Thomas L. Higgins

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

Objective:To update the Mortality Probability Model at intensive care unit (ICU) admission (MPM0-II) using contemporary data. Design: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. Setting:One hundred thirty-five ICUs at 98 hospitals. Patients:Patients in the Project IMPACT database eligible for MPM0-II scoring. Interventions:None. Measurements and Main Results: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. Conclusions: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.

ICU Admission Score for Predicting Morbidity and Mortality Risk After Coronary Artery Bypass Grafting

BACKGROUND This study was performed to develop an intensive care unit (ICU) admission risk score based on preoperative condition and intraoperative events. This score provides a tool with which to judge the effects of ICU quality of care on outcome. METHODS Data were collected prospectively on 4,918 patients (study group n = 2,793 and a validation data set n = 2,125) undergoing coronary artery bypass grafting alone or combined with a valve or carotid procedure between January 1, 1993, and March 31, 1995. Data were analyzed by univariate and multiple logistic regression with the end points of hospital mortality and serious ICU morbidity (stroke, low cardiac output, myocardial infarction, prolonged ventilation, serious infection, renal failure, or death). RESULTS Eight risk factors predicted hospital mortality at ICU admission, and these factors and five others predicted morbidity. A clinical score, weighted equally for morbidity and mortality, was developed. All models fit according to the Hosmer-Lemeshow goodness-of-fit test. This score applies equally well to patients undergoing isolated coronary artery bypass grafting. CONCLUSIONS This model is complementary to our previously reported preoperative model, allowing the process of ICU care to be measured independent of the operative care. Sequential scoring also allows updated prognoses at different points in the continuum of care.

Increased risk and decreased morbidity of coronary artery bypass grafting between 1986 and 1994

BACKGROUND The collective impact of advances in medical, surgical, and anesthetic care on the characteristics and outcomes of patients who undergo coronary artery bypass grafting was assessed. METHODS We compared the demographic and clinical characteristics, preoperative risk factors, morbidity, and mortality of two groups of patients who underwent coronary artery bypass grafting in isolation or in combination with other procedures between July 1, 1986, and June 30,1988 (group 1, n = 5,051), and between January 1, 1993, and March 31, 1994 (group 2, n = 2,793). The patients were stratified according to their preoperative risk level. Outcome measures consisted of changes in preoperative risk categories; hospital mortality rates; overall and risk-adjusted major cardiac, neurologic, pulmonary, renal, and septic morbidity rates; and intensive care unit length of stay. RESULTS Changes in the distribution of risk categories, from a median of 2 to 4 on a 9-point scale (p < 0.001), indicated that patients in group 2 were at significantly higher risk than those in group 1. The risk-adjusted mortality rate did not change (2.8% to 2.9%; p = 0.15), but the risk-adjusted morbidity rate decreased significantly (14.5% to 8.8%; p < 0.001). CONCLUSIONS At our institution, patients who undergo coronary artery bypass grafting are now at greater preoperative risk at the time of hospital admission. However, their morbidity rate is significantly lower and their mortality rate is unchanged, results that we attribute to the collective impact of changes in our medical and surgical procedures.

Intensive care unit readmissions in U.s. hospitals: Patient characteristics, risk factors, and outcomes*

Objective:To examine which patient characteristics increase the risk for intensive care unit readmission and assess the association of readmission with case-mix adjusted mortality and resource use. Design:Retrospective cohort study. Setting:Ninety-seven intensive and cardiac care units at 35 hospitals in the United States. Patients:A total of 229,375 initial intensive care unit admissions during 2001 through 2009 who met inclusion criteria. Interventions:None. Measurements and Main Results:For patients who were discharged alive and candidates for readmission, we compared the characteristics of those with and without a readmission. A multivariable logistic regression analysis was used to identify potential patient-level characteristics that increase the risk for subsequent readmission. We also evaluated case-mix adjusted outcomes by comparing observed and predicted values of mortality and length of stay for patients with and without intensive care unit readmission. Among 229,375 first admissions that met inclusion criteria, 13,980 (6.1%) were eventually readmitted. Risk factors associated with the highest multivariate odds ratio for unit readmission included location before intensive care unit admission, age, comorbid conditions, diagnosis, intensive care unit length of stay, physiologic abnormalities at intensive care discharge, and discharge to a step-down unit. After adjustment for risk factors, patients who were readmitted had a four-fold greater probability of hospital mortality and a 2.5-fold increase in hospital stay compared to patients without readmission. Conclusions:Intensive care readmission is associated with patient factors that reflect a greater severity and complexity of illness, resulting in a higher risk for hospital mortality and a longer hospital stay. To improve patient safety, physicians should consider these risk factors when making intensive care discharge decisions. Because intensive care unit readmission correlates with more complex and severe illness, readmission rates require case-mix adjustment before they can be properly interpreted as quality measures.

Propofol versus midazolam for intensive care unit sedation after coronary artery bypass grafting

ObjectiveTo compare the safety and effectiveness of propofol (2,6-diisopropylphenol) to midazolam for sedation of mechanically ventilated patients after coronary artery bypass grafting. DesignOpen, randomized, prospective trial. SettingCardiothoracic intensive care unit (ICU), Cleveland Clinic Foundation. PatientsEighty-four patients with normal or moderately impaired left ventricular function who underwent elective coronary artery bypass graft surgery under high-dose opioid anesthesia. InterventionsPatients were randomly selected to receive either propofol (mean loading dose 0.24 mg/kg; mean maintenance dose 0.76 mg/kg/hr) or midazolam (mean loading dose 0.012 mg/kg; mean maintenance dose 0.018 mg/kg/hr). Infusion rates were titrated to keep patients comfortable, drowsy, and responsive to verbal stimulation. Study duration, 8 to 12 hrs; infusions were started in the ICU when patients were awake and hemodynamically stable. Measurements and Main ResultsDuring therapy, both groups had lower mean arterial pressures and heart rates compared with baseline measurements; however, the propofol group had significantly lower heart rates than the midazolam group during the first 2 hrs of infusion. The propofol group also had significantly lower blood pressure measurements 5 and 10 mins after the initial dose, although there was no difference during infusion. Baseline cardiac output was measured before starting the infusion, and measurements were repeated during continuous infusion at 4, 8, and 12 hrs. Cardiac output values were similar. Propofol maintenance infusions ranged from 3 to 30 μg/kg/min and midazolam infusions ranged from 0.1 to 0.7 μg/kg/min. At these infusion rates. both groups had adequate sedation, based on nurse and patient evaluations; however, the propofol group used significantly lower total doses of sodium nitroprusside and supplemental opioids. ConclusionsBoth propofol and midazolam provided safe and effective sedation of coronary artery bypass graft patients recovering from high-dose opioid anesthesia. The reduced need for both antihypertensive medication and opioids seen in the propofol group may be advantageous. However, the hypotension seen after the initial bolus dose of propofol may be a concern. No difference between the two drugs could be demonstrated in time to extubation or ICU discharge, although it is probable that time to extubation was governed more by residual operative opioids than the study agents. (Crit Care Med 1994; 22:1415–1423)

Quantifying risk and assessing outcome in cardiac surgery

Quality improvement, research, and reporting of outcome results can be stratified by preoperative risk by using a logistic regression equation or scores to correct for multiple risk factors. The more than 30-fold mortality differences between lowest and highest risk patients make it critical to stratify outcome results by patient severity. Probabilities are not predictions, however, and caution must be exercised when applying scores to individuals. Outcome assessment will grow in its importance to professionals, initially in the guise of quality reporting and improvement, but increasingly as a tool for risk assessment, patient counseling, and directing therapeutic decisions based on more complete information about patient subgroups. Physicians may be called on for recommendations in choosing systems for their hospitals and communities. Therefore, it is important to have an understanding of how such systems are developed, what factors indicate adequate performance of a system, and how such systems of risk stratification should be applied in practice.

The association between ICU readmission rate and patient outcomes

Objective:To examine the association between ICU readmission rates and case-mix–adjusted outcomes. Design:Retrospective cohort study of ICU admissions from 2002 to 2010. Setting:One hundred five ICUs at 46 United States hospitals. Patients:Of 369,129 admissions, 263,082 were first admissions that were alive at ICU discharge and candidates for readmission. Interventions:None. Measurements and Main Results:The median unit readmission rate was 5.9% (intraquartile range 5.1%–7.0%). Across all admissions, hospital mortality for patients with and without readmission was 21.3% vs. 3.6%, mean ICU stay 4.9 days vs. 3.4 days, and hospital stay 13.3 days vs. 4.5 days, respectively. We stratified ICUs according to their readmission rate: high (>7%), moderate (5%–7%), and low (<5%) rates. Observed and case-mix–adjusted hospital mortality, ICU and hospital lengths of stay were examined by readmission rate strata. Observed outcomes were much worse in the high readmission rate units. But after adjusting for patient and institutional differences, there was no association between level of unit readmission rate and case-mix–adjusted mortality. The difference between observed and predicted mortality was −0.4%, 0.4%, and −1.1%, for the high, medium, and low readmission rate strata, respectively. Additionally, the difference between observed and expected ICU length of stay was approximately zero for the three strata. Conclusions:Patients readmitted to ICUs have increased hospital mortality and lengths of stay. After case-mix adjustment, there were no significant differences in standardized mortality or case-mix–adjusted lengths of stay between units with high readmission rates compared to units with moderate or low rates. The use of readmission as a quality measure should only be implemented if patient case-mix is taken into account.

Quantifying Risk and Benchmarking Performance in the Adult Intensive Care Unit

Morbidity, mortality, and length-of-stay outcomes in patients receiving critical care are difficult to interpret unless they are risk-stratified for diagnosis, presenting severity of illness, and other patient characteristics. Acuity adjustment systems for adults include the Acute Physiology And Chronic Health Evaluation (APACHE), the Mortality Probability Model (MPM), and the Simplified Acute Physiology Score (SAPS). All have recently been updated and recalibrated to reflect contemporary results. Specialized scores are also available for patient subpopulations where general acuity scores have drawbacks. Demand for outcomes data is likely to grow with pay-for-performance initiatives as well as for routine clinical, prognostic, administrative, and research applications. It is important for clinicians to understand how these scores are derived and how they are properly applied to quantify patient severity of illness and benchmark intensive care unit performance.

The risk of coronary artery surgery in women: A matched comparison using preoperative severity of illness scoring

OBJECTIVE To evaluate the effect of gender on outcomes of coronary artery bypass surgery using a weighted preoperative severity of illness scoring system. DESIGN Retrospective database review. SETTING Tertiary care teaching hospital. PARTICIPANTS The patient population consisted of 2,800 consecutive coronary artery bypass graft (CABG) patients (658 women, 2,142 men), with or without concurrent procedures, operated on between January 1, 1993 and March 31, 1994. MEASUREMENTS AND MAIN RESULTS Patients were stratified for severity of illness using a 13-element scoring system. The distribution of severity of illness scores and severity of illness-stratified morbidity, hospital mortality, and intensive care unit (ICU) length of stay were compared by chi-square and Fischers exact test where appropriate. Median duration of intubation and median duration of ICU length of stay were examined by the median test. Female versus male unadjusted mortality (4.9% v 3.0%), total morbidity (15.0% v 9.2%), and average initial ICU length of stay (92.62% v 60.56 hours) were statistically different. Female patients also had significantly more of the following postoperative morbidities: central nervous system complications (focal neurologic deficits, patients > or = 65 years 3.20% v 1.54%; global neurologic deficits, patients > or = 65 years 2.75% v 1.25%), duration of perioperative ventilation that includes the intubation time in the operating room until extubation in the ICU (average = 77.36 hours v 49.20 hours; median = 21.87 v 20.26 hours), and average initial ICU length of stay (average = 92.62 hours v 60.56 hours; median = 42.33 hours v 27.91 hours). However, distribution of severity scores was also different. Women had significantly more preoperative risk factors (p < 0.05): age 65 to 74 years (45.1% v 36.6%), age > or = 75 years (21.3% v 11.9%), chronic obstructive pulmonary disease (10.8% v 6.4%), hematocrit less than 34% (21.9% v 5.5%), diabetes (34.8% v 21.8%), weight less than 65 kg (37.4% v 6.2%), and operative mitral valve insufficiency (9.6% v 6.0%). Stratified by severity, no statistically significant gender differences were found for mortality, morbidity, or ICU length of stay. CONCLUSIONS Gender does not appear to be an independent risk factor for perioperative morbidity, mortality, or excessive ICU length of stay when patients are stratified by preoperative risk in this severity of illness scoring system.

Prospective validation of the intensive care unit admission Mortality Probability Model (MPM0-III).

Objective:To validate performance characteristics of the intensive care unit (ICU) admission mortality probability model, version III (MPM0-III) on Project IMPACT data submitted in 2004 and 2005. This data set was external from the MPM0-III developmental and internal validation data collected between 2001 and 2004. Design:Retrospective analysis of clinical data collected concurrently with care. Setting:One hundred three (103) adult ICUs in North America that voluntarily collect and submit data to Project IMPACT. Subjects:A total of 55,459 patients who were eligible for MPM scoring (age ≥18; first ICU admission for hospitalization, excludes burns, coronary care, and cardiac surgical patients). Interventions:None. Measurements:Prevalence of MPM risk factors and their relationship to hospital mortality; calibration and discrimination of MPM0-III model applied to new data. Main Results:Seventy-eight ICUs contributed data to both this study and the original development set. Fifty-six ICUs from the original MPM0-III study were replaced by 25 new ICUs in this external validation set. Patient characteristics (type of patient, risk factors, and resuscitation status) were similar to the original 2001–2004 cohort, except for slightly more patients on mechanical ventilation at admission (32% vs. 27%, p < 0.01) and the percentage of patients having no MPM0-III risk factors except age (11% vs. 14%, p < 0.01). Observed deaths were 7331 (13.2%) vs. 7456 predicted, yielding a standardized mortality ratio of 0.983, 95% CI (0.963–1.001). Conclusions:MPM0-III calibrates on a new population of 55,459 North American patients who include many patients from new ICUs, which helps confirm that the model is robust and was not overfitted to the development sample. Although Project IMPACT participants change over time, 2004–2005 patient risk factors and their relationship to hospital mortality have not significantly changed. The increase in mechanically ventilated patients and reduction in admissions with no risk factors are trends worth following.