Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction

Background and PurposeMechanical thrombectomy greatly improves stroke outcomes. Nonetheless, some patients fall short of full recovery despite good reperfusion. The purpose of this study was to develop machine learning (ML) models for the pre-interventional prediction of functional outcome at 3 months of thrombectomy in acute ischemic stroke (AIS), using clinical and auto-extractable radiological information consistently available upon first emergency evaluation.Materials and MethodsA two-center retrospective cohort of 293 patients with AIS who underwent thrombectomy was analyzed. ML models were developed to predict dichotomized modified Rankin score at 90 days (mRS-90) using clinical and imaging features, both separately and combined. Conventional and experimental imaging biomarkers were quantified using automated image-processing software from non-contract computed tomography (CT) and computed tomography angiography (CTA). Shapley Additive Explanation (SHAP) was applied for model interpretability and predictor importance analysis of the optimal model.ResultsMerging clinical and imaging features returned the best results for mRS-90 prediction. The best performing classifier was Extreme Gradient Boosting (XGB) with an area under the receiver operating characteristic curve (AUC) = 84% using selected features. The most important classifying features were age, baseline National Institutes of Health Stroke Scale (NIHSS), occlusion side, degree of brain atrophy [primarily represented by cortical cerebrospinal fluid (CSF) volume and lateral ventricle volume], early ischemic core [primarily represented by e-Alberta Stroke Program Early CT Score (ASPECTS)], and collateral circulation deficit volume on CTA.ConclusionMachine learning that is applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method in the pre-interventional prediction of stroke prognosis. Interpretable models allow for exploring which initial features contribute the most to post-thrombectomy outcome prediction overall and for each individual patient outcome.</jats:sec

Comparison of Stroke Risk Factors between Symptomatic and Asymptomatic Patients in a North American Moyamoya Disease Cohort

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; Risk factors for stroke in symptomatic and asymptomatic moyamoya disease (MMD) patients have recently been reported in a Japanese cohort. Such information in a North American population is lacking. &lt;b&gt;&lt;i&gt;Objective:&lt;/i&gt;&lt;/b&gt; We sought to elucidate the prevalence of stroke risk factors among North American patients with ischemic, hemorrhagic, and asymptomatic MMD. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; We retrospectively reviewed our institution’s database between 1990 and 2021. We excluded cases of moyamoya syndrome. We divided 119 patients into 3 groups based on the onset pattern; ischemic, hemorrhagic, and asymptomatic. We compared the prevalence of well-known stroke risk factors (diabetes, hypertension, etc.) between these three groups of patients. In the asymptomatic group, we analyzed the prevalence of cerebrovascular events on follow-up from the time of diagnosis. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Overall, 119 patients with MMD were available with predominately White ethnicity (80.7%). The mean age was 39 years, and 73.9% were female. Patients presented with ischemic stroke (82%) and hemorrhagic stroke (11%); 7% of patients were asymptomatic. The prevalence of stroke risk factors did not differ among ischemic, hemorrhagic, or asymptomatic MMD patients. In 8 asymptomatic patients, there was 81.8 months (SD ±51.0) of follow-up, and none of them developed any cerebrovascular events. &lt;b&gt;&lt;i&gt;Conclusions:&lt;/i&gt;&lt;/b&gt; No significant differences in the prevalence of stroke risk factors between MMD cohorts were found, corroborating evidence provided in a recent Japanese-based study. There were no apparent associations between stroke risk factors and interval cerebrovascular events in an asymptomatic group of MMD patients. </jats:p

Abstract Number ‐ 95: Common Data Elements Analysis of Mechanical Thrombectomy Clinical Trials for Acute Ischemic Large Core Stroke

Introduction Various clinical trials addressing large core acute ischemic stroke (AIS) are ongoing from multiple international groups. Future development of clinical guidelines depends on meta‐analyses of these trials calling for a degree of homogeneity of elements across the different studies. The aim of this common data element study was to provide an overview of the key features of pertinent large core infarct trials. Methods PubMed and ClinicalTrials.gov databases were screened for published and ongoing clinical trials assessing mechanical thrombectomy in patients with AIS with large core infarct. Nested Knowledge AutoLit living review platform was utilized to categorize primary and secondary outcomes as well as inclusion and exclusion criteria for patient selection in the clinical trials. Results The most reported data element was ASPECTS score, but with varied definitions of what compromises large core. Non‐utility‐weighted modified Rankin Score (mRS) was reported in 6/7 studies as the primary outcome, while the utility‐weighted mRS was the outcome of interest in the TESLA trial, all of them at the 3‐months mark, with only LASTE looking for mRS shift at the 6‐months mark. Secondary outcomes had more variations. Mortality is reported separately only in 4/7 trials, all at the 3‐month mark. Additionally, the TENSION trial reports the frequency of serious adverse events, including mortality, at the 1‐week and 12‐month mark. Conclusions In the published and the ongoing large core trials, there is a large degree of variability in the collected data elements. Differences in definition and timepoints renders reaching a unified standard difficult, which hinders high quality meta‐analyses and cohesive evidence‐driven synthesis

Sleep pattern in the dromedary camel: a behavioral and polysomnography study

Abstract Study Objectives To investigate sleep patterns in the camel by combining behavioral and polysomnography (PSG) methods. Methods A noninvasive PSG study was conducted over four nights on four animals. Additionally, video recordings were used to monitor the sleep behaviors associated with different vigilance states. Results During the night, short periods of sporadic sleep-like behavior corresponding to a specific posture, sternal recumbency (SR) with the head lying down on the ground, were observed. The PSG results showed rapid shifts between five vigilance states, including wakefulness, drowsiness, rapid eye movement (REM) sleep, non-REM (NREM) sleep, and rumination. The camels typically slept only 1.7 hours per night, subdivided into 0.5 hours of REM sleep and 1.2 hours of NREM sleep. Camels spent most of the night being awake (2.3 hours), ruminating (2.4 hours), or drowsing (1.9 hours). Various combinations of transitions between the different vigilance states were observed, with a notable transition into REM sleep directly from drowsiness (9%) or wakefulness (4%). Behavioral postures were found to correlate with PSG vigilance states, thereby allowing a reliable prediction of the sleep stage based on SR and the head position (erected, motionless, or lying down on the ground). Notably, 100% of REM sleep occurred during the Head Lying Down-SR posture. Conclusions The camel is a diurnal species with a polyphasic sleep pattern at night. The best correlation between PSG and ethogram data indicates that sleep duration can be predicted by the behavioral method, provided that drowsiness is considered a part of sleep. </jats:sec

Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study

Background Computed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring. Materials and Methods Brain CTA images of 56 patients with anterior LVO were retrospectively processed. Twelve readers of various clinical training, including junior neuroradiologists, senior neuroradiologists, and neurologists graded collateral flow using visual CTA-CS scale in two sessions separated by a washout period. Reference standard was the consensus of three expert readers. Duration of reading time, inter-rater reliability, and statistical comparison of readers’ performance metrics were analyzed between the e-CTA assisted and unassisted sessions. Results e-CTA assistance resulted in significant increase in mean accuracy (58.6% to 67.5%, p = 0.003), mean F1 score (0.574 to 0.676, p = 0.002), mean precision (58.8% to 68%, p = 0.007), and mean recall (58.7% to 69.9%, p = 0.002), especially with slight filling deficit (CTA-CS 2 and 3). Mean reading time was reduced across all readers (103.4 to 59.7 s, p = 0.001), and inter-rater agreement in CTA-CS assessment was increased (Krippendorff's alpha 0.366 to 0.676). Optimized occlusion laterality detection was also noted with mean accuracy (92.9% to 96.8%, p = 0.009). Conclusion Automated assistance for CTA-CS using e-CTA software provided helpful decision support for readers in terms of improving scoring accuracy and reading efficiency for physicians with a range of experience and training backgrounds and leading to significant improvements in inter-rater agreement. </jats:sec

sj-docx-1-ine-10.1177_15910199221150470 - Supplemental material for Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study

Supplemental material, sj-docx-1-ine-10.1177_15910199221150470 for Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study by Mohamed Sobhi Jabal, David F. Kallmes, George Harston, Norbert Campeau, Kara Schwartz, Steven Messina, Carrie Carr, John Benson, Jason Little, Alex Nagelschneider, Ajay Madhavan, Deena Nasr, Sherry Braksick, James Klaas, Eugene Scharf, Cem Bilgin and Waleed Brinjikji in Interventional Neuroradiology</p

Surgical site infection after gastrointestinal surgery in children : an international, multicentre, prospective cohort study

Introduction Surgical site infection (SSI) is one of the most common healthcare-associated infections (HAIs). However, there is a lack of data available about SSI in children worldwide, especially from low-income and middle-income countries. This study aimed to estimate the incidence of SSI in children and associations between SSI and morbidity across human development settings. Methods A multicentre, international, prospective, validated cohort study of children aged under 16 years undergoing clean-contaminated, contaminated or dirty gastrointestinal surgery. Any hospital in the world providing paediatric surgery was eligible to contribute data between January and July 2016. The primary outcome was the incidence of SSI by 30 days. Relationships between explanatory variables and SSI were examined using multilevel logistic regression. Countries were stratified into high development, middle development and low development groups using the United Nations Human Development Index (HDI). Results Of 1159 children across 181 hospitals in 51 countries, 523 (45 center dot 1%) children were from high HDI, 397 (34 center dot 2%) from middle HDI and 239 (20 center dot 6%) from low HDI countries. The 30-day SSI rate was 6.3% (33/523) in high HDI, 12 center dot 8% (51/397) in middle HDI and 24 center dot 7% (59/239) in low HDI countries. SSI was associated with higher incidence of 30-day mortality, intervention, organ-space infection and other HAIs, with the highest rates seen in low HDI countries. Median length of stay in patients who had an SSI was longer (7.0 days), compared with 3.0 days in patients who did not have an SSI. Use of laparoscopy was associated with significantly lower SSI rates, even after accounting for HDI. Conclusion The odds of SSI in children is nearly four times greater in low HDI compared with high HDI countries. Policies to reduce SSI should be prioritised as part of the wider global agenda.Peer reviewe

Off-Label Use of Woven EndoBridge Device for Intracranial Brain Aneurysm Treatment: Modeling of Occlusion Outcome

The Woven EndoBridge (WEB) device is emerging as a novel therapy for intracranial aneurysms, but its use for off-label indications requires further study. Using machine learning, we aimed to develop predictive models for complete occlusion after off-label WEB treatment and to identify factors associated with occlusion outcomes. This multicenter, retrospective study included 162 patients who underwent off-label WEB treatment for intracranial aneurysms. Baseline, morphological, and procedural variables were utilized to develop machine-learning models predicting complete occlusion. Model interpretation was performed to determine significant predictors. Ordinal regression was also performed with occlusion status as an ordinal outcome from better (Raymond Roy Occlusion Classification [RROC] grade 1) to worse (RROC grade 3) status. Odds ratios (OR) with 95% confidence intervals (CI) were reported. The best performing model achieved an AUROC of 0.8 for predicting complete occlusion. Larger neck diameter and daughter sac were significant independent predictors of incomplete occlusion. On multivariable ordinal regression, higher RROC grades (OR 1.86, 95% CI 1.25-2.82), larger neck diameter (OR 1.69, 95% CI 1.09-2.65), and presence of daughter sacs (OR 2.26, 95% CI 0.99-5.15) were associated with worse aneurysm occlusion after WEB treatment, independent of other factors. This study found that larger neck diameter and daughter sacs were associated with worse occlusion after WEB therapy for aneurysms. The machine learning approach identified anatomical factors related to occlusion outcomes that may help guide patient selection and monitoring with this technology. Further validation is needed