Mark van den Boogaard

Publications (24) View all

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  Available from: Mark van den Boogaard

  Article: Comparison and clinical suitability of eight prediction models for cardiac surgery-related acute kidney injury.

  Harmke D Kiers, Mark van den Boogaard, Micha C J Schoenmakers, Johannes G van der Hoeven, Henry A van Swieten, Suzanne Heemskerk, Peter Pickkers
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  ABSTRACT: Background Cardiac surgery-related acute kidney injury (CS-AKI) results in increased morbidity and mortality. Different models have been developed to identify patients at risk of CS-AKI. While models that predict dialysis and CS-AKI defined by the RIFLE criteria are available, their predictive power and clinical applicability have not been compared head to head.Methods Of 1388 consecutive adult cardiac surgery patients operated with cardiopulmonary bypass, risk scores of eight prediction models were calculated. Four models were only applicable to a subgroup of patients. The area under the receiver operating curve (AUROC) was calculated for all levels of CS-AKI and for need for dialysis (AKI-D) for each risk model and compared for the models applicable to the largest subgroup (n = 1243).ResultsThe incidence of AKI-D was 1.9% and for CS-AKI 9.3%. The models of Rahmanian, Palomba and Aronson could not be used for preoperative risk assessment as postoperative data are necessary. The three best AUROCs for AKI-D were of the model of Thakar: 0.93 [95% confidence interval (CI) 0.91-0.94], Fortescue: 0.88 (95% CI 0.87-0.90) and Wijeysundera: 0.87 (95% CI 0.85-0.89). The three best AUROCs for CS-AKI-risk were 0.75 (95% CI 0.73-0.78), 0.74 (95% CI 0.71-0.76) and 0.70 (95% CI 0.73-0.78), for Thakar, Mehta and both Fortescue and Wijeysundera, respectively. The model of Thakar performed significantly better compared with the models of Mehta, Rahmanian, Fortescue and Wijeysundera (all P-values <0.01) at different levels of severity of CS-AKI.Conclusions The Thakar model offers the best discriminative value to predict CS-AKI and is applicable in a preoperative setting and for all patients undergoing cardiac surgery.
  Nephrology Dialysis Transplantation 12/2012; · 3.40 Impact Factor
• Article: Reducing sensory input in critically ill patients: are eyemasks a blind spot?

  Koen S Simons, Mark van den Boogaard, Cornelis Pc de Jager
  Critical care (London, England) 07/2012; 16(4):439. · 4.61 Impact Factor
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  Article: Delirium: A organ dysfunction like any other.

  Mark van den Boogaard, Peter Pickkers, Lisette Schoonhoven
  Critical care medicine 07/2012; 40(7):2270-1. · 6.37 Impact Factor
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  Article: Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study.

  M van den Boogaard, P Pickkers, A J C Slooter, M A Kuiper, P E Spronk, P H J van der Voort, J G van der Hoeven, R Donders, T van Achterberg, L Schoonhoven
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  ABSTRACT: To develop and validate a delirium prediction model for adult intensive care patients and determine its additional value compared with prediction by caregivers. Observational multicentre study. Five intensive care units in the Netherlands (two university hospitals and three university affiliated teaching hospitals). 3056 intensive care patients aged 18 years or over. Development of delirium (defined as at least one positive delirium screening) during patients' stay in intensive care. The model was developed using 1613 consecutive intensive care patients in one hospital and temporally validated using 549 patients from the same hospital. For external validation, data were collected from 894 patients in four other hospitals. The prediction (PRE-DELIRIC) model contains 10 risk factors-age, APACHE-II score, admission group, coma, infection, metabolic acidosis, use of sedatives and morphine, urea concentration, and urgent admission. The model had an area under the receiver operating characteristics curve of 0.87 (95% confidence interval 0.85 to 0.89) and 0.86 after bootstrapping. Temporal validation and external validation resulted in areas under the curve of 0.89 (0.86 to 0.92) and 0.84 (0.82 to 0.87). The pooled area under the receiver operating characteristics curve (n=3056) was 0.85 (0.84 to 0.87). The area under the curve for nurses' and physicians' predictions (n=124) was significantly lower at 0.59 (0.49 to 0.70) for both. The PRE-DELIRIC model for intensive care patients consists of 10 risk factors that are readily available within 24 hours after intensive care admission and has a high predictive value. Clinical prediction by nurses and physicians performed significantly worse. The model allows for early prediction of delirium and initiation of preventive measures. Trial registration Clinical trials NCT00604773 (development study) and NCT00961389 (validation study).
  BMJ (Clinical research ed.). 01/2012; 344:e420.
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  Article: Haloperidol prophylaxis in critically ill patients with a high risk for delirium.

  Mark van den Boogaard, Lisette Schoonhoven, Theo van Achterberg, Johannes G van der Hoeven, Peter Pickkers
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  ABSTRACT: INTRODUCTION: Delirium is associated with increased morbidity and mortality. We implemented a delirium prevention policy in intensive care unit (ICU) patients with a high risk to develop delirium, and evaluated if our policy resulted in quality improvement of relevant delirium outcome measures. METHODS: A before/after evaluation of a delirium prevention project using prophylactic treatment with haloperidol. Patients with a predicted risk for delirium of [greater than or equal to]50%, or with a history of alcohol abuse or dementia, were identified. According to the prevention protocol these patients received haloperidol 1mg/8hrs. Evaluation was primarily focused on delirium incidence, delirium free days without coma and 28-day mortality. Results of prophylactic treatment were compared with a historical control group and a contemporary group that did not receive haloperidol prophylaxis mainly due to non-compliance to the protocol mostly during the implementation phase. RESULTS: In 12 months 177 patients received haloperidol prophylaxis. Except for sepsis, patient characteristics were comparable between the prevention and the historical (N=299) group. Predicted chance to develop delirium was 75+/-19% and 73+/-22%, respectively. Haloperidol prophylaxis resulted in a lower delirium incidence (65% vs. 75%, p=0.01), and more delirium-free-days (median 20 days [IQR 8-27] vs. median 13 days [3-27], p=0.003) in the intervention group compared to the control group. Cox-regression analysis adjusted for sepsis showed a hazard rate of 0.80 (95% confidence interval 0.66-0.98) for 28-day mortality. Beneficial effects of haloperidol appeared most pronounced in the patients with the highest risk for delirium. Furthermore, haloperidol prophylaxis resulted in less ICU re-admissions (11% vs. 18%, p=0.03) and unplanned removal of tubes/lines (12% vs. 19%, p=0.02). Haloperidol was stopped in 12 patients because of QTc-time prolongation (n=9), renal failure (n=1) or suspected neurological side-effects (n=2). No other side-effects were reported. Patients who were not treated during the intervention period (N=59) showed similar results compared to the untreated historical control group. CONCLUSIONS: Our evaluation study suggests that prophylactic treatment with low dose haloperidol in critically ill patients with a high risk for delirium probably has beneficial effects. These results warrant confirmation in a randomized controlled trial. Trial registration: clinicaltrial.gov Identifier: NCT01187667.
  Critical care (London, England) 01/2013; 17(1):R9. · 4.61 Impact Factor