Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic
Background The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. This study aimed to elucidat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2024-07-01
|
| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/31/1/e101074.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850099411016744960 |
|---|---|
| author | Takeshi Nakamura Satoaki Matoba Masahiro Nishi Kenji Yanishi |
| author_facet | Takeshi Nakamura Satoaki Matoba Masahiro Nishi Kenji Yanishi |
| author_sort | Takeshi Nakamura |
| collection | DOAJ |
| description | Background The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. This study aimed to elucidate the adaptability of prediction models for 30-day mortality in patients with ACS during the pandemic periods.Methods A total of 2041 consecutive patients with ACS were included from 32 institutions between December 2020 and April 2023. The dataset comprised patients who were admitted for ACS and underwent coronary angiography for the diagnosis during hospitalisation. The prediction accuracy of the Global Registry of Acute Coronary Events (GRACE) and a machine learning model, KOTOMI, was evaluated for 30-day mortality in patients with ST-elevation acute myocardial infarction (STEMI) and non-ST-elevation acute coronary syndrome (NSTE-ACS).Results The area under the receiver operating characteristics curve (AUROC) was 0.85 (95% CI 0.81 to 0.89) in the GRACE and 0.87 (95% CI 0.82 to 0.91) in the KOTOMI for STEMI. The difference of 0.020 (95% CI −0.098–0.13) was not significant. For NSTE-ACS, the respective AUROCs were 0.82 (95% CI 0.73 to 0.91) in the GRACE and 0.83 (95% CI 0.74 to 0.91) in the KOTOMI, also demonstrating insignificant difference of 0.010 (95% CI −0.023 to 0.25). The prediction accuracy of both models had consistency in patients with STEMI and insignificant variation in patients with NSTE-ACS between the pandemic periods.Conclusions The prediction models maintained high accuracy for 30-day mortality of patients with ACS even in the pandemic periods, despite marginal variation observed. |
| format | Article |
| id | doaj-art-9447f95ae32b46c7b68fb31ebbe2fa79 |
| institution | DOAJ |
| issn | 2632-1009 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-9447f95ae32b46c7b68fb31ebbe2fa792025-08-20T02:40:30ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092024-07-0131110.1136/bmjhci-2024-101074Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemicTakeshi Nakamura0Satoaki Matoba1Masahiro Nishi2Kenji Yanishi3Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan1 Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, JapanDepartment of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, JapanDepartment of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, JapanBackground The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. This study aimed to elucidate the adaptability of prediction models for 30-day mortality in patients with ACS during the pandemic periods.Methods A total of 2041 consecutive patients with ACS were included from 32 institutions between December 2020 and April 2023. The dataset comprised patients who were admitted for ACS and underwent coronary angiography for the diagnosis during hospitalisation. The prediction accuracy of the Global Registry of Acute Coronary Events (GRACE) and a machine learning model, KOTOMI, was evaluated for 30-day mortality in patients with ST-elevation acute myocardial infarction (STEMI) and non-ST-elevation acute coronary syndrome (NSTE-ACS).Results The area under the receiver operating characteristics curve (AUROC) was 0.85 (95% CI 0.81 to 0.89) in the GRACE and 0.87 (95% CI 0.82 to 0.91) in the KOTOMI for STEMI. The difference of 0.020 (95% CI −0.098–0.13) was not significant. For NSTE-ACS, the respective AUROCs were 0.82 (95% CI 0.73 to 0.91) in the GRACE and 0.83 (95% CI 0.74 to 0.91) in the KOTOMI, also demonstrating insignificant difference of 0.010 (95% CI −0.023 to 0.25). The prediction accuracy of both models had consistency in patients with STEMI and insignificant variation in patients with NSTE-ACS between the pandemic periods.Conclusions The prediction models maintained high accuracy for 30-day mortality of patients with ACS even in the pandemic periods, despite marginal variation observed.https://informatics.bmj.com/content/31/1/e101074.full |
| spellingShingle | Takeshi Nakamura Satoaki Matoba Masahiro Nishi Kenji Yanishi Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic BMJ Health & Care Informatics |
| title | Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic |
| title_full | Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic |
| title_fullStr | Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic |
| title_full_unstemmed | Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic |
| title_short | Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic |
| title_sort | adaptability of prognostic prediction models for patients with acute coronary syndrome during the covid 19 pandemic |
| url | https://informatics.bmj.com/content/31/1/e101074.full |
| work_keys_str_mv | AT takeshinakamura adaptabilityofprognosticpredictionmodelsforpatientswithacutecoronarysyndromeduringthecovid19pandemic AT satoakimatoba adaptabilityofprognosticpredictionmodelsforpatientswithacutecoronarysyndromeduringthecovid19pandemic AT masahironishi adaptabilityofprognosticpredictionmodelsforpatientswithacutecoronarysyndromeduringthecovid19pandemic AT kenjiyanishi adaptabilityofprognosticpredictionmodelsforpatientswithacutecoronarysyndromeduringthecovid19pandemic |