Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients*

Zimmerman, Jack E.; Kramer, Andrew A.; McNair, Douglas S.; Malila, Fern M.

doi:10.1097/01.ccm.0000215112.84523.f0

Cited by 1,464 publications

(1,322 citation statements)

References 52 publications

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“…The Acute Physiology and Chronic Health Evaluation (APACHE) IV score [17], the Sequential Organ Failure Assessment (SOFA) [18] score, the Oxygenation Index (OI) [19], and the Berlin classification [15,16] were determined in the first 24 h after admission.…”

Section: Established Disease Severity Scoring Systemsmentioning

confidence: 99%

Plasma suPAR as a prognostic biological marker for ICU mortality in ARDS patients

et al. 2015

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Purpose: We investigated the prognostic value of plasma soluble urokinase plasminogen activator receptor (suPAR) on day 1 in patients with the acute respiratory distress syndrome (ARDS) for intensive care unit (ICU) mortality and compared it with established disease severity scores on day 1. Methods: suPAR was determined batchwise in plasma obtained within 24 h after admission. Results: 632 ARDS patients were included. Significantly (P = 0.02) higher median levels of suPAR were found with increasing severity of ARDS: 5.9 ng/ml [IQR 3.1-12.8] in mild ARDS (n = 82), 8.4 ng/ml .0] in moderate ARDS (n = 333), and 9.0 ng/ml .0] in severe ARDS (n = 217). Non-survivors had higher median levels of suPAR [12.5 ng/ml (IQR 5.1-19.5) vs. 7.4 ng/ml (3.9-13.6), P \ 0.001]. The area under the receiver operator characteristic curve (ROC-AUC) for mortality of suPAR (0.62) was lower than the ROC-AUC of the APACHE IV score (0.72, P = 0.007), higher than that of the ARDS definition classification (0.53, P = 0.005), and did not differ from that of the SOFA score (0.68, P = 0.07) and the oxygenation index (OI) (0.58, P = 0.29). Plasma suPAR did not improve the discrimination of the established disease severity scores, but did improve net reclassification of the APACHE score (29 %), SOFA score (23 %), OI (38 %), and Berlin definition classification (39 %). Conclusion: As a single biological marker, the prognostic value for death of plasma suPAR in ARDS patients is low. Plasma suPAR, however, improves the net reclassification, suggesting a potential role for suPAR in ICU mortality prediction models.

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Section: Established Disease Severity Scoring Systemsmentioning

confidence: 99%

Plasma suPAR as a prognostic biological marker for ICU mortality in ARDS patients

et al. 2015

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“…In order to use these scores for risk adjustment, we excluded all patients who were admitted to the ICU more than 48 hours after hospital admission since it 8 is unlikely the scores will accurately measure the severity of patients after that. These scores are used for the over 3 million patients in this hospital network and have similar predictive power as the APACHE and SAPS scores with c statistic in the 0.88 range (Zimmerman et al 2006, Moreno et al 2005. See Escobar et al (2008) for further description of these severity scores.…”

Section: Datamentioning

confidence: 99%

The Impact of Delays on Service Times in the Intensive Care Unit

Chan¹,

Farias

Escobar

2017

Management Science

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Mainstream queueing models are frequently employed in modeling healthcare delivery in a number of settings, and further used in making operational decisions for the same. The vast majority of these queueing models assume that the service requirements of a job are independent of the state of the queue upon its arrival. In a healthcare setting, this assumption is equivalent to ignoring the effects of delay experienced by a patient awaiting care. However, it is only natural to conjecture that long delays may have adverse effects on patient outcomes and can potentially lead to longer lengths of stay (LOS) when the patient ultimately does receive care. At a very coarse level, prior research confirms these natural conjectures. This work sets out to understand these delay issues from an operational perspective. In particular, using data of nearly 6,000 Emergency Department (ED) visits, we use an instrumental variable approach to empirically measure how congestion in the Intensive Care Unit (ICU) can lead to delays in boarding from the ED to the ICU and measure the impact on the patient's ICU LOS.Capturing these empirically observed effects in a queueing model is challenging as the effect introduces potentially long range correlations in service and inter-arrival times. As such, we consider the problem of how to incorporate these measured delay effects into a queueing model and characterize approximations to various quantities of interest when the service time of a job is adversely impacted by the delay experienced by that job. Our findings suggest that this delay effect can be substantial and ignoring it when using queueing models to model healthcare delivery systems may result in significant under-provisioning.

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“…The M4 model turned out to be superior to M3 and its AUC surpassed the desired level of 0.8 [42] thus it was selected as the model for predicting severity of pediatric asthma exacerbations. The model is presented graphically in Figure 3.…”

Section: Developed Modelmentioning

confidence: 99%

Implementing an Integrative Multi-agent Clinical Decision Support System with Open Source Software

et al. 2010

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This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at two hours following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.

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Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients*

Cited by 1,464 publications

References 52 publications

Plasma suPAR as a prognostic biological marker for ICU mortality in ARDS patients

Plasma suPAR as a prognostic biological marker for ICU mortality in ARDS patients

The Impact of Delays on Service Times in the Intensive Care Unit

Implementing an Integrative Multi-agent Clinical Decision Support System with Open Source Software

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