Factors Affecting Patients with Concurrent Deep Neck Infection and Lemierre's Syndrome

Sequential Impact of Diabetes Mellitus on Deep Neck Infections: Comparison of the Clinical Characteristics of Patients with and without Diabetes Mellitus

BACKGROUND

Deep neck infections (DNIs) can compromise the airway and are associated with high morbidity and mortality rates. Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia that is associated with several comorbidities. We compared the clinical characteristics of DNI patients with and without DM.

METHODS

This study recorded the relevant clinical variables of 383 patients with DNIs between November 2016 and September 2022; of those patients, 147 (38.38%) had DM. The clinical factors between DNI patients with and without DM were assessed.

RESULTS

Patients with DM were older (p < 0.001), had higher white blood cell counts (p = 0.029) and C-reactive protein levels (CRP, p < 0.001), had a greater number of deep neck spaces (p = 0.002) compared to patients without DM, and had longer hospital stays (p < 0.001). Klebsiella pneumoniae was cultured more frequently from patients with DM than those without DM (p = 0.002). A higher CRP level (OR = 1.0094, 95% CI: 1.0047-1.0142, p < 0.001) was a significant independent risk factor for DM patients with prolonged hospitalization. The lengths of hospital stays in patients with poorly controlled DM were longer than those with well-controlled DM (p = 0.027).

CONCLUSIONS

DNI disease severity and outcomes were worse in patients with DM than those without DM. Antibiotics effective against Klebsiella pneumoniae should be used for DNI patients with DM. DNI patients with DM and high CRP levels had more prolonged hospitalizations. Appropriate blood glucose control is essential for DNI patients with DM.

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

BACKGROUND

Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis.

METHODS

Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients' clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared.

RESULTS

In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively.

CONCLUSIONS

Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment.

Assessment of the Elderly Adult Patients with Deep Neck Infection: A Retrospective Study

Background: Deep neck infection (DNI) is a potentially lethal infectious disease affecting middle-aged adults and can compromise the airway. There are limited data on the prognosis and outcomes of elderly (aged > 65 years) DNI patients, who tend to be immunocompromised. This study analyzed the clinical characteristics of elderly and adult (aged 18-65 years) DNI patients. Methods: Between November 2016 and November 2022, 398 patients with DNIs, including 113 elderly patients, were admitted to our hospital and enrolled in this study. The relevant clinical variables were investigated and compared. Results: The elderly DNI patients had longer hospital stays (P < .001), higher C-reactive protein levels (P = .021), higher blood sugar levels (P = .012), and a higher likelihood of diabetes mellitus (P = .025) than the adult patients. The higher blood sugar level is an independent risk factor for elderly (odds ratio = 1.005, 95% confidence intervals 1.002-1.008, P < .001). Moreover, the rates of intubation to protect the airway (P = .005) and surgical incision and drainage (I&D; P = .010) were higher in the elderly group. However, there were no group differences in pathogen distributions. Conclusion: The elderly DNI patients in this study had a more severe disease course, and poorer prognosis than the adult patients, as well as higher rates of intubation and I&D. However, the pathogen distributions did not differ significantly between the groups. Prompt intervention and treatment are important for elderly DNI patients.

Assessment of Factors Associated With Long-Term Hospitalization in Patients With a Deep Neck Infection

BACKGROUND

Deep neck infection (DNI) is a severe infectious disorder of deep neck spaces that can cause serious complications. Long-term hospitalization is when a patient spends more time in the hospital than originally expected for a disease. There are few studies assessing the risk factors associated with long-term hospitalization for a DNI. This study investigated the factors causing DNI patients to experience long-term hospitalization.

METHODS

Long-term hospitalization is defined as a length of hospital stay exceeding 28 days (> 4 weeks) in this research. A total of 362 subjects with a DNI between October 2017 and November 2022 were recruited. Among these patients, 20 required long-term hospitalization. The relevant clinical variables were assessed.

RESULTS

In a univariate analysis, C-reactive protein (odds ratio [OR] = 1.003, 95% CI: 1.000-1.007, P = .044), involvement of ≥3 deep neck spaces (OR = 2.836, 95% CI: 1.140-7.050, P = .024), and mediastinitis (OR = 8.102, 95% CI: 3.041-21.58, P < .001) were significant risk factors for long-term hospitalization in DNI patients. In a multivariate analysis, mediastinitis (OR = 6.018, 95% CI: 2.058-17.59, P = .001) was a significant independent risk factor for long-term hospitalization for a DNI. There were no significant differences in pathogens between the patients with and without long-term hospitalization (all P > .05). However, the rates of no growth of specific pathogens were significantly different between patients with and without long-term hospitalization, and those with long-term hospitalization had greater rates of growth of specific pathogens (P = .032). The rate of tracheostomy in patients with long-term hospitalization was higher than for those without (P < .001). Nevertheless, the rates of surgical incision and drainage between patients with and without long-term hospitalization did not achieve statistical significance (P = .069).

CONCLUSIONS

Deep neck infection (DNI) is a critical, life-threatening disease that could lead to long-term hospitalization. The higher CRP and involvement of ≥3 deep neck spaces were significant risk factors in univariate analysis, while concurrent mediastinitis was an independent risk factor associated with long-term hospitalization. We suggest intensive care and prompt airway protection for DNI patients with concurrent mediastinitis.

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Background: Deep neck infection (DNI) can lead to airway obstruction. Rather than intubation, some patients need tracheostomy to secure the airway. However, no study has used deep learning (DL) artificial intelligence (AI) to predict the need for tracheostomy in DNI patients. Thus, the purpose of this study was to develop a DL framework to predict the need for tracheostomy in DNI patients. Methods: 392 patients with DNI were enrolled in this study between August 2016 and April 2022; 80% of the patients (n = 317) were randomly assigned to a training group for model validation, and the remaining 20% (n = 75) were assigned to the test group to determine model accuracy. The k-nearest neighbor method was applied to analyze the clinical and computed tomography (CT) data of the patients. The predictions of the model with regard to the need for tracheostomy were compared with actual decisions made by clinical experts. Results: No significant differences were observed in clinical or CT parameters between the training group and test groups. The DL model yielded a prediction accuracy of 78.66% (59/75 cases). The sensitivity and specificity values were 62.50% and 80.60%, respectively. Conclusions: We demonstrated a DL framework to predict the need for tracheostomy in DNI patients based on clinical and CT data. The model has potential for clinical application; in particular, it may assist less experienced clinicians to determine whether tracheostomy is necessary in cases of DNI.