Usefulness of serum procalcitonin for necrotizing fasciitis as an early diagnostic tool

Early diagnosis of necrotizing fasciitis: Imaging techniques and their combined application

Necrotizing fasciitis is a clinical, surgical emergency characterized by an insidious onset, rapid progression, and a high mortality rate. The disease's mortality rate has remained high for many years, mainly because of its atypical clinical presentation, which prevents many cases from being diagnosed early and accurately, resulting in patients who may die from uncontrollable septic shock and multi-organ failure. But unfortunately, no diagnostic indicator can provide a certain early diagnosis of NF, and clinical judgement of NF is still based on the results of various ancillary tests combined with the patient's medical history, clinical manifestations, and the physician's experience. This review provides a brief overview of the epidemiological features of NF and then discusses the most important laboratory indicators and scoring systems currently employed to diagnose NF. Finally, the latest progress of several imaging techniques in the early diagnosis of NF and their combined application with other diagnostic indices are highlighted. We point out promising research directions based on an objective evaluation of the advantages and shortcomings of different methods, which provide a basis for further improving the early diagnosis of NF.

Causative Micro-Organisms in Necrotizing Fasciitis and their Influence on Inflammatory Parameters and Clinical Outcome

Background: Necrotizing fasciitis (NF) is a potentially fatal soft tissue infection. Four types of pathogens can be distinguished in the pathogen spectrum, although there are strong regional differences with regard to the most common pathogens. Patients and Methods: All cases of NF between 2003 and 2021 with an identified causative agent were analyzed retrospectively. The cases were divided into three groups: polymicrobial, gram-positive and gram-negative. Demographic factors, localization of infection, inflammatory parameters, and clinical outcome were compared between the three groups. Results: A total of 95 cases were analyzed, 41% of which were caused by multiple pathogens. A gram-positive pathogen was reported in 40% of cases and a gram-negative pathogen in 19%. There were significant differences between the three groups with respect to age (with patients in the gram-negative group being on average the oldest) and intensive care unit admissions (which was most frequent in the polymicrobial group). Conclusions: The pathogen spectrum of NF has rarely been studied in a large patient population. Gram-positive pathogens account for the majority of monomicrobial infections in our study. Nevertheless, we recommend calculated broad-spectrum antibiotic therapy given the high number of polymicrobial infections and gram-negative infections. Gram-negative infections may be associated with increased mortality, elevated procalcitonin levels, and are relatively frequent in NF of the lower extremities.

Time Is Fascia: Laboratory and Anamnestic Risk Indicators for Necrotizing Fasciitis

Background: Necrotizing fasciitis is a potentially fatal soft tissue infection in which the timing of surgical intervention significantly affects clinical outcome. The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score, the Site other than the lower limb, Immunosuppression, Age <60 years, Renal impairment (creatinine >141), and Inflammatory markers (CRP ≥150, WCC >25) (SIARI) score, and procalcitonin levels are intended to aid in the decision-making process to differentiate between necrotizing fasciitis and soft tissue infections. Methods: A retrospective analysis of cases of necrotizing fasciitis and soft tissue infections treated at Klinikum Nürnberg was performed. The two scores as well as procalcitonin levels were tested for their diagnostic value and a new score, the Laboratory and Anamnestic Risk Indicator for Necrotizing Fasciitis (LARINF), was created based on the results. Results: Procalcitonin, LRINEC score, and SIARI score showed insufficient discriminatory ability in our study. The newly created LARINF score combined laboratory parameters of the LRINEC score (hemoglobin and C-reactive protein) with procalcitonin and three comorbidities, resulting in a sensitivity of 84% and a specificity of 75% and the highest area under the receiver operating characteristic (ROC) curve of 0.83. Conclusions: The LARINF score is a novel decision-supporting tool. The decision, in which cases surgical exploration should be initiated, remains a clinical one. However, the score seems to provide an improved basis for identifying a rare clinical picture.

Classification Model for Diabetic Foot, Necrotizing Fasciitis, and Osteomyelitis

Simple Summary

Necrotizing fasciitis (NF) and osteomyelitis (OM) are severe complications in patients with diabetic foot ulcers (DFUs). Although NF and OM often cause results including limb amputation and death, definite diagnoses of these are challenging. To aid the prompt and proper diagnosis of NF and OM in patients with DFU, we developed and evaluated a novel prediction model based on machine learning technology. In summary, our prediction model appropriately discriminated the NF and OM from diabetic foot. Moreover, this prediction model has advantages in that it is based on the demographic data and routine laboratory results, which requires no additional examinations which are complicated or expensive.

Abstract

Diabetic foot ulcers (DFUs) and their life-threatening complications, such as necrotizing fasciitis (NF) and osteomyelitis (OM), increase the healthcare cost, morbidity and mortality in patients with diabetes mellitus. While the early recognition of these complications could improve the clinical outcome of diabetic patients, it is not straightforward to achieve in the usual clinical settings. In this study, we proposed a classification model for diabetic foot, NF and OM. To select features for the classification model, multidisciplinary teams were organized and data were collected based on a literature search and automatic platform. A dataset of 1581 patients (728 diabetic foot, 76 NF, and 777 OM) was divided into training and validation datasets at a ratio of 7:3 to be analyzed. The final prediction models based on training dataset exhibited areas under the receiver operating curve (AUC) of the 0.80 and 0.73 for NF model and OM model, respectively, in validation sets. In conclusion, our classification models for NF and OM showed remarkable discriminatory power and easy applicability in patients with DFU.

Distinctions between Fournier's gangrene and lower extremity necrotising fasciitis: microbiology and factors affecting mortality

OBJECTIVES

In this study, we aimed to illustrate distinctions between Fournier's gangrene (FG) and lower extremity necrotising fasciitis (NF) and screen out possible risk factors of poor prognosis for each cohort.

METHODS

The medical records of qualified patients with NF admitted to the Second People's Hospital of Yibin from January 2016 to June 2021 were retrospectively reviewed. All participants were anatomically categorised into FG and lower extremity NF groups, and their baseline data and microbiological results were compared. Further comparisons of critical parameters were conducted between survivors and nonsurvivors within each group.

RESULTS

A total of 49 patients were included in the study with a median age of 58 years, and overall mortality was 20.4%. There were 18 patients with FG and 31 patients with lower extremity NF. A microbiology distinction was found-the predominance of gram-negative infection in FG and gram-positive infection in lower extremity NF. High Fournier's gangrene severity index scores (greater than 7), advanced age, procalcitonin and D-dimer value were identified as risk factors for FG, and the presentation of sepsis was an alarming indicator for lower extremity NF.

CONCLUSIONS

The distinction of microbiology might provide advice for appropriate antibacterial administrations. In addition, with practical prognostic predicting tools, clinicians might be able to identify patients at increased risk and intervene promptly to avoid unfavourable outcomes.