Rate and impact of multidrug-resistant organisms in patients with aneurysmal subarachnoid hemorrhage

Development of a model to predict the risk of multi-drug resistant organism infections in ruptured intracranial aneurysms patients with hospital-acquired pneumonia in the neurological intensive care unit

OBJECTIVE

This study was developed to explore the incidence of multi-drug resistant organism (MDRO) infections among ruptured intracranial aneurysms(RIA) patient with hospital-acquired pneumonia(HAP) in the neurological intensive care unit (NICU), and to establish risk factors related to the development of these infections.

METHODS

We collected clinical and laboratory data from 328 eligible patients from January 2018 to December 2022. Bacterial culture results were used to assess MDRO strain distributions, and risk factors related to MDRO infection incidence were identified through logistic regression analyses. These risk factors were further used to establish a predictive model for the incidence of MDRO infections, after which this model underwent internal validation.

RESULTS

In this study cohort, 26.5 % of RIA patients with HAP developed MDRO infections (87/328). The most common MDRO pathogens in these patients included Multidrug-resistant Klebsiella pneumoniae (34.31 %) and Multidrug-resistant Acinetobacter baumannii (27.45 %). Six MDRO risk factors, namely, diabetes (P = 0.032), tracheotomy (P = 0.004), history of mechanical ventilation (P = 0.033), lower albumin levels (P < 0.001), hydrocephalus (P < 0.001) and Glasgow Coma Scale (GCS) score ≤8 (P = 0.032) were all independently correlated with MDRO infection incidence. The prediction model exhibited satisfactory discrimination (area under the curve [AUC], 0.842) and calibration (slope, 1.000), with a decision curve analysis further supporting the clinical utility of this model.

CONCLUSIONS

In summary, risk factors and bacterial distributions associated with MDRO infections among RIA patients with HAP in the NICU were herein assessed. The developed predictive model can aid clinicians to identify and screen high-risk patients for preventing MDRO infections.

Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data

Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops in some patients, who then require placement of a permanent ventriculoperitoneal (VP) shunt. The aim of this study was to employ recurrent neural network (RNN)-based machine learning techniques to identify patients who require VP shunt placement at an early stage. This retrospective single-centre study included all patients who were diagnosed with aSAH and treated in the intensive care unit (ICU) between November 2010 and May 2020 (n = 602). More than 120 parameters were analysed, including routine neurocritical care data, vital signs and blood gas analyses. Various machine learning techniques, including RNNs and gradient boosting machines, were evaluated for their ability to predict VP shunt dependency. VP-shunt dependency could be predicted using an RNN after just one day of ICU stay, with an AUC-ROC of 0.77 (CI: 0.75-0.79). The accuracy of the prediction improved after four days of observation (Day 4: AUC-ROC 0.81, CI: 0.79-0.84). At that point, the accuracy of the prediction was 76% (CI: 75.98-83.09%), with a sensitivity of 85% (CI: 83-88%) and a specificity of 74% (CI: 71-78%). RNN-based machine learning has the potential to predict VP shunt dependency on Day 4 after ictus in aSAH patients using routine data collected in the ICU. The use of machine learning may allow early identification of patients with specific therapeutic needs and accelerate the execution of required procedures.

Development and Validation of a Nomogram Prediction Model for Multidrug-Resistant Organisms Infection in a Neurosurgical Intensive Care Unit

Objective

To develop a predictive model for assessing the risk of multidrug-resistant organisms (MDROs) infection and validate its effectiveness.

We conducted a study on a total of 2516 patients admitted to the neurosurgery intensive care unit (NICU) of a Grade-III hospital in Nantong City, Jiangsu Province, China, between January 2014 and February 2022. Patients meeting the inclusion criteria were selected using convenience sampling. The patients were randomly divided into modeling and validation groups in a 7:3 ratio. To address the category imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to adjust the MDROs infection ratio from 203:1558 to 812:609 in the training set. Univariate analysis and logistic regression analysis were performed to identify risk factors associated with MDROs infection in the NICU. A risk prediction model was developed, and a nomogram was created. Receiver operating characteristic (ROC) analysis was used to assess the predictive performance of the model.

Patients and Methods

Results

Logistic regression analysis revealed that sex, hospitalization time, febrile time, invasive operations, postoperative prophylactic use of antibiotics, mechanical ventilator time, central venous catheter indwelling time, urethral catheter indwelling time, ALB, PLT, WBC, and L% were independent predictors of MDROs infection in the NICU. The area under the ROC curve for the training set and validation set were 0.880 (95% CI: 0.857-0.904) and 0.831 (95% CI: 0.786-0.876), respectively. The model's prediction curve closely matched the ideal curve, indicating excellent predictive performance.

Conclusion

The prediction model developed in this study demonstrates good accuracy in assessing the risk of MDROs infection. It serves as a valuable tool for neurosurgical intensive care practitioners, providing an objective means to effectively evaluate and target the risk of MDROs infection.

Risk predict model using multi-drug resistant organism infection from Neuro-ICU patients: a retrospective cohort study

The aim of this study was to analyze the current situation and risk factors of multi-drug-resistant organism (MDRO) infection in Neuro-intensive care unit (ICU) patients, and to develop the risk predict model. The data was collected from the patients discharged from Neuro-ICU of grade-A tertiary hospital at Guizhou province from January 2018 to April 2020. Binary Logistics regression was used to analyze the data. The model was examined by receiver operating characteristic curve (ROC). The grouped data was used to verify the sensitivity and specificity of the model. A total of 297 patients were included, 131 patients infected with MDRO. The infection rate was 44.11%. The results of binary Logistics regression showed that tracheal intubation, artery blood pressure monitoring, fever, antibiotics, pneumonia were independent risk factors for MDRO infection in Neuro-ICU (P < 0.05), AUC = 0.887. The sensitivity and specificity of ROC curve was 86.3% and 76.9%. The risk prediction model had a good predictive effect on the risk of MDRO infection in Neuro ICU, which can evaluate the risk and provide reference for preventive treatment and nursing intervention.

Multidrug-resistant organisms (MDROs) in patients with subarachnoid hemorrhage (SAH)

Patient care in a neurointensive care unit (neuro-ICU) is challenging. Multidrug-resistant organisms (MDROs) are increasingly common in the routine clinical practice. We evaluated the impact of infection with MDROs on outcomes in patients with subarachnoid hemorrhage (SAH). A single-center retrospective analysis of SAH cases involving patients treated in the neuro-ICU was performed. The outcome was assessed 6 months after SAH using the modified Rankin Scale [mRS, favorable (0–2) and unfavorable (3–6)]. Data were compared by matched-pair analysis. Patient characteristics were well matched in the MDRO (n = 61) and control (n = 61) groups. In this center, one nurse was assigned to a two-bed room. If a MDRO was detected, the patient was isolated, and the nurse was assigned to the patient infected with the MDRO. In the MDRO group, 29 patients (48%) had a favorable outcome, while 25 patients (41%) in the control group had a favorable outcome; the difference was not significant (p > 0.05). Independent prognostic factors for unfavorable outcomes were worse status at admission (OR = 3.1), concomitant intracerebral hematoma (ICH) (OR = 3.7), and delayed cerebral ischemia (DCI) (OR = 6.8). Infection with MRDOs did not have a negative impact on the outcome in SAH patients. Slightly better outcomes were observed in SAH patients infected with MDROs, suggesting the benefit of individual care.

Incidence, impact and risk factors for multidrug-resistant organisms (MDRO) in patients with major trauma: a European Multicenter Cohort Study

INTRODUCTION

The burden of MDRO in health systems is a global issue, and a growing problem. We conducted a European multicenter cohort study to assess the incidence, impact and risk factors for multidrug-resistant organisms in patients with major trauma. We conducted this study because the predictive factors and effects of MDRO in severely injured patients are not yet described. Our hypothesis is that positive detection of MDRO in severely injured patients is associated with a less favorable outcome.

METHODS

Retrospective study of four level-1 trauma centers including all patients after major trauma with an injury severity score (ISS) ≥ 9 admitted to an intensive care unit (ICU) between 2013 and 2017. Outcome was measured using the Glasgow outcome scale (GOS).

RESULTS

Of 4131 included patients, 95 (2.3%) had a positive screening for MDRO. Risk factors for MDRO were male gender (OR 1.73 [95% CI 1.04-2.89]), ISS (OR 1.01 [95% CI 1.00-1.03]), PRBC's given (OR 1.73 [95% CI 1.09-2.78]), ICU stay > 48 h (OR 4.01 [95% CI 2.06-7.81]) and mechanical ventilation (OR 1.85 [95% CI 1.01-3.38]). A positive MDRO infection correlates with worse outcome. MDRO positive cases GOS: good recovery = 0.6%, moderate disability = 2.1%, severe disability = 5.6%, vegetative state = 5.7% (p < 0.001).

CONCLUSIONS

MDRO in severely injured patients are rare but associated with a worse outcome at hospital discharge. We identified potential risk factors for MDRO in severely injured patients. Based on our results, we recommend a standardized screening procedure for major trauma patients.