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Douillard J, Lentz S, Ganjian S, Agdeppa S, Ho N, Lin JC, Han P. Predictive Value of LACE Scores for Pediatric Readmissions. Perm J 2024; 28:9-15. [PMID: 38389442 PMCID: PMC11232907 DOI: 10.7812/tpp/23.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
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.
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Affiliation(s)
- Jelena Douillard
- Southern California Permanente Medical Group, Los Angeles, CA, USA
| | - Sarah Lentz
- Kaiser Foundation Hospital, Los Angeles, CA, USA
| | | | - Sherill Agdeppa
- Southern California Permanente Medical Group, Los Angeles, CA, USA
| | - Ngoc Ho
- Southern California Permanente Medical Group, Los Angeles, CA, USA
| | - Jane Chieh Lin
- Southern California Permanente Medical Group, Los Angeles, CA, USA
| | - Paul Han
- Southern California Permanente Medical Group, Los Angeles, CA, USA
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Sun CH, Chou YY, Lee YS, Weng SC, Lin CF, Kuo FH, Hsu PS, Lin SY. Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:348. [PMID: 36612671 PMCID: PMC9819393 DOI: 10.3390/ijerph20010348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/07/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
(1) Background: Elders have higher rates of rehospitalization, especially those with functional decline. We aimed to investigate potential predictors of 30-day readmission risk by comprehensive geriatric assessment (CGA) in hospitalized patients aged 65 years or older and to examine the predictive ability of the LACE index and HOSPITAL score in older patients with a combination of malnutrition and physical dysfunction. (2) Methods: We included patients admitted to a geriatric ward in a tertiary hospital from July 2012 to August 2018. CGA components including cognitive, functional, nutritional, and social parameters were assessed at admission and recorded, as well as clinical information. The association factors with 30-day hospital readmission were analyzed by multivariate logistic regression analysis. The predictive ability of the LACE and HOSPITAL score was assessed using receiver operator characteristic curve analysis. (3) Results: During the study period, 1509 patients admitted to a ward were recorded. Of these patients, 233 (15.4%) were readmitted within 30 days. Those who were readmitted presented with higher comorbidity numbers and poorer performance of CGA, including gait ability, activities of daily living (ADL), and nutritional status. Multivariate regression analysis showed that male gender and moderately impaired gait ability were independently correlated with 30-day hospital readmissions, while other components such as functional impairment (as ADL) and nutritional status were not associated with 30-day rehospitalization. The receiver operating characteristics for the LACE index and HOSPITAL score showed that both predicting scores performed poorly at predicting 30-day hospital readmission (C-statistic = 0.59) and did not perform better in any of the subgroups. (4) Conclusions: Our study showed that only some components of CGA, mobile disability, and gender were independently associated with increased risk of readmission. However, the LACE index and HOSPITAL score had a poor discriminating ability for predicting 30-day hospitalization in all and subgroup patients. Further identifiers are required to better estimate the 30-day readmission rates in this patient population.
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Affiliation(s)
- Chia-Hui Sun
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yin-Yi Chou
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yu-Shan Lee
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Shuo-Chun Weng
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Cheng-Fu Lin
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Occupational Medicine, Department of Emergency, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Fu-Hsuan Kuo
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Pi-Shan Hsu
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Shih-Yi Lin
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
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Rajaguru V, Kim TH, Shin J, Lee SG, Han W. Ability of the LACE Index to Predict 30-Day Readmissions in Patients with Acute Myocardial Infarction. J Pers Med 2022; 12:jpm12071085. [PMID: 35887582 PMCID: PMC9318277 DOI: 10.3390/jpm12071085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/26/2022] [Accepted: 06/28/2022] [Indexed: 11/16/2022] Open
Abstract
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.
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Affiliation(s)
- Vasuki Rajaguru
- Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul 03722, Korea; (V.R.); (T.H.K.)
| | - Tae Hyun Kim
- Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul 03722, Korea; (V.R.); (T.H.K.)
| | - Jaeyong Shin
- Department of Preventive Medicine, College of Medicine, Yonsei University, Seoul 03722, Korea; (J.S.); (S.G.L.)
| | - Sang Gyu Lee
- Department of Preventive Medicine, College of Medicine, Yonsei University, Seoul 03722, Korea; (J.S.); (S.G.L.)
| | - Whiejong Han
- Department of Global Health Security, Graduate School of Public Health, Yonsei University, Seoul 03722, Korea
- Correspondence:
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Rajaguru V, Han W, Kim TH, Shin J, Lee SG. LACE Index to Predict the High Risk of 30-Day Readmission: A Systematic Review and Meta-Analysis. J Pers Med 2022; 12:jpm12040545. [PMID: 35455661 PMCID: PMC9024499 DOI: 10.3390/jpm12040545] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 02/01/2023] Open
Abstract
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.
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Affiliation(s)
- Vasuki Rajaguru
- Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul 03722, Korea; (V.R.); (W.H.); (T.H.K.)
| | - Whiejong Han
- Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul 03722, Korea; (V.R.); (W.H.); (T.H.K.)
| | - Tae Hyun Kim
- Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul 03722, Korea; (V.R.); (W.H.); (T.H.K.)
| | - Jaeyong Shin
- Department of Preventive Medicine, College of Medicine, Yonsei University, Seoul 03722, Korea;
- Institute of Health Services Research, Yonsei University, Seoul 03722, Korea
| | - Sang Gyu Lee
- Department of Preventive Medicine, College of Medicine, Yonsei University, Seoul 03722, Korea;
- Correspondence:
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Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18105110. [PMID: 34065894 PMCID: PMC8150657 DOI: 10.3390/ijerph18105110] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 04/22/2021] [Accepted: 04/29/2021] [Indexed: 12/02/2022]
Abstract
Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.
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Chakrabarti B, Lane S, Jenks T, Higgins J, Kanwar E, Allen M, Wotton D. Predictors of 30-day readmission following hospitalisation with community-acquired pneumonia. BMJ Open Respir Res 2021; 8:8/1/e000883. [PMID: 33771814 PMCID: PMC8006847 DOI: 10.1136/bmjresp-2021-000883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/01/2021] [Accepted: 03/02/2021] [Indexed: 12/11/2022] Open
Abstract
Background There is a paucity of UK data to aid healthcare professionals in predicting which patients hospitalised with community-acquired pneumonia (CAP) are at greatest risk of 30-day readmission and to determine which readmissions may occur soonest. Methods An analysis of CAP cases admitted to nine UK hospitals participating in the Advancing Quality Pneumonia Programme. Results An analysis was performed of 12 157 subjects hospitalised with CAP in the Advancing Quality Programme Database. 26% of those discharged were readmitted within 30 days with readmission predicted by comorbidity including non-metastatic cancer, diabetes with complications and chronic kidney disease. 41% and 66% of readmissions occurred within 7 and 14 days of discharge, respectively. Patients readmitted within 14 days were more likely to have metastatic cancer (6.6% vs 4.5%; p=0.03) compared with those readmitted at 15–30 days. Conclusions A quarter of patients hospitalised for CAP are readmitted within 30 days; of those, two-thirds are readmitted within 2 weeks. Further research is required to determine whether such readmissions might be preventable through imple menting measures including in-hospital cross-specialty comorbidity management, convalescence in intermediate care, targeted rehabilitation and advanced care planning.
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Affiliation(s)
| | - Steven Lane
- Biostatistics, University of Liverpool, Liverpool, UK
| | - Tom Jenks
- Advancing Quality Alliance, Salford, UK
| | | | | | - Martin Allen
- University Hospitals of North Midlands, Stoke, UK
| | - Dan Wotton
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
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