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Topic and purpose
Patients on general wards often show signs of deterioration hours before a cardiac arrest or transfer to intensive care. This bibliography gathers evidence on two ways hospitals try to catch those signs earlier: rapid response teams and automated early warning models built into the electronic health record.
Annotations
Chan, P. S., Jain, R., Nallamothu, B. K., Berg, R. A., & Sasson, C. (2010). Rapid response teams: A systematic review and meta-analysis. Archives of Internal Medicine, 170(1), 18–26. https://doi.org/10.1001/archinternmed.2009.424
This meta-analysis pooled 18 studies with nearly 1.3 million hospital admissions. In adults, introducing a rapid response team was associated with a 33.8% reduction in cardiopulmonary arrests outside the ICU (relative risk 0.66) but not with lower hospital mortality (relative risk 0.96). In children, arrests outside the ICU fell by 37.7% and mortality by 21.4%, although the pooled pediatric mortality estimate was not robust in sensitivity analyses. Most included studies were before-and-after designs, which can't rule out other changes over time. The review is useful because it separates the outcome rapid response teams clearly affect (arrests on the ward) from the one they may not (overall mortality).
Escobar, G. J., Liu, V. X., Schuler, A., Lawson, B., Greene, J. D., & Kipnis, P. (2020). Automated identification of adults at risk for in-hospital clinical deterioration. New England Journal of Medicine, 383(20), 1951–1960. https://doi.org/10.1056/NEJMsa2001090
At 19 hospitals, a validated model calculated each ward patient's risk of deterioration in real time from electronic record data. When a patient reached the alert threshold, nurses monitoring remotely reviewed the record and contacted the hospital's rapid response team. Across 548,838 non-ICU hospitalizations, 43,949 reached the alert threshold. Mortality within 30 days of an alert was lower at hospitals where the program was running than at those where it had not yet started (adjusted relative risk 0.84). The staggered rollout created a natural comparison group, but the study was not randomized. Its key nursing lesson is that the alert worked as part of a response system, with nurses reviewing each alert, not as a stand-alone score.
Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., Ghous, M., & Singh, K. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065–1070. https://doi.org/10.1001/jamainternmed.2021.2626
This retrospective study tested a commercial sepsis prediction model, used at hundreds of US hospitals, in 38,455 hospitalizations at one academic health system. Sepsis occurred in 7% of hospitalizations. The model's area under the curve was 0.63, which shows poor discrimination. It missed 67% of patients with sepsis while generating alerts for 18% of all hospitalized patients, creating a heavy burden of alert fatigue. This study is a caution that a warning tool should be validated locally before nurses are asked to act on its alerts.
Synthesis
Together, the sources suggest that rapid response systems reduce arrests on the ward, that automated alerts can help when they are paired with a reliable human response, and that an alert is only as useful as its accuracy. For nursing practice, the evidence supports escalating early using a rapid response team, and asking how well any warning score has been tested in the hospital where it is used.
