Prediction and Management of Strangulated Bowel Obstruction: A Multiomics Model Analysis

Abstract BackgroundDistinguishing strangulated bowel obstruction (StBO) from simple bowel obstruction (SiBO) still poses a challenge for emergency surgeons. We aimed to construct a predictive model that could distinctly discriminate StBO from SiBO based on the degree of bowel ischemia.MethodsA total of 281 patients diagnosed with intestinal obstruction were enrolled. According to pathological confirmation, patients were divided into a simple bowel obstruction (SiBO, n=236) group and a strangulated bowel obstruction (StBO, n=45) group. The clinical characteristics, laboratory tests and radiomics were compared between the groups via univariate analysis. Binary logistic regression was applied to identify independent risk factors, and then predictive models based on radiomics and multiomics models were constructed. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were calculated to assess the accuracy of the predicted models. Finally, via stratification analysis, we validated the multiomics model in the prediction of transmural necrosis. ResultsOf the 281 patients with SBO, 45 (16.0%) were found to have StBO, while 236(84.0%) with SiBO. Via univariate analysis, clinical characteristics including pain duration (p=0.036), abdominal pain(p=0.018), tenderness (p=0.020), rebound tenderness (p<0.001), bowel sounds (p=0.014), and laboratory parameters like white blood cell (WBC) (p=0.029), neutrophil (NE)% (p=0.007), low levels of sodium (p=0.009), abnormal potassium (p=0.003), high levels of blood urea nitrogen (BUN) (p<0.001) and glucose (p=0.002), as well as the radiomics consisting of mesenteric fluid (p=0.018), ascites (p=0.002), bowel spiral signs (p<0.001) and edema of bowel wall (p=0.037) were closely related to bowel ischemia. The ascites (OR=4.067) and bowel spiral signs (OR=5.506) were identified as independent risk factors of StBO in the radiomics model, the AUC for which was 0.706 (95%CI, 0.617–0.795). In the multivariate analysis, seven risk factors including pain duration≤3days (OR=3.775), rebound tenderness (OR=5.201), low-to-absent bowel sounds (OR=5.006), low levels of potassium (OR=3.696) and sodium (OR=3.753), high levels of BUN (OR=4.349), high radiomics score (OR=11.264) were identified. The area under the receiver operating characteristics (ROC) curve of the model was 0.857(95%CI, 0.793-0.920). The score of the mutiomics model can be calculated as following formula (1.328*Pd+1.649*Rt+1.611*Bs+1.307*K+1.323*Na+1.470*BUN+2.422*Rad-6.009). In the stratification of risk scores, the proportion of patients with transmural necrosis was significantly greater in the high-risk group (24%) than in the medium-risk group (3%). No transmural necrosis was found in the low-risk group.ConclusionThe novel multiomics model consisting of risk factors for pain duration, rebound tenderness, bowel sounds, potassium, sodium, and BUN levels and radiomics offers a useful tool for predicting StBO. Clinical management can be performed according to the multiomics score; for patients with low risk (scores≤ -3.91), conservative treatment is recommended. For the high-risk group (risk scores> -1.472), there was a strong suggestion for detection with laparotomy. For the remaining patients (-3.091< risk scores ≤ -1.472), dynamic observation is suggested.

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Advanced stage, elevated LDH, primary sites, but not adolescent (A) age (≥ 15 years) as risk factors for disease progression in childhood (C) and adolescent (A) mature B-NHL: Report of the FAB/LMB 96 international trial

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.15_suppl.10032 ◽

2009 ◽

Vol 27 (15_suppl) ◽

pp. 10032-10032

Author(s):

M. S. Cairo ◽

R. Sposto ◽

M. Gerrard ◽

I. Waxman ◽

S. Goldman ◽

...

Keyword(s):

Risk Factors ◽

Multivariate Analysis ◽

Risk Factor ◽

Risk Group ◽

Significant Risk ◽

High Risk Group ◽

Stage Iii ◽

Group A ◽

Independent Risk Factors ◽

Group B

10032 Background: We recently reported the results in C & A with low risk (group A), intermediate risk (group B) and high risk (group C) mature B-NHL treated on FAB/LMB 96 (Gerrard et al, Br J Haematol, 2008; Patte et al, Blood, 2007; Cairo et al, Blood, 2007, respectively). Adolescent age (15–21 yrs) has historically been considered to be an independent risk factor for poor outcome in subsets of mature B-NHL (Hochberg/Cairo et al, Br J Haematol, 2008; Burkhardt et al, Br J Haematol 2005; Cairo et al, Br J Haematol, 2003). Methods: We analyzed the EFS of all pts treated on FAB/LMB 96 and the following risk factors were significant in a univariate and Cox multivariate analysis: age (<15 vs ≥15 yrs), stage I/II vs III/IV, primary sites, LDH <2 vs ≥2 NL and histology (DLBCL vs BL/BLL). Results: 1111 pts (15%, 15–21 years) were treated with group A (N = 132), group B (N = 744), and group C (N = 235) therapy. Five year EFS (CI95) for all, A, B, C pts was 86% (84%,88%), 98% (93%, 100%), 87%% (84%, 89%), and 79%% (73%,84%), respectively. Age (≥15 yrs), LDH ≥2NL, stage III/IV, and BM+/CNS+ and histology were significant univariate risk factors for decreased EFS (P<0.045, <0.0001, <0.0001, <0.0001, and <0.0001 respectively). Multivariate analysis demonstrated age ≥15 yrs and DLBCL histology were no longer independent significant risk factors (p = .82 and 0.08, respectively), but LDH (RR 2.0, p = .001), stage III/IV (RR 3.8, p<0.001), and primary sites including PMBL (RR 4.0, p<.001) and BM+/CNS+ (RR 2.8, p<0.001) were independent significant risk factors for poorer outcome. Conclusions: With the use of modern short but intense FAB-LMB 96 therapy, adolescent age is no longer a poor risk factor in children with mature B-NHL. The independent risk factors identified in this study (stage, LDH, primary site) for decreased EFS in C & A mature B-NHL will form the basis of the next risk adapted international pediatric mature B-NHL trial. No significant financial relationships to disclose.

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Risk scoring model to predict recurrence in locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy (NACT).

Journal of Global Oncology ◽

10.1200/jgo.2019.5.suppl.98 ◽

2019 ◽

Vol 5 (suppl) ◽

pp. 98-98

Author(s):

Sushma Agrawal ◽

Prabhakar Mishra ◽

Punita Lal ◽

Gaurav Agarwal ◽

Amit Agarwal ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Risk Group ◽

Locally Advanced ◽

High Risk Group ◽

Low Risk ◽

Scoring Model ◽

Group Score ◽

Risk Scoring ◽

Independent Risk Factors

98 Background: Complete response (CR) to NACT portends favorable long term outcomes in LABC. There is a need for a tool to risk categorise patients for recurrence risk (RR), so that intensification of treatment can be offered to women with high risk of recurrence. Methods: A prospectively maintained database of LABC (between January 2007 to December 2012), who received NACT followed by definitive surgery, radiotherapy and endocrine therapy in endocrine sensitive disease was retrospectively analyzed for clinico-pathological and treatment factors affecting disease free survival (DFS). A risk scoring model was developed on the basis of beta coefficients of identified independent risk factors for DFS. Results: The incidence of loco-regional relapse was 8% and that of distant metastases was 32% in a dataset of 206 patients at a median follow-up of 47 months (IQR 24-62 mo). The independent risk factors for recurrence were index T stage [HR 1.8 (0.9-3.6)], N stage [HR 1.7 (0.4 – 4.7)], grade [HR 1.8 (0.8-4.2)], age less than and more than 40 years [HR 1.6 (0.4-0.9)], pathologic CR [HR 4.3 (1.7- 10.7)], intrinsic subtype [HR 2.2 (1.3-3.7)], and type of surgery (BCS vs MRM) [HR 2.2 (1.3-3.6)]. The ROC of the model for the prediction of recurrence was 0.67 (95 % CI: 0.61-0.75). The results of this model were validated by dividing the population into 3 risk groups: low risk (score less than 12), intermediate risk group (score between 13-15), high risk group (score 16 or more). The chances of recurrence are 16% versus 34% versus 57% in low, intermediate and high risk group respectively. Presence of three risk factors implies low risk, five intermediate and more than five high risk. Conclusions: The risk scoring model developed by us predicts RR and can be used for selecting patients for treatment intensification in high risk category.

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MD Anderson Scoring System (MDACC) Predicts Outcomes After Hematopoietic Stem Cell Transplantation (HSCT) Better Than Other Prognostic Classifications In MDS

Blood ◽

10.1182/blood.v122.21.3340.3340 ◽

2013 ◽

Vol 122 (21) ◽

pp. 3340-3340

Author(s):

Piyanuch Kongtim ◽

Uday R Popat ◽

Marcos de Lima ◽

Guillermo Garcia-Manero ◽

Elias J. Jabbour ◽

...

Keyword(s):

High Risk ◽

Scoring System ◽

Risk Group ◽

Univariate Analysis ◽

Scoring Systems ◽

Risk Groups ◽

High Risk Group ◽

Risk Scores ◽

Hematopoietic Stem ◽

Very High

Abstract MDS is a heterogeneous group of hematopoietic stem cell disorders. Various prognostic models have been established to categorize patients with MDS including the International Prognostic Scoring System (IPSS), the Revised-IPSS (r-IPSS) and MDACC Scoring System. In this analysis, we compared those three classification schemas for their outcome predictability after HSCT. We analyzed 291 MDS patients with a median age of 55 (interquartile range (IQR) 47-60.7 years) who underwent HSCT between January 2001 and December 2011. Histology by WHO classification included RA/RARS 48 (16.5%), RCMD 28 (9.6%), RAEB-1 59 (20.2%), RAEB-2 63 (21.7%), MDS unclassified 67 (23%), and CMML 26 (9%). Of 291, 117 patients (40.2%) had therapy related MDS (t-MDS). Conditioning regimen was myeloablative in 201 patients (69.1%) and reduced intensity in 90 patients (30.9%). Donors were matched related (MRD), matched unrelated (MUD), mismatched (MMD) in 131 (45%), 114 (39.2%) and 46 (15.8%) patients respectively. Risk categorization was performed by IPSS, r-IPSS and MDACC scoring systems at the time of diagnosis. IPSS, r-IPSS and MDACC scoring systems could be assessed in 239 (82.1%), 241 (82.8%) and 231 (79.4%) patients respectively. The median follow up time of 109 survivors was 45 months. The median time from diagnosis to HSCT was 7.3 months (IQR 4.6-12.4 months). Three-year overall survival (OS) was 38.1% (95%CI 32.3-43.9) with 3-year event free survival (EFS) of 34.2% (95%CI 28.4-40). Cumulative relapse incidence (RI) at 3-year was 28.8% (95%CI 23.3-34.5). Cumulative incidence of treatment related mortality (TRM) at 3 year post-transplant was 27.9% (95%CI 22.6-33.6). In univariate analysis, IPSS and r-IPSS were able to differentiate 2 risk groups for OS and EFS. High risk group per IPSS and very high risk group per r-IPSS had lower OS with hazard ratio (HR) of 2.4 to 3.1, lower EFS with HR of 2.2 to 2.7. While IPSS could not predict RI, very high risk group by r-IPSS had higher RI with HR of 3.6 compared with lower risk groups. Both IPSS and r-IPSS did not identify different risk groups for TRM. On the other hand, MDACC scoring system was able to identify 4 different risk groups for EFS and OS in univariate analysis. Three-year OS was 68%, 46.1%, 30.3% and 11.4% for patients with MDACC risk score of 0-4, 5-6, 7-8 and ≥9 respectively (p<0.001) (figure1). Three-year EFS with MDACC risk score of 0-4, 5-6, 7-8 and ≥9 was 61.7%, 40.8%, 28.1% and 7.4% respectively (p<0.001). For RI and TRM, only MDACC risk scores of ≥9 was associated with poor outcomes with 3-year RI of 38.9% and 3-year TRM of 41.7% compared with 13.3% and 15.5% in risk scores of 0-4 (p=0.01 and p=0.01 respectively). In multivariate analysis, MDACC score, matched unrelated and mismatched donors were associated with inferior OS (table1). As a summary, MDACC risk scoring system for MDS better differentiates prognostic groups than IPSS or r-IPSS. Considering the high frequency of t-MDS among transplanted MDS patients, we propose that MDACC scoring system should be used for prognostic classification for hematopoietic transplantation. Disclosures: No relevant conflicts of interest to declare.

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Comparative risk assessment for the development of cardiovascular diseases in the Hungarian general and Roma population

Scientific Reports ◽

10.1038/s41598-021-82689-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Peter Piko ◽

Zsigmond Kosa ◽

Janos Sandor ◽

Roza Adany

Keyword(s):

Risk Factors ◽

Risk Assessment ◽

Cardiovascular Risk ◽

Cardiovascular Diseases ◽

High Risk ◽

Risk Group ◽

High Risk Group ◽

Average Risk ◽

Cvd Risk ◽

Roma Population

AbstractCardiovascular diseases (CVDs) are the number one cause of death globally, and the early identification of high risk is crucial to prevent the disease and to reduce healthcare costs. Short life expectancy and increased mortality among the Roma are generally accepted (although not indeed proven by mortality analyses) which can be partially explained by the high prevalence of cardiovascular risk factors (CVRF) among them. This study aims to elaborate on the prevalence of the most important CVD risk factors, assess the estimation of a 10-year risk of development of fatal and nonfatal CVDs based on the most used risk assessment scoring models, and to compare the Hungarian general (HG) and Roma (HR) populations. In 2018 a complex health survey was accomplished on the HG (n = 380) and HR (n = 347) populations. The prevalence of CVRS was defined and 10-year cardiovascular risk was estimated for both study populations using the following systems: Framingham Risk Score for hard coronary heart disease (FRSCHD) and for cardiovascular disease (FRSCVD), Systematic COronary Risk Evaluation (SCORE), ACC/AHA Pooled Cohort Equations (PCE) and Revised Pooled Cohort Equations (RPCE). After the risk scores had been calculated, the populations were divided into risk categories and all subjects were classified. For all CVD risk estimation scores, the average of the estimated risk was higher among Roma compared to the HG independently of the gender. The proportion of high-risk group in the Hungarian Roma males population was on average 1.5–3 times higher than in the general one. Among Roma females, the average risk value was higher than in the HG one. The proportion of high-risk group in the Hungarian Roma females population was on average 2–3 times higher compared to the distribution of females in the general population. Our results show that both genders in the Hungarian Roma population have a significantly higher risk for a 10-year development of cardiovascular diseases and dying from them compared to the HG one. Therefore, cardiovascular interventions should be focusing not only on reducing smoking among Roma but on improving health literacy and service provision regarding prevention, early recognition, and treatment of lipid disorders and diabetes among them.

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Risk factors for cholesterol polyp formation in the gallbladder are closely related to lipid metabolism

Lipids in Health and Disease ◽

10.1186/s12944-021-01452-6 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Zhihao Yu ◽

Changlin Yang ◽

Xuesong Bai ◽

Guibin Yao ◽

Xia Qian ◽

...

Keyword(s):

Risk Factors ◽

Low Density Lipoprotein ◽

Univariate Analysis ◽

Density Lipoprotein ◽

High Volume ◽

Population Characteristics ◽

Polyp Formation ◽

Logistic Analysis ◽

Cholesterol Polyp ◽

Independent Risk Factors

Abstract Background The purpose of this study was to assess the risk factors for cholesterol polyp formation in the gallbladder. Methods This was a multicenter retrospective study based on pathology. From January 2016 to December 2019, patients who underwent cholecystectomy and non-polyp participants confirmed by continuous ultrasound follow-ups were reviewed. Patients in the cholesterol polyp group were recruited from three high-volume centers with a diagnosis of pathologically confirmed cholesterol polyps larger than 10 mm. Population characteristics and medical data were collected within 24 h of admission before surgery. The non-polyp group included participants from the hospital physical examination center database. They had at least two ultrasound examinations with an interval longer than 180 days. Data from the final follow-up of the non-polyp group were analyzed. The risk factors for cholesterol polyp formation were analyzed by comparing the two groups. Results A total of 4714 participants were recruited, including 376 cholesterol polyp patients and 4338 non-polyp participants. In univariate analysis, clinical risk factors for cholesterol polyps were age, male sex, higher body mass index (BMI), higher low-density lipoprotein (LDL), lower high-density lipoprotein (HDL), and higher aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels. In multivariate logistic analysis, independent risk factors were age > 50 years (odds ratio [OR] = 3.02, 95% confidence interval [CI] 2.33–3.91, P < 0.001], LDL > 2.89 mmol/L (OR = 1.38, 95% CI 1.08–1.78, P = 0.011), lower HDL (OR = 1.78 95% CI 1.32–2.44, P < 0.001), AST > 40 IU/L (OR = 3.55, 95% CI 2.07–6.07, P < 0.001), and BMI > 25 kg/m 2 (OR = 1.32, 95% CI 1.01–1.72, P = 0.037). Conclusions Age, LDL, HDL, AST, and BMI are strong risk factors for cholesterol polyp formation. Older overweight patients with polyps, accompanied by abnormal lipid levels, are at high risk for cholesterol polyps.

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AB0809 PADUA PREDICTION SCORE COMBINED WITH SERUM ALBUMIN FOR THE IDENTIFICATION OF VENOUS THROMBOEMBOLISM OF HOSPITALIZED PATIENTS IN THE DEPARTMENT OF RHEUMATOLOGY

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3729 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1429.1-1429

Author(s):

Q. Peng ◽

L. Long ◽

J. Liu

Keyword(s):

Risk Factors ◽

Venous Thromboembolism ◽

Serum Albumin ◽

Risk Group ◽

Assessment Model ◽

Prediction Score ◽

Risk Assessment Model ◽

Systemic Autoimmune Diseases ◽

Independent Risk Factors ◽

The Difference

Background:Venous thromboembolism (VTE) includes thrombotic disease of venous system, but primarily includes lower extremity deep vein thrombosis (DVT) and pulmonary embolism (PE). Population-based epidemiological studies have shown an association between systemic autoimmune diseases and VTE[1]. The Padua prediction score(PPS) is a new 20-point risk assessment model proposed by Professor Barbar et al[2] in 2010. A large number of researches have shown that low serum albumin concentration is associated with an increased risk of VTE [3],but there is a lack of studies on serum albumin in VTE, and there are no reports on PPS in rheumatology inpatients.Objectives:To investigate the status of VTE in patients in the department of rheumatology, and to explore the value of PPS combined with serum albumin in the identification of VTE in this patient population.Methods:Baseline data of inpatients in rheumatology department were collected at Sichuan Provincial People’s Hospital from September 2018 to September 2020. Occurrence of VTE was compared between high and low risk groups. PPSs were analyzed in VTE and non-VTE patients. Multivariate logistic regression was used to analyze the independent risk factors of VTE. The receiver operating characteristic curve was used to evaluate the probablity of value of rheumatic inpatients with VTE assessed by PPS,serum albumin and PPS with serum albumin. P<0.05 indicates that the difference was statistically significant.Results:A total of 2282 patients were included in this study, and 50(2.2%) had symptomatic VTE. Among the symptomatic VTE cases,38(1.6%) had DVT only,8(0.4%) had PE only, and 4(0.2%) were diagnosed with DVT and PE. PPSs in VTE and non-VTE groups were 3.00(2.00~6.00) and2.00(1.00~2.00) respectively (P< 0.05). One hundred and eighty-eight cases was divided into high-risk group of VTE (PPS≥4), while 2094 cases (PPS<4) were in the low-risk group. Logistic regression analysis showed that known thrombophilic condition, history of VTE, reduced mobility, and D-dimer were independent risk factors of VTE in rheumatology patients, the odd ration(OR) values were 161.90, 26.08, 8.73,and1.04. Serum albumin was the independent protection factor [OR= 0.92(95%CI:0.87~0.98)]. The AUC of PPS model, serum albumin model and the combined predictive model were 0.77, 0.75, 0.84, respectively. The difference between the combined prediction model and PPS model was statistically significant (Z=3.813, P<0.05). The optimal sensitivity of PPS and serum albumin models is 60%, 82%, respectively, and the optimal specificity of is 82.5%,58.6%, respectively. The combination model corresponds to a sensitivity of 62% and a specificity of 90.4%.Conclusion:The incidence of symptomatic VTE was relatively higher in hospitalized patients in rheumatology department. Serum albumin was the protective factor. The combination of albumin and PPS can improve the accuracy of screening for VTE in rheumatology in-patients.References:[1]Tamaki H,Khasnis A.Venous thromboembolism in systemic autoimmune diseases: A narrative review with emphasis on primary systemic vasculitides.[J].Vasc Med, 2015, 20: 369-76.[2]Barbar S, Noventa F, Rossetto V,et al. A risk assessment model for the identification of hospitalized medical patients at risk for venous thromboembolism: the Padua Prediction Score[J]. J Thromb Haemost,2010,8(11):2450–2457.[3]Kunutsor SK,Seidu S,Katechia DT et al. Inverse association between serum albumin and future risk of venous thromboembolism: interrelationship with high sensitivity C-reactive protein.[J].Ann Med, 2018, 50: 240-248.Disclosure of Interests:None declared

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A model for predicting carotid instability plaque in high risk group of stroke individuals

10.21203/rs.2.13605/v1 ◽

2019 ◽

Author(s):

Junxiong Yin ◽

Chuanyong Yu ◽

Hongxing Liu ◽

Mingyang Du ◽

Feng Sun ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Predictive Model ◽

Doppler Ultrasound ◽

Scoring System ◽

Risk Group ◽

High Risk Group ◽

High Risk Population ◽

Auc Value ◽

Validation Set

Abstract Objective: To establish a predictive model of carotid vulnerable plaque through systematic screening of high-risk population for stroke.Patients and methods: All community residents who participated in the screening of stroke high-risk population by the China National Stroke Screening and Prevention Project (CNSSPP). A total of 19 risk factors were analyzed. Individuals were randomly divided into Derivation Set group and Validation Set group. According to carotid ultrasonography, the derivation set group patients were divided into instability plaque group and non-instability plaque group. Univariate and multivariable logistic regression were taken for risk factors. A predictive model scoring system were established by the coefficient. The AUC value of both derivation and validation set group were used to verify the effectiveness of the model.Results: A total of 2841 high-risk stroke patients were enrolled in this study, 266 (9.4%) patients were found instability plaque. According to the results of Doppler ultrasound, Derivation Set group were divided into instability plaque group (174 cases) and non-instability plaque group (1720 cases). The independent risk factors for carotid instability plaque were: male (OR 1.966, 95%CI 1.406-2.749)，older age (50-59, OR 6.012, 95%CI 1.410-25.629; 60-69, OR 13.915, 95%CI 3.381-57.267;≥70, OR 31.267, 95%CI 7.472-130.83) , married(OR 1.780, 95%CI 1.186-2.672)，LDL-c(OR 2.015, 95%CI 1.443-2.814), and HDL-C(OR 2.130, 95%CI 1.360-3.338). A predictive scoring system was created, range 0-10. The cut-off value of prediction model score is 6.5. The AUC value of derivation and validation set group were 0.738 and 0.737.Conclusion：For a high risk group of stroke individual, We provide a model that could distinguishing those who have a high probability of having carotid instability plaque. When resident’s predictive model score exceeds 6.5, the incidence of carotid instability plaque is high, carotid artery Doppler ultrasound would be checked immediately. This model can be helpful in the primary prevention of stroke.

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Development of Predictive Nomograms for Clinical Use to Quantify the Risk of Amputation in Patients with Diabetic Foot Ulcer

Journal of Diabetes Research ◽

10.1155/2021/6621035 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Bocheng Peng ◽

Rui Min ◽

Yiqin Liao ◽

Aixi Yu

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Prediction Model ◽

Predictive Factors ◽

Diabetic Foot ◽

Univariate Analysis ◽

Foot Ulcer ◽

Diabetic Foot Ulcer ◽

Single Factor ◽

Independent Risk Factors

Objective. To determine the novel proposed nomogram model accuracy in the prediction of the lower-extremity amputations (LEA) risk in diabetic foot ulcer (DFU). Methods and Materials. In this retrospective study, data of 125 patients with diabetic foot ulcer who met the research criteria in Zhongnan Hospital of Wuhan University from January 2015 to December 2019 were collected by filling in the clinical investigation case report form. Firstly, univariate analysis was used to find the primary predictive factors of amputation in patients with diabetic foot ulcer. Secondly, single factor and multiple factor logistic regression analysis were employed to screen the independent influencing factors of amputation introducing the primary predictive factors selected from the univariate analysis. Thirdly, the independent influencing factors were applied to build a prediction model of amputation risk in patients with diabetic foot ulcer by using R4.3; then, the nomogram was established according to the selected variables visually. Finally, the performance of the prediction model was evaluated and verified by receiver working characteristic (ROC) curve, corrected calibration curve, and clinical decision curve. Results. 7 primary predictive factors were selected by univariate analysis from 21 variables, including the course of diabetes, peripheral angiopathy of diabetic (PAD), glycosylated hemoglobin A1c (HbA1c), white blood cells (WBC), albumin (ALB), blood uric acid (BUA), and fibrinogen (FIB); single factor logistic regression analysis showed that albumin was a protective factor for amputation in patients with diabetic foot ulcer, and the other six factors were risk factors. Multivariate logical regression analysis illustrated that only five factors (the course of diabetes, PAD, HbA1c, WBC, and FIB) were independent risk factors for amputation in patients with diabetic foot ulcer. According to the area under curve (AUC) of ROC was 0.876 and corrected calibration curve of the nomogram displayed good fitting ability, the model established by these 5 independent risk factors exhibited good ability to predict the risk of amputation. The decision analysis curve (DCA) indicated that the nomogram model was more practical and accurate when the risk threshold was between 6% and 91%. Conclusion. Our novel proposed nomogram showed that the course of diabetes, PAD, HbA1c, WBC, and FIB are the independent risk factors of amputation in patients with DFU. This prediction model was well developed and behaved a great accurate value for LEA so as to provide a useful tool for screening LEA risk and preventing DFU from developing into amputation.

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A Novel Ferroptosis-Related Gene Signature Robustly Predicts Clinical Prognosis And Tumor Immunity in Patients With Kidney Renal Clear Cell Carcinoma

10.21203/rs.3.rs-390254/v1 ◽

2021 ◽

Author(s):

Wei Song ◽

Weiting Kang ◽

Qi Zhang

Keyword(s):

High Risk ◽

Clear Cell ◽

Risk Group ◽

Gene Signature ◽

Renal Clear Cell Carcinoma ◽

High Risk Group ◽

Risk Scores ◽

Related Gene ◽

Clinical Prognosis ◽

Validation Set

Abstract Objective: This study aimed to construct a ferroptosis-related gene signature to predict clinical prognosis and tumor immunity in patients with kidney renal clear cell carcinoma (KIRC).Methods: The mRNA expression profiles and corresponding clinical data of KIRC patients were downloaded from The Cancer Genome Atlas (TCGA), which were randomly divided into training (398 patients) and validation set (132 patients). The iron death related (IDR) prediction model was constructed based on training set and 60 ferroptosis-related genes from previous literatures, followed by prognostic performance evaluation and verification using the validation set. Moreover, functional enrichment, immune cell infiltration, metagene clusters correlation, and TIDE scoring analyses were performed. Results: In total, 23 ferroptosis-related genes were significantly associated with overall survival (OS). The IDR prediction model (a 10-gene signature) was then constructed to stratify patients into two risk groups. The OS of KIRC patients with high-risk scores was significantly shorter than those with low-risk scores. Moreover, the risk score was confirmed as an independent prognostic predictor for OS. The positive and negative correlated genes with this model were significantly enriched in p53 signaling pathway, and cGMP-PKG signaling pathway. The patients in the high-risk group had higher ratios of plasma cells, T cells CD8, and T cells regulatory Tregs. Furthermore, IgG, HCK, LCK, and Interferson metagenes were significantly correlated with risk score. By TIDE score analysis, patients in the high-risk group could benefit from immunotherapy.Conclusions: The identified ferroptosis-related gene signature is significantly correlated with clinical prognosis and immune immunity in KIRC patients.

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Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

10.21203/rs.3.rs-349666/v1 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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