Citations

The efficacy and safety of extracorporeal membrane oxygenation (ECMO) support during transcatheter aortic valve replacement (TAVR) remains unknown. We conducted a meta-analysis to compare benefit and risk of ECMO in TAVR patients. Bibliographic databases were searched from inception to January 1, 2024. Included studies involved patients ≥18 years old undergoing TAVR and using ECMO emergently or prophylactically. Mortality and procedure success were primary outcomes. Peri- or postoperative complications were the secondary outcomes. We identified 11 observational studies, including 2,275 participants (415 ECMO and 1,860 non-ECMO). The unadjusted mortality risk in ECMO-supported patient was higher than non-ECMO patients (odds ratio [OR] 1.73). The mortality unadjusted risk remained high (OR 3.89) and statistically significant for prophylactic ECMO. Prophylactic ECMO had lower mortality risk compared with emergent ECMO (OR 0.17). Extracorporeal membrane oxygenation-supported patients had lower procedural success rate (OR 0.10). Extracorporeal membrane oxygenation patients undergoing TAVR had significantly increased risk of bleeding (OR 3.32), renal failure (OR 2.38), postoperative myocardial infarction (OR 1.89), and stroke (OR 2.32) compared with non-ECMO patients. Clinical results are not improved by ECMO support in patients with high-risk TAVR. Prophylactic ECMO outperforms emergent. Overall, ECMO support increases mortality and postoperative complications. Transcatheter aortic valve replacement outcomes may improve with prophylactic ECMO in high-risk situations.

The evidence on indications, outcomes, and complications with the use of extracorporeal membrane oxygenation (ECMO) in the setting of interstitial lung disease (ILD) is limited in the existing literature. We performed a systematic review and meta-analysis for the use of ECMO in the setting of ILD to study the prognostic factors associated with in-hospital mortality. Eighteen unique studies with a total of 1,356 patients on ECMO for ILD were identified out of which 76.5% were on ECMO as a bridge to transplant (BTT) and the rest as a bridge to recovery (BTR). The overall in-hospital mortality was 45.76%, with 71.3% and 37.8% for BTR and BTT, respectively. Among the various prognostic factors, mortality was lower with younger age (mean difference = 3.15, 95% confidence interval [CI] = 0.82–5.49), use of awake veno-arterial (VA)-ECMO compared to veno-venous (VV)-ECMO (unadjusted odds ratio [OR] = 0.22, 95% CI = 0.13–0.37) in the overall cohort. In the setting of BTT, the use of VA-ECMO had a decreased hazard ratio (HR) compared to VV-ECMO (adjusted HR = 0.34, 95% CI = 0.15–0.81, p = 0.015). The findings of our meta-analysis are critical but are derived from retrospective studies with small sample sizes and thus are of low to very low-GRADE certainty.

Objective To provide insights into the nature, risk factors, impact and existing measures for reporting and preventing violence in the healthcare system. The under-reporting of violence against healthcare workers (HCWs) globally highlights the need for increased public awareness and education. Methods The Violence Study of Healthcare Workers and Systems study used a survey questionnaire created using Research Electronic Data Capture (REDCap) forms and distributed from 6 June to 9 August 2022. Logistic regression analysis evaluated violence predictors, including gender, age, years of experience, institution type, respondent profession and night shift frequency. A χ 2 test was performed to determine the association between gender and different violence forms. Results A total of 5405 responses from 79 countries were analysed. India, the USA and Venezuela were the top three contributors. Female respondents comprised 53%. The majority (45%) fell within the 26–35 age group. Medical students (21%), consultants (20%), residents/fellows (15%) and nurses (10%) constituted highest responders. Nearly 55% HCWs reported firsthand violence experience, and 16% reported violence against their colleagues. Perpetrators were identified as patients or family members in over 50% of cases, while supervisor-incited violence accounted for 16%. Around 80% stated that violence incidence either remained constant or increased during the COVID-19 pandemic. Among HCWs who experienced violence, 55% felt less motivated or more dissatisfied with their jobs afterward, and 25% expressed willingness to quit. Univariate analysis revealed that HCWs aged 26–65 years, nurses, physicians, ancillary staff, those working in public settings, with >1 year of experience, and frequent night shift workers were at significantly higher risk of experiencing violence. These results remained significant in multivariate analysis, except for the 55–65 age group, which lost statistical significance. Conclusion This global cross-sectional study highlights that a majority of HCWs have experienced violence, and the incidence either increased or remained the same during the COVID-19 pandemic. This has resulted in decreased job satisfaction.

Background and Objective: The association of non-alcoholic fatty liver disease (NAFLD) and metabolic-associated fatty liver disease (MAFLD) with intensive care unit (ICU) admissions and the need for mechanical ventilation and disease severity in COVID-19 patients. Material and Methods: A systematic literature review was conducted on the databases: Cochrane, Embase, PubMed, ScienceDirect, and the Web of Science from January 2019 to June 2022. Studies evaluating MAFLD using laboratory methods, non-invasive imaging, or liver biopsy were included. The study protocol was registered in PROSPERO (ID CRD42022313259), and PRISMA guidelines were followed. The NIH quality assessment tool was used for quality assessment. RevMan version 5.3 software was used for pooled analysis. A sensitivity analysis was performed to assess the result’s stability. Results: A total of 37,974 patients from 17 studies were assessed for the association between MAFLD and ICU admission. A total of 3396 COVID-19 patients required ICU admission: 1236 (20.41%) in the MAFLD group and 2160 (6.77%) in the non-MAFLD group. The odds ratio was 1.86 for ICU admission, p = 0.007, and a (95% CI) of [1.18–2.91]. A total of 37,166 patients from 13 studies were included in the need for invasive mechanical ventilation analysis. A total of 1676 patients required mechanical ventilation: 805 in the MAFLD group (14.20% of all MAFLD patients) and 871 patients in the non-MAFLD group (2.76% of all non-MAFLD patients). The odds ratio was 2.05, p = 0.02, and a (95% CI) of [1.12–3.74]. A total of 5286 patients from 14 studies were included in the COVID-19 disease severity analysis. Severe COVID-19 was seen in 1623 patients, with 33.17% (901/2716) of MAFLD patients and 28.09% (722/2570) of non-MAFLD patients having severe disease. The odds ratio was 1.59 for disease severity, p = 0.010, and a (95% CI) of [1.12–2.26]. Conclusions: Our meta-analysis suggests that there are significantly increased odds of ICU admissions, a need for invasive mechanical ventilation, and disease severity in MAFLD patients who acquire COVID-19.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Introduction: Acinetobacter baumannii (AB) is a multidrug-resistant pathogen commonly associated with nosocomial infections. The resistance profile and ability to produce biofilm make it a complicated organism to treat effectively. Cefoperazone sulbactam (CS) is commonly used to treat AB, but the associated data are scarce. Methods: We conducted a systematic review of articles downloaded from Cochrane, Embase, PubMed, Scopus, and Web of Science (through June 2022) to study the efficacy of CS in treating AB infections. Our review evaluated patients treated with CS alone and CS in combination with other antibiotics separately. The following outcomes were studied: clinical cure, microbiological cure, and mortality from any cause. Results: We included 16 studies where CS was used for the treatment of AB infections. This included 11 studies where CS was used alone and 10 studies where CS was used in combination. The outcomes were similar in both groups. We found that the pooled clinical cure, microbiological cure, and mortality with CS alone for AB were 70%, 44%, and 20%, respectively. The pooled clinical cure, microbiological cure, and mortality when CS was used in combination with other antibiotics were 72%, 43%, and 21%, respectively. Conclusions: CS alone or in combination needs to be further explored for the treatment of AB infections. There is a need for randomized controlled trials with comparator drugs to evaluate the drug’s effectiveness.

Objective: Artificial intelligence and machine learning (AI/ML) are increasingly being applied to big clinical data to tackle research questions that cannot be answered with traditional statistical methods. The field is still in its nascent stages and there is a paucity of guidelines for conducting and reporting AI/ML research in hypertension. The objective was to apply the HUMANE checklist to survey the present landscape of AI/ML in hypertension to inform the development of hypertension-specific guidelines and recommendations. Design and method: The HUMANE checklist was developed by global clinical and AI/ML experts through the Delphi method. It assesses the quality of medical AI/ML articles based on whether they cover subjects expected in any peer-reviewed, clinical or AI/ML research publication. A cooping review was carried out to identify articles presenting original research in AI/ML and hypertension published in 2019–2021. Two independent reviewers applied the checklist to each article and in the case of discordance, the response was adjudicated by an AI/ML expert. Results were analysed to assess compliance with the survey (% of papers satisfying checklist requirements). Results: A total of 63 manuscripts was reviewed. A summary of results is shown in Figure 1. Highest compliance was seen for items relating to general article presentation, with compliance ranging from 68% to 98% (description of statistical analysis methods and background context, respectively). Lowest compliance was seen with checklist items relating to clinical research and AI/ML methods. 44% of reviewed articles described the demographics of their dataset and 48% stated their inclusion/exclusion criteria. Nonetheless, datasets were deemed appropriate for investigative aims in 93% of articles. 30% of manuscripts reported their calibration measures, while 73% stated their performance metrics. Internal validation was carried out in 75% of studies, but external validity was assessed in only 14% of cases. Algorithmic bias was addressed in 11% of papers. Conclusions: Application of AI/ML methods in hypertension research is growing, but the majority of current work has major shortfalls in reporting quality, model validation and algorithmic bias. Our study identifies areas of improvement to enable the full realisation of the potential of AI/ML in hypertension.