Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU.

OBJECTIVES: To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU. DESIGN: A retrospective cohort study. SETTING: Five university hospitals in the Netherlands between 2002 and 2015. PATIENTS: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model's generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers (C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality. CONCLUSIONS: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations.

To warrant clinical adoption AI models require a multi-faceted implementation evaluation.

Despite artificial intelligence (AI) technology progresses at unprecedented rate, our ability to translate these advancements into clinical value and adoption at the bedside remains comparatively limited. This paper reviews the current use of implementation outcomes in randomized controlled trials evaluating AI-based clinical decision support and found limited adoption. To advance trust and clinical adoption of AI, there is a need to bridge the gap between traditional quantitative metrics and implementation outcomes to better grasp the reasons behind the success or failure of AI systems and improve their translation into clinical value.

Association between fat-free mass and survival in critically ill patients with COVID-19: A prospective cohort study.

BACKGROUND: Most critically ill patients with COVID-19 experience malnutrition and weight loss associated with negative clinical outcomes. Our primary aim was to assess body composition during acute and late phase of illness in these patients in relation to clinical outcome and secondary to tailored nutrition support. METHODS: This prospective cohort study included adult critically ill patients with COVID-19. Body composition (fat-free mass [FFM] [exposure of interest], fat mass [FM], skeletal muscle mass [SMM], and phase angle [PA]) was determined with multifrequency bioelectrical impedance analyses in the acute and late phase. Nutrition support data were collected simultaneously. Clinical outcome was defined as intensive care unit (ICU) survival (primary outcome) and 30-90 days thereafter, duration of mechanical ventilation, and length of ICU stay and length of hospital stay (LOS). Nonparametric tests and regression analyses were performed. RESULTS: We included 70 patients (73% male, median age 60 years). Upon admission, median BMI was 30 kg/m2 , 54% had obesity (BMI > 30 kg/m2 ). Median weight change during ICU stay was -3 kg: +3 kg FM and -6 kg FFM (-4 kg SMM). Body composition changed significantly (P < 0.001). Regarding clinical outcome, only low PA was associated with prolonged LOS (odds ratio = 0.83, 95% CI = 0.72-0.96; P = 0.015). Patients with optimal protein intake (>80%) during acute phase maintained significantly more FFM (2.7 kg, P = 0.047) in the late phase compared with patients who received <80%. CONCLUSION: FFM decreased significantly during acute and late phase of illness, but we observed no association with ICU survival. Only low PA was associated with prolonged LOS. FFM wasting likely occurred because of disease severity and immobility.

A study protocol to develop and test an e-health intervention in follow-up service for intensive care survivors' relatives.

BACKGROUND: The negative impact on long-term health-related outcomes among relatives of critically ill patients in the intensive care unit (ICU) has been well described. High-quality ICU specialized follow-up care, which is easily accessible with digital innovation and which is designed by and with relevant stakeholders (i.e., ICU patients' relatives and nurses), should be considered to reduce these impairments in the psychological and social domains. AIM: The programme's aim is to develop and test an e-health intervention in a follow-up service to support ICU patients' relatives. Here, the protocol for the overall study programme will be described. STUDY DESIGN: The overall study comprises a mixed-methods, multicentre research design with qualitative and quantitative study parts. The study population is ICU patients' adult relatives and ICU nurses. The main outcomes are the experiences of these stakeholders with the newly developed e-health intervention. There will be no predefined selection based on age, gender, and level of education to maximize diversity throughout the study programme. After the participants provide informed consent, data will be gathered through focus groups (n = 5) among relatives and individual interviews (n = 20) among nurses exploring the needs and priorities of a digital follow-up service. The findings will be explored further for priority considerations among members of the patient/relative organization (aiming n = 150), which will serve as a basis for digital prototypes of the e-health intervention. Assessment of the intervention will be followed during an iterative process with investigator-developed questionnaires. Finally, symptoms of anxiety and depression will be measured with the 14-item Dutch version of the 'Hospital Anxiety and Depression Scale', and symptoms of posttraumatic stress will be measured with the 21-item Dutch version of the 'Impact of Events Scale-Revised' to indicate the effectiveness of digital support among ICU patients' relatives. RELEVANCE TO CLINICAL PRACTICE: The e-health intervention to be developed during this research programme can possibly bridge the gap in integrated ICU follow-up care by providing relevant information, self-monitoring and stimulating self-care among ICU patients' relatives.

Cognitive and psychological recovery patterns across different care pathways 12 months after hospitalization for COVID-19: A multicenter cohort study (CO-FLOW).

BACKGROUND: The comparison of recovery patterns for different care pathways following COVID-19 is necessary for optimizing rehabilitation strategies. OBJECTIVES: To evaluate cognitive and psychological outcomes across different care pathways up to 12 months after hospitalization for COVID-19. METHODS: CO-FLOW is an ongoing multicenter prospective cohort study with assessments at 3, 6, and 12 months after hospitalization for COVID-19. The main outcomes are cognitive deficits (Montreal Cognitive Assessment, score <26), cognitive failure (Cognitive Failure Questionnaire, score >43), posttraumatic stress disorder (PTSD; Impact of Event Scale-Revised, score ≥33), and anxiety and depression (Hospital Anxiety and Depression Scale, subscale score ≥11). RESULTS: In total, data from 617 participants were analyzed. Mean age was 59.7 (SD 11.4) years and 188 (31%) were female. Significant recovery occurred within the first 6 months post-discharge (p ≤ 0.001). Cognitive deficits persisted in 21% (101/474), and psychological problems in 15% (74/482) of people at 12 months. Significantly improved cognition scores were reported for people who did not receive rehabilitation ('No-rehab'; 124/617, 20%; mean difference, MD 2.32, 95% CI 1.47 to 3.17; p<0.001), those who received community-based rehabilitation ('Com-rehab'; 327/617, 53%; MD 1.27, 95% CI 0.77 to 1.78; p<0.001), and those who received medical rehabilitation ('Med-rehab'; 86/617, 14%; MD 1.63, 95% CI 0.17 to 3.10; p = 0.029). Med-rehab participants experienced more cognitive failure from 3 to 6 months (MD 4.24, 95% 1.63 to 6.84; p = 0.001). Com-rehab showed recovery for PTSD (MD -2.43, 95% -3.50 to -1.37; p<0.001), anxiety (MD -0.67, 95% -1.02 to -0.32; p<0.001), and depression (MD -0.60, 95% -0.96 to -0.25; p<0.001), but symptoms persisted at 12 months. CONCLUSIONS: Survivors of COVID-19 showed cognitive and psychological recovery, especially within the first 6 months after hospitalization. Most persistent problems were related to cognitive functioning at 12 months. Recovery differed rehabilitation settings. Additional cognitive or psychological support might be warranted in people who medical or community-based rehabilitation.

Symptoms persisting after hospitalisation for COVID-19: 12 months interim results of the CO-FLOW study.

Introduction: A large proportion of patients experience a wide range of sequelae after acute COVID-19, especially after severe illness. The long-term health sequelae need to be assessed. Our objective was to longitudinally assess persistence of symptoms and clusters of symptoms up to 12 months after hospitalisation for COVID-19 and to assess determinants of the main persistent symptoms. Methods: In this multicenter prospective cohort study patients with COVID-19 are followed up for 2 years with measurements at 3, 6, 12 and 24 months after hospital discharge. Here, we present interim results regarding persistent symptoms up to 12 months. Results: We included 492 patients; mean±sd age was 60.2±10.7 years, 335 (68.1%) were males, median length of hospital stay was 11 (6.0-27.0) days. At 3 months after discharge 97.0% of the patients had at least one persisting symptom, this declined to 95.5% and 92.0% at 6 and 12 months, respectively (p=0.010). Muscle weakness, exertional dyspnoea, fatigue, and memory and concentration problems were the most prevalent symptoms with rates over 50% during follow-up. Over time, muscle weakness, hair loss and exertional dyspnoea decreased significantly (p<0.001), while other symptoms such as fatigue, concentration and memory problems, anosmia and ageusia persisted. Symptoms from the physical and respiratory cluster declined significantly over time, in contrast to the fatigue and cognitive symptom clusters. Conclusion: The majority of patients experienced COVID-19 sequelae up to 12 months after severe infection. Whereas physical and respiratory symptoms showed slow gradual decline, fatigue and cognitive symptoms did not evidently resolve over time.

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study.

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 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.

Effect of intensive care unit-specific virtual reality (ICU-VR) to improve psychological well-being in ICU survivors: study protocol for an international, multicentre, randomised controlled trial-the HORIZON-IC study.

INTRODUCTION: A substantial proportion of intensive care unit (ICU) survivors develop psychological impairments after ICU treatment, part of the postintensive care syndrome, resulting in a decreased quality of life. Recent data suggest that an ICU-specific virtual reality intervention (ICU-VR) for post-ICU patients is feasible and safe, improves satisfaction with ICU aftercare, and might improve psychological sequelae. In the present trial, we firstly aim to determine whether ICU-VR is effective in mitigating post-traumatic stress disorder (PTSD)-related symptoms and secondly to determine the optimal timing for initiation with ICU-VR. METHODS AND ANALYSIS: This international, multicentre, randomised controlled trial will be conducted in 10 hospitals. Between December 2021 and April 2023, we aim to include 300 patients who have been admitted to the ICU ≥72 hours and were mechanically ventilated ≥24 hours. Patients will be followed for 12 consecutive months. Patients will be randomised in a 1:1:1 ratio to the early ICU-VR group, the late ICU-VR group, or the usual care group. All patients will receive usual care, including a mandatory ICU follow-up clinic visit 3 months after ICU discharge. Patients in the early ICU-VR group will receive ICU-VR within 2 weeks after ICU discharge. Patients in the late VR group will receive ICU-VR during the post-ICU follow-up visit. The primary objective is to assess the effect of ICU-VR on PTSD-related symptoms. Secondary objectives are to determine optimal timing for ICU-VR, to assess the effects on anxiety-related and depression-related symptoms and health-related quality of life, and to assess patient satisfaction with ICU aftercare and perspectives on ICU-VR. ETHICS AND DISSEMINATION: The Medical Ethics Committee United, Nieuwegein, the Netherlands, approved this study and local approval was obtained from each participating centre (NL78555.100.21). Our findings will be disseminated by presentation of the results at (inter)national conferences and publication in scientific, peer-reviewed journals. TRIAL REGISTRATION NUMBER: NL9812.

Physical recovery across care pathways up to 12 months after hospitalization for COVID-19: A multicenter prospective cohort study (CO-FLOW).

Backgroud: The sudden COVID-19 pandemic forced quick development of care pathways for patients with different needs. Trajectories of physical recovery in hospitalized patients for COVID-19 following different care pathways are unknown. We aimed to assess trajectories of physical recovery and levels of physical function reached within the different care pathways. Additionally, we assessed differences in physical function across care pathways at follow-up visits. Methods: This multicenter prospective cohort study of adults who had been hospitalized for COVID-19 was performed in 10 centers, including 7 hospitals (1 academic and 6 regional hospitals) and 3 rehabilitation centers (1 medical rehabilitation center and 2 skilled nursing facilities), located in the Netherlands. Study visits were performed at 3, 6, and 12 months post-hospital discharge and included assessment of cardiorespiratory fitness (6 min walk test [6MWT], 1 min sit-to-stand test [1MSTST]), muscle strength (maximum handgrip strength [HGS]) and mobility (de Morton Mobility Index [DEMMI]). Findings: We report findings for 582 patients who had been discharged from hospital between March 24, 2020 and June 17, 2021. Patients had a median age of 60·0 years, 68·9% (401/582) were male, 94·6% (561/582) had received oxygen therapy, and 35·2% (205/582) mechanical ventilation. We followed patients across four different rehabilitation settings: no rehabilitation (No-rehab, 19·6% [114/582]), community-based rehabilitation (Com-rehab, 54·1% [315/582]), medical rehabilitation (Med-rehab, 13·7% [80/582]), and rehabilitation in a skilled nursing facility (SNF-rehab, 12·5% [73/582]). Overall, outcomes in 6MWT (14·9 meters [95% CI 7·4 to 22·4]), 1MSTST (2·2 repetitions [1·5 to 2·8]), and HGS (3·5 kg [2·9 to 4·0]) improved significantly (p<0·001) from 3 to 6 months and only HGS from 6 to 12 months (2·5 kg [1·8 to 3·1]; p<0·001). DEMMI scores did not significantly improve over time. At 3 months, percentage of normative values reached in 1MSTST differed significantly (p<0.001) across care pathways, with largest impairments in Med- and SNF-rehab groups. At 12 months these differences were no longer significant, reaching, overall, 90·5% on 6MWD, 75·4% on 1MSTST, and 106·9% on HGS. Interpretation: Overall, physical function improved after hospitalization for COVID-19, with largest improvement within 6 months post-discharge. Patients with rehabilitation after hospital discharge improved in more than one component of physical function, whereas patients without rehabilitation improved solely in muscle strength. Patients who received rehabilitation, and particularly patients with Med- and SNF-rehab, had more severe impairment in physical function at 3 months, but reached equal levels at 12 months compared to patients without follow-up treatment. Our findings indicate the importance of rehabilitation. Funding: ZonMw, Rijndam Rehabilitation, Laurens (The Netherlands).

Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): An external validation study.

BACKGROUND: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations. We therefore aimed to determine the generalizability of the previously developed machine learning concept. METHODS: We externally validated the machine learning concept in gastrointestinal and oncology surgery patients admitted to 3 nonacademic hospitals in The Netherlands between January 2017 and June 2021, who remained admitted 2 days after surgery. Primary outcome was the ability to predict hospital interventions after the second postoperative day, which were defined as unplanned reoperations, radiological interventions, and/or intravenous antibiotics administration. Four forest models were locally trained and evaluated with respect to area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: All models were trained on 1,693 epsiodes, of which 731 (29.9%) required a hospital intervention and demonstrated strong performance (area under the receiver operating characteristics curve only varied 4%). The best model achieved an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [0.81-0.85]), sensitivity of 77.9% (0.67-0.87), specificity of 79.2% (0.72-0.85), positive predictive value of 61.6% (0.54-0.69), and negative predictive value of 89.3% (0.85-0.93). CONCLUSION: This study showed that a previously developed machine learning concept can predict safe discharge in different surgical populations and hospital settings (academic versus nonacademic) by training a model on local patient data. Given its high accuracy, integration of the machine learning concept into the clinical workflow could expedite surgical discharge and aid hospitals in addressing capacity challenges by reducing avoidable bed-days.

Increasing cardio-thoracic productivity at Erasmus MC.

The Thoraxcenter of Erasmus MC started an improvement project in 2015 in order to increase the number of open-heart surgeries by 150 for three consecutive years (450 in total, +46%), and to decrease the access time from 12-14 to 2-3 weeks by the end of 2016. This was required to attain economy of scale in a highly competitive market. In this paper we describe the first year of the project, focusing on its structure and interventions taken, resulting in 165 additional open-heart surgeries carried out in 2016 and a significantly shorter access time of 2-3 weeks.

Intensive Care Unit-Specific Virtual Reality for Critically Ill Patients With COVID-19: Multicenter Randomized Controlled Trial.

BACKGROUND: Although psychological sequelae after intensive care unit (ICU) treatment are considered quite intrusive, robustly effective interventions to treat or prevent these long-term sequelae are lacking. Recently, it was demonstrated that ICU-specific virtual reality (ICU-VR) is a feasible and acceptable intervention with potential mental health benefits. However, its effect on mental health and ICU aftercare in COVID-19 ICU survivors is unknown. OBJECTIVE: This study aimed to explore the effects of ICU-VR on mental health and on patients' perceived quality of, satisfaction with, and rating of ICU aftercare among COVID-19 ICU survivors. METHODS: This was a multicenter randomized controlled trial. Patients were randomized to either the ICU-VR (intervention) or the control group. All patients were invited to an COVID-19 post-ICU follow-up clinic 3 months after hospital discharge, during which patients in the intervention group received ICU-VR. One month and 3 months later (4 and 6 months after hospital discharge), mental health, quality of life, perceived quality, satisfaction with, and rating of ICU aftercare were scored using questionnaires. RESULTS: Eighty-nine patients (median age 58 years; 63 males, 70%) were included. The prevalence and severity of psychological distress were limited throughout follow-up, and no differences in psychological distress or quality of life were observed between the groups. ICU-VR improved satisfaction with (mean score 8.7, SD 1.6 vs 7.6, SD 1.6 [ICU-VR vs control]; t64=-2.82, P=.006) and overall rating of ICU aftercare (mean overall rating of aftercare 8.9, SD 0.9 vs 7.8, SD 1.7 [ICU-VR vs control]; t64=-3.25; P=.002) compared to controls. ICU-VR added to the quality of ICU aftercare according to 81% of the patients, and all patients would recommend ICU-VR to other ICU survivors. CONCLUSIONS: ICU-VR is a feasible and acceptable innovative method to improve satisfaction with and rating of ICU aftercare and adds to its perceived quality. We observed a low prevalence of psychological distress after ICU treatment for COVID-19, and ICU-VR did not improve psychological recovery or quality of life. Future research is needed to confirm our results in other critical illness survivors to potentially facilitate ICU-VR's widespread availability and application during follow-up. TRIAL REGISTRATION: Netherlands Trial Register NL8835; https://www.trialregister.nl/trial/8835. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-021-05271-z.

Persistent Health Problems beyond Pulmonary Recovery up to 6 Months after Hospitalization for COVID-19: A Longitudinal Study of Respiratory, Physical, and Psychological Outcomes.

Rationale: Data on longitudinal recovery after hospitalization for coronavirus disease (COVID-19) currently remain scarce, just as outcomes beyond 3 months of follow-up do. Objectives: To evaluate the sequelae up to 6 months after hospitalization for COVID-19 by considering 1) recovery as it relates to pulmonary function, radiological abnormalities, physical and mental health status, and health-related quality of life (HR-QoL) and 2) the predictors of the most clinically relevant sequelae. Methods: Patients were evaluated at 6 weeks, 3 months, and 6 months after hospitalization by using pulmonary function testing, radiological evaluation, and online questionnaires on the physical and mental health status and HR-QoL. Outcomes were analyzed using repeated-measurement analyses. Results: Ninety-two patients were included (mean age, 58.2 ± 12.3 yr; 58 [63.0%] men). The estimated percentage of patients with impaired forced vital capacity improved from 25% at 6 weeks to 11% at 6 months; for impaired diffusion capacity, this percentage improved from 63% to 46%. Radiologically, ground-glass opacity decreased but fibrosis persisted. The majority of patients (89.1%) still reported one or more symptoms 6 months after discharge. Fatigue decreased significantly over time (P = 0.006). Nonetheless, fatigue remained in 51% of the patients at 6 months. HR-QoL (nearly) normalized in most domains at 6 months, except for physical role functioning, with persistent fatigue and the length of hospitalization being the most important predictors. Conclusions: During the first 6 months after hospitalization for COVID-19, most patients demonstrated continuing recovery across all health domains, but persistent sequelae were frequent. Fatigue was the most frequent residual and persistent symptom up to 6 months after hospitalization, importantly impacting HR-QoL.

Virtual reality for relatives of ICU patients to improve psychological sequelae: study protocol for a multicentre, randomised controlled trial.

INTRODUCTION: Intensive care unit (ICU) admission of a relative might lead to psychological distress and complicated grief (post-intensive care syndrome-family; PICS-F). Evidence suggests that increased distress during ICU stay increases risk of PICS-F, resulting in difficulty returning to their normal lives after the ICU experience. Effective interventions to improve PICS-F are currently lacking. In the present trial, we hypothesised that information provision using ICU-specific Virtual Reality for Family members/relatives (ICU-VR-F) may improve understanding of the ICU and subsequently improve psychological well-being and quality of life in relatives of patients admitted to the ICU. METHODS AND ANALYSIS: This multicentre, clustered randomised controlled trial will be conducted from January to December 2021 in the mixed medical-surgical ICUs of four hospitals in Rotterdam, the Netherlands. We aim to include adult relatives of 160 ICU patients with an expected ICU length of stay over 72 hours. Participants will be randomised clustered per patient in a 1:1 ratio to either the intervention or control group. Participants allocated to the intervention group will receive ICU-VR-F, an information video that can be watched in VR, while the control group will receive usual care. Initiation of ICU-VR-F will be during their hospital visit unless participants cannot visit the hospital due to COVID-19 regulations, then VR can be watched digitally at home. The primary objective is to study the effect of ICU-VR-F on psychological well-being and quality of life up to 6 months after the patients' ICU discharge. The secondary outcome is the degree of understanding of ICU treatment and ICU modalities. ETHICS AND DISSEMINATION: The Medical Ethics Committee of the Erasmus Medical Centre, Rotterdam, the Netherlands, approved the study and local approval was obtained from each participating centre (NL73670.078.20). Our findings will be disseminated by presentation of the results at (inter)national conferences and publication in scientific, peer-reviewed journals. TRIAL REGISTRATION NUMBER: Netherlands Trial Register (TrialRegister.nl, NL9220).