Effectiveness of automated alerting system compared to usual care for the management of sepsis
Abstract There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are se...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-022-00650-5 |
| _version_ | 1797642414463123456 |
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| author | Zhongheng Zhang Lin Chen Ping Xu Qing Wang Jianjun Zhang Kun Chen Casey M. Clements Leo Anthony Celi Vitaly Herasevich Yucai Hong |
| author_facet | Zhongheng Zhang Lin Chen Ping Xu Qing Wang Jianjun Zhang Kun Chen Casey M. Clements Leo Anthony Celi Vitaly Herasevich Yucai Hong |
| author_sort | Zhongheng Zhang |
| collection | DOAJ |
| description | Abstract There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73–1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51–0.90) and ward (RR: 0.71; 95% CI: 0.61–0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39–0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63–0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit. |
| first_indexed | 2024-03-11T13:59:55Z |
| format | Article |
| id | doaj.art-0f274eab3e5b4c7081f5ba9e8f1982f2 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-11T13:59:55Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-0f274eab3e5b4c7081f5ba9e8f1982f22023-11-02T04:40:44ZengNature Portfolionpj Digital Medicine2398-63522022-07-015111010.1038/s41746-022-00650-5Effectiveness of automated alerting system compared to usual care for the management of sepsisZhongheng Zhang0Lin Chen1Ping Xu2Qing Wang3Jianjun Zhang4Kun Chen5Casey M. Clements6Leo Anthony Celi7Vitaly Herasevich8Yucai Hong9Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of MedicineEmergency Department, Zigong Fourth People’s HospitalDepartment of Surgery, University of VirginiaEmergency Department, Zigong Fourth People’s HospitalDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of MedicineDepartment of Emergency Medicine, Mayo ClinicDepartment of Biostatistics, Harvard T H Chan School of Public HealthDepartment of Anesthesiology and Perioperative Medicine, Division of Critical Care Medicine, Mayo ClinicDepartment of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineAbstract There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73–1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51–0.90) and ward (RR: 0.71; 95% CI: 0.61–0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39–0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63–0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit.https://doi.org/10.1038/s41746-022-00650-5 |
| spellingShingle | Zhongheng Zhang Lin Chen Ping Xu Qing Wang Jianjun Zhang Kun Chen Casey M. Clements Leo Anthony Celi Vitaly Herasevich Yucai Hong Effectiveness of automated alerting system compared to usual care for the management of sepsis npj Digital Medicine |
| title | Effectiveness of automated alerting system compared to usual care for the management of sepsis |
| title_full | Effectiveness of automated alerting system compared to usual care for the management of sepsis |
| title_fullStr | Effectiveness of automated alerting system compared to usual care for the management of sepsis |
| title_full_unstemmed | Effectiveness of automated alerting system compared to usual care for the management of sepsis |
| title_short | Effectiveness of automated alerting system compared to usual care for the management of sepsis |
| title_sort | effectiveness of automated alerting system compared to usual care for the management of sepsis |
| url | https://doi.org/10.1038/s41746-022-00650-5 |
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