Urine Output and Mortality in Patients Resuscitated from out of Hospital Cardiac Arrest.

BACKGROUND: Limited data exist regarding urine output (UO) as a prognostic marker in out-of-hospital-cardiac-arrest (OHCA) survivors undergoing targeted temperature management (TTM). METHODS: We included 247 comatose adult patients who underwent TTM after OHCA between 2007 and 2017, excluding patients with end-stage renal disease. Three groups were defined based on mean hourly UO during the first 24 h: Group 1 (<0.5 mL/kg/h, n = 73), Group 2 (0.5-1 mL/kg/h, n = 81) and Group 3 (>1 mL/kg/h, n = 93). Serum creatinine was used to classify acute kidney injury (AKI). The primary and secondary outcomes respectively were in-hospital mortality and favorable neurological outcome at hospital discharge (modified Rankin Scale [mRS]<3). RESULTS: In-hospital mortality decreased incrementally as UO increased (adjusted OR 0.9 per 0.1 mL/kg/h higher; p = 0.002). UO < 0.5 mL/kg/h was strongly associated with higher in-hospital mortality (adjusted OR 4.2 [1.6-10.8], p = 0.003) and less favorable neurological outcomes (adjusted OR 0.4 [0.2-0.8], p = 0.007). Even among patients without AKI, lower UO portended higher mortality (40% vs 15% vs 9% for UO groups 1, 2, and 3 respectively, p < 0.001). CONCLUSION: Higher UO is incrementally associated with lower in-hospital mortality and better neurological outcomes. Oliguria may be a more sensitive early prognostic marker than creatinine-based AKI after OHCA.

Year 2022 in Review - Intensive care medicine - Cardiovascular and general issues.

In 2022, intensive medicine all around the world gradually began to return to standard tracks, although we could still observe the effects of the pandemic waves of the disease COVID-19. In the literature, we could note the publication of research studies of "violently terminated" pandemics and new works. This review article presents a selection of the most interesting published articles in general intensive care medicine and those focusing on cardiovascular issues.Copyright © 2022, Czech Medical Association J.E. Purkyne. All rights reserved.

Prognostic Value of the Radiographic Assessment of Lung Edema Score in Mechanically Ventilated ICU Patients.

INTRODUCTION: The Radiographic Assessment of Lung Edema (RALE) score provides a semi-quantitative measure of pulmonary edema. In patients with acute respiratory distress syndrome (ARDS), the RALE score is associated with mortality. In mechanically ventilated patients in the intensive care unit (ICU) with respiratory failure not due to ARDS, a variable degree of lung edema is observed as well. We aimed to evaluate the prognostic value of RALE in mechanically ventilated ICU patients. METHODS: Secondary analysis of patients enrolled in the 'Diagnosis of Acute Respiratory Distress Syndrome' (DARTS) project with an available chest X-ray (CXR) at baseline. Where present, additional CXRs at day 1 were analysed. The primary endpoint was 30-day mortality. Outcomes were also stratified for ARDS subgroups (no ARDS, non-COVID-ARDS and COVID-ARDS). RESULTS: 422 patients were included, of which 84 had an additional CXR the following day. Baseline RALE scores were not associated with 30-day mortality in the entire cohort (OR: 1.01, 95% CI: 0.98-1.03, p = 0.66), nor in subgroups of ARDS patients. Early changes in RALE score (baseline to day 1) were only associated with mortality in a subgroup of ARDS patients (OR: 1.21, 95% CI: 1.02-1.51, p = 0.04), after correcting for other known prognostic factors. CONCLUSIONS: The prognostic value of the RALE score cannot be extended to mechanically ventilated ICU patients in general. Only in ARDS patients, early changes in RALE score were associated with mortality.

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.

Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.

Formation of feedback in the structures of poly-agent functionals

The research aim is to improve the efficiency of poly-agent functional monitoring information system by feedback creation. The signs list at the entrance functionality varies according to the characteristics of the signal at the output. The signal characteristics at the functional output are improved by changing the features list of the results of observations at its input. Building poly-agent functionalities by the monitoring information system (MIS) is improved. The MIS is a software implementation of the information technology of intelligent monitoring (ITLM). This paper describes the use of ITLM for forecasting the incidence of COVID-19 disease in the Ukrainian population. The information technology is designed to work under conditions of crisis monitoring. During the pandemic, the properties of the monitoring objects change, and the informativeness of the accumulated results of monitoring decreases. It is proposed to adapt the list of features of the array of input data (AID) to change the informativeness of the observation results. A method for informativeness identifying the AID features of a poly-agent functional based on the results of constructing agents with structural tasks is proposed. AID increasing informativeness by signs list optimizing according to signals' characteristics at the agents' output with the structural tasks of MIS is experimentally confirmed. © 2021 Elsevier B.V.. All rights reserved.

Primary Palliative Care in Dementia.

Primary palliative care is a fundamental aspect of high-quality care for patients with a serious illness such as dementia. The clinician caring for a patient and family suffering with dementia can provide primary palliative care in numerous ways. Perhaps the most important aspects are high quality communication while sharing a diagnosis, counseling the patient through progression of illness and prognostication, and referral to hospice when appropriate. COVID-19 presents additional risks of intensive care requirement and mortality which we must help patients and families navigate. Throughout all of these discussions, the astute clinician must monitor the patient's decision making capacity and balance respect for autonomy with protection against uninformed consent. Excellent primary palliative care also involves discussion of deprescribing medications of uncertain benefit such as long term use of cholinesterase inhibitors and memantine and being vigilant in the monitoring of pain with its relationship to behavioral disturbance in patients with dementia. Clinicians should follow a standardized approach to pain management in this vulnerable population. Caregiver burden is high for patients with dementia and comprehensive care should also address this burden and implement reduction strategies. When these aspects of care are particularly complex or initial managements strategies fall short, palliative care specialists can be an important additional resource not only for the patient and family, but for the care team struggling to guide the way through a disease with innumerable challenges.

The Prognostic Capacity of the Radiographic Assessment for Lung Edema Score in Patients With COVID-19 Acute Respiratory Distress Syndrome-An International Multicenter Observational Study.

Background: The radiographic assessment for lung edema (RALE) score has an association with mortality in patients with acute respiratory distress syndrome (ARDS). It is uncertain whether the RALE scores at the start of invasive ventilation or changes thereof in the next days have prognostic capacities in patients with COVID-19 ARDS. Aims and Objectives: To determine the prognostic capacity of the RALE score for mortality and duration of invasive ventilation in patients with COVID-19 ARDS. Methods: An international multicenter observational study included consecutive patients from 6 ICUs. Trained observers scored the first available chest X-ray (CXR) obtained within 48 h after the start of invasive ventilation ("baseline CXR") and each CXRs thereafter up to day 14 ("follow-up CXR"). The primary endpoint was mortality at day 90. The secondary endpoint was the number of days free from the ventilator and alive at day 28 (VFD-28). Results: A total of 350 CXRs were scored in 139 patients with COVID-19 ARDS. The RALE score of the baseline CXR was high and was not different between survivors and non-survivors (33 [24-38] vs. 30 [25-38], P = 0.602). The RALE score of the baseline CXR had no association with mortality (hazard ratio [HR], 1.24 [95% CI 0.88-1.76]; P = 0.222; area under the receiver operating characteristic curve (AUROC) 0.50 [0.40-0.60]). A change in the RALE score over the first 14 days of invasive ventilation, however, had an independent association with mortality (HR, 1.03 [95% CI 1.01-1.05]; P < 0.001). When the event of death was considered, there was no significant association between the RALE score of the baseline CXR and the probability of being liberated from the ventilator (HR 1.02 [95% CI 0.99-1.04]; P = 0.08). Conclusion: In this cohort of patients with COVID-19 ARDS, with high RALE scores of the baseline CXR, the RALE score of the baseline CXR had no prognostic capacity, but an increase in the RALE score in the next days had an association with higher mortality.

Kidney Recovery and Death in Critically Ill Patients With COVID-19-Associated Acute Kidney Injury Treated With Dialysis: The STOP-COVID Cohort Study.

RATIONALE & OBJECTIVE: Acute kidney injury treated with kidney replacement therapy (AKI-KRT) occurs frequently in critically ill patients with coronavirus disease 2019 (COVID-19). We examined the clinical factors that determine kidney recovery in this population. STUDY DESIGN: Multicenter cohort study. SETTING & PARTICIPANTS: 4,221 adults not receiving KRT who were admitted to intensive care units at 68 US hospitals with COVID-19 from March 1 to June 22, 2020 (the "ICU cohort"). Among these, 876 developed AKI-KRT after admission to the ICU (the "AKI-KRT subcohort"). EXPOSURE: The ICU cohort was analyzed using AKI severity as the exposure. For the AKI-KRT subcohort, exposures included demographics, comorbidities, initial mode of KRT, and markers of illness severity at the time of KRT initiation. OUTCOME: The outcome for the ICU cohort was estimated glomerular filtration rate (eGFR) at hospital discharge. A 3-level outcome (death, kidney nonrecovery, and kidney recovery at discharge) was analyzed for the AKI-KRT subcohort. ANALYTICAL APPROACH: The ICU cohort was characterized using descriptive analyses. The AKI-KRT subcohort was characterized with both descriptive analyses and multinomial logistic regression to assess factors associated with kidney nonrecovery while accounting for death. RESULTS: Among a total of 4,221 patients in the ICU cohort, 2,361 (56%) developed AKI, including 876 (21%) who received KRT. More severe AKI was associated with higher mortality. Among survivors, more severe AKI was associated with an increased rate of kidney nonrecovery and lower kidney function at discharge. Among the 876 patients with AKI-KRT, 588 (67%) died, 95 (11%) had kidney nonrecovery, and 193 (22%) had kidney recovery by the time of discharge. The odds of kidney nonrecovery was greater for lower baseline eGFR, with ORs of 2.09 (95% CI, 1.09-4.04), 4.27 (95% CI, 1.99-9.17), and 8.69 (95% CI, 3.07-24.55) for baseline eGFR 31-60, 16-30, ≤15 mL/min/1.73 m2, respectively, compared with eGFR > 60 mL/min/1.73 m2. Oliguria at the time of KRT initiation was also associated with nonrecovery (ORs of 2.10 [95% CI, 1.14-3.88] and 4.02 [95% CI, 1.72-9.39] for patients with 50-499 and <50 mL/d of urine, respectively, compared to ≥500 mL/d of urine). LIMITATIONS: Later recovery events may not have been captured due to lack of postdischarge follow-up. CONCLUSIONS: Lower baseline eGFR and reduced urine output at the time of KRT initiation are each strongly and independently associated with kidney nonrecovery among critically ill patients with COVID-19.

Palliative Care: Critical Concepts for the Geropsychiatrist.

Psychiatrists can make a significant contribution to improving quality end-of-life care for psychiatric patients, beyond managing their psychiatric and psychological conditions. Geriatric psychiatrists can build expertise in enhancing end-of-life care when caring for older adults with serious illnesses and their families, given the biopsychosociospiritual approach that significantly overlaps with palliative and hospice care approaches. To effectively add quality to end-of-life care, it is essential for psychiatrists to understand the core principles and practices of palliative and hospice care, learn basic symptom management skills, and hone the ability to have crucial conversations regarding prognosis and advance care planning. Also important is recognizing when to refer to hospice and palliative medicine subspecialists. This article provides an overview of palliative and hospice care, uses a case study to illustrate components of palliative and hospice care relevant to geriatric psychiatry practice, and comments on considerations pertinent to the coronavirus disease 2019 (COVID-19) pandemic.

Lung Ultrasound Severity Index: Development and Usefulness in Patients with Suspected SARS-Cov-2 Pneumonia-A Prospective Study.

Coronavirus disease 2019 (COVID-19) has spread across the world with a strong impact on populations and health systems. Lung ultrasound is increasingly employed in clinical practice but a standard approach and data on the accuracy of lung ultrasound are still needed. Our study's objective was to evaluate lung ultrasound diagnostic and prognostic characteristics in patients with suspected COVID-19. We conducted a monocentric, prospective, observational study. Patients with respiratory distress and suspected COVID-19 consecutively admitted to the Emergency Medicine Unit were enrolled. Lung ultrasound examinations were performed blindly to clinical data. Outcomes were diagnosis of COVID-19 pneumonia and in-hospital mortality. One hundred fifty-nine patients were included in our study; 66% were males and 63.5% had a final diagnosis of COVID-19. COVID-19 patients had a higher mortality rate (18.8% vs. 6.9%, p = 0.04) and Lung Ultrasound Severity Index (16.14 [8.71] vs. 10.08 [8.92], p < 0.001) compared with non-COVID-19 patients. This model proved able to distinguish between positive and negative cases with an area under the receiver operating characteristic (AUROC) equal to 0.72 (95% confidence interval [CI]: 0.64-0.78) and to predict in-hospital mortality with an AUROC equal to 0.81 (95% CI: 0.74-0.86) in the whole population and an AUROC equal to 0.76 (95% CI: 0.66-0.84) in COVID-19 patients. The Lung Ultrasound Severity Index can be a useful tool in diagnosing COVID-19 in patients with a high pretest probability of having the disease and to identify, among them, those with a worse prognosis.

A Case Report of COVID-19 in New Orleans, Louisiana: Highlighting the Complexities of Prognostication in a Critically Ill Patient.

Palliative care teams and intensive care teams have experience providing goals-of-care guidance for critically ill patients and families. Critical coronavirus disease 2019 (COVID-19) infection is defined as infection requiring intensive care unit care, respiratory support, and often multiorgan involvement. This case presents a 53-year-old critically ill COVID-19 patient in multisystem organ failure who appeared hours from death despite best medical efforts. Comfort-focused care and compassionate extubation were offered after all medical teams felt near certain that death was imminent. Overnight, while options were being considered by the family, the patient began to markedly improve hemodynamically and was extubated several days later. Weeks later, the patient survived the hospital stay and was discharged to rehabilitation. After rehabilitation he returned home, able to walk, communicate freely, and independently perform all activities of daily living. Dialysis was no longer necessary and was stopped. The challenges of assisting in goals-of-care conversations for patients with serious COVID-19 infection are discussed.

Dead space estimates may not be independently associated with 28-day mortality in COVID-19 ARDS.

BACKGROUND: Estimates for dead space ventilation have been shown to be independently associated with an increased risk of mortality in the acute respiratory distress syndrome and small case series of COVID-19-related ARDS. METHODS: Secondary analysis from the PRoVENT-COVID study. The PRoVENT-COVID is a national, multicenter, retrospective observational study done at 22 intensive care units in the Netherlands. Consecutive patients aged at least 18 years were eligible for participation if they had received invasive ventilation for COVID-19 at a participating ICU during the first month of the national outbreak in the Netherlands. The aim was to quantify the dynamics and determine the prognostic value of surrogate markers of wasted ventilation in patients with COVID-19-related ARDS. RESULTS: A total of 927 consecutive patients admitted with COVID-19-related ARDS were included in this study. Estimations of wasted ventilation such as the estimated dead space fraction (by Harris-Benedict and direct method) and ventilatory ratio were significantly higher in non-survivors than survivors at baseline and during the following days of mechanical ventilation (p < 0.001). The end-tidal-to-arterial PCO2 ratio was lower in non-survivors than in survivors (p < 0.001). As ARDS severity increased, mortality increased with successive tertiles of dead space fraction by Harris-Benedict and by direct estimation, and with an increase in the VR. The same trend was observed with decreased levels in the tertiles for the end-tidal-to-arterial PCO2 ratio. After adjustment for a base risk model that included chronic comorbidities and ventilation- and oxygenation-parameters, none of the dead space estimates measured at the start of ventilation or the following days were significantly associated with 28-day mortality. CONCLUSIONS: There is significant impairment of ventilation in the early course of COVID-19-related ARDS but quantification of this impairment does not add prognostic information when added to a baseline risk model. TRIAL REGISTRATION: ISRCTN04346342. Registered 15 April 2020. Retrospectively registered.

A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients.

As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve patient outcomes. Existing prognostic models mostly consider the impact of biomarkers at presentation on the risk of a single patient outcome at a single follow up time. We collected data for 553 Polymerase Chain Reaction (PCR)-positive COVID-19 patients admitted to hospital whose eventual outcomes were known. The data collected for the patients included demographics, comorbidities and laboratory values taken at admission and throughout the course of hospitalization. We trained multivariate Markov prognostic models to identify high-risk patients at admission along with a dynamic measure of risk incorporating time-dependent changes in patients' laboratory values. From the set of factors available upon admission, the Markov model determined that age >80 years, history of coronary artery disease and chronic obstructive pulmonary disease increased mortality risk. The lab values upon admission most associated with mortality included neutrophil percentage, red blood cells (RBC), red cell distribution width (RDW), protein levels, platelets count, albumin levels and mean corpuscular hemoglobin concentration (MCHC). Incorporating dynamic changes in lab values throughout hospitalization lead to dramatic gains in the predictive accuracy of the model and indicated a catalogue of variables for determining high-risk patients including eosinophil percentage, white blood cells (WBC), platelets, pCO2, RDW, large unstained cells (LUC) count, alkaline phosphatase and albumin. Our prognostic model highlights the nuance of determining risk for COVID-19 patients and indicates that, rather than a single variable, a range of factors (at different points in hospitalization) are needed for effective risk stratification.

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.

BACKGROUND: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. METHOD: Between March 1 and April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: (1) a Cox regression model and (2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. RESULTS: Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI) 73.8-91.1 and 90.0%, 95% CI 81.2-95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI 91.1-94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI 85.7-88.2), p = 0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. CONCLUSION: We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

Correlation of chest radiography findings with the severity and progression of COVID-19 pneumonia.

PURPOSE: Aim is to assess the temporal changes and prognostic value of chest radiograph (CXR) in COVID-19 patients. MATERIAL AND METHODS: We performed a retrospective study of confirmed COVID-19 patients presented to the emergency between March 07-17, 2020. Clinical & radiological findings were reviewed. Clinical outcomes were classified into critical & non-critical based on severity. Two independent radiologists graded frontal view CXRs into COVID-19 pneumonia category 1 (CoV-P1) with <4 zones and CoV-P2 with ≥4 zones involvement. Interobserver agreement of CoV-P category for the CXR preceding the clinical outcome was assessed using Kendall's τ coefficient. Association between CXR findings and clinical deterioration was calculated along with temporal changes of CXR findings with disease progression. RESULTS: Sixty-two patients were evaluated for clinical features. 56 of these (total: 325 CXRs) were evaluated for radiological findings. Common patterns were progression from lower to upper zones, peripheral to diffuse involvement, & from ground glass opacities to consolidation. Consolidations starting peripherally were noted in 76%, 93% and 48% with critical outcomes, respectively. The interobserver agreement of the CoV-P category of CXRs in the critical and non-critical outcome groups were good and excellent, respectively (τ coefficient = 0.6 & 1.0). Significant association was observed between CoV-P2 and clinical deterioration into a critical status (χ2 = 27.7, p = 0.0001) with high sensitivity (95%) and specificity (71%) within a median interval time of 2 days (range: 0-4 days). CONCLUSION: Involvement of predominantly 4 or more zones on frontal chest radiograph can be used as predictive prognostic indicator of poorer outcome in COVID-19 patients.

Prognostication and Proactive Planning in COVID-19.

Accurate prognostication is challenging in the setting of SARS-CoV-2, the virus responsible for COVID-19, due to rapidly changing data, studies that are not generalizable, and lack of morbidity and functional outcomes in survivors. To provide meaningful guidance to patients, existing mortality data must be considered and appropriately applied. Although most people infected with SARS-CoV-2 will recover, mortality increases with age and comorbidity in those who develop severe illness.