Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data
Objective:. To determine the extent to which within-hospital temporal clustering of postoperative complications is observed in the American College of Surgeons, National Surgical Quality Improvement Program (ACS-NSQIP). Background:. ACS-NSQIP relies on periodic and on-demand reports for quality benc...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Health
2024-09-01
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| Series: | Annals of Surgery Open |
| Online Access: | http://journals.lww.com/10.1097/AS9.0000000000000483 |
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| author | Mark E. Cohen, PhD Yaoming Liu, PhD Clifford Y. Ko, MD, MS, MSHS, FACS Bruce L. Hall, MD, PhD, MBA, FACS |
| author_facet | Mark E. Cohen, PhD Yaoming Liu, PhD Clifford Y. Ko, MD, MS, MSHS, FACS Bruce L. Hall, MD, PhD, MBA, FACS |
| author_sort | Mark E. Cohen, PhD |
| collection | DOAJ |
| description | Objective:. To determine the extent to which within-hospital temporal clustering of postoperative complications is observed in the American College of Surgeons, National Surgical Quality Improvement Program (ACS-NSQIP).
Background:. ACS-NSQIP relies on periodic and on-demand reports for quality benchmarking. However, if rapid increases in postoperative complication rates (clusters) are common, other reporting methods might be valuable additions to the program. This article focuses on estimating the incidence of within-hospital temporal clusters.
Methods:. ACS-NSQIP data from 1,547,440 patients, in 425 hospitals, over a 2-year period was examined. Hospital-specific Cox proportional hazards regression was used to estimate the incidence of mortality, morbidity, and surgical site infection (SSI) over a 30-day postoperative period, with risk adjustment for patient and procedure and with additional adjustments for linear trend, day-of-week, and season. Clusters were identified using scan statistics, and cluster counts were compared, using unpaired and paired t tests, for different levels of adjustment and when randomization of cases across time eliminated all temporal influences.
Results:. Temporal clusters were rarely observed. When clustering was adjusted only for patient and procedure risk, an annual average of 0.31, 0.85, and 0.51 clusters were observed per hospital for mortality, morbidity, and SSI, respectively. The number of clusters dropped after adjustment for linear trend, day-of-week, and season (0.31–0.24; P = 0.012; 0.85–0.80; P = 0.034; and 0.51–0.36; P < 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively. There was 1 significant difference in the number of clusters when comparing data with all adjustments and after data were randomized (0.24 and 0.25 for mortality; P = 0.853; 0.80 and 0.82 for morbidity; P = 0.529; and 0.36 and 0.46 [randomized data had more clusters] for SSI; P = 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively.
Conclusions:. Temporal clusters of postoperative complications were rarely observed in ACS-NSQIP data. The described methodology may be useful in assessing clustering in other surgical arenas. |
| format | Article |
| id | doaj-art-519bb381bd2b4cfb93bc68f32bf4fb0a |
| institution | Kabale University |
| issn | 2691-3593 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wolters Kluwer Health |
| record_format | Article |
| series | Annals of Surgery Open |
| spelling | doaj-art-519bb381bd2b4cfb93bc68f32bf4fb0a2025-01-24T09:18:49ZengWolters Kluwer HealthAnnals of Surgery Open2691-35932024-09-0153e48310.1097/AS9.0000000000000483202409000-00030Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP DataMark E. Cohen, PhD0Yaoming Liu, PhD1Clifford Y. Ko, MD, MS, MSHS, FACS2Bruce L. Hall, MD, PhD, MBA, FACS3From the * Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, ILFrom the * Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, ILFrom the * Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, ILFrom the * Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, ILObjective:. To determine the extent to which within-hospital temporal clustering of postoperative complications is observed in the American College of Surgeons, National Surgical Quality Improvement Program (ACS-NSQIP). Background:. ACS-NSQIP relies on periodic and on-demand reports for quality benchmarking. However, if rapid increases in postoperative complication rates (clusters) are common, other reporting methods might be valuable additions to the program. This article focuses on estimating the incidence of within-hospital temporal clusters. Methods:. ACS-NSQIP data from 1,547,440 patients, in 425 hospitals, over a 2-year period was examined. Hospital-specific Cox proportional hazards regression was used to estimate the incidence of mortality, morbidity, and surgical site infection (SSI) over a 30-day postoperative period, with risk adjustment for patient and procedure and with additional adjustments for linear trend, day-of-week, and season. Clusters were identified using scan statistics, and cluster counts were compared, using unpaired and paired t tests, for different levels of adjustment and when randomization of cases across time eliminated all temporal influences. Results:. Temporal clusters were rarely observed. When clustering was adjusted only for patient and procedure risk, an annual average of 0.31, 0.85, and 0.51 clusters were observed per hospital for mortality, morbidity, and SSI, respectively. The number of clusters dropped after adjustment for linear trend, day-of-week, and season (0.31–0.24; P = 0.012; 0.85–0.80; P = 0.034; and 0.51–0.36; P < 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively. There was 1 significant difference in the number of clusters when comparing data with all adjustments and after data were randomized (0.24 and 0.25 for mortality; P = 0.853; 0.80 and 0.82 for morbidity; P = 0.529; and 0.36 and 0.46 [randomized data had more clusters] for SSI; P = 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively. Conclusions:. Temporal clusters of postoperative complications were rarely observed in ACS-NSQIP data. The described methodology may be useful in assessing clustering in other surgical arenas.http://journals.lww.com/10.1097/AS9.0000000000000483 |
| spellingShingle | Mark E. Cohen, PhD Yaoming Liu, PhD Clifford Y. Ko, MD, MS, MSHS, FACS Bruce L. Hall, MD, PhD, MBA, FACS Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data Annals of Surgery Open |
| title | Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data |
| title_full | Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data |
| title_fullStr | Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data |
| title_full_unstemmed | Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data |
| title_short | Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data |
| title_sort | within hospital temporal clustering of postoperative complications and implications for safety monitoring and benchmarking using acs nsqip data |
| url | http://journals.lww.com/10.1097/AS9.0000000000000483 |
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