Effect of a rapid response system for patients in shock on time to treatment and mortality during 5 years*

Sebat, Frank; Musthafa, Amjad A.; Johnson, David; Kramer, Andrew A.; Shoffner, Debbie; Eliason, Mark J.; Henry, Kristen; Spurlock, Bruce

doi:10.1097/01.ccm.0000287593.54658.89

Cited by 178 publications

(125 citation statements)

References 37 publications

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“…Condition-specific protocols, checklists, and ''bundles'' 36 tailored to a ward environment (as opposed to the ICU or to the entire hospital) might prevent deteriorations in these patients, as has been reported for sepsis. 37 The second route is to improve the overall capabilities of rapid response and ''code blue'' teams. Such research would need to include a more careful assessment of what commonalities exist among patients who were and were not successfully ''rescued'' by these teams.…”

Section: Discussionmentioning

confidence: 99%

Intra‐hospital transfers to a higher level of care: Contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS)

Escobar¹,

Greene²,

Gardner³

et al. 2010

Journal of Hospital Medicine

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BACKGROUND:Patients who experience intra‐hospital transfers to a higher level of care (eg, ward to intensive care unit [ICU]) are known to have high mortality. However, these findings have been based on single‐center studies or studies that employ ICU admissions as the denominator.OBJECTIVE:To employ automated bed history data to examine outcomes of intra‐hospital transfers using all hospital admissions as the denominator.DESIGN:Retrospective cohort study.SETTING:A total of 19 acute care hospitals.PATIENTS:A total of 150,495 patients, who experienced 210,470 hospitalizations, admitted to these hospitals between November 1st, 2006 and January 31st, 2008.MEASUREMENTS:Predictors were age, sex, admission type, admission diagnosis, physiologic derangement on admission, and pre‐existing illness burden; outcomes were: 1) occurrence of intra‐hospital transfer, 2) death following admission to the hospital, 3) death following transfer, and 4) total hospital length of stay (LOS).RESULTS:A total of 7,868 hospitalizations that began with admission to either a general medical surgical ward or to a transitional care unit (TCU) had at least one transfer to a higher level of care. These hospitalizations constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers.CONCLUSIONS:Patients transferred to higher level of care following admission to the hospital have excess mortality and LOS. Journal of Hospital Medicine 2010;. © 2010 Society of Hospital Medicine.

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Section: Discussionmentioning

confidence: 99%

Intra‐hospital transfers to a higher level of care: Contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS)

Escobar¹,

Greene²,

Gardner³

et al. 2010

Journal of Hospital Medicine

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“…heart rate, blood pressure, respiratory rate) and other parameters to 'track' a patient's condition, detect deterioration early and 'trigger' appropriate clinical intervention. The National Institute for Health and Care Excellence recommends that such systems be used for all hospitalised patients [3] as delayed interventions for acutely ill patients leads to poor outcomes [4,5]. National guidance recommends the use of EWS in maternity [1,6].…”

Section: Introductionmentioning

confidence: 99%

A national survey of obstetric early warning systems in the United Kingdom: five years on

et al. 2014

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SummaryThe Confidential Enquiries into Maternal Deaths in the UK have recommended obstetric early warning systems for early identification of clinical deterioration to reduce maternal morbidity and mortality. This survey explored early warning systems currently used by maternity units in the UK. An electronic questionnaire was sent to all 205 lead obstetric anaesthetists under the auspices of the Obstetric Anaesthetists' Association, generating 130 (63%) responses. All respondents reported use of an obstetric early warning system, compared with 19% in a similar survey in 2007. Respondents agreed that the six most important physiological parameters to record were respiratory rate, heart rate, temperature, systolic and diastolic blood pressure and oxygen saturation. One hundred and eighteen (91%) lead anaesthetists agreed that early warning systems helped to prevent obstetric morbidity. Staffing pressures were perceived as the greatest barrier to their use, and improved audit, education and training for healthcare professionals were identified as priority areas.

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“…Both sepsis and septic shock are known to be associated with high morbidity, longer hospital stay and increased health care cost (Kumar et al 2011). Often, the risk of sepsis-related adverse outcomes can be reduced by early treatment (Sebat et al 2007), thus a scoring system that allows precise tracking of changes in sepsis-related disease severity is of great importance.…”

Section: Sepsis Mimic-ii and The Surviving Sepsis Campaign Guidelinementioning

confidence: 99%

Learning (predictive) risk scores in the presence of censoring due to interventions

Dyagilev

Saria

2015

Mach Learn

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A large and diverse set of measurements are regularly collected during a patient's hospital stay to monitor their health status. Tools for integrating these measurements into severity scores, that accurately track changes in illness severity, can improve clinicians ability to provide timely interventions. Existing approaches for creating such scores either 1) rely on experts to fully specify the severity score, 2) infer a score using detailed models of disease progression, or 3) train a predictive score, using supervised learning, by regressing against a surrogate marker of severity such as the presence of downstream adverse events. The first approach does not extend to diseases where an accurate score cannot be elicited from experts. The second assumes that the progression of disease can be accurately modeled, limiting its application to populations with simple, well-understood disease dynamics. The third approach, also most commonly used, often produces scores that suffer from bias due to treatment-related censoring (Paxton et al, 2013). Specifically, since the downstream outcomes used for their training are observed only noisily and are influenced by treatment administration patterns, these scores do not generalize well when treatment administration patterns change. We propose a novel ranking based framework for disease severity score learning (DSSL). DSSL exploits the following key observation: while it is challenging for experts to quantify the disease severity at any given time, it is often easy to compare the disease severity at two different times. Extending existing ranking algorithms, DSSL learns a function that maps a vector of patient's measurements to a scalar severity score subject to two constraints. First, the resulting score should be consistent with the expert's ranking of the disease severity state. Second, changes in score between consecutive periods should be smooth. We apply DSSL to the problem of learning a sepsis severity score using a large, real-

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Effect of a rapid response system for patients in shock on time to treatment and mortality during 5 years*

Abstract: Over time, a rapid response system for patients in shock continued to reduce time to treatment, resulting in a continued decrease in mortality. By year 5, only three patients needed to be treated to save one additional life.

Cited by 178 publications

References 37 publications

Intra‐hospital transfers to a higher level of care: Contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS)

Intra‐hospital transfers to a higher level of care: Contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS)

A national survey of obstetric early warning systems in the United Kingdom: five years on

Learning (predictive) risk scores in the presence of censoring due to interventions

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