MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival.

Background: Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Methods: Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. Results: We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Conclusions: Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

Postoperative delirium is associated with grey matter brain volume loss.

Delirium is associated with long-term cognitive dysfunction and with increased brain atrophy. However, it is unclear whether these problems result from or predisposes to delirium. We aimed to investigate preoperative to postoperative brain changes, as well as the role of delirium in these changes over time. We investigated the effects of surgery and postoperative delirium with brain MRIs made before and 3 months after major elective surgery in 299 elderly patients, and an MRI with a 3 months follow-up MRI in 48 non-surgical control participants. To study the effects of surgery and delirium, we compared brain volumes, white matter hyperintensities and brain infarcts between baseline and follow-up MRIs, using multiple regression analyses adjusting for possible confounders. Within the patients group, 37 persons (12%) developed postoperative delirium. Surgical patients showed a greater decrease in grey matter volume than non-surgical control participants [linear regression: B (95% confidence interval) = -0.65% of intracranial volume (-1.01 to -0.29, P < 0.005)]. Within the surgery group, delirium was associated with a greater decrease in grey matter volume [B (95% confidence interval): -0.44% of intracranial volume (-0.82 to -0.06, P = 0.02)]. Furthermore, within the patients, delirium was associated with a non-significantly increased risk of a new postoperative brain infarct [logistic regression: odds ratio (95% confidence interval): 2.8 (0.7-11.1), P = 0.14]. Our study was the first to investigate the association between delirium and preoperative to postoperative brain volume changes, suggesting that delirium is associated with increased progression of grey matter volume loss.

Functional brain network and trail making test changes following major surgery and postoperative delirium: a prospective, multicentre, observational cohort study.

BACKGROUND: Delirium is a frequent complication after surgery in older adults and is associated with an increased risk of long-term cognitive impairment and dementia. Disturbances in functional brain networks were previously reported during delirium. We hypothesised that alterations in functional brain networks persist after remission of postoperative delirium and that functional brain network alterations are associated with long-term cognitive impairment. METHODS: In this prospective, multicentre, observational cohort study, we included older patients who underwent clinical assessments (including the Trail Making Test B [TMT-B]) and resting-state functional MRI (rs-fMRI) before and 3 months after elective surgery. Delirium was assessed on the first seven postoperative days. RESULTS: Of the 554 enrolled patients, 246 remained after strict motion correction, of whom 38 (16%) developed postoperative delirium. The rs-fMRI functional connectivity strength increased 3 months after surgery in the total study population (ß=0.006; 95% confidence interval [CI]: 0.001-0.011; P=0.013), but it decreased after postoperative delirium (ß=-0.015; 95% CI: -0.028 to 0.002; P=0.023). No difference in TMT-B scores was found at follow-up between patients with and without postoperative delirium. Patients with decreased functional connectivity strength declined in TMT-B scores compared with those who did not (ß=11.04; 95% CI: 0.85-21.2; P=0.034). CONCLUSIONS: Postoperative delirium was associated with decreased brain functional connectivity strength after 3 months, suggesting that delirium has a long-lasting impact on brain networks. The decreased connectivity strength was associated with significant cognitive deterioration after major surgery. CLINICAL TRIAL REGISTRATION: NCT02265263.

Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).

BACKGROUND: Evaluating patients' experiences is essential when incorporating the patients' perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended questions is widely recognized. Natural language processing (NLP) can automate the analysis of open-ended questions for an efficient approach to patient-centeredness. METHODS: We developed the Artificial Intelligence Patient-Reported Experience Measures (AI-PREM) tool, consisting of a new, open-ended questionnaire, an NLP pipeline to analyze the answers using sentiment analysis and topic modeling, and a visualization to guide physicians through the results. The questionnaire and NLP pipeline were iteratively developed and validated in a clinical context. RESULTS: The final AI-PREM consisted of five open-ended questions about the provided information, personal approach, collaboration between healthcare professionals, organization of care, and other experiences. The AI-PREM was sent to 867 vestibular schwannoma patients, 534 of which responded. The sentiment analysis model attained an F1 score of 0.97 for positive texts and 0.63 for negative texts. There was a 90% overlap between automatically and manually extracted topics. The visualization was hierarchically structured into three stages: the sentiment per question, the topics per sentiment and question, and the original patient responses per topic. CONCLUSIONS: The AI-PREM tool is a comprehensive method that combines a validated, open-ended questionnaire with a well-performing NLP pipeline and visualization. Thematically organizing and quantifying patient feedback reduces the time invested by healthcare professionals to evaluate and prioritize patient experiences without being confined to the limited answer options of closed-ended questions.

Delirium and long-term psychopathology following surgery in older adults.

OBJECTIVE: To describe the risk of postoperative delirium and long-term psychopathology (depression, anxiety or post-traumatic stress syndrome (PTSS)) in older adults. METHODS: 255 elderly patients (≥ 65 years) undergoing major surgery (planned surgical time > 60 min) in a tertiary hospital were compared to 76 non-surgical controls from general practice. Patients were assessed twice daily for postoperative delirium using the Confusion Assessment Method (CAM(-ICU)), nursing delirium screening scale (NuDESC) and validated chart review. Before surgery and 3 and 12 months thereafter, the participants filled in the Hospital Anxiety and Depression Scale (HADS), the Geriatric Depression Scale (GDS-15) and the Post-Traumatic Stress Syndrome-14-Questions Inventory (PTSS-14). Non-surgical controls filled in the same questionnaires with similar follow-up. RESULTS: Patients were more often male, had higher American Society of Anesthesiologists scores and more often had a spouse compared to controls (p < 0.005). Forty-three patients (18%) developed postoperative delirium, who were significantly older, had higher ASA scores and lower estimated IQ scores compared to the patients who did not develop delirium (p < 0.05). There were no differences in psychopathology at baseline and 3-month follow-up between patients and controls. At 12-months, surgical patients less frequently scored positive for depression (7% versus 16%) and anxiety (2% versus 10%) compared to nonsurgical controls (p < 0.05). We did not observe differences in occurrence of psychopathology between patients who had and had not developed postoperative delirium. CONCLUSION: Our results suggest that the older surgical population, with or without postoperative delirium, does not appear to be at greater risk of developing psychopathology. WHY DOES THIS PAPER MATTER?: The older surgical population does not appear to be at greater risk of developing psychopathology, neither seems this risk influenced by the occurrence of postoperative delirium.

Preoperative MRI brain phenotypes are related to postoperative delirium in older individuals.

The underlying structural correlates of predisposition to postoperative delirium remain largely unknown. A combined analysis of preoperative brain magnetic resonance imaging (MRI) markers could improve our understanding of the pathophysiology of delirium. Therefore, we aimed to identify different MRI brain phenotypes in older patients scheduled for major elective surgery, and to assess the relation between these phenotypes and postoperative delirium. Markers of neurodegenerative and neurovascular brain changes were determined from MRI brain scans in older patients (n = 161, mean age 71, standard deviation 5 years), of whom 24 (15%) developed delirium. A hierarchical cluster analysis was performed. We found six distinct groups of patients with different MRI brain phenotypes. Logistic regression analysis showed a higher odds of developing postoperative delirium in individuals with multi-burden pathology (n = 15 (9%), odds ratio (95% confidence interval): 3.8 (1.1-13.0)). In conclusion, these results indicate that different MRI brain phenotypes are related to a different risk of developing delirium after major elective surgery. MRI brain phenotypes could assist in an improved understanding of the structural correlates of predisposition to postoperative delirium.

Preoperative brain MRI features and occurrence of postoperative delirium.

OBJECTIVE: Delirium is a frequent complication after surgery with important negative outcomes for affected patients and society. However, it is still largely unknown why some patients have a predisposition for delirium and others not. To increase our understanding of the neural substrate of postoperative delirium, we studied the association between preoperative brain MRI features and the occurrence of delirium after major surgery. METHODS: A group of 413 patients without dementia (Mean 72 years, SD: 5) was included in a prospective observational two-center study design. The study was conducted at Charité Universitätsmedizin (Berlin, Germany) and the University Medical Center Utrecht (Utrecht, The Netherlands). We measured preoperative brain volumes (total brain, gray matter, white matter), white matter hyperintensity volume and shape, brain infarcts and cerebral perfusion, and used logistic regression analysis adjusted for age, sex, intracranial volume, study center and type of surgery. RESULTS: Postoperative delirium was present in a total of 70 patients (17%). Preoperative cortical brain infarcts increased the risk of postoperative delirium, although this did not reach statistical significance (OR (95%CI): 1.63 (0.84-3.18). Furthermore, we found a trend for an association of a more complex shape of white matter hyperintensities with occurrence of postoperative delirium (OR (95%CI): 0.97 (0.95-1.00)). Preoperative brain volumes, white matter hyperintensity volume, and cerebral perfusion were not associated with occurrence of postoperative delirium. CONCLUSION: Our study suggests that patients with preoperative cortical brain infarcts and those with a more complex white matter hyperintensity shape may have a predisposition for developing delirium after major surgery.

The association between brain volume, cortical brain infarcts, and physical frailty.

Physical frailty is an age-associated syndrome of decreased reserve leading to vulnerability to physiological stressors and associated with negative outcomes. The underlying structural brain abnormalities of physical frailty are unclear. We investigated the association between brain volume, cortical brain infarcts, and physical frailty. In this multicenter study, 214 nondemented participants were classified as frail (n = 32), prefrail (n = 107), or nonfrail (n = 75) based on the Fried frailty phenotype. The associations between frailty and brain volumes and cortical brain infarcts were investigated by linear or logistic regression analyses. Participants in the frail group showed a lower total brain volume (-19.67 mL [95% confidence interval -37.84 to -1.50]) and lower gray matter volume (-12.19 mL [95% confidence interval -23.84 to -0.54]) compared to nonfrail participants. Frailty was associated with cortical brain infarcts [frail 16% [n = 5], prefrail 11% [n = 12], and nonfrail 3% [n = 2]). Reduced total brain volume and gray matter volume and increased cortical brain infarcts seem therefore to be part of the structural substrate of the physical frailty phenotype.