Standardizing sepsis screening and management via a tele-ICU program improves patient care.

OBJECTIVE: This article evaluates the feasibility of a tele-intensive care unit (ICU) nurse-driven early identification and treatment process for severe sepsis patients in improving compliance to evidence-based practice. MATERIALS AND METHODS: Florence Nightingale identified that by using science, logic, and compassion to manipulate the patient care environment nurses could create the best possible conditions for healing to occur. Nurses in a tele-ICU used this premise to initiate a standardized screening and data collection program using a custom-built document sharing application that conformed to the Surviving Sepsis Campaign (SSC) criteria for identification and treatment of severe sepsis. RESULTS: The tele-ICU nurses performed 89,921 screens on 36,353 ICU admissions to 161 ICU beds across a geographical range of 500 miles. Between January 1, 2006 and December 31, 2008, tele-ICU nurses identified 5,437 patients as meeting the criteria for severe sepsis. Statistically significant increases in compliance with SSC's bundled care recommendations were realized during this study period with four initial elements: antibiotic administration increased from 55% in 2006 to 74% in 2008 (p=0.001), serum lactate measurement increased from 50% to 66% (p=0.001), the initial fluid bolus of ≥ 20 mL/kg increased from 23% to 70% (p=0.001), and central line placement increased from 33% to 50% (p=0.001). CONCLUSIONS: A tele-ICU nurse-driven process can prompt earlier identification and improve compliance to evidence-based practice bundles for complex disease states such as severe sepsis.

Patients alter power of breathing as the primary response to changes in pressure support ventilation.

INTRODUCTION: The patient-ventilator relationship is dynamic as the patient's health fluctuates and the ventilator settings are modified. Spontaneously breathing patients respond to mechanical ventilation by changing their patterns of breathing. This study measured the physiologic response when pressure support (PS) settings were modified during mechanical ventilation. METHODS: Subjects were instrumented with a non-invasive pressure, flow, and carbon dioxide airway sensor to estimate tidal volume, respiratory rate, minute ventilation, and end-tidal CO2. Additionally, a catheter was used to measure esophageal pressure and estimate effort exerted during breathing. Respiratory function measurements were obtained while PS settings were adjusted 569 times between 5 and 25 cmH2O. RESULTS: Data was collected on 248 patients. The primary patient response to changes in PS was to adjusting effort (power of breathing) followed by adjusting tidal volume. Changes in respiratory rate were less definite while changes in minute ventilation and end-tidal CO2 appeared unrelated to the change in PS. CONCLUSION: The data indicates that patients maintain a set minute ventilation by adjusting their breathing rate, volume, and power. The data indicates that the subjects regulate their Ve and PetCO2 by adjusting power of breathing and breathing pattern.

Engineering risk analysis of a hospital oxygen supply system.

Reports from the Food and Drug Administration (FDA) and the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) have emphasized the potential for injury to patients caused by failures in oxygen supply systems. This article presents a model of patient risk related to the process of supplying oxygen at a single university hospital. One of the goals of the article is to illustrate how probabilistic risk analysis (PRA) can be used by hospitals to assess and mitigate risk and, therefore, to meet JCAHO requirements. PRA techniques are useful to 1) model the reliability of a complex system and 2) assess the cost-effectiveness of different risk mitigation measures. The authors focus on the risk estimation step, describing in detail their modeling of the oxygen supply system and analysis of the results. For the hospital that the authors study (20,000 admissions yearly), the total expected number of fatalities from oxygen system failure is 44 over a 30-year time horizon. The greatest contribution to the risk (94% of the expected number of fatalities) comes from problems that involve the supply network (e.g., damage to structure and poisoning) as opposed to incidents that occur inside patient rooms. Although the threat to patient safety is not dramatic, health care organizations should be concerned about potential failures of their oxygen system because improving this system could avoid low-probability, high-consequence failures at a low cost.

Noninvasive Estimation of Respiratory Mechanics in Spontaneously Breathing Ventilated Patients: A Constrained Optimization Approach.

This paper presents a method for breath-by-breath noninvasive estimation of respiratory resistance and elastance in mechanically ventilated patients. For passive patients, well-established approaches exist. However, when patients are breathing spontaneously, taking into account the diaphragmatic effort in the estimation process is still an open challenge. Mechanical ventilators require maneuvers to obtain reliable estimates for respiratory mechanics parameters. Such maneuvers interfere with the desired ventilation pattern to be delivered to the patient. Alternatively, invasive procedures are needed. The method presented in this paper is a noninvasive way requiring only measurements of airway pressure and flow that are routinely available for ventilated patients. It is based on a first-order single-compartment model of the respiratory system, from which a cost function is constructed as the sum of squared errors between model-based airway pressure predictions and actual measurements. Physiological considerations are translated into mathematical constraints that restrict the space of feasible solutions and make the resulting optimization problem strictly convex. Existing quadratic programming techniques are used to efficiently find the minimizing solution, which yields an estimate of the respiratory system resistance and elastance. The method is illustrated via numerical examples and experimental data from animal tests. Results show that taking into account the patient effort consistently improves the estimation of respiratory mechanics. The method is suitable for real-time patient monitoring, providing clinicians with noninvasive measurements that could be used for diagnosis and therapy optimization.

Report of a case series of ultra low-frequency oscillations in cardiac output in critically ill adults with sepsis, systemic inflammatory response syndrome, and multiple organ dysfunction syndrome.

Healthy physiological systems exhibit irregular variability whereas diseased systems display decreased signal variability or greater regularity. The objective of this article is to report a case series of critically ill adults who displayed ultra low-frequency periodic sinusoidal oscillations in cardiac output (ULF-CO) that were discovered during a clinical study testing software for continuous physiological monitoring. Data were collected from 13 critically ill surgical and trauma patients who required continuous cardiac output monitoring. Physiologic data were collected from clinical monitors. The computerized time series of cases displaying CO oscillations were manually reviewed. Ten patients with sepsis or the systemic inflammatory response syndrome exhibited 18 episodes of ultra low-frequency periodic oscillations (ULF-CO) with frequencies ranging from 0.0028 to 0.000053 Hz (periods, 6 to 316 min). Intensive care unit mortality rate was 50%. The amplitude and coefficient of variation of cardiac output during ULF-CO ranged from 0.1-4.6 L and 3.9-14.3%, respectively. Duration of ULF-CO ranged from 4-108.1 h. ULF-CO could not be explained as a result of patterned artifact from measurement error or therapeutic intervention. ULF-CO may be a pathophysiologic marker that might serve the diagnosis, prognosis, and treatment of critical illness.

Real-time noninvasive estimation of intrapleural pressure in mechanically ventilated patients: a feasibility study.

A method for real-time noninvasive estimation of intrapleural pressure in mechanically ventilated patients is proposed. The method employs a simple first-order lung mechanics model that is fitted in real-time to flow and pressure signals acquired non-invasively at the opening of the patient airways, in order to estimate lung resistance (RL), lung compliance (CL) and intrapleural pressure (Ppl) continuously in time. Estimation is achieved by minimizing the sum of squared residuals between measured and model predicted airway pressure using a modified Recursive Least Squares (RLS) approach. Particularly, two different RLS algorithms, namely the conventional RLS with Exponential Forgetting (EF-RLS) and the RLS with Vector-type Forgetting Factor (VFF-RLS), are considered in this study and their performances are first evaluated using simulated data. Simulations suggest that the conventional EF-RLS algorithm is not suitable for our purposes, whereas the VFF-RLS method provides satisfactory results. The potential of the VFF-RLS based method is then proved on experimental data collected from a mechanically ventilated pig. Results show that the method provides continuous estimated lung resistance and compliance in normal physiological ranges and pleural pressure in good agreement with invasive esophageal pressure measurements.