Management of invasive pulmonary aspergillosis in non-neutropenic critically ill patients

During recent years, a rising incidence of invasive pulmonary aspergillosis (IPA) in non-neutropenic critically ill patients has been reported. Critically ill patients are prone to develop disturbances in immunoregulation during their stay in the ICU, which render them more vulnerable for fungal infections. Risk factors such as chronic obstructive pulmonary disease (COPD), prolonged use of steroids, advanced liver disease, chronic renal replacement therapy, near-drowning and diabetes mellitus have been described. Diagnosis of IPA may be difficult and obtaining histo- or cytopathological demonstration of the fungus in order to meet the gold standard for IPA is not always feasible in these patients. Laboratory markers used as a non-invasive diagnostic tool, such as the galactomannan antigen test (GM), 1,3-β-glucan, and Aspergillus PCR, show varying results. Antifungal therapy might be considered in patients with persistent pulmonary infection who exhibit risk factors together with positive cultures or sequentially positive GM and Aspergillus PCR in serum, in whom voriconazole is the drug of choice. The benefit of combination antifungal therapy lacks sufficient evidence so far, but this treatment might be considered in patients with breakthrough infections or refractory disease.
Introduction
Invasive pulmonary aspergillosis (IPA) has emerged as an important cause of morbidity and mortality in patients receiving intensive chemotherapy, allogeneic stem cell transplantation, and solid organ transplantation. However, during recent years, several reports have described a rising incidence of IPA in critically ill patients admitted to the intensive care unit (ICU), even in the absence of an apparent predisposing immunodeficiency [1–6]. The incidence of IPA in the ICU ranges from 0.3% to as much as 5.8% [2, 3, 6] and it carries an overall mortality rate exceeding 80%, with an attributable mortality ofalmost 20% [4, 5]. This high mortality is at least partially related to difficulties in timely diagnosis, caused by insensitive and non-specific clinical signs and lack of unequivocal diagnostic criteria.
In this review, we will describe the pathophysiological mechanisms and risk factors for IPA in non-neutropenic critically ill patients, limitations and advances in the diagnostic process, and alterations in treatment with antifungal therapy. A Medline/PubMed search was performed for all articles about IPA in critically ill patients in relation to risk factors, diagnosis and antifungal therapy. All publication types of human studies in the English language were searched and an extraction of relevant articles was made for the purpose of this narrative review.
Pathophysiological mechanisms and risk factors
Aspergillus spp. are opportunistic moulds that cause both allergic and invasive syndromes. The genus Aspergillus contains approximately 175 species, only a minority of them have been associated with human disease. Infections are caused mostly by Aspergillusfumigatus; next in line are Aspergillus flavus, Aspergillus terreus, Aspergillus niger andAspergillus nidulans [7]. Aspergillus is found in soil, water, food, and in the air and grows on a wide variety of organic material, such as decaying vegetation. The conidia (spores) are easily aerosolised. The route of transmission is by air. Although exposure is universal, invasive infection occurs almost exclusively in immunocompromised individuals. Infections have frequently been described in patients with haematological malignancies and solid organ transplant recipients, but also in patients undergoing chronic intermittent haemodialysis in whom these infections were associated with hospital construction and/or ventilation systems contaminated with Aspergillus spp. [8]. Even hospital water is a frequently overlooked source of nosocomial aspergillosis [9, 10].
Natural antifungal defence in humans is based on normal mucosal barriers and an intact macrophage and neutrophil function. Alveolar macrophages form the first line of defence against inhaled Aspergillus conidia that reach the alveoli. Macrophages normally are capable of killing the conidia and preventing germination, by releasing cytokines such as tumour necrosis factor (TNF)-α and macrophage inflammatory protein (MIP)-1α [11]. During neutropenia, TNF-α and MIP-1α synthesis is reduced and the conidia can germinate to form hyphae. T-cell mediated acquired immunity also has an important role in protecting against fungal infections, as Aspergillus antigens are able to induce T-helper (Th)-1 and Th-2 type reactivity [12, 13]. Th-1 reactivity is displayed by an increase of interferon-γ and interleukin (IL)-12 and has protective effects against infection. In contrast, Th-2 reactivity is characterised by production of IL-4 and IL-10 and leads to disease progression, at least in a murine model of IPA [12, 14, 15].
Critically ill patients in ICU exhibit a complex change in immune function characterised by deactivation of macrophages and an altered cellular response due to the severity of illness which is also termed “immunoparalysis” [16, 17]. This immunologic derangement might explain why Aspergillus infections are able to develop in critically ill patients who do not display the predisposing classical risk factors [17, 18]. Many other factors will negatively influence the immune function during critical illness, such as (acute) hyperglycaemia [19] and the use of corticosteroids [20–23]. Corticosteroids have profound effects on the distribution and function of neutrophils, monocytes, and lymphocytes and they directly stimulate the growth of Aspergillus fumigatus in vitro possibly via sterol binding proteins in the fungus [21]. In particular intravenous corticosteroids treatment in patients with chronic obstructive pulmonary disease (COPD), is associated with a rising incidence of IPA [1, 24–26]. Also broad-spectrum antibiotics, which affect the distribution of normal flora, have been described as a risk factor [27]. However, not every critically ill patient in the ICU is at risk for developing invasive fungal infections. Apparently, other specific – patient-related – predisposing conditions seem to be associated with the development of IPA, in which COPD and other chronic lung disease [1–4, 28–32], diabetes mellitus [2, 31, 33], acute liver failure/advanced liver cirrhosis [2–4, 34], chronic renal failure [8, 35], and near drowning [4, 36–38] have been described (Table 1). In these, mainly retrospective, studies a mean in-hospital mortality of 80% was found in patients with highly suspected or proven IPA in the presence of at least one of these underlying conditions, despite antifungal therapy. Remarkably, patients who were suspected to be colonised only with Aspergillus spp. (i. e. no signs of pulmonary infection) demonstrated even a high in-hospital mortality rate [3, 31], which might suggest that colonisation should be considered as a potentially important finding.
Table 1Risk factors for IPA in non-neutropenic critically ill patients in the ICURisk factorReferenceCOPD in combination with prolonged corticosteroid use [1, 24–26]High-dose systemic corticosteroids > 3 weeks (e. g. prednisone equivalent
> 20 mg/day) [2, 31, 33]Chronic renal failure with RRT [8, 35]Liver cirrhosis/acute hepatic failure [2–4, 34]Near-drowning [4, 36–38]Diabetes mellitus [2, 3, 31, 33]COPD, Chronic obstructive pulmonary disease; RRT,
renal replacement therapy
Diagnostic features
A few years ago, a consensus for standard definitions and diagnosis for invasive fungal infections in immunocompromised patients with cancer and recipients of haematopoietic stem cell transplants was established by the European Organization for Research and Treatment of Cancer and the Mycoses Study Group (EORTC/MSG) in which three levels of probability for invasive fungal infections are proposed: “proven”, “probable” and “possible” [39]. However, these guidelines are intended for use in clinical trials and for epidemiologic research and, moreover, are focused on patients with classical risk factors such as neutropenia, malignancies and after haematopoietic stem cell transplantation, and it may not be possible to extrapolate them to the non-neutropenic critically ill patient.
The diagnosis of IPA in non-neutropenic critically ill patients is difficult because signs and symptoms are non-specific, and the initiation of additional diagnostic examinations is often delayed because of a low clinical suspicion. For the timing of suspicion of IPA in these patients, the combination of persistent or rapid developing infiltrative abnormalities on thoracic imaging and/or a persistent pulmonary infection despite broad spectrum antibiotics accompanied by one or more predisposing conditions (Table 1) might be the moment for triggering further diagnostic exploration. Although histopathological evidence of IPA is defined as the gold standard due to a very high tropism for blood vessels [39], trans-bronchial biopsy or surgical lung biopsy via mini-thoracotomy may not justify the risk of this invasive procedure in critically ill patients on mechanical ventilation with, sometimes, severe bleeding diathesis. Therefore, the diagnostic process will contain thoracic imaging and microbiological examination by means of direct microscopy and culture of sputum or broncho-alveolar lavage fluid (BALF). Excluding the possibility of contamination during the pre-analytical phase of a sample, isolation of Aspergillus spp. in the respiratory tract may represent three clinical situations: (1) evidence of current disease, (2) true colonisation, or (3) a marker for the future development of invasive disease. In the immunocompetent host, cultures of Aspergillus in respiratory secretions are usually a result of colonisation [32]; in the immunocompromised host, however, they may indicate invasive disease [6, 7, 39]. The positive predictive value of cultures in general is as high as 80–90% [2, 6, 40], although in some groups of patients (e. g. after lung transplantation) specificity is much lower [41]. Sensitivity of cultures in the diagnosis of IPA is poor [2, 6, 40]. Direct microscopic examination of sputum or BAL, stained with specific fluorescent stain for chitin (a fungal cell wall component) is easy to perform, rapid to read, and improves the sensitivity of microbiological examination [40, 42]. Direct visualisation of the hyphae makes it possible to discriminate between septate (e. g. Aspergillus, Fusarium and Scedosporium) and non-septate (e. g. Mucorales) moulds. Ubiquitous moulds of the order Mucorales cause serious infection in immunocompromised patients and, in contrast to Aspergillus, Fusarium and Scedosporium spp., they are not susceptible to voriconazole, which is an important factor when choosing a pre-emptive antifungal drug. The extra value of a positive culture is that growing of the fungus enables identification and susceptibility testing to antifungal drugs. This is important in view of the antifungal resistance of – for example – Aspergillus fumigatus isolates to voriconazole [43]. Nonetheless, reliance on microscopy and/or culture alone results in substantial underdiagnosis due to the low sensitivity. Fibre-bronchoscopy with inspection of the tracheobronchial tree, sampling of deep airway secretions and BAL can be helpful; the macroscopic finding of ulcerative lesions and/or pseudomembranes together with a positive microscopy and/or culture is highly suggestive for Aspergillus-related tracheobronchitis.
Chest computerised tomography (CT) has proved to be an important tool for the diagnosis of IPA in neutropenic, severely immunocompromised patients, even in the absence of evident lesions on a conventional chest X-ray. Radiological findings might include nodules with rapid growth and/or cavitations. A `halo sign' (a pulmonary mass surrounded by a zone of lower attenuation with ground-glass opacification produced by adjacent haemorrhage) and/or the `air crescent sign' (crescentic radiolucencies around a nodular area of consolidation) may be present [44–50]. The frequency of the halo sign in patients with IPA is relatively high in the early stages of the disease, but becomes progressively lower with the passage of time [51]. Combining the halo sign and the air crescent sign, the sensitivity for IPA is more than 80% as specificity reaches 60–98% [47]. However, thoracic imaging in mechanically ventilated ICU patients is less helpful due to many confounding factors such as atelectasis and, sometimes major, pleural effusions. A lower sensitivity (5–24%) of the halo sign and air crescent sign in non-neutropenic patients has been reported in the literature [3, 6, 52]. Due to the high tropism for blood vessels, IPA might be complicated by localisations in the central nervous system. Therefore, in patients with documented or highly suspected IPA, CT scanning or magnetic resonance imaging (MRI) of the brain should be considered to exclude dissemination to the brain.
In the past decade, non-invasive diagnostic tests, serological and molecular, have focused on the detection of surrogate markers for Aspergillus spp., such as the galactomannan (GM) antigen, 1,3-β-glucan and the detection of Aspergillus DNA by PCR. GM is a major Aspergillus cell-wall component that is released during the growth phase of the fungus, and detection of GM would be indicative for invasive disease [53]. Many studies have been done in order to investigate the value of the commercial Platelia Aspergillus assay (BioRad™, Marnes-La-Coquette, France) as a diagnostic tool for IPA, but mainly in patients with haematological malignancies [54–57]. The specificity of the GM assay for diagnosing IPA is at least 85%, as demonstrated by these studies, but the sensitivity of the assay varied considerably between 29% and 100% depending on the cut-off value. The most important finding of these studies was that in around two-third of patients, circulating antigen could be detected at a mean of 8 days before a probable diagnosis was made by a combination of radiographic findings and Aspergillus isolation [53, 54]. In a recent meta-analysis, 27 studies were included regarding the value of the GM serum assay for surveillance of IPA in high-risk patients. The median sensitivity for proven cases was 71% (specificity 89%); for proven or probable cases, median sensitivity and specificity were 61% and 93%, respectively [58]. Specificity increased to 95% using a cut-off value of 1.5 in cases with proven or probable IPA. Because GM is a water-soluble carbohydrate, it can also be detected in BALF. Although the Platelia ELISA (enzyme-linked immunosorbent assay) is not validated for detection of GM in this fluid, there is an increasing tendency to use these samples for diagnosis of IPA. In small clinical studies among patients with haematological malignancies and in solid organ transplant recipients, the sensitivity of the GM EIA (enzyme immuno-assay) applied to BALF ranges from 85% to 100% with a high index cut-off (> 1.5) to define positivity [59–62]. However, there are several clinical circumstances that might influence the diagnostic performance of the GM antigen test in either serum or BALF. First, the false-positive reactivity which might be caused by gastro-intestinal translocation of fungal GM from contaminated food or drink, as demonstrated in small children [63], and the use of the intravenous antibiotics piperacillin–tazobactam and amoxicillin–clavulanic acid, which is associated with serum ELISA reactivity in patients without evidence of IPA [64–67]. Second, an important factor that affects the release of GM antigens is antifungal drug therapy. Different animal and human studies have shown decreased sensitivity of the GM assay when (prophylactic) antifungal drugs were used [63, 68, 69]. In several prospective studies that assessed the performance of antigen detection, patients received antifungal prophylaxis with itraconazole, which might have a significant effect on the sensitivity of the assay [53, 54]. Third, when the Platelia Aspergillus ELISA kit was launched in Europe about a decade ago, another cut-off serum ratio was recommended than at present. Over the past years, several studies suggested lower cut-off values, ranging from 0.5 to 1 [39, 53, 54, 70–72]. It is clear that alterations in cut-off level will change the performance of the assay. Finally, the studies show that monitoring GM levels is crucial in order to diagnose (and eventually monitor treatment outcome) correctly, which means that the assay has to be performed twice weekly, preferably on receipt of the specimen. In critically ill patients without classical risk factors for IPA, the diagnostic value of the GM assay has been investigated only in one retrospective study and demonstrated a sensitivity of only 53% in patients with proven or probable IPA (cut-off value 1.0) [3]. Thus, it has to be stressed that the available data from patients with (haematological) malignancies and after solid organ transplantation can not be extrapolated to the critically ill patient in general. In the meantime, due to lack of more reliable, non-invasive diagnostic tests, the GM assay could be used as an additive tool in the diagnostic work-up of IPA.
The 1,3-β-glucan is a cell wall component of many filamentous fungi and yeasts, including Aspergillus spp. and Candida spp. Reproducible assay results, with high specificity and a high positive predictive value, demonstrated that use of an assay to detect serum 1,3-β-glucan derived from fungal cell walls is a useful diagnostic adjunct for invasive fungal infection [73]. In addition, false-positive tests have been found in patients after haemodialysis, cardiopulmonary bypass surgery, high-dose immunoglobulin treatment, and after exposure to glucan-containing gauze [33]. Furthermore, in a recent small prospective study among ICU patients, serum glucan levels did not appear to be specific for fungal infections, as serum glucan levels were also elevated in bacterial infections [74]. Hence, the usefulness of 1,3-β-glucan in the diagnosis of IPA has to be further evaluated.
Amplification of nucleic acid by PCR technology for the diagnosis of IPA is being increasingly studied. It can be applied to serum and BAL specimens [75–79]. Experience is limited to patients with haematological malignancies. White et al. evaluated the performance of a real-time PCR in whole blood in a group of patients with haematological malignancies and showed sensitivity of 92.3% and specificity of 94.6% for the diagnosis of IPA with good agreement of the GM ELISA [80]. They concluded that a negative PCR obtained twice weekly allowed a wait-and-see approach concerning starting antifungal treatment. However, comparable to the GM antigen test, there are a number of factors that potentially have an impact upon the clinical sensitivity of PCR. The magnitude of the quantitative PCR signal falls with antifungal therapy, thereby causing false-negative PCR results [81] while the (transient) colonising presence of Aspergillus in the respiratory tract may suggest a low positive predictive value [78]. Furthermore, patients at risk for IPA are often prescribed a multitude of drugs and fluids, all of which may act as non-specific inhibitors of the PCR. For example, anticoagulants inhibit PCR, thereby limiting its sensitivity [82].
One might conclude that the use of GM antigen test, 1,3-β-glucan, and Aspergillus PCR as serological and molecular markers cannot be advocated for routine use in critically ill patients, and caution is warranted in the interpretation of positive test results in patients without a clinical suspicion of pulmonary infection as well as negative test results in patients with persisting pneumonia. However, the finding of sequentially positive GM tests in serum or BALF – using higher cut-off values – together with a positive Aspergillus PCR, in a patient with persisting pulmonary infection who carries one or more risk factors, is highly indicative for IPA and might justify treatment with antifungal therapy.
Antifungal therapy
There have been important developments in antifungal drugs in the past few years, although amphotericin B deoxycholate has been the standard therapy for IPA for decades. However, multiple studies have now established not only its lack of efficacy due to an increasing antifungal resistance but also demonstrated unacceptable toxicity of this compound, in particular nephrotoxicity [83, 84]. Continuous infusion of amphotericin B deoxycholate over 24 h may reduce its nephrotoxicity [85, 86], although the efficacy may be reduced due to lower peak serum levels [87]. The use of lipid formulations may also reduce toxicity and have been studied extensively for empirical use in febrile neutropenia with the same efficacy rate as conventional amphotericin B [88]. However, lipid formulations are more expensive, and the initial use of higher doses does not improve efficacy and is associated with greater toxicity than lower doses, which suggests that high doses may not be routinely warranted.
Among the triazoles, itraconazole has activity against Aspergillus, but its clinical utility in critically ill patients with IPA has been limited by drug interactions and toxicity as well as erratic bioavailability of the oral suspensions [89]. Furthermore, strains of Aspergillusfumigatus resistant to itraconazole have already been described [90]. A large multicentre randomised trial established that voriconazole provides higher response rates and better survival than amphotericin B in the treatment of “probable or proven” IPA among patients with haematological diseases with fewer drug-related adverse events [91]. As a result of this study, voriconazole is increasingly recommended as initial therapy for IPA [92]. Voriconazole is available for intravenous and oral use. It is rapidly absorbed within 2 h after oral administration and the oral bioavailability is over 90% [93]. Clearance is hepatic via N-oxidation by the hepatic cytochrome P450 (CYP) isoenzymes, CYP2C19, CYP2C9 and CYP3A4 [93], which makes the potential for drug interactions considerable. For instance, voriconazole considerably reduces the clearance of intravenous midazolam [94], and fatal interactions with highly active antiretroviral therapy (HAART) have been described [95]. As voriconazole has limited aqueous solubility, the intravenous form includes the solvent vehicle sulfobutylether beta cyclodextrin sodium [96]. The clearance of sulfobutylether beta cyclodextrin sodium is linearly related to creatinine clearance and accumulation has been described in subjects with moderate to severe renal impairment [96], although in animal experiments the frequency of acute toxicity of sulfobutylether beta cyclodextrin sodium is low. Target organs for toxic effects are the kidney and liver, causing obstruction of renal tubules and necrosis in the liver respectively [96]. Because of this potential toxicity, it is recommended to treat patients with moderate to severe renal failure and who are on renal replacement therapy only with the oral form of voriconazole, if feasible. However, in critically ill patients safe oral administration of drugs is difficult to accomplish as, for instance, gastric reflux, gastro-intestinal bleeding and impaired function of the intestine are frequent co-morbidities, leading to potentially insufficient intestinal absorption of the drug. Furthermore, it is important to recognise that some patients may have inadequate levels of the oral drug, particularly if a standard dose of 200 mg twice daily is used rather than the recommended 4 mg/kg twice daily dose which has been studied for the intravenous formulation [97]. At present, no clinical data are available regarding the bioavailability of the oral form of voriconazole in critically ill patients. It might be considered that the fear of potential adverse effects from intravenous solutions does not justify the risk of insufficient treatment by oral solutions. Moreover, limited clinical data showed no obvious toxicity in patients undergoing intermittent haemodialysis who were treated with daily 400–800 mg of intravenous voriconazole during 2 weeks [96], and the pharmacokinetics of voriconazole appears not to be affected by continuous renal replacement therapy (CRRT). On the basis of pharmacokinetics, dose reduction is not recommended in patients receiving CRRT [98, 99]. Regarding the efficacy of voriconazole, there is a potential concern with (long-term) voriconazole therapy as occasional breakthrough infections with yeasts and moulds, with decreased susceptibility to voriconazole, have been reported [43, 100, 101].
Posaconazole is a promising new triazole with broad-spectrum antifungal profile and has shown activity for salvage treatment of IPA in patients who are refractory to or intolerant of conventional therapy. A recent multicentre, prospective study among haematological and non-neutropenic patients with refractory IPA demonstrated a 42% overall success rate for posaconazole recipients versus 26% for control subjects [102]. Posaconazole is, however, only available for oral administration, which makes it probably less applicable in critically ill patients who are susceptible to impaired drug absorption in the digestive tract.
Echinocandins are a novel class of parenterally administered semi-synthetic lipopeptides with a pathogen-specific mechanism for non-competitive inhibition of biosynthesis of the fungus cell-wall enzyme complex 1,3-β-D-glucan [103]. The echinocandins have documented in vivo activity against Candida spp. and Aspergillus spp. At present, there are three approved echinocandins, caspofungin, anidulafungin and micafungin, of which particularly caspofungin has demonstrated efficacy for the treatment of IPA. The first clinical trial to document the efficacy of caspofungin was among patients with “proven or probable” IPA who had treatment failure with (liposomal) amphotericin B, itraconazole or voriconazole, or who were intolerant to these antifungal drugs [104]. Caspofungin seems to be as effective as and generally better tolerated than liposomal amphotericin B when given as empirical antifungal therapy in patients with persistent fever and neutropenia [105]. However, prospective randomised clinical trials aimed at the treatment of IPA in (non-neutropenic) critically ill patients are lacking.
In general, there is no conclusive evidence that extended-spectrum triazoles are superior to echinocandins or polyenes, or vice versa, for monotherapy of IPA in ICU patients. We need prospective randomised controlled trials to solve this issue in critically ill patients. Table 2 gives an overview of treatment options with antifungal drugs for IPA.
Table 2Treatment options with antifungal drugs for IPA in critically ill patients in the ICUSettingFirst choiceAlternativesPrimary therapy of IPAVoriconazole 6 mg/kg q 12 h i. v. on day 1, then 4 mg/kg
q 12 h i. v.Liposomal amphotericin B 3-5 mg/kg/day i. v.ororVoriconazole 400 mg q 12 h oral on day 1, then 200 mg q 12 h
oral aAmphotericin B deoxycholate 1 mg/kg/day i. v.orCaspofungin 70 mg i. v. on day 1, then 50 mg/day i. v. ba Oral administration is recommended only in patients with intact intestinal
absorption; b In patients with moderate to severe hepatic failure, dose reduction
is recommended to 35 mg/day i. v.
Combination therapy
Because the efficacy of antifungal therapy for IPA is poor, with more than 50% of all patients experiencing failure of first-line therapies [106, 107] empirical administration of combination antifungal regiments for proven or probable IPA may be an important strategy to improve outcome.
Theoretically, there are several foreseeable advantages of combination therapy, such as a widened spectrum and potency of drug reactivity, more rapid antifungal effect, synergy, and a reduced risk of antifungal resistance [108]. The available antifungal drugs target four different cell functions: cell membrane integrity (polyenes), ergosterol biosynthesis (azoles, allylamines), DNA synthesis (pyrimidine analogues) and cell-wall integrity (echinocandins). Although antifungal drugs are targeted against specific cell functions, many drugs also have pleiotropic effects that may inhibit other elements of fungal homeostasis [108]. For instance, azoles inhibit many cytochrome-dependent enzymes of the fungal respiration chain and amphotericin B generates oxidative species that damage fungal mitochondrial function and enhance macrophage fungal killing [109]. These subtle effects could be enhanced when one antifungal is applied together with a second drug, resulting in synergy. Conversely, the combination could act antagonistically, e. g. when one antifungal agent affects the targets of the other one.
Only a few clinical studies with small numbers of patients have tried to address the need for combination antifungal therapy [110–113] . These studies have important limitations because they lack a control group and because of many other uncontrolled factors including the choice of combination, the duration of therapy, concomitant antibacterial or antiviral treatment, the lack of adequate follow-up to estimate relapses, and no discrimination between primary and sequential therapy. Results from those studies should therefore be viewed with caution. Large, adequately powered, prospective clinical trials are needed but they might be difficult to perform because of inadequate enrolment, differences in providing benefits, difficulty in documenting fungal infection, controversy regarding endpoints, the lack of surrogate markers to correlate in-vitro evidence to outcome prognosis, and the associated costs [114]. Nevertheless, combination antifungal therapy might be considered for certain clinical conditions such as refractory disease or breakthrough infections [115, 116].
Conclusion
Recent data indicate that IPA may be an underestimated opportunistic fungal infection in critically ill patients, even in the absence of severe pre-existent immunological disorders, and carries a high mortality rate. A decrease in immune function or dysregulation of the immune system due to the severity of illness, together with specific underlying risk factors such as COPD, diabetes mellitus, chronic renal replacement therapy, advanced liver disease, and long-term use of steroids, might explain the relatively high occurrence of IPA among these patients. The high mortality rate is partially related to difficulties in timely diagnosis because of non-specific signs and symptoms, low clinical suspicion, and time delay due to high risks for invasive procedures to obtain histopathological evidence for diagnosing IPA. The presence of a persistent pulmonary infection despite broad-spectrum antibiotics or abnormal thoracic imaging by CT scanning together with one of these risk factors should trigger further diagnostic exploration by collecting respiratory secretions and/or laboratory markers. Meeting the gold standard alone should not be the threshold for starting antifungal therapy, considering the high mortality rate. Invasive infection in patients with negative cultures might be supported by positive serological and molecular markers such as galactomannan antigen testing and Aspergillus PCR, which requires at least two sequentially positive samples. Antifungal therapy might be considered when persistent pneumonia with positive cultures for Aspergillus spp. or sequentially positive GM and Aspergillus PCR are present, accompanied with one of those risk factors for which voriconazole appears to be the first-line treatment. Combination therapy might be considered in breakthrough infections with moulds or yeasts or in refractory disease, although clear evidence is lacking.

invasive pulmonary aspergillosis
non-neutropenic critically ill
antifungal therapy
serological markers


What is new in iron overload?

Children with severe chronic hemolytic anemia or congenital erythroblastopenia are transfusion dependent. Long-term transfusion therapy prolongs life but results in a toxic accumulation of iron in the organs. The human body cannot actively eliminate excess iron. Therefore, the use of a chelating agent is required to promote excretion of iron. So far, iron chelation has been done by subcutaneous infusion of deferoxamine given over 10 h, 5–6 days per week. Compliance is poor and chelation often insufficient. Ferritin measurements and sometimes liver biopsies are used to evaluate the iron burden in the body. At the present time, new iron chelators that can be given orally are available. Furthermore, magnetic resonance imaging (MRI) assessment of tissue iron is a noninvasive and highly reproducible method, which is able to quantitate organ iron burden. In conclusion, iron overload can be measured more accurately with noninvasive methods such as MRI. Deferasirox is a once-daily oral therapy for treating transfusional iron overload, which improves patient compliance and quality of life.
Introduction
Children suffering from severe chronic hemolytic anemia such as β-thalassemia, or congenital erythroblastopenia are at risk of developing severe iron overload. Regular blood transfusions are potentially life saving. However, every single transfusion of one unit of blood brings 200 mg of iron into the body of the recipient. Patients receiving 3 U every 4 weeks (39 U per year) will accumulate 7.8 g of iron by the end of the year, in addition to the amount absorbed by the gut. The human body has no mechanism for active excretion of excess iron. Repeated transfusions lead to elevated iron levels that result in major organ damage. Excess iron is deposited largely in the liver, spleen, myocardium, and several endocrine organs. In young patients, growth and maturation is slowed, and in all age groups, liver disease, diabetes mellitus, and other complications develop. Once the body’s storage capacity is exceeded, free iron catalyses the formation of highly reactive hydroxyl radicals, which cause membrane damage and protein denaturation. This process leads to tissue damage and ultimately to significant morbidity and mortality [3].
Measuring iron levels
(Table 1)
Table 1Comparison of methods for evaluating iron overload InvasiveCostLimitationValidationFerritinNoLowInfection/inflammationYesLICYesMedianFibrosis/cirrhosisYesSQUIDNoHighNot easily availableYesMRINoHighDedicated personYesLIC liver iron content, SQUID superconducting quantum interference device, MRI magnetic resonance imaging
There are several methods of assessing body iron load. As 90% of excess iron is deposited in the liver, most techniques focus on measuring liver iron levels, and it is widely accepted that liver iron content (LIC) provides an accurate measure of whole-body iron concentration. The invasive nature of the liver biopsy means that other markers such as serum ferritin levels are frequently employed. Other approaches using biomagnetic susceptometry and magnetic resonance imaging (MRI) are also being assessed in order to identify an accurate, low-risk, and convenient approach to assessing patient iron status.
Ferritin
Serum ferritin is easily measured using a commercially available kit. A ferritin constantly below 2,500 mg/l has been shown to reduce the risk of cardiac complications, but a target value of 1,000 mg/l is recommended. Factors such as inflammation, ascorbate status, and hepatitis can affect serum ferritin levels. Therefore, results should be interpreted with caution and a trend in the evolution of serial measurements is a better index than day-to-day variation. In patients with transfusion-dependent diseases, chelation should be initiated after 10–20 blood transfusions or when ferritin level rises above 1,000 mg/l.
Liver biopsy
LIC measurement is the most accurate method for assessing body iron (Fig. 1). It also provides information about the severity of the liver disease [8]. Liver samples can be obtained by percutaneous or transjugular access. LIC measurement from a needle biopsy has been associated with a coefficient of variation of less than 10% in livers with no advanced disease. However, it has been shown that variability can be much higher in fibrotic and cirrhotic livers. Values of LIC are expressed as milligrams of iron per gram of dry liver weight.
Fig. 1Liver biopsy (Perls stain): iron deposits are in blue. Courtesy of B. Turlin, Pathology Department, University Hospital of Rennes
Liver biopsy is an invasive technique that is associated with some pain and at risk of hemorrhage and infection. It is not indicated for routine assessment.
Superconducting quantum interference device (SQUID)
SQUID is capable of measuring very small changes in magnetic flux. Iron stored as ferritin and hemosiderin is the only relevant paramagnetic material in the human body. The magnitude of paramagnetic response is directly related to the amount of iron in a certain volume of tissue. This is a noninvasive method, with a linear correlation with LIC assessed by biopsy. However, equipment availability is extremely limited [6].
Magnetic resonance imaging (MRI)
MRI measures tissue iron concentration indirectly by detecting the paramagnetic influences of storage iron (ferritin and hemosiderin) on proton resonance behavior. With MRI, the transmitted signal is a microwave, which excites water protons in the body to higher magnetic energy states. As these water protons relax back to the unexcited state, they emit microwaves that are received and interpreted by the scanner. Iron deposits act like little magnets when placed in a strong magnetic field, disrupting coherence among the protons and darkening the image more quickly (Fig. 2) [13]. LIC determined using MRI shows excellent correlation with that obtained from liver biopsy. Furthermore, MRI has the ability to evaluate the entire organ and gives more accurate measurement of LIC, particularly in patients with heterogeneous iron content (Fig. 3). It requires a dedicated imaging method and equipment operator [13].
Fig. 2Gradient echo images of liver collected by J. Wood [13] at four different echo times. The top four images were collected from a patient having a liver iron of 6 mg/g. The bottom four images were collected from a normal volunteer. All images darken as the echo time (TE) lengthens, but the iron-heavy tissue darkens faster. The half-life of this process is called T2* and the rate is called R2* (R2* = 1,000/T2*)Fig. 3Gradient echo imaging illustrating discordant iron loading of the liver and heart. a Heavy liver iron loading (dark tissue) with heart sparing image. b Heavy cardiac iron loading with no liver deposition. From [13]
Iron chelation therapy
Within 1–2 years of initiation of regular blood transfusions, iron starts to accumulate in the body. The use of a chelating agent then becomes mandatory [6]. At the present time, three different therapies are available (Table 2).
Table 2Comparison between iron chelators (adapted from [4])CharacteristicsDeferoxamine (Desferal)Deferiprone (Ferriprox)Deferasirox (Exjade)Route of administrationSubcutaneous or intravenousOralOralHalf-life20 min2–3 h8–16 hRoutes of iron excretionUrine/stoolUrinestoolDose range20–60 mg/kg per day50–60 mg/kg per day20–30 mg/kg/dayGuidelines for monitoring therapyAudiometry and eye exams annuallyComplete blood count weekly; ALT level monthly for first 3–6 months then every 6 monthsSerum creatinine level monthly; ALT level monthlySerum ferritinSerum ferritinSerum ferritinAssessment of liver iron annuallyAssessment of liver iron annuallyAssessment of liver iron annuallyAssessment of cardiac iron annually after 10 years of ageAssessment of cardiac iron annually after 10 years of ageAssessment of cardiac iron annually after 10 years of ageAdvantagesLong-term experienceOrally activeOrally activeEffective in maintaining normal or near-normal iron storesSafety profile well establishedOnce-daily administrationReversal of cardiac disease with intensive therapyEnhanced removal of cardiac ironDemonstrated equivalency to deferoxamine at higher dosesMay be combined with deferiproneMay be combined with deferoxamineTrials in several hematologic disordersDisadvantagesRequires parenteral infusionMay not achieve negative iron balance in all patients at 75 mg/kg per dayLimited long-term dataEar, eye, bone toxicityRisk of agranulocytosisNeed to monitor renal functionPoor complianceMay not achieve negative iron balance in all patients at highest dose
Deferoxamine mesylate (Desferal) is the current standard for iron chelation therapy. The long-term efficacy of this approach has been demonstrated, limiting organ damage and preventing premature death. Deferoxamine has a very short half-life in plasma (5–10 min). Therefore, deferoxamine is administered parenterally, either subcutaneously 8–12 h a day at least 5 days a week, or intravenously, through a Port-a-Cath for continuous infusion. Low compliance with the prolonged subcutaneous administration is the main reason of ineffective treatment. Toxic side effects such as auditory, ocular, and neurotoxic abnormalities are also drawbacks in the therapy.
Deferiprone (Ferriprox) is one of the hundreds of oral chelators that have been tested. It was shown to have comparable efficacy to subcutaneous deferoxamine. It has been used as a monotherapy or in combination with deferoxamine [7, 9]. It increases urinary excretion of iron and is more effective than deferoxamine in the removal of excess iron from the heart, as shown from MRI T2* studies [1, 9, 10, 11]. Deferiprone and its iron complex are cleared from the plasma with a half-life of 47–134 min. Therefore, it should be taken three times a day. The major toxic side effects are agranulocytosis, musculoskeletal and joint pains, gastric intolerance, and zinc deficiency. Those side effects are considered to be reversible. The combination of deferoxamine and deferiprone is most effective when used in the following way: deferiprone (80–110 mg/kg per day) during the day and deferoxamine (40–60 mg/kg per day) at least 3 nights a week. No new toxic effects have been reported for the combination.
Deferasirox (Exjade) has been tested recently in clinical trials [2, 3, 12]. Its long half-life makes it suitable for once-daily oral administration. Efficacy at a dose of 20–30 mg/kg per day is at least equivalent to that of deferoxamine (≥35 mg/kg per day 5 days weekly) in a subgroup of patients with higher hepatic iron burdens. The most common toxic side effects are skin rash, increase in transaminases and creatinine, and gastrointestinal symptoms such as nausea, vomiting, diarrhea, and abdominal pain. The skin rash and gastrointestinal symptoms tend to decrease spontaneously with time and an increase in creatinine may require a dose adjustment. The increase in transaminases is usually linked to an insufficient dose of deferasirox and an increase in the hepatic iron overload. A higher dose of deferasirox will result in an improvement in transaminases.
Discussion
Chronic iron overload due to blood transfusions leads to significant morbidity and early mortality unless adequate chelation therapy is administered. Deferoxamine is the reference chelation therapy, but compliance to the treatment is often poor because it must be administered by prolonged subcutaneous or intravenous infusion. Oral chelators are now available: Deferasirox has the advantage of being administered once daily, with a low toxicity profile. Furthermore, its cost effectiveness has also been demonstrated [5]. MRI has emerged as the dominant technique by which to evaluate iron overload because of its sensitivity, reproducibility, and ability to image multiple organs in the body during a single imaging session.


Biomechanics of Traumatic Brain Injury: Influences of the Morphologic Heterogeneities of the Cerebral Cortex

Traumatic brain injury (TBI) can be caused by accidents and often leads to permanent health issues or even death. Brain injury criteria are used for assessing the probability of TBI, if a certain mechanical load is applied. The currently used injury criteria in the automotive industry are based on global head kinematics. New methods, based on finite element modeling, use brain injury criteria at lower scale levels, e.g., tissue-based injury criteria. However, most current computational head models lack the anatomical details of the cerebrum. To investigate the influence of the morphologic heterogeneities of the cerebral cortex, a numerical model of a representative part of the cerebral cortex with a detailed geometry has been developed. Several different geometries containing gyri and sulci have been developed for this model. Also, a homogeneous geometry has been made to analyze the relative importance of the heterogeneities. The loading conditions are based on a computational head model simulation. The results of this model indicate that the heterogeneities have an influence on the equivalent stress. The maximum equivalent stress in the heterogeneous models is increased by a factor of about 1.3–1.9 with respect to the homogeneous model, whereas the mean equivalent stress is increased by at most 10%. This implies that tissue-based injury criteria may not be accurately applied to most computational head models used nowadays, which do not account for sulci and gyri.
Introduction
The brain is often one of the most seriously injured parts of the human body in case of a road traffic crash situation.1,21,41 The incidence rate and mortality rate in Europe are estimated to be 235 and 15.4 per 100,000 of the population per year, respectively.41 Traumatic brain injury (TBI) is therefore considered as a widespread problem. Understanding the mechanisms inducing TBI is necessary for reducing the number of occurrences, e.g., by developing more appropriate protective systems and diagnostic tools.
Brain injury criteria are used for the assessment of the probability of TBI for certain mechanical loading conditions. The most commonly used injury criterion in the automotive industry is the Head Injury Criterion (HIC).16,43 It is developed to predict TBI resulting from a translational acceleration of the head. One of the drawbacks of the HIC is that it is based on global kinematic data to predict TBI, whereas actual brain damage is caused at the cellular level as a consequence of tissue strains and stresses.35 Furthermore, it is based on experimental data, in which only anterior–posterior contact loading has been applied to human cadavers, not accounting for angular accelerations of the head. For a better approximation of the relation between TBI and a mechanical load, more advanced methods have been developed. For instance, three-dimensional finite element (FE) head models have been developed to predict brain injury.2,6,7,9,17,22,38,42,45,47 With these numerical head models, different injury mechanisms and loading conditions can be distinguished. However, in these models, the heterogeneous anatomy of the cerebrum is usually represented by a relatively homogeneous geometry. A comparison between the homogeneous geometry of a typical finite element head model and the complex structure of a real brain is given in Fig. 1. The main function of the heterogeneous morphology is to increase the cortical surface in order to obtain a more complex level of the brain functions.34 The most recent numerical head models include ventricles and the invaginations of the dura mater, but none include the convolutions of the cerebral cortex. Consequently, the stresses and strains that are predicted from these models likely do not represent actual tissue stresses and strains, at least in the cortex. Therefore, although tissue-based injury criteria may be used, their accuracy is expected to be limited. This might prohibit the direct use of tissue-based injury criteria. Such criteria predict injury at the tissue level and are based on in vitro and in vivo experiments.3,4,10,11,13,31,32 For a direct application of tissue-based injury criteria in a computational head model, a more detailed description of the biomechanical behavior of the cerebrum may be required, which can be achieved by including its morphologic heterogeneities in these models. A few two-dimensional FE models of the brain containing the convolutions of the cerebral cortex have been described in literature. Miller et al.30 compared different modeling techniques for the relative motion between the brain and the cranium. Nishimoto and Murakami33 developed a model to investigate the relation between brain injury and the HIC. However, these models have not been developed with the purpose of investigating the local biomechanics at the level of these convolutions. No conclusions have been drawn from these studies on the biomechanical influence of the heterogeneities of the cerebral cortex, due to the limited spatial resolution of the mesh.Figure 1(a) Numerical head model developed by Claessens.6,7 (b) Lateral view of the human brain. Adapted from Welker et al.44
Physical experiments have been conducted in several studies to investigate the biomechanical consequences of the heterogeneities of the cerebrum.14 In a study by Bradshaw et al.,5 a gel-filled chamber that represented the brain and skull in a coronal plane including the falx cerebri and the sulci of the cerebral cortex was subjected to a rotation with a peak acceleration of approximately 7800 rad s−2. An increase of the maximum principle strain in the cerebral cortex due to the sulci was found.
The aim of this study is to investigate the biomechanical influences of the morphologic heterogeneities in the cerebral cortex. To achieve this, several two-dimensional FE models with detailed geometries of a part of the cerebral cortex have been developed. Also, an FE model with a homogeneous morphology of the cortex has been made. The loading conditions are based on simulations with a computational head model as used by Brands et al.6 The results of the simulations of the heterogeneous models will be compared to those of the homogeneous model.
Methods
In this study, plane strain models of small sections of the cerebrum are made using the FE code Abaqus 6.6-1 (HKS, Providence, USA). An explicit time integration is used, anticipating a dynamic load with a high magnitude and a short duration. The time increments are limited by the stability condition, which is determined in the global estimator function in Abaqus.
Geometries
To investigate the influence of the heterogeneities of the cerebral cortex, a homogeneous model and three heterogeneous models have been developed. The heterogeneous models, which are shown in Figs. 2a, 2c, and 2d have detailed geometries of a small part of the cerebrum, including also a part of the cerebrospinal fluid (CSF). The cranium is modeled by a boundary constraint, as will be detailed further on. Since the dura mater and the arachnoid are connected to the inside of the cranium in the region that is modeled,29,34 it is assumed that they can be ignored for this situation. The pia mater, which is a thin and delicate membrane covering the brain,29,34 is also not included, since it is expected to have no mechanical influence for the used loading conditions. The same assumption is used for the arachnoid trabeculae, which extend from the arachnoid to the pia mater and are less existent inside the sulci.34Figure 2(a) Heterogeneous geometry 1 and (b) its spatial discretization. (c) Heterogeneous geometry 2. (d) Heterogeneous geometry 3. (e) Homogeneous geometry
The first geometry has one narrow sulcus on the right hand side and a small part of a sulcus on the left hand side. The second geometry contains two deeper and wider sulci than the other two geometries. The third geometry consists of one vertical sulcus and one partly horizontal sulcus, where horizontal and vertical refer to the x- and y-direction, respectively. These geometries, which represent typical stylized shapes of the cerebral cortex, are based on the topological studies by Mai et al.27 The left and right boundaries of the models are chosen to be periodic, i.e., the internal geometries near the opposite boundaries match. The periodicity of the boundary conditions will be explained further on. The models do not distinguish between gray (cerebral cortex) and white matter. In Fig. 2e, the homogeneous model is shown. Similar to the heterogeneous models, it also consists of CSF and brain tissue, but it does not contain any gyri and sulci. The outer dimensions of each model are 32 mm by 24 mm. The meshes consist of bi-linear, quadrilateral, reduced integration elements with hourglass control. The heterogeneous models also contain a small number of triangular elements. The total number of elements of the heterogeneous models ranges from 4243 to 4533 elements. The homogeneous geometry consists of 3072 elements.
Material Properties
For the material properties of the CSF, a nearly incompressible, low shear modulus elastic solid has been assumed, since the shear stress in the brain tissue due to the applied loading conditions is estimated to be about a factor 104 higher than that in the CSF. The material properties are listed in Table 1. The shear modulus of CSF is estimated from the loading conditions that are described further on by using  in which G is the elastic shear modulus, η is the viscosity, γ is the estimated shear strain, and  is the estimated shear rate. Because two different loading conditions have been used, also two different estimates for the CSF shear modulus have been used. However, with these shear moduli being much lower than that of the brain tissue, the exact values of these estimates do not affect the outcome of this study. The bulk modulus is obtained from literature.2,47Table 1Linear material parameters Bulk modulus (GPa)Shear modulus (Pa)Time constant (s)CSF2.20.036a∞0.12b∞Brain tissue2.5182.9∞98840.00013835.50.012231.20.3567.14.623.6112.12.7954.3aShear modulus in case of loading condition AbShear modulus in case of loading condition B
The material properties of the brain tissue are described by a non-linear viscoelastic constitutive model that has been developed by Hrapko et al.19 This model was found to accurately describe the response of brain tissue for large deformations in both shear and compression. This model is extended here to account for compressibility.
The constitutive model consists of an elastic part, denoted by the subscript ‘e’, and a (deviatoric) viscoelastic part, denoted by the subscript ‘ve’, with N viscoelastic modes. The total Cauchy stress tensor  is written asin which the superscripts ‘h’ and ‘d’ denote the hydrostatic and the deviatoric part, respectively. For simplicity, the subscript i indicating the number of the viscoelastic mode will be omitted from this point on. The hydrostatic part of Eq. (1) is defined aswhere K is the bulk modulus and  is the volume change ratio.
The deviatoric elastic mode describes a non-linear response to the deformation gradient tensor F, which is given bywhere G∞ is the elastic shear modulus,  is the isochoric part of the Finger tensor B, and  and  are the first and second invariant of the isochoric Finger tensor  respectively. A, C, and b are fitting parameters describing the non-linearity of the elastic response.
The third term on the right hand side of Eq. (1) consists of the summation of the viscoelastic modes. The deformation gradient tensor F is partitioned into an elastic deformation gradient tensor  and a viscous deformation gradient tensor  by assuming multiplicative decomposition25,36:
The decomposition involves a fictitious intermediate state, which could exist after application of merely the viscous deformation gradient tensor  This is the stress-free state, which after application of the elastic deformation tensor  transforms into the final state. The third term on the right hand side of Eq. (1) describes the viscoelastic contribution to the stress as follows:with G the shear modulus,  the isochoric part of the elastic Finger tensor  and a a fitting parameter.
The viscous deformation  is assumed to be volume-invariant, i.e.,  and  The viscous rate of deformation tensor is calculated from the flow rule aswhere the dynamic viscosity η is a function of the scalar equivalent stress measure  for which the Ellis model is adopted:with subscripts 0 and ∞ denoting the initial and infinite values, respectively. The initial value for the viscosity is defined as η0 = Gλ, whereas the infinite viscosity is defined as η∞ = kη0. Here, λ refers to the time constant.
Although differences between the material properties of the gray and white matter may exist, these differences are not well characterized. Therefore, no distinction between gray and white matter has been made in this study, except for the investigation of the influence of varying the material properties of gray matter with respect to those of white matter (see the Discussion and Conclusions). For simulating a head impact situation representative of road traffic accidents, an extra viscoelastic mode with a smaller time constant has been added to the behavior as characterized by Hrapko et al.19 The extra mode18 is based on the experimental data from Hrapko and co-workers in combination with the data by Shen et al.39 The linear material properties are listed in Table 1. The values of the non-linear viscoelastic parameters are shown in Table 2.Table 2Non-linear material parameters for brain tissueElasticViscousA = 0.73τ0 = 9.7 PaC = 15.6n = 1.65a = 1k = 0.39b = 1
Boundary Conditions
The boundary conditions have been chosen such that they represent the biomechanical influences of the surroundings on the cerebral cortex model. Figure 3 shows the labeling of the corner nodes and the boundaries. The symbols x and y denote the components of position vector  with respect to a Cartesian vector basis  whereas u and v are the components of the displacement vector  with respect to this basis.Figure 3Labeling of corner nodes and boundaries
The Young’s modulus of the cranium is much higher than that of brain tissue.19,46 Still, in a contact loading situation of the head the deformation of the skull is important, because it initiates strain waves in the brain tissue. In this study, however, only inertial loading of the head is considered and therefore the cranium is assumed to be rigid. The cranium is incorporated in the boundary condition at Γ3. Because of the low shear modulus of the CSF, the influence of the rigid constraint associated with the cranium at boundary Γ3 in the x-direction can be neglected. Provided no rotation of the model occurs, the constraint equation for all nodes on boundary Γ3 iswith vs the vertical displacement of the skull.
The boundaries Γ2 and Γ4 are subjected to periodic boundary conditions23:
These constraints imply that throughout the deformation process the shapes of the opposite boundaries, Γ2 and Γ4, remain identical to each other, while the tractions on opposite boundaries are opposite to satisfy stress continuity, which can be written aswith  the Cauchy stress tensor and  the unit outward normal vector of boundary Γi.
The lower boundary, Γ1, of the brain tissue in the model lies adjacent to brain tissue in neighboring regions. Therefore, boundary Γ1 has to be constrained accordingly. The applied constraint on Γ1 is obtained by tying all nodal displacements on Γ1 to a linear interpolation between the displacements of corner nodes C1 and C2. For any node on boundary Γ1, this results inwith the subscript 0 denoting the initial configuration. The displacements of corner nodes C1 and C2 are prescribed and calculated from the applied loading conditions.
The loading conditions of the cerebral cortex model (micro-level in Fig. 4) are based on the loading conditions that have been used by Brands et al.6 for a three-dimensional numerical head model (macro-level in Fig. 4). In that model, an eccentric rotation has been applied to the skull to simulate an angular head acceleration around the neck-shoulder joint in the sagittal plane in the anterior–posterior direction. The eccentricity has been chosen to represent a typical neck length. The axis of rotation has been positioned at 155 mm below the anatomical origin, i.e., the ear hole projected to the sagittal plane. The rotation of the head model consists of two successive sine functions that describe the angular acceleration:Figure 4The loading conditions of the cerebral cortex model (micro-level) are derived from the region of interest in a parasagittal cross-section (15 mm offset from the midsagittal plane) of the head model (macro-level). Shown at the macro-level is the equivalent stress field of the head model at 10 ms
In Eqs. (12) and (13), the angular acceleration  is given in rad s−2.
The loading conditions are applied to the cerebral cortex model by means of body forces. In all integration points of the elements in the model, a non-uniform body force is imposed that reversely simulates the inertial forces:in which  represents the distributed load per unit of volume, ρ is mass density, t is time, and  refers to the acceleration in the x-direction that is represented by these body forces. Note that for the head model the loading conditions contain an angular component, whereas the cerebral cortex model uses translational loading conditions. Because only a small part of the head is modeled and because of the small rotation of the head model with a maximum of 4°, the loading of the cerebral cortex model is assumed to be translational in x-direction only.
The loading conditions of the cerebral cortex model, i.e., the representative accelerations  are calculated from the head model (from the region indicated in Fig. 4) in two different approaches: In the first approach, the input accelerations of the head model are used to define the loading condition of the cerebral cortex model. This approach will be referred to as loading condition A.The translational acceleration  can be calculated usingwith  the angular acceleration, which is defined by Eqs. (12) and (13), and r the radius from the axis of rotation (neck-shoulder) in the head model to a point in the region of interest. The radius r is a function of the y-position in the cerebral cortex model. It varies between r(0) = 0.251 m at boundary Γ1 to r(0.024) = 0.275 m at Γ3. The accelerations  at Γ1 and Γ3 are depicted in Fig. 5a. All other accelerations are interpolated linearly between these two boundaries, thereby creating a gradient across the height of the model. The acceleration gradient is important for the resulting shear stresses. Figure 5b shows the acceleration profile of the cerebral cortex model. The accelerations are used to calculate the body forces as a function of both time and y-position.The disadvantage of this loading condition is that a spatially constant acceleration gradient is assumed and therefore it does not account for the influence of the geometry of the cranium. To account for the geometry of the head, another loading condition has been developed that is described next.The second approach, loading condition B, uses output accelerations from a global head model simulation as the input of the cerebral cortex model. For this, a modified version of the head model, as used by Brands et al.,6 has been employed in the simulation code Madymo, in which the constitutive model for brain tissue by Hrapko et al.19 has been implemented. The accelerations obtained from the region inside the box in Fig. 4 from the head model are imposed on the cerebral cortex model. Hence, the influence of the geometry of the head is modeled indirectly by means of an acceleration profile that is obtained from the head model.The displacements of the brain tissue in the head model in the field of interest are almost entirely in the x-direction justifying the assumption of inertial loading (of the cerebral cortex model) in the x-direction only. In Fig. 6, the acceleration profiles as a function of the y-position are shown at 5, 10, and 20 ms. Similar to loading condition A, the accelerations are used to calculate the body forces as a function of both time and y-position.Figure 5Loading condition A. (a) Acceleration at the upper and lower boundary of the cerebral cortex model. (b) Acceleration profiles at different timesFigure 6Loading condition B: displacement (top) and acceleration (bottom) profiles derived from the output of the head model
In order to quantify the influence of the morphologic heterogeneities, the equivalent stress  is used, in which  is the deviatoric part of the Cauchy stress tensor  The equivalent stress is chosen, because the simulations are based on an angular acceleration of the head, in which deviatoric stresses are considered to be the most important.30 The maximum principal strain is considered important as well with respect to diffuse axonal brain injury.3,30 Therefore, also the maximum principal logarithmic strain is used to quantify the influence of the morphologic heterogeneities.
Results
Figure 7 depicts the development in time of the equivalent stress fields for the homogeneous model (top row) and the heterogeneous models from the simulation with loading condition A. Stress concentrations are present in the heterogeneous models at the surface of the brain tissue between two gyri at 5, 10, and 20 ms. Near boundary Γ1, all heterogeneous models have lower equivalent stresses compared to the homogeneous model at 20 ms.Figure 7The equivalent stress fields as a result of loading condition A
In order to obtain a good comparison of the results for all geometries during the complete simulation time, the maximum and mean equivalent stress from the simulations with loading condition A are shown in Fig. 8 as a function of time. It shows the stresses in the brain tissue only. It can be noticed that the heterogeneous models have a higher maximum equivalent stress than the homogeneous model. Among the heterogeneous configurations, geometry 1 causes a noticeably lower maximum equivalent stress of 112 Pa compared to geometries 2 and 3, with a maximum equivalent stress of approximately 156 Pa. The large maximum equivalent stress in heterogeneous geometry 2 lasts longer than the stresses of the other geometries. The maximum equivalent stress of the homogeneous model reaches a value of 80 Pa. The mean equivalent stresses are nearly the same for all geometries.Figure 8Maximum and mean equivalent stress for the heterogeneous and homogeneous models as a result of loading condition A
To investigate the influence of the heterogeneities, the equivalent stress of the cerebral cortex in the heterogeneous models is taken relative to that of the homogeneous model. For the maximum equivalent stress, this will be done by taking the maximum values, whereas for the mean equivalent stress, this will be done by taking the time averaged values. The maximum equivalent stress of the heterogeneous models 1, 2, and 3 is 1.31, 1.84, and 1.83 times higher than the homogeneous model, respectively. The mean equivalent stress of the heterogeneous models 1, 2, and 3 with respect to the homogeneous model is 1.09, 1.08, and 1.10, respectively.
The equivalent stress fields obtained with loading condition B are displayed in Fig. 9. During the beginning of the simulation, the equivalent stress fields in the brain tissue are comparable for all models. When the field of higher equivalent stress moves downwards, the heterogeneities result in local peak stress concentrations, which can be seen at 10 ms for geometries 1 and 3. Later on, at 20 ms, the heterogeneous geometries 1 and 3 have less influence on the equivalent stress fields. The differences of geometry 2 with respect to geometries 1 and 3 are a consequence of the deeper sulci in geometry 2.Figure 9The equivalent stress fields as a result of loading condition B
The maximum and mean equivalent stress of the cerebral cortex as a function of time obtained with loading condition B is shown in Fig. 10. The maximum equivalent stress is higher for the heterogeneous models than for the homogeneous models, but not for the complete duration of the simulation. After about 10–15 ms, the maximum equivalent stress of the heterogeneous models drops to approximately the same magnitude as the one obtained for the homogeneous model. For the heterogeneous models, the maximum equivalent stress reaches values of approximately 470, 565, and 624 Pa for geometries 1, 2, and 3, respectively. The homogeneous model has a maximum equivalent stress reaching 325 Pa. Also, the moment in time at which the maximum occurs differs from one geometry to the other. The mean equivalent stress values of all the geometries are similar.Figure 10Maximum and mean equivalent stress for the heterogeneous and homogeneous models as a result of loading condition B
To quantify the influence of the heterogeneities, the equivalent stress of the brain tissue of the heterogeneous models is taken relative to the homogeneous model in the same manner as described previously for loading condition A. The maximum equivalent stress of the heterogeneous models 1, 2, and 3 has increased by 1.44, 1.74, and 1.92 with respect the homogeneous model, respectively. The mean equivalent stress of the heterogeneous models 1, 2, and 3 is 0.97, 0.99, and again 0.99 relative to the homogeneous model, respectively.
The distribution of maximum principal strains for loading condition B at 10 ms is shown in Fig. 11. One can notice that a concentration of maximum principal strains at 10 ms occurs in the same location as the equivalent stress concentration at 10 ms (Fig. 9), both in case of loading condition B. The same method for the quantification of the influence of the heterogeneities is used, but with the maximum principal strain instead of the equivalent stress. For the simulations with loading condition A, the peak maximum principal strain in the brain tissue of the heterogeneous models 1, 2, and 3 has increased with respect to the homogeneous model by 1.22, 1.92, and 1.80, respectively. If loading condition B is used, the increases are 1.43, 1.84, and 1.90, respectively.Figure 11The maximum principal logarithmic strain field as a result of loading condition B at 10 ms
Discussion and Conclusions
In this study, the influences of the heterogeneities in the cerebral cortex were investigated. This was done with FE models of several different geometries from small detailed parts of the cortex. In a preliminary study, the boundary constraints were tested. The loading conditions were derived from a numerical head model.
In order to determine which constraints on the boundaries would represent the surroundings best, a preliminary study was conducted in which several different constraints were applied to boundaries Γ1 and Γ3. The different conditions on boundary Γ3, i.e., rigid constraint or slip-condition in the x-direction, did not contribute to differences in the results of the brain tissue. This was probably caused by the low shear modulus of the CSF. For boundary Γ1, several different boundary conditions were tested and compared to the results of models with the same width, but with twice the height of the models in this study. By comparing the ‘normal’ and the ‘high’ models, the boundary condition at boundary Γ1 that represented adjacent brain tissue could be determined. The periodic boundary condition that was applied to boundaries Γ2 and Γ4, was used because of the assumed periodicity in the cerebral cortex.
Since the loading conditions were dynamic they could not be applied to the model by directly imposing a deformation, which would induce boundary effects. For this reason, an indirect deformation was imposed by means of body forces. This approach worked well for the first 20 ms of the simulation, which had a total duration of 30 ms. After 20 ms the deformations of the brain tissue differed from the deformations derived from the head model that were indirectly applied to the model (Figs. 6 and 9). Therefore, only the first 20 ms of the simulation are considered to be realistic.
Loading condition A was derived from the acceleration pulse that has been applied to a head model.6,12 The equivalent stress fields of the homogeneous models caused by loading condition A (Fig. 7) showed no similarities to the equivalent stress field of the head model (Fig. 4). Provided that the equivalent stress field of the head model is realistic, loading condition A can be considered unrealistic. Loading condition B was obtained from the resulting accelerations in the region of interest of the head model. The equivalent stress fields of the homogeneous models from simulations with loading condition B (Fig. 9) were approximately similar to the stress fields in the corresponding region of the head model (Fig. 4) during the first 20 ms. Nevertheless, differences in the equivalent stress fields existed at the surface of the cortex. This is due to the CSF layer in the cerebral cortex model being described elastically with a low shear modulus, as opposed to the head model, which contains a relatively stiff CSF/dura layer. In spite of all these obvious differences, by comparing these two loading conditions, it was shown that the different loading conditions have hardly any effect on the relative mean and maximum equivalent stress and the relative peak maximum principal logarithmic strain. Hence, the mechanical influences of the heterogeneities of the cerebral cortex seem to be independent of the loading conditions.
The constitutive model for brain tissue was based on experiments on porcine white matter.19 This was used as a substitute for human brain tissue, as it was readily available and it allowed to conduct experiments with a shorter post-mortem time.12,26 Experiments conducted by Prange et al.37 on fresh human brain tissue indicated that its mechanical properties are approximately 30% stiffer than those of fresh porcine brain tissue. Although the constitutive model for brain tissue in the cerebral cortex model was not based on human brain tissue, the geometries of the models were based on the human cerebrum. This assumption is expected to have more effect on the absolute equivalent stress than on the relative equivalent stress, which is dominated by the heterogeneities of the model. Furthermore, no distinction between the mechanical properties of white and gray matter was made, so that the material properties of the cerebral cortex, which consists of gray matter, were based on experiments on white matter, as well. In their results, Prange et al.37 found differences between the material properties of the corpus callosum, i.e., the white matter that connects the two cerebral hemispheres, and the cerebral cortex, but not between the corona radiata, i.e., the white matter that lies inferior to the cerebral cortex, and the cerebral cortex. Nevertheless, in several other studies of head and brain models, different material properties were used for gray and white matter based on the assumption that white matter was more fibrous than gray matter.2,28,30,47 Therefore, simulations of the cerebral cortex model with shear moduli for gray matter ranging from 75% to 125% with respect to the shear moduli white matter were conducted as well. The results of these simulations indicated that the material properties had an effect on the magnitude of the equivalent stress, but not on the regions in which the peak equivalent stress was observed. The equivalent stress of the heterogeneous model relative to the homogeneous model was hardly affected by the different material properties.
In this model, the meninges and the blood vessels were considered to have no mechanical influence on the cerebral cortex for the used loading conditions. Although Jin et al.20 suggested that the pia-arachnoid complex can have a mechanical influence on the brain tissue during an impact, it is still not clear if it would affect the influence due to the gyrification of the cerebral cortex. In a study performed by Ho and Kleiven,17 it was found that the vasculature of the brain can be neglected, as far as the mechanical influences are concerned in a head model not containing the gyrification of the cortex.
In order to validate this model, a comparison with physical experiments is required. However, data of physical experiments at a typical length scale of this level is rather limited. Parallel to this study, the results of physical experiments, in which brain slices have been accelerated, showed increased equivalent strains near the sulci.24 Furthermore, some studies showed that angular accelerations of the head induce high stress concentrations in and near the cerebral cortex.8,15 Another method of validation can be performed by comparing the results of the model to clinically observed injury. It has been shown that small cortical infarcts exist in diffuse brain injury at the bottom of the sulci.40 This is in accordance with the locations of the high stress and strain regions in the cerebral cortex model.
The two loading conditions and the different geometries resulted in different equivalent stress fields. The simulations with loading condition A resulted in a lower mean and maximum equivalent stress compared to the simulations with loading condition B. However, relative to the homogeneous model, it was observed that the equivalent stress was almost independent of the different loading conditions used in this study. The differences between the several heterogeneous geometries had more influence on the relative mean and maximum equivalent stress. The morphologic heterogeneities of the cerebral cortex led to an increase of the maximum equivalent stress by a factor of about 1.3–1.9, depending mostly on the geometry, whereas the relative mean equivalent stress values of all the geometries were 1.1 and 1.0 for loading condition A and B, respectively. Furthermore, the peak maximum principal logarithmic strain was increased by a factor of about 1.2–1.9 due to the morphologic heterogeneities of the cerebral cortex. This is a strong indication that predictions of brain injury obtained from head models with a homogeneous cerebrum should be interpreted with care. To obtain a more accurate assessment of injury, the influence of the morphologic heterogeneities in the cerebral cortex should be accounted for.

traumatic brain injury
cerebral cortex
finite element model
cerebrum
brain tissue
inhomogeneities