Sample Solution

The understanding of risk factors associated with intensive care unit (ICU) mortality is an important element in health-care improvement. ICU mortality rates can be used to identify areas for potential improvement and guide further research initiatives. The aim of this proposal is to identify and characterize the risk factors associated with ICU mortality, as well as develop strategies to predict those factors in order to reduce mortality.

This proposal is divided into three main objectives: 1) To review existing literature on risk factors associated with ICU mortality; 2) To investigate the impact of relevant comorbidities, laboratory values, and other clinical parameters on ICU outcomes; 3) To develop prediction models that can accurately assess a patient’s probability of survival based on identified risk factors.

In order to achieve these objectives, we will use a combination of retrospective database reviews and analytic methods including logistic regression analyses. We will analyze data from multiple sources such as electronic medical records (EMR), hospital discharge databases, claims data sets and surveys conducted among healthcare workers in the field who have direct experience caring for critically ill patients in the ICU setting. Specifically, we plan to collect information regarding patient demographics (age/gender), laboratory values (blood pressure/hemoglobin levels), comorbidities (heart disease/diabetes), disease severity scores at admission (APACHE II score/SOFA score), treatment type (mechanical ventilation status / dialysis status). These variables will then be analyzed using advanced modeling techniques such as multivariable logistic regression analyses in order to identify which specific characteristics are most strongly correlated with high or low likelihoods of survival from an ICU stay.

Finally, using our collected data set and statistical analysis results, we intend to create predictive models tailored towards optimizing outcomes related to extreme illness events treated within an intensive care unit by designing protocols aimed at predicting high-risk patients before they arrive at an emergency department so that they may receive more intensive monitoring upon arrival or even preemptive interventions when appropriate. By doing so, it is hoped that this project may yield valuable insight into the complex realm of critical care medicine while also helping providers better anticipate adverse events before they occur thereby decreasing morbidity and mortality due to catastrophic conditions managed within the ICUs.