Factors for a clinical prediction score to determine complication development after cellulitis diagnosis in adult patients

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In extreme cases, it may lead to multiorgan failure and death. We sought to analyze the clinical factors that contribute to the development of complicated disease, including demographics, clinical presentation, initial vital signs, and laboratory studies. Methods Our study is a retrospective cohort study carried out in a university-based tertiary care hospital in Bangkok, Thailand. All adult patients who presented with cellulitis from January 1, 2018, to December 31, 2022, were evaluated for eligibility and inclusion in this study. All related variables for both outcomes, septicemia and necrotizing fasciitis, were gathered from electronic medical records and analyzed. Results Of the 1,560 visits to this hospital, 47 cases reported at least one complication, with septicemia noted in 27 visits (6.68%) and necrotizing fasciitis in 20 visits (1.27%). From the multivariable logistic regression analysis, six variables emerge as predictors of cellulitis complications. These are: Age≥ 65 years, Body Mass Index ≥ 30 kg/m 2 , diabetes mellitus, body temperature ≥ 37.3°C, systolic blood pressure < 100 mmHg, and involvement of lower extremities. The predictive score was developed from these factors and was named the Ramathibodi Necrotizing Fasciitis/Septicemia (RAMA-NFS) Prediction Score. Our predictive score has an accuracy of 82.3% (95% CI 0.77-0.88). Patients in the high-risk group (RAMA NFS score > 6) have a likelihood ratio of 3.7 times to develop complications of cellulitis. Conclusion In our study, the RAMA-NFS Prediction Score predicts complications in adult patients who present with cellulitis. External validation of this predictive score is still needed for further practical application. Figures Figure 1 Figure 2 I. Introduction Background Cellulitis is defined as a bacterial infection of the skin or soft tissue, which can progress to septicemia or necrotizing fasciitis if not treated. The incidence of cellulitis in patients with positive hemoculture ranges from 4% to 30%, depending on the population and diagnostic criteria. 1,5 The most prevalent bacteria cultured are Streptococcus species and Staphylococcus aureus. These organisms account for 66% to 73.7% of all cellulitis-related bacteria. Moreover, Methicillin-Resistant Staphylococcus has been identified as a growing problem in hospital-acquired infections. 3,5,18,19 Up to 3% of all Emergency Department visits are due to skin and soft tissue infections 20 , with the rate of hospitalization varying depending on the severity of disease and the patient's comorbidities. Around 7% of patients with cellulitis are hospitalized, while mortalities range from 1% to 2.5%, depending on the study. 2,3 Diabetes mellitus, chronic kidney disease, and peripheral arterial disease account for a higher rate of hospitalization and prolongation of hospital stays. One study from Siriraj Hospital in Thailand found that 20.6% of patients with cellulitis received inpatient care, with an overall mortality rate of 0.3%. 3 Apart from the patient's comorbidities, several elements are regarded as risk factors for hospitalization, such as, increased age, immunodeficiency status, and area of skin involved. 4 The New England Journal of Medicine 5 suggests obtaining blood cultures in patients with cellulitis who have systemic symptoms (fever or chills), lymphedema with superimposed cellulitis, or tissue exposure to a non-sterilized body of water. It also suggests admitting patients who have failed outpatient management or those with rapidly spreading infection. Various risk factors contribute to differing outcomes in patients with cellulitis. Proper care must be provided to prevent complications such as septicemia or necrotizing fasciitis. Further studies are needed to evaluate high-risk patients with cellulitis at risk for limb-threatening or life-threatening complications. An appropriate scoring system is a way to identify a patient's risk and assist physicians in making decisions on proper management and early intervention, both in the outpatient and emergency care settings. Additionally, the scoring system should guide disposition decisions and indications for hospitalization, thus helping to reduce unnecessary health-care costs or length of stay. Objective To explore factors and generate a scoring system in patients with skin or subcutaneous tissue infections to predict associated complication development. II. Methods Study Design We used the retrospective cohort, single-center model for this study. Study Setting Our study was conducted at Ramathibodi Hospital, a university-based tertiary care hospital in Bangkok, Thailand. Sample Size The sample size for our study was calculated based on a previous study by Lee et al., 6 which gathered data from patients with cellulitis with two different outcomes (positive hemoculture group and negative hemoculture group). The data from Table 1 and Table 2 of this study were used to calculate the sample size using Stata version 16.1 through a two-sample comparison of proportions and means. The assumptions were as follows: Alpha = 0.05 (one side), Power = 0.8, and N2/N1 = 0.010. The smallest sample size that would produce a significantly different result was a total of 164 patients, comprising 15 patients in the positive hemoculture group (N1) and 149 patients in the negative hemoculture group (N2). Participants Patients diagnosed with skin and soft tissue infection (coded as ICD-10 L03.9) during the study period were reviewed from the electronic medical record (EMR) for eligibility. The inclusion criteria were: 18 years of age or older, diagnosed with cellulitis, and capable of follow-up for at least 1 month after the diagnosis of cellulitis. Duration of study The study period was January 1, 2018 through December 31, 2022. Data Collection and Study Variables From all medical records of cellulitis patients, 2,766 individuals presented with cellulitis within the 5-year study period, and 1,572 patients were eligible for our study based on inclusion criteria. The study variables were recorded for all eligible patients, including demographic data, characteristics of current cellulitis, past medical histories, initial vital signs, and laboratory test results. Outcomes of Interest The outcome was complications, defined as septicemia or necrotizing fasciitis occurring in the affected area within 1 month of the diagnosis of cellulitis. Septicemia was defined as the presence of a positive blood culture result with cellulitis identified as the cause. Necrotizing fasciitis was considered a complication when diagnosed in the patient's medical records. Statistical Analysis Statistical analysis was conducted using STATA version 16.1 to create a prediction score. All eligible patients were categorized into two groups based on the presence of complications. Baseline characteristics were described using counts and percentages for categorical data, means and standard deviations for continuous data with a normal distribution, and medians and interquartile ranges (IQR) for continuous variables with a non-normal distribution. Data variables from both groups were compared using t-tests and exact probability tests for continuous and categorical data, respectively. Potential predictors significantly associated with complications (p-value < 0.05) were divided into three levels by multivariable logistic regression. Regression coefficients for each level were divided by the smallest coefficient and rounded to produce the prediction score. The predictive power of the score was represented using the area under the ROC curve and a 95% confidence interval. Furthermore, the score-predicted risk and observed risk in our population were compared to demonstrate the predictive power of the score. Lastly, using this score, we categorized our patients into three groups: low-risk, moderate-risk, and high-risk groups. Positive likelihood ratios, 95% confidence intervals, and p-values were calculated for each group. III. Results During the 5-year study period, 2,767 patient visits at Ramathibodi Hospital had a diagnosis of cellulitis. Among them, 1,560 patients were eligible for inclusion in this study. Of these visits, 47 patients developed complications from cellulitis, including the outcomes of septicemia (n = 27) and necrotizing fasciitis (n = 20). Table 1 lists the clinical characteristics of the patients, sub-divided by the development of complications with a cellulitis diagnosis. In our study, complications were observed more frequently in males, with a total of 25 visits (53.19%). The mean ages in the complication and non-complication groups were 65.78 ± 15.78 years and 57.51 ± 18.97 years, respectively. Patients with complications from cellulitis had a significantly higher mean weight (75.53 ± 22.34 kg) and a higher mean BMI (29.56 ± 9.53 kg/m 2 ) compared to the non-complication group. A higher incidence of diabetes mellitus was identified as a significant comorbidity in the complication group (55.32%) versus the non-complication group (20.16%). These two groups did not differ significantly with respect to prior wound, purulent features, or arterial lactate. However, cellulitis of the lower extremities tended to have significantly more complications (89.36% versus 53.28%). According to patients' vital signs, those with complications from cellulitis had a significantly higher mean body temperature (37.56 ± 0.96 °C), respiratory rate (21.02 ± 3.56 breaths per minute), and lower mean oxygen saturation (96 ± 5.23 %). Table 1. Characteristics of patients with cellulitis. Results are categorized as complication or no complication after a diagnosis of cellulitis. Variables Complication (N1=47) No Complication (N2=1513) P-value Male 25 (53.19%) 574 (37.94%) 0.047 Age (mean ± SD) 65.78 ± 15.78 57.51 ± 18.97 0.003 Weight (kg) (mean ± SD) 75.53 ± 22.34 65.43 ± 17.82 < 0.001 Height (m) (mean ± SD) 1.60 ± 0.09 1.59 ± 0.10 0.335 Body mass index (kg/m 2 ) (mean ± SD) 29.56 ± 9.53 25.60 ± 7.57 < 0.001 Patient location Emergency department Outpatient department 34 (72.34%) 13 (27.66%) 241 (15.93%) 1272 (84.07%) < 0.001 Underlying diseases Diabetes mellitus Autoimmune disease Malignancy Cirrhosis HIV infection Peripheral artery disease Chronic venous insufficiency Lymphatic obstruction 26 (55.32%) 3 (6.38%) 6 (12.77%) 2 (4.26%) 0 0 7 (14.89%) 2 (4.26%) 305 (20.16%) 58 (3.83%) 157 (10.38%) 17 (1.12%) 13 (0.86%) 24 (1.59%) 87 (5.75% 19 (1.26%) < 0.001 0.427 0.625 0.110 1.000 1.000 0.020 0.130 Prior wound or infected wound 12 (25.53%) 223 (14.74%) 0.059 Initial vital signs (mean ± SD) Body temperature (°C) Respiratory rate (breaths per minute) Oxygen saturation (%) Heart rate (beats per minute) Systolic blood pressure (mmHg) 37.56 ± 0.96 21.02 ± 3.56 96.00 ± 5.23 90.24 ± 19.05 129.26 ± 24.84 36.92 ± 0.69 19.76 ± 1.53 97.99 ± 1.56 84.94 ± 15.82 134.25 ± 21.80 < 0.001 < 0.001 < 0.001 0.027 0.124 Decrease level of consciousness 1, 2.13 14, 0.93 0.369 Involvement of lower extremities 42, 89.36 804, 53.28 < 0.001 Purulent 7, 14.89 138, 9.16 0.197 Laboratory investigations White blood cell count (cumm) (median, IQR) Polymorphonuclear cell count (%) (mean ± SD) Bicarbonate (mmol/L) (mean ± SD) Creatinine (mg/dL) (median, IQR) Venous Lactate (mmol/L) (median, IQR) Arterial Lactate (mmol/L) (median, IQR) 11145 (5600-15170) 77.20 ± 20.02 22.32 ± 3.40 1.10 (0.77-1.62) 2.35 (1.60-3.50) 2.77 (1.30-6.20) 8480 (6550-11850) 70.00 ± 14.46 23.53 ± 3.26 0.86 (0.69-1.18) 2.00 (1.50-6.20) 2.40 (1.40-3.20) 0.047 0.002 0.019 0.030 0.040 0.656 Positive hemoculture (blood culture) 27 (6.77%) (total N=399) Necrotizing fasciitis 20 (1.28%) (total N=1560) Multivariable logistic regression analysis was conducted to identify predictors of complication development following diagnosis of cellulitis, as demonstrated in Table 2. The item score was determined by age (≥ 65 years), BMI (≥ 30 kg/m 2 ), the presence of diabetes mellitus, elevated body temperature (BT ≥ 37.3 °C), low systolic blood pressure (SBP < 100 mmHg), and involvement of lower extremities. The resulting prediction score was named the “Ramathibodi Necrotizing Fasciitis/Septicemia (RAMA-NFS) Prediction Score”. The Area Under ROC of the clinical prediction score showed 82.3% (95% CI, 0.77-0.88) predictive power for complications after a cellulitis diagnosis (Figure 1). Additionally, the calibration of the prediction score depicted the observed risk and predicted risk in adult cellulitis patients (Figure 2). Our clinical prediction scores were then categorized into three groups: low risk (score 6). The probabilities of each score group are shown in Table 3. Table 2 . Predictors of complication development and the assigned item score in case of adult cellulitis (multivariable logistic regression analysis) Predictors Category Adjusted Odd Ratio 95% CI P-value Coefficient Score Age≥ 65 years No Yes 1.00 1.61 Reference 0.82-3.14 - 0.164 - 0.48 0 1 Body Mass Index ≥ 30 kg/m 2 No Yes 1.00 1.73 Reference 0.87-3.43 - 0.115 - 0.55 0 1 Diabetes mellitus No Yes 1.00 2.56 Reference 1.30-5.07 - 0.007 - 0.94 0 2 Body temperature ≥ 37.3°C No Yes 1.00 3.59 Reference 1.90-6.80 - <0.001 - 1.28 0 2.5 Systolic blood pressure < 100 mmHg No Yes 1.00 4.90 Reference 1.39-17.31 - 0.013 - 1.60 0 3.5 Involvement of lower extremities No Yes 1.00 3.95 Reference 1.49-10.53 - 0.006 - 1.37 0 3 Table 3. Probability categories in the RAMA-NFS Prediction Score for adult cellulitis patients Probability categories Score Complication (N, %) No complication (N, %) LHR+ 95% CI P-value Low <4 5, 11.36 727, 57.42 0.20 0.09-0.45 6 22, 50.00 171, 13.51 3.70 2.67-5.13 <0.001 Mean ± SD 6.61 ± 2.41 3.32 ± 2.52 <0.001 IV. Discussion This study aimed to develop an initial prediction model that could assist physicians in determining the risk of complication development after diagnosing cellulitis in adult patients. The RAMA-NFS Prediction Score shows AUROC 82.3% (with 95% CI, 76.5–88.1). This indicates good correlation of the six identified variables (age ≥ 65 years, Body Mass Index ≥ 30 kg/m 2 , diabetes mellitus, elevated body temperature, low systolic blood pressure, and involvement of lower extremities) to predict complications of septicemia or necrotizing fasciitis after cellulitis diagnosis. In previous studies 4 , 7 cellulitis complications were more commonly observed in male patients. This was also identified in our study with P-value of 0.047. Concerning underlying diseases, diabetes mellitus emerged as the most significant factor in this context. Our study's outcomes align with those of Allen’s et al previous study 15 , showing the clinical impact of diabetes mellitus on a patient's immune system. Surprisingly, comorbidities of chronic venous insufficiency (N1 = 7, N2 = 87), HIV (N1 = 0, N2 = 17), lymphatic obstruction (N1 = 2, N2 = 19) and peripheral artery disease (N1 = 0, N2 = 24) were not significant and did not emerge as independent predictors of complication in patients with cellulitis. The study by Chamli et.al 12 showed that 94.9% of cellulitis cases occur in the lower extremities. The higher prevalence of lower extremity involvement in cellulitis from our study aligns with these findings. Furthermore, higher body weight and BMI contributed to the development of cellulitis complications, consistent with the study from Tianyi et al. 8 and Njim et al 9 . These findings highlight the importance of metabolic disease as a factor that increases the likelihood of adverse outcomes. Our study also demonstrates differences in findings compared with previous studies. Specifically, our study highlights the importance of patients' initial vital signs, which were identified as significant factors in the development of cellulitis complications. The cut-off point for temperature in our study, derived from Harrison’s et al 10 definition of fever, is slightly lower than that from a prior study 11 , in which a temperature of more than 100°F indicated a higher rate of patient admission. Despite several studies 11 , 16 demonstrating select laboratory investigations (such as WBC count, PMN, or serum lactate) are indicative of more severe cellulitis, the clinical significance of laboratory investigation in our study is shown to have a lower impact. This allows for the use of the RAMA-NFS Prediction Score to be used early on in a patient’s diagnostic work up without the need for laboratory blood work. This increases the speed of decision making regarding early antibiotic interventions, early disposition decisions, and in turn, may reduce unnecessary hospitalizations and health-care costs. Our findings demonstrate that by considering a combination of a patient's age, BMI, underlying diseases, vital signs, and the location of cellulitis, it is possible to predict the likelihood of complications arising from cellulitis. This information can be valuable in assisting physicians with their clinical decision-making. We suggest that if patients were categorized into a low-risk group (score < 4), the probability of cellulitis developing into complications would be low with positive likelihood ratio 0.1 (95% CI, 0.03–0.25; p 6) of patients, there is a high probability of complications developing after cellulitis diagnosis with positive likelihood ratio 6.38 (95% CI, 3.28–12.32; p < 0.001). We would hypothesize that the high-risk group might benefit from more aggressive management, including obtaining hemocultures, admission for close monitoring of hemodynamic status, and administration of early antibiotics. For patients identified as moderate risk for developing complications, further discussion should be provided to the patient and their relatives regarding the risks and benefits of the treatment plan, and patients should be followed up to detect any potential complications. Some studies on clinical scores for cellulitis patients have been published. One study proposed the Melbourne ASSET (Area, Systemic features, Swelling, Eye, Tenderness) Score, 13 demonstrated as a tool to guide physicians regarding when to start intravenous antibiotics in children with cellulitis. In terms of necrotizing fasciitis, Wong et al. 17 proposed the LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) score, which consists of laboratory items to identify the risk of developing early necrotizing fasciitis. Another multi-center, prospective cohort study 14 developed a risk score for predicting MRSA probability in adult patients with cellulitis. Our study is the first to identify and provide a predictive score for complications in adult patients after a cellulitis diagnosis, especially in a university-based, tertiary care hospital setting. A strength of the RAMA-NFS Prediction Score is the application early on without need of laboratory data for application. However, its use in real clinical situations still requires further validation. Limitations Our center is a high-volume, urban, university-based, tertiary care hospital, thus patients are more likely to have complicated underlying diseases or a higher risk of exposure to advanced organisms compared to those in community-based rural hospitals. Because this study is conducted at a single center, there may be limitations in generalizability to other populations of patients of different demographics. Therefore, further external validation of our predictive score is needed using independently obtained patient data of other hospital systems to validate the model. Additionally, the absence of randomization and use of a retrospective study makes controlling for confounding variables and establishing a causal relationship of the identified outcomes difficult. To improve generalizability and causality, a multi-center prospective investigation could be pursued. Declarations Ethical Approval This study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and was approved by the Committee on Human Rights Related to Research Involving Human Subjects, Faculty of Medicine, Mahidol University, Ramathibodi Hospital (COA.MURA2023/461). The study was also reviewed by the Yale University Human Research Protection Program Institutional Review Boards and received exemption status (IRB ID: 2000036328). Consent to Participate Not applicable, as the study involved retrospective data collection of de-identified electronic medical records. Consent for Publication Not applicable. Availability of Data and Materials The respective dataset is stored on a password protected excel document on the secure Ramathibodi Hospital server. This data is accessible by faculty who have been identified in the approved IRB. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Funding Not applicable. Conflicts of interest None References Chira S., Miller L. (2010). Staphylococcus aureus is the most common identified cause of cellulitis: A systematic review. Epidemiology & Infection, 138(3), 313-317. Cranendonk, D. R., Lavrijsen, A. P. M., Prins, J. M., & Wiersinga, W. J. (2017). Cellulitis: current insights into pathophysiology and clinical management. The Netherlands journal of medicine, 75(9), 366–378. Sirijatuphat R, Somngam W, Thamlikitkul V. Epidemiology of Cellulitis at a University-Based Tertiary Care Hospital in Thailand. J Med Assoc Thai 2019;102(1):78-85. Singh B, Singh S, Khichiy S, Ghatge A. Clinical Presentation of Soft-tissue Infections and its Management: A Study of 100 Cases. Niger J Surg. 2017;23(2):86-91. Swartz M.N. (2004). Clinical practice. Cellulitis. 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Yadav K, Eagles D, Perry JJ, Taljaard M, Sandino-Gold G, Nemnom M-J, et al. High-dose cephalexin for cellulitis: A pilot randomized controlled trial. Canadian Journal of Emergency Medicine. 2023;25(1):22–30. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Apr, 2024 Reviews received at journal 14 Apr, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviews received at journal 09 Mar, 2024 Reviewers agreed at journal 01 Mar, 2024 Reviewers agreed at journal 01 Mar, 2024 Reviewers invited by journal 01 Mar, 2024 Editor assigned by journal 29 Jan, 2024 Submission checks completed at journal 29 Jan, 2024 First submitted to journal 02 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3830385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269839514,"identity":"d193c288-62a2-40ac-923e-32813a84143b","order_by":0,"name":"Welawat Tienpratarn","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Welawat","middleName":"","lastName":"Tienpratarn","suffix":""},{"id":269839515,"identity":"5cba3fe3-5c71-4566-a9f1-e2af192adfb5","order_by":1,"name":"Chaiyaporn Yuksen","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Chaiyaporn","middleName":"","lastName":"Yuksen","suffix":""},{"id":269839516,"identity":"f578f5d8-3950-4e0c-ba8a-30efb1855cc7","order_by":2,"name":"Joseph Daniel Pauly","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACxgYYwcDcwPCBgUEGxJQgUgtjA+MMBgYegloQ+oAkMw8xWpjb2x8+LtzBkMc/I7FN2rbNjoefgfngbR58FvScMTaeeYahWOIGUEtuWzKPZANbsjVeLTNy2KR52xgSGyBamHkMDvCYSePXkv78N0jLfJAWy7Z6HvsD/N8IaEkwYwZp2QDSwth2mMeAgYcNvxagX6RntkkUG5552GzZc+44j8RhNmPLOXi0GAJD7HNhm02e3PHkgzd+lFXL8bc3P7zxBp+WBmBAAyMiAchmkWBkYwBz8QJ5qBqQFuYPDH8IKB8Fo2AUjIIRCQDItElPSJUmEAAAAABJRU5ErkJggg==","orcid":"","institution":"Yale University","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"Daniel","lastName":"Pauly","suffix":""},{"id":269839517,"identity":"39a8184f-c658-4959-988d-bdcb7ed915c4","order_by":3,"name":"Diana Vu","email":"","orcid":"","institution":"George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Vu","suffix":""},{"id":269839518,"identity":"010087f6-f0cf-444a-a944-bb4e5cd6a425","order_by":4,"name":"Anisa Noiwong Benbourenane","email":"","orcid":"","institution":"George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Anisa","middleName":"Noiwong","lastName":"Benbourenane","suffix":""},{"id":269839519,"identity":"5089e3dd-788f-4ef3-a725-4da4c16d15a6","order_by":5,"name":"Nuttamon Sangskul","email":"","orcid":"","institution":"Mahidol University","correspondingAuthor":false,"prefix":"","firstName":"Nuttamon","middleName":"","lastName":"Sangskul","suffix":""}],"badges":[],"createdAt":"2024-01-02 21:14:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3830385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3830385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50442475,"identity":"bfea58fc-5776-43f3-91dc-f2135bc8effa","added_by":"auto","created_at":"2024-01-31 15:22:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":241326,"visible":true,"origin":"","legend":"\u003cp\u003eThe Area under ROC curve and 95% Confidence Interval of the predictive power of the RAMA-NFS Prediction Score for complication development in adult cellulitis patients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3830385/v1/c5e7b2497a28c558485a6749.png"},{"id":50442476,"identity":"d46c49de-1059-4539-9cc8-bd06aaa810ce","added_by":"auto","created_at":"2024-01-31 15:22:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190599,"visible":true,"origin":"","legend":"\u003cp\u003eObserved risk (circles) vs score predicted risk (solid line) of complication development in adult cellulitis patients\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3830385/v1/5634e36f1e968c736391bc99.png"},{"id":50443694,"identity":"c95bcc5c-53eb-4114-a062-99b6ee3493f9","added_by":"auto","created_at":"2024-01-31 15:30:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":677877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3830385/v1/1e73fdc9-60f8-4642-ad41-f0fb5b3d4386.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors for a clinical prediction score to determine complication development after cellulitis diagnosis in adult patients","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCellulitis is defined as a bacterial infection of the skin or soft tissue, which can progress to septicemia or necrotizing fasciitis if not treated. The incidence of cellulitis in patients with positive hemoculture ranges from 4% to 30%, depending on the population and diagnostic criteria.\u003csup\u003e1,5\u0026nbsp;\u003c/sup\u003eThe most prevalent bacteria cultured are Streptococcus species and Staphylococcus aureus. These organisms account for 66% to 73.7% of all cellulitis-related bacteria. Moreover, Methicillin-Resistant Staphylococcus has been identified as a growing problem in hospital-acquired infections.\u003csup\u003e3,5,18,19\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eUp to 3% of all Emergency Department visits are due to skin and soft tissue infections\u003csup\u003e20\u003c/sup\u003e, with the rate of hospitalization varying depending on the severity of disease and the patient\u0026apos;s comorbidities. Around 7% of patients with cellulitis are hospitalized, while mortalities range from 1% to 2.5%, depending on the study.\u003csup\u003e2,3\u003c/sup\u003e Diabetes mellitus, chronic kidney disease, and peripheral arterial disease account for a higher rate of hospitalization and prolongation of hospital stays. One study from Siriraj Hospital in Thailand found that 20.6% of patients with cellulitis received inpatient care, with an overall mortality rate of 0.3%.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eApart from the patient\u0026apos;s comorbidities, several elements are regarded as risk factors for hospitalization, such as, increased age, immunodeficiency status, and area of skin involved.\u003csup\u003e4\u003c/sup\u003e The New England Journal of Medicine\u003csup\u003e5\u0026nbsp;\u003c/sup\u003esuggests obtaining blood cultures in patients with cellulitis who have systemic symptoms (fever or chills), lymphedema with superimposed cellulitis, or tissue exposure to a non-sterilized body of water. It also suggests admitting patients who have failed outpatient management or those with rapidly spreading infection.\u003c/p\u003e\n\u003cp\u003eVarious risk factors contribute to differing outcomes in patients with cellulitis. Proper care must be provided to prevent complications such as septicemia or necrotizing fasciitis. Further studies are needed to evaluate high-risk patients with cellulitis at risk for limb-threatening or life-threatening complications. An appropriate scoring system is a way to identify a patient\u0026apos;s risk and assist physicians in making decisions on proper management and early intervention, both in the outpatient and emergency care settings. Additionally, the scoring system should guide disposition decisions and indications for hospitalization, thus helping to reduce unnecessary health-care costs or length of stay.\u003c/p\u003e\n\u003cp\u003eObjective\u003c/p\u003e\n\u003cp\u003eTo explore factors and generate a scoring system in patients with skin or subcutaneous tissue infections to predict associated complication development.\u003c/p\u003e"},{"header":"II. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe used the retrospective cohort, single-center model for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003e Our study was conducted at Ramathibodi Hospital, a university-based tertiary care hospital in Bangkok, Thailand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample Size\u003c/h2\u003e \u003cp\u003eThe sample size for our study was calculated based on a previous study by Lee et al.,\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e which gathered data from patients with cellulitis with two different outcomes (positive hemoculture group and negative hemoculture group). The data from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e of this study were used to calculate the sample size using Stata version 16.1 through a two-sample comparison of proportions and means. The assumptions were as follows: Alpha\u0026thinsp;=\u0026thinsp;0.05 (one side), Power\u0026thinsp;=\u0026thinsp;0.8, and N2/N1\u0026thinsp;=\u0026thinsp;0.010. The smallest sample size that would produce a significantly different result was a total of 164 patients, comprising 15 patients in the positive hemoculture group (N1) and 149 patients in the negative hemoculture group (N2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003ePatients diagnosed with skin and soft tissue infection (coded as ICD-10 L03.9) during the study period were reviewed from the electronic medical record (EMR) for eligibility. The inclusion criteria were: 18 years of age or older, diagnosed with cellulitis, and capable of follow-up for at least 1 month after the diagnosis of cellulitis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDuration of study\u003c/h2\u003e \u003cp\u003eThe study period was January 1, 2018 through December 31, 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Study Variables\u003c/h2\u003e \u003cp\u003eFrom all medical records of cellulitis patients, 2,766 individuals presented with cellulitis within the 5-year study period, and 1,572 patients were eligible for our study based on inclusion criteria. The study variables were recorded for all eligible patients, including demographic data, characteristics of current cellulitis, past medical histories, initial vital signs, and laboratory test results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes of Interest\u003c/h2\u003e \u003cp\u003eThe outcome was complications, defined as septicemia or necrotizing fasciitis occurring in the affected area within 1 month of the diagnosis of cellulitis. Septicemia was defined as the presence of a positive blood culture result with cellulitis identified as the cause. Necrotizing fasciitis was considered a complication when diagnosed in the patient's medical records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using STATA version 16.1 to create a prediction score. All eligible patients were categorized into two groups based on the presence of complications. Baseline characteristics were described using counts and percentages for categorical data, means and standard deviations for continuous data with a normal distribution, and medians and interquartile ranges (IQR) for continuous variables with a non-normal distribution. Data variables from both groups were compared using t-tests and exact probability tests for continuous and categorical data, respectively. Potential predictors significantly associated with complications (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were divided into three levels by multivariable logistic regression. Regression coefficients for each level were divided by the smallest coefficient and rounded to produce the prediction score. The predictive power of the score was represented using the area under the ROC curve and a 95% confidence interval. Furthermore, the score-predicted risk and observed risk in our population were compared to demonstrate the predictive power of the score. Lastly, using this score, we categorized our patients into three groups: low-risk, moderate-risk, and high-risk groups. Positive likelihood ratios, 95% confidence intervals, and p-values were calculated for each group.\u003c/p\u003e \u003c/div\u003e"},{"header":"III. Results","content":"\u003cp\u003eDuring the 5-year study period, 2,767 patient visits at Ramathibodi Hospital had a diagnosis of cellulitis. Among them, 1,560 patients were eligible for inclusion in this study. Of these visits, 47 patients developed complications from cellulitis, including the outcomes of septicemia (n = 27) and necrotizing fasciitis (n = 20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 lists the clinical characteristics of the patients, sub-divided by the development of complications with a cellulitis diagnosis. In our study, complications were observed more frequently in males, with a total of 25 visits (53.19%). The mean ages in the complication and non-complication groups were 65.78 \u0026plusmn; 15.78 years and 57.51 \u0026plusmn; 18.97 years, respectively. Patients with complications from cellulitis had a significantly higher mean weight (75.53 \u0026plusmn; 22.34 kg) and a higher mean BMI (29.56 \u0026plusmn; 9.53 kg/m\u003csup\u003e2\u003c/sup\u003e) compared to the non-complication group. A higher incidence of diabetes mellitus was identified as a significant comorbidity in the complication group (55.32%) versus the non-complication group (20.16%). These two groups did not differ significantly with respect to prior wound, purulent features, or arterial lactate. However, cellulitis of the lower extremities tended to have significantly more complications (89.36% versus 53.28%). According to patients\u0026apos; vital signs, those with complications from cellulitis had a significantly higher mean body temperature (37.56 \u0026plusmn; 0.96 \u0026deg;C), respiratory rate (21.02 \u0026plusmn; 3.56 breaths per minute), and lower mean oxygen saturation (96 \u0026plusmn; 5.23 %).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics of patients with cellulitis. Results are categorized as complication or no complication after a diagnosis of cellulitis.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplication\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N1=47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Complication\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N2=1513)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e25 (53.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e574 (37.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eAge (mean \u0026plusmn; SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e65.78 \u0026plusmn; 15.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e57.51 \u0026plusmn; 18.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eWeight (kg) (mean \u0026plusmn; SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e75.53 \u0026plusmn; 22.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e65.43 \u0026plusmn; 17.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (m) (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e1.60 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e1.59 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e) (mean \u0026plusmn; SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e29.56 \u0026plusmn; 9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e25.60 \u0026plusmn; 7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003ePatient location\u0026nbsp;\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eEmergency department\u003c/li\u003e\n \u003cli\u003eOutpatient department\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34 (72.34%)\u003c/p\u003e\n \u003cp\u003e13 (27.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e241 (15.93%)\u003c/p\u003e\n \u003cp\u003e1272 (84.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eUnderlying diseases\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eDiabetes mellitus\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAutoimmune disease\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMalignancy\u003c/li\u003e\n \u003cli\u003eCirrhosis\u003c/li\u003e\n \u003cli\u003eHIV infection\u003c/li\u003e\n \u003cli\u003ePeripheral artery disease\u003c/li\u003e\n \u003cli\u003eChronic venous insufficiency\u003c/li\u003e\n \u003cli\u003eLymphatic obstruction\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (55.32%)\u003c/p\u003e\n \u003cp\u003e3 (6.38%)\u003c/p\u003e\n \u003cp\u003e6 (12.77%)\u003c/p\u003e\n \u003cp\u003e2 (4.26%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e7 (14.89%)\u003c/p\u003e\n \u003cp\u003e2 (4.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e305 (20.16%)\u003c/p\u003e\n \u003cp\u003e58 (3.83%)\u003c/p\u003e\n \u003cp\u003e157 (10.38%)\u003c/p\u003e\n \u003cp\u003e17 (1.12%)\u003c/p\u003e\n \u003cp\u003e13 (0.86%)\u003c/p\u003e\n \u003cp\u003e24 (1.59%)\u003c/p\u003e\n \u003cp\u003e87 (5.75%\u003c/p\u003e\n \u003cp\u003e19 (1.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003ePrior wound or infected wound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e12 (25.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e223 (14.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eInitial vital signs (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eBody temperature (\u0026deg;C)\u003c/li\u003e\n \u003cli\u003eRespiratory rate (breaths per minute)\u003c/li\u003e\n \u003cli\u003eOxygen saturation (%)\u003c/li\u003e\n \u003cli\u003eHeart rate (beats per minute)\u003c/li\u003e\n \u003cli\u003eSystolic blood pressure (mmHg)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.56 \u0026plusmn; 0.96\u003c/p\u003e\n \u003cp\u003e21.02 \u0026plusmn; 3.56\u003c/p\u003e\n \u003cp\u003e96.00 \u0026plusmn; 5.23\u003c/p\u003e\n \u003cp\u003e90.24 \u0026plusmn; 19.05\u003c/p\u003e\n \u003cp\u003e129.26 \u0026plusmn; 24.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36.92 \u0026plusmn; 0.69\u003c/p\u003e\n \u003cp\u003e19.76 \u0026plusmn; 1.53\u003c/p\u003e\n \u003cp\u003e97.99 \u0026plusmn; 1.56\u003c/p\u003e\n \u003cp\u003e84.94 \u0026plusmn; 15.82\u003c/p\u003e\n \u003cp\u003e134.25 \u0026plusmn; 21.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eDecrease level of consciousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e1, 2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e14, 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eInvolvement of lower extremities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e42, 89.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e804, 53.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003ePurulent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e7, 14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e138, 9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.80152671755725%\" valign=\"top\"\u003e\n \u003cp\u003eLaboratory investigations\u0026nbsp;\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eWhite blood cell count (cumm) (median, IQR)\u003c/li\u003e\n \u003cli\u003ePolymorphonuclear cell count (%)\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e(mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eBicarbonate (mmol/L) (mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/li\u003e\n \u003cli\u003eCreatinine (mg/dL) (median, IQR)\u003c/li\u003e\n \u003cli\u003eVenous Lactate (mmol/L) (median, IQR)\u003c/li\u003e\n \u003cli\u003eArterial Lactate (mmol/L) (median, IQR)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.290076335877863%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11145 (5600-15170)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77.20 \u0026plusmn; 20.02\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22.32 \u0026plusmn; 3.40\u003c/p\u003e\n \u003cp\u003e1.10 (0.77-1.62)\u003c/p\u003e\n \u003cp\u003e2.35 (1.60-3.50)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.77 (1.30-6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.30534351145038%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8480 (6550-11850)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70.00 \u0026plusmn; 14.46\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23.53 \u0026plusmn; 3.26\u003c/p\u003e\n \u003cp\u003e0.86 (0.69-1.18)\u003c/p\u003e\n \u003cp\u003e2.00 (1.50-6.20)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.40 (1.40-3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.603053435114504%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003ePositive hemoculture (blood culture)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.68292682926829%\" colspan=\"2\"\u003e\n \u003cp\u003e27 (6.77%) (total N=399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003eNecrotizing fasciitis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.68292682926829%\" colspan=\"2\"\u003e\n \u003cp\u003e20 (1.28%) (total N=1560)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMultivariable logistic regression analysis was conducted to identify predictors of complication development following diagnosis of cellulitis, as demonstrated in Table 2. The item score was determined by age (\u0026ge; 65 years), BMI (\u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e), the presence of diabetes mellitus, elevated body temperature (BT \u0026ge; 37.3 \u0026deg;C), low systolic blood pressure (SBP \u0026lt; 100 mmHg), and involvement of lower extremities. The resulting prediction score was named the \u0026ldquo;Ramathibodi Necrotizing Fasciitis/Septicemia (RAMA-NFS) Prediction Score\u0026rdquo;. The Area Under ROC of the clinical prediction score showed 82.3% (95% CI, 0.77-0.88) predictive power for complications after a cellulitis diagnosis (Figure 1). Additionally, the calibration of the prediction score depicted the observed risk and predicted risk in adult cellulitis patients (Figure 2). Our clinical prediction scores were then categorized into three groups: low risk (score \u0026lt; 4), moderate risk (score 4-6), and high risk (score \u0026gt; 6). The probabilities of each score group are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Predictors of complication development and the assigned item score in case of adult cellulitis (multivariable logistic regression analysis)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"687\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.41860465116279%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.337209302325581%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.261627906976743%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOdd Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.081395348837209%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.901162790697674%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936046511627907%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.41860465116279%\"\u003e\n \u003cp\u003eAge\u0026ge; 65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.337209302325581%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.261627906976743%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.081395348837209%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e0.82-3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.901162790697674%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936046511627907%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.41860465116279%\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003cp\u003e\u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.337209302325581%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.261627906976743%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.081395348837209%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e0.87-3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.901162790697674%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936046511627907%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.41860465116279%\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.337209302325581%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.261627906976743%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.081395348837209%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e1.30-5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.901162790697674%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936046511627907%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.41860465116279%\"\u003e\n \u003cp\u003eBody temperature\u003c/p\u003e\n \u003cp\u003e\u0026ge; 37.3\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.337209302325581%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.261627906976743%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.081395348837209%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e1.90-6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.901162790697674%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936046511627907%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.41860465116279%\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003cp\u003e\u0026lt; 100 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.337209302325581%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.261627906976743%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.081395348837209%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e1.39-17.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.901162790697674%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936046511627907%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.41860465116279%\"\u003e\n \u003cp\u003eInvolvement of lower extremities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.337209302325581%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.261627906976743%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.081395348837209%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e1.49-10.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.901162790697674%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.936046511627907%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.063953488372093%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Probability categories in the RAMA-NFS Prediction Score for adult cellulitis patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.847352024922117%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProbability categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190031152647975%\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91277258566978%\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplication\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.24922118380062%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo complication\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLHR+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.018691588785046%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.903426791277258%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.847352024922117%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190031152647975%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91277258566978%\" valign=\"top\"\u003e\n \u003cp\u003e5, 11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.24922118380062%\" valign=\"top\"\u003e\n \u003cp\u003e727, 57.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.018691588785046%\" valign=\"top\"\u003e\n \u003cp\u003e0.09-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.903426791277258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.847352024922117%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190031152647975%\" valign=\"top\"\u003e\n \u003cp\u003e4-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91277258566978%\" valign=\"top\"\u003e\n \u003cp\u003e17, 38.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.24922118380062%\" valign=\"top\"\u003e\n \u003cp\u003e368, 29.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"top\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.018691588785046%\" valign=\"top\"\u003e\n \u003cp\u003e0.91-1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.903426791277258%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.847352024922117%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190031152647975%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91277258566978%\" valign=\"top\"\u003e\n \u003cp\u003e22, 50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.24922118380062%\" valign=\"top\"\u003e\n \u003cp\u003e171, 13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.018691588785046%\" valign=\"top\"\u003e\n \u003cp\u003e2.67-5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.903426791277258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.847352024922117%\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190031152647975%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.91277258566978%\" valign=\"top\"\u003e\n \u003cp\u003e6.61 \u0026plusmn; 2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.24922118380062%\" valign=\"top\"\u003e\n \u003cp\u003e3.32 \u0026plusmn; 2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.878504672897197%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.018691588785046%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.903426791277258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"IV. Discussion","content":"\u003cp\u003eThis study aimed to develop an initial prediction model that could assist physicians in determining the risk of complication development after diagnosing cellulitis in adult patients. The RAMA-NFS Prediction Score shows AUROC 82.3% (with 95% CI, 76.5\u0026ndash;88.1). This indicates good correlation of the six identified variables (age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, Body Mass Index\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e, diabetes mellitus, elevated body temperature, low systolic blood pressure, and involvement of lower extremities) to predict complications of septicemia or necrotizing fasciitis after cellulitis diagnosis.\u003c/p\u003e \u003cp\u003eIn previous studies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e cellulitis complications were more commonly observed in male patients. This was also identified in our study with P-value of 0.047. Concerning underlying diseases, diabetes mellitus emerged as the most significant factor in this context. Our study's outcomes align with those of Allen\u0026rsquo;s et al previous study\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, showing the clinical impact of diabetes mellitus on a patient's immune system. Surprisingly, comorbidities of chronic venous insufficiency (N1\u0026thinsp;=\u0026thinsp;7, N2\u0026thinsp;=\u0026thinsp;87), HIV (N1\u0026thinsp;=\u0026thinsp;0, N2\u0026thinsp;=\u0026thinsp;17), lymphatic obstruction (N1\u0026thinsp;=\u0026thinsp;2, N2\u0026thinsp;=\u0026thinsp;19) and peripheral artery disease (N1\u0026thinsp;=\u0026thinsp;0, N2\u0026thinsp;=\u0026thinsp;24) were not significant and did not emerge as independent predictors of complication in patients with cellulitis.\u003c/p\u003e \u003cp\u003eThe study by Chamli et.al\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e showed that 94.9% of cellulitis cases occur in the lower extremities. The higher prevalence of lower extremity involvement in cellulitis from our study aligns with these findings. Furthermore, higher body weight and BMI contributed to the development of cellulitis complications, consistent with the study from Tianyi et al.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and Njim et al\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These findings highlight the importance of metabolic disease as a factor that increases the likelihood of adverse outcomes.\u003c/p\u003e \u003cp\u003eOur study also demonstrates differences in findings compared with previous studies. Specifically, our study highlights the importance of patients' initial vital signs, which were identified as significant factors in the development of cellulitis complications. The cut-off point for temperature in our study, derived from Harrison\u0026rsquo;s et al\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e definition of fever, is slightly lower than that from a prior study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, in which a temperature of more than 100\u0026deg;F indicated a higher rate of patient admission. Despite several studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e demonstrating select laboratory investigations (such as WBC count, PMN, or serum lactate) are indicative of more severe cellulitis, the clinical significance of laboratory investigation in our study is shown to have a lower impact. This allows for the use of the RAMA-NFS Prediction Score to be used early on in a patient\u0026rsquo;s diagnostic work up without the need for laboratory blood work. This increases the speed of decision making regarding early antibiotic interventions, early disposition decisions, and in turn, may reduce unnecessary hospitalizations and health-care costs.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that by considering a combination of a patient's age, BMI, underlying diseases, vital signs, and the location of cellulitis, it is possible to predict the likelihood of complications arising from cellulitis. This information can be valuable in assisting physicians with their clinical decision-making. We suggest that if patients were categorized into a low-risk group (score\u0026thinsp;\u0026lt;\u0026thinsp;4), the probability of cellulitis developing into complications would be low with positive likelihood ratio 0.1 (95% CI, 0.03\u0026ndash;0.25; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, management for this group of patients can be limited to an outpatient setting. Conversely, in the high-risk group (score\u0026thinsp;\u0026gt;\u0026thinsp;6) of patients, there is a high probability of complications developing after cellulitis diagnosis with positive likelihood ratio 6.38 (95% CI, 3.28\u0026ndash;12.32; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). We would hypothesize that the high-risk group might benefit from more aggressive management, including obtaining hemocultures, admission for close monitoring of hemodynamic status, and administration of early antibiotics. For patients identified as moderate risk for developing complications, further discussion should be provided to the patient and their relatives regarding the risks and benefits of the treatment plan, and patients should be followed up to detect any potential complications.\u003c/p\u003e \u003cp\u003eSome studies on clinical scores for cellulitis patients have been published. One study proposed the Melbourne ASSET (Area, Systemic features, Swelling, Eye, Tenderness) Score,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e demonstrated as a tool to guide physicians regarding when to start intravenous antibiotics in children with cellulitis. In terms of necrotizing fasciitis, Wong et al.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e proposed the LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) score, which consists of laboratory items to identify the risk of developing early necrotizing fasciitis. Another multi-center, prospective cohort study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e developed a risk score for predicting MRSA probability in adult patients with cellulitis. Our study is the first to identify and provide a predictive score for complications in adult patients after a cellulitis diagnosis, especially in a university-based, tertiary care hospital setting. A strength of the RAMA-NFS Prediction Score is the application early on without need of laboratory data for application. However, its use in real clinical situations still requires further validation.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur center is a high-volume, urban, university-based, tertiary care hospital, thus patients are more likely to have complicated underlying diseases or a higher risk of exposure to advanced organisms compared to those in community-based rural hospitals. Because this study is conducted at a single center, there may be limitations in generalizability to other populations of patients of different demographics. Therefore, further external validation of our predictive score is needed using independently obtained patient data of other hospital systems to validate the model. Additionally, the absence of randomization and use of a retrospective study makes controlling for confounding variables and establishing a causal relationship of the identified outcomes difficult. To improve generalizability and causality, a multi-center prospective investigation could be pursued.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and was approved by the Committee on Human Rights Related to Research Involving Human Subjects, Faculty of Medicine, Mahidol University, Ramathibodi Hospital (COA.MURA2023/461). The study was also reviewed by the Yale University Human Research Protection Program Institutional Review Boards and received exemption status (IRB ID: 2000036328). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as the study involved retrospective data collection of de-identified electronic medical records. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe respective dataset is stored on a password protected excel document on the secure Ramathibodi Hospital server. This data is accessible by faculty who have been identified in the approved IRB. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflicts of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChira S., Miller L. (2010). Staphylococcus aureus is the most common identified cause of cellulitis: A systematic review. Epidemiology \u0026amp; Infection, 138(3), 313-317.\u003c/li\u003e\n\u003cli\u003eCranendonk, D. R., Lavrijsen, A. P. M., Prins, J. M., \u0026amp; Wiersinga, W. J. (2017). Cellulitis: current insights into pathophysiology and clinical management. The Netherlands journal of medicine, 75(9), 366\u0026ndash;378.\u003c/li\u003e\n\u003cli\u003eSirijatuphat R, Somngam W, Thamlikitkul V. Epidemiology of Cellulitis at a University-Based Tertiary Care Hospital in Thailand. J Med Assoc Thai 2019;102(1):78-85.\u003c/li\u003e\n\u003cli\u003eSingh B, Singh S, Khichiy S, Ghatge A. Clinical Presentation of Soft-tissue Infections and its Management: A Study of 100 Cases. Niger J Surg. 2017;23(2):86-91.\u003c/li\u003e\n\u003cli\u003eSwartz M.N. (2004). Clinical practice. Cellulitis. The New England journal of medicine, 350(9), 904\u0026ndash;912.\u003c/li\u003e\n\u003cli\u003eLee, C.Y., Kunin, C.M., Chang, C. et al. Development of a prediction model for bacteremia in hospitalized adults with cellulitis to aid in the efficient use of blood cultures: a retrospective cohort study. BMC Infect Dis. 16, 581 (2016).\u003c/li\u003e\n\u003cli\u003eBhagat TS, Kumar L, Garg P, Goel A, Aggarwal A, Gupta S. To study the clinical profile and management of cellulitis of lower limb in northern India. Int J Low Extrem Wounds [Internet]. 2023;22(1):44\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eTianyi F-L, Mbanga CM, Danwang C, Agbor VN. Risk factors and complications of lower limb cellulitis in Africa: a systematic review. BMJ Open [Internet]. 2018;8(7):e021175.\u003c/li\u003e\n\u003cli\u003eNjim T, Aminde LN, Agbor VN, Toukam LD, Kashaf SS, Ohuma EO. Risk factors of lower limb cellulitis in a level-two healthcare facility in Cameroon: a case-control study. BMC Infect Dis [Internet]. 2017;17(1). \u003c/li\u003e\n\u003cli\u003eHarrison\u0026rsquo;s principles of internal medicine. 20th ed. McGraw-Hill Education/Medical; 2018.\u003c/li\u003e\n\u003cli\u003eVolz KA, Canham L, Kaplan E, Sanchez LD, Shapiro NI, Grossman SA. Identifying patients with cellulitis who are likely to require inpatient admission after a stay in an ED observation unit. Am J Emerg Med [Internet]. 2013;31(2):360\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eChamli A, Jaber K, Ben Lagha I, Malek BS, Rabhi F, Doss N, et al. Factors associated with acute and recurrent erysipelas in a young population: a retrospective of 147 cases. La Tunisie M\u0026eacute;dicale. 2021;99(08\u0026ndash;09):886.\u003c/li\u003e\n\u003cli\u003eIbrahim LF, Hopper SM, Donath S, Salvin B, Babl FE, Bryant PA. Development and validation of a cellulitis risk score: The Melbourne ASSET score. Pediatrics [Internet]. 2019;143(2). \u003c/li\u003e\n\u003cli\u003eZasowski EJ, Trinh TD, Claeys KC, Dryden M, Shlyapnikov S, Bassetti M, et al. International validation of a methicillin-resistant staphylococcus aureus risk assessment tool for skin and soft tissue infections. Infect Dis Ther [Internet]. 2022;11(6):2253\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eAllen M, Gluck J, Benson E. Renal disease and diabetes increase the risk of failed outpatient management of cellulitic hand infections: A retrospective cohort study [Internet]. U.S. National Library of Medicine; 2023. \u003c/li\u003e\n\u003cli\u003eOkmen H;Sari ND;Ulusan K;Tunay A;Idiz UO; Clinical and laboratory parameters for differential diagnosis of necrotizing faciitis and cellulitis [Internet]. U.S. National Library of Medicine; [cited 2023 Nov 27]. \u003c/li\u003e\n\u003cli\u003eWong CH;Khin LW;Heng KS;Tan KC;Low CO; The LRINEC (laboratory risk indicator for necrotizing fasciitis) score: A tool for distinguishing necrotizing fasciitis from other soft tissue infections [Internet]. U.S. National Library of Medicine; [cited 2023 Nov 27]. \u003c/li\u003e\n\u003cli\u003eS; PRJ. Necrotizing fasciitis [Internet]. U.S. National Library of Medicine; [cited 2023 Nov 27]. \u003c/li\u003e\n\u003cli\u003eSeeleang K, Manning ML, Saks M, Winstead Y. Skin and soft tissue infection management, outcomes, and follow-up in the Emergency Department of an urban academic hospital. Advanced Emergency Nursing Journal. 2014;36(4):348\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eYadav K, Eagles D, Perry JJ, Taljaard M, Sandino-Gold G, Nemnom M-J, et al. High-dose cephalexin for cellulitis: A pilot randomized controlled trial. Canadian Journal of Emergency Medicine. 2023;25(1):22\u0026ndash;30. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijem","sideBox":"Learn more about [International Journal of Emergency Medicine](https://intjem.biomedcentral.com/)","snPcode":"12245","submissionUrl":"https://submission.nature.com/new-submission/12245/3","title":"International Journal of Emergency Medicine","twitterHandle":"@IntJEmergMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3830385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3830385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Cellulitis is defined as a bacterial infection of the skin and subcutaneous tissue that can cause multiple complications, such as sepsis and necrotizing fasciitis. In extreme cases, it may lead to multiorgan failure and death. We sought to analyze the clinical factors that contribute to the development of complicated disease, including demographics, clinical presentation, initial vital signs, and laboratory studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Our study is a retrospective cohort study carried out in a university-based tertiary care hospital in Bangkok, Thailand. All adult patients who presented with cellulitis from January 1, 2018, to December 31, 2022, were evaluated for eligibility and inclusion in this study. All related variables for both outcomes, septicemia and necrotizing fasciitis, were gathered from electronic medical records and analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Of the 1,560 visits to this hospital, 47 cases reported at least one complication, with septicemia noted in 27 visits (6.68%) and necrotizing fasciitis in 20 visits (1.27%). From the multivariable logistic regression analysis, six variables emerge as predictors of cellulitis complications. These are: Age≥ 65 years, Body Mass Index ≥ 30 kg/m\u003csup\u003e2\u003c/sup\u003e, diabetes mellitus, body temperature ≥ 37.3°C, systolic blood pressure \u0026lt; 100 mmHg, and involvement of lower extremities. The predictive score was developed from these factors and was named the Ramathibodi Necrotizing Fasciitis/Septicemia (RAMA-NFS) Prediction Score. Our predictive score has an accuracy of 82.3% (95% CI 0.77-0.88). Patients in the high-risk group (RAMA NFS score \u0026gt; 6) have a likelihood ratio of 3.7 times to develop complications of cellulitis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e In our study, the RAMA-NFS Prediction Score predicts complications in adult patients who present with cellulitis. External validation of this predictive score is still needed for further practical application.\u003c/p\u003e","manuscriptTitle":"Factors for a clinical prediction score to determine complication development after cellulitis diagnosis in adult patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 15:21:57","doi":"10.21203/rs.3.rs-3830385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-15T12:01:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-15T02:44:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"bd1c2555-048c-4386-9c20-02742e6aa06d","date":"2024-04-05T15:06:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-09T12:53:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9bd049d6-a27e-4528-a042-b66b0d4989ba","date":"2024-03-01T12:51:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a3d1c56e-b822-4f03-83ae-0b2a5b7a2c3f","date":"2024-03-01T12:45:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-01T12:41:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-29T09:29:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-29T09:29:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Emergency Medicine","date":"2024-01-02T21:00:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijem","sideBox":"Learn more about [International Journal of Emergency Medicine](https://intjem.biomedcentral.com/)","snPcode":"12245","submissionUrl":"https://submission.nature.com/new-submission/12245/3","title":"International Journal of Emergency Medicine","twitterHandle":"@IntJEmergMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8269a19-fae3-4422-9e55-38b842b3d416","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-14T22:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-31 15:21:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3830385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3830385","identity":"rs-3830385","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}