Higgins TL, Teres D, Copes WS, et al. Assessing contemporary intensive care unit outcome: An updated Mortality Probability Admission Model (MPM0-III)

Tufts University, Бостон, Georgia, United States
Critical Care Medicine (Impact Factor: 6.31). 04/2007; 35(3):827-35. DOI: 10.1097/01.CCM.0000257337.63529.9F
Source: PubMed


To update the Mortality Probability Model at intensive care unit (ICU) admission (MPM0-II) using contemporary data.
Retrospective analysis of data from 124,855 patients admitted to 135 ICUs at 98 hospitals participating in Project IMPACT between 2001 and 2004. Independent variables considered were 15 MPM0-II variables, time before ICU admission, and code status. Univariate analysis and multivariate logistic regression were used to identify risk factors associated with hospital mortality.
One hundred thirty-five ICUs at 98 hospitals.
Patients in the Project IMPACT database eligible for MPM0-II scoring.
Hospital mortality rate in the current data set was 13.8% vs. 20.8% in the MPM0-II cohort. All MPM0-II variables remained associated with mortality. Clinical conditions with high relative risks in MPM0-II also had high relative risks in MPM0-III. Gastrointestinal bleeding is now associated with lower mortality risk. Two factors have been added to MPM0-III: "full code" resuscitation status at ICU admission, and "zero factor" (absence of all MPM0-II risk factors except age). Seven two-way interactions between MPM0-II variables and age were included and reflect the declining marginal contribution of acute and chronic medical conditions to mortality risk with increasing age. Lead time before ICU admission and pre-ICU location influenced individual outcomes but did not improve model discrimination or calibration. MPM0-III calibrates well by graphic comparison of actual vs. expected mortality, overall standardized mortality ratio (1.018; 95% confidence interval, 0.996-1.040) and a low Hosmer-Lemeshow goodness-of-fit statistic (11.62; p = .31). The area under the receiver operating characteristic curve was 0.823.
MPM0-II risk factors remain relevant in predicting ICU outcome, but the 1993 model significantly overpredicts mortality in contemporary practice. With the advantage of a much larger sample size and the addition of new variables and interaction effects, MPM0-III provides more accurate comparisons of actual vs. expected ICU outcomes.

74 Reads
  • Source
    • "Clinical characteristics at the index ICU admission mark patients at increased risk of ICU readmission. Readmitted patients had higher Mortality Prediction Model III (MPM- III) scores, [4] increased vasopressor use, and were more likely ventilated than patients who were never readmitted. Furthermore , patients with comorbid conditions (chronic cardiovascular disease, chronic respiratory disease, and baseline serum creatinine, 2 mg/dl) were more likely to be readmitted. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Many patients need readmission to intensive care unit (recidivism) which make ICU moderation burdensome. Readmitted patients mostly carry poor prognosis compared to newly admitted ones, in addition to the bad psychological impact for both patient and his family. Study design In this retrospective study data of the admitted patients to the pulmonary critical care unit, Mansoura University Hospital included: demographic, clinical, laboratory, and ventilatory data in addition time of discharge and readmission were collected, analyzed and interpreted. Aim The aim of this work is to study the predictors of pulmonary critical care recidivism. Patients and methods In this retrospective study 1562 pulmonary critical care unit patients admitted to pulmonary critical care unit, Mansoura University Hospital from August 2009 till the end of December 2013 were subjected to: recording of demographic data, body mass index, admission severity scoring, type of respiratory failure, presence of co morbidity, need for pressors, presence of acute kidney injury at the time of admission, duration of mechanical ventilation, protocolized versus non protocolized weaning, need for tracheostomy, time of discharge, and discharging oxygen saturation using pulse oxymetry. Results Of the total number was 1562 patients 69 patients were transferred to other ICUs. From the remaining 1493 patients, 327 died within the first 24 h of ICU admission and 1166 survived, 395 patients needed readmission and 771 were non readmission. The incidence of recidivism was more in: patients with type II respiratory failure (66.8%), age above 50 years (69.9%), BMI above 35 (70.4%), non recovered acute kidney injury (53.2%), pressor receivers (87.6%), who underwent tracheostomy (67.8%), had longer duration of mechanical ventilation (17 ± 7 days vs. 9 ± 4 days in non readmitted) and patients who were discharged between 8 pm and 8 am (72.4%) on hot days (82.1%), in all the p value was <0.005. On the other hand, there was no statistically significant difference in both readmitted and non readmitted patients as regards: sex and weaning method (protocolized 49.4% or non protocolized 50.6%), in all the p value was >0.005. Conclusion Age above 50 years, obesity, non recovered AKI, presence of type II respiratory failure, nocturnal and hot day discharge, need for pressors and tracheostomy are considered to be predictors of recidivism to pulmonary critical care unit.
    10/2014; 63(4). DOI:10.1016/j.ejcdt.2014.05.006
  • Source
    • "Newer versions of major adult prognostic models have been developed overseas [19] to overcome performance degradation of older generation models. The APACHE has been upgraded to APACHE IV [5]; SAPS and MPM have been upgraded to SAPS3 [8] [9] and MPM III [10], respectively. Although these new generation models are reported to perform well overseas [19], they cannot be implemented in ANZ ICUs because the data required for these models are not routinely collected by ANZICS. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of this study is to develop and validate a new mortality prediction model (Australian and New Zealand Risk of Death [ANZROD]) for Australian and New Zealand intensive care units (ICUs) and compare its performance with the existing Acute Physiology and Chronic Health Evaluation (APACHE) III-j. All ICU admissions from 2004 to 2009 were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Hospital mortality was modeled using logistic regression with training (two third) and validation (one third) data sets. Predictor variables included APACHE III score components, source of admission to ICU and hospital, lead time, elective surgery, treatment limitation, ventilation status, and APACHE III diagnoses. Model performance was assessed by standardized mortality ratio, Hosmer-Lemeshow C and H statistics, Brier score, Cox calibration regression, area under the receiver operating characteristic curve, and calibration curves. There were 456605 patients available for model development and validation. Observed mortality was 11.3%. Performance measures (standardized mortality ratio, Hosmer-Lemeshow C and H statistics, and receiver operating characteristic curve) for the ANZROD and APACHE III-j model in the validation data set were 1.01, 104.9 and 111.4, and 0.902; 0.84, 1596.6 and 2087.3, and 0.885, respectively. The ANZROD has better calibration; discrimination compared with the APACHE III-j. Further research is required to validate performance over time and in specific subgroups of ICU population.
    Journal of critical care 09/2013; 28(6). DOI:10.1016/j.jcrc.2013.07.058 · 2.00 Impact Factor
  • Source
    • "These studies have repeatedly demonstrated 'model fade': the over prediction of mortality when risk adjustment models are applied to more recent data [12]. As a result, the Simplified Acute Physiology Score (SAPS) [13], Mortality Probability Model (MPM) [14], and Acute Physiology and Chronic Health Evaluation (APACHE) [15] have required repeated updating. The developers of these systems have attributed mortality over prediction to increased treatment effectiveness, improved care before ICU admission, and more frequent discharge to post-acute care facilities [13-15]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Introduction A decrease in disease-specific mortality over the last twenty years has been reported for patients admitted to United States (US) hospitals, but data for intensive care patients are lacking. The aim of this study was to describe changes in hospital mortality and case-mix using clinical data for patients admitted to multiple US ICUs over the last 24 years. Methods We carried out a retrospective time series analysis of hospital mortality using clinical data collected from 1988 to 2012. We also examined the impact of ICU admission diagnosis and other clinical characteristics on mortality over time. The potential impact of hospital discharge destination on mortality was also assessed using data from 2001 to 2012. Results For 482,601 ICU admissions there was a 35% relative decrease in mortality from 1988 to 2012 despite an increase in age and severity of illness. This decrease varied greatly by diagnosis. Mortality fell by >60% for patients with chronic obstructive pulmonary disease, seizures and surgery for aortic dissection and subarachnoid hemorrhage. Mortality fell by 51% to 59% for six diagnoses, 41% to 50% for seven diagnoses, and 10% to 40% for seven diagnoses. The decrease in mortality from 2001 to 2012 was accompanied by an increase in discharge to post-acute care facilities and a decrease in discharge to home. Conclusions Hospital mortality for patients admitted to US ICUs has decreased significantly over the past two decades despite an increase in the severity of illness. Decreases in mortality were diagnosis specific and appear attributable to improvements in the quality of care, but changes in discharge destination and other confounders may also be responsible.
    Critical care (London, England) 04/2013; 17(2):R81. DOI:10.1186/cc12695 · 4.48 Impact Factor
Show more