after discharge, and closer follow-ups may be arranged in this high-risk population.

Does Fitzpatrick Score Predict Flap Loss? Microsurgical Breast Reconstruction Outcomes of Varying Skin Color.

This is a retrospective study conducted on patients who underwent any type of autologous flap breast reconstruction performed by the two senior authors at an academic center between January 2010 and December 2021. The sole primary outcome variable was flap loss. Patient skin tone was assessed using the Fitzpatrick scale on clinical photographs of patients. A total of 1115 pateints underwent autologous flap breast reconstruction, of which only 56 met both exclusion and inclusion criteria with 58 individual breasts being included in the final study population. The most common race of subjects was White (n = 33; 56.9%) while the most common Fitzpatrick score skin tone was type II (n = 22; 37.9%). The Cochran-Armitage test of trend showed a statistically significant linear trend, P = 0.006, with darker Fitzpatrick score skin tones associated with a higher proportion of flap loss in patients who had vascular compromise. A logistic regression showed that none of the predictor variables were significant. Patients with darker Fitzpatrick skin tones were associated with flap loss after vascular compromise. To prevent flap loss in patients who have darker Fitzpatrick score skin tones, more aggressive flap monitoring should be taken into consideration in the immediate postoperative setting.

LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital.

BackgroundThe LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).MethodsThis was a retrospective study. Data were extracted from the hospital's electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.ResultsOf the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75–0.81). The Hosmer–Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.ConclusionThe LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be considered to improve the model performance of the LACE index with other acute conditions by using administrative data.

Ability of the LACE Index to Predict 30-Day Readmissions in Patients with Acute Myocardial Infarction.

Aims: This study aimed to utilize the existing LACE index (length of stay, acuity of admission, comorbidity index and emergency room visit in the past six months) to predict the risk of 30-day readmission and to find the associated factors in patients with AMI. Methods: This was a retrospective study and LACE index scores were calculated for patients admitted with AMI between 2015 and 2019. Data were utilized from the hospital’s electronic medical record. Multivariate logistic regression was performed to find the association between covariates and 30-day readmission. The risk prediction ability of the LACE index for 30-day readmission was analyzed by receiver operating characteristic curves with the C statistic. Results: A total of 205 (5.7%) patients were readmitted within 30 days. The odds ratio of older age group (OR = 1.78, 95% CI: 1.54–2.05), admission via emergency ward (OR = 1.45; 95% CI: 1.42–1.54) and LACE score ≥10 (OR = 2.71; 95% CI: 1.03–4.37) were highly associated with 30-day readmissions and statistically significant. The receiver operating characteristic curve C statistic of the LACE index for AMI patients was 0.78 (95% CI: 0.75–0.80) and showed favorable discrimination in the prediction of 30-day readmission. Conclusion: The LACE index showed a good discrimination to predict the risk of 30-day readmission for hospitalized patients with AMI. Further study would be recommended to focus on additional factors that can be used to predict the risk of 30-day readmission; this should be considered to improve the model performance of the LACE index for other acute conditions by using the national-based administrative data.

Use of the LACE+ index to predict readmissions after single-level lumbar fusion.

Spinal fusion is one of the most common neurosurgical procedures. The LACE (length of stay, acuity of admission, Charlson Comorbidity Index [CCI] score, and emergency department [ED] visits within the previous 6 months) index was developed to predict readmission but has not been tested in a large, homogeneous spinal fusion population. The present study evaluated use of the LACE+ score for outcome prediction after lumbar fusion. LACE+ scores were calculated for all patients (n = 1598) with complete information who underwent single-level, posterior-only lumbar fusion at a single university medical system. Logistic regression was performed to assess the ability of the LACE+ score as a continuous variable to predict hospital readmissions within 30 days (30D), 30-90 days (30-90D), and 90 days (90D) of the index operation. Secondary outcome measures included ED visits and reoperations. Subsequently, patients with LACE+ scores in the bottom decile were exact matched to the patients with scores in the top 4 deciles to control for sociodemographic and procedural variables. Among all patients, increased LACE+ score significantly predicted higher rates of readmissions in the 30D (p < 0.001), 30-90D (p = 0.001), and 90D (p < 0.001) postoperative windows. LACE+ score also predicted risk of ED visits at all 3 time points and reoperations at 30-90D and 90D. When patients with LACE+ scores in the bottom decile were compared with patients with scores in the top 4 deciles, higher LACE+ score predicted higher risk of readmissions at 30D (p = 0.009) and 90D (p = 0.005). No significant difference in hospital readmissions was observed between the exact-matched cohorts. The present results suggest that the LACE+ score demonstrates utility in predicting readmissions within 30 and 90 days after single-level lumbar fusion. Future research is warranted that utilizes the LACE+ index to identify strategies to support high-risk patients in a prospective population.

LACE Index to Predict the High Risk of 30-Day Readmission: A Systematic Review and Meta-Analysis.

The LACE index accounts for: Length of stay (L), Acuity of admission (A), Comorbidities (C), and recent Emergency department use (E). This study aimed to explore the LACE index to predict the high risk of 30-day readmission in patients with diverse disease conditions by an updated systematic review. A systematic review carried out by electronic databases from 2011–2021. The studies included a LACE index score for 30-day of readmission and patients with all types of diseases and were published in the English language. The meta-analysis was performed by using a random-effects model with a 95% confidence interval. Of 3300 records, a total of 16 studies met the inclusion criteria. The country of publication was primarily the USA (n = 7) and study designs were retrospective and perspective cohorts. The average mean age was 64 years. The C-statistics was 0.55 to 0.81. The pooled random effects of relative risk readmission were overall (RR, 0.20; 95% CI, 0.12–0.34) and it was favorable. The subgroup analysis of the opted disease-based relative risk of readmissions of all causes, cardiovascular and pulmonary diseases, and neurological diseases were consistent and statistically significant at p < 0.001 level. Current evidence of this review suggested that incorporating a high-risk LACE index showed favorable to risk prediction and could be applied to predict 30-day readmission with chronic conditions. Future study would be planned to predict the high risk of 30-day readmission in acute clinical care for utility, and applicability of promising LACE index in South Korean hospitals.

455 Predicting Outcomes Following Single-Level Lumbar Fusion

INTRODUCTION: Spinal fusions represent one of the most common neurosurgical procedures. The LACE (Length of Stay, Acuity of Admission, Charlson Comorbidity Index (CCI) Score, Emergency Department (ED) visits within the previous 6 months) index was developed to predict readmissions, but has not been tested in a large, homogenous spinal fusion population. METHODS: LACE+ scores were calculated for all patients with complete information who underwent single-level, posterior-only lumbar fusion at a single, university medical system (n = 1598). Logistic regression was performed to assess the ability of the LACE+ score as a continuous variable to predict hospital readmissions within 90 days (90D) of the index operation. Secondary outcome measures included ED visits and reoperations. Subsequently, the risk of adverse postsurgical events was compared between the lowest and the highest quartile of LACE+ scores, then between the bottom decile and top 4 deciles of LACE+ scores. Finally, patients with the bottom decile of LACE+ scores were exact matched to the top 4 deciles to control for sociodemographic and procedural variables. RESULTS: Among all patients, increased LACE+ score significantly predicted higher rates of readmissions in the 30-day (30D), 30-90-day (30-90D), and 90D postoperative windows (p &lt; 0.001, p = 0.001, p &lt; 0.001, respectively). LACE+ score also predicted risk of ED visits at all 3 timepoints, and reoperations at 30-90D and 90D. When comparing the lowest quartile of LACE+ scores to the highest quartile, higher LACE+ score predicted higher risk of readmissions at 30D, 30-90D, and 90D following the index surgery (p = 0.003, p = 0.016, p &lt; 0.001, respectively). When comparing the bottom decile of LACE+ scores to the top 4 deciles, higher LACE+ score predicted higher risk of readmissions at 30D (p = 0.009) and 90D (p = 0.005). Between exact-matched cohorts, no significant difference was observed in hospital readmissions. CONCLUSION: The LACE+ index may be a powerful tool in predicting other important postsurgical outcomes.

Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions.

SummaryBackgroundEmergency readmission poses an additional burden on both patients and healthcare systems. Risk stratification is the first step of transitional care interventions targeted at reducing readmission. To accurately predict the short- and intermediate-term risks of readmission and provide information for further temporal risk stratification, we developed and validated an interpretable machine learning risk scoring system.MethodsIn this retrospective study, all emergency admission episodes from January 1st 2009 to December 31st 2016 at a tertiary hospital in Singapore were assessed. The primary outcome was time to emergency readmission within 90 days post discharge. The Score for Emergency ReAdmission Prediction (SERAP) tool was derived via an interpretable machine learning-based system for time-to-event outcomes. SERAP is six-variable survival score, and takes the number of emergency admissions last year, age, history of malignancy, history of renal diseases, serum creatinine level, and serum albumin level during index admission into consideration.FindingsA total of 293,589 ED admission episodes were finally included in the whole cohort. Among them, 203,748 episodes were included in the training cohort, 50,937 episodes in the validation cohort, and 38,904 in the testing cohort. Readmission within 90 days was documented in 80,213 (27.3%) episodes, with a median time to emergency readmission of 22 days (Interquartile range: 8-47). For different time points, the readmission rates observed in the whole cohort were 6.7% at 7 days, 10.6% at 14 days, 13.6% at 21 days, 16.4% at 30 days, and 23.0% at 60 days. In the testing cohort, the SERAP achieved an integrated area under the curve of 0.737 (95% confidence interval: 0.730-0.743). For a specific 30-day readmission prediction, SERAP outperformed the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, and Emergency department visits in past six months) and the HOSPITAL score (Hemoglobin at discharge, discharge from an Oncology service, Sodium level at discharge, Procedure during the index admission, Index Type of admission, number of Admissions during the last 12 months, and Length of stay). Besides 30-day readmission, SERAP can predict readmission rates at any time point during the 90-day period.InterpretationBetter performance in risk prediction was achieved by the SERAP than other existing scores, and accurate information about time to emergency readmission was generated for further temporal risk stratification and clinical decision-making. In the future, external validation studies are needed to evaluate the SERAP at different settings and assess their real-world performance.FundingThis study was supported by the Singapore National Medical Research Council under the PULSES Center Grant, and Duke-NUS Medical School.

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.

REadmission PREvention in SepSis: Development and Validation of a Prediction Model.

Hospital 30-day readmissions remain a major quality and cost indicator. Traditional readmission risk scores, such as LACE (length of stay, acuity of admission, Charlson comorbidity index, and emergency department visits), may be suboptimal in special patient populations, such as those with sepsis. As sepsis survivorship improves, there is a need to determine which variables might be associated with a decrease in 30-day readmission. We completed a retrospective analysis reviewing patients with sepsis who had unplanned 30-day readmissions. Multivariate regression analysis was performed for the REadmission PREvention in SepSis (REPRESS) model, which evaluated age, length of stay, Charlson disease count, Richmond Agitation-Sedation Scale score, discharge to a skilled nursing facility, and mobility for predictive significance in hospital readmission. Our REPRESS model performed better when compared with LACE for predicting readmission risk in a sepsis population.

Predicting patient outcomes after far lateral lumbar discectomy

IntroductionThe LACE+ (Length of Stay, Acuity of Admission, Charlson Comorbidity Index (CCI) Score, Emergency Department (ED) visits within the previous 6 months) index has never been tested in a purely spine surgery population. This study assesses the ability of LACE + to predict adverse patient outcomes following discectomy for far lateral disc herniation (FLDH). Patients and MethodsData were obtained for patients (n = 144) who underwent far lateral lumbar discectomy at a single, multi-hospital academic medical center (2013–2020). LACE + scores were calculated for all patients with complete information (n = 100). The influence of confounding variables was assessed and controlled with stepwise regression. Logistic regression was used to test the ability of LACE + to predict risk of unplanned hospital readmission, ED visits, outpatient office visits, and reoperation after surgery. ResultsMean age of the population was 61.72 ± 11.55 years, 69 (47.9 %) were female, and 126 (87.5 %) were non-Hispanic white. Patients underwent either open (n = 92) or endoscopic (n = 52) surgery. Each point increase in LACE + score significantly predicted, in the 30-day (30D) and 30−90-day (30−90D) post-discharge window, higher risk of readmission (p = 0.005, p = 0.009; respectively) and ED visits (p = 0.045). Increasing LACE + also predicted, in the 30D and 90-day (90D) post-discharge window, risk of reoperation (p = 0.022, p = 0.016; respectively), and repeat neurosurgical intervention (p = 0.026, p = 0.026; respectively). Increasing LACE + score also predicted risk of reoperation (p = 0.011) within 30 days of initial surgery. ConclusionsLACE + may be suitable for characterizing risk of adverse perioperative events for patients undergoing far lateral discectomy.

The LACE+Index as a Predictor of 90-Day Supratentorial Tumor Surgery Outcomes.

The LACE+ (Length of stay, Acuity of admission, Charlson Comorbidity Index [CCI] score, and Emergency department [ED] visits in the past 6 mo) index risk-prediction tool has never been successfully tested in a neurosurgery population. To assess the ability of LACE+ to predict adverse outcomes after supratentorial brain tumor surgery. LACE+ scores were retrospectively calculated for all patients (n=624) who underwent surgery for supratentorial tumors at the University of Pennsylvania Health System (2017-2019). Confounding variables were controlled with coarsened exact matching. The frequency of unplanned hospital readmission, ED visits, and death was compared for patients with different LACE+ score quartiles (Q1, Q2, Q3, and Q4). A total of 134 patients were matched between Q1 and Q4; 152 patients were matched between Q2 and Q4; and 192 patients were matched between Q3 and Q4. Patients with higher LACE+scores were significantly more likely to be readmitted within 90 d (90D) of discharge for Q1 vs Q4 (21.88%vs 46.88%, P=.005) and Q2 vs Q4 (27.03%vs 55.41%, P=.001). Patients with larger LACE+scores also had significantly increased risk of 90D ED visits for Q1 vs Q4 (13.33%vs 30.00%, P=.027) and Q2 vs Q4 (22.54%vs 39.44%, P=.039). LACE+score also correlated with death within 90D of surgery for Q2 vs Q4 (2.63%vs 15.79%, P=.003) and with death at any point after surgery/during follow-up for Q1 vs Q4 (7.46%vs 28.36%, P=.002), Q2 vs Q4 (15.79%vs 31.58%, P=.011), and Q3 vs Q4 (18.75%vs 31.25%, P=.047). LACE+ may be suitable for characterizing risk of certain perioperative events in a patient population undergoing supratentorial brain tumor resection.