Early goal-directed therapy in the treatment of severe sepsis and septic shock

A processor-based system for analyzing physiologic data and medical care is provided wherein the patient data is analyzed to construct images that are representative of a patient's condition. The processor provides a self-modulating analysis, which is responsive to the occurrence of additional data items to increase the information contained in the images. Identifications of modes of physiologic failure by the analysis of the generated images provides for earlier recognition and intervention and improved protocolization of testing and treatment.

Patient safety processor

Sepsis is one of the main causes of morbidity and mortality in critically ill patients despite the use of modern antibiotics and resuscitation therapies. Outcomes in sepsis have improved overall, probably because of an enhanced focus on early diagnosis and other improvements in supportive care, but mortality rates still remain unacceptably high. The diagnosis and definition of sepsis is a critical problem due to the heterogeneity of this disease process. Although it is apparent that much more needs to be done to advance our understanding, sepsis and related terms remain difficult to define. A 1991 consensus conference developed initial definitions that systemic inflammatory response syndrome (SIRS) to infection would be called sepsis. Definitions of sepsis and septic shock were revised in 2001 to incorporate the threshold values for organ damage. In early 2016, the new definitions of sepsis and septic shock have changed dramatically. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. The consensus document describes organ dysfunction as an acute increase in total Sequential Organ Failure Assessment (SOFA) score two points consequently to the infection. A significant change in the new definitions is the elimination of any mention of SIRS. The Sepsis-3 Task Force also introduced a new bedside index, called the qSOFA, to identify outside of critical care units patients with suspected infection who are likely to develop sepsis. Recently updated the consensus definitions improved specificity compared with the previous descriptions.

Changing Definitions of Sepsis

Respiratory alarm monitoring and rapid response team alerts on hospital general floors are based on detection of simple numeric threshold breaches. Although some uncontrolled observation trials in select patient populations have been encouraging, randomized controlled trials suggest that this simplistic approach may not reduce the unexpected death rate in this complex environment. The purpose of this review is to examine the history and scientific basis for threshold alarms and to compare thresholds with the actual pathophysiologic patterns of evolving death which must be timely detected. The Pubmed database was searched for articles relating to methods for triggering rapid response teams and respiratory alarms and these were contrasted with the fundamental timed pathophysiologic patterns of death which evolve due to sepsis, congestive heart failure, pulmonary embolism, hypoventilation, narcotic overdose, and sleep apnea. In contrast to the simplicity of the numeric threshold breach method of generating alerts, the actual patterns of evolving death are complex and do not share common features until near death. On hospital general floors, unexpected clinical instability leading to death often progresses along three distinct patterns which can be designated as Types I, II and III. Type I is a pattern comprised of hyperventilation compensated respiratory failure typical of congestive heart failure and sepsis. Here, early hyperventilation and respiratory alkalosis can conceal the onset of instability. Type II is the pattern of classic CO2 narcosis. Type III occurs only during sleep and is a pattern of ventilation and SPO2 cycling caused by instability of ventilation and/or upper airway control followed by precipitous and fatal oxygen desaturation if arousal failure is induced by narcotics and/or sedation. The traditional threshold breach method of detecting instability on hospital wards was not scientifically derived; explaining the failure of threshold based monitoring and rapid response team activation in randomized trials. Furthermore, the thresholds themselves are arbitrary and capricious. There are three common fundamental pathophysiologic patterns of unexpected hospital death. These patterns are too complex for early detection by any unifying numeric threshold. New methods and technologies which detect and identify the actual patterns of evolving death should be investigated.

https://escholarship.org/content/qt05p3z32s/qt05p3z32s.pdf?t=lrej4e

Patterns of unexpected in-hospital deaths: a root cause analysis.

The disclosed embodiments relate to pulse oximetry. An exemplary pulse oximeter comprises a probe that is adapted to be attached to a body part of a patient to create a signal indicative of an oxygen saturation of blood of the patient, and a processor that is adapted to receive the signal produced by the probe, to calculate an SPO2 value based on the signal, to detect a plurality of pattern types of SPO2 indicative of pathophysiologic events, and to produce an output indicative of a detected one of the plurality of pattern types.

System and method for automatic detection of a plurality of spo2 time series pattern types

Evaluation of patient satisfaction with second intention healing versus primary surgical closure.

The development of organ dysfunction is a key contributor to morbidity and mortality in sepsis. End-tidal carbon dioxide levels measured by non-invasive end-tidal capnography (ETCO2) may provide a rapid assessment of a patient’s underlying metabolic status. The objective of this study was to explore the association between ETCO2 and (1) organ dysfunction [sequential organ failure assessment (SOFA) score], and (2) serum lactate levels in febrile emergency department (ED) patients. Prospective, observational cohort study of a convenience sample of 97 adult (age 18 years or older) patients presented to an academic urban ED with a fever and suspected infection. The outcomes were ED SOFA score and serum lactate level. Based on prior studies, we categorized an ETCO2 2, and an abnormal lactate as >4 mmol/L. The correlation of ETCO2 with SOFA and lactate level was analyzed using Pearson correlation coefficient. Operating characteristics were calculated with 95% confidence intervals, along with the area under the curve (AUC). Among 97 patients enrolled, 5 (5%) had an abnormal lactate and 34 (35%) had a SOFA score >2. A significant correlation was found between ETCO2 and SOFA score (r = −0.35, p 2 had an AUC of 0.69. ETCO2 of 2. ETCO2 4 with an AUC of 0.62. We found a small, but statistically significant correlation, between ETCO2 and SOFA scores; however, based on questionable operating characteristics, the test seems to have limited ability to meaningfully impact clinical decision making. Larger confirmatory studies are required before final assessment.

/pdf/evaluation-of-end-tidal-carbon-dioxide-role-in-predicting-28bfz3wpav.pdf

Evaluation of end-tidal carbon dioxide role in predicting elevated SOFA scores and lactic acidosis.

418: Evaluation of End-Tidal Carbon Dioxide Role in Predicting Elevated SOFA Scores and Lactic Acidosis

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Evaluation of patient satisfaction with second intention healing versus primary surgical closure.

Evaluation of end-tidal carbon dioxide role in predicting elevated SOFA scores and lactic acidosis.

418: Evaluation of End-Tidal Carbon Dioxide Role in Predicting Elevated SOFA Scores and Lactic Acidosis