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Abstract Background Resilience, or the ability to bounce back from stress, is a key psychological factor that is associated with ongoing functional independence and higher quality of life in older adults in the context of chronic health conditions. Emerging research has explored resilience and patient-reported outcomes after TKA. Our primary aim was to explore the relationship between resilience and acute hospital length of stay after total knee arthroplasty (TKA). Methods A prospective observational study recruited 75 participants one month before total knee arthroplasty from two Australian hospitals. Two preoperative psychological measures were used: the Brief Resilience Scale, and for comparison, the Depression, Anxiety and Stress Scale-21 (DASS-21). We collected sociodemographic, medical and surgical details, patient-reported pain, function, fatigue and quality of life one month before TKA. Health service data describing acute hospital length of stay, inpatient rehabilitation use, and physiotherapy occasions of service were collected after TKA. Non-parametric analysis was used to determine any differences in length of stay between those with low or high resilience and DASS-21 scores. Secondary regression analysis explored the preoperative factors affecting acute hospital length of stay. Results No significant difference was detected in length of stay between those with a low or a high resilience score before TKA. However, the group reporting psychological symptoms as measured by the DASS-21 before TKA had a significantly longer acute hospital length of stay after TKA compared to those with no psychological symptoms [median length of stay 6 (IQR 2.5) days vs. 5 (IQR 2) days, respectively (Mann-Whitney U = 495.5, P=0.03)]. Multivariate regression analysis showed that anesthetic risk score and fatigue were significant predictors of length of stay, with the overall model demonstrating significance (χ2=12.426, df = 4, P=0.014). Conclusions No association was detected between the brief resilience score before TKA and acute hospital length of stay after TKA, however, symptoms on the DASS-21 were associated with longer acute hospital length of stay. Preoperative screening for psychological symptoms using the DASS-21 is useful for health services to identify those at higher risk of longer acute hospital length of stay after TKA.

This study explored the challenges faced by 23 primary early career teachers (ECTs) in their second, third or fourth years of teaching. Qualitative data were collected through an online semistructured interview. Three themes were evident: parents, workload and behaviour management. These results were also reflected in the review of the literature. All three themes have been linked to increased ECT stress and have been found to undermine ECTs' self-confidence. The results suggest that some challenges are potentially aligned to the stage of ECTs' career development and that ECTs face different challenges as their teaching experience levels increase. A range of cohort specific themes were also identified. For second year ECTs, many of the challenges may potentially reflect their limited experience within the profession. As such, the challenges focused more on the logistics of doing the job and adjustment to the emotional and time demands of teaching. By the third and fourth year of teaching, the cohort-specific themes identified in the present study had changed to reflect broader school-based and interpersonal relationship issues. Three recommendations are made in the light of the research findings.

Background The National Institutes of Health Stroke Scale (NIHSS) scores have been used to evaluate acute ischaemic stroke (AIS) severity in clinical settings. Through the International Classification of Diseases, Tenth Revision Code (ICD-10), documentation of NIHSS scores has been made possible for administrative purposes and has since been increasingly adopted in insurance claims. Per Centres for Medicare & Medicaid Services guidelines, the stroke ICD-10 diagnosis code must be documented by the treating physician. Accuracy of the administratively collected NIHSS compared with expert clinical evaluation as documented in the Paul Coverdell registry is however still uncertain.Methods Leveraging a linked dataset comprised of the Paul Coverdell National Acute Stroke Program (PCNASP) clinical registry and matched individuals on Medicare Claims data, we sampled patients aged 65 and above admitted for AIS across nine states, from January 2017 to December 2020. We excluded those lacking documentation for either clinical or ICD-10-based NIHSS scores. We then examined score concordance from both databases and measured discordance as the absolute difference between the PCNASP and ICD-10-based NIHSS scores.Results Among 87 996 matched patients, mean NIHSS scores for PCNASP and Medicare ICD-10 were 7.19 (95% CI 7.14 to 7.24) and 7.32 (95% CI 7.27 to 7.37), respectively. Concordance between the two scores was high as indicated by an intraclass correlation coefficient of 0.93.Conclusion The high concordance between clinical and ICD-10 NIHSS scores highlights the latter’s potential as measure of stroke severity derived from structured claims data.

Abstract Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.

Previous observational studies of the diabetes drugs metformin vs. sulfonylureas have yielded mixed results about whether metformin reduces the risk of dementia, relative to the sulfonylureas. Here, the authors apply a novel competing risks approach to emulate dementia-related target trials in electronic health records of diabetic patients and a complementary systems pharmacology evaluation on human neural cells.

BackgroundThe last few years have seen major advances in blood biomarkers for Alzheimer's Disease (AD) with the development of ultrasensitive immunoassays, promising to transform how we diagnose, prognose, and track progression of neurodegenerative dementias.MethodsWe evaluated a panel of four novel ultrasensitive electrochemiluminescence (ECL) immunoassays against presumed CNS derived proteins of interest in AD in plasma [phosphorylated-Tau181 (pTau181), total Tau (tTau), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP)]. Two sets of banked plasma samples from the Massachusetts Alzheimer's Disease Research Center's longitudinal cohort study were examined: A longitudinal prognostic sample (n = 85) consisting of individuals with mild cognitive impairment (MCI) and 4 years of follow-up and a cross-sectional sample (n = 238) consisting of individuals with AD, other neurodegenerative diseases (OND), and normal cognition (CN).ResultsParticipants with MCI who progressed to dementia due to probable AD during follow-up had higher baseline plasma concentrations of pTau181, NfL, and GFAP compared to non-progressors. The best prognostic discrimination was observed with pTau181 (AUC = 0.83, 1.7-fold increase) and GFAP (AUC = 0.83, 1.6-fold increase). Participants with autopsy- and/or biomarker verified AD had higher plasma levels of pTau181, tTau and GFAP compared to CN and OND, while NfL was elevated in AD and further increased in OND. The best diagnostic discrimination was observed with pTau181 (AD vs CN: AUC = 0.90, 2-fold increase; AD vs. OND: AUC = 0.84, 1.5-fold increase) but tTau, NfL, and GFAP also showed good discrimination between AD and CN (AUC = 0.81–0.85; 1.5–2.2 fold increase).ConclusionsThese new ultrasensitive ECL plasma assays for pTau181, tTau, NfL, and GFAP demonstrated diagnostic utility for detection of AD. Moreover, the absolute baseline plasma levels of pTau181 and GFAP reflect cognitive decline over the next 4 years, providing prognostic information that may have utility in both clinical practice and clinical trial populations.

BackgroundElectronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. ObjectiveThe aim of this study was to evaluate whether natural language processing (NLP)–powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. MethodsIn this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. ResultsNAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P