Intensive Care Unit Resources Research Articles

Neuropsychiatric assessment and management of the ICU survivor.

Any survivor among the millions of patients admitted to the intensive care unit (ICU) for critical illness each year is susceptible to persistent health problems that continue after discharge and may lead to post-intensive care syndrome (PICS), defined as new or worsening dysfunction from physical impairment, cognitive impairment, or emotional impairment, or a combination. Considering the increased rates of ICU survival and the growing elderly population more likely to utilize ICU resources, critical care practitioners have broadened their focus on outcomes and care of ICU survivors to include the acute post-ICU survival period as well as months and even years after ICU discharge. This review focuses on the neuropsychiatric aspects of PICS in ICU survivors including diagnostic, screening, and treatment recommendations. It also highlights the value of post-ICU clinics and the unique role of the consultation psychiatrist in the care of this patient population.

Can the intensivists predict the outcomes of critically ill patients on the appropriateness of intensive care unit admission for limited intensive care unit resources ?

Can the intensivists predict the outcomes of critically ill patients on the appropriateness of intensive care unit admission for limited intensive care unit resources ?

COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

The coronavirus pandemic overwhelmed many countries and their healthcare systems. Shortage of testing kits and Intensive-Care-Unit (ICU) beds for critical patients have become a norm in most developing countries. This has prompted the need to rapidly identify the COVID-19 patients to stop the spread of the virus and also to find critical patients. The latter is imperative for determining the state of critically ill patients as quickly as possible. This will lower the number of deaths from the infection. In this paper, we propose a cloud-based smartphone application for the early prognosis of COVID-19 infected patients and also predict their mortality risk using their symptoms. Moreover, we heuristically identified the most important symptoms necessary for making such predictions. We have successfully reduced the number of features by almost half for the prognosis and by more than a third for forecasting the mortality risk, compared to the contemporary studies. The application makes the real-time analysis using machine learning models, designed and stored in the cloud. Our machine learning model demonstrates an accuracy, precision, recall, and F1 score of 97.72%, 100%, 95.55%, and 97.70%, respectively, in identifying the COVID-19 infected patients and with an accuracy, precision, recall, and F1 score of 90.83%, 88.47%, 92.94%, and 90.65%, respectively, in forecasting the mortality risk from the infection. The real-time cloud-based approach yields faster responses, which is critical in the time of pandemic for mitigating the infection spread and aiding in the efficient management of the limited ICU resources.

Differences in Characteristics, Hospital Care, and Outcomes between Acute Critically Ill Emergency Department Patients Receiving Palliative Care and Usual Care

Background: The early integration of palliative care in the emergency department (ED-PC) provides several benefits, including improved quality of life with optimal comfort measures, and symptom control. Whether palliative care could affect the intensive care unit admissions, hospital care and resource utilization requires further investigation. Aim: To determine the differences in inpatient characteristics, hospital care, survival, and resource utilization between patients receiving palliative care (ED-PC) and usual care (UC). Design: Retrospective observational study. Setting/participants: We enrolled consecutive, acute, critically ill patients admitted to the emergency intensive care unit at Taipei Veterans General Hospital from 1 February 2018 to 31 January 2020. Results: A total of 1273 patients were evaluated for unmet palliative care needs; 685 patients received ED-PC and 588 received UC. The palliative care patients were more severely frail (AOR 2.217 (1.295–3.797), p = 0.004), had functional deterioration with three ADLs (AOR 1.348 (1.040–1.748), p = 0.024), biopsychosocial discomfort (AOR 1.696 (1.315–2.187), p < 0.001), higher Taiwan Triage and Acuity Scale 1 (p = 0.024), higher in-hospital mortality (AOR 1.983 (1.540–2.555), p < 0.001), were four times more likely to sign an DNR (AOR 4.536 (2.522–8.158), p < 0.001), and were twice as likely to sign an DNR at admission (AOR 2.1331.619–2.811), p < 0.001). Palliative care patients received less epinephrine (AOR 0.424 (0.265–0.678), p < 0.001), more frequent withdrawal of an endotracheal tube (AOR 8.780 (1.122–68.720), p = 0.038), and more narcotics (AOR1.675 (1.132–2.477), p = 0.010). Palliative care patients exhibited lower 7-day, 30-day, and 90-day survival rates (p < 0.001). There was no significant difference in the hospital length of stay (LOS) (21.2 ± 26.6 vs. 21.7 ± 20.6, p = 0.709) nor total hospital expenses (293,169 ± 350,043 vs. 294,161 ± 315,275, p = 0.958). Conclusion: Acute critically ill patients receiving palliative care were more frail, more critical, and had higher in-hospital mortality. Palliative care patients received less epinephrine, more endotracheal extubation, and more narcotics. There was no difference in the hospital LOS or hospital costs between the palliative and usual care groups. The synthesis of ED-PC is new but achievable with potential benefits to align care with patient goals.

Community, Hope, and Resilience: Parental Perspectives on Peer Support in Neonatology

To describe the perspective of parents who participated in peer-to-peer support meetings with parents of children in a neonatal intensive care unit (NICU) and veteran resource parents with previous NICU experience. During a longitudinal evaluation in a tertiary care NICU, participating parents were asked to evaluate meetings; with open-ended questions, they were asked about their perspectives. Results were analyzed using mixed methods. Forty-five NICU parents participated over a 10-week study period. They were followed longitudinally after attending at least 1 of the 10 meetings offered; 95% of parents (43 of 45) reported that the meeting was useful to them and gave an overall evaluation of 8.7 out of 10 (average). For each meeting, all the subjects on the checklist of the moderators (veteran resource parents) were discussed with new parents. When describing why and how the meetings were useful to them in their answers to open-ended questions, NICU parents reported 3 major themes: (1) decreasing isolation and being a community (73%), (2) hope and resilience (63%), and (3) getting practical "parent" information (32%). Sharing stories with parents who also had experienced loss, sadness, and grief, NICU parents trusted that it was possible to adapt and thrive. The meetings normalized parents' emotions (92%), decreased negative emotions (eg, anger, sadness, guilt), empowered them in their parental role, and helped them communicate with loved ones and providers. Peer support meetings are a unique and useful means to support parents. Future investigations will investigate whether and how this type of intervention can improve clinical outcomes.

Tackling the Waves of COVID-19: A Planning Model for Intrahospital Resource Allocation.

Background: The current pandemic requires hospitals to ensure care not only for the growing number of COVID-19 patients but also regular patients. Hospital resources must be allocated accordingly. Objective: To provide hospitals with a planning model to optimally allocate resources to intensive care units given a certain incidence of COVID-19 cases. Methods: The analysis included 334 cases from four adjacent counties south-west of Munich. From length of stay and type of ward [general ward (NOR), intensive care unit (ICU)] probabilities of case numbers within a hospital at a certain time point were derived. The epidemiological situation was simulated by the effective reproduction number R, the infection rates in mid-August 2020 in the counties, and the German hospitalization rate. Simulation results are compared with real data from 2nd and 3rd wave (September 2020-May 2021). Results: With R = 2, a hospitalization rate of 17%, mitigation measures implemented on day 9 (i.e., 7-day incidence surpassing 50/100,000), the peak occupancy was reached on day 22 (155.1 beds) for the normal ward and on day 25 (44.9 beds) for the intensive care unit. A higher R led to higher occupancy rates. Simulated number of infections and intensive care unit occupancy was concordant in validation with real data obtained from the 2nd and 3rd waves in Germany. Conclusion: Hospitals could expect a peak occupancy of normal ward and intensive care unit within ~5-11 days after infections reached their peak and critical resources could be allocated accordingly. This delay (in particular for the peak of intensive care unit occupancy) might give options for timely preparation of additional intensive care unit resources.

SARS-CoV-2-induced autophagy dysregulation may cause neuronal dysfunction in COVID-19.

SARS-CoV-2-induced autophagy dysregulation may cause neuronal dysfunction in COVID-19.

In-Situ Simulation for Intensive Care Nurses During the COVID-19 Pandemic in Italy: Advantages and Challenges

The COVID-19 pandemic required a global increase in intensive care unit (ICU) resources and the recruitment of a great number of nurses without any tprior critical care experience. The opportunities for traditional education and supervised clinical training were limited to prevent infections. The massive increase of nursing staff resources required a rethinking of the usual educational strategies for newly acquired nurses. This short communication describes our experience of an "in-situ" simulation training course in an Italian tertiary level hospital. A series of two-part classes were structured with short lectures on fundamental principles of intensive care nursing and brief hands-on sessions, and a set of simulated scenarios, based upon the most common situations to be faced in the ICU. In-situ simulation offers greater realism and transferability and represents a cost-effective strategy, avoiding the costs and the maintenance of a dedicated simulation center. The simulated multidisciplinary teamwork in the real ICU setting contributes to an effective experiential learning, improving staff familiarity with devices, equipment, and environment, and allows trainees to improve both technical and nontechnical skills.

Impact of previous cardiac conditions in prognosis and clinical management of patient with COVID-19 infection

Abstract Background Recent studies suggest a higher mortality rate because of COVID-19 in patients with previous cardiac conditions compared to those without. Given the limited resources of intensive care units (ICU) during the pandemic outbreak, this fact has important implications. Purpose The main purpose of this study was to compare the 30-day mortality of the COVID-19 infection in patients with and without previous cardiac conditions. The secondary end point was to assess the differences in clinical severity of the infection (as development of Acute Respiratory Distress Syndrome – ARDS) and ICU admission amongst these patients. Methods A total of 1708 consecutive patients were prospectively included. The inclusion criteria were: a confirmed positive diagnosis of COVID-19 infection by PCR and being admitted to our centre between 18th and 23rd March 2020 and 22nd August and 9th January 2021. Patients were classified in two groups according to the presence of previous cardiac conditions (defined as previous history of myocardial infarction, heart failure and atrial fibrillation). Other comorbidities were extensively explored and Charlson Comorbidity Index was calculated. A propensity-score matching was performed and 145 patients with previous cardiac conditions were matched with 145 patients without. Results The group of patients with a previous cardiac condition included 421 patients (24.6%). The crude analysis showed a higher 30-day mortality rate among patients with previous cardiac affections (35.6% vs. 14.6%, p&amp;lt;0.001). They were also less likely to be admitted to the ICU (9.8% vs. 6.2%, p=0.022) and had a higher prevalence ARDS (48.9% vs. 33.9%, p&amp;lt;0.001). In the matched cohort, there were no significant differences between both groups regarding mortality (24.8% in the group of patients with previous cardiac conditions vs. 31.0%, p=0.272) nor ARDS prevalence (50.3% vs. 53.1%, p=0.655). There was a trend toward patients with previous cardiac conditions to be less likely to be admitted to the ICU (4.8% vs. 9.7%, p=0.090). Conclusions Patients with a personal history of previous cardiac conditions were less likely to be admitted to the ICU. However, our results show that when comparing cohorts with similar comorbidity burden, a previous cardiopathy “per se” does not significantly increase the risk of death in patients with a concomitant COVID infection. Funding Acknowledgement Type of funding sources: None. Mortality unmatched vs matched cohort

Outcomes of critically ill patients according to the perception of intensivists on the appropriateness of intensive care unit admission.

BackgroundIt is important for intensivists to determine which patient may benefit from intensive care unit (ICU) admission. We aimed to assess the outcomes of patients perceived as non-beneficially or beneficially admitted to the ICU and evaluate whether their prognosis was consistent with the intensivists’ perception.MethodsA prospective observational study was conducted on patients admitted to the medical ICU of a tertiary referral center between February and April 2014. The perceptions of four intensivists at admission (day 1) and on day 3 were investigated as non-beneficial admission, beneficial admission, or indeterminate state.ResultsA total of 210 patients were enrolled. On days 1 and 3, 22 (10%) and 23 (11%) patients were judged as having non-beneficial admission; 166 (79%) and 159 (79%), beneficial admission; and 22 (10%) and 21 (10%), indeterminate state, respectively. The ICU mortality rates of each group on day 1 were 59%, 23%, and 59%, respectively; their 6-month mortality rates were 100%, 48%, and 82%, respectively. The perceptions of non-beneficial admission or indeterminate state were the significant predictors of ICU mortality (day 3; odds ratio [OR], 4.049; 95% confidence interval [CI], 1.892–8.664; P<0.001) and 6-month mortality (day 1: OR, 4.983; 95% CI, 1.260–19.703; P=0.022; day 3: OR, 4.459; 95% CI, 1.162–17.121; P=0.029).ConclusionsThe outcomes of patients perceived as having non-beneficial admission were extremely poor. The intensivists’ perception was important in predicting patients’ outcomes and was more consistent with long-term prognosis than with immediate outcomes. The intensivists’ role can be reflected in limited ICU resource utilization.

A descriptive study of the surge response and outcomes of ICU patients with COVID-19 during first wave in Nordic countries.

BackgroundWe sought to provide a description of surge response strategies and characteristics, clinical management and outcomes of patients with severe COVID‐19 in the intensive care unit (ICU) during the first wave of the pandemic in Denmark, Finland, Iceland, Norway and Sweden.MethodsRepresentatives from the national ICU registries for each of the five countries provided clinical data and a description of the strategies to allocate ICU resources and increase the ICU capacity during the pandemic. All adult patients admitted to the ICU for COVID‐19 disease during the first wave of COVID‐19 were included. The clinical characteristics, ICU management and outcomes of individual countries were described with descriptive statistics.ResultsMost countries more than doubled their ICU capacity during the pandemic. For patients positive for SARS‐CoV‐2, the ratio of requiring ICU admission for COVID‐19 varied substantially (1.6%–6.7%). Apart from age (proportion of patients aged 65 years or over between 29% and 62%), baseline characteristics, chronic comorbidity burden and acute presentations of COVID‐19 disease were similar among the five countries. While utilization of invasive mechanical ventilation was high (59%–85%) in all countries, the proportion of patients receiving renal replacement therapy (7%–26%) and various experimental therapies for COVID‐19 disease varied substantially (e.g. use of hydroxychloroquine 0%–85%). Crude ICU mortality ranged from 11% to 33%.ConclusionThere was substantial variability in the critical care response in Nordic ICUs to the first wave of COVID‐19 pandemic, including usage of experimental medications. While ICU mortality was low in all countries, the observed variability warrants further attention.

Clinical characteristics of critically ill pregnant women with different admission methods to intensive care unit: data analysis from 2006 to 2019 in the university hospital

To compare the clinical characteristics of critically ill pregnant women admitted to the intensive care unit (ICU) with different admission methods, in order to make more effective and rational use of ICU resources. A retrospective study was conducted. The clinical data of critically ill pregnant women admitted to ICU of Peking University Third Hospital from January 2006 to July 2019 were analyzed. According to the admission mode to ICU, the pregnant women were divided into emergency admission group (transferred to ICU on the same day or the next day due to critical illness) and planned admission group (transferred to ICU 2 days after admitting in obstetric ward). The clinical characteristics of ICU critical pregnant women, such as the incidence, causes of admission, severity of the disease, main treatment measures, mortality, and medical expenses were collected, and a comparative analysis between the two groups was performed. During the nearly 14 years, a total of 576 critical pregnant women in ICU were enrolled, accounting for 0.8% (576/71 790) of the total number of obstetric inpatients and 4.6% (576/12 412) of the total number of ICU inpatients. Seven maternal deaths accounted for 1.2% of all critically pregnant women transferred to ICU, and the overall mortality of pregnant women was 10/100 thousand. Of the 576 critically pregnant women, there were 327 patients (56.8%) in the emergency admission group and 249 patients (43.2%) in the planned admission group. Compared with the planned admission group, the proportion of elective cesarean section in the emergency admission group was significantly lower (17.7% vs. 94.0%, P < 0.01), and the proportion of emergency cesarean section was significantly higher (65.1% vs. 2.4%, P < 0.01), the acute physiology and chronic health evaluation (APACHE II, APACHE III) scores, simplified acute physiology score II (SAPS II) and Marshall score were significantly higher [APACHE II score: 6.0 (4.0, 9.8) vs. 4.0 (3.0, 7.0), APACHE III score: 14.0 (11.0, 20.3) vs. 12.0 (9.0, 16.0), SAPS II score: 8 (0, 12) vs. 3 (0, 8), Marshall score: 2 (1, 4) vs. 1 (1, 3), all P < 0.01]. The length of ICU stay in the emergency admission group was significantly longer than that in the planned admission group [days: 2 (1, 5) vs. 2 (1, 3), P < 0.01], and the total length of hospital stay was significantly shorter [days: 9 (7, 13) vs. 13 (10, 18), P < 0.01]. Both in the emergency admission group and the planned admission group, obstetric factors were the main reason for admission, 60.9% (199/327) and 70.3% (175/249), respectively. The proportion of postpartum hemorrhage was the highest [35.2% (115/327) and 57.0% (142/249)], followed by preeclampsia/eclampsia [7.0% (23/327) and 7.6% (19/249)]. Only 7 of the 19 critically pregnant women with puerperal infection were planned admission. All 21 patients with acute fatty liver of pregnancy (AFLP) during pregnancy were emergency admission. Among the emergency and planned admission patients, 73 patients (22.3%) and 42 patients (16.9%) required mechanical ventilation (duration of mechanical ventilation > 24 hours), 99 patients (30.3%) and 35 patients (14.1%) needed vasoactive agents, 67 patients (20.5%) and 20 patients (8.0%) received hemodynamic monitoring, and 123 patients (37.6%) and 154 patients (61.8%) were given anticoagulation therapy, respectively. In terms of severity score of critical pregnant women, there were significant differences in APACHE II, APACHE III, SAPS II and Marshall scores of pregnant women with different diseases. Among them, the APACHE III, SAPS II and Marshall scores of AFLP were the highest [21.0 (15.0, 32.5), 12.0 (6.0, 16.5) and 6.0 (3.5, 8.0), respectively]. The APACHE II and APACHE III scores of postpartum hemorrhage were the lowest [4.0 (3.0, 7.0), 12.0 (10.0, 16.0)]. The SAPS II score of pneumonia was the lowest [2.0 (0, 14.0)]. The Marshall score for puerperal infection was the lowest [1.0 (0, 3.0)]. In terms of the total medical expenses, the cost in the emergency admission group was significantly lower than that in the planned admission group [10 thousand Yuan: 3.1 (2.0, 4.7) vs. 4.1 (2.9, 5.8), P < 0.05]. Compared with the critically ill pregnant women who planned to be admitted to ICU, the patients emergency admitted to ICU were more complicated and urgent, and the severity of the condition was scored higher. At present, the severity scoring system commonly used in ICU can only partly evaluate the severity of critically ill pregnant women, therefore, it is necessary to design the specific severity scoring system for critically ill pregnant women to effectively and rationally use the precious ICU resources.

COVID-19 pandemic in ICU. Limited resources for many patients: approaches and criteria for triaging.

The COVID-19 pandemic has shattered the illusion that healthcare resource shortages that require rationing are problems restricted to low- and middle-income countries. During the pandemic surges, many high-income countries have been confronted with unprecedented demands for healthcare systems that dramatically exceeded available resources. Hospitals capacities were overwhelmed, and physicians working in intensive care units (ICUs) were often forced to deny admissions to patients in desperate need of intensive care. To support these difficult decisions, many scientific societies and governmental bodies have developed guidelines on the triage of patients in need of mechanical ventilation and other life-support treatments. The ethical approaches underlying these guidelines were grounded on egalitarian or utilitarian principles. Thus far, however, consensus on the approaches used, and, above all, on the solutions adopted have been limited, giving rise to a clash of opinions that has further complicated health professionals' ability to respond optimally to their patients' needs. As the COVID-19 crisis moves toward a phase of what some have called "pandemic normalcy," the need to debate the merits and demerits of the individual decisions made in the allocation of ICU resources seems less pressing. Instead, the aims of the authors are: 1) to critically review the approaches and criteria used for triaging patients to be admitted in ICU; 2) to clarify how macro- and micro-allocation choices, in their interdependance, can condition decision-making processes regarding the care of individual patients; 3) to reflect on the need for decision-makers and professionals working in ICUs to maintain a proper degree of "honesty" towards citizens and patients regarding the causes of the resource shortages and the decision-making processes, which, in different ways routinely and in crisis times, involve the need to make "tragic choices" at both levels.

Quality of life in elderly ICU survivors before the COVID-19 pandemic: a systematic review and meta-analysis of cohort studies

ObjectivesThe influence of age on intensive care unit (ICU) decision-making is complex, and it is unclear if it is based on expected subjective or objective patient outcomes. To address recent...

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.

COVID-19 Critical Illness: A Data-Driven Review.

The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges in critical care medicine, including extreme demand for intensive care unit (ICU) resources and rapidly evolving understanding of a novel disease. Up to one-third of hospitalized patients with COVID-19 experience critical illness. The most common form of organ failure in COVID-19 critical illness is acute hypoxemic respiratory failure, which clinically presents as acute respiratory distress syndrome (ARDS) in three-quarters of ICU patients. Noninvasive respiratory support modalities are being used with increasing frequency given their potential to reduce the need for intubation. Determining optimal patient selection for and timing of intubation remains a challenge. Management of mechanically ventilated patients with COVID-19 largely mirrors that of non-COVID-19 ARDS. Organ failure is common and portends a poor prognosis. Mortality rates have improved over the course of the pandemic, likely owing to increasing disease familiarity, data-driven pharmacologics, and improved adherence to evidence-based critical care.

Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning.

Background: Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration over time. We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches. Methods: For model description, we extracted data from the first 3 ICU days after ARDS diagnosis from patients included in the publicly available MIMIC-III database. Disease progression was tracked along those 3 ICU days to assess lung severity according to Berlin criteria. Three robust supervised ML techniques were implemented using Python 3.7 (Light Gradient Boosting Machine (LightGBM); Random Forest (RF); and eXtreme Gradient Boosting (XGBoost)) for predicting MV duration. For external validation, we used the publicly available multicenter database eICU. Results: A total of 2466 and 5153 patients in MIMIC-III and eICU databases, respectively, received MV for >48 h. Median MV duration of extracted patients was 6.5 days (IQR 4.4–9.8 days) in MIMIC-III and 5.0 days (IQR 3.0–9.0 days) in eICU. LightGBM was the best model in predicting MV duration after ARDS onset in MIMIC-III with a root mean square error (RMSE) of 6.10–6.41 days, and it was externally validated in eICU with RMSE of 5.87–6.08 days. The best early prediction model was obtained with data captured in the 2nd day. Conclusions: Supervised ML can make early and accurate predictions of MV duration in ARDS after onset over time across ICUs. Supervised ML models might have important implications for optimizing ICU resource utilization and high acute cost reduction of MV.

Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning.

Background: Patients with acute type A aortic dissection are usually transferred to the intensive care unit (ICU) after surgery. Prolonged ICU length of stay (ICU-LOS) is associated with higher level of care and higher mortality. We aimed to develop and validate machine learning models for predicting ICU-LOS after acute type A aortic dissection surgery.Methods: A total of 353 patients with acute type A aortic dissection transferred to ICU after surgery from September 2016 to August 2019 were included. The patients were randomly divided into the training dataset (70%) and the validation dataset (30%). Eighty-four preoperative and intraoperative factors were collected for each patient. ICU-LOS was divided into four intervals (<4, 4–7, 7–10, and >10 days) according to interquartile range. Kendall correlation coefficient was used to identify factors associated with ICU-LOS. Five classic classifiers, Naive Bayes, Linear Regression, Decision Tree, Random Forest, and Gradient Boosting Decision Tree, were developed to predict ICU-LOS. Area under the curve (AUC) was used to evaluate the models' performance.Results: The mean age of patients was 51.0 ± 10.9 years and 307 (87.0%) were males. Twelve predictors were identified for ICU-LOS, namely, D-dimer, serum creatinine, lactate dehydrogenase, cardiopulmonary bypass time, fasting blood glucose, white blood cell count, surgical time, aortic cross-clamping time, with Marfan's syndrome, without Marfan's syndrome, without aortic aneurysm, and platelet count. Random Forest yielded the highest performance, with an AUC of 0.991 (95% confidence interval [CI]: 0.978–1.000) and 0.837 (95% CI: 0.766–0.908) in the training and validation datasets, respectively.Conclusions: Machine learning has the potential to predict ICU-LOS for acute type A aortic dissection. This tool could improve the management of ICU resources and patient-throughput planning, and allow better communication with patients and their families.

Ward nurses' experiences and perceptions of the critical care outreach service: A qualitative study undertaken in a large teaching hospital in the West of Ireland.

The critical care outreach service (CCOS) was established in a large teaching hospital in the West of Ireland in 2014 to provide an extension of CCOS between the intensive care unit (ICU) and the wards outside ICU. With the ever-increasing pressure on ICU resources, CCOS seems to offer an additional service to patients who are critically ill and deteriorating in hospital wards. The aim of this study is to explore ward nurses' experiences and perceptions of the CCOS. A qualitative descriptive methodology aimed to obtain a descriptive account of ward nurses' experiences of the CCOS. Data were collected using semi-structured interviews. Twelve nurses from the West of Ireland participated in the study. Braun and Clarke's six-step thematic analysis was used to analyse the data. Three core themes identified in this study included clinical support and decision-making; sharing knowledge and skills, and communication. All 12 nurses identified the positive impacts that they felt the current outreach service provided to them on a daily basis. Study findings indicate that nurses value the role of the CCOS in supporting the care of the critically ill and deteriorating patients at ward level. The CCOS has become invaluable to the ward nurses in their daily practice especially at a time when resources are lacking and patient acuity is on the rise.

Importance of critical care staffing and standard intensive care therapy in the COVID-19 era: a descriptive study of the first epidemic wave at a Swiss tertiary intensive care unit.

Mortality rates of COVID-19 patients hospitalised in intensive care units (ICUs) are generally high. Availability of ICU resources might influence clinical outcomes. The aim of this study was to examine the clinical course of the 42 patients treated during the first epidemic wave between 2 March and 20 May 2020 at the tertiary ICU of the Bern University Hospital, where staffing, equipment and drugs were not limited. For this descriptive study, retrospective data of the first COVID-19 wave in an interdisciplinary adult ICU of a Swiss University hospital was used. The study included data regarding healthcare staffing and COVID-19 patients. The primary outcome was the ICU mortality in COVID-19 patients. Patients&rsquo; median age was 61 years (range 32&ndash;86), simplified acute physiology score (SAPS-II) was 46 (13&ndash;90), 81% of the patients were males, 79% were mechanically ventilated (3 of them on extracorporeal membrane oxygenation), 31% were under renal replacement therapy and 21% received steroids. All patients were fully anticoagulated from the time of admission. No off-label experimental antiviral or anti-inflammatory drugs were used with the exception of one patient, and antibiotic prescription was restrictive. Nurse-to-patient ratio was 1:1 during all shifts, and the physician-to-patient ratio was 1:4 (day shift) and 1:10 (night shift). Infectious disease specialists and physiotherapists were present every day. The median ICU length of stay was 10 days (1&ndash;38) days, and ICU and hospital mortality rates were 7% and 12%, respectively. Careful intensive care treatment, without off-label drug use but including steroids in selected cases, combined with an interdisciplinary approach and provision of sufficient human resources, were associated with low ICU and hospital mortality rates despite high disease severity. Availability of qualified human resources may have an important impact on the outcome of COVID-19.