Background: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern... Show moreBackground: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients. Methods: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 `wave' in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values. Results: We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80-0.84]) compared to the National early warning score (0.72 [0.69-0.74]) and the Modified early warning score (0.67 [0.65-0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [- 0.09 to 0.14], slope = 0.79 [0.73-0.86]). Conclusions: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed. Show less
Woittiez, N.J.C.; Prehn, J. van; Immerseel, F. van; Goossens, E.; Bauer, M.P.; Ramspek, C.L.; ... ; Ludikhuize, J. 2022
Background: Septicaemia with intravascular haemolysis is a rare, but often fatal, presentation of Clostridium perfringens infection. C. perfringens is a Gram-positive, anaerobic bacterium that can... Show moreBackground: Septicaemia with intravascular haemolysis is a rare, but often fatal, presentation of Clostridium perfringens infection. C. perfringens is a Gram-positive, anaerobic bacterium that can produce multiple toxins. Toxinotyping is not performed regularly.Methods: This article describes two human cases of C. perfringens infections. Toxinotyping was performed using polymerase chain reaction (PCR). Additionally, a structured review of the literature was performed which searched specifically for cases of C. perfringens infection with haemolysis.Results: Both cases were identified as toxinotype A strains and both cases were fatal. Also, both cases showed marked haemolysis during their clinical course, which is assumed to have played a significant role in their outcome. In total, 83 references were identified describing human C. perfringens infection with haemolysis. Mortality rates have been stable over the last 10 years at 80%. Toxinotyping has been performed in a total of six cases. Of the four cases analysed by PCR, all were identified as toxinotype A.Conclusions: Haemolytic C. perfringens infections are rare but are fatal in most cases. Toxinotyping is performed rarely. The authors advocate increased use of toxinotyping to gain insight into pathophysiology and more effective interventions. (C) 2021 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. Show less
Brunsveld-Reinders, A.H.; Ludikhuize, J.; Arbous, M.S.; Dijkgraaf, M.G.W.; Jonge, E. de; COMET Study Grp 2017
Objective: To describe the effect of implementation of a Rapid Response System (RRS) on the composite endpoint of cardiopulmonary arrest, unplanned ICU admission, or death. Design: Pragmatic... Show moreObjective: To describe the effect of implementation of a Rapid Response System (RRS) on the composite endpoint of cardiopulmonary arrest, unplanned ICU admission, or death. Design: Pragmatic prospective Dutch multi-center before-after trial, Cost and Outcomes analysis of Medical Emergency Teams (COMET) trial. Setting: Twelve hospitals participated, each including two surgical and two non-surgical wards between April 2009 and November 2011. The Modified Early Warning Score (MEWS) and Situation-Background-Assessment-Recommendation (SBAR) instruments were implemented over seven months. The Rapid Response Team (RRT) was then implemented during the following 17 months. The effects of implementing the RRT were measured in the last 5 months of this period. Patients: All patients 18 years and older admitted to the study wards were included. Measurements and main results: In total 166,569 patients were included in the study representing 1,031,172 hospital admission days. No differences were observed in patient demographics between periods. The composite endpoint of cardiopulmonary arrest, unplanned ICU admission, or death per 1000 admissions was significantly reduced in the RRT versus the before phase, adjusted odds ratio (OR) 0.847 (95% CI 0.725-0.989, p=0.036). Cardiopulmonary arrests and in hospital mortality were also significantly reduced, OR 0.607 (95 CI 0.393-0.937, p=0.018) and OR 0.802 (95% CI 0.644-1.0, p=0.05) respectively. Unplanned ICU admissions showed a declining trend, OR 0.878 (95% CI 0.755-1.021, p=0.092) whereas severity of illness at the moment of ICU admission was not different between periods. Conclusions: In this study, introduction of nationwide implementation of RRSs was associated with a decrease in the composite endpoint of cardiopulmonary arrests, unplanned ICU admissions and mortality in patients on general hospital wards. These findings support the implementation of RRSs in hospitals to reduce severe adverse events. Show less
Ludikhuize, J.; Dongelmans, D.A.; Smorenburg, S.M.; Gans-Langelaar, M.; Jonge, E. de; Rooij, S.E. de 2012
