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Background:
Acute pancreatitis (AP) varies in severity, prompting development of systems aimed at predicting prognosis to help guide therapy. Although several prediction approaches are available, their test characteristics and clinical utility are not completely understood.
Purpose:
To evaluate the test characteristics (prognostic accuracy, incr...
Context in source publication
Context 1
... systematic re- view focused on studies providing information at levels 2 through 6. The key questions addressed here are summarized in Table 1 of Supplement 1. We devel- oped a standardized protocol (unregistered) on 2 Feb- ruary 2015, amended it on 13 April 2015, and followed the amended version in the overall review. ...
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Citations
... As with many areas of medicine where multiple options are still being studied, no single approach has emerged as definitively superior to others in large-scale comparisons. [11][12][13][14][15][16] While there is no 'gold standard' prognostic score for predicting SAP, the Bedside Index of Severity of Acute Pancreatitis (BISAP) score is one of the more accurate. Due to its simplicity and the fact that it does not require data from 48 hours after admission, BISAP is particularly applicable in everyday clinical practice. ...
Acute pancreatitis (AP) is a heterogeneous inflammation of the pancreas, most frequently attributable to gallstones or alcohol. AP accounts for an estimated 300 000 patients admitted each year in the USA, and an estimated US$2.6 billion/year in hospitalization costs. Disease severity is classified as mild, moderate, or severe, dependent on the presence or degree of concomitant organ failure. Locally, pancreatitis may be complicated by fluid collections, necrosis, infection, and hemorrhage. Infection of necrotizing pancreatitis (NP) is associated with a doubling of mortality risk. The modern management of AP is evolving. Recent data suggest a shift from normal saline to lactated Ringer’s solution, and from aggressive to more judicious volume resuscitation. Similarly, while historical wisdom advocated keeping patients nothing by mouth to ‘rest the pancreas’, recent data convincingly show fewer complications and reduced mortality with early enteral nutrition, when tolerated by the patient. The use of antibiotics in NP is controversial. Current recommendations suggest reserving antibiotics for cases with highly suspected or confirmed infected necrosis, as well as in patients with biliary pancreatitis complicated by acute cholecystitis or cholangitis. Regarding the management of local complications, control of acute hemorrhage can be attained either endovascularly or via laparotomy. Abdominal compartment syndrome is associated with a mortality risk of 50%–75%. Routine monitoring of intra-abdominal pressure is recommended in patients at high risk. Pancreatic pseudocysts require intervention in symptomatic patients or those with infection or other complications. Endoscopic transmural drainage may be considered as the first step when technically feasible. Necrotizing pancreatitis without suspicion of infection is often managed medically, while the delay, drain, debride approach remains the standard of care for the vast majority of infected pancreatic necrosis. Robotic surgery, in appropriately selected patients, allows for a one-step approach, and merits further study to explore its initially promising results.
... Risk stratification is important for triaging patients to the appropriate level of care and for deciding the aggressiveness of intervention. For this reason, approximately 20 scoring systems have been developed and evaluated since 1974 for AP severity assessment and mortality prediction [5], including the commonly used APACHE II [6], Ranson score [7], and Bedside Index for Severity in Acute Pancreatitis (BISAP) [8]. ...
... One major limitation of the scoring systems is that they are frequently cumbersome to calculate (such as APACHE II score) in clinical practice [5], and physicians continue to rely mainly on comorbidities, vital signs, and certain laboratory data to determine the severity for triaging and managing patients. This traditional strategy is experience depended and might lead to a significant number of AP patients unnecessarily admitted to the intensive care unit (ICU) for observation. ...
... AP is a potentially deadly disease; however, the clinical outcomes vary broadly among different cases. Thus, it is important to predict patient outcomes to facilitate clinicians to choose the appropriate level of care (i.e., transferred to a general ward or continue monitored in an ICU), to determine the aggressiveness of treatment, and to counsel patients' prognosis [5]. Although several guidelines recommend the application of certain scoring systems in clinical decision-making [21][22][23], it remains uncertain on how the results should guide therapy. ...
Background
Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model to calculate the risk of mortality in patients with AP admitted in intensive care unit (ICU) and compared it with existing scoring systems.
Methods
A gradient-boosting ML (XGBoost) model was developed and externally validated based on two public databases: Medical Information Mart for Intensive Care (MIMIC, training cohort) and the eICU Collaborative Research Database (eICU-CRD, validation cohort). We compared the performance of the XGBoost model with validated clinical risk scoring systems (the APACHE IV, SOFA, and Bedside Index for Severity in Acute Pancreatitis [BISAP]) by area under receiver operating characteristic curve (AUC) analysis. SHAP (SHapley Additive exPlanations) method was applied to provide the explanation behind the prediction outcome.
Results
The XGBoost model performed better than the clinical scoring systems in correctly predicting mortality risk of AP patients, achieving an AUC of 0.89 (95% CI: 0.84–0.94). When set the sensitivity at 100% for death prediction, the model had a specificity of 38%, much higher than the APACHE IV, SOFA and BISAP score, which had a specificity of 1%, 16% and 1% respectively.
Conclusions
This model might increase identification of very low-risk patients who can be safely monitored in a general ward for management. By making the model explainable, physicians would be able to better understand the reasoning behind the prediction.
... and 0.022 (95% CI −0.176-0.164), respectively, suggesting COPD, chronic obstructive pulmonary disease; SOFA, sequential organ failure assessment; PaO 2 , arterial oxygen partial pressure; PaCO 2 , arterial carbon dioxide partial pressure Age has long been recognized as a significant indicator of poor prognosis in AP, [17] as demonstrated by its inclusion in both the APACHE II score and Ranson score, as a predictive factor. This association is likely due to the increased likelihood of comorbid conditions with advancing age. ...
Background
There is currently a lack of nomograms specifically designed for predicting the risk of death in diabetic patients with severe acute pancreatitis (SAP). The objective of this study was to develop a nomogram tailored to diabetic patients with SAP to predict overall survival.
Methods
Diabetic patients diagnosed with SAP between January 1, 2018 and December 31, 2023 were included in the study. Risk factors were identified through least absolute shrinkage and selection operator regression analysis. Subsequently, a novel nomogram model was developed through multivariable logistic regression analysis. The predictive performance of the nomogram was evaluated using metrics such as the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).
Results
A total of 206 patients were included in the analysis, with 171 in the survival group and 35 in the deceased group. Multivariate logistic regression indicated that age, platelet, total bilirubin, and potassium were independent prognostic factors for the survival of diabetic patients with SAP. The nomogram demonstrated a performance comparable to sequential organ failure assessment ( P = 0.570). Additionally, the calibration curve showed satisfactory predictive accuracy, and the DCA highlighted the clinical application value of the nomogram.
Conclusion
We have identified key demographic and laboratory parameters that are associated with the survival of diabetic patients with SAP. These parameters have been utilized to create a precise and user-friendly nomogram, which could be an effective and valuable clinical tool for clinicians.
... In clinical practice, several disease severity prediction scores, such as the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Bedside Index of Severity in Acute Pancreatitis (BISAP) score, and Ranson score, are utilized to predict outcomes in AP patients. 4 However, the fluctuating hematological parameters often lead to variations in these risk scores, and the definitive test characteristics and clinical utility of severity scores for AP remain uncertain. ...
... Furthermore, these tests provide inherent benefits in regard to ease of access and cost-effectiveness when compared with intricate prognostic scoring systems. 4 In the same way, reticulocytes are immature RBC that are intermediate between juvenile and mature erythrocytes. Greater immaturity is associated with higher light scatter, indicating that the fluorescence intensity of reticulocytes is a measure of their immaturity. ...
Background and Aim
Observational studies have shown that there is a connection between blood biomarkers and the occurrence of acute pancreatitis (AP). Nevertheless, the causal relationships are still not clear. The purpose of this study was to evaluate causal association between biomarkers and AP.
Method(s)
A bidirectional two‐sample Mendelian randomization (MR) analysis was applied to investigate the causal association between blood biomarkers and AP. Summary statistics obtained from genome‐wide association studies were utilized for this analysis. The primary statistical approach employed was the inverse variance weighted (IVW) method, complemented by sensitivity analyses aimed at assessing heterogeneity and pleiotropy. Furthermore, a multivariable MR (MVMR) analysis was performed to adjust for confounders.
Results
A total of 11 red blood cell (RBC) traits, 6 white blood cell traits, platelet count, and 30 blood biomarkers were analyzed in this study. Genetically predicted RBC count (IVW odds ratio [OR] = 1.144, P = 0.004), the high light scatter reticulocyte count (HLSR) (OR = 1.127, P = 0.022), blood glucose (BG) (OR = 1.480, P = 0.019), and leptin (OR = 1.234, P = 0.050) were suggestively associated with an increased risk of AP. Reverse MR analysis showed no causal effect of AP on RBC, HLSR, BG, and leptin (IVW P > 0.05). Sensitivity analyses and MVMR analysis still supported the earlier causality.
Conclusion(s)
Our findings provide evidence of a suggestive association between RBC count, HLSR, BG, and leptin with an increased susceptibility to AP. These findings aid in our comprehension of the cause of AP and may be used as potential prognostic markers or predictors of severity with AP.
... The financial burden of AP is substantial, with costs reaching up to $2.6 billion annually [2]. Despite a decline in mortality rates over the past decade, AP continues to present considerable mortality risks, particularly in high-risk groups; approximately 25% of patients develop severe AP which has a mortality rate of 20% [3]. ...
Background/Objectives: Effective management of acute pancreatitis (AP) hinges on prompt volume resuscitation and is adversely affected by delays in diagnosis. Given diverse clinical settings (tertiary care vs. community hospitals), further investigation is needed to understand the impact of the initial setting to which patients presented on clinical outcomes and quality of care. This study aimed to compare outcomes and quality indicators between AP patients who first presented to the emergency department (ED) of a tertiary care center and AP patients transferred from community hospitals. Methods: This study included AP patients managed at our tertiary care hospital between 2008 and 2018. We compared demographics and outcomes, including length of stay (LOS), intensive care unit (ICU) admission, rates of local and systemic complications, re-admission rates, and one-year mortality in transferred patients and those admitted from the ED. Quality indicators of interest included duration of volume resuscitation, time until advancement to enteral feeding, pain requiring opioid medication [measured in morphine milliequivalent (MME) dosing], and surgical referrals for cholecystectomy. Categorical variables were analyzed by chi-square or Fisher’s exact test; continuous variables were compared using Kruskal–Wallis tests. Regression was performed to assess the impact of transfer status on our outcomes of interest. Results: Our cohort of 882 AP patients comprised 648 patients admitted from the ED and 234 patients transferred from a community hospital. Transferred patients were older (54.6 vs. 51.0 years old, p < 0.01) and had less frequent alcohol use (28% vs. 39%, p < 0.01). Transferred patients had a significantly greater frequency of gallstone AP (40% vs. 23%), but a lower frequency of alcohol AP (16% vs. 22%) and idiopathic AP (29% vs. 41%) (p < 0.001). Regarding clinical outcomes, transferred patients had significantly higher rates of severe AP (revised Atlanta classification) (10% vs. 2% severe, p < 0.001) and ICU admission (8% vs. 2%, p < 0.001) and longer median LOS (5 vs. 4 days, p < 0.001). Regarding quality indicators, there was no significant difference in the number of days of intravenous fluid administration, or days until advancement to enteral feeding, pain requiring opioid pain medication, or rates of surgical referral for cholecystectomy. Conclusions: Though the quality of care was similar in both groups, transferred patients had more severe AP with higher rates of systemic complications and ICU admissions and longer LOS, with no difference in quality indicators between groups.
... Many risk scores to predict the severity of acute pancreatitis have been developed, but few have consistently shown reliability, validity, and prediction over time and hence they perform poorly as clinical decision tools 8 . In a systematic review and meta-analysis, a total of 94 studies evaluating 18 scores in 53 547 patients were identified 9 . The investigators concluded that the test characteristics and clinical utility of severity scores in acute pancreatitis remain uncertain 9 . ...
... In a systematic review and meta-analysis, a total of 94 studies evaluating 18 scores in 53 547 patients were identified 9 . The investigators concluded that the test characteristics and clinical utility of severity scores in acute pancreatitis remain uncertain 9 . ...
Key points
Severe acute pancreatitis is a potentially life-threatening condition that can cause multi-organ failure
Early initiation of supportive treatment and determination of severity to identify patients in, or at risk of, organ failure to provide them with timely critical care support aimed at reversing organ failure are critical
Lack of understanding about the natural course of the disease and limited treatment targets are a concern
The cornerstones of management of acute pancreatitis are fluid resuscitation, adequate pain relief, and the early initiation and maintenance of nutrition
The step-up approach is the preferred strategy for managing necrotizing pancreatitis, with endoscopic drainage preferred to surgical intervention
... Apart from age, other risk factors can be modified through timely and aggressive treatment, which is crucial for enhancing patient outcomes. Older age has long been recognized as a significant indicator of poor prognosis in AP [23], as evidenced by its inclusion in both the APACHE II score and Ranson score as a predictive factor. This association is likely attributed to the higher likelihood of comorbid conditions as age increases [24]. ...
Purpose
There is a lack of adequate models specifically designed for elderly patients with severe acute pancreatitis (SAP) to predict the risk of death. This study aimed to develop a nomogram for predicting the overall survival of SAP in elderly patients.
Methods
Elderly patients diagnosed with SAP between January 1, 2017 and December 31, 2022 were included in the study. Risk factors were identified through least absolute shrinkage and selection operator regression analysis. Subsequently, a novel nomogram model was developed using multivariable logistic regression analysis. The predictive performance of the nomogram was evaluated using metrics such as the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).
Results
A total of 326 patients were included in the analysis, with 260 in the survival group and 66 in the deceased group. Multivariate logistic regression indicated that age, respiratory rate, arterial pH, total bilirubin, and calcium were independent prognostic factors for the survival of SAP patients. The nomogram demonstrated a performance comparable to sequential organ failure assessment (P = 0.065). Additionally, the calibration curve showed satisfactory predictive accuracy, and the DCA highlighted the clinical application value of the nomogram.
Conclusion
We have identified key demographic and laboratory parameters that are associated with the survival of elderly patients with SAP. These parameters have been utilized to create a precise and user-friendly nomogram, which could be an effective and valuable clinical tool for clinicians.
... However, around 20% of the patients develop acute severe pancreatitis and the death rate is about 30% [4]. Although several models have been developed to predict pancreatitis-related outcomes, their accuracy is unsatisfactory [5,6]. At the present, there are many clinical scoring systems for the early classification of acute pancreatitis severity, among which Acute Physiological and Chronic Health Score (APACHE) II and Acute Pancreatitis Severity Bed Side Index (BISAP) are widely used in clinical practice [7]. ...
Background
Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis.
Materials and methods
In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People’s Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model’s performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU).
Results
Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978–0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790–0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis.
Conclusion
The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition.
Trial Registration
This is a retrospective study.
... These scores often provide delayed predictive results (e.g. the Ranson score is standardised to 48 hours), are cumbersome to use (e.g. APACHE II includes more than 13 variables), or lack repeated validation (as is the case with most existing scores) [35]. Finally, none of the above scores consider obesity or visceral fat. ...
Background
Obesity substantially contributes to the onset of acute pancreatitis (AP) and influences its progression to severe AP. Although body mass index (BMI) is a widely used anthropometric parameter, it fails to delineate the distribution pattern of adipose tissue. To circumvent this shortcoming, the predictive efficacies of novel anthropometric indicators of visceral obesity, such as lipid accumulation products (LAP), cardiometabolic index (CMI), body roundness index (BRI), visceral adiposity index (VAI), A Body Shape Index (ABSI), and Chinese visceral adiposity index (CVAI) were examined to assess the severity of AP.
Method
The body parameters and laboratory indices of 283 patients with hyperlipidemic acute pancreatitis (HLAP) were retrospectively analysed, and the six novel anthropometric indicators of visceral obesity were calculated. The severity of HLAP was determined using the revised Atlanta classification. The correlation between the six indicators and HLAP severity was evaluated, and the predictive efficacy of the indicators was assessed using area under the curve (AUC). The differences in diagnostic values of the six indicators were also compared using the DeLong test.
Results
Patients with moderate to severe AP had higher VAI, CMI, and LAP than patients with mild AP (all P < 0.001). The highest AUC in predicting HLAP severity was observed for VAI, with a value of 0.733 and 95% confidence interval of 0.678–0.784.
Conclusions
This study demonstrated significant correlations between HLAP severity and VAI, CMI, and LAP indicators. These indicators, particularly VAI, which displayed the highest predictive power, were instrumental in forecasting and evaluating the severity of HLAP.
... According to the revised Atlanta classification, the severity of AP can be defined as mild, moderately severe, or severe [7]. Given the unpredictable course of AP, a plethora of clinical and biochemical scoring systems have been developed to predict the prognosis of AP, such as Acute Physiology and Chronic Health Evaluation II and Ranson's score [8,9]. These scoring systems are based on a combination of vital signs, certain laboratory data, and radiographic findings, which are frequently cumbersome to calculate and thus limited their application in clinical practice [9,10]. ...
Background
Acute pancreatitis (AP) has heterogeneous clinical features, and identifying clinically relevant sub-phenotypes is useful. We aimed to identify novel sub-phenotypes in hospitalized AP patients using longitudinal total serum calcium (TSC) trajectories.
Methods
AP patients had at least two TSC measurements during the first 24 h of hospitalization in the US-based critical care database (Medical Information Mart for Intensive Care-III (MIMIC-III) and MIMIC-IV were included. Group-based trajectory modeling was used to identify calcium trajectory phenotypes, and patient characteristics and treatment outcomes were compared between the phenotypes.
Results
A total of 4518 admissions were included in the analysis. Four TSC trajectory groups were identified: “Very low TSC, slow resolvers” (n = 65; 1.4% of the cohort); “Moderately low TSC” (n = 559; 12.4%); “Stable normal-calcium” (n = 3875; 85.8%); and “Fluctuating high TSC” (n = 19; 0.4%). The “Very low TSC, slow resolvers” had the lowest initial, maximum, minimum, and mean TSC, and highest SOFA score, creatinine and glucose level. In contrast, the “Stable normal-calcium” had the fewest ICU admission, antibiotic use, intubation and renal replace treatment. In adjusted analysis, significantly higher in-hospital mortality was noted among “Very low TSC, slow resolvers” (odds ratio [OR], 7.2; 95% CI, 3.7 to 14.0), “moderately low TSC” (OR, 5.0; 95% CI, 3.8 to 6.7), and “Fluctuating high TSC” (OR, 5.6; 95% CI, 1.5 to 20.6) compared with the “Stable normal-calcium” group.
Conclusions
We identified four novel sub-phenotypes of patients with AP, with significant variability in clinical outcomes. Not only the absolute TSC levels but also their trajectories were significantly associated with in-hospital mortality.
