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The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.

Post-cardiac arrest brain injury (PCABI) is caused by initial ischaemia and subsequent reperfusion of the brain following resuscitation. In those who are admitted to intensive care unit after cardiac arrest, PCABI manifests as coma, and is the main cause of mortality and long-term disability. This review describes the mechanisms of PCABI, its treatment options, its outcomes, and the suggested strategies for outcome prediction.

Colorectal cancer (CRC) is a leading cause of death worldwide, despite progress made in detection and management through surgery, chemotherapy, radiotherapy, and immunotherapy. Novel therapeutic agents have improved survival in both the adjuvant and advanced disease settings, albeit with an increased risk of toxicity and cost. However, metastatic disease continues to have a poor long-term prognosis and significant challenges remain due to late stage diagnosis and treatment failure. Biomarkers are a key tool in early detection, prognostication, survival, and predicting treatment response. The past three decades have seen advances in genomics and molecular pathology of cancer biomarkers, allowing for greater individualization of therapy with a positive impact on survival outcomes. Clinically useful predictive biomarkers aid clinical decision making, such as the presence of KRAS gene mutations predicting benefit from epidermal growth factor receptor (EGFR) inhibiting antibodies. However, few biomarkers have been translated into clinical practice highlighting the need for further investigation. We review a range of protein, DNA and RNA-based biomarkers under investigation for diagnostic, predictive, and prognostic properties for CRC. In particular, long non-coding RNAs (lncRNA), have been investigated as biomarkers in a range of cancers including colorectal cancer. Specifically, we evaluate the potential role of lncRNA plasmacytoma variant translocation 1 (PVT1), an oncogene, as a diagnostic, prognostic, and therapeutic biomarker in colorectal cancer.

BackgroundIn patients with pulmonary arterial hypertension (PAH), hospital-based field walk testing is used as a clinical study endpoint and to estimate risk and guide treatment. The relationships between indicators of clinical risk and remote monitored physical activity is unknown. Here we report the relationship between baseline parameters that indicate clinical risk and remote monitored cardiac and physical activity measures.Methods80 patients were recruited to the arrhythmia sub-study of the United Kingdom National cohort study of Heritable and Idiopathic PAH (NCT01907295), and implanted with an insertable cardiac monitor (LinQ, Medtronic) and remote monitoring established through a regulatory approved system. Daily physical activity, heart rate, and heart rate variability was related to baseline World Health Organisation functional class (WHO-FC); N-terminal-Pro B-type natriuretic peptide (NTProBNP) and COMPERA 2.0 risk score.ResultsDaily physical activity was reduced with a high WHO-FC (WHO-FC 1 vrs 3&4, P<0.001, figure 1), increased NTProBNP (P<0.001), and increased COMPERA 2.0 risk score (P<0.001), and correlated with incremental shuttle walk’test (p<0.01). Indicators of cardiopulmonary function including heart rate variability and night heart rate were also related to WHO-FC, NTproBNP and COMPERA 2.0 risk (P<0.01).Abstract S91 Figure 1Physical activity (minutes/day) stratified by baseline World Health Organization (WHO) functional class. WHO Functional Class I (median: 288.8; IQR: 267.1 – 301.8). II (median: 189.9; IQR: 144.4 – 224.6). III (median: 107.1; IQR: 62.1 – 160.4), and IV (median: 31.6: IQR: 17.0 – 38.9). ANOVA with Dunnett’s t-test; WHO functional classes I and III (P<0.001); WHO functional classes I and IV (P<0.001)ConclusionDaily, remote measured physical activity, heart rate variability, and night heart rate are related to established measures of clinical risk in patients with PAH.

ObjectivesTo determine the prognostic value of patterns of right ventricular (RV) adaptation in patients with pulmonary arterial hypertension (PAH), assessed using cardiac magnetic resonance (CMR) imaging at baseline and follow-up.MethodsConsecutive patients with PAH from the ASPIRE registry were included in the baseline cohort. Patients who received PAH therapy and had follow-up CMR assessment were included in the follow-up cohort. A right ventricular end-systolic volume index adjusted for age and sex (RVESVI%pred) threshold of 227% and ventricular mass index (VMI) threshold of 0.53 were used to stratify patients into four different volume/mass groups: VollowMasslow (low RVESVI%pred and VMI), VollowMasshigh (low RVESVI%pred and high VMI), VolhighMasslow (high RVESVI%pred and low VMI) and VolhighMasshigh (high RVESVI%pred and VMI). At the baseline assessment, One-way ANOVA test and Chi-squared tests were used to compare the variables of the groups. Transition of the groups from baseline to follow-up assessment were studied and illustrated using alluvial graph. At follow-up, the prognoses of the groups were compared using Kaplan-Meier plots.ResultsA total of 564 patients with PAH were identified, 250 (44.0%) died during follow-up (median 4.85 years, interquartile range 4.05). At baseline assessment, VollowMasslow was associated with CMR and right heart catheterisation metrics predictive of improved prognosis. There were 126 patients who underwent follow up CMR (median 1.11 years, interquartile range 0.78). At both baseline and follow-up assessments, VolhighMasslow group had worse prognosis than the VollowMasslow group (p<0.001). At follow-up, patients with VollowMasslow had lower mortality than VollowMasshigh, VolhighMasslow and VolhighMasshigh (p<0.001). With PAH therapy, 73.5% of VollowMasslow remained in this group, whereas 56.5% and 29.0% of VollowMasshigh and VolhighMasshighpatients transitioned into VollowMasslow, respectively. In contrast, only 17.4% of VolhighMasslow transitioned into VollowMasslow.Abstract S90 Figure 1ConclusionsCMR can be used to assess for RV adaptation in patients with PAH and has prognostic value at baseline and follow-up. Patients with evidence of maladaptive remodelling (VolhighMasslow) are at high risk of treatment failure with PAH therapies and should be considered for early intensification of treatment and lung transplantation. Presence of significant RV reverse remodelling (VollowMasslow) at follow-up on CMR indicates good prognosis and effective treatment.

PurposeTo assess the ability of quantitative pupillometry [using the Neurological Pupil index (NPi)] to predict an unfavorable neurological outcome after cardiac arrest (CA).MethodsWe performed a prospective international multicenter study (10 centers) in adult comatose CA patients. Quantitative NPi and standard manual pupillary light reflex (sPLR)—blinded to clinicians and outcome assessors—were recorded in parallel from day 1 to 3 after CA. Primary study endpoint was to compare the value of NPi versus sPLR to predict 3-month Cerebral Performance Category (CPC), dichotomized as favorable (CPC 1–2: full recovery or moderate disability) versus unfavorable outcome (CPC 3–5: severe disability, vegetative state, or death).ResultsAt any time between day 1 and 3, an NPi ≤ 2 (n = 456 patients) had a 51% (95% CI 49–53) negative predictive value and a 100% positive predictive value [PPV; 0% (0–2) false-positive rate], with a 100% (98–100) specificity and 32% (27–38) sensitivity for the prediction of unfavorable outcome. Compared with NPi, sPLR had significantly lower PPV and significantly lower specificity (p  < 0.001 at day 1 and 2; p  = 0.06 at day 3). The combination of NPi ≤ 2 with bilaterally absent somatosensory evoked potentials (SSEP; n = 188 patients) provided higher sensitivity [58% (49–67) vs. 48% (39–57) for SSEP alone], with comparable specificity [100% (94–100)].ConclusionsQuantitative NPi had excellent ability to predict an unfavorable outcome from day 1 after CA, with no false positives, and significantly higher specificity than standard manual pupillary examination. The addition of NPi to SSEP increased sensitivity of outcome prediction, while maintaining 100% specificity.

Abstract Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing “automation revolutions,” namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective.

The objective of this review is to provide an update on prognostication in patients with advanced cancer and to discuss future directions for research in this field. Accurate prognostication of survival for patients with advanced cancer is vital, as patient life expectancy informs many important personal and clinical decisions. The most common prognostic approach is clinician prediction of survival (CPS) using temporal, surprise, or probabilistic questions. The surprise and probabilistic questions may be more accurate than the temporal approach, partly by limiting the time frame of prediction. Prognostic models such as the Glasgow Prognostic Score (GPS), Palliative Performance Scale (PPS), Palliative Prognostic Score (PaP), Palliative Prognostic Index (PPI), or Prognosis in Palliative Care Study (PiPS) predictor model may augment CPS. However, care must be taken to select the appropriate tool since prognostic accuracy varies by patient population, setting, and time frame of prediction. In addition to life expectancy, patients and caregivers often desire that expected treatment outcomes and bodily changes be communicated to them in a sensible manner at an appropriate time. We propose the following 10 major themes for future prognostication research: (1) enhancing prognostic accuracy, (2) improving reliability and reproducibility of prognosis, (3) identifying the appropriate prognostic tool for a given setting, (4) predicting the risks and benefits of cancer therapies, (5) predicting survival for pediatric populations, (6) translating prognostic knowledge into practice, (7) understanding the impact of prognostic uncertainty, (8) communicating prognosis, (9) clarifying outcomes associated with delivery of prognostic information, and (10) standardizing prognostic terminology.

PurposeNeurofilament light (NfL) is a biomarker reflecting neurodegeneration and acute neuronal injury, and an increase is found following hypoxic brain damage. We assessed the ability of plasma NfL to predict outcome in comatose patients after out-of-hospital cardiac arrest (OHCA). We also compared plasma NfL concentrations between patients treated with two different targets of arterial carbon dioxide tension (PaCO2), arterial oxygen tension (PaO2), and mean arterial pressure (MAP).MethodsWe measured NfL concentrations in plasma obtained at intensive care unit admission and at 24, 48, and 72 h after OHCA. We assessed neurological outcome at 6 months and defined a good outcome as Cerebral Performance Category (CPC) 1–2 and poor outcome as CPC 3–5.ResultsSix-month outcome was good in 73/112 (65%) patients. Forty-eight hours after OHCA, the median NfL concentration was 19 (interquartile range [IQR] 11–31) pg/ml in patients with good outcome and 2343 (587–5829) pg/ml in those with poor outcome, p < 0.001. NfL predicted poor outcome with an area under the receiver operating characteristic curve (AUROC) of 0.98 (95% confidence interval [CI] 0.97–1.00) at 24 h, 0.98 (0.97–1.00) at 48 h, and 0.98 (0.95–1.00) at 72 h. NfL concentrations were lower in the higher MAP (80–100 mmHg) group than in the lower MAP (65–75 mmHg) group at 48 h (median, 23 vs. 43 pg/ml, p = 0.04). PaCO2 and PaO2 targets did not associate with NfL levels.ConclusionsNfL demonstrated excellent prognostic accuracy after OHCA. Higher MAP was associated with lower NfL concentrations.

Introduction The aim of this systematic review was to summarize the current literature on wearable technologies in oncology patients for the purpose of prognostication, treatment monitoring, and rehabilitation planning. Methods A search was conducted in Medline ALL, Cochrane Central Register of Controlled Trials, Embase, Emcare, CINAHL, Scopus, and Web of Science, up until February 2022. Articles were included if they reported on consumer grade and/or non-commercial wearable devices in the setting of either prognostication, treatment monitoring or rehabilitation. Results We found 199 studies reporting on 18 513 patients suitable for inclusion. One hundred and eleven studies used wearable device data primarily for the purposes of rehabilitation, 68 for treatment monitoring, and 20 for prognostication. The most commonly-reported brands of wearable devices were ActiGraph (71 studies; 36%), Fitbit (37 studies; 19%), Garmin (13 studies; 7%), and ActivPAL (11 studies; 6%). Daily minutes of physical activity were measured in 121 studies (61%), and daily step counts were measured in 93 studies (47%). Adherence was reported in 86 studies, and ranged from 40% to 100%; of these, 63 (74%) reported adherence in excess of 80%. Conclusion Wearable devices may provide valuable data for the purposes of treatment monitoring, prognostication, and rehabilitation. Future studies should investigate live-time monitoring of collected data, which may facilitate directed interventions. This article summarizes the current literature on wearable technologies in oncology patients for the purpose of prognostication, treatment monitoring, and rehabilitation planning.