Effect of the Early Introduction of Mild Mobilization Performed by Nurses on the Recovery of Patients in the Intensive Care Unit

Rationale: Recent studies have reported the effectiveness of the early introduction of rehabilitation for preventing muscle weakness in patients in the intensive care unit (ICU). The early introduction of full-scale rehabilitation by a physical therapist is difficult in some cases because of disease severity and/or patient conditions. However, mild mobilization by a nurse (MMN), as a part of standard care performed, may have a positive effect on patient recovery. We examined the effect of the early introduction of MMN on the recovery of patients in the ICU. Methods: We retrospectively examined patients admitted to Niigata University Hospital’s ICU during between April 2014 and March 2015 who were receiving mechanical ventilation for 7 days or more. Patients were divided into two groups according to the date of initiation of MMN: group L comprised patients for whom MMN was started after 72 hours and group E comprised patients for whom MMN was started within 72 hours after ICU admission. The data were analyzed using the Fisher test, Mann-Whitney U test, and Wilcoxon test. Statistical significance was defined as P Results: Sixty-three patients were included: 42 patients in group L and 21 in group E. There was no significant difference between the two groups in patients’ background, including the type of illness, steroid use, presence of sepsis or diabetes, and sequential organ failure assessment (SOFA) score on ICU admission;however, the SOFA score at ICU discharge was significantly decreased in group E compared to that in group L (6.21 versus 4.30;P = 0.034). Conclusion: Our results indicate that MMN may reduce disease severity if started within 72 hours after ICU admission.

Research on the Application of Evidence-Based Quality Control Circle to Improve the Implementation Rate of Airway Management Measures in Adult Critically Ill Patients

Objective: To explore the effect of evidence-based quality control circle (QCC) in improving the implementation rate of airway management measures in adult critically ill patients. Methods: Based on the Joanna Briggs Institute (JBI) evidence-based health care model, the best evidence of airway management in adult critically ill patients was obtained and applied to the clinic. Results: The total implementation rate of airway management measures in adult critically ill patients increased from 23.62% before the implementation of quality control circle to 88.82%, and the pulmonary infection rate in critically ill patients decreased from 42.31% to 21.74%, with statistical significance between the two groups (P 0.05). Conclusion: Evidence-based quality control circle activities can standardize the practice standards of airway management in critically ill patients, reduce the occurrence of patients’ airway related complications, and improve clinical outcomes.

Molecular Detection of Carbapenemase Genes in Extensive Drug Resistant Acinetobacter baumannii Clinical Isolates from ICU Patients, Khartoum

Background: The emergence of carbapenemase producing Acinetobacter baumannii is increasingly reported nowadays and constitutes a major problem to the intensive care unit (ICU) patients with notable extensive-drug resistance ability. The study investigates carbapenemase producing A. baumannii strains exhibiting an extensively drug-resistant (XDR) phenotype, isolated from ICU patients in Khartoum. Methods: A total of 100 nonduplicate Gram-negative coccobacilli strains were obtained from microbiology laboratory of ICU patients’ clinical isolates. Molecular identification of A. baumannii was performed by targeting 16S rRNA gene using specifically designed primers. Then, XDR strains were determined by susceptibility testing (disc diffusion). For detection of carbapenemase genes Polymerase chain reaction (PCR) was carried out. Result: Of 100 ICU clinical isolates, 38 (38.0%) was confirmed A. baumannii strains, those strains showed 100% carbapenem resistance and 60.5% extensive drug resistance to the antibiotics tested. The frequency of carbapenemase producer was 57.9% (22/38) of carbapenem resistance A. baumannii (CRAB). The most common carbapenemase associated with resistance was blaOXA gene followed by blaNDM and blaGES A. baumannii isolates. The co-occurrence of blaOXA-48-like and blaNDM, blaOXA-23-like and blaOXA-51, and blaNDM-1 and blaOXA-51 was detected in 22.7%, 18.2% strains and 4.5% respectively. A unique characteristic of our findings was the coharbouring of the genes blaNDM-1, blaOXA-23-like, blaOXA-51 and blaOXA-143 in 9.1% strains (2/22), and this was the first report in the Khartoum city, Sudan. Conclusion: We have demonstrated for the first time a high prevalence of XDR-carbapenemase producing A. baumannii clinical isolates from ICU patients in Khartoum. Also an emergent blaOXA-143 was reported as High-Risk Clones. This highlights the routine mentoring of XDR-carbapenemase producing A. baumannii to avoid clone dissemination in our region hospitals.

Diagnostic accuracy of ePOS score in predicting DNR labeling after ICU admission:A prospective observational study(ePOS-DNR)

Background Resuscitation can sometimes be futile and making a do-not-resuscitate(DNR)decision is in the best interest of the patient.The electronic poor outcome screening(ePOS)score was developed to predict 6-month poor outcomes of critically ill patients.We explored the diagnostic accuracy of the ePOS score in predicting DNR decisions in the intensive care unit(ICU).Methods This study was conducted at the ICU of a tertiary referral hospital in Saudi Arabia between March and May 2023.Prospectively,we calculated ePOS scores for all eligible consecutive admissions after 48 h in the ICU and recorded the DNR orders.The ability of the score to predict DNR was explored using logistic regression.Youden's ideal cut-off value was calculated using the DeLong method,and different diagnostic accuracy measures were generated with corresponding 95%confidence intervals(CIs).Results We enrolled 857 patients,125 received a DNR order and 732 did not.The average ePOS score of DNR and non-DNR patients was 28.2±10.7 and 15.2±9.7,respectively.ePOS score,as a predictor of DNR order,had an area under receiver operator characteristic(AUROC)curve of 81.8%(95%CI:79.0 to 84.3,P<0.001).Youden's ideal cut-off value>17 was associated with a sensitivity of 87.2(95%CI:80.0 to 92.5,P<0.001),specificity of 63.9(95%CI:60.3 to 67.4,P<0.001),positive predictive value of 29.2(95%CI:24.6 to 33.8,P<0.001),negative predictive value of 96.7(95%CI:95.1 to 98.3,P<0.001),and diagnostic odds ratio 12.1(95%CI:7.0 to 20.8,P<0.001).Conclusions In this study,the ePOS score performed well as a diagnostic test for patients who will be labeled as DNR during their ICU stay.A cut-off score>17 may help guide clinical decisions to withhold or commence resuscitative measures.

Alveolar Hemorrhage and Acute Respiratory Distress Syndrome Associated with Pulmonary Cement Following Percutaneous Vertebroplasty with Polymethylmethacrylate

We report a rare complication of diffuse alveolar hemorrhage and respiratory failure following percutaneous vertebroplasty in a patient who has evidence of cement leakage. Cement injection was done two days prior to presentation and covered 2 vertebral levels for osteoporosis induced fractures.

Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future

Sepsis remains a major challenge internationally for healthcare systems.Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management.In sepsis,mortality increases with every hour left untreated.Artificial intelligence(AI)is transforming worldwide healthcare delivery at present.This review has outlined how AI can augment strategies to address this global disease burden.AI and machine learning(ML)algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods.Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs.This gives providers additional time to plan and execute treatment plans,thereby avoiding increasing complications associated with delayed diagnosis of sepsis.The potential for cost savings with AI implementation is also discussed,including improving workflow efficiencies,reducing administrative costs,and improving healthcare outcomes.Despite these advantages,clinicians have been slow to adopt AI into clinical practice.Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use.Furthermore,the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies.Finally,we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology。