Temperature-Neutrophils-Multiple Organ Failure Grading as a Prognostic Indicator in Fournier Gangrene

Abstract

Background:

Severity scoring systems are used widely to predict prognosis in managing various diseases and to tailor the treatment of patients in a personalized way, not in a general concept, by making a risk assessment. This study examines the importance of the Temperature-Neutrophils-Multiple Organ Failure (TNM) scoring system, a new scoring system, in evaluating the prognosis in patients with Fournier gangrene (FG).

Patients and Methods:

Patients who were operated on with the diagnosis of FG in our clinic between 2012 and 2022 were analyzed with a single-center cross-sectional retrospective study design. Demographic data (age, gender), pre-operative evaluation, body temperature, neutrophil ratio, presence of multiple organ failure, TNM score, and post-operative survival data were recorded. The patients were grouped as those with post-operative hospital mortality (group 1) and without (group 2).

Results:

The study included 167 patients. Twenty-two (13.2%) of the patients were in group 1 and 145 (86.8%) were in group 2. According to the TNM score, the frequency of stage 3–4 was higher in group 1 than in group 2 (p < 0.001). Patients ≥65 years of age had a 4.80 (95% confidence interval [CI], 1.87–12.29) times greater mortality risk than patients <65. Patients with comorbid disease had a 4.56 (95% CI, 1.47–14.14) times greater risk of mortality than patients without. Patients with TNM scores 3–4 had a 9.38 (95% CI, 3.01–29.28) times greater risk of exit than patients with scores 1–2.

Conclusions:

The TNM system is a new scoring system that is created quickly using simple laboratory and clinical data in patients with FG and is useful in predicting mortality. Therefore, its clinical use will benefit FG and other deep soft tissue infections.

Disease scoring systems are used widely in all fields of medicine today in the management of complex diseases and the creation of individualized treatment algorithms by using various laboratory and imaging data, especially vital signs of patients (pulse, blood pressure, temperature, respiratory rate, saturation).^1–3 However, for the use of any scoring system in daily clinical practice, the applicability of this system depends on its ease and practicality and the ability to calculate the parameters to be used in scoring systems using simple clinicopathologic data. Nevertheless, since using clinical scoring systems in the 1980s, clinicians have widely used these systems.⁴

Fournier gangrene (FG) is a life-threatening fasciitis condition characterized by diffuse soft tissue necrosis and sepsis, starting from the perineal, perianal, and genital regions and spreading to the abdominal wall if left untreated.⁵ Despite its low prevalence, advanced antibiotic therapy and surgical debridement are performed, but its mortality is relatively high, and mortality rates vary between 20% and 30%.⁶

In recent years, various scoring systems have been developed not only to reduce mortality and prevent complications but also to determine the severity of the disease and to provide appropriate medical support to the patient.⁷ Among these, the Fournier gangrene severity index (FGSI) developed by Laor et al.⁸ and Uludağ FGSI (UFGSI) developed by Yilmazlar et al.⁹ are used widely.

The Temperature-Neutrophils-Multiple Organ Failure (TNM) score was first suggested by Schietroma et al.¹⁰ in 2020 to detect the severity of intra-abdominal sepsis. It was found to be quite valuable for demonstrating mortality in practice. Therefore, this retrospective observational study aims to evaluate the importance of the TNM score determined at the time of diagnosis in predicting mortality in patients with FG.

Patients and Methods

Patient selection

The data of patients who were operated on with the diagnosis of FG in our clinic between January 2012 and January 2022 were analyzed using a cross-sectional retrospective study design. Patients who were operated on for FG, pre-operative vital follow-ups, and pre-operative blood tests were included in the study. Patients with hematologic pathology, a history of malignancy, an autoimmune disease, a history of organ failure, and incomplete data were excluded from the study.

Data collecting

Demographic data (age, gender), body mass index (BMI), pre-operative laboratory values, American Society of Anesthesiologists (ASA) score, pre-operative body temperature (in degrees Celcius), neutrophil ratio (%), presence of organ failure, TNM score, FGSI, culture results from necrotic tissue, vacuum-assisted wound closure method, number of debridements, length of hospital stay (days) and post-operative survival data were recorded. In addition, FGSI was calculated using the patient's age, vital signs, and pre-operative laboratory values to evaluate the disease.

Study design

The patients who were operated on after the diagnosis of FG were followed up in the post-operative hospital. The patients were grouped as patients with in-hospital mortality (group 1) and without (group 2) in the post-operative follow-up. In addition, risk factors for post-operative mortality were determined.

Temperature-Neutrophils-Multiple Organ Failure

The TNM score consists of three components: temperature (T), neutrophil ratio (N), and multiorgan failure (M). The TNM stages were calculated according to the patient's vital values and clinical status at the time of admission to the hospital. The temperature was the oral temperature in centigrade at the time of admission to the hospital. The neutrophil ratio was obtained from a complete blood count. Organ failure was determined according to renal, respiratory, and cardiovascular system inadequacies. The distribution of the patients according to their classes and their classification in stages (stages 0–4) were made after the T, N, and M categories were defined (Table 1).

Table 1.

Temperature-Neutrophils-Multiple Organ Failure Grading

TNM score
Temperature (T)	Maximum daily temperature (°C)
T0	36.4°C–37.4°C
T1	37.5°C–38.4°C
T2	38.5°C–39.0°C
T3	39.1°C–39.5°C
T4	>39.5°C; <36.4°C
Neutrophil (N)	%
N0	40–74
N1	75–85
N2	86–90
N3	>90; <40
Multiple organ failure (M)	Organ failure
M0	No organ failure
M1	One organ failure
M2	Two or more organ failures

Stage	TNM	Clinical profile
0	T0N0M0	Mild sepsis
1		Mild sepsis
1a	T1; N0,N1; M0
1b	T2; N0,N1; M0
2		Moderate sepsis
2a	T3; N0,N1,N2; M0
2b	T3; N0,N1,N2; M0
3		Severe sepsis
3a	Any T; N3; M0
3b	Any T; any N; M1
4	Any T; any N; M2	Septic shock

Organ failure

All patients were examined systematically for organ failure in the pre-operative period. It was evaluated regarding cardiovascular, respiratory, renal, hepatic, hematologic, and neurological failure.

Diagnostic criteria for cardiovascular failure were heart rate <54 beats/min, mean arterial pressure <49 mm Hg, ventricular tachycardia/fibrillation, serum pH <7.24, Paco₂ <49 mm Hg, or inotropic drug requirement. Diagnostic criteria for respiratory failure were Paco₂ >50 mm Hg, po₂ <60, respiratory rate <5 per minute, or >49 per minute. Diagnostic criteria for renal failure were urine output <479 mL/24 hours, blood urea nitrogen >100 mg/dL, and creatinine >3.5 mg/dL. Diagnostic criteria for liver failure were coagulopathy (international normalized ratio [INR] >1.5), encephalopathy, and bilirubin >6 mg/dL. Diagnostic criteria for hematologic failure were leukocytes <1,000/mm³, platelets <20,000/mm³, and hematocrit <20 % in the complete blood count. The diagnostic criteria for neurologic failure were a Glasgow Coma Score <6.¹¹

Study approval

The study design is a single-center cross-sectional retrospective study. Permission was obtained from the chief physician of the tertiary hospital where we conducted the study. The data of the patients were obtained from the hospital's information operating system and electronic database. In the study, no new drug or surgical technique was applied to the patients with FG diagnosis. There was no intervention in the treatment and clinical follow-up of the patients.

Statistical analysis

In the analysis of the data, the mean and standard deviation were used while making the statistics of the continuous data showing normal distribution in the scales. Median, minimum, and maximum values were used for continuous or ordinal data that did not show normal distribution. Frequency (n) and percentage (%) values were used to define categorical variables. The Independent t-test was used to compare the means of the two groups. The Mann-Whitney U test compared two groups with non-normally distributed continuous or ordinal data. The χ² test was used to evaluate the relation between categorical variables. To evaluate the incidence of post-operative hospital mortality, logistic regression analysis was performed using the backward and enter methods with statistically significant variables. The statistical significance level of the data was taken as p < 0.05. The software www.e-picos.com (MedicRes) (New York) and the statistics program MedCalc Software (Version 16.8, Ostend, Belgium) were used to evaluate the data.

Results

During the study, 186 patients were operated on with the diagnosis of FG in our clinic. Two patients with chronic lymphocytic leukemia, one with acute myeloid leukemia, two with lymphoma, five with a history of malignancy, two with autoimmune disease, three with a history of organ failure, and four patients with missing data were excluded from the study. The study included 167 patients.

The mean age of the patients was 52.78 ± 14.27 years. Fifty (29.9%) of the patients were female and 117 (70.1%) were male. The mean body mass index (BMI) was 30.01 ± 4.92 kg/m². Comorbid disease was present in 90 (53.9%) of the patients. Of the patients, 56 had diabetes mellitus, 45 had hypertension, 12 had peripheral circulatory disorders, five had extremity paralysis, four had dementia, and three had Alzheimer's disease. Fifty-one (30.5%) of the patients had a single comorbid disease and 39 (23.4%) had more than one comorbid disease.

The FGSI mean was 6.44 ± 3.39. The median number of debridements was two (range, 1–12). Vacuum-assisted wound closure set was used in 87 (52.1%) patients. Microbiologic culture samples were taken from necrotic tissue after debridement in all patients. Microbiologic growth occurred in 154 (92.2%) patients.

The patients were grouped and compared according to the mortality status in the post-operative hospital (Table 2). Twenty-two (13.2%) of the patients were in group 1 and 145 (86.8%) were in group 2. The mean age was 66.36 ± 12.27 years in group 1 and 50.72 ± 13.42 years in group 2. The FGSI was 10.36 ± 3.79 years in group 1 and 5.84 ± 2.91 years in group 2. The mean age and FGSI were statistically significantly higher in group 1 than in group 2 (p < 0.001).

Table 2.

Socio-Demographic and Clinical Data of the Groups According to the Post-Operative Mortality Status

	All patients (n = 167)	Group 1 (n = 22)	Group 2 (n = 145)
	x ± SD	x ± SD	x ± SD	p
Age (y)	52.78 ± 14.27	66.36 ± 12.27	50.72 ± 13.42	< 0.001
BMI (kg/m²)	30.01 ± 4.92	29.61 ± 4.65	30.42 ± 4.97	0.961
Hospital stay (d)	16.16 ± 13.4911 (1-49)	15.50 ± 12.5013 (1-35)	16.26 ± 13.6811 (1-49)	0.808
FGSI	6.44 ± 3.396.4 (1-18)	10.36 ± 3.799.6 (6-18)	5.84 ± 2.915.3 (1-15)	< 0.001
Number of debridements	3.07 ± 2.972 (1-12)	3.27 ± 4.083 (1-12)	3.04 ± 2.792 (1-9)	0.735
	n (%)	n (%)	n (%)
Age
<65	131 (78.4)	11 (50)	120 (82.8)	< 0.001
≥65	36 (21.6)	11 (50)	25 (17.2)
Gender
Female	50 (29.9)	8 (36.4)	42 (28.9)	0.480
Male	117 (70.1)	14 (63.6)	103 (71.1)
Comorbid disease	90 (53.9)	18 (81.8)	72 (49.7)	0.005
ASA score
2, 3	125 (74.9)	11 (50)	114 (78.6)	0.004
4, 5	42 (25.1)	11 (50)	31 (21.4)
Temperature (T) (°C)
37.5°C–38.4°C	45 (26.9)	4 (18.2)	41 (28.3)	0.028
38.5°C–39.0°C	54 (32.3)	9 (40.9)	45 (31)
39.1°C–39.5°C	54 (32.3)	4 (18.2)	50 (34.5)
>39.5°C; <36.4°C	14 (8.4)	5 (22.7)	9 (6.2)
Neutrophil (N) %
40–74	24 (14.4)	3 (13.6)	21 (14.5)	0.002
75–85	84 (50.3)	4 (18.2)	80 (55.2)
86–90	34 (20.4)	7 (31.8)	27 (18.6)
>90; <40	25 (15)	8 (36.4)	17 (11.7)
Pre-operative organ failure (M)
No	115 (68.9)	6 (27.3)	109 (75.2)	< 0.001
One	30 (18)	6 (27.3)	24 (16.6)
Two or more	22 (13.2)	10 (45.4)	12 (8.2)
TNM
1, 2	102 (61.1)	4 (18.2)	98 (67.6)	< 0.001
3, 4	65 (38.9)	18 (81.8)	47 (32.4)
Vacuum-assisted closure	87 (52.1)	12 (54.5)	75 (51.7)	0.805
Micro-organism growth	154 (92.2)	20 (90.9)	134 (92.4)	0.806

SD = standard deviation; BMI = body mass index; FGSI = Fournier gangrene severity index; ASA = American Society of Anesthesiologists; TNM = Temperature-Neutrophils-Multiple Organ Failure.

Eleven (50%) patients in group 1 and 25 (17.2%) patients in group 2 were in the ≥65 age group. Eleven (50%) patients in group 1 and 25 (17.2%) in group 2 had comorbid disease history. The frequency of ASA score 4–5 was 50% in group 1 and 21.4% in group 2. The presence of comorbid disease, age ≥65 years, and ASA score 4–5 frequency were higher in group 1 than in group 2 (p < 0.001).

There was no statistically significant difference between the groups regarding gender distribution, BMI, length of hospital stay, number of debridements, use of vacuum-assisted wound closure method, and microorganism growth (p > 0.05).

The values of the TNM score components (temperature, neutrophil, and multiple organ failure) were more advanced in group 1 than in group 2 (p = 0.028, p = 0.002, and p < 0.001, respectively). The TNM score stages were grouped as 1–2 and 3–4. According to the TNM score, the number of patients at stage 3–4 was 18 (81.8%) in group 1 and 47 (32.4%) in group 2. The frequency of stage 3–4 was statistically significantly higher in group 1 than in group 2 (p < 0.001). The sensitivity of the high TNM score (score 3–4) in predicting post-operative mortality was 81.8% and the specificity was 67.6%.

Grading according to TNM was done. Of the patients in group 1, two (9.1%) were at stage 1; two (9.1%) were at stage 2; eight (36.4%) were at stage 3; and 10 (% 45.4) were at stage 4. Of the patients in group 2, 60 (41.4%) were at stage 1; 38 (26.2%) were at stage 2; 35 (24.1%) were at stage 3; and 12 (8.3%) were at stage 4. According to the TNM score, the patients in group 1 were at a more advanced stage than group 2 (p < 0.001; Table 3 and (Fig. 1).

FIG. 1.

Post-operative mortality rate according to Temperature-Neutrophils-Multiple Organ Failure (TNM) score.

Table 3.

Distribution of Groups According to TNM Score

	All patients (n = 167)	Group 1 (n = 22)	Group 2 (n = 145)	Clinical profile
	n	n (%)	n (%)	Clinical profile
Stage 1	62 (37, 1)	2 (9, 1)	60 (41, 4)	Mild sepsis
1A	35	1	34
1B	27	1	26
Stage 2	40 (23, 9)	2 (9, 1)	38 (26, 2)	Moderate sepsis
2A	23	1	22
2B	17	1	16
Stage 3	43 (25, 7)	8 (36, 4)	35 (24, 1)	Severe sepsis
3A	13	2	11
3B	30	6	24
Stage 4	22 (13, 3)	10 (45, 4)	12 (8, 3)	Septic shock

TNM = Temperature-Neutrophils-Multiple Organ Failure.

The risk factors for mortality during the post-operative hospital stay were examined. Patients ≥65 years of age had a 4.80 (95% Confidence Interval: 1.87-12.29) times greater mortality risk than patients <65. Patients with the comorbid disease had a 4.56 (95% confidence interval [CI], 1.47–14.14) times higher risk of mortality than patients without. Patients with TNM scores 3–4 had a 9.38 (95% CI, 3.01–29.28) times higher risk of exit than patients with scores of 1–2 (Table 4).

Table 4.

Multivariant Analysis of Risk Factors for Post-Operative Hospital Mortality

	OR	95% CI	p
Gender (male)	1.34	0.59–2.98	> 0.05
Age (≥65)	4.80	1.87–12.29	< 0.05
Comorbid disease (yes)	4.56	1.47–14.14	< 0.05
TNM score (3, 4)	9.38	3.01–29.28	< 0.05

OR = odds ratio; CI = confidence interval; TNM = Temperature-Neutrophils-Multiple Organ Failure.

Dıscussıon

Fournier gangrene, previously known to be of idiopathic origin,¹² has now been found to be idiopathic in less than one-quarter of the patients after the etiologic causes have been clarified.¹³ Despite clarifying its etiology and pathophysiology with medical advances, mortality rates in FG are still high.¹⁴ Predisposing factors include genitourinary infections, diabetes mellitus, immune suppressive states, chronic alcoholism, and genitourinary traumas.¹⁵

Mortality rates vary between 20% and 50% in the literature,¹⁶ whereas this rate can reach up to 88% in patients with sepsis in FG.¹⁷ The current study, with a mortality rate of 13.2%, revealed the relation of TNM, a new scoring system, with mortality in patients with FG. In the study, although the clinical profile is milder and mortality is less in stage 1 patients, the clinical profile becomes more severe toward stage 4, and mortality increases. In addition, our study showed that advanced age, male gender, comorbidity, and TNM score increase mortality rates.

Currently, FGSI and UFGSI are among the most common scoring systems used to assess the severity of the disease in FG. Among the factors included in these scoring systems and associated with mortality in the literature are vital signs such as pulse, blood pressure, and respiratory rate; There are laboratory data such as hematocrit, white blood cell count, sodium, potassium, creatinine, lactate, albumin, and bicarbonate.^18–21 However, most studies have shown that only a few factors are associated with mortality.²² Because the organism's vital signs and laboratory biochemical values are interrelated mechanisms and in a situation in which the system cannot tolerate (systemic inflammatory response syndrome, sepsis, severe sepsis, septic shock), all values are affected by the effect of domino stones. For this reason, it is imperative to reach the most information with minor parameters by using vital and laboratory data, which are easy to implement and not have too many parameters. Therefore, the TNM scoring system is effective in clinical use because it covers the data used in complex scoring systems.

In line with the data we have shown in our study, organ failure is directly proportional to mortality rates. It has been shown that a blood urea level of >50 mg/dL in patients with FG is associated with high mortality rates.²³ In addition, it has been reported that renal failure detected during hospital admission is associated with high mortality rates.^24,25 In addition, in a cohort study involving 1,641 patients, the development of renal failure in patients with FG was an essential mortality predictor with an odds ratio of 5.3.²⁶

In the definition of FG by John Alfred Fournier, three basic features were defined: unknown origin, young age, and male gender.²⁷ However, it has been revealed that advanced age is associated with mortality.²⁸ Therefore, although age is not among the parameters in FGSI, one of the widely used scoring systems in FG, age is included in the parameters in UFGSI. In line with the data we have shown in our study, being 65 years and older is directly related to mortality, and the odds ratio was found to be 4.80. Similar to our study, in a retrospective study including 150 patients in Turkey, the relation between advanced age and mortality was found to be odds ratio (OR), 1.15; 95% CI, 1.019–1.291; p = 0.023.²⁹ In addition, it has taken its place in the literature in various studies showing that being over 60 affects the results negatively.^30,31

The study's limitations are that it is a single retrospective center, and the number of patients is relatively low. The heterogeneity of the group included in the study is among the limitations. In addition, the study's need for more data, such as antibiotic therapy, debridement method and timing, whether to open a colostomy or not, is a significant limitation. An important limitation is that the extent of FG should have been evaluated in the study. In addition, hypothermia and neutropenia can be seen from time to time in systemic inflammatory response syndrome. The fact that these patients cannot be evaluated separately affects the reliability of the study. In addition, the fact that organ failure has yet to be evaluated in detail on an organ basis is among the limitations. However, the study's strengths are its simple applicability at the bedside and the fact that it reflects the basic parameters of severe infection and gives essential information about mortality. In addition, it will contribute to the existing literature because it is the only study demonstrating the relation between TNM and mortality in patients with FG.

Conclusions

In summary, in FG, elderly patients, patients with comorbid diseases, and patients with high TNM scores also have higher mortality rates. Because the TNM scoring system is based on easily accessible laboratory and clinical data, it provides maximal results in predicting the disease's mortality with minimal data without many parameters. In line with the data shown in our study, it can be used in deep soft tissue infections and intra-abdominal infections caused by serious infective processes, as well as in FG.

Footnotes

Authors' Contributions

Conception: Tazeoglu. Design: Tazeoglu. Supervision: Colak. Materials: Tazeoglu, Benli. Data collection and/or processing: Tazeoglu. Analysis and/or interpretation: Tazeoglu. Literature review: Tazeoglu. Writing: Tazeoglu and Benli. Critical review: Tazeoglu.

Funding Information

The authors received no financial support for this article's research, authorship or publication.

Author Disclosure Statement

The authors declare no conflict of interest.

References

Moreno

, Miranda

, Matos

, et al. Mortality after discharge from intensive care: The impact of organ system failure and nursing workload use at discharge. Intensive Care Med, 2001; 27(6):999–1004; doi: 10.1007/s001340100966

Vincent

, Ferreira

, Moreno

. Scoring systems for assessing organ dysfunction and survival. Crit Care Clin, 2000; 16(2):353–366; doi: 10.1016/s0749-0704(05)70114-7

Rapsang

, Shyam

. Scoring systems in the intensive care unit: A compendium. Indian J Crit Care Med, 2014; 18(4):220–228; doi: 10.4103/0972-5229.130573

Fleig

, Brenck

, Wolff

, et al. Scoring systems in intensive care medicine: Principles, models, application and limits. [Article in German.] Anaesthesist, 2011; 60(10):963–974; doi: 10.1007/s00101-011-1942-8

Sroczyński

, Sebastian

, Rudnicki

, et al. A complex approach to the treatment of Fournier's gangrene. Adv Clin Exp Med, 2013; 22(1):131–135.

Singh

, Ahmed

, Aydin

, et al. Fournier's gangrene. A clinical review. Arch Ital Urol Androl, 2016; 88(3):157–164; doi: 10.4081/aiua.2016.3.157

Roghmann

, von Bodman

, Löppenberg

, et al. Is there a need for the Fournier's gangrene severity index? Comparison of scoring systems for outcome prediction in patients with Fournier's gangrene. BJU Int, 2012; 110(9):1359–1365; doi: 10.1111/j.1464-410X.2012.11082.x

Laor

, Palmer

, Tolia

, et al. Outcome prediction in patients with Fournier's gangrene. J Urol, 1995; 154(1):89–92.

Yilmazlar

, Ozturk

, Ozguc

, et al. Fournier's gangrene: An analysis of 80 patients and a novel scoring system. Tech Coloproctol, 2010; 14(3):217–223; doi: 10.1007/s10151-010-0592-1

10.

Schietroma

, Pessia

, Mattei

, et al. Temperature-neutrophils-multiple organ failure grading for complicated ıntra-abdominal ınfections. Surg Infect, 2020; 21(1):69–74; doi: 10.1089/sur.2019.092

11.

Timsit

, Fosse

, Troché

, et al. Calibration and discrimination by daily Logistic Organ Dysfunction scoring comparatively with daily Sequential Organ Failure Assessment scoring for predicting hospital mortality in critically ill patients. Critical care medicine, 2002; 30(9):2003–2013; doi: 10.1097/00003246-200209000-00009.

12.

Hakkarainen

, Kopari

, Pham

, et al. Necrotizing soft tissue infections: Review and current concepts in treatment, systems of care, and outcomes. Curr Probl Surg, 2014; 51(8):344–362; doi: 10.1067/j.cpsurg.2014.06.001

13.

Smith

, Bunker

, Dinneen

. Fournier's gangrene. Br J Urol, 1998; 81(3):347–355; doi: 10.1046/j.1464-410x.1998.00532.x

14.

Montrief

, Long

, Koyfman

, et al. Fournier Gangrene: A Review for Emergency Clinicians. J Emerg Med, 2019; 57(4):488–500; doi: 10.1016/j.jemermed.2019.06.023

15.

Martínez-Rodríguez

, Ponce de León

, Caparrós

, et al. Fournier's gangrene: A monographic urology center experience with twenty patients. Urol Int, 2009; 83(3):323–328; doi: 10.1159/000241676

16.

Zhang

, Xiao

, Wang

, et al. Bibliometric insights in fournier's gangrene: Research landscapes, turning points, and global trends. Front Surg, 2023; 10:1057486; doi: 10.3389/fsurg.2023.1057486

17.

El-Qushayri

, Khalaf

, Dahy

, et al. Fournier's gangrene mortality: A 17-year systematic review and meta-analysis. Int J Infect Dis, 2020; 92:218–225; doi: 10.1016/j.ijid.2019.12.030

18.

Altarac

, Katušin

, Crnica

, et al. Fournier's gangrene: Etiology and outcome analysis of 41 patients. Urol Int, 2012; 88(3):289–293; doi: 10.1159/000335507

19.

Erol

, Tuncel

, Hanci

, et al. Fournier's gangrene: overview of prognostic factors and definition of new prognostic parameter. Urology, 2010; 75(5):1193–1198; doi: 10.1016/j.urology.2009.08.090

20.

Yeniyol

, Suelozgen

, Arslan

, et al. Fournier's gangrene: Experience with 25 patients and use of Fournier's gangrene severity index score. Urology, 2004; 64(2):218–222; doi: 10.1016/j.urology.2004.03.049

21.

Chawla

, Gallop

, Mydlo

. Fournier's gangrene: An analysis of repeated surgical debridement. Eur Urol, 2003; 43(5):572–575; doi: 10.1016/s0302-2838(03)00102-7

22.

Lin

, Ou

, Tzai

, et al. Validation and simplification of Fournier's gangrene severity index. Int J Urol, 2014; 21(7):696–701; doi: 10.1111/iju.12426

23.

Clayton

, Fowler

Jr , Sharifi

, et al. Causes, presentation and survival of fifty-seven patients with necrotizing fasciitis of the male genitalia. Surg Gynecol Obstet, 1990; 170(1):49–55.

24.

Benjelloun el

, Souiki

, Yakla

, et al. Fournier's gangrene: Our experience with 50 patients and analysis of factors affecting mortality. World J Emerg Surg, 2013; 8(1):13; doi: 10.1186/1749-7922-8-13

25.

Roghmann

, von Bodman

, Löppenberg

26.

Sorensen

, Krieger

, Rivara

, et al. Fournier's gangrene: Management and mortality predictors in a population based study. J Urol, 2009; 182(6):2742–2747; doi: 10.1016/j.juro.2009.08.050

27.

Paty

, Smith

. Gangrene and Fournier's gangrene. Urol Clin North Am, 1992; 19(1):149–162

28.

Eke

. Fournier's gangrene: A review of 1726 cases. Br J Surg, 2000; 87(6):718–728; doi: 10.1046/j.1365-2168.2000.01497.x

29.

Tosun

, Akıncı

, Küçük

. Risk factors for mortality in Fournier's gangrene of anorectal origin. Ulus Travma Acil Cerrahi Derg, 2022; 28(8):1128–1133; doi: 10.14744/tjtes.2021.97866

30.

Yaghan

, Al-Jaberi

, Bani-Hani

. Fournier's gangrene: changing face of the disease. Dis Colon Rectum, 2000; 43(9):1300–1308; doi: 10.1007/BF02237442

31.

Shin

, Gong

, An

, et al. Delta neutrophil index as a prognostic factor for mortality in patients with Fournier's gangrene. Int J Urol, 2022; 29(11):1287–1293; doi: 10.1111/iju.14981