Survival outcomes used to generate version 9 American Joint Committee on Cancer staging system for anal cancer.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including anal cancer, is the standard for cancer staging in the United States. The AJCC staging criteria are dynamic, and periodic updates are conducted to optimize AJCC staging definitions through a panel of experts charged with evaluating new evidence to implement changes. With greater availability of large data sets, the AJCC has since restructured and updated its processes, incorporating prospectively collected data to validate stage group revisions in the version 9 AJCC staging system, including anal cancer. Survival analysis using AJCC eighth edition staging guidelines revealed a lack of hierarchical order in which stage IIIA anal cancer was associated with a better prognosis than stage IIB disease, suggesting that, for anal cancer, tumor (T) category has a greater effect on survival than lymph node (N) category. Accordingly, version 9 stage groups have been appropriately adjusted to reflect contemporary long-term outcomes. This article highlights the changes to the now published AJCC staging system for anal cancer, which: (1) redefined stage IIB as T1-T2N1M0 disease, (2) redefined stage IIIA as T3N0-N1M0 disease, and (3) eliminated stage 0 disease from its guidelines altogether.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology.

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.

Data-driven optimization of version 9 American Joint Committee on Cancer staging system for anal cancer.

INTRODUCTION: The American Joint Committee on Cancer (AJCC) staging system undergoes periodic revisions to maintain contemporary survival outcomes related to stage. Recently, the AJCC has developed a novel, systematic approach incorporating survival data to refine stage groupings. The objective of this study was to demonstrate data-driven optimization of the version 9 AJCC staging system for anal cancer assessed through a defined validation approach. METHODS: The National Cancer Database was queried for patients diagnosed with anal cancer in 2012 through 2017. Kaplan-Meier methods analyzed 5-year survival by individual clinical T category, N category, M category, and overall stage. Cox proportional hazards models validated overall survival of the revised TNM stage groupings. RESULTS: Overall, 24,328 cases of anal cancer were included. Evaluation of the 8th edition AJCC stage groups demonstrated a lack of hierarchical prognostic order. Survival at 5 years for stage I was 84.4%, 77.4% for stage IIA, and 63.7% for stage IIB; however, stage IIIA disease demonstrated a 73.0% survival, followed by 58.4% for stage IIIB, 59.9% for stage IIIC, and 22.5% for stage IV (p <.001). Thus, stage IIB was redefined as T1-2N1M0, whereas Stage IIIA was redefined as T3N0-1M0. Reevaluation of 5-year survival based on data-informed stage groupings now demonstrates hierarchical prognostic order and validated via Cox proportional hazards models. CONCLUSION: The 8th edition AJCC survival data demonstrated a lack of hierarchical prognostic order and informed revised stage groupings in the version 9 AJCC staging system for anal cancer. Thus, a validated data-driven optimization approach can be implemented for staging revisions across all disease sites moving forward.

The Use of Wearable Devices in Oncology Patients: A Systematic Review.

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.

Metabolic tumor volume predicts outcome in patients with advanced stage follicular lymphoma from the RELEVANCE trial.

BACKGROUND: We investigated the prognostic value of baseline positron emission tomography (PET) parameters for patients with treatment-naïve follicular lymphoma (FL) in the phase III RELEVANCE trial, comparing the immunomodulatory combination of lenalidomide and rituximab (R2) versus R-chemotherapy (R-chemo), with both regimens followed by R maintenance therapy. PATIENTS AND METHODS: Baseline characteristics of the entire PET-evaluable population (n = 406/1032) were well balanced between treatment arms. The maximal standard uptake value (SUVmax) and the standardized maximal distance between tow lesions (SDmax) were extracted, the standardized distance between two lesions the furthest apart, were extracted. The total metabolic tumor volume (TMTV) was computed using the 41% SUVmax method. RESULTS: With a median follow-up of 6.5 years, the 6-year progression-free survival (PFS) was 57.8%, the median TMTV was 284 cm3, SUVmax was 11.3 and SDmax was 0.32 m-1, with no significant difference between arms. High TMTV (>510 cm3) and FLIPI were associated with an inferior PFS (P = 0.013 and P = 0.006, respectively), whereas SUVmax and SDmax were not (P = 0.08 and P = 0.12, respectively). In multivariable analysis, follicular lymphoma international prognostic index (FLIPI) and TMTV remained significantly associated with PFS (P = 0.0119 and P = 0.0379, respectively). These two adverse factors combined stratified the overall population into three risk groups: patients with no risk factors (40%), with one factor (44%), or with both (16%), with a 6-year PFS of 67.7%, 54.5%, and 41.0%, respectively. No significant interaction between treatment arms and TMTV or FLIPI (P = 0.31 or P = 0.59, respectively) was observed. The high-risk group (high TMTV and FLIPI 3-5) had a similar PFS in both arms (P = 0.45) with a median PFS of 68.4% in the R-chemo arm versus 71.4% in the R2 arm. CONCLUSIONS: Baseline TMTV is predictive of PFS, independently of FLIPI, in patients with advanced FL even in the context of antibody maintenance.

Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome.

PURPOSE: Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome. METHODS: [18F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score. RESULTS: Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001). CONCLUSIONS: Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome.

Outcomes after intervention for enteral nutrition in patients with amyotrophic lateral sclerosis in multidisciplinary clinics.

INTRODUCTION/AIMS: Patients with amyotrophic lateral sclerosis (ALS) are susceptible to malnutrition, with appropriate management of nutritional interventions an active area of investigation. We sought to determine the impact of gastrostomy tube placement in ALS patients, exploring the correlation between forced vital capacity (FVC), malnutrition, and perioperative complications. METHODS: A retrospective review was performed of clinically diagnosed ALS patients treated at two multidisciplinary clinics (University of Kansas, University of Nebraska) from January 2009 to September 2020 who were referred for gastrostomy. Data collected included demographics, disease characteristics, and key gastrostomy related dates/outcomes. RESULTS: Two hundred thirty-nine patients were included with a median age of 65 years and median of 589 days from symptom onset to gastrostomy (interquartile range, 404-943). The population was predominantly Non-Hispanic White with bulbar-onset ALS. 30-day mortality was 4% and 30-day morbidity was 13%. Weight loss, body mass index, and predicted FVC at placement showed no increased 30-day morbidity or mortality association. Bulbar-onset ALS patients exhibited higher overall mortality postplacement than limb onset (odds ratio: 1.85, 95% confidence interval: 1.03-3.33). There was a 5% incidence of symptoms suggestive of refeeding syndrome. DISCUSSION: Rates of major/minor complications and 30-day mortality related to gastrostomy placement in our population were similar compared with prior studies in ALS. The lack of difference in outcomes based on FVC at procedure may suggest this is not predictive of outcome, or perhaps, high-quality perioperative respiratory management. Alternative reasons may account for the increased morbidity and mortality of gastrostomy placement in the ALS population.

The impact of macro- and micro-steatosis on the outcomes of patients who undergo liver transplant: Analysis of the UNOS-STAR database.

BACKGROUND AND AIMS: The presence of steatosis in a donor liver and its relation to post-transplantation outcomes are not well defined. This study evaluates the effect of the presence and severity of micro- and macro-steatosis of a donor graft on post-transplantation outcomes. METHODS: The UNOS-STAR registry (2005-2019) was used to select patients who received a liver transplant graft with hepatic steatosis. The study cohort was stratified by the presence of macro- or micro-vesicular steatosis, and further stratified by histologic grade of steatosis. The primary endpoints of all-cause mortality and graft failure were compared using sequential Cox regression analysis. Analysis of specific causes of mortality was further performed. RESULTS: There were 9184 with no macro-steatosis (control), 150 with grade 3 macro-steatosis, 822 with grade 2 macro-steatosis and 12 585 with grade 1 macro-steatosis. There were 10 320 without micro-steatosis (control), 478 with grade 3 micro-steatosis, 1539 with grade 2 micro-steatosis and 10 404 with grade 1 micro-steatosis. There was no significant difference in all-cause mortality or graft failure among recipients who received a donor organ with any evidence of macro- or micro-steatosis, compared to those receiving non-steatotic grafts. There was increased mortality due to cardiac arrest among recipients of a grade 2 macro-steatosis donor organ. CONCLUSION: This study shows no significant difference in all-cause mortality or graft failure among recipients who received a donor liver with any degree of micro- or macro-steatosis. Further analysis identified increased mortality due to specific aetiologies among recipients receiving donor organs with varying grades of macro- and micro-steatosis.

Methylation profiling in the contemporary management of meningioma.

BACKGROUND: The last decade has seen major international research efforts focus on better understanding disease heterogeneity in meningioma. Multiple molecular platforms have generated significant biological and clinical utility, and there is a need to translate these findings into routine clinical practice. Here we review the role of DNA methylation profiling in meningioma and advocate for its widespread adoption. METHODS: We review modern DNA methylation-based classification and outcome prediction tools in meningioma. Biological classifiers, which were constructed agnostic to outcome using unsupervised approaches, outcome predictors, and liquid biopsy models are discussed in detail. RESULTS: DNA methylation has been used for biological classification and outcome in meningioma with considerable success. Several groups have proposed novel molecular classification systems which share similar features with one another and outperform WHO grade in their ability to predict outcome and explain subgroup-specific biological processes. In addition, recent studies have suggested a role for methylation-based liquid-biopsy in meningioma, which represents an exciting avenue for further exploration. CONCLUSIONS: DNA methylation profiling has been revolutionary in meningioma. There is a need for widespread adoption of these approaches to personalize care and inform clinical trial design.

Skeletal Muscle Area on CT: Determination of an Optimal Height Scaling Power and Testing for Mortality Risk Prediction.

BACKGROUND. Sarcopenia is commonly assessed on CT by use of the skeletal muscle index (SMI), which is calculated as the skeletal muscle area (SMA) at L3 divided by patient height squared (i.e., a height scaling power of 2). OBJECTIVE. The purpose of this study was to determine the optimal height scaling power for SMA measurements on CT and to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. METHODS. This retrospective study included 16,575 patients (6985 men, 9590 women; mean age, 56.4 years) who underwent abdominal CT from December 2012 through October 2018. The SMA at L3 was determined using automated software. The sample was stratified into two groups: 5459 patients without major medical conditions (based on ICD-9 and ICD-10 codes) who were included in the analysis for determining the optimal height scaling power and 11,116 patients with major medical conditions who were included for the purpose of testing this power. The optimal scaling power was determined by allometric analysis (whereby regression coefficients were fitted to log-linear sex-specific models relating height to SMA) and by analysis of statistical independence of SMI from height across scaling powers. Cox proportional hazards models were used to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. RESULTS. In allometric analysis, the regression coefficient of log(height) in patients 40 years old and younger was 1.02 in men and 1.08 in women, and in patients older than 40 years old, it was 1.07 in men and 1.10 in women (all p < .05 vs regression coefficient of 2). In analyses for statistical independence of SMI from height, the optimal height scaling power (i.e., those yielding correlations closest to 0) was, in patients 40 years old and younger, 0.97 in men and 1.08 in women, whereas in patients older than 40 years old, it was 1.03 in men and 1.09 in women. In the Cox model used for testing, SMI predicted all-cause mortality with a higher concordance index using of a height scaling power of 1 rather than 2 in men (0.675 vs 0.663, p < .001) and in women (0.664 vs 0.653, p < .001). CONCLUSION. The findings support a height scaling power of 1, rather than a conventional power of 2, for SMI computation. CLINICAL IMPACT. A revised height scaling power for SMI could impact the utility of CT-based sarcopenia diagnoses in risk assessment.

One-Year Clinical Outcomes and Prognostic Factors Following Revascularization in Patients With Acute Limb Ischemia　- Results From the RESCUE ALI Study.

BACKGROUND: Acute limb ischemia (ALI) is a limb- and life-threatening condition and urgent treatment including revascularization should be offered to patients unless the limb is irreversibly ischemic. The aim of this study was to investigate 1-year clinical outcomes and prognostic factors following revascularization in patients with ALI.Methods and Results: A retrospective, multicenter, nonrandomized study examined 185 consecutive patients with ALI treated by surgical revascularization (SR), endovascular revascularization (ER), or hybrid revascularization (HR) in 6 Japanese medical centers from January 2015 to August 2021. The 1-year amputation-free survival (AFS) rate was estimated to be 69.2% (95% confidence interval [CI], 62.8-76.2%). There were no significant differences among SR, ER, and HR regarding both technical success and perioperative complications. Multivariate analysis revealed that Rutherford category IIb and III ischemia (hazard ratio [HR]: 1.86; 95% CI: 1.06-3.25), supra- to infrapopliteal lesion (HR: 2.06; 95% CI: 1.08-3.95), and technical failure (HR: 2.58; 95% CI: 1.49-4.46) were independent risk factors for 1-year AFS. CONCLUSIONS: Rutherford category IIb and III ischemia, supra- to infrapopliteal lesions, and technical failures were identified as independent risk factors for 1-year AFS. Furthermore, patients with multiple risk factors had a lower AFS rate.

Novel prognostication biomarker adipophilin reveals a metabolic shift in uveal melanoma and new therapeutic opportunities.

Metastatic uveal melanoma remains incurable at present. We previously demonstrated that loss of BAP1 gene expression in tumour cells triggers molecular mechanisms of immunosuppression in the tumour microenvironment (TME) of metastatic uveal melanoma. Adipophilin is a structural protein of lipid droplets involved in fat storage within mammalian cells, and its expression has been identified in uveal melanoma. We comprehensively evaluated adipophilin expression at the RNA (PLIN2) and protein levels of 80 patients of the GDC-TCGA-UM study and in a local cohort of 43 primary uveal melanoma samples respectively. PLIN2 expression is a survival prognosticator biomarker in uveal melanoma. Loss of adipophilin expression is significantly associated with monosomy 3 status and nuclear BAP1 losses in uveal melanoma tumours. Integrative transcriptomic and secretome studies show a relationship between transient loss of adipophilin expression and increased levels of tumour-associated macrophages and hypoxia genes, suggesting PLIN2-dependent changes in oxygen and lipid metabolism in the TME of low and high-metastatic risk uveal melanoma. We designed four adipophilin-based multigene signatures for uveal melanoma prognostication using a transcriptomic and secretome survival-functional network approach. Adipophilin-based multigene signatures were validated in BAP1-positive and BAP1-negative uveal melanoma cell lines using a next-generation RNA sequencing approach. We identified existing small molecules, mostly adrenergic, retinoid, and glucocorticoid receptor agonists, MEK, and RAF inhibitors, with the potential to reverse this multigene signature expression in uveal melanoma. Some of these molecules were able to impact tumour cell viability, and carvedilol, an adrenergic receptor antagonist, restored PLIN2 levels, mimicking the expression of normoxia/lipid storage signatures and reversing the expression of hypoxia/lipolysis signatures in co-cultures of uveal melanoma cells with human macrophages. These findings open up a new research line for understanding the lipid metabolic regulation of immune responses, with implications for therapeutic innovation in uveal melanoma. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Prognostic value of early EEG abnormalities in severe stroke patients requiring mechanical ventilation: a pre-planned analysis of the SPICE prospective multicenter study.

INTRODUCTION: Prognostication of outcome in severe stroke patients necessitating invasive mechanical ventilation poses significant challenges. The objective of this study was to assess the prognostic significance and prevalence of early electroencephalogram (EEG) abnormalities in adult stroke patients receiving mechanical ventilation. METHODS: This study is a pre-planned ancillary investigation within the prospective multicenter SPICE cohort study (2017-2019), conducted in 33 intensive care units (ICUs) in the Paris area, France. We included adult stroke patients requiring invasive mechanical ventilation, who underwent at least one intermittent EEG examination during their ICU stay. The primary endpoint was the functional neurological outcome at one year, determined using the modified Rankin scale (mRS), and dichotomized as unfavorable (mRS 4-6, indicating severe disability or death) or favorable (mRS 0-3). Multivariable regression analyses were employed to identify EEG abnormalities associated with functional outcomes. RESULTS: Of the 364 patients enrolled in the SPICE study, 153 patients (49 ischemic strokes, 52 intracranial hemorrhages, and 52 subarachnoid hemorrhages) underwent at least one EEG at a median time of 4 (interquartile range 2-7) days post-stroke. Rates of diffuse slowing (70% vs. 63%, p = 0.37), focal slowing (38% vs. 32%, p = 0.15), periodic discharges (2.3% vs. 3.7%, p = 0.9), and electrographic seizures (4.5% vs. 3.7%, p = 0.4) were comparable between patients with unfavorable and favorable outcomes. Following adjustment for potential confounders, an unreactive EEG background to auditory and pain stimulations (OR 6.02, 95% CI 2.27-15.99) was independently associated with unfavorable outcomes. An unreactive EEG predicted unfavorable outcome with a specificity of 48% (95% CI 40-56), sensitivity of 79% (95% CI 72-85), and positive predictive value (PPV) of 74% (95% CI 67-81). Conversely, a benign EEG (defined as continuous and reactive background activity without seizure, periodic discharges, triphasic waves, or burst suppression) predicted favorable outcome with a specificity of 89% (95% CI 84-94), and a sensitivity of 37% (95% CI 30-45). CONCLUSION: The absence of EEG reactivity independently predicts unfavorable outcomes at one year in severe stroke patients requiring mechanical ventilation in the ICU, although its prognostic value remains limited. Conversely, a benign EEG pattern was associated with a favorable outcome.

Plasma glial fibrillary acidic protein and tau: predictors of neurological outcome after cardiac arrest.

BACKGROUND: The purpose was to evaluate glial fibrillary acidic protein (GFAP) and total-tau in plasma as predictors of poor neurological outcome after out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA), including comparisons with neurofilament light (NFL) and neuron-specific enolase (NSE). METHODS: Retrospective multicentre observational study of patients admitted to an intensive care unit (ICU) in three hospitals in Sweden 2014-2018. Blood samples were collected at ICU admission, 12 h, and 48 h post-cardiac arrest. Poor neurological outcome was defined as Cerebral Performance Category 3-5 at 2-6 months after cardiac arrest. Plasma samples were retrospectively analysed for GFAP, tau, and NFL. Serum NSE was analysed in clinical care. Prognostic performances were tested with the area under the receiver operating characteristics curve (AUC). RESULTS: Of the 428 included patients, 328 were OHCA, and 100 were IHCA. At ICU admission, 12 h and 48 h post-cardiac arrest, GFAP predicted neurological outcome after OHCA with AUC (95% CI) 0.76 (0.70-0.82), 0.86 (0.81-0.90) and 0.91 (0.87-0.96), and after IHCA with AUC (95% CI) 0.77 (0.66-0.87), 0.83 (0.74-0.92) and 0.83 (0.71-0.95). At the same time points, tau predicted outcome after OHCA with AUC (95% CI) 0.72 (0.66-0.79), 0.75 (0.69-0.81), and 0.93 (0.89-0.96) and after IHCA with AUC (95% CI) 0.61 (0.49-0.74), 0.68 (0.56-0.79), and 0.77 (0.65-0.90). Adding the change in biomarker levels between time points did not improve predictive accuracy compared to the last time point. In a subset of patients, GFAP at 12 h and 48 h, as well as tau at 48 h, offered similar predictive value as NSE at 48 h (the earliest time point NSE is recommended in guidelines) after both OHCA and IHCA. The predictive performance of NFL was similar or superior to GFAP and tau at all time points after OHCA and IHCA. CONCLUSION: GFAP and tau are promising biomarkers for neuroprognostication, with the highest predictive performance at 48 h after OHCA, but not superior to NFL. The predictive ability of GFAP may be sufficiently high for clinical use at 12 h after cardiac arrest.

Prognostication for surgically treated papillary renal cell carcinoma: which model is the optimal choice?

Tweetable abstract Surgically treated papillary renal cell carcinoma shows distinct prognosis and needs specific prognostic models for counseling, follow-up and high-risk patient identification. Our goal is to summarize and compare currently recommended models.

Factors related to accurate clinicians' prediction of survival: an international multicenter study in East Asia.

PURPOSE: Recent guidelines for prognostic evaluation recommend clinicians' prediction of survival (CPS) for survival prediction in patients with advanced cancer. However, CPS is often inaccurate and optimistic. Studies on factors associated with overestimation or underestimation of CPS are limited. We aimed to investigate the factors associated with the overestimation and underestimation of CPS in patients with far-advanced cancer. METHODS: The current study was a secondary analysis of an international multicenter prospective cohort study, which enrolled newly admitted patients with advanced cancer in palliative care units (PCUs) in Japan, Korea, and Taiwan from 2017 to 2018. We obtained the temporal CPS at enrollment and performed multivariate logistic regression analysis to identify the factors associated with "underestimation (less than 33% of actual survival)" and "overestimation (more than 33% of actual survival)." RESULTS: A total of 2571 patients were assessed and admitted in 37 PCUs between January 2017 and September 2018. Older age (adjusted odds ratio [aOR] 1.01; 95% confidence interval [CI] 1.01-1.02; P < 0.01) and reduced oral intake (aOR 0.68; 95% CI 0.51-0.89; P < 0.01) were identified as significant factors associated with underestimation. Dyspnea (aOR 1.28; 95% CI 1.06-1.54; P = 0.01) and hyperactive delirium (aOR 1.34; 95% CI 1.05-1.72; P = 0.02) were identified as significant factors associated with overestimation. CONCLUSION: Older age was related to underestimation, while dyspnea and hyperactive delirium were related to overestimation of CPS for patients with weeks of survival. However, reduced oral intake was less likely to lead to underestimation.

Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study.

BACKGROUND: Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions. METHODS: Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software. RESULTS: The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML's accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions. CONCLUSION: Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

'How long do you think?' Unresponsive dying patients in a specialist palliative care service: A consecutive cohort study.

BACKGROUND: Predicting length of time to death once the person is unresponsive and deemed to be dying remains uncertain. Knowing approximately how many hours or days dying loved ones have left is crucial for families and clinicians to guide decision-making and plan end-of-life care. AIM: To determine the length of time between becoming unresponsive and death, and whether age, gender, diagnosis or location-of-care predicted length of time to death. DESIGN: Retrospective cohort study. Time from allocation of an Australia-modified Karnofsky Performance Status (AKPS) 10 to death was analysed using descriptive narrative. Interval-censored survival analysis was used to determine the duration of patient's final phase of life, taking into account variation across age, gender, diagnosis and location of death. SETTING/PARTICIPANTS: A total of 786 patients, 18 years of age or over, who received specialist palliative care: as hospice in-patients, in the community and in aged care homes, between January 1st and October 31st, 2022. RESULTS: The time to death after a change to AKPS 10 is 2 days (n = 382; mean = 2.1; median = 1). Having adjusted for age, cancer, gender, the standard deviation of AKPS for the 7-day period prior to death, the likelihood of death within 2 days is 47%, with 84% of patients dying within 4 days. CONCLUSION: This study provides valuable new knowledge to support clinicians' confidence when responding to the 'how long' question and can inform decision-making at end-of-life. Further research using the AKPS could provide greater certainty for answering 'how long' questions across the illness trajectory.

Outcome prediction in comatose cardiac arrest patients with initial shockable and non-shockable rhythms.

BACKGROUND: Prognosis after out-of-hospital cardiac arrest (OHCA) is presumed poorer in patients with non-shockable than shockable rhythms, frequently leading to treatment withdrawal. Multimodal outcome prediction is recommended 72 h post-arrest in still comatose patients, not considering initial rhythms. We investigated accuracy of outcome predictors in all comatose OHCA survivors, with a particular focus on shockable vs. non-shockable rhythms. METHODS: In this observational NORCAST sub-study, patients still comatose 72 h post-arrest were stratified by shockable vs. non-shockable rhythms for outcome prediction analyzes. Good outcome was defined as cerebral performance category 1-2 within 6 months. False positive rate (FPR) was used for poor and sensitivity for good outcome prediction accuracy. RESULTS: Overall, 72/128 (56%) patients with shockable and 12/50 (24%) with non-shockable rhythms had good outcome (p < .001). For poor outcome prediction, absent pupillary light reflexes (PLR) and corneal reflexes (clinical predictors) 72 h after sedation withdrawal, PLR 96 h post-arrest, and somatosensory evoked potentials (SSEP), all had FPR <0.1% in both groups. Unreactive EEG and neuron-specific enolase (NSE) >60 µg/L 24-72 h post-arrest had better precision in shockable patients. For good outcome, the clinical predictors, SSEP and CT, had 86%-100% sensitivity in both groups. For NSE, sensitivity varied from 22% to 69% 24-72 h post-arrest. The outcome predictors indicated severe brain injury proportionally more often in patients with non-shockable than with shockable rhythms. For all patients, clinical predictors, CT, and SSEP, predicted poor and good outcome with high accuracy. CONCLUSION: Outcome prediction accuracy was comparable for shockable and non-shockable rhythms. PLR and corneal reflexes had best precision 72 h after sedation withdrawal and 96 h post-arrest.

Relevance of Continuous EEG versus Routine EEG for Outcome Prediction after Traumatic Brain Injury.

INTRODUCTION: In a cohort of adult patients with disturbance of consciousness after TBI, we aimed to explore the relationship of continuous video-EEG (cEEG) versus routine EEG (rEEG) with mortality and functional outcome. METHODS: This is a post hoc analysis of a randomized controlled trial (CERTA), in which adults with disorder of consciousness and needing EEG (excluding those with proven seizures/SE just before) were randomized 1:1 to cEEG or two rEEG. In TBI patients, correlation between EEG duration, mortality, and modified Rankin score (mRs, good 0-2) at 6 months was assessed. RESULTS: Among 364 patients, 44 presenting with consciousness impairment after TBI were included; 29 randomized to cEEG and 15 to rEEG. Mortality (p = 0.88) and functional outcome (p = 0.58) at 6 months were similar between groups. There was a nonsignificant tendency toward more seizure/status epilepticus detection with cEEG (p = 0.08). In multivariable regression, cEEG was not related to functional outcome (OR: 0.75 [0.13-4.24], p = 0.745) or mortality (OR: 7.11 [0.51-99.32], p = 0.145). CONCLUSION: Despite allowing increased seizure detections in TBI patients, cEEG does not seem to be associated with better functional outcome or mortality over rEEG. Pending larger trials, repeated rEEG might be acceptable in post-TBI disorder of consciousness, especially in resource-limited environments.