Cerebral microbleeds are not associated with postoperative delirium and postoperative cognitive dysfunction in older individuals

BACKGROUND: Cerebral microbleeds (CMB) occur in the context of cerebral small vessel disease. Other brain MRI markers of cerebral small vessel disease are associated with the occurrence of postoperative delirium (POD) and postoperative cognitive dysfunction (POCD), but for CMB this is unknown. We aimed to study the association between CMB and the occurrence of POD and POCD in older individuals. METHODS: The current study consists of 65 patients (72±5 years) from the BIOCOG study, which is a prospective, observational study of patients who underwent an elective surgery of at least 60 minutes. Patients in the current study received a preoperative cerebral MRI scan including a 3D susceptibility-weighted imaging sequence to detect CMB. The occurrence of POD was screened for twice a day until postoperative day 7 by using the DSM-5, NuDesc, CAM, and CAM-ICU. The occurrence of POCD was determined by the reliable change index model at 7 days after surgery or discharge, respectively, and 3 months after surgery. Statistical analyses consisted of logistic regression adjusted for age and gender. RESULTS: A total of 39 CMB were detected in 17 patients (26%) prior to surgery. POD occurred in 14 out of 65 patients (22%). POCD at 7 days after surgery occurred in 11 out of 54 patients (20%) and in 3 out of 40 patients at the 3 month follow-up (8%). Preoperative CMB were not associated with the occurrence of POD (OR (95%-CI): 0.28 (0.05, 1.57); p = 0.147) or POCD at 7 days after surgery (0.76 (0.16, 3.54); p = 0.727) or at 3 months follow-up (0.61 (0.03, 11.64); p = 0.740). CONCLUSION: We did not find an association between preoperative CMB and the occurrence of POD or POCD. TRIAL REGISTRATION: clinicaltrials.gov (NCT02265263) on 23 September 2014

Diagnosis Classification in the Emergency Room Using Natural Language Processing

Diagnosis classification in the emergency room (ER) is a complex task. We developed several natural language processing classification models, looking both at the full classification task of 132 diagnostic categories and at several clinically applicable samples consisting of two diagnoses that are hard to distinguish

Predicting Depression Risk in Patients with Cancer Using Multimodal Data

When patients with cancer develop depression, it is often left untreated. We developed a prediction model for depression risk within the first month after starting cancer treatment using machine learning and Natural Language Processing (NLP) models. The LASSO logistic regression model based on structured data performed well, whereas the NLP model based on only clinician notes did poorly. After further validation, prediction models for depression risk could lead to earlier identification and treatment of vulnerable patients, ultimately improving cancer care and treatment adherence

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study

Background: Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients. Objective: This study aimed to develop a prediction model for depression risk within the first month of cancer treatment. Methods: We included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008 and 2021. Machine learning models (eg, least absolute shrinkage and selection operator [LASSO] logistic regression) and natural language processing models (Bidirectional Encoder Representations from Transformers [BERT]) were used to develop multimodal prediction models using both electronic health record data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5387, 33%) using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis to assess initial clinical impact use. Results: Among 16,159 patients, 437 (2.7%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression models based on the structured data (AUROC 0.74, 95% CI 0.71-0.78) and structured data with email classification scores (AUROC 0.74, 95% CI 0.71-0.78) had the best discriminative performance. The BERT models based on clinician notes and structured data with email classification scores had AUROCs around 0.71. The logistic regression model based on email classification scores alone performed poorly (AUROC 0.54, 95% CI 0.52-0.56), and the model based solely on clinician notes had the worst performance (AUROC 0.50, 95% CI 0.49-0.52). Calibration was good for the logistic regression models, whereas the BERT models produced overly extreme risk estimates even after recalibration. There was a small range of decision thresholds for which the best-performing model showed promising clinical effectiveness use. The risks were underestimated for female and Black patients. Conclusions: The results demonstrated the potential and limitations of machine learning and multimodal models for predicting depression risk in patients with cancer. Future research is needed to further validate these models, refine the outcome label and predictors related to mental health, and address biases across subgroups

Different phenotypes of neuropsychiatric systemic lupus erythematosus are related to a distinct pattern of structural changes on brain MRI

Objectives The underlying structural brain correlates of neuropsychiatric involvement in systemic lupus erythematosus (NPSLE) remain unclear, thus hindering correct diagnosis. We compared brain tissue volumes between a clinically well-defined cohort of patients with NPSLE and SLE patients with neuropsychiatric syndromes not attributed to SLE (non-NPSLE). Within the NPSLE patients, we also examined differences between patients with two distinct disease phenotypes: ischemic and inflammatory. Methods In this prospective (May 2007 to April 2015) cohort study, we included 38 NPSLE patients (26 inflammatory and 12 ischemic) and 117 non-NPSLE patients. All patients underwent a 3-T brain MRI scan that was used to automatically determine white matter, grey matter, white matter hyperintensities (WMH) and total brain volumes. Group differences in brain tissue volumes were studied with linear regression analyses corrected for age, gender, and total intracranial volume and expressed as B values and 95% confidence intervals. Results NPSLE patients showed higher WMH volume compared to non-NPSLE patients (p = 0.004). NPSLE inflammatory patients showed lower total brain (p = 0.014) and white matter volumes (p = 0.020), and higher WMH volume (p = 0.002) compared to non-NPSLE patients. Additionally, NPSLE inflammatory patients showed lower white matter (p = 0.020) and total brain volumes (p = 0.038) compared to NPSLE ischemic patients. Conclusion We showed that different phenotypes of NPSLE were related to distinct patterns of underlying structural brain MRI changes. Especially the inflammatory phenotype of NPSLE was associated with the most pronounced brain volume changes, which might facilitate the diagnostic process in SLE patients with neuropsychiatric symptoms

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 deliriu

The association between frailty and MRI features of cerebral small vessel disease.

Frailty is a common syndrome in older individuals that is associated with poor cognitive outcome. The underlying brain correlates of frailty are unclear. The aim of this study was to investigate the association between frailty and MRI features of cerebral small vessel disease in a group of non-demented older individuals. We included 170 participants who were classified as frail (n = 30), pre-frail (n = 85) or non-frail (n = 55). The association of frailty and white matter hyperintensity volume and shape features, lacunar infarcts and cerebral perfusion was investigated by regression analyses adjusted for age and sex. Frail and pre-frail participants were older, more often female and showed higher white matter hyperintensity volume (0.69 [95%-CI 0.08 to 1.31], p = 0.03 respectively 0.43 [95%-CI: 0.04 to 0.82], p = 0.03) compared to non-frail participants. Frail participants showed a non-significant trend, and pre-frail participants showed a more complex shape of white matter hyperintensities (concavity index: 0.04 [95%-CI: 0.03 to 0.08], p = 0.03; fractal dimensions: 0.07 [95%-CI: 0.00 to 0.15], p = 0.05) compared to non-frail participants. No between group differences were found in gray matter perfusion or in the presence of lacunar infarcts. In conclusion, increased white matter hyperintensity volume and a more complex white matter hyperintensity shape may be structural brain correlates of the frailty phenotype

Don't be misled: 3 misconceptions about external validation of clinical prediction models

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies