Background: Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients.Methods: Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high‐income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication.Results: In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer‐Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R² (4.7%).Conclusions: Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head‐to‐head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

Angioedema without concomitant urticaria is a well-known complication of treatment with the recombinant tissue-type plasminogen activator (r-tPA) alteplase and its genetically modified variant tenecteplase. It is potentially lethal when causing airway obstruction and can require intubation. The latest guideline for the early management of patients with acute ischemic stroke from the American Heart Association/American Stroke Association advises to treat this complication initially by interfering with the histamine pathway. This article aims to clarify the pathophysiological mechanism of r-tPA-induced angioedema and provides several arguments that this condition is primarily bradykinin-mediated and hence should be treated initially by intervening with the bradykinin pathway. Second, other—less frequently reported—adverse symptoms after r-tPA therapy and their proposed pathophysiological mechanisms leading to specific treatment are described. This manuscript describes the need for an update of the section “3.5 IV alteplase” from the American Heart Association/American Stroke Association guideline to treat this r-tPA-induced angioedema adequately and prevent potentially fatal outcomes.

Objectives: Delirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models. Setting: Single-site university hospital. Design: Secondary analysis of prospective cohort study. Participants and inclusion: CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients. Primary outcome measures: Discrimination, calibration and clinical usefulness. Results: 14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from −0.02 to 0.34, slopes from −0.74 to 1.96 and scaled Brier from −1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai et al (c-index: 0.739; (95% CI: 0.664 to 0.813); intercept: −0.018; slope: 1.96; scaled Brier: 0.049) and Litaker et al (c-index: 0.706 (95% CI: 0.590 to 0.823); intercept: −0.015; slope: 0.995; scaled Brier: 0.088). For the remaining CPMs, model discrimination was considered poor with corresponding c-indices <0.70. Conclusion: Our head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.

Postoperative pancreatic fistula is a frequent and potentially lethal complication after pancreatoduodenectomy. Several models have been developed to predict postoperative pancreatic fistula risk. This study was performed to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist that provides guidelines on reporting prediction models to enhance transparency and to help in the decision-making regarding the implementation of the appropriate risk models into clinical practice. Studies that described prediction models to predict postoperative pancreatic fistula after pancreatoduodenectomy were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The TRIPOD checklist was used to evaluate the adherence rate. The area under the curve and other performance measures were extracted if reported. A quadrant matrix chart is created to plot the area under the curve against TRIPOD adherence rate to find models with a combination of above-average TRIPOD adherence and area under the curve. In total, 52 predictive models were included (23 development, 15 external validation, 4 incremental value, and 10 development and external validation). No risk model achieved 100% adherence to the TRIPOD. The mean adherence rate was 65%. Most authors failed to report on missing data and actions to blind assessment of predictors. Thirteen models had an above-average performance for TRIPOD checklist adherence and area under the curve. Although the average TRIPOD adherence rate for postoperative pancreatic fistula models after pancreatoduodenectomy was 65%, higher compared to other published models, it does not meet TRIPOD standards for transparency. This study identified 13 models that performed above average in TRIPOD adherence and area under the curve, which could be the appropriate models to be used in clinical practice.

Background: Pancreatoduodenectomy (PD) is the only cure for periampullary and pancreatic cancer. It has morbidity rates of 40–60%, with severe complications in 30%. Prediction models to predict complications are crucial. A risk model for severe complications was developed by Schroder et al. based on BMI, ASA classification and Hounsfield Units of the pancreatic body on the preoperative CT scan. These variables were independent predictors for severe complications upon internal validation. Our aim was to externally validate this model using an independent cohort of patients. Methods: A retrospective analysis was performed on 318 patients who underwent PD at our institution from 2013 to 2021. The outcome of interest was severe complications Clavien–Dindo ≥ IIIa. Model calibration, discrimination and performance were assessed. Results: A total of 308 patients were included. Patients with incomplete data were excluded. A total of 89 (28.9%) patients had severe complications. The externally validated model achieved: C-index = 0.67 (95% CI: 0.60–0.73), regression coefficient = 0.37, intercept = 0.13, Brier score = 0.25. Conclusions: The performance ability, discriminative power, and calibration of this model were acceptable. Our risk calculator can help surgeons identify high-risk patients for post-operative complications to improve shared decision-making and tailor perioperative management.

Background: In patients with a beta‐lactam antibiotic (BA) allergy label, avoidance of the whole group of BAs leads to displacement of first‐line therapies potentially influencing patient outcomes and antimicrobial resistance. Studies previously published on BA cross‐reactivity use different sets of BA and seem to contain conflicting recommendations on safe BA alternatives in case of (suspected) BA allergy. Methods: The objectives were (i) to identify discrepancies between studies regarding cross‐allergy in BA and (ii) to identify research gaps. The tables and errata of 4 studies (Romano et al., Trubiano et al., Zagursky et al., SWAB‐guideline by Wijnakker et al.) were evaluated and compared head‐to‐head. Results: A total of 51 antibiotics were covered by the four authors, theoretically leading to 2550 potential recommendations regarding alternatives in case of specific allergies. Internal discrepancies existed in 2 tables. Since none of the tables included all 51 BAs, in 356 situations data were lacking regarding specific alternatives. In 1104 situations, only one author gave advice about a specific alternative. Harmony and disharmony between authors could be evaluated in 1090 cases. The advice regarding alternative BAs was 696 times in harmony (482 safe, 214 unsafe), while discrepancies were found in 394 cases. This led to a different advice (safe vs. unsafe) in 272 cases or 69%. Conclusion: Disharmony between authors was identified in 36% of the cases. In 69%, this led to a clinically relevant, different advice. This indicates the need for synchronisation of cross‐reactivity tables and answering remaining research gaps.