Clinical characteristics, risk factors and microbiology of infections in patients receiving chronic hemodialysis

Development of an investigation form for hemodialysis infection outbreak: Identifying sources in the early stage

BACKGROUND

There are many infectious factors causing the outbreak of hemodialysis infection, which may easily lead to the delay of investigation and treatment. This study aimed to develop an investigation form for hemodialysis infection outbreak (HIO), and to identify sources of outbreak in early stage.

METHODS

After an exhaustive literature review, we used the Delphi method to determine the indicators and relative risk scores of the assessment tools through 2 rounds of specialist consultation and overall consideration of the opinions and suggestions of 18 specialists.

RESULTS

A total of 87 studies of HIOs were eligible for inclusion. The mean authority coefficient (Cr) was 0.89. Kendall's W coefficient of the specialist consultation was 0.359 after 2 rounds of consultation (P < .005), suggesting that the specialists had similar opinions. Based on 4 primary items and 13 secondary items of the source of HIO, and tripartite distribution characteristics of infected patients, we constructed the investigation form.

CONCLUSIONS

The investigation form may be implemented during the initial phase of an outbreak investigation, it is a prerequisite for taking effective control measures, avoiding HIO occurrence. However, the efficacy of the investigation form needs to be further evaluated.

Comparison of risk prediction models for the progression of pelvic inflammatory disease patients to sepsis: Cox regression model and machine learning model

Introduction

The present study presents the development and validation of a clinical prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to predict the probability of pelvic inflammatory disease (PID) progressing to sepsis.

Methods

A retrospective cohort study was conducted, gathering clinical data of patients diagnosed with PID between 2008 and 2019 from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who met the Sepsis 3.0 diagnostic criteria were selected, with sepsis as the outcome. Univariate Cox regression and stepwise Cox regression were used to screen variables for constructing a nomogram. Moreover, an RSF model was created using machine learning algorithms. To verify the model's performance, a calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve were utilized. Furthermore, the capabilities of the two models for estimating the incidence of sepsis in PID patients within 3 and 7 days were compared.

Results

A total of 1064 PID patients were included, of whom 54 had progressed to sepsis. The established nomogram highlighted dialysis, reduced platelet (PLT) counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts as significant predictive factors. The areas under the curve (AUCs) of the nomogram for prediction of PID progression to sepsis at 3-day and 7-day (3-/7-day) in the training set and the validation set were 0.886/0.863 and 0.824/0.726, respectively, and the C-index of the model was 0.8905. The RSF displayed excellent performance, with AUCs of 0.939/0.919 and 0.712/0.571 for 3-/7-day risk prediction in the training set and validation set, respectively.

Conclusion

The nomogram accurately predicted the incidence of sepsis in PID patients, and relevant risk factors were identified. While the RSF model outperformed the Cox regression models in predicting sepsis incidence, its performance exhibited some instability. On the other hand, the Cox regression-based nomogram displayed stable performance and improved interpretability, thereby supporting clinical decision-making in PID treatment.

Blood stream Infections in chronic hemodialysis patients - characteristics and outcomes

INTRODUCTION

Bloodstream Infections (BSI) are a major cause of death and hospitalization among hemodialysis (HD) patients. The rates of BSI among HD patients vary and are influenced by local patient and pathogen characteristics. Modifications in local infection prevention protocols in light of active surveillance of BSI has been shown to improve clinical outcomes. The aim of this study was to further explore factors associated with BSI in a contemporary cohort of HD patients at a public teaching hospital dialysis center in Israel.

METHODS

This was a retrospective cohort study of HD patients with a BSI in the years 2014 to 2018. The primary outcome was the occurrence of BSI. Secondary outcomes were to describe the causative pathogens of BSI, and to assess for risk factors for BSI, and mortality.

RESULTS

Included were 251 patients. The mean age was 68.5 ± 13.4 years, 66.9% were male. The mean time from initiation of dialysis was 34.76 ± 40.77 months, interquartile range (IQR) 1-47.5 months and the follow up period of the cohort was 25.17 ± 15.9 months. During the observation period, 44 patients (17.5%) developed 54 BSI events, while 10 of them (3.9% of the whole cohort) developed recurrent BSI events. Gram-negative microorganisms caused 46.3% of all BSI events. 31.4% of these BSI were caused by resistant bacteria. In a multivariate logistic regression analysis, patients receiving dialysis through a central line had a significantly increased risk for BSI adjusted Odds Ratio (aOR) 3.907, p = 0.005, whereas patients' weight was mildly protective (aOR 0.971, p = 0.024).

CONCLUSIONS

We noted an increased prevalence of gram-negative pathogens in the etiology of BSI in HD patients. Based on our findings, additional empirical antibiotics addressing gram negative bacteria have been added to our empirical treatment protocol. Our findings highlight the need to follow local epidemiology for implementing appropriate preventative measures and for tailoring appropriate empiric antibiotic therapy.

Access- and non-access-related infections among patients receiving haemodialysis: Experience of an academic centre in Oman

Objective

The aim of this study was to examine the epidemiology of access and non-access-related infections in patients receiving haemodialysis at an academic tertiary hospital in Oman.

Methods

This was a retrospective observational study of 287 hospitalized patients who received haemodialysis during the period January 2018 to December 2019 at Sultan Qaboos University Hospital, Muscat, Oman.

Results

A total of 202 different infections were documented in 142 of the 287 patients (49.5%). Pneumonia was the most common infection in the patients examined, accounting for 24.8% (50/202) of the total infections. This was followed by bloodstream infections, with a prevalence of 19.8% (40/202). Klebsiella pneumoniae was the most prevalent isolate (19.0%; 47/248). The highest number of multidrug-resistant infections were caused by multidrug-resistant K. pneumoniae (29.9%; 23/77).

Conclusions

Infections in patients undergoing haemodialysis are common and are dominated by non-access-related infections. Pneumonia was found to be the most prevalent infection in this population. Gram-negative bacteria, predominantly K. pneumoniae, were the most prevalent isolates. The study reported an alarming number of multidrug-resistant organisms, accounting for 31.0% of the total bacterial isolates from various clinical specimens.

The Epidemiology of Multidrug-Resistant Sepsis among Chronic Hemodialysis Patients

Sepsis is one of the leading causes of hospitalization and death among hemodialysis patients. Infections due to multidrug-resistant organisms (MDROs) are common among these patients, but empiric broad-spectrum coverage for every septic patient is associated with unfavorable outcomes. A retrospective case–control study was conducted at Shamir Medical Center, Israel (July 2016–April 2020), to determine predictors of MDRO infections among septic (per SEPSIS-3) ambulatory adult hemodialysis patients with permanent dialysis access (i.e., fistula, graft, or tunneled Perm-A-Cath). MDROs were determined according to established definitions. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to construct a prediction score and determine its performance. Of 509 patients, 225 (44%) had microbiologically confirmed infection, and 79 patients (35% of 225) had MDROs. The eventual independent predictors of MDRO infections were Perm-A-Cath access (vs. fistula or graft, aOR = 3, CI-95% = 2.1–4.2) and recent hospitalization in the previous three months (aOR = 2.3, CI-95% = 1.6–3.3). The score to predict MDRO sepsis with the highest performances contained seven parameters and displayed an area under the receiver operating characteristic curve (ROC AUC) of 0.74. This study could aid in defining a group of hemodialysis patients for which empiric broad-spectrum agents could be safely avoided.