Outcome prediction in critical care: the Mortality Probability Models

PURPOSE OF REVIEW

The comparison of morbidity, mortality, and length-of-stay outcomes in patients receiving critical care requires adjustment based on their presenting illness. These adjustments are made with severity-of-illness models. These models must be periodically updated to reflect current medical practices. This article will review the history of the Mortality Probability Model (MPM), discuss why and how it was recently updated, and outline examples of MPM use.

RECENT FINDINGS

All severity-of-illness models have limitations, especially if a unit's patient population becomes highly specialized. In these situations, customized models may provide better accuracy. The MPMs include those calculated at admission (MPM0) and additional models at 24, 48, and 72 h (MPM 24, MPM 48, and MPM 72). The model is now in its third iteration (MPM 0-III). Length of stay (LOS) and subgroup models have also been developed.

SUMMARY

Understanding appropriate application of models such as MPM is important as transparency in healthcare drives demand for severity-adjusted outcomes data.

Assessing organizational performance in intensive care units: a French experience

OBJECTIVE

The objective of the study was to assess and to explain variation of organizational performance in intensive care units (ICUs).

DESIGN

This was a prospective multicenter study.

SETTING

The study involved 26 ICUs located in the Paris area, France, participating in a regional database.

METHODS

Data were collected through answers of 1000 ICU personnel to the Culture, Organization, and Management in Intensive Care questionnaire and from the database. Organizational performance was assessed through a composite score related to 5 dimensions: coordination and adaptation to uncertainty, communication, conflict management, organizational change, and organizational learning, Skills developed in relationship with patients and their families. Statistical comparisons between ICUs were performed by analysis of variance with a Scheffé pairwise procedure. A multilevel regression model was used to analyze both individual and structural variables explaining differences of ICU's organizational performance.

RESULTS

The organizational performance score differed among ICUs. Some cultural values were negatively correlated with a high level of organizational performance, suggesting improvement potential. Several individual and structural factors were also related to the quality of ICU organization, including absence of burnout, older staff, satisfaction to work, and high workload (P < .02 for each).

CONCLUSIONS

A benchmarking approach can be used by ICU managers to assess the organizational performance of their ICU based on a validated questionnaire. Differences are mainly explained by cultural values and individual well-being factors, introducing new requirements for managing human resources in ICUs.

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 value and limits of severity adjusted mortality for ICU patients

Intensive care unit (ICU) data systems serve a variety of valuable functions for ICU directors, quality-of-care managers, safety officers and health service researchers. Although controversial, severity adjusted mortality cross-linked with resource measures may provide additional value when comparisons are made to similar types of ICUs. This article describes several options for improving the standardized mortality ratio. An essential ingredient for fostering wider application of ICU data systems is through automated information technology to assure high quality data input and to minimize the burden of manual data collection effort.

Current Issues Affecting Critical Care Practice

Internal and external forces continue to impact the practice of critical care medicine. Contracting for purchase of hospital services will soon be guided by the presence or absence of specific hospital attributes, including computerized physician order entry, evidence-based hospital referral, and the staffing model for intensive care unit care. The Leapfrog group, a consortium of health care purchasers, prefers to contract with hospitals that have implemented full-time intensive care unit physician staffing. This requirement is likely to increase the demand for critical care specialists. Personnel shortages are already apparent in critical care nursing and are expected to occur with physician specialists as well. Other current issues include the demand for increasingly detailed information on intensive care unit outcomes, a focus on patient safety, and cost control.

The clinical management database

The clinical management database utilizes ICU patient data in aggregate to examine quality of care and resource utilization at the population level. As clinicians become accountable for efficiency and quality, this type of database is essential to understand the results of care. This article reviews the challenges of evaluating cost and quality including the potential for bias and measurement error. A practical approach to starting a database is outlined with examples and suggestions.