In-line filtration of intravenous infusion may reduce organ dysfunction of adult critical patients
The potential harmful effects of particle-contaminated infusions for critically ill adult patients are yet unclear. So far, only significant improved outcome in critically ill children and new-borns was demonstrated when using in-line filters, but for adult patients, evidence is still missing.
This single-centre, retrospective controlled cohort study assessed the effect of in-line filtration of intravenous fluids with finer 0.2 or 1.2 μm vs 5.0 μm filters in critically ill adult patients. From a total of n = 3215 adult patients, n = 3012 patients were selected by propensity score matching (adjusting for sex, age, and surgery group) and assigned to either a fine filter cohort (with 0.2/1.2 μm filters, n = 1506, time period from February 2013 to January 2014) or a control filter cohort (with 5.0 μm filters, n = 1506, time period from April 2014 to March 2015). The cohorts were compared regarding the occurrence of severe vasoplegia, organ dysfunctions (lung, kidney, and brain), inflammation, in-hospital complications (myocardial infarction, ischemic stroke, pneumonia, and sepsis), in-hospital mortality, and length of ICU and hospital stay.
Comparing fine filter vs control filter cohort, respiratory dysfunction (Horowitz index 206 (119–290) vs 191 (104.75–280); P = 0.04), pneumonia (11.4% vs 14.4%; P = 0.02), sepsis (9.6% vs 12.2%; P = 0.03), interleukin-6 (471.5 (258.8–1062.8) ng/l vs 540.5 (284.5–1147.5) ng/l; P = 0.01), and length of ICU (1.2 (0.6–4.9) vs 1.7 (0.8–6.9) days; P < 0.01) and hospital stay (14.0 (9.2–22.2) vs 14.8 (10.0–26.8) days; P = 0.01) were reduced. Rate of severe vasoplegia (21.0% vs 19.6%; P > 0.20) and acute kidney injury (11.8% vs 13.7%; P = 0.11) was not significantly different between the cohorts.
In-line filtration with finer 0.2 and 1.2 μm filters may be associated with less organ dysfunction and less inflammation in critically ill adult patients.
The study was registered at ClinicalTrials.gov (number: NCT02281604).
KeywordsIntensive care Infusion management In-line filtration Particles Organ dysfunction Inflammation
Acute kidney injury
Acute Kidney Injury Network
Acute respiratory distress syndrome
Area under the ROC curve
Case report forms/electronical case report forms
Extracorporeal membrane oxygenation
The Intensive Care Delirium Screening Checklist
Intensive care unit
Kidney Disease Improving Global Outcomes
Mann-Whitney U estimator
Statistical analysis plan
Simplified Acute Physiology Score
Systemic inflammatory response syndrome
Sequential Organ Failure Assessment score
Therapeutic Intervention Scoring System
The intravenous administration of both fluids and drugs represents one of the main therapy pillars within intensive care medicine. Contamination with particles, however, and their potential harmful effects, especially for critically ill patients is a suspected issue [1, 2, 3, 4]. Recent studies [5, 6, 7] showed that the particle load is significantly higher without in-line filters and that in-line filtration may be an effective tool in preventing particle contamination to patients. Today, at least three different commercial in-line filter systems are available. The 5.0-μm in-line filter reduces rough particles (glass, rubber, plastic). The potential advantage of the finer positively charged 0.2 and 1.2 μm in-line filters is that they can hold back not only glass, rubber, and plastic particles but also particles from drug incompatibilities, air, microorganisms (bacteria size 1–3 μm), and smaller endotoxins . Therefore, catheter-related bloodstream infections are a potential target of prevention via in-line filtration, too.
In general, potential systemic effects of microparticles on different organs may depend on material, size, amount, and patient population. Recent studies focussed on critically ill children and new-borns showing a significant reduction of complications, e.g. thrombosis, sepsis, and organ failure as well as a reduction of length of stay in ICU [9, 10, 11, 12], while others [13, 14] found no benefits. Furthermore, post-mortem studies on patients with acute respiratory distress syndrome (ARDS) suggest a harmful effect of those particles especially for the lungs and have shown that infusion therapy can lead to a particle-induced mechanic vascular occlusion and to intravascular formation of foreign bodies [15, 16, 17, 18]. In vitro studies demonstrated a particle-induced modulation of the immune system . The German Society of Anaesthesiology and Intensive Care Medicine and the Berufsverband Deutscher Anästhesisten both strongly suggest the use of particle filters with size always adapted to the type of fluid or drug (chosen as small as possible due to the suspected harmful effects of very small particles in the range of 2–100 μm) . Organisations such as the PDA (Parenteral Drug Association) Europe offer regular training for physicians to transfer actual knowledge about potential harmful effects of particles for patients and also participate actively in research activities on particles in parenteral drugs. But the evidence on the benefits for adult intensive care patients is still unclear.
The aim of this study was to elucidate if the use of the finer 0.2 or 1.2 μm in-line filters was superior in comparison to the larger 5 μm in-line filter within the framework of i.v. fluid and drug management for adult critical ill patients.
Materials and methods
This single-centre, retrospective controlled cohort study covering the period between February 2013 and April 2015 assessed the effects of in-line filtration of intravenous fluids on the reduction of complications in critically ill adult patients of a tertiary German hospital.
Approval of the study protocol was obtained from the local ethics committee (Goethe-University Frankfurt am Main, ref. 8/14 from 30 January 2014). The study was registered at ClinicalTrials.gov (NCT02281604). Funding was exclusively provided by internal departmental funding.
Patient enrolment and cohorts
All adult (age ≥ 18 years) critical patients who were admitted to the University Hospital Frankfurt between February 2013 and March 2015 and had at least 1 admission to the 34-bed intensive care unit (ICU) were included. Admission to the ICU for more than one time during the same hospital stay was not an a priori exclusion criteria, and the information of these ICU stays was cumulated. Patients admitted to the hospital more than one time during this period (ranging from returns after weeks to months or years) were not excluded from the study, but only the first hospital stay was used.
While patients admitted to the ICU between February 2013 and January 2014 were assigned to the fine filter cohort (finer 0.2 or 1.2 μm in-line filters; n = 1621), patients admitted between April 2014 and March 2015 were assigned to the control filter cohort (larger 5 μm in-line filters; n = 1594).
To account for differences in patient characteristics (exact matching for surgery group and sex, nearest neighbour matching with respect to age), a propensity score matching was performed, which resulted in the final number of n = 1506 subjects for both cohorts.
No dropout occurred as routine data could be followed up until hospital discharge and no patient consent was needed.
Microparticles are suspected to be responsible for inflammation of any kind that could lead to different organ manifestations. Inflammation on its own can cause vasodilatation via increased NO production, possibly leading to severe vasoplegia, also known as vasodilatory shock. As this complex syndrome can in fact only be assessed clinically by the vasopressor dosage necessary to maintain an acceptable mean arterial blood pressure, we tried to identify patients suffering from this syndrome by choosing high dose norepinephrine as one of the most appropriate potential surrogate parameters .
Primary endpoint of this epidemiological study was the rate of severe vasoplegia [22, 23, 24] defined by a continuous norepinephrine rate ≥ 0.3 μg/kg/min for at least 1 min. Patients that did not receive any administration of norepinephrine reported in the data files were counted as patients with dose zero due to the fact that only records exist if patients did receive norepinephrine. Values of norepinephrine rates > 1.0 μg/kg/min were discarded as unrealistic on our ICU. In addition, we analysed the rate of patients receiving norepinephrine and also methylene blue (which is an additional option to treat vasoplegia) to obtain a more comprehensive overview on this endpoint.
Multiple-organ dysfunction (assessed by maximal SOFA score, additionally SAPS II and TISS-10/TISS-28).
Incidence and severity of lung dysfunction (defined by minimal Horowitz index (obtained by the ratio of the values of paO2 and FIO2) and ARDS score  (obtained by calculating the scores with the standard definition from the minimal Horowitz index of each patient (0–100 = severe ARDS (score 3), 101–200 = moderate ARDS (score 2), 201–300 = mild ARDS (score 1), 301–… = no ARDS (score 0))). Information on duration of mechanical ventilation or airway pressures was not present.
Incidence and severity of acute kidney injury (AKI) was defined by a modified AKIN  score based only on creatinine values before the first dialysis without information on urine output (0 = no AKI, 1 = mild AKI, 2 = moderate AKI, 3 = severe AKI) and by the dialysis rate as albuminuria and glomerular filtration rate were not available in this retrospective study to analyse the current recommended score from KDIGO (Kidney Disease Improving Global Outcome) (https://kdigo.org/).
Incidence of delirium (maximal delirium score defined by ICDSC standard screening).
Grade of inflammation (by maximal interleukin-6 (IL-6) values [27, 28]). Interleukin-6 levels were dosed routinely every 6 or at least 12 h (at 4 am and 4 pm) in our intensive care unit and, according to specific individual needs for a critical patient, additionally also occasionally at individual extra time points in between, whereas other inflammation markers, e.g. C-reactive protein or procalcitonin, were not determined routinely. Blood samples were sent to the central lab where an electro-chemiluminescence immunoassay (ECLIA) test was performed. The measurement usually takes 30–60 min in case of an emergency blood sample. As any unspecific activation of macrophages generally leads to an elevation of IL-6 levels in patients’ serum at first, this marker is sensitive in detecting ongoing inflammation .
Length of stay on ICU (cumulated time in days defined by admission/discharge dates and times) and in hospital (time in days defined by admission/discharge date and time).
In-hospital mortality over 28 days and over total hospital stay (defined by discharge code).
Rate of in-hospital complications and morbidities (acute myocardial infarction, acute ischemic stroke, pneumonia, sepsis, defined by their ICD-10 codes from diagnoses).
Incidence and severity of lung/cardiac dysfunction (assessed by rate of ECMO and cumulated ECMO time, defined by start/end dates and times).
Patients who need higher norepinephrine rates might also suffer from sepsis or septic shock, but especially, inflammation after cardiopulmonary bypass triggers vasoplegia as well. In contrast, the diagnosis sepsis is reasonably different from inflammation and vasoplegia; therefore, we analysed these two endpoints independently.
Data collection and statistical analysis
Routine data were obtained from the hospital information systems ORBIS (AGFA HealthCare, Bonn, Germany)  and the electronic patient data management system Metavision (iMDsoft, Tel Aviv, Israel) , which was first established in 2013 on the ICU. No additional study documentation on CRFs/eCRFs was performed. The documentation of the patient-related medical data was performed by using a pseudonymised patient ID or case number. No characteristics that would allow the direct identification of certain patients during the data analysis were transmitted. The study data were protected from foreign access, and only members of the study team had access to it.
The exported data were screened for apparent inconsistencies (e.g. inverted start/end dates, values being clearly out of the normal reference range by many orders) and missing information (e.g. type of discharge, measurements, medication) to detect documentation errors. Missing information was corrected manually, where possible, based on the original full patient file from the source hospital information systems AGFA ORBIS or Metavision. Patients with missing laboratory or score values for an endpoint were only excluded from that endpoint but not from the study. For patients not mentioned in the therapeutic intervention data (e.g. norepinephrine, dialysis), it was assumed that those patients had no intervention (e.g. zero doses, no dialysis). For patients with cardiac surgery, the potential event of myocardial infarction was not taken into account, as well as for patients with neurosurgery not the potential event of ischemic stroke (such patients were treated as having no event) to exclude potential pre-existing diseases.
The information about different ICU stays during the same hospital stay was then summarised, and patients were assigned to either the fine filter cohort (if all ICU stays were during the fine filter period), the control cohort (if all ICU stays were during the control filter period), or excluded (if neither of the two).
Statistical analysis was performed with the use of the statistic software R (R Foundation for Statistical Computing, Vienna, Austria) version 3.4.1  with the package “MatchIt” version 3.0.2 for propensity score matching. Additional analysis was performed with BiAS (epsilon-Verlag, Frankfurt/Main, Germany, maintained by Institute of Biostatistics, Goethe University Frankfurt, Germany) for Windows version 11.06 . Although if due to the large number of patients per cohort that depended only on the time period, the patient characteristics were most likely already approximately equally distributed in each cohort, we aimed to minimise potential bias and effects in the outcomes as best as possible. Therefore, in the total cohort of n = 3215 patients, a propensity score matching adjusting pairwise for the main characteristics—surgery group, sex, and age in each cohort—was introduced that lead to a total of n = 3012 patients (n = 1506 each per fine filter and control filter cohort). The propensity score matching was performed completely independent from the later outcome variables (such as Horowitz, creatinine, IL-6), and especially, no patient selection was done. For other minor characteristics (such as scores or baseline values), no matching was performed, as this would need an even much higher amount of patients to match for more characteristics equally in both cohorts. Further pre-tests (Shapiro-Wilk) were performed to determine if any of the endpoints or their logarithmised values followed a normal distribution. As deviations from normal distribution could be confirmed in all tests for the continuous endpoints, the Mann-Whitney U tests were performed for group comparisons.
There was no need to perform any significance correction for multiple testing, as our scope was to analyse the outcomes for different secondary endpoints independently from each other and from the main endpoint.
The main hypothesis was to test if the use of the finer 0.2 or 1.2 μm in-line filters was superior in comparison to the larger 5 μm in-line filter with respect to the incidence rate of severe vasoplegia.
For sample size estimation, a risk reduction of 20% by the usage of the finer 0.2 or 1.2 μm in-line filters from an incidence rate of 10 to 8% for the primary endpoint was considered as clinically relevant. This difference could be detected with a chi-square test with at least 80% power and a significance level of α ≤ 0.05, if about n = 1400 subjects were enrolled in each cohort, as calculated by G*Power (Heinrich Heine University of Duesseldorf, Germany)  in the original study analysis plan. The evaluation of the therapy effect was performed by a logistic regression (with regressor variables age, sex, surgery group, and cohort), and the test decision (for difference between the cohorts) was carried out with the Wald test for the coefficients of the therapy effect with R (package “survey” version 3.33). The risk reduction was quantified as odds ratio (OR) with 95% confidence interval.
The secondary hypotheses were to test if the use of the finer 0.2 or 1.2 μm in-line filters was superior to the use of the larger 5 μm in-line filter regarding the incidence of the secondary endpoints. Risk reduction was quantified as risk ratio (RR) with 95% confidence interval. Both cohorts were compared regarding the differences of incidence rates and their 95% confidence interval (CI), of mean ± standard error (SE) for continuous or median values with quartiles for at least ordinal endpoints. Differing from that, median values were also used for continuous endpoints if their distribution was not approximately symmetric.
In addition, appropriate tests (exact Fisher or chi-square for rates and Mann-Whitney U for all other endpoints) for significance were performed. Effect size was reported as risk ratio (range from 0 to infinity) for binary endpoints and by the Mann-Whitney U (MW) estimator (range from 0 to 1) for at least ordinal endpoints, each with corresponding 95% CI. Where appropriate, additionally, also linear or logistic regression models dependent on cohort, age, sex, surgery group, and baseline values were used to perform a Wald test for significance of the cohort and the related odds ratio was reported where appropriate. The latter models provide the advantage of considering potential influence factors (e.g. baseline values) but are less robust than non-parametric tests, on which we primarily based our results.
Baseline characteristics of patients (propensity score matched)
Fine filter cohort (n = 1506)
Control filter cohort (n = 1506)
68 (58.0–75), 65.7 ± 0.3
66 (57–74), 63.9 ± 0.3
Approximate match, P < 0.01, MW 0.54 (0.52–0.56)
Male sex (n; %)
Exact match, P = 1.00
Surgery groups (n; %)
Exact match, P = 1.00
Visceral and endocrine
Oral and maxillofacial
Discarded for matching (n)
Initial SOFA scoreb
Initial SAPS IIc
< 0.01, MW 0.56 (0.52–0.59)
Initial Horowitz indexf
Initial creatinine value (mg/dl)g
Initial delirium scoreh
< 0.01, MW 0.55 (0.51–0.59)
Initial interleukin-6 value (ng/l)i
Primary outcome parameters (vasoplegia)
Fine filter cohort (n = 1506)
Control filter cohort (n = 1506)
Vasoplegia (n; %)
316; 21.0% (19.0–23.1%)
295; 19.6% (17.6–21.7%)
> 0.20, RR 1.07 (0.93–1.23)
> 0.20, OR 1.05 (0.87–1.27)
Maximum rate of norepinephrine (μg/kg/min)
0.09 (0.03–0.23), 0.16 ± 0.01
0.05 (0.00–0.20), 0.14 ± 0.01
< 0.01, MW 0.58 (0.56–0.60)
Patients receiving norepinephrine (n; %)
1249; 82.9% (80.9–84.8%)
1000; 66.4% (64.0–68.8%)
< 0.01, RR 1.25 (1.20–1.30)
< 0.01, OR 1.17 (1.14–1.21)
Patients receiving methylene blue (n; %)
147; 9.8% (8.3–11.4%)
153; 10.2% (8.7–11.8%)
Secondary outcome parameters (multiple-organ dysfunction, lung dysfunction, acute kidney injury, brain dysfunction)
Fine filter cohort (n = 1506)
Control filter cohort (n = 1506)
Maximal SOFA score
Maximal SAPS II score
Maximal TISS-10 score
Maximal TISS-28 score
Minimal Horowitz index
0.04, MW 0.52 (0.50–0.55)
0.01, MW 0.47 (0.45–0.49)
0, no ARDS (n; %)
1, mild ARDS (n; %)
2, moderate ARDS (n; %)
3, severe ARDS (n; %)
Heart/lung dysfunction (ECMO)
Patients on ECMO (n; %)
30; 2.0% (1.3–2.8%)
38; 2.5% (1.8–3.4%)
Cumulative duration (days) without first day
0.01, MW 0.33 (0.19–0.46)
Cumulative duration (days)
0.02, MW 0.33 (0.20–0.47)
Acute kidney injury (AKI)
Maximal creatinine value before dialysis (mg/dl)
Maximal creatinine value before dialysis per interval (n; %)
> 6.00 mg/dl
Modified AKIN score rate (no AKI/mild AKI vs moderate AKI/severe AKI)
Modified AKIN score per classes
0, no AKI (n; %)
1, mild AKI (n; %)
2, moderate AKI (n; %)
3, severe AKI (n; %)
Dialysis rate (n; %)
235; 15.6% (13.8–17.5%)
247; 16.4% (14.6–18.4%)
Maximal delirium score
Secondary outcome parameters (inflammation, cytokines)
Fine filter cohort (n = 1506)
Control filter cohort (n = 1506)
Maximal interleukin-6 value (ng/l)
0.01, MW 0.47 (0.45–0.49)
Maximal interleukin-6 per interval (n; %)
> 500.00 ng/l
Secondary outcome parameters (length of stay, mortality, in-hospital complications, and morbidity rates)
Fine filter cohort (n = 1506)
Control filter cohort (n = 1506)
Length of stay
ICU stay (days)
< 0.01, MW 0.46 (0.44–0.48)
ICU stay survivors (days)
< 0.01, MW 0.46 (0.44–0.49)
ICU stay non-survivors (days)
In-hospital stay (days)
0.01, MW 0.47 (0.45–0.49)
In-hospital stay survivors (days)
0.01, MW 0.46 (0.44–0.49)
In-hospital stay non-survivors (days)
In-hospital 28-day mortality rate (n; %)
133; 8.8% (7.4–10.4%)
155; 10.3% (8.8–11.9%)
In-hospital mortality rate (n; %)
198; 13.1% (11.5–15.0%)
213; 14.1% (12.4–16.0%)
In-hospital complications and morbidities
Myocardial infarction (n; %)
26; 1.7% (1.1–2.5%)
25; 1.7% (1.1–2.4%)
Ischemic stroke (n; %)
35; 2.3% (1.6–3.2%)
21; 1.4% (0.9–2.1%)
Pneumonia (n; %)
172; 11.4% (9.9–13.1%)
217; 14.4% (12.7–16.3%)
0.02, RR 0.79 (0.66–0.96)
< 0.01, OR 0.73 (0.58–0.91)
Sepsis (n; %)
145; 9.6% (8.2–11.2%)
183; 12.2% (10.5–13.9%)
0.03, RR 0.79 (0.64–0.97)
0.01, OR 0.73 (0.57–0.94)
Composite endpointc (n; %)
405; 26.9% (24.7–29.2%)
437; 29.0% (26.7–31.4%)
0.05, OR 0.84 (0.71–1.00)
The overall in-hospital mortality rate was comparable (13.1% vs 14.1%; P > 0.20). Similarly, the 28-day mortality was comparable (8.8% vs 10.3%; P = 0.19) (Table 5).
Analysing routine ICD-10 codes, typical in-hospital complications were significantly lower in the fine filter cohort (pneumonia 11.4% vs 14.4%, P = 0.02, and sepsis 9.6% vs 12.2%, P = 0.03) (Table 5). For rarer complications such as ischemic stroke (1.7% vs 1.7%; P > 0.20) and myocardial infarction (2.3% vs 1.4%; P = 0.06), no significant difference was found (Table 5) between the two cohorts. In addition, a composite endpoint including in-hospital mortality, myocardial infarction, ischemic stroke, pneumonia, and sepsis showed no significant difference between the two cohorts (26.9% vs 29.0%; P > 0.20) (Table 5).
In our propensity-matched cohort analysis, the routine use of finer 0.2 and 1.2 μm in-line filters compared to larger 5 μm control filters had no beneficial effects on the rate of catecholamines and on AKI, but was associated with decreased markers of inflammation and a decreased risk of respiratory dysfunction, sepsis, pneumonia, and slightly reduced length of ICU and in-hospital stay. In this respect, we postulate a clinical relevance for patients on intensive care units.
Microparticle-contaminated infusions and drugs may have adverse effects in critically ill patients and are suspected to be responsible for unspecific inflammation that might lead to different organ manifestations. Post-mortem studies have demonstrated that a high amount of particles enters the patient if no in-line filters have been used. This might already be challenging for a healthy patient but could be even worse for severely ill patients. So far, no significant benefits of finer 0.2 and 1.2 μm in-line filters in comparison to no filters were found in a previous adult study .
Recent studies including critically ill children and new-borns instead did show a significant reduction of organ dysfunction by using in-line filters (finer 0.2 μm for aqueous solutions and 1.2 μm for lipid-containing mixtures in the filter cohort [9, 10, 11] as well as only 0.2 μm for aqueous solutions but no filters for lipid-containing mixtures in the filter cohort ). Interestingly, all mentioned studies used no filter in the control cohort compared to the larger 5 μm filter as intervention but that was used in our control cohort.
We initially chose to investigate a possible and clinical important manifestation of inflammation, vascular dilatation, leading to severe vasoplegia. However, vasoplegia is a complex syndrome and we tried to identify this by simply choosing high dose norepinephrine as a surrogate parameter. As a limitation, norepinephrine alone might not be the best surrogate for vasoplegia, as other reasons on a surgical ICU such as ongoing blood loss and other forms of shock might have an additional effect on our readout system.
We explain the positive outcome of some secondary endpoints by the fact that these data are also theoretically able to model manifestations of inflammation. It has to be stated that all our readout systems do not have a proven direct link to microparticle infusion. To the best of our knowledge, the underlying mechanisms are not yet understood. The clinical data at this moment only support the hypothesis that infused particles somehow augment a systemic inflammation response. Therefore, specific subgroups of patients with a reduced immune response might especially benefit from the use of in-line filtration with finer 0.2 and 1.2 μm filters. Our additional subgroup analysis showing significantly better results for IL-6 values for the fine filter cohort within the only cardiac surgery group supports this assumption, comparable to results of Sasse et al. . Exaggerated inflammation levels after cardiopulmonary bypass surgery are a well-known phenomenon, and additional triggers of inflammation should be avoided if possible. Based on our additional basic IL-6 kinetics analysis on all included patients, we conclude furthermore that the beneficial effects of the finer in-line filters are more likely given by reducing the maximal IL-6 value than by reducing the peak time to maximal inflammation.
Based on these data, the benefits for (finer) filters will remain uncertain. We hope that prospective studies will address the theory of microparticle-induced inflammation and evaluate the effects of the finer filters.
Our study had several limitations. As the study was retrospective and depended on an automated readout system, no additional data could be gathered afterwards. Besides that, being a mono-centre study, composition of patients could vary in other centres but we assume, as many results hold also for various subgroups, that this difference is not essential. Furthermore, our analysis includes mainly surgical ICU patients. The accuracy of the encoded routine ICD-10 diagnoses in general has not been assessed, but we assume that for this short time period of 2 years, the coding pattern did not change relevantly. A simple intraoperative factor we cannot statistically exclude and which might be a potential bias could be seen in different surgical and anaesthesiologic teams performing the procedures. Therefore, especially, fluid management regarding type and amount could differ between the time periods. Unfortunately, we cannot normalise the amount of fluids administered to body weight, as we do not have all information to types and volumes given. But we assume due to equal pair matching in each cohort that the received fluids were approximately the same, given the high number of patients which help avoiding bigger random effects. At any time period, mostly crystalloid fluids were used in general. As we did not analyse data regarding urine output, we have to acknowledge that we lose a significant percentage of AKI cases. Furthermore, we do not have additional data about ventilation length and patients’ oxygenation.
Advantages of our propensity-matched study design are mainly statistical: first, the large number of potentially interesting endpoints being investigated and the high number of patients included that did not need to be enrolled, and second, the retrospective approach using routine data which saved time, staff, and costs. Due to the fact that we matched the cohorts equally in number, age, sex, and surgery discipline, at least no bias due to these patient characteristics should have arisen. However, we cannot exclude mild differences regarding other potentially important characteristics such as comorbidities.
We suggest that in-line filtration with finer 0.2 and 1.2 μm filters could reduce systemic inflammation and maybe morbidity in critically ill adult patients and hence improve safety of intensive care therapy. But further prospective studies might be warranted to investigate these effects in detail.
We thank Mrs. Annette Bartholmes (Division of Software and Information Systems, Department of Information and Communication Technology, University Hospital Frankfurt) for kindly providing us additional data.
ES, PM, HN, and KZ wrote the manuscript. EH, KA, SL, and PH critically read the manuscript. RE read the manuscript. KZ, PM, and EH designed the study and the statistical analysis plan. HN and ES critically reviewed the study and the statistical analysis plan. ES performed the statistical analysis. EH provided senior statistical support and critically reviewed the results of the statistical analysis. RE contributed to the initial data sampling and assisted in the preliminary data analysis. PM, HN, and KZ evaluated and critically revised the medical results of the statistical analysis. KA provided all the data. All authors read and approved the final manuscript.
The study was conducted with the internal department funding only.
Ethics approval and consent to participate
The study was approved by the local ethics committee (Goethe University Frankfurt am Main, ref. 8/14 from 30 January 2014), which waived the need for informed consent.
Consent for publication
The authors declare that they have no competing interests.
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