End-of-life decision-making in critically ill old patients with and without coronavirus disease 2019.

BACKGROUND: There are few studies on the differences in end-of-life decisions making in critically ill patients with and without coronavirus disease 2019 (COVID-19). This study aimed to investigate the independent factors that predicted the decision to withdraw or withhold life-sustaining treatments (LST) in critically ill patients and if these decisions were based on different variables for critically ill patients with COVID-19 compared to those for critically ill patients with other diagnoses in a Swedish intensive care unit. METHODS: This observational pilot study was performed at Sahlgrenska University Hospital, Gothenburg, Sweden. Patients ≥65 years were included from 1 March 2020 to 30 April 2021. The association between a decision to limit LST and a priori selected variables including sex, age, Simplified Acute Physiology Score 3 (SAPS 3), Clinical Frailty Scale ≥4, Charlson Comorbidity Index, Body Mass Index, living at home, invasive and non-invasive mechanical ventilation was assessed using a univariate and multivariable logistic regression model and presented as odds ratio with corresponding 95% confidence intervals. RESULTS: There were 394 patients included in this study, 131 in the non-COVID-19 group and 263 in the COVID-19 group. For the non-COVID-19 cohort, the univariate analysis demonstrated that age and SAPS 3 were significantly associated with the decision to withdraw or withhold life-sustaining treatments, and this association remained in the multivariable analysis, with odds ratios of 1.10 (1.03-1.19) p = .009 and 1.06 (1.03-1.10) p < .001, respectively. For the COVID-19 cohort, the univariate analysis indicated that age, SAPS 3, and Charlson comorbidity index were significantly associated with the decision to withdraw or withhold life-sustaining treatments. However, in multivariable analysis, only the Charlson comorbidity index remained independently associated with the decision to withdraw or withhold life-sustaining treatments, with an odds ratio of 1.26 (1.07-1.49), p = .006. CONCLUSION: Decisions to withdraw or withhold life-sustaining treatments were based on other variables for the critically ill COVID-19 cohort compared to those for the critically ill non-COVID-19 cohort. Further studies are warranted to forge a common path for ethical end-of-life decision-making in critically ill patients.

Machine learning analysis of heart rate variability to detect delayed cerebral ischemia in subarachnoid hemorrhage.

OBJECTIVES: Approximately 30% of patients with aneurysmal subarachnoid hemorrhage (aSAH) develop delayed cerebral ischemia (DCI). DCI is associated with increased mortality and persistent neurological deficits. This study aimed to analyze heart rate variability (HRV) data from patients with aSAH using machine learning to evaluate whether specific patterns could be found in patients developing DCI. MATERIAL & METHODS: This is an extended, in-depth analysis of all HRV data from a previous study wherein HRV data were collected prospectively from a cohort of 64 patients with aSAH admitted to Sahlgrenska University Hospital, Gothenburg, Sweden, from 2015 to 2016. The method used for analyzing HRV is based on several data processing steps combined with the random forest supervised machine learning algorithm. RESULTS: HRV data were available in 55 patients, but since data quality was significantly low in 19 patients, these were excluded. Twelve patients developed DCI. The machine learning process identified 71% of all DCI cases. However, the results also demonstrated a tendency to identify DCI in non-DCI patients, resulting in a specificity of 57%. CONCLUSIONS: These data suggest that machine learning applied to HRV data might help identify patients with DCI in the future; however, whereas the sensitivity in the present study was acceptable, the specificity was low. Possible confounders such as severity of illness and therapy may have affected the result. Future studies should focus on developing a robust method for detecting DCI using real-time HRV data and explore the limits of this technology in terms of its reliability and accuracy.

Reasons for physician-related variability in end-of-life decision-making in intensive care.

BACKGROUND: There is increasing evidence that the individual physician is the main factor influencing variability in end-of-life decision-making in intensive care units. End-of-life decisions are complex and should be adapted to each patient. Physician-related variability is problematic as it may result in unequal assessments that affect patient outcomes. The primary aim of this study was to investigate factors contributing to physician-related variability in end-of-life decision-making. METHOD: This is a qualitative substudy of a previously conducted study. In-depth thematic analysis of semistructured interviews with 19 critical care specialists from five different Swedish intensive care units was performed. Interviews took place between 1 February 2017 and 31 May 2017. RESULTS: Factors influencing physician-related variability consisted of different assessment of patient preferences, as well as intensivists' personality and values. Personality was expressed mainly through pace and determination in the decision-making process. Personal prejudices appeared in decisions, but few respondents had personally witnessed this. Avoidance of criticism and conflicts as well as individual strategies for emotional coping were other factors that influenced physician-related variability. Many respondents feared criticism for making their assessments, and the challenging nature of end-of-life decision-making lead to avoidance as well as emotional stress. CONCLUSION: Variability in end-of-life decision-making is an important topic that needs further investigation. It is imperative that such variability be acknowledged and addressed in a more formal and transparent manner. The ethical issues faced by intensivists have recently been compounded by the devastating impact of the COVID-19 pandemic, demonstrating in profound terms the importance of the topic.

Association between inflammatory response and outcome after subarachnoid haemorrhage.

OBJECTIVES: Recent reports suggest an association between the inflammatory response after aneurysmal subarachnoid haemorrhage (aSAH) and patients' outcome. The primary aim of this study was to identify a potential association between the inflammatory response after aSAH and 1-year outcome. The secondary aim was to investigate whether the inflammatory response after aSAH could predict the development of delayed cerebral ischaemia (DCI). MATERIALS AND METHODS: This prospective observational pilot study included patients with an aSAH admitted to Sahlgrenska University Hospital, Gothenburg, Sweden, between May 2015 and October 2016. The patients were stratified according to the extended Glasgow Outcome Scale (GOSE) as having an unfavourable (score: 1-4) or favourable outcome (score: 5-8). Furthermore, patients were stratified depending on development of DCI or not. Patient data and blood samples were collected and analysed at admission and after 10 days. RESULTS: Elevated serum concentrations of inflammatory markers such as tumour necrosis factor-α and interleukin (IL)-6, IL-1Ra, C-reactive protein and intercellular adhesion molecule-1 were detected in patients with unfavourable outcome. When adjustments for Glasgow coma scale were made, only IL-1Ra remained significantly associated with poor outcome (p = 0.012). The inflammatory response after aSAH was not predictive of the development of DCI. CONCLUSION: Elevated serum concentrations of inflammatory markers were associated with poor neurological outcome 1-year after aSAH. However, inflammatory markers are affected by many clinical events, and when adjustments were made, only IL-1Ra remained significantly associated with poor outcome. The robustness of these results needs to be tested in a larger trial.

Cerebral ischemia detection using artificial intelligence (CIDAI)-A study protocol.

BACKGROUND: The onset of cerebral ischemia is difficult to predict in patients with altered consciousness using the methods available. We hypothesize that changes in Heart Rate Variability (HRV), Near-Infrared Spectroscopy (NIRS), and Electroencephalography (EEG) correlated with clinical data and processed by artificial intelligence (AI) can indicate the development of imminent cerebral ischemia and reperfusion, respectively. This study aimed to develop a method that enables detection of imminent cerebral ischemia in unconscious patients, noninvasively and with the support of AI. METHODS: This prospective observational study will include patients undergoing elective surgery for carotid endarterectomy and patients undergoing acute endovascular embolectomy for cerebral arterial embolism. HRV, NIRS, and EEG measurements and clinical information on patient status will be collected and processed using machine learning. The study will take place at Sahlgrenska University Hospital, Gothenburg, Sweden. Inclusion will start in September 2020, and patients will be included until a robust model can be constructed. By analyzing changes in HRV, EEG, and NIRS measurements in conjunction with cerebral ischemia or cerebral reperfusion, it should be possible to train artificial neural networks to detect patterns of impending cerebral ischemia. The analysis will be performed using machine learning with long short-term memory artificial neural networks combined with convolutional layers to identify patterns consistent with cerebral ischemia and reperfusion. DISCUSSION: Early signs of cerebral ischemia could be detected more rapidly by identifying patterns in integrated, continuously collected physiological data processed by AI. Clinicians could then be alerted, and appropriate actions could be taken to improve patient outcomes.

Heart rate variability monitoring for the detection of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

BACKGROUND: Delayed cerebral ischemia (DCI) is a major cause of impaired outcome after aneurysmal subarachnoidal hemorrhage (aSAH). In this observational cohort study we investigated whether changes in heart rate variability (HRV) that precede DCI could be detected. METHODS: Sixty-four patients with aSAH were included. HRV data were collected for up to 10 days and analyzed offline. Correlation with clinical status and/or radiologic findings was investigated. A linear mixed model was used for the evaluation of HRV parameters over time in patients with and without DCI. Extended Glasgow outcome scale score was assessed after 1 year. RESULTS: In 55 patients HRV data could be analyzed. Fifteen patients developed DCI. No changes in HRV parameters were observed 24 hours before onset of DCI. Mean of the HRV parameters in the first 48 hours did not correlate with the development of DCI. Low/high frequency (LF/HF) ratio increased more in patients developing DCI (ß -0.07 (95% confidence interval, 0.12-0.01); P = .012). Lower STDRR (standard deviation of RR intervals), RMSSD (root mean square of the successive differences between adjacent RR intervals), and total power (P = .003, P = .007 and P = .004 respectively) in the first 48 hours were seen in patients who died within 1 year. CONCLUSION: Impaired HRV correlated with 1-year mortality and LF/HF ratio increased more in patients developing DCI. Even though DCI could not be detected by the intermittent analysis of HRV used in this study, continuous HRV monitoring may have potential in the detection of DCI after aSAH using different methods of analysis.

Age, SAPS 3 and female sex are associated with decisions to withdraw or withhold intensive care.

BACKGROUND: Intensive care treat critically ill patients. When intensive care is not considered beneficial for the patient, decisions to withdraw or withhold treatments are made. We aimed to identify independent patient variables that increase the odds for receiving a decision to withdraw or withhold intensive care. METHODS: Registry study using data from the Swedish Intensive Care Registry (SIR) 2014-2016. Age, condition at admission, including co-morbidities (Simplified Acute Physiology Score version 3, SAPS 3), diagnosis, sex, and decisions on treatment limitations were extracted. Patient data were divided into a full care (FC) group, and a withhold or withdraw (WW) treatment group. RESULTS: Of all 97 095 cases, 47.1% were 61-80 years old, 41.9% were women and 58.1% men. 14 996 (15.4%) were allocated to the WW group and 82 149 (84.6%) to the FC group. The WW group, compared with the FC group, was older (P < 0.001), had higher SAPS 3 (P < 0.001) and were predominantly female (P < 0.001). Compared to patients 16-20 years old, patients >81 years old had 11 times higher odds of being allocated to the WW group. Higher SAPS 3 (continuous) increased the odds of being allocated to the WW group by odds ratio [OR] 1.085, (CI 1.084-1.087). Female sex increased the odds of being allocated to the WW group by 18% (1.18; CI 1.13- 1.23). CONCLUSION: Older age, higher SAPS 3 at admission and female sex were found to be independent variables that increased the odds to receive a decision to withdraw or withhold intensive care.

Mental fatigue assessment may add information after aneurysmal subarachnoid hemorrhage.

BACKGROUND: Mental fatigue, as part of cognitive dysfunction, has been reported to be common after subarachnoid hemorrhage and it significantly affects quality of life. AIMS OF THE STUDY: The aim of this study was to assess mental fatigue one year after an aneurysmal subarachnoid hemorrhage and to correlate the degree of mental fatigue to functional outcome assessed with the Extended Glasgow Outcome Scale (GOSE). METHODS: One year after an aneurysmal subarachnoid hemorrhage, the GOSE was assessed and a questionnaire for self-assessment of mental fatigue, the Mental Fatigue Scale, was distributed to all included patients. The maximum score is 42 and a score of ≥10.5 indicates mental fatigue. RESULTS: All patients with GOSE 8, indicating full recovery, had a mental fatigue score of <10.5. A linear correlation between the GOSE and the mental fatigue score was observed (p < 0.0001). CONCLUSIONS: Patients with a favorable outcome and GOSE 5-7 could benefit from the assessments of mental fatigue in order to receive satisfactory rehabilitation.

Impact of Acute Cardiac Complications After Subarachnoid Hemorrhage on Long-Term Mortality and Cardiovascular Events.

BACKGROUND: Cardiac complications frequently occur after subarachnoid hemorrhage (SAH) and are associated with an increased risk of neurological complications and poor outcomes. The aim of this study was to evaluate the impact of acute cardiac complications after SAH on long-term mortality and cardiovascular events. METHODS: All patients admitted to our Neuro intensive care unit with verified SAH from January 2010 to April 2015, and electrocardiogram, echocardiogram, and troponin T or NTproBNP data obtained within 72 h of admission were included in the study. Mortality data were obtained from the Swedish population register. Data regarding cause of death and hospitalization for cardiovascular events were obtained from the Swedish Board of Health and Welfare. RESULTS: A total of 455 patients were included in the study analysis. There were 102 deaths during the study period. Cardiac troponin release (HR 1.08, CI 1.02-1.15 per 100 ng/l, p = 0.019), NTproBNP (HR 1.05, CI 1.01-1.09 per 1000 ng/l, p = 0.018), and ST-T abnormalities (HR 1.53, CI 1.02-2.29, p = 0.040) were independently associated with an increased risk of death. However, these associations were significant only during the first 3 months after the hemorrhage. Cardiac events were observed in 25 patients, and cerebrovascular events were observed in 62 patients during the study period. ST-T abnormalities were independently associated with an increased risk of cardiac events (HR 5.52, CI 2.07-14.7, p < 0.001), and stress cardiomyopathy was independently associated with an increased risk of cerebrovascular events (HR 3.65, CI 1.55-8.58, p = 0.003). CONCLUSION: Cardiac complications after SAH are associated with an increased risk of short-term death. Patients with electrocardiogram abnormalities and stress cardiomyopathy need appropriate follow-up for the identification of cardiac disease or risk factors for cardiovascular disease.