Haematological inflammatory markers for indicating ischemic bowel in patients with incarcerated abdominal wall hernias

Bostancı, Mustafa Taner; Yılmaz, İbrahim; Seki, Ahmet; Saydam, Mehmet; Koşmaz, Koray; Kaya, İsmail

doi:10.1007/s10029-021-02518-1

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Khan et al 14 showed that hematological markers can be helpful in considering the diagnosis of acute mesenteric ischemia in emergency situations. With this, Bostancı et al 15 showed that preoperative elevation of NLR, Platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) values in patients with incarcerated abdominal wall hernia may indicate intestinal ischemia and bowel resection (p<0.05). Ceylan et al 16 In the study conducted by SV, they created a model that aims to predict whether there is intestinal necrosis in patients with SV only by blood tests.…”

Section: Discussionmentioning

confidence: 99%

Can inflammatory markers predict intestinal wall ischemia in sigmoid volvulus?

Ertenlice,

Göktaş,

Pak

et al. 2023

JOCS

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Aims: Sigmoid volvulus (SV), which causes closed-loop obstruction as a result of the rotation of the sigmoid colon around its mesentery, causes serious morbidity and mortality, especially in elderly patients, when necrosis develops in the intestine. In this study, it was investigated whether inflammatory markers, which guide the diagnosis and prognosis of many intra-abdominal pathologies, can predict the presence of ischemia in the intestine in patients with SV. Methods: In the study, patients who were operated on for SV in our clinic between January 2019 and March 2022 were retrospectively screened, and the demographic, clinicopathological, and laboratory parameters of the patients were examined. Data were analyzed with the Student’s T test and Mann-Whitney U-test. Results: Of the 47 patients included in the study, 28 (59.6%) were male and 19 (40.4%) were female. Intestinal necrosis was detected in 23 (48.9%) patients who underwent surgery. The mean age of the patient population was 70 (±19). There was no statistical difference between the two groups according to age (p=0.338). White blood cell count (WBC) (p=0.686); Neutrophil count (p=0.949); Lymphocyte count (p=0.790); Monocyte count (p=0.898); Neutrophil-lymphocyte ratio (NLR) (p=0.733); Platelet count (p=0.766); RDW value (p=0.725); There was no statistically significant difference between the two groups in lactate level (p=0.289), C-reactive protein (CRP) (p=0.212), Amylase (p=0.975) and Lactate dehydrogenase (LDH) (p=0.974) levels. Conclusion: Detection of intestinal necrosis is a vital condition for the patient. Inflammatory biomarkers obtained from preoperative routine blood parameters were not found to be significant in estimating the presence of intestinal necrosis in the SV clinic. It was concluded that large series are needed in this regard.

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Section: Discussionmentioning

confidence: 99%

Can inflammatory markers predict intestinal wall ischemia in sigmoid volvulus?

Ertenlice,

Göktaş,

Pak

et al. 2023

JOCS

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“…21 FPR [17,49] 1) Fib/Plt [49] 2) Fib/preAlb [17] 22 GLR [19][20][21] 1) Gran-to-Lymph ratio [19] 2) GGT to Lymph ratio [20] 3) Glc-to-Lymph ratio [21] 1) Gran/Lymph [19] 2) GGT/Lymph [20] 3) Glc/Lymph [21] 23 GPR [50] GGT to Plt ratio GGT/Plt 24 HALP [13] HGB × Alb × Lymph/Plt 25 HII [51] Haematological inflammatory index [51] Plt×Lymph/Neu×100…”

Section: Inverse Proportionalmentioning

confidence: 99%

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

Tan

Zhao

et al. 2023

BMC Cancer

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Background Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. Methods Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set’s prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. Results From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. Conclusions This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

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