Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan
Abstract
:1. Introduction
2. Materials and Methods
2.1. Setting, Data Source, and Ethical Concerns
2.2. Study Cohort, Enrolled Hospitalizations, and 30-Day Readmissions
2.3. The LACE Index and HOSPITAL Score Models for Prediction of Readmission
2.4. Microsimulation Model
2.5. Error Analysis and Statistical Analysis
2.6. Experimentation of Microsimulation Model
3. Results
3.1. Study Population and 30-Day Readmission Rates
3.2. Discrimination and Calibration of the LACE and HOSPITAL Models
3.3. Cost of Intervention and Readmission Rate Reduction of the LACE Index and HOSPITAL Score Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
30-Day Readmission Prediction Models | Sensitivity (%) | (95% CI) | Specificity | (95% CI) |
---|---|---|---|---|
LACE index | 81.8 | (59.7–94.8) | 44.6 | (32.3–57.5) |
HOSPITAL score | 90.9 | (70.8–98.9) | 41.5 | (29.4–54.4) |
Attending physician 1 | 23 | (13–37) | 84 | (75–90) |
Variables | Type, Value | Lower Limit | Mode | Upper Limit |
---|---|---|---|---|
Readmission rate | Constant = 0.25 | - | - | - |
Successful rate of preventive intervention | Constant = 0.5 | - | - | - |
LACE index—Sensitivity | Triangular distribution | 0.597 | 0.818 | 0.948 |
HOSPITAL score—Sensitivity | Triangular distribution | 0.708 | 0.909 | 0.989 |
Attending physician—Sensitivity | Triangular distribution | 0.13 | 0.23 | 0.37 |
LACE index—Specificity | Triangular distribution | 0.323 | 0.446 | 0.575 |
HOSPITAL score—Specificity | Triangular distribution | 0.294 | 0.415 | 0.544 |
Attending physician—Specificity | Triangular distribution | 0.75 | 0.84 | 0.90 |
Appendix B
No. | Studies Used LACE Index | Population (n) | Area under Curve (AUC) | (95% CI) 1 | Discrimination Power 2 |
---|---|---|---|---|---|
1. | Cotter et al., (2012) [12] | UK population, n = 507 | 0.57 | NA | Poor |
2. | Van Walraven et al., (2012) [44] 3 | Medical records, n = 500,000 | 0.771 | (0.767–0.775) | Clearly useful |
3. | Wang et al., (2014) [45] | Heart failure patients, n = 253 | 0.664 | (0.575–0.752) | Possibly helpful |
4. | Low et al., (2015) [46] | Singapore population, n = 5862 | 0.628 | (0.602–0.653) | Possibly helpful |
5. | Yazdan-Ashoori et al., (2016) [47] | Heart failure patients, n = 378 | 0.58 | (0.57–0.61) | Poor |
6. | Damery et al., (2017) [48] | Inpatient health records, n = 7107 | 0.773 | (0.768–0.779) | Clearly useful |
7. | Low et al., (2017) [49] | Singapore, elder inpatients, n = 17,006 | 0.595 | (0.581–0.608) | Poor |
8. | Robinson et al., (2017) [13] | Inpatient health records, n = 432 | 0.58 | (0.48–0.68) | Poor |
9. | Baig et al., (2018) [50] | New Zealand population, n = 213,440 | 0.752 | NA | Clearly useful |
10. | Hakim et al., (2018) [51] | COPD 4 patients, n = 2662 | 0.63 | (0.62–0.65) | Possibly helpful |
11. | Miller et al., (2018) [37] | Inpatients, n = 359 | 0.620 | (0.521–0.718) | Possibly helpful |
12. | Saluk et al., (2018) [52] | Radical cystectomy patients, n = 3470 | 0.581 | (0.556-0.606) | Poor |
13. | Caplan et al., (2019) [53] 3 | Brain tumor population, n = 352 | 0.58 | NA | Poor |
14. | Caplan et al., (2019) [54] 3 | Craniotomy patients, n = 238 | 0.69 | NA | Possibly helpful |
15. | Ibrahim et al., (2019) [55] | Heart failure patients, n = 730 | 0.551 | (0.503–0.598) | Poor |
16. | Robinson et al., (2019) [56] | Inpatients, n = 1916 | 0.598 | (0.58–0.64) | Poor |
17. | Shaffer et al., (2019) [57] | Surgical patients, n = 192,670 | 0.82 | NA | Possibly helpful |
No. | Studies Used HOSPITAL Score | Population (n) | Area under Curve (AUC) | (95% CI) 1 | Discrimination Power 2 |
---|---|---|---|---|---|
1. | Aubert et al., (2016) [10] | Switzerland, inpatients, n = 346 | 0.70 | (0.62–0.79) | Possibly helpful |
2. | Donze et al., (2016) [58] | Inpatients, n = 117,065 | 0.72 | (0.72–0.72) | Possibly helpful |
3. | Kim et al., (2016) [59] | Inpatients, n = 4208 | 0.65 | NA | Possibly helpful |
4. | Robinson (2016) [60] | Inpatients, n = 998 | 0.77 | (0.73–0.81) | Clearly useful |
5. | Aubert et al., (2017) [61] | Inpatients, n = 117,065 | 0.69 | (0.68–0.69) | Possibly helpful |
6. | Burke et al., (2017) [8] | Inpatients, n = 9181 | 0.68 | (0.67–0.71) | Possibly helpful |
7. | Robinson et al., (2017) [13] | Inpatient health records, n = 432 | 0.75 | (0.67–0.83) | Possibly helpful |
8. | Ibrahim et al., (2019) [55] | Heart failure inpatients, n = 730 | 0.595 | (0.549–0.641) | Poor |
9. | Robinson et al., (2019) [56] | Inpatients, n = 1916 | 0.675 | (0.65–0.70) | Possibly helpful |
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Demographics and Parameters | All Acute Hospitalizations (n = 87) | Acute Hospitalization Followed by 30-Day Readmission (n = 22) | Acute Hospitalization with Successful Discharge (n = 65) | - | - | |||
---|---|---|---|---|---|---|---|---|
Mean | (SD 1) | Mean | (SD 1) | Mean | (SD 1) | p-Value | Sig. 2 | |
Personal demographics | ||||||||
Gender | - | - | - | - | - | - | 0.055 | - |
Female, no. (%) | 40 | (46.0) | 14 | (63.6) | 26 | (40.0) | - | - |
Male, no. (%) | 47 | (54.0) | 8 | (36.4) | 39 | (60.0) | - | - |
Age | 87.4 | (12.4) | 89.0 | (8.1) | 86.9 | (13.6) | 0.482 | - |
LACE prediction model | ||||||||
Length of stay (L) | 5.6 | (0.7) | 5.6 | (0.6) | 2.6 | (0.6) | 0.654 | - |
Acuity of admission (A) | 2.7 | (0.9) | 2.9 | (0.6) | 2.6 | (0.6) | 0.21 | - |
Comorbidities (C) | 1.7 | (0.9) | 1.6 | (0.9) | 1.7 | (0.9) | 0.864 | - |
Emergency department visits (E) | 1.9 | (1.3) | 2.5 | (1.1) | 1.7 | (1.1) | 0.017 | * |
LACE index | 11.8 | (2.6) | 12.5 | (1.8) | 11.5 | (1.8) | 0.053 | - |
HOSPITAL prediction model 3 | ||||||||
Hemoglobin level (H) | 0.7 | (0.5) | 0.9 | (0.4) | 0.6 | (0.5) | 0.009 | ** |
Sodium level (S) | 0.3 | (0.4) | 0.1 | (0.3) | 0.3 | (0.5) | 0.009 | ** |
Procedure during the index admission (P) | 0.3 | (0.5) | 0.3 | (0.5) | 0.3 | (0.5) | 0.967 | - |
Index type of admission (IT) | 0.9 | (0.3) | 0.9 | (0.3) | 0.8 | (0.4) | 0.465 | - |
Number of admissions (A) | 1.7 | (1.2) | 2.3 | (1.2) | 1.5 | (1.2) | 0.007 | ** |
Length of stay (L) | 1.9 | (0.4) | 2.0 | (0.0) | 1.9 | (0.4) | 0.083 | - |
HOSPITAL score | 5.8 | (1.6) | 6.6 | (1.4) | 5.5 | (1.6) | 0.003 | ** |
30-Day Readmission Prediction Models | Area under Curve (AUC) | (95% C.I.) | p-Value | Sig. 1 | - |
LACE index | 0.598 | (0.474–0.722) | 0.170 | - | - |
HOSPITAL score | 0.691 | (0.573–0.808) | 0.008 | ** | - |
30-Day Readmission Prediction Models | Accuracy (%) | Sensitivity (%) | (95% CI) | Specificity (%) | (95% CI) |
LACE index | 54.5 | 81.8 | (59.7–94.8) | 44.6 | (32.3–57.5) |
HOSPITAL score | 54.0 | 90.9 | (70.8–98.9) | 41.5 | (29.4–54.4) |
30-Day Readmission Prediction Models 1 | 30-Day Readmission Rate Reduction (%) | (IQR 3) | Cost of Preventive Intervention (%) 4 | (IQR 2) |
---|---|---|---|---|
LACE index | 39.2 | (39.1–39.4) | 66.8 | (61.0–72.6) |
HOSPITAL score | 43.4 | (43.3–43.5) | 72.0 | (63.1–79.4) |
Attending physician | 10.1 | (9.8–10.3) | 18.6 | (17.2–19.9) |
All intervention | 50.0 | (50.0–50.0) | 100 (reference) | - |
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Su, M.-C.; Wang, Y.-J.; Chen, T.-J.; Chiu, S.-H.; Chang, H.-T.; Huang, M.-S.; Hu, L.-H.; Li, C.-C.; Yang, S.-J.; Wu, J.-C.; et al. Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan. Int. J. Environ. Res. Public Health 2020, 17, 927. https://doi.org/10.3390/ijerph17030927
Su M-C, Wang Y-J, Chen T-J, Chiu S-H, Chang H-T, Huang M-S, Hu L-H, Li C-C, Yang S-J, Wu J-C, et al. Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan. International Journal of Environmental Research and Public Health. 2020; 17(3):927. https://doi.org/10.3390/ijerph17030927
Chicago/Turabian StyleSu, Mei-Chin, Yi-Jen Wang, Tzeng-Ji Chen, Shiao-Hui Chiu, Hsiao-Ting Chang, Mei-Shu Huang, Li-Hui Hu, Chu-Chuan Li, Su-Ju Yang, Jau-Ching Wu, and et al. 2020. "Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan" International Journal of Environmental Research and Public Health 17, no. 3: 927. https://doi.org/10.3390/ijerph17030927