Applicability of the APACHE II model to a lower middle income country
To determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data.Patients meeting APACHE II inclusion criteria admitted to 18 ICUs in Sri Lanka over three consecutive months had data necessary for the calculation of APACHE II, probabilities prospec...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2017
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| _version_ | 1797051915515723776 |
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| author | Haniffa, R Pubudu De Silva, A Weerathunga, P Mukaka, M Athapattu, P Munasinghe, S Mahesh, B Mahipala, P De Silva, T Abayadeera, A Jayasinghe, S de Keizer, N Dondorp, A |
| author_facet | Haniffa, R Pubudu De Silva, A Weerathunga, P Mukaka, M Athapattu, P Munasinghe, S Mahesh, B Mahipala, P De Silva, T Abayadeera, A Jayasinghe, S de Keizer, N Dondorp, A |
| author_sort | Haniffa, R |
| collection | OXFORD |
| description | To determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data.Patients meeting APACHE II inclusion criteria admitted to 18 ICUs in Sri Lanka over three consecutive months had data necessary for the calculation of APACHE II, probabilities prospectively extracted from case notes. APACHE II physiology score (APS), probabilities, Standardised (ICU) Mortality Ratio (SMR), discrimination (AUROC), and calibration (C-statistic) were calculated, both by imputing missing measurements with normal values and by Multiple Imputation using Chained Equations (MICE).From a total of 995 patients admitted during the study period, 736 had APACHE II probabilities calculated. Data availability for APS calculation ranged from 70.6% to 88.4% for bedside observations and 18.7% to 63.4% for invasive measurements. SMR (95% CI) was 1.27 (1.17, 1.40) and 0.46 (0.44, 0.49), AUROC (95% CI) was 0.70 (0.65, 0.76) and 0.74 (0.68, 0.80), and C-statistic was 68.8 and 156.6 for normal value imputation and MICE, respectively.An incomplete dataset confounds interpretation of prognostic model performance in LMICs, wherein imputation using normal values is not a suitable strategy. Improving data availability, researching imputation methods and developing setting-adapted and simpler prognostic models are warranted. |
| first_indexed | 2024-03-06T18:25:58Z |
| format | Journal article |
| id | oxford-uuid:07f9134c-a02d-4333-a75a-31f37b5f6cff |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:25:58Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:07f9134c-a02d-4333-a75a-31f37b5f6cff2022-03-26T09:10:23ZApplicability of the APACHE II model to a lower middle income countryJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:07f9134c-a02d-4333-a75a-31f37b5f6cffEnglishSymplectic Elements at OxfordElsevier2017Haniffa, RPubudu De Silva, AWeerathunga, PMukaka, MAthapattu, PMunasinghe, SMahesh, BMahipala, PDe Silva, TAbayadeera, AJayasinghe, Sde Keizer, NDondorp, ATo determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data.Patients meeting APACHE II inclusion criteria admitted to 18 ICUs in Sri Lanka over three consecutive months had data necessary for the calculation of APACHE II, probabilities prospectively extracted from case notes. APACHE II physiology score (APS), probabilities, Standardised (ICU) Mortality Ratio (SMR), discrimination (AUROC), and calibration (C-statistic) were calculated, both by imputing missing measurements with normal values and by Multiple Imputation using Chained Equations (MICE).From a total of 995 patients admitted during the study period, 736 had APACHE II probabilities calculated. Data availability for APS calculation ranged from 70.6% to 88.4% for bedside observations and 18.7% to 63.4% for invasive measurements. SMR (95% CI) was 1.27 (1.17, 1.40) and 0.46 (0.44, 0.49), AUROC (95% CI) was 0.70 (0.65, 0.76) and 0.74 (0.68, 0.80), and C-statistic was 68.8 and 156.6 for normal value imputation and MICE, respectively.An incomplete dataset confounds interpretation of prognostic model performance in LMICs, wherein imputation using normal values is not a suitable strategy. Improving data availability, researching imputation methods and developing setting-adapted and simpler prognostic models are warranted. |
| spellingShingle | Haniffa, R Pubudu De Silva, A Weerathunga, P Mukaka, M Athapattu, P Munasinghe, S Mahesh, B Mahipala, P De Silva, T Abayadeera, A Jayasinghe, S de Keizer, N Dondorp, A Applicability of the APACHE II model to a lower middle income country |
| title | Applicability of the APACHE II model to a lower middle income country |
| title_full | Applicability of the APACHE II model to a lower middle income country |
| title_fullStr | Applicability of the APACHE II model to a lower middle income country |
| title_full_unstemmed | Applicability of the APACHE II model to a lower middle income country |
| title_short | Applicability of the APACHE II model to a lower middle income country |
| title_sort | applicability of the apache ii model to a lower middle income country |
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