Fever effects and treatment in critical care: Literature review

Kiekkas, Panagiotis; Αρέθα, Διαμάντω; Bakalis, Nick; Karpouhtsi, Irini; Marneras, Chris; Baltopoulos, George

doi:10.1016/j.aucc.2012.10.004

Cited by 34 publications

(34 citation statements)

References 52 publications

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“…It is well documented that fever is associated with illness severity and mortality . Indeed, patients with febrile septic shock who receive external cooling to achieve normothermia have significantly decreased vasopressor dose requirements after 12 h of treatment ( P <.001) and lower 14‐day mortality ( P =.013) and shock reversal is also more common ( P =.021) vs those whose fever is untreated, inferring that persistent fever is related to increased cost due to increased duration of shock …”

Section: Discussionmentioning

confidence: 99%

Clinical factors affecting costs in patients receiving systemic antifungal therapy in intensive care units in Greece: Results from the ESTIMATOR study

Armaganidis

Nanas

Antoniadou

et al. 2017

Mycoses

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Summary Invasive fungal infections are common in intensive care units (ICUs) but there is a great variability in factors affecting costs of different antifungal treatment strategies in clinical practice. To determine factors affecting treatment cost in adult ICU patients with or without documented invasive fungal infection receiving systemic antifungal therapy (SAT) we have performed a prospective, multicentre, observational study enrolling patients receiving SAT in participating ICUs in Greece. During the study period, 155 patients received SAT at 14 participating ICUs: 37 (23.9%) for proven fungal infection before treatment began, 10 (6.5%) prophylactically, 77 (49.7%) empirically and 31 (20.0%) pre‐emptively; 66 patients receiving early SAT (55.9%) were subsequently confirmed to have proven infection with Candida spp. (eight while on treatment). The most frequently used antifungal drugs were echinocandins (89/155; 57.4%), fluconazole (31/155; 20%) and itraconazole (20/155; 12.9%). Mean total cost per patient by SAT strategy was €20 458 (proven), €15 054 (prophylaxis), €23 594 (empiric) and €22 184 (pre‐emptive). Factors associated with significantly increased cost were initial treatment failure, length of stay (LOS) in ICU before starting SAT (i.e. from admission until treatment start), fever and proven candidaemia (all P≤.05). Conclusion: Early administration of antifungal drugs was not a substantial component of total hospital costs. However, there was a significant adverse impact on costs with increasing LOS in febrile patients in ICU for whom diagnosis of fungaemia was delayed before starting SAT, and with initial treatment failure. Awareness of potential candidaemia and initiation of pre‐emptive or empirical strategy as early appropriate treatment may improve ICU patient outcomes while reducing direct medical costs.

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Section: Discussionmentioning

confidence: 99%

Clinical factors affecting costs in patients receiving systemic antifungal therapy in intensive care units in Greece: Results from the ESTIMATOR study

Armaganidis

Nanas

Antoniadou

et al. 2017

Mycoses

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“…Antipyretic medication in critically COVID-19 patients may reduce the risk of haemodynamic instability and hypoxic tissue damage, in particular for elderly patients and/or those with comorbidities [73]. These patients are more vulnerable to elevated physiological demands during high-grade fever presenting an increased risk of dehydration or circulatory dysregulation [74].…”

Section: Other Therapiesmentioning

confidence: 99%

COVID-19 multidisciplinary high dependency unit: the Milan model

et al. 2020

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COVID-19 is a complex and heterogeneous disease. The pathogenesis and the complications of the disease are not fully elucidated, and increasing evidence shows that SARS-CoV-2 causes a systemic inflammatory disease rather than a pulmonary disease. The management of hospitalized patients in COVID-19 dedicated units is advisable for segregation purpose as well as for infection control. In this article we present the standard operating procedures of our COVID-19 high dependency unit of the Policlinico Hospital, in Milan. Our high dependency unit is based on a multidisciplinary approach. We think that the multidisciplinary involvement of several figures can better identify treatable traits of COVID-19 disease, early identify patients who can quickly deteriorate, particularly patients with multiple comorbidities, and better manage complications related to off-label treatments. Although no generalizable to other hospitals and different healthcare settings, we think that our experience and our point of view can be helpful for countries and hospitals that are now starting to face the COVID-19 outbreak.

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“…Fever can be caused due to any medical irregularities such as bacterial, parasitic, viral infections or any such non-infectious causes [2]. It is comprehended as the most common medical symptom among 30% and 70% of seriously ill children and elders, respectively [3]. Though, it is an auto-reflexive outburst of bodily immune-mechanism, consequences of fever have seen as a great concern to a large set of parents and medical practitioners world-wide [4,5].…”

Section: Introductionmentioning

confidence: 99%

Design of a low cost and novel Naive Bayes classifier based NodeMCU web server for fever type detection

Ray¹,

Dash²,

De³

2018

biomedicalresearch

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This study presents a novel NodeMCU based web server to validate cost-effective and smart e-health diagnosis. Proposed work implements a novel Naïve Bayes Classifier for automatic fever type detection as a proof of concept. The application is currently accessible on local network. Network users (patients) are first required to submit eight symptoms e.g. "fatigue", "fever", "chills", "sore throat", "cough", "headache", "muscle pain", and "sneezing" on web portal in form of yes "y" or no "n". Implied Naïve Bayes Classifier engine predicts the probability of occurrence of fever either from flu or common cold as per symptoms provided earlier. The patients are simultaneously diagnosed by a medical practitioner from whom patient wise predictive percentage was received. The probabilistic values were then paired against flu and common cold typed fever patients independently. 22 patients were voluntarily gone through this experiment where p (0.089>0.05) and p (0.068>0.05) were not found to be statistically significant (i.e. no difference between proposed classifier and doctor's diagnosis) for flu and common cold fever types, respectively.

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Fever effects and treatment in critical care: Literature review

Cited by 34 publications

References 52 publications

Clinical factors affecting costs in patients receiving systemic antifungal therapy in intensive care units in Greece: Results from the ESTIMATOR study

Clinical factors affecting costs in patients receiving systemic antifungal therapy in intensive care units in Greece: Results from the ESTIMATOR study

COVID-19 multidisciplinary high dependency unit: the Milan model

Design of a low cost and novel Naive Bayes classifier based NodeMCU web server for fever type detection

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