Role of Algorithms for the Medical Intensive Care Unit

Role of Algorithms for the Medical Intensive Care Unit

 

The management of patients in the intensive care unit is complex. Many times decisions regarding the best option for the diagnosis of an entity or management need to be made in a short period of time. Over the last 5 years we have been using in the Medical Intensive Care Unit of The Cleveland Clinic Foundation different algorithms for the diagnosis and management of conditions commonly present in the critically ill.

The algorithms are intended to be used as general guidelines and cannot be substituted for sound clinical judgment in the individual patient. By outlining a general approach, we are not denying the need to individualize our decision in particular patients; however, we think that providing this approach can reduce mistakes in those hectic moments when these decisions are made.

Introduction

The importance of tight glycemic control in the intensive care unit (ICU) has resulted in numerous protocols and algorithms for adjusting intravenous insulin delivery. The algorithms are typically implemented as written instructions, with calculations performed bedside by ICU staff whenever a new glucose value is available—typically every 1 to 4 h. Of the algorithms routinely used, the majority have been developed and tested based on the experiences of nurses and doctors at different institutions.

 

Objective

Studies showing improved outcomes with tight glycemic control in the intensive care unit (ICU) have resulted in a substantial number of new insulin delivery algorithms being proposed. The present study highlights mechanisms used in the better-known approaches, examines what might be critical differences among them, and uses systems theory to characterize the conditions under which each can be expected to perform best.

Methods

Algorithm dose (ΔI/ΔG) and step (response to a persistent elevation in glucose) response curves were calculated for written instruction algorithms, developed at the Providence Heart and Vascular Institute (Portland [P] protocol), the University of Washington (UW), and Yale University (Y), together with similar curves for the Glucommander (GM) and proportional integral derivative (PID) computer algorithms. From the simulated curves, different mechanisms used to adjust insulin delivery were identified.

Results

All algorithms increased insulin delivery in response to persistent hyperglycemia, but the mechanism used altered the algorithm's sensitivity to glucose, or gain, in the GM, UW, and Y protocols, while leaving it unchanged for the P protocol and PID algorithm.

Conclusions

The increase in insulin delivery in response to persistent hyperglycemia observed with all the algorithms can be expected to bring subjects who respond to insulin to targeted glucose ranges. However, because the PID and P protocols did not alter the insulin delivery response curves, these algorithms can be expected to take longer to achieve target glucose levels in individuals who are insulin resistant and/or are exposed to increased carbohydrate loads