1
|
Setiati S, Ardian LJ, Fitriana I, Azwar MK. Improvement of scoring system used before discharge to predict 30-day all-cause unplanned readmission in geriatric population: a prospective cohort study. BMC Geriatr 2024; 24:281. [PMID: 38528454 DOI: 10.1186/s12877-024-04875-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/05/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Data taken from tertiary referral hospitals in Indonesia suggested readmission rate in older population ranging between 18.1 and 36.3%. Thus, it is crucial to identify high risk patients who were readmitted. Our previous study found several important predictors, despite unsatisfactory discrimination value. METHODS We aimed to investigate whether comprehensive geriatric assessment (CGA) -based modification to the published seven-point scoring system may increase the discrimination value. We conducted a prospective cohort study in July-September 2022 and recruited patients aged 60 years and older admitted to the non-surgical ward and intensive coronary care unit. The ROC curve was made based on the four variables included in the prior study. We conducted bivariate and multivariate analyses, and derived a new scoring system with its discrimination value. RESULTS Of 235 subjects, the incidence of readmission was 32.3% (95% CI 26-38%). We established a new scoring system consisting of 4 components. The scoring system had maximum score of 21 and incorporated malignancy (6 points), delirium (4 points), length of stay ≥ 10 days (4 points), and being at risk of malnutrition or malnourished (7 points), with a good calibration test. The C-statistic value was 0.835 (95% CI 0.781-0.880). The optimal cut-off point was ≥ 8 with a sensitivity of 90.8% and a specificity of 54.7%. CONCLUSIONS Malignancy, delirium, length of stay ≥ 10 days, and being at risk of malnutrition or malnourished are predictors for 30-day all-cause unplanned readmission. The sensitive scoring system is a strong model to identify whether an individual is at higher risk for readmission. The new CGA-based scoring system had higher discrimination value than that of the previous seven-point scoring system.
Collapse
Affiliation(s)
- Siti Setiati
- Division of Geriatrics, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia-Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
| | - Laurentius Johan Ardian
- Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia-Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Ika Fitriana
- Division of Geriatrics, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia-Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Muhammad Khifzhon Azwar
- Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia-Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| |
Collapse
|
2
|
Setiati S, Laksmi PW, Aryana IGPS, Sunarti S, Widajanti N, Dwipa L, Seto E, Istanti R, Ardian LJ, Chotimah SC. Frailty state among Indonesian elderly: prevalence, associated factors, and frailty state transition. BMC Geriatr 2019; 19:182. [PMID: 31269921 PMCID: PMC6609407 DOI: 10.1186/s12877-019-1198-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 06/24/2019] [Indexed: 12/16/2022] Open
Abstract
Background Information about frailty status and its transition is important to inform clinical decisions. Predicting frailty transition is beneficial for its prevention. While Indonesia is the 4th largest geriatric population in Asia, data about frailty transition is limited. This study aimed to obtain data on prevalence of frailty, its risk factors, frailty state transition and its prognostic factors, as well as to develop prognostic score for frailty state transition. Methods Multicenter study on subjects aged ≥60 years old was done to obtain the prevalence of frailty status and to identify risk factors of frailty. Prospective cohort over 12 months was done to obtain data on frailty state transition. Multiple logistic regression analysis was performed to identify its prognostic factors from several clinical data, which then were utilized to develop prognostic score for frailty state worsening. Results Cross-sectional data from 448 subjects showed that 25.2% of the subjects were frail based on Frailty index-40 items. Risk factors of frailty were age (OR 2.72; 95% CI 1.58–4.76), functional status (OR 2.89; 95% CI 1.79–4.67), and nutritional status (OR 3.75; 95% CI 2.29–6.13). Data from the 162 subjects who completed the cohort showed 27.2% of the cohort had frailty state worsening. Prognostic factors for frailty state worsening were being 70 years or older (OR 3.9; 95% CI 1.2–12.3, p < 0.05), negative QoL, i.e., fair and poor QoL (OR 2.5; 95% CI 1.1–5.9, p < 0.05), and slow gait speed (OR 2.8; 95% CI 1.3–6.4, p < 0.05). The internal validation of the prognostic score consisted of those three variables showed good performance. Conclusion The prevalence of frailty in this study among Indonesian elderly in outpatient setting was 25.2%. The risk factors of frailty were age, functional status and nutritional status. The prognostic factors for frailty state worsening were being 70 years old or older, negative QoL (fair or poor quality of life), and slow gait speed. A prognostic score to predict frailty state worsening in 12 months had been developed.
Collapse
Affiliation(s)
- Siti Setiati
- Division of Geriatric, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia. .,Clinical Epidemiology and Evidence Based Medicine Unit, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jalan Pangeran Diponegoro No. 71, Jakarta, 10430, Indonesia.
| | - Purwita Wijaya Laksmi
- Division of Geriatric, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - I G P Suka Aryana
- Division of Geriatric, Departement of Internal Medicine, Faculty of Medicine, Universitas Udayana, Sanglah Teaching Hospital, Bali, Bali, Indonesia
| | - Sri Sunarti
- Division of Geriatric, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, dr. Syaiful Anwar Hospital, Malang, East Java, Indonesia
| | - Novira Widajanti
- Division of Geriatric, Departement of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Dr. Soetomo General Hospital, Surabaya, East Java, Indonesia
| | - Lazuardhi Dwipa
- Division of Geriatric,Department of Internal Medicine, Faculty of Medicine, Universitas Padjajaran, Hasan Sadikin General Hospital, Bandung, West Java, Indonesia
| | - Euphemia Seto
- Division of Geriatric, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Rahmi Istanti
- Division of Geriatric, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Laurentius Johan Ardian
- Division of Geriatric, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Sabrina Chusnul Chotimah
- Division of Geriatric, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| |
Collapse
|