Assessing contemporary intensive care unit outcome: An updated Mortality Probability Admission Model (MPM0-III)*

Higgins, Thomas L.; Teres, Daniel; Copes, Wayne S.; Nathanson, Brian H.; Stark, Maureen; Kramer, Andrew A.

doi:10.1097/01.ccm.0000257337.63529.9f

Cited by 284 publications

(254 citation statements)

References 25 publications

Supporting

Mentioning

244

Contrasting

Unclassified

Order By: Relevance

“…APACHE IV, SAPS 3, and MPM 0 III prediction of mortality were calculated using published formulae. [15][16][17][18] APACHE III predictions of mortality were obtained from the institutional APACHE III database using proprietary software provided by the Cerner Corporation.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and Influence of Resuscitation Status on Model Performance

Keegan

Gajic

Afessa

2012

Chest

View full text Add to dashboard Cite

The assessment of quality of care in the practice of medicine has become increasingly important. [1][2][3] The practice of critical care medicine has been especially scrutinized, at least in part because of the enormous costs of providing critical care services. 4,5 Aside from external pressures, monitoring and improvement of quality of care are important to clinicians. 6,7 Misuse of quality measures may occur and "[risks] stigmatizing an entire institution," 8 so it is imperative that any assessment of the quality of care delivered in the ICU involves a consideration of the severity of patient illness using a reliable measure. The Joint Commission 9 has proposed severity-adjusted mortality rate as a specifi c measure that should be recorded.Prognostic scoring systems have been developed by the critical care community in an effort to quantify the severity of illness of a given patient or group of patients. 10-12 Adjustment for severity of illness enables monitoring of the performance of an ICU over time and for comparison of ICUs in the same or different hospitals. It is imperative that such severity adjustments be as accurate as possible. 13 Many prognostic models exist, suggesting that the optimum model has not been established. The three most commonly used adult-ICU prognostic scoring systems are APACHE (Acute Physiology and Chronic Health Evaluation), the Simplified Acute Physiology Score (SAPS), and the Mortality Probability Conclusions: APACHE III and IV had similar discriminatory capability and both were better than SAPS 3, which was better than MPM 0 III. The calibrations of the models studied were poor. Overall, models with more predictor variables performed better than those with fewer. The addition of resuscitation status did not improve APACHE III or IV or SAPS 3 prediction. CHEST 2012; 142(4):851 -858Abbreviations: APACHE 5 Acute Physiology and Chronic Health Evaluation; AUC 5 area under the receiver operator characteristic curve; DNR 5 do not resuscitate; MPM 5 Mortality Probability Model; ROC 5 receiver operating characteristic; SAPS 5 Simplifi ed Acute Physiology Score

show abstract

Section: Methodsmentioning

confidence: 99%

“…Any prognostic model will have a limited effective life span. 10,14 [15][16][17][18] A signifi cant number of deaths in patients admitted to ICUs occur after a decision to forgo life-sustaining therapy. 19 Of the three major prognostic models, only MPM includes the presence of a do-not-resuscitate (DNR) order as a predictor variable.…”

Section: Methodsmentioning

confidence: 99%

Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and Influence of Resuscitation Status on Model Performance

Keegan

Gajic

Afessa

2012

Chest

View full text Add to dashboard Cite

show abstract

“…18,19,25,34,35 Th e Mortality Prediction Model, version III (MPM 0 -III) was used to model severity of illness at ICU admission. 39 Because MPM 0 -III is not valid in patients admitted for coronary conditions, cardiac surgery, or burns, they were excluded from the analysis. 37 Only fi rst ICU admissions were included because MPM0-III has not been validated in ICU readmissions.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Utility of ICU Readmissions as a Quality Metric

Brown

Ratcliffe

Halpern

2015

Chest

View full text Add to dashboard Cite

BACKGROUND: ICU readmissions are associated with increased mortality and costs; however, it is unclear whether these outcomes are caused by readmissions or by residual confounding by illness severity. An assessment of temporal changes in ICU readmission in response to a specific policy change could help disentangle these possibilities. We sought to determine whether ICU readmission rates changed aft er 2003 Accreditation Council for Graduate Medical Education Resident Duty Hours reform ("reform") and whether there were temporally corresponding changes in other ICU outcomes.

show abstract

“…We excluded patients from analysis for the following reasons: (1) ineligible for severity of illness assessment as calculated by the MPM 0 -III; and (2) admission to the ICU with limitations on care beyond a simple do-not-resuscitate order. MPM 0 -III is a validated measure of patient acuity and probability of in-hospital death (15,16). To further augment the stability of estimates obtained from within-ICU analyses, we restricted the sample to ICUs contributing data to IMPACT for at least 1 year, and to ICUs contributing at least 20 patients per quarter-year.…”

Section: Study Populationmentioning

confidence: 99%

Mortality among Patients Admitted to Strained Intensive Care Units

Gabler

Ratcliffe²,

Wagner³

et al. 2013

Am J Respir Crit Care Med

131

124

View full text Add to dashboard Cite

Rationale: The aging population may strain intensive care unit (ICU) capacity and adversely affect patient outcomes. Existing fluctuations in demand for ICU care offer an opportunity to explore such relationships. Objectives: To determine whether transient increases in ICU strain influence patient mortality, and to identify characteristics of ICUs that are resilient to surges in capacity strain. physician staffing models (interaction P value ¼ 0.02). The relationship between census and mortality was stronger when the census was composed of higher acuity patients (interaction P value , 0.01). Averaging strain over the first 3 days of patients' ICU stays yielded similar results except that the proportion of new admissions was now also associated with mortality (OR, 1.04 for each 10% increase; 95% CI: 1.02, 1.06). Conclusions: Several sources of ICU strain are associated with small but potentially important increases in patient mortality, particularly in ICUs employing closed staffing models. Although closed ICUs may promote favorable outcomes under static conditions, they are susceptible to being overwhelmed by patient influxes.Keywords: critical care; resource allocation; intensive care unit; physician staffing; regionalization Intensive care units (ICUs) in the United States will encounter increased demand for critical care in the next two decades because of the aging of the American population (1). This sustained increase in baseline demand will compound the effects of routine fluctuations in demand for critical care, such as from influenza epidemics and mass casualties. Concomitant increases in critical care supply are unlikely, due to projected staffing shortages and fiscal constraints (1-6). Thus, ICUs will be increasingly challenged to deliver high-quality care under conditions of increased capacity strain (7).We reported that several measures of ICU capacity strain near the time that patients are discharged from ICUs may enhance the efficiency of critical care delivery without harming patients (8). However, mixed evidence exists regarding the effects of capacity strain closer to the time of ICU admission, when patients' trajectories are often determined by the care they receive. European studies have found associations between increased ICU workload and decreased patient safety (9, 10). By contrast, a large U.S. study found that one metric of strain, ICU census on the day of patients' admissions, was not associated with mortality (11).We therefore sought, in the largest study of capacity strain to date, to determine whether several metrics of strain are associated with in-hospital mortality. We also sought to determine whether certain types of ICUs are more "elastic"-that is, better able to accommodate increases in strain without experiencing worse patient outcomes. METHODS Study DesignWe performed a retrospective cohort study of patients admitted to U.S. ICUs included in the Project IMPACT database (Cerner Corporation, Author Contributions: All authors were involved in the study design, data analysis and/...

show abstract

Assessing contemporary intensive care unit outcome: An updated Mortality Probability Admission Model (MPM0-III)*

Cited by 284 publications

References 25 publications

Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and Influence of Resuscitation Status on Model Performance

Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and Influence of Resuscitation Status on Model Performance

Assessing the Utility of ICU Readmissions as a Quality Metric

Mortality among Patients Admitted to Strained Intensive Care Units

Contact Info

Product

Resources

About