Daniel Teres

Findings of the First Consensus Conference on Medical Emergency Teams

Background:Studies have established that physiologic instability and services mismatching precede adverse events in hospitalized patients. In response to these considerations, the concept of a Rapid Response System (RRS) has emerged. The responding team is commonly known as a medical emergency team (MET), rapid response team (RRT), or critical care outreach (CCO). Studies show that an RRS may improve outcome, but questions remain regarding the benefit, design elements, and advisability of implementing a MET system. Methods:In June 2005 an International Conference on Medical Emergency Teams (ICMET) included experts in patient safety, hospital medicine, critical care medicine, and METs. Seven of 25 had no experience with an RRS, and the remainder had experience with one of the three major forms of RRS. After preconference telephone and e-mail conversations by the panelists in which questions to be discussed were characterized, literature reviewed, and preliminary answers created, the panelists convened for 2 days to create a consensus document. Four major content areas were addressed: What is a MET response? Is there a MET syndrome? What are barriers to METS? How should outcome be measured? Panelists considered whether all hospitals should implement an RRS. Results:Patients needing an RRS intervention are suddenly critically ill and have a mismatch of resources to needs. Hospitals should implement an RRS, which consists of four elements: an afferent, “crisis detection” and “response triggering” mechanism; an efferent, predetermined rapid response team; a governance/administrative structure to supply and organize resources; and a mechanism to evaluate crisis antecedents and promote hospital process improvement to prevent future events.

Critical care delivery in the intensive care unit: Defining clinical roles and the best practice model

Patients receiving medical care in intensive care units (ICUs) account for nearly 30% of acute care hospital costs, yet these patients occupy only 10% of inpatient beds (1, 2). In 1984, the Office of Technology Assessment concluded that 80% of hospitals in the United States had ICUs, >20% of hospital budgets were expended on the care of intensive care patients, and approximately 1% of the gross national product was expended for intensive care services (3). With the aging of the U.S. population, greater demand for critical care services will occur. At the same time, market forces are evolving that may constrain both hospitals’ and practitioners’ abilities to provide this increasing need for critical care services. In addition, managed care organizations are requesting justification for services provided in the ICU and for demonstration of both efficiency and efficacy. Hospital administrators are continually seeking methods to provide effective and efficient care to their ICU patients. As a result of these social and economic pressures, there is a need to provide more data about the type and quality of clinical care provided in the ICU. In response, two task forces were convened by the Society of Critical Care Medicine leadership. One task force (models task force) was asked to review available information on critical care delivery in the ICU and to ascertain, if possible, a “best” practice model. The other task force was asked to define the role and practice of an intensivist. The task force memberships were diverse, representing all the disciplines that actively participate in the delivery of health care to patients in the ICU. The models task force membership consisted of 31 healthcare professionals and practitioners, including statisticians and representatives from industry, pharmacy, nursing, respiratory care, and physicians from the specialties of surgery, internal medicine, pediatrics, and anesthesia. These healthcare professionals represented the practice of critical care medicine in multiple settings, including nonteaching community hospitals, community hospitals with teaching programs, academic institutions, military hospitals, critical care medicine private practice, full-time academic practice, and consultative critical care practice. This article is the consensus report of the two task forces. The objectives of this report include the following: (1) to describe the types and settings of critical care practice (2); to describe the clinical roles of members of the ICU healthcare team (3); to examine available outcome data pertaining to the types of critical care practice (4); to attempt to define a “best” practice model; and (5) to propose additional research that should be undertaken to answer important questions regarding the practice of critical care medicine. The data and recommendations contained within this report are sometimes based on consensus expert opinion; however, where possible, recommendations are promulgated based on levels of evidence as outlined by Sacket in 1989 (4) and further modified by Taylor in 1997 (5) (see Appendix 1).

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.

A method for assessing the clinical performance and cost-effectiveness of intensive care units : a multicenter inception cohort study

ObjectivesTo present an approach for assessing intensive care unit (ICU) performance which takes into account both economic and clinical performance while adjusting for severity of illness. To present a graphic display which permits comparisons among a group of hospitals. DesignA multicenter, inception cohort study. SettingTwenty-five ICUs in U.S. hospitals that participated in the European and North American Study of Severity Systems for ICU Patients. PatientsConsecutive patients (n = 3,397) admitted to ICUs in participating hospitals between September 30, 1991 and December 27, 1991. Excluded were coronary care patients, burn patients, cardiac surgery patients and patients aged <18 yrs. Measurements and Main ResultsThe clinical performance index is the difference between observed hospital survival rate and survival rate predicted by the Mortality Probability Model measuring severity of illness at ICU admission. The economic performance (resource use) measure is a length of stay index, Weighted Hospital Days, which weights ICU days more heavily than non-ICU days. The economic performance index is the difference between actual mean resource use and the resource use predicted by a regression including severity of illness and percent of surgical patients. Both the clinical and economic performance indices are standardized to show how far a particular hospital is from the overall mean and are graphed together. Most of the 25 hospitals lie within 1 SD of the mean on both clinical and economic performance scales. The graph makes it easy to identify those hospitals that are outside this range. There is no evidence of a tradeoff between high clinical performance and high economic performance; i.e., it is possible to achieve both. ConclusionsCross-indexing of clinical and economic ICU performance is easy to calculate. It has potential as a research and evaluation tool used by physicians, hospital administrators, payers, and others. (Crit Care Med 1994; 22:1385–1391

Mortality probability models for patients in the intensive care unit for 48 or 72 hours: A prospective, multicenter study

ObjectiveTo develop models in the Mortality Probability Model (MPM II) system to estimate the probability of hospital mortality at 48 and 72 hrs in the intensive care unit (ICU), and to test whether the 24-hr Mortality Probability Model (MPM24), developed for use at 24 hrs in the ICU, can be used on a daily basis beyond 24 hrs. DesignA prospective, multicenter study to develop and validate models, using a cohort of consecutive admissions. SettingSix adult medical and surgical ICUs in Massachusetts and New York adjusted to reflect 137 ICUs in 12 countries. PatientsConsecutive admissions (n = 6,290) to the Massachusetts/New York ICUs were studied. Of these patients, 3,023 and 2,233 patients remained in the ICU and had complete data at 48 and 72 hrs, respectively. Patients <18 yrs of age, burn patients, coronary care patients, and cardiac surgical patients were excluded. Outcome MeasureVital status at the time of hospital discharge. ResultsThe models consist of five variables measured at the time of ICU admission and eight variables ascertained at 24-hr intervals. The 24-hr model demonstrated poor calibration and discrimination at 48 and 72 hrs. The newly developed 48− and 72-hr models—MPM48 and MPM72—contain the same 13 variables and coefficients as the MPM24. The models differ only in their constant terms, which increase in a manner that reflects the increasing probability of mortality with increasing length of stay in the ICU. These constant terms were adjusted by a factor determined from the relationship between the data from the six Massachusetts and New York ICUs and a more extensive data set, from which the ICU admission Mortality Probability Model (MPM0) and MPM24 were developed. This latter data set was assembled from ICUs in 12 countries. The MPM48 and MPM72 calibrated and discriminated well, based on goodness-of-fit tests and area under the receiver operating characteristic curve. ConclusionsModels developed for use among ICU patients at one time period are not transferable without modification to other time periods. The MPM48 and MPM72 calibrated well to their respective time periods, and they are intended for use at specific points in time. The increasing constant terms and associated increase in the probability of hospital mortality exemplify a common clinical adage that if a patients clinical profile stays the same, he or she is actually getting worse. (Crit Care Med 1994; 22:1351–1358)

Outcome prediction for individual intensive care patients: useful, misused, or abused?

Probabilities of hospital mortality provide meaningful information in many contexts, such as in discussions of patient prognosis by intensive care physicians, in patient stratification for analysis of clinical trial data by researchers, and in hospital reimbursement analysis by insurers. Use of probabilities as binary predictors based on a cut point can be misleading for making treatment decisions for individual patients, however, even when model performance is good overall. Alternative models for estimating severity of illness in intensive care unit (ICU) patients, while demonstrating good agreement for describing patients in the aggregate, are shown to differ considerably for individual patients. This suggests that identifying patients unlikely to benefit from ICU care by using models must be approached with considerable caution.

Effect of changing patient mix on the performance of an intensive care unit severity-of-illness model: how to distinguish a general from a specialty intensive care unit.

OBJECTIVE To analyze the effects of patient mix diversity on performance of an intensive care unit (ICU) severity-of-illness model. DESIGN Multiple patient populations were created using computer simulations. A customized version of the Mortality Probability Model (MPM) II admission model was used to ascertain probabilities of hospital mortality. Performance of the model was assessed using discrimination (area under the receiver operating characteristic curve) and calibration (goodness-of-fit testing). SETTING Intensive care units. PATIENTS Data were collected from 4,224 ICU patients from two Massachusetts hospitals (Baystate Medical Center, Springfield, MA; University of Massachusetts Medical Center, Worcester, MA) and two New York hospitals (Albany Medical Center, Albany, NY; Ellis Hospital, Schenectady, NY). INTERVENTIONS Random samples were taken from a database. The percentage of patients with each model disease characteristic was varied by assigning weights (ranging from 0 to 10) to patients with a disease characteristic. Three simulations were run for each of 15 model variables at each of 16 weights, totaling 720 simulations. MEASUREMENTS AND MAIN RESULTS The area under the receiver operating characteristic curve and model fit were assessed in each random sample. Removing patients with a given disease characteristic did not affect discrimination or calibration. Increasing frequency of patients with each disease characteristic above the original frequency caused discrimination and calibration to deteriorate. Model fit was more robust to increases in less frequently occurring patient conditions. From the goodness-of-fit test, a critical percentage for each admission model variable was determined for each disease characteristic, defined as the percentage at which the average p value for the test over the three replications decreased to < .10. CONCLUSIONS The concept of critical percentages is potentially clinically important. It might provide an easy first step in checking applicability of a given severity-of-illness model and in defining a general medical-surgical ICU. If the critical percentages are exceeded, as might occur in a highly specialized ICU, the model would not be accurate. Alternative modeling approaches might be to customize the model coefficients to the population for more accurate probabilities or to develop specialized models. The MPM approach remained robust for a large variation in patient mix factors.

Length of stay data as a guide to hospital economic performance for ICU patients.

Context. Length of stay data are increasingly used to monitor ICU economic performance. How such material is presented greatly affects its utility. Objective. To develop a weighted length of stay index and to estimate expected length of stay. To assess alternative ways to summarize weighted length of stay to evaluate ICU economic performance. Design. Retrospective database study. Subjects. Data for 751 ICU patients in 1998 at two hospitals used to develop weighted length of stay index. Data on 42,237 patients from 72 ICUs used as the basis of economic performance evaluation. Main Outcome Measures. Difference between actual and expected weighted length of stay, where expected weighted length of stay is based on patient clinical characteristics. Results. Length of stay statistically explains approximately 85 to 90% of interpatient variation in hospital costs. The first ICU day is approximately four times as expensive, and other ICU days approximately 2.5 times as expensive, as non-ICU hospital days. In a regression model for weighted length of stay, patient clinical characteristics explain 26% of variation. ICU economic performance can be measured by excess weighted length of stay of a “typical” patient or by occurrence of long excess weighted lengths of stay. Although different summary measures of performance are highly correlated, choice of measure affects relative ranking of some ICUs’ performance Conclusion. Providers of statistical data on ICU economic performance should adjust length of stay for patient characteristics and provide multiple summary measures of the statistical distribution, including measures that address both the typical patient and outliers.

A comparison of infections in different ICUs within the same hospital

Infections identified between 1981 and 1983 in a hospitals medical/surgical, pediatric, neonatal, coronary care, and cardiac surgery ICUs were compared. Among 14,360 admissions, 1840 infections occurred in 1360 patients. Total infection rates ranged from 1.0% (cardiac surgery ICU) to 23.5% (medical/surgical ICU). Rates of ICU-acquired infection ranged from 0.8% (cardiac surgery ICU) to 11.2% (medical/surgical ICU), indicating that only about half of infections in the latter unit were acquired from within.Primary bacteremias comprised 14.5% of neonatal ICU infections, a rate 500% higher than in other ICUs. Meningitis and genitourinary infections were more common in pediatric and coronary care ICUs. Candida and Pseudomonas species and Klebsiella-Enterobacter-Ser-ratia were most common in the medical/surgical ICU. Survival rate of infected patients was over 87% in pediatric and neonatal ICUs, compared with only 55.4% in the medical/surgical ICU.These differences in types and rates of infection have an important bearing on infection-control activities in the ICU, and also provide a yardstick against which similar institutions can gauge their ICU infection status.

Validation of the mortality prediction model for ICU patients

We tested recently developed admission and 24-h models of hospital mortality on 1,997 consecutive admissions to a general medical/surgical ICU. This study population was independent of the group used to develop the models. The admission prediction model estimated each patients probability of hospital mortality based on seven routinely collected admission variables. The 24-h model utilized seven variables routinely available at 24 h in the ICU. The admission model accurately described the mortality experience of the new cohort, while the 24-h model did not.Advantages of the admission model are that it is evaluable at the time of ICU admission, is independent of ICU treatment, and can be used to stratify patients by severity of illness, thereby making ICU comparisons possible. Its excellent goodness-of-fit, correct classification rate, sensitivity, and specificity suggest that this model is now ready for multihospital testing.