Application of a Standardized Treatment Paradigm as a Strategy to Achieve Optimal Onco-Functional Balance in Glioma Surgery

Background@#Gliomas, characterized by their invasive persistence and tendency to affect critical brain regions, pose a challenge in surgical resection due to the risk of neurological deficits. This study focuses on a personalized approach to achieving an optimal onco-functional balance in glioma resections, emphasizing maximal tumor removal while preserving the quality of life. @*Methods@#A retrospective analysis of 57 awake surgical resections of gliomas at the NationalUniversity Hospital, Singapore, was conducted. The inclusion criteria were based on diagnosis, functional boundaries determined by direct electrical stimulation, preoperative Karnofsky Performance Status score, and absence of multifocal disease on MRI. The treatment approach included comprehensive neuropsychological evaluation, determination of suitability for awake surgery, and standard asleepawake-asleep anesthesia protocol. Tumor resection techniques and postoperative care were systematically followed. @*Results@#The study included 53 patients (55.5% male, average age 39 years), predominantlyright-handed. Over half reported seizures as their chief complaint. Tumors were mostly low-grade gliomas. Positive mapping of the primary motor cortex was conducted in all cases, with awake surgery completed in 77.2% of cases. New neurological deficits were observed in 26.3% of patients at 1 month after operation; most showed significant improvement at 6 months. @*Conclusion@#The standardized treatment paradigm effectively achieved an optimal onco-functional balance in glioma patients. While some patients experienced neurological deficits postoperatively, the majority recovered to their preoperative baseline within 3 months. The approach prioritizes patient empowerment and customized utilization of functional mapping techniques, considering the challenge of preserving diverse languages in a multilingual patient population.

Risk factors of post-anaesthesia care unit delirium in patients undergoing non-cardiac surgery in Singapore

INTRODUCTION@#Post-anaesthesia care unit (PACU) delirium affects 5%-45% of patients after surgery and is associated with postoperative delirium and increased mortality. Up to 40% of PACU delirium is preventable, but it remains under-recognised due to a lack of awareness of its diagnosis. The nursing delirium screening scale (Nu-DESC) has been validated for diagnosing PACU delirium, but is not routinely used locally. This study aimed to use Nu-DESC to establish the incidence and risk factors of PACU delirium in patients undergoing non-cardiac surgery in the surgical population.@*METHODS@#We conducted an audit of eligible patients undergoing major surgery in three public hospitals in Singapore over 1 week. Patients were assessed for delirium 30-60 min following their arrival in PACU using Nu-DESC, with a total score of ≥2 indicative of delirium.@*RESULTS@#A total of 478 patients were assessed. The overall incidence rate of PACU delirium was 18/478 (3.8%), and the incidence was 9/146 (6.2%) in patients aged > 65 years. Post-anaesthesia care unit delirium was more common in females, patients with malignancy and those who underwent longer operations. Logistic regression analysis showed that the use of bispectral index (P < 0.001) and the presence of malignancy (P < 0.001) were significantly associated with a higher incidence of PACU delirium.@*CONCLUSION@#In this first local study, the incidence of PACU delirium was 3.8%, increasing to 6.2% in those aged > 65 years. Understanding these risk factors will form the basis for which protocols can be established to optimise resource management and prevent long-term morbidities and mortality in PACU delirium.

Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: international observational federated study based on electronic health records through the 4CE consortium ARDS after SARS-CoV-2 infection on young adult

PurposeIn young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. MethodsA retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. ResultsAmong the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS ( 7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%-although not significantly associated with ARDS), and diabetes (32%). ConclusionTrough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Incidence and risk factors of delirium in post-anaesthesia care unit

INTRODUCTION@#Post-anaesthesia care unit (PACU) delirium is a potentially preventable condition that results in a significant long-term effect. In a multicentre prospective cohort study, we investigate the incidence and risk factors of postoperative delirium in elderly patients undergoing major non-cardiac surgery.@*METHODS@#Patients were consented and recruited from 4 major hospitals in Singapore. Research ethics approval was obtained. Patients older than 65 years undergoing non-cardiac surgery >2 hours were recruited. Baseline perioperative data were collected. Preoperative baseline cognition was obtained. Patients were assessed in the post-anaesthesia care unit for delirium 30-60 minutes after arrival using the Nursing Delirium Screening Scale (Nu-DESC).@*RESULTS@#Ninety-eight patients completed the study. Eleven patients (11.2%) had postoperative delirium. Patients who had PACU delirium were older (74.6±3.2 versus 70.6±4.4 years, P=0.005). Univariate analysis showed those who had PACU delirium are more likely to be ASA 3 (63.6% vs 31.0%, P=0.019), had estimated glomerular filtration rate (eGFR) of >60mL/min/1.73m2 (36.4% vs 10.6%, P=0.013), higher HbA1C value (7.8±1.2 vs 6.6±0.9, P=0.011), raised random blood glucose (10.0±5.0mmol/L vs 6.5±2.4mmol/L, P=0.0066), and moderate-severe depression (18.2% vs 1.1%, P=0.033). They are more likely to stay longer in hospital (median 8 days [range 4-18] vs 4 days [range 2-8], P=0.049). Raised random blood glucose is independently associated with increased PACU delirium on multivariate analysis.

Multinational Prevalence of Neurological Phenotypes in Patients Hospitalized with COVID-19

OBJECTIVENeurological complications can worsen outcomes in COVID-19. We defined the prevalence of a wide range of neurological conditions among patients hospitalized with COVID-19 in geographically diverse multinational populations. METHODSUsing electronic health record (EHR) data from 348 participating hospitals across 6 countries and 3 continents between January and September 2020, we performed a cross-sectional study of hospitalized adult and pediatric patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test, both with and without severe COVID-19. We assessed the frequency of each disease category and 3-character International Classification of Disease (ICD) code of neurological diseases by countries, sites, time before and after admission for COVID-19, and COVID-19 severity. RESULTSAmong the 35,177 hospitalized patients with SARS-CoV-2 infection, there was increased prevalence of disorders of consciousness (5.8%, 95% confidence interval [CI]: 3.7%-7.8%, pFDR<.001) and unspecified disorders of the brain (8.1%, 95%CI: 5.7%-10.5%, pFDR<.001), compared to pre-admission prevalence. During hospitalization, patients who experienced severe COVID-19 status had 22% (95%CI: 19%-25%) increase in the relative risk (RR) of disorders of consciousness, 24% (95%CI: 13%-35%) increase in other cerebrovascular diseases, 34% (95%CI: 20%-50%) increase in nontraumatic intracranial hemorrhage, 37% (95%CI: 17%-60%) increase in encephalitis and/or myelitis, and 72% (95%CI: 67%-77%) increase in myopathy compared to those who never experienced severe disease. INTERPRETATIONUsing an international network and common EHR data elements, we highlight an increase in the prevalence of central and peripheral neurological phenotypes in patients hospitalized with SARS-CoV-2 infection, particularly among those with severe disease.

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

ObjectivesTo perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. DesignRetrospective cohort study. SettingThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. ParticipantsPatients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measuresPatients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. ResultsOf 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. ConclusionsLaboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

Validation of a Derived International Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) includes hundreds of hospitals internationally using a federated computational approach to COVID-19 research using the EHR. ObjectiveWe sought to develop and validate a standard definition of COVID-19 severity from readily accessible EHR data across the Consortium. MethodsWe developed an EHR-based severity algorithm and validated it on patient hospitalization data from 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also used a machine learning approach to compare selected predictors of severity to the 4CE algorithm at one site. ResultsThe 4CE severity algorithm performed with pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of single code categories for acuity were unacceptably inaccurate - varying by up to 0.65 across sites. A multivariate machine learning approach identified codes resulting in mean AUC 0.956 (95% CI: 0.952, 0.959) compared to 0.903 (95% CI: 0.886, 0.921) using expert-derived codes. Billing codes were poor proxies of ICU admission, with 49% precision and recall compared against chart review at one partner institution. DiscussionWe developed a proxy measure of severity that proved resilient to coding variability internationally by using a set of 6 code classes. In contrast, machine-learning approaches may tend to overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold standard outcomes, possibly due to pandemic conditions. ConclusionWe developed an EHR-based algorithm for COVID-19 severity and validated it at 12 international sites.