Rapid emergence of antibacterial resistance by bacterial isolates from patients of gynecological infections in Punjab, Pakistan.

BACKGROUND PROBLEM: Antibiotic resistance is considered a global problem, where highly emerging antibacterial resistance is posing a great threat to treat infections. OBJECTIVES: The present study is aimed to highlight emerging resistance of bacteria to commonly used antibiotics in Pakistan. METHODS: The samples for bacterial culture were obtained using high vaginal swabs (HVS) from female genitalia. The specimens were incubated and identified for subsequent pathogenic bacterial growth. There were 370 bacterial isolates from 520 females provisionally diagnosed for gynecological infections. RESULTS: Out of total 9 isolated bacteria, Escherichia coli were most common isolates (41.6%) followed by Staphylococcus aureus (15.4%), and coagulase negative staphylococci (12.2%). E. coli exhibited maximum susceptibility to meropenem (98.2%) and imipenem (97.7%) and least to amoxicillin (9.8%) and ampicillin (9.6%). The highest incidence of bacterial infection was found in age group of 26-35 years. Among Gram-positive bacteria, 98.7%, 88.2% and 81.3% were susceptible to vancomycin, cefuroxime and linezolid respectively. For Gram-negative bacteria, 94.3%, 93.6% were susceptible to cefoperazone/sulbactam and meropenem respectively. Recurrence of infection developed among 3.27% of the patients, while two patients died with the infection. CONCLUSION: Maximum resistance was shown by co-trimoxazole (81.4%), followed by amoxicillin (80.2%). After identifying the causative strain, the susceptible antibiotic should be administered to reduce antibiotic resistance and better control of gynecological infections in female Pakistani population.

A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis.

AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful contribution to healthcare globally, they must be trusted by patients and healthcare professionals alike, and service the needs of patients in diverse regions and segments of the population. We developed an AI virtual assistant which provides patients with triage and diagnostic information. Crucially, the system is based on a generative model, which allows for relatively straightforward re-parameterization to reflect local disease and risk factor burden in diverse regions and population segments. This is an appealing property, particularly when considering the potential of AI systems to improve the provision of healthcare on a global scale in many regions and for both developing and developed countries. We performed a prospective validation study of the accuracy and safety of the AI system and human doctors. Importantly, we assessed the accuracy and safety of both the AI and human doctors independently against identical clinical cases and, unlike previous studies, also accounted for the information gathering process of both agents. Overall, we found that the AI system is able to provide patients with triage and diagnostic information with a level of clinical accuracy and safety comparable to that of human doctors. Through this approach and study, we hope to start building trust in AI-powered systems by directly comparing their performance to human doctors, who do not always agree with each other on the cause of patients' symptoms or the most appropriate triage recommendation.

Ethical Issues in Patient Safety Research: A Systematic Review of the Literature.

As many as 1 in 10 patients is harmed while receiving hospital care in wealthy countries. The risk of health care-associated infection in some developing countries is as much as 20 times higher. In response, in many global regions, increased attention has turned to the implementation of a broad program of safety research, encompassing a variety of methods. Although important international ethical guidelines for research exist, literature has been emerging in the last 20 years that begins to apply such guidelines to patient safety research specifically. This paper provides a review of the literature related to ethics, oversight, and patient safety research; identifies issues highlighted in articles as being of ethical relevance; describes areas of consensus regarding how to respond to these ethical issues; and highlights areas where additional ethical analysis and discussion are needed to provide guidance to those in the field.

Influenza vaccination for immunocompromised patients: summary of a systematic review and meta-analysis.

Vaccination of immunocompromised patients is recommended in many national guidelines to protect against severe or complicated influenza infection. However, due to uncertainties over the evidence base, implementation is frequently patchy and dependent on individual clinical discretion. We conducted a systematic review and meta-analysis to assess the evidence for influenza vaccination in this patient group. Healthcare databases and grey literature were searched and screened for eligibility. Data extraction and assessments of risk of bias were undertaken in duplicate, and results were synthesised narratively and using meta-analysis where possible. Our data show that whilst the serological response following vaccination of immunocompromised patients is less vigorous than in healthy controls, clinical protection is still meaningful, with only mild variation in adverse events between aetiological groups. Although we encountered significant clinical and statistical heterogeneity in many of our meta-analyses, we advocate that immunocompromised patients should be targeted for influenza vaccination.

Influenza vaccination for immunocompromised patients: systematic review and meta-analysis from a public health policy perspective.

BACKGROUND: Immunocompromised patients are vulnerable to severe or complicated influenza infection. Vaccination is widely recommended for this group. This systematic review and meta-analysis assesses influenza vaccination for immunocompromised patients in terms of preventing influenza-like illness and laboratory confirmed influenza, serological response and adverse events. METHODOLOGY/PRINCIPAL FINDINGS: Electronic databases and grey literature were searched and records were screened against eligibility criteria. Data extraction and risk of bias assessments were performed in duplicate. Results were synthesised narratively and meta-analyses were conducted where feasible. Heterogeneity was assessed using I(2) and publication bias was assessed using Begg's funnel plot and Egger's regression test. Many of the 209 eligible studies included an unclear or high risk of bias. Meta-analyses showed a significant effect of preventing influenza-like illness (odds ratio [OR]=0.23; 95% confidence interval [CI]=0.16-0.34; p<0.001) and laboratory confirmed influenza infection (OR=0.15; 95% CI=0.03-0.63; p=0.01) through vaccinating immunocompromised patie nts compared to placebo or unvaccinated controls. We found no difference in the odds of influenza-like illness compared to vaccinated immunocompetent controls. The pooled odds of seroconversion were lower in vaccinated patients compared to immunocompetent controls for seasonal influenza A(H1N1), A(H3N2) and B. A similar trend was identified for seroprotection. Meta-analyses of seroconversion showed higher odds in vaccinated patients compared to placebo or unvaccinated controls, although this reached significance for influenza B only. Publication bias was not detected and narrative synthesis supported our findings. No consistent evidence of safety concerns was identified. CONCLUSIONS/SIGNIFICANCE: Infection prevention and control strategies should recommend vaccinating immunocompromised patients. Potential for bias and confounding and the presence of heterogeneity mean the evidence reviewed is generally weak, although the directions of effects are consistent. Areas for further research are identified.