Pre-resuscitation factors associated with mortality in 49,130 cases of in-hospital cardiac arrest: a report from the National Registry for Cardiopulmonary Resuscitation.

AIM: To evaluate key pre-arrest factors and their collective ability to predict post-cardiopulmonary arrest mortality. CPR is often initiated indiscriminately after in-hospital cardiopulmonary arrest. Improved understanding of pre-arrest factors associated with mortality may inform advance care planning. METHODS: A cohort of 49,130 adults who experienced pulseless cardiopulmonary arrest from January 2000 to September 2004 was obtained from 366 US hospitals participating in the National Registry for Cardiopulmonary Resuscitation (NRCPR). Logistic regression with bootstrapping was used to model in-hospital mortality, which included those discharged in unfavorable and severely worsened neurologic state (Cerebral Performance Category >/=3). RESULTS: Overall in-hospital mortality was 84.1%. Advanced age, black race, non-cardiac, non-surgical illness category, pre-existing malignancy, acute stroke, trauma, septicemia, hepatic insufficiency, general floor or Emergency Department location, and pre-arrest use of vasopressors or assisted/mechanical ventilation were independently predictive of in-hospital mortality. Retained peri-arrest factors including cardiac monitoring, and shockable initial pulseless rhythms, were strongly associated with survival. The validation model's AUROC curve (0.77) revealed fair performance. CONCLUSIONS: Predictive pre-resuscitation factors may supplement patient-specific information available at bedside to assist in revising resuscitation plans during the patient's hospitalization.

Subgroup mortality probability models: are they necessary for specialized intensive care units?

OBJECTIVES: To examine the sensitivity of the performance of the latest Mortality Probability Model at intensive care unit admission (MPM0-III) to case-mix variations and to determine how specialized models for these subgroups would affect intensive care unit performance assessment. MPM0-III is an important benchmarking tool for intensive care units in Project IMPACT. Overall, MPM0-III has excellent discrimination and calibration but its performance varies on six common patient subsets. DESIGN: A total of 124,171 patients in six subgroups (complex cardiovascular, trauma, elective surgery, medical, neurosurgery, and emergency surgery) were divided randomly into development (60%) and validation (40%) groups. A logistic regression model was developed to predict hospital mortality for each subgroup, using MPM0-III variables. Model performance was evaluated on the validation sets, using Hosmer-Lemeshow and receiver operating characteristic statistics. Intensive care unit standardized mortality ratios, using the subgroup models and MPM0-III, were compared. A sensitivity analysis was used to identify the occurrence of each subgroup associated with degraded MPM0-III performance. SETTING: One hundred thirty-five intensive care units at 98 hospitals participating in Project IMPACT between 2001 and 2004. ICUs with <100 patient records were excluded. PATIENTS: Consecutive intensive care unit patients in the Project IMPACT database who were eligible for MPM0-II scoring. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Hospital mortality and standardized mortality ratio values by intensive care unit. All six subgroup models had good performance on their validation sets. Intensive care unit standardized mortality ratios calculated with MPM0-III and the subgroup models were nearly identical, with MPM0-III identifying 33 of 135 as significant standardized mortality ratio outliers and the subgroup models identifying 35 of 135, with 33 overlapping. Sensitivity analysis indicated that MPM0-III calibration degraded substantially only when patient mix varied significantly from that of the data set on which MPM0-III was based. CONCLUSION: We recommend users of MPM make MPM0-III their primary model. Subgroup models may have utility when evaluating highly specialized intensive care units or in research on specific, homogeneous populations.

A revised method to assess intensive care unit clinical performance and resource utilization.

OBJECTIVE: In 1994, Rapoport et al. published a two-dimensional graphical tool for benchmarking intensive care units (ICUs) using a Mortality Probability Model (MPM0-II) to assess clinical performance and a Weighted Hospital Days scale (WHD-94) to assess resource utilization. MPM0-II and WHD-94 do not calibrate on contemporary data, giving users of the graph an inflated assessment of their ICU's performance. MPM0-II was recently updated (MPM0-III) but not the model for predicting resource utilization. The objective was to develop a new WHD model and revised Rapoport-Teres graph. DESIGN: Multicenter cohort study. SETTING: One hundred thirty-five ICUs in 98 hospitals participating in Project IMPACT. PATIENTS: Patients were 124,855 MPM0-II eligible Project IMPACT patients treated between March 2001 and June 2004. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: WHD was redefined as 4 units for the first day of each ICU stay, 2.5 units for each additional ICU day, and 1 unit for each non-ICU day after the first ICU discharge. Stepwise linear regression was used to construct a model to predict ICU-specific log average WHD from 39 candidate variables available in Project IMPACT. The updated WHD model has four independent variables: percent of patients dying in the hospital, percent of unscheduled surgical patients, percent of patients on mechanical ventilation within 1 hr of ICU admission, and percent discharged from the ICU to an external post-acute care facility. The first three variables increase average WHD and the last decreases it. The new model has good performance (R = 0.47) and, when combined with MPM0-II, provides a well-calibrated Rapoport-Teres graph. CONCLUSIONS: A new WHD model has been derived from a large, contemporary critical care database and, when used with MPM0-III, updates a popular method for benchmarking ICUs. Project IMPACT participants will likely perceive a decline in their ICU performance coordinates due to the recalibrated graph and should instead focus on their unit's performance relative to their peers.

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.

Variability in trauma center outcomes for patients with moderate intracranial injury.

BACKGROUND: The variability of outcome between Trauma Centers has not been extensively studied as a possible avenue for performance improvement. Trauma Center variability in severity-adjusted survival for patients with moderate intracranial injury (MII) was studied in order to determine the association of MII-related process of care variables with outcomes. The analytic results were supplemented with peer review of MII patients with unexpected outcomes and identified potential process of care variables. METHODS: A retrospective cohort study was undertaken based on data submitted to a statewide trauma center database from July '95 through June '98. MII patients had one or more selected ICD-9-CM codes with an AIS-90 severity score of 3 or 4 but no higher. Severity adjustment was done using case matching and a logistic function based New Model that appropriately accounts for patients intubated on Emergency Department arrival. MII-related process of care variables derived from the database were identified and their relationship with outcome were evaluated individually and using multivariate methods. Trauma center personnel conducted standardized peer reviews. RESULTS: The study included data from 6765 patients treated at 26 trauma centers. Two centers (2PZW) had significantly more survivors than expected by both severity adjustment methods. Three had significantly fewer survivors than expected (3NZW). By several measures, patients treated in the 2PZW centers were more seriously injured and older than those in the 3NZW centers. CT of the head performed in the treating hospital was the only process of care variable associated with outcome in multivariate evaluations. Peer review also found little association between process of care variables and patient outcomes. However, peer review reported that 23.7% of unexpected deaths identified by case matching or the New Model were preventable or potentially preventable. Peer review also identified as medically unnecessary significant percentages of patients with unexpectedly long stays in hospital (26.4%) or in ICU (17.3%) identified by case matching. Nearly 45% of unexpected complications were judged preventable or potentially preventable. CONCLUSIONS: Two severity adjustment methods identified significant variability in trauma center outcomes for patients with MII. The difference in outcomes between the centers with better than expected (2PZW) and poorer than expected outcomes (3NZW) was substantial. Peer review identified significant opportunities for reducing unexpected deaths, stays in hospital and in ICU, and the occurrence of complications. Trauma registry data and peer reviews found little relationship between available process of care variables and patient outcomes. This study should stimulate discussions to understand reasons for outcome variability and ways to reduce it.

Assessment of injury severity : The triage index

Injury severity scales of proven reliability and validity are essential for the appropriate allocation of therapeutic resources, for prediction of outcome, and for evaluation of the quantity and quality of emergencyh medical care in differing facilities and over time. Quantitation of injury severity in the field is particulary necessary. Existing scales are too imprecese to permit comparisons of management or systems of care. In this paper, the autors present the triage index, a measure of injury severity based on five simple variables observed in a design data set of 1084 patients. The triage index has been developed with state-of-the-art multivariate statistical techniques, meets the requirements of an interval ranking scale and has been both validated and assessed for interuser reliability. The triage index is proposed as a validated system of early, rapid, noninvasive, accurate patient assessment permiting appropriate matching of trauma victims with available therapeuitic resources as a means of reducing mortality and morbidity(AU)