The diagnostic potential of paramagnetic rim lesions (PRLs) has been previously established; however, the prognostic significance of these lesions has not previously been consistently described. This study aimed to establish the prognostic role of PRLs in MS with respect to the Expanded Disability Status Scale (EDSS) and rates of disability progression. Databases of PubMed, EMBASE, Scopus and reference lists of selected articles were searched up to 29/04/2023. The review was conducted in accordance with PRISMA guidelines and was registered prospectively on PROSPERO (CRD42023422052). 7 studies were included in the final review. All of the eligible studies found that patients with PRLs tend to have higher baseline EDSS scores. Longitudinal assessments revealed greater EDSS progression in patients with PRLs over time in most studies. However, the effect of location of PRLs within the central nervous system were not assessed across the studies. Only one study investigated progression independent of relapse activity (PIRA) and showed that this clinical entity occurred in a greater proportion in patients with PRLs. This review supports PRLs as a predictor of EDSS progression. This measure has widespread applicability, however further multicentre studies are needed. Future research should explore the impact of PRLs on silent disability, PIRA, take into account different MS phenotypes and the topography of PRLs in prognosis.

Introduction: Identifying the evolving needs of patients with advanced heart failure (AdHF) and triaging those at high risk of death can facilitate timely referrals to palliative care and advance patient-centered individualized care. There are limited models specific for patients with end-stage HF. We aim to identify risk factors associated with up to three-year all-cause mortality (ACM) and describe prognostic models developed or validated in AdHF populations. Methods: Frameworks proposed by Arksey, O\'Malley, and Levac were adopted for this scoping review. We searched the Medline, EMBASE, PubMed, CINAHL, Cochrane library, Web of Science and gray literature databases for articles published between January 2010 and September 2020. Primary studies that included adults aged ≥ 18 years, diagnosed with AdHF defined as New York Heart Association class III/IV, American Heart Association/American College of Cardiology Stage D, end-stage HF, and assessed for risk factors associated with up to three-year ACM using multivariate analysis were included. Studies were appraised using the Quality of Prognostic Studies tool. Data were analyzed using a narrative synthesis approach. Results: We reviewed 167 risk factors that were associated with up to three-year ACM and prognostic models specific to AdHF patients across 65 articles with low-to-moderate bias. Studies were mostly based in Western and/or European cohorts (n = 60), in the acute care setting (n = 56), and derived from clinical trials (n = 40). Risk factors were grouped into six domains. Variables related to cardiovascular and overall health were frequently assessed. Ten prognostic models developed/validated on AdHF patients displayed acceptable model performance [area under the curve (AUC) range: 0.71-0.81]. Among the ten models, the model for end-stage-liver disease (MELD-XI) and acute decompensated HF with N-terminal pro b-type natriuretic peptide (ADHF/proBNP) model attained the highest discriminatory performance against short-term ACM (AUC: 0.81). Conclusions: To enable timely referrals to palliative care interventions, further research is required to develop or validate prognostic models that consider the evolving landscape of AdHF management.

(1) Background: Prognostication in patients with cancer receiving palliative radiotherapy remains a challenge. To improve the process, we aim to identify prognostic factors in this population from the literature and offer evidence-based recommendations on prognostication in patients undergoing palliative radiotherapy for non-curable or advanced cancers. (2) Methods: A systematic review was performed on the medical literature from 2005 to 2023 to extract papers on the prognosis of palliative radiotherapy patients with advanced cancer. The initial selection was performed by at least two authors to determine study relevance to the target area. Studies were then classified based on type and evidence quality to determine final recommendations. (3) Results: The literature search returned 57 papers to be evaluated. Clinical and biological prognostic factors were identified from these papers to improve clinical decision making or construct prognostic models. Twenty prognostic models were identified for clinical use. There is moderate evidence supporting (i) evidence-based factors (patient, clinical, disease, and lab) in guiding decision making around palliative radiation; (ii) that certain biological factors are of importance; (iii) prognostication models in patients with advanced cancer; and that (iv) SBRT or re-irradiation use can be guided by predictions of survival by prognostic scores or clinicians. Patients with more favorable prognoses are generally better suited to SBRT or re-irradiation, and the use of prognostic models can aid in this decision making. (4) Conclusions: This evaluation has identified several factors or tools to aid in prognosis and clinical decision making. Future studies should aim to further validate these tools and factors in a clinical setting, including the leveraging of electronic medical records for data availability. To increase our understanding of how causal factors interact with palliative radiotherapy, future studies should also examine and include prediction of response to radiation as an outcome.

BACKGROUND: Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.
OBJECTIVE: To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.
METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.
RESULTS: Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.
CONCLUSIONS: This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

BACKGROUND: Low skeletal muscle mass (LSMM) and/or, function associated with an increased risk of treatment-related toxicities and inferior overall survival (OS) among adults with solid malignancies. However, the association between LSMM and treatment-related toxicities among adults with haematologic malignancies remains unclear.
METHODS: Using a pre-published protocol (CRD42020197814), we searched seven bibliographic databases from inception to 08/2021 for studies reporting the impact of LSMM among adults ≥18 years with a known haematologic malignancy. The primary outcome of interest was OS, and secondary outcomes included progression free survival (PFS) and non-relapse mortality (NRM). These effect sizes were quantified in terms of hazards ratio (HR) along with 95% confidence interval (CI) and pooled across studies using a DerSimonian-Laird random-effects model. Heterogeneity was assessed using the Cochran\'s Q and the I2 statistic. All hypothesis testing was two-sided with an alpha of 0.05.
RESULTS: Of 3791 studies screened, we identified 20 studies involving 3468 patients with a mean age of 60 years; 44% were female and the most common malignancy was diffuse large B-cell lymphoma (42%). Most studies measured muscle mass using single slice computed tomography imaging at the L3 level. The presence of LSMM was associated with worse OS (pooled HR = 1.81, 95% CI = 1.48-2.22, P < 0.001) with moderate heterogeneity (Cochran\'s Q, I2 = 60.4%), PFS (pooled HR = 1.61, 95% CI = 1.28-2.02, P < 0.001) with moderate heterogeneity (Cochran\'s Q, I2 = 66.0%). Similarly, LSMM was associated with worse NRM (HR = 1.72, 95% CI = 1.34-2.22, P < 0.001) with little evidence of heterogeneity (Cochran\'s Q, I2 = 0.0%).
CONCLUSIONS: LSMM is associated with worse survival outcomes among adults with haematologic malignancies. Further research into understanding the underlying mechanism of this association and mitigating the negative effects of LSMM among adults with haematologic malignancies is needed.

OBJECTIVE: To perform a systematic review and meta-analysis to evaluate the association of various measures of glycemic variability, including time-domain and complexity-domain, with short-term mortality in patients with critical illness.
METHODS: We searched Embase Classic +, MEDLINE, and the Cochrane Database of Systematic Reviews from inception to November 3, 2023.
METHODS: We included English language studies that assessed metrics of glycemic variation or complexity and short-term mortality in patients admitted to the ICU.
METHODS: Two authors performed independent data abstraction and risk-of-bias assessments. We used a random-effects model to pool binary and continuous data and summarized estimates of effect using odds ratios and mean difference. We used the Quality in Prognosis Studies tool to assess risk of bias and the Grading of Recommendations, Assessment, Development and Evaluations to assess certainty of pooled estimates.
RESULTS: We included 41 studies (n = 162,259). We demonstrate that increased sd, coefficient of variance, glycemic lability index, and decreased time in range are probably associated with increased mortality in critically ill patients (moderate certainty) and that increased mean absolute glucose, mean amplitude of glycemic excursion, and detrended fluctuation analysis may be associated with increased mortality (low certainty).
CONCLUSIONS: We found a consistent association between increased measures of glycemic variability and higher short-term mortality in patient with critical illness. Further research should focus on standardized measurements of glycemic variation and complexity, along with their utility as therapeutic targets and prognostic markers.

BACKGROUND: 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%.
CONCLUSIONS: 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.

Vital signs are routinely measured in secondary healthcare settings and can be used to detect clinical problems, guide treatment, and monitor response to treatment. Vital signs are less frequently measured in palliative care settings. Reasons for this are unclear. This scoping review aimed to assess the generic use of vital signs in palliative care, and its role in prognostication for adult patients with cancer receiving palliative care. Medline, Embase, and CINAHL were searched for articles involving adult patients with advanced cancer receiving palliative care who had their vital signs measured. Twenty-six articles were identified in which one or a combination of vital signs, with or without other clinical parameters, was used to prognosticate for patients. An additional three articles investigated the generic use of vital signs in patients with advanced cancer. There was significant heterogeneity between identified studies, with some indication that changes in vital signs may indicate that a patient is close to death. However, other studies suggested that patients may maintain normal vital signs until the time of death. Further studies are needed to explore whether abnormal vital signs may be used as a prognostic indicator for patients with cancer receiving palliative care.

UNASSIGNED: Despite significant progress in cardiopulmonary resuscitation and post-cardiac arrest care, favorable outcome in out-of hospital sudden cardiac arrest patients remains low. One of the main reasons for mortality in these patients is withdrawal of life-sustaining treatment. There is a need for precise and equitable prognostication tools to support families in avoiding premature or inappropriate WLST. Heart rate (HR) and heart rate variability (HRV) have been noted for their association with outcome, and are positioned to be a useful modality for prognostication.
UNASSIGNED: The aim of this scoping review is to rigorously explore which electrocardiography features have been shown to predict functional outcome in post-cardiac arrest patients.
UNASSIGNED: The search was performed in Pubmed, EMBASE, and SCOPUS for studies published from January 1, 2011, to September 29, 2022, including papers in English or Korean.
UNASSIGNED: Seven studies were included with a total of 1359 patients. Four studies evaluated HR, one study evaluated RR inverval, and two studies evaluated HRV. All studies were retrospective, with 3 multi-center and 4 single-center studies. All seven studies were inclusive of patients who underwent targeted temperature management (TTM) after cardiac arrest, and two studies included patients without TTM. Five studies used cerebral performance category to assess functional outcome, two studies used Glasgow outcome score, and one study used modified Rankin scale. Three studies measured outcome at hospital discharge, one study measured outcome at 14 days after return of spontaneous circulation, two studies measured outcome after 3 months, and one after 1 year. In all studies that evaluated HR, lower HR was associated with favorable functional outcome. Two studies found that higher complexity of HRV was associated with favorable functional outcome.
UNASSIGNED: HR and HRV showed clear associations with functional outcome in patients after CA, but cinilcial utility for prognostication is uncertain.

Prognostication of comatose patients after cardiac arrest aims to identify patients with a large probability of favourable or unfavouble outcome, usually within the first week after the event. Electroencephalography (EEG) is a technique that is increasingly used for this purpose and has many advantages, such as its non-invasive nature and the possibility to monitor the evolution of brain function over time. At the same time, use of EEG in a critical care environment faces a number of challenges. This narrative review describes the current role and future applications of EEG for outcome prediction of comatose patients with postanoxic encephalopathy.