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.

CO-FLOW: COvid-19 Follow-up care paths and Long-term Outcomes Within the Dutch health care system: study protocol of a multicenter prospective cohort study following patients 2 years after hospital discharge.

BACKGROUND: First studies indicate that up to 6 months after hospital discharge, coronavirus disease 2019 (COVID-19) causes severe physical, cognitive, and psychological impairments, which may affect participation and health-related quality of life (HRQoL). After hospitalization for COVID-19, a number of patients are referred to medical rehabilitation centers or skilled nursing facilities for further treatment, while others go home with or without aftercare. The aftercare paths include 1] community-based rehabilitation; 2] in- and outpatient medical rehabilitation; 3] inpatient rehabilitation in skilled nursing facilities; and 4] sheltered care (inpatient). These aftercare paths and the trajectories of recovery after COVID-19 urgently need long-term in-depth evaluation to optimize and personalize treatment. CO-FLOW aims, by following the outcomes and aftercare paths of all COVID-19 patients after hospital discharge, to systematically study over a 2-year period: 1] trajectories of physical, cognitive, and psychological recovery; 2] patient flows, healthcare utilization, patient satisfaction with aftercare, and barriers/facilitators regarding aftercare as experienced by healthcare professionals; 3] effects of physical, cognitive, and psychological outcomes on participation and HRQoL; and 4] predictors for long-term recovery, health care utilization, and patient satisfaction with aftercare. METHODS: CO-FLOW is a multicenter prospective cohort study in the mid-west of the Netherlands with a 2-year follow-up period. Measurements comprise non-invasive clinical tests and patient reported outcome measures from a combined rehabilitation, pulmonary, and intensive care perspective. Measurements are performed at 3, 6, 12, and 24 months after hospital discharge and, if applicable, at rehabilitation discharge. CO-FLOW aims to include at least 500 patients who survived hospitalization for COVID-19, aged ≥18 years. DISCUSSION: CO-FLOW will provide in-depth knowledge on the long-term sequelae of COVID-19 and the quality of current aftercare paths for patients who survived hospitalization. This knowledge is a prerequisite to facilitate the right care in the right place for COVID-19 and comparable future infectious diseases. TRIAL REGISTRATION: The Netherlands Trial Register (NTR), https://www.trialregister.nl . Registered: 12-06-2020, CO-FLOW trialregister no. NL8710.

Peripheral Perfusion Index Predicts Hypotension during Fluid Withdrawal by Continuous Veno-Venous Hemofiltration in Critically Ill Patients.

AIM: Peripheral perfusion may predict harmful hypovolemic hypotension during fluid withdrawal by continuous veno-venous hemofiltration (CVVH) in critically ill patients with acute kidney injury. METHODS: Twenty-three critically ill AKI patients were subjected to progressive fluid withdrawal. Systemic hemodynamics and peripheral perfusion index (PPI) by pulse oximetry, forearm-to-fingertip skin temperature gradient (Tskin-diff) and tissue oxygen saturation (StO2, near infra-red spectroscopy) were measured. RESULTS: Most hemodynamic values decreased with fluid withdrawal, particularly in the hypotensive group, except for stroke volume (SV) and cardiac output, which decreased to a great extent in the non-hypotensive patients. Increases in systemic vascular resistance (SVR) were less in hypotension. Baseline pulse pressure and PPI were lower in hypotensive (n = 10) than non-hypotensive patients and subsequent PPI values paralleled SV decreases. A baseline PPI ≤0.82 AU predicted hypotension with a sensitivity of 70%, and a specificity of 92% (AUC 0.80 ± 0.11, p = 0.004). CONCLUSION: Progressive fluid withdrawal during CVVH is poorly tolerated in patients with less increases in SVR. The occurrence of hypotension can be predicted by low baseline PPI.

Nitroglycerin reverts clinical manifestations of poor peripheral perfusion in patients with circulatory shock.

INTRODUCTION: Recent clinical studies have shown a relationship between abnormalities in peripheral perfusion and unfavorable outcome in patients with circulatory shock. Nitroglycerin is effective in restoring alterations in microcirculatory blood flow. The aim of this study was to investigate whether nitroglycerin could correct the parameters of abnormal peripheral circulation in resuscitated circulatory shock patients. METHODS: This interventional study recruited patients who had circulatory shock and who persisted with abnormal peripheral perfusion despite normalization of global hemodynamic parameters. Nitroglycerin started at 2 mg/hour and doubled stepwise (4, 8, and 16 mg/hour) each 15 minutes until an improvement in peripheral perfusion was observed. Peripheral circulation parameters included capillary refill time (CRT), skin-temperature gradient (Tskin-diff), perfusion index (PI), and tissue oxygen saturation (StO2) during a reactive hyperemia test (RincStO2). Measurements were performed before, at the maximum dose, and after cessation of nitroglycerin infusion. Data were analyzed by using linear model for repeated measurements and are presented as mean (standard error). RESULTS: Of the 15 patients included, four patients (27%) responded with an initial nitroglycerin dose of 2 mg/hour. In all patients, nitroglycerin infusion resulted in significant changes in CRT, Tskin-diff, and PI toward normal at the maximum dose of nitroglycerin: from 9.4 (0.6) seconds to 4.8 (0.3) seconds (P < 0.05), from 3.3 °C (0.7 °C) to 0.7 °C (0.6 °C) (P < 0.05), and from [log] -0.5% (0.2%) to 0.7% (0.1%) (P < 0.05), respectively. Similar changes in StO2 and RincStO2 were observed: from 75% (3.4%) to 84% (2.7%) (P < 0.05) and 1.9%/second (0.08%/second) to 2.8%/second (0.05%/second) (P < 0.05), respectively. The magnitude of changes in StO2 was more pronounced for StO2 of less than 75%: 11% versus 4%, respectively (P < 0.05). CONCLUSIONS: Dose-dependent infusion of nitroglycerin reverted abnormal peripheral perfusion and poor tissue oxygenation in patients following circulatory shock resuscitation. Individual requirements of nitroglycerin dose to improve peripheral circulation vary between patients. A simple and fast physical examination of peripheral circulation at the bedside can be used to titrate nitroglycerin infusion.

Clinical assessment of peripheral perfusion to predict postoperative complications after major abdominal surgery early: a prospective observational study in adults.

INTRODUCTION: Altered peripheral perfusion is strongly associated with poor outcome in critically ill patients. We wanted to determine whether repeated assessments of peripheral perfusion during the days following surgery could help to early identify patients that are more likely to develop postoperative complications. METHODS: Haemodynamic measurements and peripheral perfusion parameters were collected one day prior to surgery, directly after surgery (D0) and on the first (D1), second (D2) and third (D3) postoperative days. Peripheral perfusion assessment consisted of capillary refill time (CRT), peripheral perfusion index (PPI) and forearm-to-fingertip skin temperature gradient (T(skin-diff)). Generalized linear mixed models were used to predict severe complications within ten days after surgery based on Clavien-Dindo classification. RESULTS: We prospectively followed 137 consecutive patients, from among whom 111 were included in the analysis. Severe complications were observed in 19 patients (17.0%). Postoperatively, peripheral perfusion parameters were significantly altered in patients who subsequently developed severe complications compared to those who did not, and these parameters persisted over time. CRT was altered at D0, and PPI and T(skin-diff) were altered on D1 and D2, respectively. Among the different peripheral perfusion parameters, the diagnostic accuracy in predicting severe postoperative complications was highest for CRT on D2 (area under the receiver operating characteristic curve = 0.91 (95% confidence interval (CI) = 0.83 to 0.92)) with a sensitivity of 0.79 (95% CI = 0.54 to 0.94) and a specificity of 0.93 (95% CI = 0.86 to 0.97). Generalized mixed-model analysis demonstrated that abnormal peripheral perfusion on D2 and D3 was an independent predictor of severe postoperative complications (D2 odds ratio (OR) = 8.4, 95% CI = 2.7 to 25.9; D2 OR = 6.4, 95% CI = 2.1 to 19.6). CONCLUSIONS: In a group of patients assessed following major abdominal surgery, peripheral perfusion alterations were associated with the development of severe complications independently of systemic haemodynamics. Further research is needed to confirm these findings and to explore in more detail the effects of peripheral perfusion-targeted resuscitation following major abdominal surgery.

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.

From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.

PURPOSE: Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benefits is necessary to improve neonatal and pediatric care for critically ill patients. This systematic review seeks to assess the maturity of AI models in neonatal and pediatric intensive care unit (NICU and PICU) treatment, and their risk of bias and objectives. METHODS: We conducted a systematic search in Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar. Studies using AI models during NICU or PICU stay were eligible for inclusion. Study design, objective, dataset size, level of validation, risk of bias, and technological readiness of the models were extracted. RESULTS: Out of the 1257 identified studies 262 were included. The majority of studies was conducted in the NICU (66%) and most had a high risk of bias (77%). An insufficient sample size was the main cause for this high risk of bias. No studies were identified that integrated an AI model in routine clinical practice and the majority of the studies remained in the prototyping and model development phase. CONCLUSION: The majority of AI models remain within the testing and prototyping phase and have a high risk of bias. Bridging the gap between designing and clinical implementation of AI models is needed to warrant safe and trustworthy AI models. Specific guidelines and approaches can help improve clinical outcome with usage of AI.

Key Research Areas for Building and Deploying a Common Data Model for an Intensive Medicine Data Space in Europe and Beyond.

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation.

Peripheral perfusion index as an early predictor for central hypovolemia in awake healthy volunteers.

BACKGROUND: In healthy volunteers, we investigated the ability of the pulse oximeter-derived peripheral perfusion index (PPI) to detect progressive reductions in central blood volume. METHODS: Twenty-five awake, spontaneously breathing, healthy male volunteers were subjected to progressive reductions in central blood volume by inducing stepwise lower body negative pressure (LBNP) with 20 mm Hg for 5 minutes per step, from 0 to -20, -40, -60, and back to 0 mm Hg. Throughout the procedure, stroke volume (SV), heart rate (HR), and mean arterial blood pressure were recorded using volume-clamp finger plethysmography. Assessment of the PPI was done by pulse oximetry. Additionally, the forearm-to-fingertip skin-temperature gradient was measured. Data are presented as mean±SE. PPI underwent log transformation and is presented as median (25th-75th). RESULTS: Of the 25 subjects, one did not complete the study because of cardiovascular collapse. After the first LBNP step (-20 mm Hg), PPI decreased from 2.2 (1.6-3.3) to 1.2 (0.8-1.6) (P=0.007) and SV decreased from 116±3.0 mL to 104±2.6 mL (P=0.02). The magnitude of the PPI decrease (41%±6.0%) was statistically different from that observed for SV (9%±1.3%) and HR (3%±1.9%). During progression of LBNP, SV decreased and HR increased progressively with the increased applied negative pressure, whereas the PPI remained low throughout the remainder of the protocol and returned to baseline values when LBNP was released. At -60 mm Hg LBNP, SV decreased and HR increased by 36%±0.9% and 33%±2.4% from baseline, respectively. Mean arterial blood pressure remained in the same range throughout the experiment. CONCLUSIONS: These results indicate that the pulse oximeter-derived PPI may be a valuable adjunct diagnostic tool to detect early clinically significant central hypovolemia, before the onset of cardiovascular decompensation in healthy volunteers.

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.

Persistent peripheral and microcirculatory perfusion alterations after out-of-hospital cardiac arrest are associated with poor survival.

OBJECTIVE: To evaluate sublingual microcirculatory and peripheral tissue perfusion parameters in relation to systemic hemodynamics during and after therapeutic hypothermia following out-of-hospital cardiac arrest. DESIGN: Prospective observational study. SETTING: Intensive cardiac care unit at a university teaching hospital. SUBJECTS: We followed 80 patients, of whom 25 were included after out-of-hospital cardiac arrest. INTERVENTION: In all patients, we induced therapeutic hypothermia to 33°C during the first 24 hrs of admission. MEASUREMENTS AND MAIN RESULTS: Complete hemodynamic measurements were obtained directly on intensive cardiac care unit admission (baseline), during induced hypothermia (T1), directly after rewarming (T2), and another 24 hrs later (T3). In addition, the sublingual microcirculation was observed using sidestream dark-field imaging, and peripheral tissue perfusion was monitored with the peripheral perfusion index, capillary refill time, tissue oxygen saturation, and forearm-to-fingertip skin temperature gradient. During hypothermia, all sublingual microcirculatory parameters decreased significantly together with peripheral capillary refill time and the peripheral perfusion index, followed by a significant increase at T2. Changes in sublingual and peripheral tissue perfusion parameters were significantly related to changes in central body temperature, but not to changes in systemic hemodynamic variables such as cardiac index or mean arterial pressure. Surprisingly, these parameters were significantly lower in nonsurvivors (n=6) at admission and after rewarming. Persistent alterations in these parameters were related with the prevalence of organ dysfunction and were highly predictive of mortality. CONCLUSIONS: Following out-of-hospital cardiac arrest, the early postresuscitation phase is characterized by abnormalities in sublingual microcirculation and peripheral tissue perfusion, which are caused by vasoconstriction due to induced systemic hypothermia and not by impaired systemic blood flow. Persistence of these alterations is associated with organ failure and death, independent of systemic hemodynamics.

Monitoring peripheral perfusion in critically ill patients at the bedside.

PURPOSE OF REVIEW: The goal of circulatory monitoring is the use of an accurate, continuous and noninvasive method that can easily assess tissue perfusion under clinical conditions. As peripheral tissues are sensitive to alterations in perfusion, the noninvasive monitoring of peripheral circulation could be used as an early marker of systemic haemodynamic derangement. We, therefore, aim to discuss the currently available methods that can be used at the bedside as well as the role of peripheral perfusion monitoring in critically ill patients. RECENT FINDINGS: The deterioration of peripheral circulation has frequently been observed in critically ill patients with the use of subjective assessment and several optical techniques. In various patient categories, more severe and persistent alterations have been associated with worse outcomes, and these associations were independent of systemic haemodynamic parameters. Interventions aimed at systemic parameters have an unpredictable effect on peripheral circulation parameters, especially during hyperdynamic conditions. Thus, it appears that changes in peripheral perfusion reflect changes in regional vasomotor tone rather than systemic blood flow. SUMMARY: Subjective assessments and optical techniques provide important information regarding peripheral circulation. Moreover, these techniques are relatively easy to implement and interpret at the bedside and can be applied during acute conditions. Further research is warranted to investigate the effects of therapeutic interventions on peripheral perfusion parameters and patient outcome.

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.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter.

OBJECTIVE: Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research. METHODS: We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine. CONCLUSION: This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.

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).

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.

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.

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.

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.