A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Distribution of the UHCMI and APACHEMI by hospital, compared for the total poulation and at each qaurtile of UHC predicted mortality.

<p>Distribution of the UHCMI and APACHEMI by hospital, compared for the total poulation and at each qaurtile of UHC predicted mortality.</p

Predicted Mortality by prediction model.

<p>Panel A: Mean and 95% CI with T-Test result. Panel B: Box and Whisker Plot for each model.</p

ROC Curve for UHC and APACHE-IV models to discriminate survivors from non-survivors.

<p>ROC Curve for UHC and APACHE-IV models to discriminate survivors from non-survivors.</p

Relationship between UHC and APACHE Models.

<p>Panel A. Linear relationship for UHC model (y-axis) and APACHE-IV (x-axis) for subgroup admitted directly from emergency department. Panel B. Bland-Altman Plot of Predicted Mortality for those patients admitted directly to the ICU from the Emergency Department: The x-axis represents the mean of the two values and the y-axis represents the difference.</p

Patient Population Characteristics and Diagnosis Frequencies.

<p>Patient Population Characteristics and Diagnosis Frequencies.</p

Relationship between UHC and APACHE Models for patients admitted through the emergency department.

<p>Panel A. Linear relationship for UHC model (y-axis) and APACHE-IV (x-axis) for subgroup admitted directly from emergency department. Panel B. Bland-Altman Plot of Predicted Mortality for those patients admitted directly to the ICU from the Emergency Department: The x-axis represents the mean of the two values and the y-axis represents the difference.</p

Mechanisms and treatment of organ failure in sepsis.

Sepsis is a dysregulated immune response to an infection that leads to organ dysfunction. Knowledge of the pathophysiology of organ failure in sepsis is crucial for optimizing the management and treatment of patients and for the development of potential new therapies. In clinical practice, six major organ systems - the cardiovascular (including the microcirculation), respiratory, renal, neurological, haematological and hepatic systems - can be assessed and monitored, whereas others, such as the gut, are less accessible. Over the past 2 decades, considerable amounts of new data have helped improve our understanding of sepsis pathophysiology, including the regulation of inflammatory pathways and the role played by immune suppression during sepsis. The effects of impaired cellular function, including mitochondrial dysfunction and altered cell death mechanisms, on the development of organ dysfunction are also being unravelled. Insights have been gained into interactions between key organs (such as the kidneys and the gut) and organ-organ crosstalk during sepsis. The important role of the microcirculation in sepsis is increasingly apparent, and new techniques have been developed that make it possible to visualize the microcirculation at the bedside, although these techniques are only research tools at present.SCOPUS: re.jinfo:eu-repo/semantics/publishe