Antimicrobial susceptibilities of specific syndromes created with organ-specific weighted incidence antibiograms (OSWIA) in patients with intra-abdominal infections

Antibiotic susceptibility patterns and trends of the gram-negative bacteria isolated from the patients in the emergency departments in China: results of SMART 2016-2019

BACKGROUND

The study aims were to evaluate the species distribution and antimicrobial resistance profile of Gram-negative pathogens isolated from specimens of intra-abdominal infections (IAI), urinary tract infections (UTI), respiratory tract infections (RTI), and blood stream infections (BSI) in emergency departments (EDs) in China.

METHODS

From 2016 to 2019, 656 isolates were collected from 18 hospitals across China. Minimum inhibitory concentrations were determined by CLSI broth microdilution and interpreted according to CLSI M100 (2021) guidelines. In addition, organ-specific weighted incidence antibiograms (OSWIAs) were constructed.

RESULTS

Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) were the most common pathogens isolated from BSI, IAI and UTI, accounting for 80% of the Gram-negative clinical isolates, while Pseudomonas aeruginosa (P. aeruginosa) was mainly isolated from RTI. E. coli showed < 10% resistance rates to amikacin, colistin, ertapenem, imipenem, meropenem and piperacillin/tazobactam. K. pneumoniae exhibited low resistance rates only to colistin (6.4%) and amikacin (17.5%) with resistance rates of 25-29% to carbapenems. P. aeruginosa exhibited low resistance rates only to amikacin (13.4%), colistin (11.6%), and tobramycin (10.8%) with over 30% resistance to all traditional antipseudomonal antimicrobials including ceftazidime, cefepime, carbapenems and levofloxacin. OSWIAs were different at different infection sites. Among them, the susceptibility of RTI to conventional antibiotics was lower than for IAI, UTI or BSI.

CONCLUSIONS

Gram-negative bacteria collected from Chinese EDs exhibited high resistance to commonly used antibiotics. Susceptibilities were organ specific for different infection sites, knowledge which will be useful for guiding empirical therapies in the clinic.

Development and validation of a nomogram to predict postsurgical intra-abdominal infection in blunt abdominal trauma patients: A multicenter retrospective study

BACKGROUND

Intra-abdominal infection is a common complication of blunt abdominal trauma. Early detection and intervention can reduce the incidence of intra-abdominal infection and improve patients' prognoses. This study aims to construct a clinical model predicting postsurgical intra-abdominal infection after blunt abdominal trauma.

METHODS

This study is a retrospective analysis of 553 patients with blunt abdominal trauma from the Department of General Surgery of 7 medical centers (2011-2021). A 7:3 ratio was used to assign patients to the derivation and validation cohorts. Patients were divided into 2 groups based on whether intra-abdominal infection occurred after blunt abdominal trauma. Multivariate logistic regression and least absolute shrinkage and selection operator regression were used to select variables to establish a nomogram. The nomogram was evaluated, and the validity of the model was further evaluated by the validation cohort.

RESULTS

A total of 113 were diagnosed with intra-abdominal infection (20.4%). Age, prehospital time, C-reactive protein, injury severity score, operation duration, intestinal injury, neutrophils, and antibiotic use were independent risk factors for intra-abdominal infection in blunt abdominal trauma patients (P < .05). The area under the receiver operating curve (area under the curve) of derivation cohort and validation cohort was 0.852 (95% confidence interval, 0.784-0.912) and 0.814 (95% confidence interval, 0.751-0.902). The P value for the Hosmer-Lemeshow test was .135 and .891 in the 2 cohorts. The calibration curve demonstrated that the nomogram had a high consistency between prediction and practical observation. The decision curve analysis also showed that the nomogram had a better potential for clinical application. To facilitate clinical application, we have developed an online at https://nomogramcgz.shinyapps.io/IAIrisk/.

CONCLUSION

The nomogram is helpful in predicting the risk of postoperative intra-abdominal infection in patients with blunt abdominal trauma and provides guidance for clinical decision-making and treatment.

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis

Background

There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows.

Objective

The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health.

Methods

We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes.

Results

We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians’ reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis.

Conclusions

The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

Novel Multiparametric Nomogram for Overall Survival Prediction in Complicated Intra-Abdominal Infection: A Multicenter Study in China

Background: Complicated intra-abdominal infections (cIAIs) in the abdominal cavity or within an abdominal organ are numerous and frequent dangerous entities in the treatment of critically ill patients. Early clinical evaluation is necessary.

Methods: This retrospective multicenter study included patients from 10 intensive care units (ICUs). Risk factors for the overall survival (OS) of patients with cIAI were selected using least absolute shrinkage and selection operator regression, and a nomogram was constructed subsequently. Calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the calibration and discriminative ability.

Results: In total, 544 patients diagnosed with cIAI were enrolled and divided into the study (n = 276) and validation (n = 268) sets. Sex, acute gastrointestinal injury, acute kidney injury, rare bacterium infection, Charlson score, and APACHE II score were identified as independent risk factors and were constructed for the nomogram. The nomogram showed marked calibration capability with a concordance index (C-index) of 0.909 and 0.831 in the study and validation set, respectively. Compared with the common clinical prognostic scoring system, the nomogram achieved the highest discrimination ability with an area under the curve (AUC) value of 0.91 and 0.83 in the study set and validation set, respectively.

Conclusions: Our newly constructed nomogram provides a useful tool for risk stratification and prognosis evaluation of cIAI.