Recognition of Sepsis in Resource-Limited Settings

Kwizera, Arthur; Adhikari, Neill K. J.; Angus, Derek C.; Dondorp, Arjen M.; Dünser, Martin W.; Festić, Emir; Kissoon, Niranjan; Martín‐Loeches, Ignacio; Lundeg, Ganbold

doi:10.1007/978-3-030-03143-5_4

Cited by 5 publications

(7 citation statements)

References 76 publications

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“…Although some signal functions and impact assessment techniques cross over from high-to low-resource settings, many do not. For example, while sepsis is frequently encountered in both high-and low-resource settings, the underlying etiology and pathophysiology may differ [70]. Thus, the care based on principles derived in resource rich settings may not be appropriate in LRS [71].…”

Section: Section IV Measuring Impact -How Is the Impact Of Early Critical Care Services In Lrs Measured?mentioning

confidence: 99%

White Paper on Early Critical Care Services in Low Resource Settings

Losonczy

Papali²,

Kivlehan³

et al. 2021

Annals of Global Health

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Section: Section IV Measuring Impact -How Is the Impact Of Early Critical Care Services In Lrs Measured?mentioning

confidence: 99%

White Paper on Early Critical Care Services in Low Resource Settings

Losonczy

Papali²,

Kivlehan³

et al. 2021

Annals of Global Health

Self Cite

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“…Sepsis is defined as a "dysregulated host response to an infection leading to end organ damage," and may be operationalized to include the quick Sequential Organ Function Assessment score. 79,80 Two fundamental principles underpin appropriate and timely sepsis management: (1) source control (i.e., appropriate antimicrobial therapy and/or an intervention for pathogen removal [e.g., surgery]); and (2) adequate resuscitation to support end-organ function (e.g., IV fluids and/or vasoactive medications to maintain organ perfusion pressure). Although a rare occurrence in expedition-based settings, providers should treat sepsis at the point of contact in all settings by securing adequate IV access, administering targeted antibiotics, and IV fluids.…”

Section: Septic Shockmentioning

confidence: 99%

“…Rapid (<1 hour) antibiotic administration, thoughtfully targeting likely infecting pathogens, relies on researching local organisms and resistance patterns. 79 IV fluid resuscitation initially should treat hypovolemia, and, in many cases, requires 20-30 mL/kg of crystalloid with frequent reassessment (e.g., POC lactate, echocardiographic assessments, skin mottling, capillary refill) to evaluate ongoing benefit. In refractory shock (e.g., mean arterial pressure ≤65 mmHg or other signs of malperfusion), vasopressor agents may be needed, with norepinephrine considered first-line therapy at present.…”

Section: Septic Shockmentioning

confidence: 99%

“…In refractory shock (e.g., mean arterial pressure ≤65 mmHg or other signs of malperfusion), vasopressor agents may be needed, with norepinephrine considered first-line therapy at present. 79 Vasopressor titration en route to definitive care should utilize an intravenous pump, although "push-dose" vasopressor administration at the point of medical contact may allow for temporary blood pressure stabilization in the interim. 81…”

Section: Septic Shockmentioning

confidence: 99%

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Critical Care Anywhere: Principles for High-Functioning Management in Low-Resource Environments

Stankiewicz

Ward

McCurdy

2021

MRAJ

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Critically ill patients can present at any time and location, and they demand high quality care. Historical experiences from military, wilderness, and disaster medicine settings have helped shape the modern concept of caring for the most severely ill with limited available resources. We introduce a method to help design a successful critical care medical support endeavor, which includes properly defining components of Navigation, Environment, Resupply, Energy, Unconventional problems, and Support (NEREUS). Additionally, we provide recommendations for optimal team personnel composition, including utilization of paramedics, critical care providers, nurses, and respiratory therapists across the spectrum of care provided at point of injury, en route to definitive care, and definitive care. A review of critical care principles relevant to the austere setting proceeds with a systematic organization according to airway, breathing, circulation, and neurologic management. Lastly, we employ our proposed method of organizing a critical care medical support endeavor to a post-hurricane scenario. In summary, this review provides the historical background, modern definition, and practical framework for successfully administering critical care in scenarios with limited available resources. We emphasize the need to appropriately adapt critical care concepts to meet the unique demands of a specific scenario.

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“…Serum lactate measurements made at frequent intervals pose a risk of infection due to the need for venipuncture or the use of a central venous catheter for blood sampling [10], and risk of anemia due to repeated blood samplings [11]. In low-and middle-income countries (LMICs), continuous serum lactate measurements may not be available in intensive care units, or in the presence of limited resources, which may increase the nancial burden [12]. A non-invasive method that could predict the course of serum lactate values without the need for repeated blood draws could alert clinicians to potential deterioration and perform rapid con rmatory testing by a blood draw.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

Arslantaş

Asuroglu

Arslantaş

et al. 2022

Preprint

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Purpose Serum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients as suggested by The Surviving Sepsis Campaign serum lactate levels should be assessed and early lactate clearance-directed therapy is associated with decreased mortality. Monitoring a patient's vital parameters and repeatedly done blood analysis may have deleterious effects on the patient and brings an economical burden. Machine learning algorithms and trend analysis are gaining importance to overcome these unwanted facts. In this context, we aimed to investigate if an artificial intelligence approach can predict lactate trends from non-invasive clinical variables of patients with sepsis. MethodsIn this retrospective study, adult patients with sepsis from the MIMIC-IV dataset who had at least two serum lactate measurements recorded within the first 6 hours of sepsis diagnosis and who also has an ICU length of stay ≥ 24 hours are evaluated and ≥1mmol/l change is considered as a trend indicator. For prediction of lactate trend Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers are evaluated. ResultsLMT algorithm outperformed other classifiers (AUC= 0.832). J48 decision tree performed worse when predicting constant lactate trend. LMT algorithm with 4 features (heart rate, oxygen saturation, lactate value before sepsis diagnosis, and time interval variables) achieved 0.821 in terms of AUC. ConclusionWe can say that machine learning models that employ logistic regression architectures, i.e. LMT algorithm achieved good results in lactate trend prediction tasks can be effectively used to assess the state of the patient whether it is stable or improving.

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Recognition of Sepsis in Resource-Limited Settings

Cited by 5 publications

References 76 publications

White Paper on Early Critical Care Services in Low Resource Settings

White Paper on Early Critical Care Services in Low Resource Settings

Critical Care Anywhere: Principles for High-Functioning Management in Low-Resource Environments

Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

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