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Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27; −0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

A clear margin is an important prognostic factor for most solid tumours treated by surgery. Intraoperative fluorescence imaging using exogenous tumour-specific fluorescent agents has shown particular benefit in improving complete resection of tumour tissue. However, signal processing for fluorescence imaging is complex, and fluorescence signal intensity does not always perfectly correlate with tumour location. Raman spectroscopy has the capacity to accurately differentiate between malignant and healthy tissue based on their molecular composition. In Raman spectroscopy, specificity is uniquely high, but signal intensity is weak and Raman measurements are mainly performed in a point-wise manner on microscopic tissue volumes, making whole-field assessment temporally unfeasible. In this review, we describe the state-of-the-art of both optical techniques, paying special attention to the combined intraoperative application of fluorescence imaging and Raman spectroscopy in current clinical research. We demonstrate how these techniques are complementary and address the technical challenges that have traditionally led them to be considered mutually exclusive for clinical implementation. Finally, we present a novel strategy that exploits the optimal characteristics of both modalities to facilitate resection with clear surgical margins.

Background: In healthcare, analysing patient-reported outcome measures (PROMs) on an aggregated level can improve and regulate healthcare for specific patient populations (meso level). This mixed-methods systematic review aimed to summarize and describe the effectiveness of quality improvement methods based on aggregated PROMs. Additionally, it aimed to describe barriers, facilitators and lessons learned when using these quality improvement methods. Methods: A mixed-methods systematic review was conducted. Embase, MEDLINE, CINAHL and the Cochrane Library were searched for studies that described, implemented or evaluated a quality improvement method based on aggregated PROMs in the curative hospital setting. Quality assessment was conducted via the Mixed Methods Appraisal Tool. Quantitative data were synthesized into a narrative summary of the characteristics and findings. For the qualitative analysis, a thematic synthesis was conducted. Results: From 2360 unique search records, 13 quantitative and three qualitative studies were included. Four quality improvement methods were identified: benchmarking, plan-do-study-act cycle, dashboards and internal statistical analysis. Five studies reported on the effectiveness of the use of aggregated PROMs, of which four identified no effect and one a positive effect. The qualitative analysis identified the following themes for facilitators and barriers: (1) conceptual (i.e. stakeholders, subjectivity of PROMs, aligning PROMs with clinical data, PROMs versus patient-reported experience measures [PREMs]); (2a) methodological—data collection (i.e. choice, timing, response rate and focus); (2b) methodological—data processing (i.e. representativeness, responsibility, case-mix control, interpretation); (3) practical (i.e. resources). Conclusion: The results showed little to no effect of quality improvement methods based on aggregated PROMs, but more empirical research is needed to investigate different quality improvement methods.

BACKGROUND: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. METHODS: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. RESULTS: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). CONCLUSIONS: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.

Background: Oncokompas is a web-based self-management application that supports cancer survivors to monitor their health-related quality of life (HRQOL) and symptoms, and to obtain personalised feedback and tailored options for supportive care. In a large randomised controlled trial among survivors of head and neck cancer, colorectal cancer, and breast cancer and (non-)Hodgkin lymphoma, Oncokompas proved to improve HRQOL, and to reduce several tumour-specific symptoms. Effect sizes were however small, and no effect was observed on the primary outcome patient activation. Therefore, this study aims to explore which subgroups of cancer survivors may especially benefit from Oncokompas. Materials and methods: Cancer survivors (n = 625) were randomly assigned to the intervention group (access to Oncokompas, n = 320) or control group (6 months waiting list, n = 305). Outcome measures were HRQOL, tumour-specific symptoms, and patient activation. Potential moderators included socio-demographic (sex, age, marital status, education, employment), clinical (tumour type, stage, time since diagnosis, treatment modality, comorbidities), and personal factors (self-efficacy, personal control, health literacy, Internet use), and patient activation, mental adjustment to cancer, HRQOL, symptoms, and need for supportive care, measured at baseline. Linear mixed models were performed to investigate potential moderators. Results: The intervention effect on HRQOL was the largest among cancer survivors with low to moderate self-efficacy, and among those with high personal control and those with high health literacy scores. Cancer survivors with higher baseline symptom scores benefitted more on head and neck (pain in the mouth, social eating, swallowing, coughing, trismus), and colorectal cancer (weight) specific symptoms. Discussion: Oncokompas seems most effective in reducing symptoms in head and neck cancer and colorectal cancer survivors who report a higher burden of tumour-specific sym

BackgroundOncokompas is a web-based self-management application that supports cancer survivors to monitor their health-related quality of life (HRQOL) and symptoms, and to obtain personalised feedback and tailored options for supportive care. In a large randomised controlled trial among survivors of head and neck cancer, colorectal cancer, and breast cancer and (non-)Hodgkin lymphoma, Oncokompas proved to improve HRQOL, and to reduce several tumour-specific symptoms. Effect sizes were however small, and no effect was observed on the primary outcome patient activation. Therefore, this study aims to explore which subgroups of cancer survivors may especially benefit from Oncokompas.Materials and methodsCancer survivors (n = 625) were randomly assigned to the intervention group (access to Oncokompas, n = 320) or control group (6 months waiting list, n = 305). Outcome measures were HRQOL, tumour-specific symptoms, and patient activation. Potential moderators included socio-demographic (sex, age, marital status, education, employment), clinical (tumour type, stage, time since diagnosis, treatment modality, comorbidities), and personal factors (self-efficacy, personal control, health literacy, Internet use), and patient activation, mental adjustment to cancer, HRQOL, symptoms, and need for supportive care, measured at baseline. Linear mixed models were performed to investigate potential moderators.ResultsThe intervention effect on HRQOL was the largest among cancer survivors with low to moderate self-efficacy, and among those with high personal control and those with high health literacy scores. Cancer survivors with higher baseline symptom scores benefitted more on head and neck (pain in the mouth, social eating, swallowing, coughing, trismus), and colorectal cancer (weight) specific symptoms.DiscussionOncokompas seems most effective in reducing symptoms in head and neck cancer and colorectal cancer survivors who report

Background: Anxiety disorders in children and young people are common and bring significant personal and societal costs. Over the last two decades, there has been a substantial increase in research evaluating psychological and pharmacological treatments for anxiety disorders in children and young people and exciting and novel research has continued as the field strives to improve efficacy and effectiveness, and accessibility of interventions. This increase in research brings potential to draw together data across studies to compare treatment approaches and advance understanding of what works, how, and for whom. There are challenges to these efforts due largely to variation in studies’ outcome measures and variation in the way study characteristics are reported, making it difficult to compare and/or combine studies, and this is likely to lead to faulty conclusions. Studies particularly vary in their reliance on child, parent, and/or assessor-based ratings across a range of outcomes, including remission of anxiety diagnosis, symptom reduction, and other domains of functioning (e.g., family relationships, peer relationships). Methods: To address these challenges, we convened a series of international activities that brought together the views of key stakeholders (i.e., researchers, mental health professionals, young people, parents/caregivers) to develop recommendations for outcome measurement to be used in treatment trials for anxiety disorders in children and young people. Results and Conclusions: This article reports the results of these activities and offers recommendations for selection and reporting of outcome measures to (a) guide future research and (b)