Prognostic clinical indexes for prediction of acute gangrenous cholecystitis and acute purulent cholecystitis

Chen, Jie; Qi, Guo-Jun; Huang, Xinyu; Wang, Yingqi

doi:10.1186/s12876-022-02582-6

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…During disease progression, these cells undergo significant metabolic rewiring to adapt to their high energy demands and changing environmental conditions [ 22 ]. Recent research has confirmed that changes in patients’ metabolic indices (including BMI, low-density lipoprotein, and total cholesterol) are associated with the prognoses of patients with acute cholecystitis [ 23 ]. Assessing body composition parameters, such as fat and muscle tissue, can be used to assess a patient’s energy reserves and expenditure.…”

Section: Discussionmentioning

confidence: 99%

Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis

Chen,

Wang,

Chen

et al. 2024

Biomol Biomed

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Increasing evidence suggests that body composition is associated with the development of acute pancreatitis (AP). This study aimed to investigate the applicability of body composition in predicting AP severity. Data of 213 patients with AP from Affiliated Hospital of Putian University (AHOPTU) were included in this study, whilst data of 173 patients with AP from Fujian Medical University Union Hospital (FMUUH) were used for external validation. Patients were classified into the non-severe and severe groups according to AP severity. After seven days of treatment, in patients from AHOPTU, the difference in skeletal muscle index before and after treatment (ΔSMI) was significantly higher (P = 0.002), while the skeletal muscle radiodensity before treatment (PreSMR) was significantly lower (P = 0.042) in the non-severe group than in the severe group. The multivariate logistic regression model also revealed that the ΔSMI and PreSMR were independent risk factors for AP severity. The optimal cut-off values of ΔSMI and PreSMR were 1.0 and 43.7, respectively. The following metabolic score (SMS) was established to predict AP severity: 0: ΔSMI < 1.0 and PreSMR < 43.7; 1: ΔSMI ≥ 1.0 and PreSMR < 43.7 or ΔSMI < 1.0 and PreSMR ≥ 43.7; 3: ΔSMI ≥ 1.0 and PreSMR ≥ 43.7. In patients from AHOPTU and FMUUH, the areas under the curves (AUC) for this model were 0.764 and 0.741, respectively. ΔSMI and PreSMR can accurately predict AP severity. It is recommended to routinely evaluate the statuses of patients with AP using the predictive model presented in this study for individualized treatment.

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Section: Discussionmentioning

confidence: 99%

Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis

Chen,

Wang,

Chen

et al. 2024

Biomol Biomed

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“…Previous studies have elucidated that white blood cell (WBC) count and leukocyte subtype cells are valuable indicators for predicting the inflammatory process in acute cholecystitis. [13][14][15][16][17] WBC count has been established as the primary biomarker for assessing the severity of cholecystitis. 4 Elevated WBC count is often observed due to the robust inflammatory response triggered by gallstone blockage.…”

Section: Introductionmentioning

confidence: 99%

The Role of Different Systemic Inflammatory Indexes Derived from Complete Blood Count in Differentiating Acute from Chronic Calculus Cholecystitis and Predicting Its Severity

Chen,

Chen

2024

JIR

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Purpose This study aimed to evaluate the diagnostic value of the different Complete blood count-derived systemic inflammation indexes, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and neutrophil to lymphocyte × platelet ratio (NLPR) in differential diagnosing the chronic calculus cholecystitis (CCC) and the acute calculus cholecystitis (ACC), as well as determining the severity of ACC. Patients and Methods 105 CCC and 88 ACC patients were enrolled. NLR, PLR, SII, and NLPR were evaluated in both cohorts as well as in different severity levels of ACC. The severity of ACC was determined based on the Tokyo Guidelines. Receiver operating characteristic (ROC) curve and Univariate/multivariate regression analyses were conducted. Results The levels of NLR, PLR, SII, and NLPR were significantly higher in the ACC group compared to the CCC group. The optimal cutoff values for NLR, PLR, SII, and NLPR were determined to be 3.89, 144.7, 896.8, and 0.031 respectively. NLR>3.89 demonstrates the highest predictive capability with an AUC of 0.801 and a sensitivity of 72.73%. Multivariate analysis showed that NLR>3.89 (OR: 4.169, p = 0.004) and NLPR>0.031 (OR: 4.304, p = 0.005) were dominant in distinguishing ACC from CCC. In ACC patients, the levels of NLR, SII, and NLPR were significantly higher in the Moderate to Severe-degree ACC (MS-ACC) group than in Mild-Degree ACC (M-ACC). NLPR > 0.044 exhibited the highest predictive ability with an AUC of 0.778 and a specificity of 91.67%. Multivariate analysis showed that NLR>6.399 (OR: 10.308, p = 0.000) was a possible independent prognostic factor for accessing the severity of ACC. Conclusion Systemic inflammation indexes can be useful in predicting the risk of ACC and MS-ACC. NLR demonstrates the best distinguishing power and sensitivity for distinguishing ACC from CCC, while NLPR shows the best predictive power and specificity for predicting the severity of ACC.

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A new method for predicting SIRS after percutaneous transhepatic gallbladder drainage

Zhang,

Yang,

Cui

et al. 2023

Sci Rep

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The occurrence of systemic inflammatory response after percutaneous transhepatic gallbladder drainage brings great risks to patients and is one of the challenges faced by clinicians. Therefore, it is of great significance to find a suitable prediction method for clinicians to intervene early and reduce the transformation of serious complications. Easy-to-obtain and objectively measured clinical features were screened, and logistic regression was used to construct a prediction model. The predictive ability of the model was evaluated by using the receiver operating characteristic curve and the decision curve in the validation set and the training set, respectively. Nine clinical features (CRP, Fever, DBIL, Obstruction, Bile properties, PCT, Length, Width, and Volume factor) were used to construct the prediction model, and the validation results showed that the prediction model had good performance in the training set (AUC = 0.83) and the validation set (AUC = 0.81). The decision curve also showed that the predictive ability of the model incorporating nine clinical features is better than that of a single clinical feature. The model we constructed can accurately predict the occurrence of SIRS, which can guide clinicians to take treatment measures and avoid the deterioration of complications.

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Prognostic clinical indexes for prediction of acute gangrenous cholecystitis and acute purulent cholecystitis

Cited by 5 publications

References 25 publications

Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis

Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis

The Role of Different Systemic Inflammatory Indexes Derived from Complete Blood Count in Differentiating Acute from Chronic Calculus Cholecystitis and Predicting Its Severity

A new method for predicting SIRS after percutaneous transhepatic gallbladder drainage

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