Publications by authors named "Pieter D"

10 Publications

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Anesthesia for euthanasia influences mRNA expression in healthy mice and after traumatic brain injury.

J Neurotrauma 2014 Oct 12;31(19):1664-71. Epub 2014 Aug 12.

Department of Anesthesiology, University Medical Center of the Johannes Gutenberg-University , Mainz, Germany .

Tissue sampling for gene expression analysis is usually performed under general anesthesia. Anesthetics are known to modulate hemodynamics, receptor-mediated signaling cascades, and outcome parameters. The present study determined the influence of anesthetic paradigms typically used for euthanization and tissue sampling on cerebral mRNA expression in mice. Naïve mice and animals with acute traumatic brain injury induced by controlled cortical impact (CCI) were randomized to the following euthanasia protocols (n=10-11/group): no anesthesia (NA), 1 min of 4 vol% isoflurane in room air (ISO), 3 min of a combination of 5 mg/kg midazolam, 0.05 mg/kg fentanyl, and 0.5 mg/kg medetomidine intraperitoneally (COMB), or 3 min of 360 mg/kg chloral hydrate intraperitoneally (CH). mRNA expression of actin-1-related gene (Act1), FBJ murine osteosarcoma viral oncogene homolog B (FosB), tumor necrosis factor alpha (TNFα), heat shock protein beta-1 (HspB1), interleukin (IL)-6, tight junction protein 1 (ZO-1), IL-1ß, cyclophilin A, micro RNA 497 (miR497), and small cajal body-specific RNA 17 were determined by real-time polymerase chain reaction (PCR) in hippocampus samples. In naïve animals, Act1 expression was downregulated in the CH group compared with NA. FosB expression was downregulated in COMB and CH groups compared with NA. CCI reduced Act1 and FosB expression, whereas HspB1 and TNFα expression increased. After CCI, HspB1 expression was significantly higher in ISO, COMB, and CH groups, and TNFα expression was elevated in ISO and COMB groups. MiR497, IL-6, and IL-1ß were upregulated after CCI but not affected by anesthetics. Effects were independent of absolute mRNA copy numbers. The data demonstrate that a few minutes of anesthesia before tissue sampling are sufficient to induce immediate mRNA changes, which seem to predominate in the early-regulated gene cluster. Anesthesia-related effects on gene expression might explain limited reproduciblity of real-time PCR data between studies or research groups and should therefore be considered for quantitative PCR data.
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http://dx.doi.org/10.1089/neu.2013.3243DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4170812PMC
October 2014

Single administration of tripeptide α-MSH(11-13) attenuates brain damage by reduced inflammation and apoptosis after experimental traumatic brain injury in mice.

PLoS One 2013 5;8(8):e71056. Epub 2013 Aug 5.

Department of Anesthesiology, Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.

Following traumatic brain injury (TBI) neuroinflammatory processes promote neuronal cell loss. Alpha-melanocyte-stimulating hormone (α-MSH) is a neuropeptide with immunomodulatory properties, which may offer neuroprotection. Due to short half-life and pigmentary side-effects of α-MSH, the C-terminal tripeptide α-MSH(11-13) may be an anti-inflammatory alternative. The present study investigated the mRNA concentrations of the precursor hormone proopiomelanocortin (POMC) and of melanocortin receptors 1 and 4 (MC1R/MC4R) in naive mice and 15 min, 6, 12, 24, and 48 h after controlled cortical impact (CCI). Regulation of POMC and MC4R expression did not change after trauma, while MC1R levels increased over time with a 3-fold maximum at 12 h compared to naive brain tissue. The effect of α-MSH(11-13) on secondary lesion volume determined in cresyl violet stained sections (intraperitoneal injection 30 min after insult of 1 mg/kg α-MSH(11-13) or 0.9% NaCl) showed a considerable smaller trauma in α-MSH(11-13) injected mice. The expression of the inflammatory markers TNF-α and IL-1β as well as the total amount of Iba-1 positive cells were not reduced. However, cell branch counting of Iba-1 positive cells revealed a reduced activation of microglia. Furthermore, tripeptide injection reduced neuronal apoptosis analyzed by cleaved caspase-3 and NeuN staining. Based on the results single α-MSH(11-13) administration offers a promising neuroprotective property by modulation of inflammation and prevention of apoptosis after traumatic brain injury.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0071056PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3733710PMC
March 2014

Hospital standardized mortality ratio: consequences of adjusting hospital mortality with indirect standardization.

PLoS One 2013 9;8(4):e59160. Epub 2013 Apr 9.

Department of Anesthesiology, University Medical Center Utrecht, Utrecht, The Netherlands.

Background: The hospital standardized mortality ratio (HSMR) is developed to evaluate and improve hospital quality. Different methods can be used to standardize the hospital mortality ratio. Our aim was to assess the validity and applicability of directly and indirectly standardized hospital mortality ratios.

Methods: Retrospective scenario analysis using routinely collected hospital data to compare deaths predicted by the indirectly standardized case-mix adjustment method with observed deaths. Discharges from Dutch hospitals in the period 2003-2009 were used to estimate the underlying prediction models. We analysed variation in indirectly standardized hospital mortality ratios (HSMRs) when changing the case-mix distributions using different scenarios. Sixty-one Dutch hospitals were included in our scenario analysis.

Results: A numerical example showed that when interaction between hospital and case-mix is present and case-mix differs between hospitals, indirectly standardized HSMRs vary between hospitals providing the same quality of care. In empirical data analysis, the differences between directly and indirectly standardized HSMRs for individual hospitals were limited.

Conclusion: Direct standardization is not affected by the presence of interaction between hospital and case-mix and is therefore theoretically preferable over indirect standardization. Since direct standardization is practically impossible when multiple predictors are included in the case-mix adjustment model, indirect standardization is the only available method to compute the HSMR. Before interpreting such indirectly standardized HSMRs the case-mix distributions of individual hospitals and the presence of interactions between hospital and case-mix should be assessed.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059160PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621877PMC
October 2013

Pioglitazone reduces secondary brain damage after experimental brain trauma by PPAR-γ-independent mechanisms.

J Neurotrauma 2011 Jun;28(6):983-93

Department of Anesthesiology, Medical Center of the Johannes Gutenberg-University, Mainz, Germany.

Inflammatory and ischemic processes contribute to the development of secondary brain damage after mechanical brain injury. Recent data suggest that thiazolidinediones (TZDs), a class of drugs approved for the treatment of non-insulin-dependent diabetes mellitus, effectively reduces inflammation and brain lesion by stimulation of the peroxisome proliferator-activated receptor-γ (PPAR-γ). The present study investigates the influence of the TZD pioglitazone and rosiglitazone on inflammation and secondary brain damage after experimental traumatic brain injury (TBI). A controlled cortical impact (CCI) injury was induced in male C57BL/6 mice to investigate following endpoints: (1) mRNA expression of PPAR-γ and PPAR-γ target genes (LPL, GLT1, and IRAP/Lnpep), and inflammatory markers (TNF-α, IL-1β, IL-6, and iNOS), at 15 min, 3 h, 6 h, 12 h, and 24 h post-trauma; (2) contusion volume, neurological function, and gene expression after 24 h in mice treated with pioglitazone (0.5 and 1 mg/kg) or rosiglitazone (5 and 10 mg/kg IP at 30 min post-trauma); and (3) the role of PPAR-γ to mediate protection was determined in animals treated with pioglitazone, the PPAR-γ inhibitor T0070907, and a combination of both. Inflammatory marker genes, but not PPAR-γ gene expression, was upregulated after trauma. Pioglitazone reduced the histological damage and inflammation in a dose-dependent fashion. In contrast, rosiglitazone failed to suppress inflammation and histological damage. PPAR-γ and PPAR-γ target gene expression was not induced by pioglitazone and rosiglitazone. In line with these results, pioglitazone-mediated protection was not reversed by T0070907. The results indicate that the neuroprotective effects of pioglitazone are not solely related to PPAR-γ-dependent mechanisms.
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http://dx.doi.org/10.1089/neu.2010.1685DOI Listing
June 2011

[Variations in Dutch National Medical Registration hardly affect the hospital standardised mortality rate (HSMR)].

Ned Tijdschr Geneeskd 2010 ;154:A2186

Kiwa Prismant, Utrecht, the Netherlands.

Objective: To analyse the variation in the registration of hospital admissions across Dutch hospitals and determine how this variation affects the Hospital Standardised Mortality Rate (HSMR).

Design: Retrospective, descriptive.

Method: We used data from the National Medical Registration (LMR), covering the records of all hospital admissions in 2005 in Dutch hospitals, to analyse the variation between hospitals in 3 variables: the number of secondary diagnoses, the percentage of unplanned admissions, and the percentage of non-specified diagnoses ('other diagnoses'). The impact of this variation on the HSMR was analysed by calculating the correlation between the HSMR and each of the variables. The correlation between the original HSMR and the HSMR without adjustment for these variables was also calculated.

Results: The variation in the percentages of unplanned admissions and admissions with a non-specified diagnosis was low. The variation in these two variables had a small or no effect on the HSMR. There was a considerable variation in the mean number of secondary diagnoses per hospital. This variation had a limited but statistically significant effect on the HSMR. The HSMR calculated without adjustments for secondary diagnoses correlated strongly with the original HSMR.

Conclusion: This analysis does not support the view that the HSMR is strongly affected by variation in the registration of hospital admissions and is therefore not reliable. Therefore, there is no need for restraint with regard to publication of the Dutch HSMR.
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October 2010

The hospital standardised mortality ratio: a powerful tool for Dutch hospitals to assess their quality of care?

Qual Saf Health Care 2010 Feb;19(1):9-13

Dr Foster Unit, Faculty of Medicine, Imperial College London EC1A 9LA, UK.

Aim Of The Study: To use the hospital standardised mortality ratio (HSMR), as a tool for Dutch hospitals to analyse their death rates by comparing their risk-adjusted mortality with the national average.

Method: The method uses routine administrative databases that are available nationally in The Netherlands--the National Medical Registration dataset for the years 2005-2007. Diagnostic groups that led to 80% of hospital deaths were included in the analysis. The method adjusts for a number of case-mix factors per diagnostic group determined through a logistic regression modelling process.

Results: In The Netherlands, the case-mix factors are primary diagnosis, age, sex, urgency of admission, length of stay, comorbidity (Charlson Index), social deprivation, source of referral and month of admission. The Dutch HSMR model performs well at predicting a patient's risk of death as measured by a c statistic of the receiver operating characteristic curve of 0.91. The ratio of the HSMR of the Dutch hospital with the highest value in 2005-2007 is 2.3 times the HSMR of the hospital with the lowest value.

Discussion: Overall hospital HSMRs and mortality at individual diagnostic group level can be monitored using statistical process control charts to give an early warning of possible problems with quality of care. The use of routine data in a standardised and robust model can be of value as a starting point for improvement of Dutch hospital outcomes. HSMRs have been calculated for several other countries.
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http://dx.doi.org/10.1136/qshc.2009.032953DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2921266PMC
February 2010

Selection of endogenous control genes for normalization of gene expression analysis after experimental brain trauma in mice.

J Neurotrauma 2008 Jul;25(7):785-94

Department of Anesthesiology, Johannes Gutenberg University, Mainz, Germany.

Quantitative measurements of gene expression require correction for tissue sample size, RNA quantity, and reverse transcription efficiency. This can be achieved by normalization with control genes. The study was designed to identify candidates not altered after brain trauma. Male C57Bl/6 mice were anesthetized with isoflurane, and a pneumatic brain trauma was induced by controlled cortical impact (CCI) on the right parietal cortex. Brains were removed at 15 min, and 3, 6, 12 and 24 h after CCI and from naive animals (n = 6 each). Absolute copies of six control genes (beta-2-microglobin [B2M], cyclophilin A, beta-actin, hypoxanthine ribosyltransferase [HPRT], porphobilinogen deaminase [PBGD], and glyceraldehyde-3-phosphate dehydrogenase [GAPDH]) and one example target gene (iNOS) were determined by real-time reverse transcription-polymerase chain reaction (RT-PCR; Lightcycler) in the traumatic focus and contralateral tissue. Control gene expression was stable until 12 h after CCI. At 24 h after CCI expression of B2M, cyclophilin A and HPRT remained stable in the contusion, while expression of beta-actin, GAPDH, and PBGD increased. Due to variations between animals (+/-85%), increases in beta-actin (+64%) and GAPDH (+59%) did not reach the level of significance. In non-contused tissue, expression of all genes dropped 24 h after CCI (range, -17% to -61%). Due to low variations between animals and stable expression after CCI, B2M and cyclophilin A seem to be suitable to serve as single normalizer. Normalization of the example target gene iNOS resulted in varying relative expression extending from onefold (PBDG) to 10-fold (HPRT). The results suggest that the knowledge of the temporal profile of control genes is essential to properly interpret results of mRNA quantification.
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http://dx.doi.org/10.1089/neu.2007.0497DOI Listing
July 2008

[The effect of specialised medical procedures on the hosptial standardised mortality ratio in cardiac centers].

Ned Tijdschr Geneeskd 2008 May;152(21):1221-7

St. Antonius Ziekenhuis, Koekoekslaan 1, 3435 CM Nieuwegein.

Objective: To examine the impact of specialised medical procedures (SMPs) on the hospital standardized mortality ratio (HSMR) in Dutch cardiac centres.

Design: Retrospective, calculation of the HSMR.

Method: Data from 2004 from the National Medical Registration (LMR) were used to calculate the HSMR in 12 cardiac centres and all other hospitals in the Netherlands. The HSMRwas then recalculated for the 12 cardiac centres excluding either percutaneous transluminal coronary angioplasty (PTCA) or open heart surgery or both to determine the impact of these SMPs on the HSMR.

Results: Exclusion of SMPs from the HSMR calculation changed the HSMR for individual cardiac centres, ranging from a 4.7% decrease to a 5.3% increase. Change in HSMR was related to the relative frequency of the two procedures at each cardiac centre. Mortality risk was lower than average for PTCA and higher than average for open heart surgery. PTCA accounted for 5.6%-20.2% of total admissions in the 12 cardiac centres. A relatively high proportion of PTCA procedures was associated with a lower HSMR, to a maximum decrease of nearly 7% in one cardiac centre. Open heart surgery accounted for 2.1%-12.6% of total admissions per cardiac centre. A relatively high proportion ofopen heart procedures was associated with an increased HSMR, to a maximum increase of nearly 8% in one cardiac centre.

Conclusion: Specialised medical procedures for heart conditions influence the HSMR of cardiac centres. The increase or decrease in HSMR is related to the relative frequency of PTCA and open heart surgery. These results can be used to help interpret the differences in HSMR among cardiac centres and other hospitals.
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May 2008

Measuring and explaining mortality in Dutch hospitals; the hospital standardized mortality rate between 2003 and 2005.

BMC Health Serv Res 2008 Apr 3;8:73. Epub 2008 Apr 3.

National Institute for Public Health and the Environment, Bilthoven, The Netherlands.

Background: Indicators of hospital quality, such as hospital standardized mortality ratios (HSMR), have been used increasingly to assess and improve hospital quality. Our aim has been to describe and explain variation in new HSMRs for the Netherlands.

Methods: HSMRs were estimated using data from the complete population of discharged patients during 2003 to 2005. We used binary logistic regression to indirectly standardize for differences in case-mix. Out of a total of 101 hospitals 89 hospitals remained in our explanatory analysis. In this analysis we explored the association between HSMRs and determinants that can and cannot be influenced by hospitals. For this analysis we used a two-level hierarchical linear regression model to explain variation in yearly HSMRs.

Results: The average HSMR decreased yearly with more than eight percent. The highest HSMR was about twice as high as the lowest HSMR in all years. More than 2/3 of the variation stemmed from between-hospital variation. Year (-), local number of general practitioners (-) and hospital type were significantly associated with the HSMR in all tested models.

Conclusion: HSMR scores vary substantially between hospitals, while rankings appear stable over time. We find no evidence that the HSMR cannot be used as an indicator to monitor and compare hospital quality. Because the standardization method is indirect, the comparisons are most relevant from a societal perspective but less so from an individual perspective. We find evidence of comparatively higher HSMRs in academic hospitals. This may result from (good quality) high-risk procedures, low quality of care or inadequate case-mix correction.
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http://dx.doi.org/10.1186/1472-6963-8-73DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2362116PMC
April 2008
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