Computed tomography based scoring system in a prospectively ascertained cohort of patients with chronic pancreatitis.
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ABSTRACT: Objective
No standardized system is currently used to report the presence or severity of parenchymal and ductal features of chronic pancreatitis (CP) on CT scan. We report a modification to the previously proposed Cambridge classification to serve this purpose.Methods
Contrast-enhanced CT scans of 158 well-phenotyped patients with CP enrolled in the North American Pancreatitis Studies (NAPS2) during 2000-2014 from the University of Pittsburgh were retrospectively reviewed by a subspecialty trained abdominal radiologist. Presence and severity (score scale 0-4) of pancreatic duct (PD) dilation, obstruction and contour irregularity, pancreatic calcifications, atrophy and extent of pancreatic involvement were recorded to grade the morphological severity of CP and stratify patients into distinct morphologic patterns. Findings were also correlated with clinical features.Results
Pancreatic atrophy, calcifications, PD dilation and PD irregularity were observed in 80%, 68%, 65%, 58% cases, respectively. An obstructive stone or PD stricture was present in 63%, and 86% had diffuse pancreatic involvement. Using these features, CP was noted to be moderate or severe in 61%, and classified morphologically as obstructive with/without calcifications, calcific but non-obstructive and non-calcific/non-obstructive in 65%, 20%, 15%, respectively. Functional abnormalities but not the presence of pain generally correlated with imaging findings.Conclusion
A structured scoring system can provide qualitative and quantitative assessment of imaging findings in CP and an opportunity for adoption into clinical practice and research for initial evaluation and longitudinal follow-up. Our findings need validation in a prospective cohort before widespread adoption.
SUBMITTER: Dasyam AK
PROVIDER: S-EPMC8126159 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVES:The Pancreatitis Activity Scoring System (PASS) has been derived by an international group of experts via a modified Delphi process. Our aim was to perform an external validation study to assess for concordance of the PASS score with high face validity clinical outcomes and determine specific meaningful thresholds to assist in application of this scoring system in a large prospectively ascertained cohort. METHODS:We analyzed data from a prospective cohort study of consecutive patients admitted to the Los Angeles County Hospital between March 2015 and March 2017. Patients were identified using an emergency department paging system and electronic alert system. Comprehensive characterization included substance use history, pancreatitis etiology, biochemical profile, and detailed clinical course. We calculated the PASS score at admission, discharge, and at 12?h increments during the hospitalization. We performed several analyses to assess the relationship between the PASS score and outcomes at various points during hospitalization as well as following discharge. Using multivariable logistic regression analysis, we assessed the relationship between admission PASS score and risk of severe pancreatitis. PASS score performance was compared to established systems used to predict severe pancreatitis. Additional inpatient outcomes assessed included local complications, length of stay, development of systemic inflammatory response syndrome (SIRS), and intensive care unit (ICU) admission. We also assessed whether the PASS score at discharge was associated with early readmission (re-hospitalization for pancreatitis symptoms and complications within 30 days of discharge). RESULTS:A total of 439 patients were enrolled, their mean age was 42 (±15) years, and 53% were male. Admission PASS score >140 was associated with moderately severe and severe pancreatitis (OR 3.5 [95% CI 2.0, 6.3]), ICU admission (OR 4.9 [2.5, 9.4]), local complications (3.0 [1.6, 5.7]), and development of SIRS (OR 2.9 [1.8, 4.5]) as well as prolongation of hospitalization by a mean of 1.5 (1.3-1.7) days. For the prediction of moderately severe/severe pancreatitis, the PASS score (AUC?=?0.71) was comparable to the more established Ranson's (AUC?=?0.63), Glasgow (AUC?=?0.72), Panc3 (AUC?=?0.57), and HAPS (AUC?=?0.54) scoring systems. Discharge PASS score >60 was associated with early readmission (OR 5.0 [2.4, 10.7]). CONCLUSIONS:The PASS score is associated with important clinical outcomes in acute pancreatitis. The ability of the score to forecast important clinical events at different points in the disease course suggests that it is a valid measure of activity in patients with acute pancreatitis.
| S-EPMC6123248 | biostudies-literature
Project description:BACKGROUND:Severity of AP is an important indicator of death rate, playing a crucial role in defining a correct dealing with a patient at his/her initial admission, in deciding on the need to transfer a patient to the intensive care unit. Many studies point out a direct relation between the death rate and the number of affected organs. In light of this, looking for the new criteria of multiple organ failure is still useful in clinical practice. Typically, assessment of multiple organ failure with patients undergoing treatment in the intensive care unit is carried out with the use of various integrated scores based both on clinical laboratory assessment of patient's condition and on data obtained by advanced imaging methods. However, many scientists point out that the facilities of diagnostic radiology, including in particularly computerised tomography, are not used to the full extent. AIM:We developed a CT score for assessment of pancreatitis severity that takes into consideration not only alterations of the pancreas but also enables evaluation of multiple organ failure with the examined patients. METHODS:We have examined 100 patients with suspected pancreatitis. Among them 30 patients had pancreatitis without alterations of the vital organs; 70 patients had alterations of the vital organs, suffered organ or multiple organ failure and received treatment in the surgery unit and intensive care unit of the Department of Surgical Conditions of Karaganda Medical University. RESULTS:Because of CT results, based on the proposed score, we assessed a degree of pancreas necrosis, analysed the relation between organ failure and degree of pancreas necrosis. Finally, we evaluated the connection between multiple organ failure and the specific failure of one organ and the presence of necrosis and death rate. CONCLUSION:The proposed score for CT-based assessment of pancreatitis severity can be used not only for identification but also for prediction of organ failure at the early stage of pancreatitis to a high accuracy as compared to conventional CT systems for assessment of the condition of patients affected by pancreatitis. It can also be used to differentiate the severity of organ failure and the number of affected organs.
| S-EPMC7061380 | biostudies-literature
Project description:ObjectivesAcute pancreatitis (AP) is a sudden onset, rapidly evolving inflammatory response with systemic inflammation and multiorgan failure (MOF) in a subset of patients. New highly accurate clinical decision support tools are needed to allow local doctors to provide expert care.MethodsAriel Dynamic Acute Pancreatitis Tracker (ADAPT) is a digital tool to guide physicians in ordering standard tests, evaluate test results and model progression using available data, propose emergent therapies. The accuracy of the severity score calculators was tested using 2 prospectively ascertained Acute Pancreatitis Patient Registry to Examine Novel Therapies in Clinical Experience cohorts (pilot University of Pittsburgh Medical Center, n = 163; international, n = 1544).ResultsThe ADAPT and post hoc expert-calculated AP severity scores were 100% concordant in both pilot and international cohorts. High-risk criteria of all 4 severity scores at admission were associated with moderately-severe or severe AP and MOF (both P < 0.0001) and prediction of no MOF was 97.8% to 98.9%. The positive predictive value for MOF was 7.5% to 14.9%.ConclusionsThe ADAPT tool showed 100% accuracy with AP predictive metrics. Prospective evaluation of ADAPT features is needed to determine if additional data can accurately predict and mitigate severe AP and MOF.
| S-EPMC8128442 | biostudies-literature
Project description:Early detection of osteoarthritis (OA) remains a diagnostic challenge owing to insensitive diagnostic techniques currently available. Herein a new semiquantitative scoring system, based upon contrast-enhanced computed tomographic (CECT) imaging, is described for further refinement of early OA disease staging. Trochlear ridge cartilage defects were surgically created in the femoropatellar joint of an adult horse (ACUC approved protocols). Seven weeks post-surgery, CECT imaging was performed on a clinical scanner after intra-articular injection of a cationic iodinated contrast agent, CA4+, into both injured and control femoropatellar joint compartments. The femoral cartilage surface was densely biopsied, and specimens were assessed for visual (Outerbridge score), functional (equilibrium compressive modulus), and biochemical (glycosaminoglycan content) measures of cartilage quality. Cartilage CECT attenuation was compared with cartilage quality measures using receiver operating characteristic curve analysis to establish attenuation thresholds for distinguishing among cartilage quality levels. CECT imaging identifies macroscopically damaged cartilage regions and in morphologically identical tissue provides moderately sensitive and specific semiquantitative segregation of cartilage quality based upon CECT attenuation, reflecting both glycosaminoglycan content and compressive stiffness of cartilage area under the curve (AUC?=?0.83 [95% confidence interval [CI]: 0.72-0.93] for distinguishing poor quality and AUC?=?0.76 [95% CI: 0.65-0.90] for distinguishing healthy quality cartilage). A semiquantitative 6-point scoring system-the Osteoarthritis Attenuation and Morphological Assessment (OAMA) score-is proposed as a tool for assessing cartilage quality from CECT images. The OAMA scoring system expands the current disease staging capability of early OA by inclusion of morphological, biochemical, and biomechanical assessments. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2138-2148, 2019.
| S-EPMC6739126 | biostudies-literature
Project description:To determine if coronary artery calcium (CAC) scoring using computed tomography at 80 kilovolt-peak (kVp) and 70-kVp and tube voltage-adapted scoring-thresholds allow for accurate risk stratification as compared to the standard 120-kVp protocol. We prospectively included 170 patients who underwent standard CAC scanning at 120-kVp and 200 milliamperes and additional scans with 80-kVp and 70-kVp tube voltage with adapted tube current to normalize image noise across scans. Novel kVp-adapted thresholds were applied to calculate CAC scores from the low-kVp scans and were compared to those from standard 120-kVp scans by assessing risk reclassification rates and agreement using Kendall's rank correlation coefficients (Τb) for risk categories bounded by 0, 1, 100, and 400. Interreader reclassification rates for the 120-kVp scans were assessed. Agreement for risk classification obtained from 80-kVp and 70-kVp scans as compared to 120-kVp was good (Τb = 0.967 and 0.915, respectively; both p < 0.001) with reclassification rates of 7.1% and 17.2%, respectively, mostly towards a lower risk category. By comparison, the interreader reclassification rate was 4.1% (Τb = 0.980, p < 0.001). Reclassification rates were dependent on body mass index (BMI) with 7.1% and 13.6% reclassifications for the 80-kVp and 70-kVp scans, respectively, in patients with a BMI < 30 kg/m2 (n = 140), and 2.9% and 7.4%, respectively, in patients with a BMI < 25 kg/m2 (n = 68). Mean effective radiation dose from the 120-kVp, the 80-kVp, and 70-kVp scans was 0.54 ± 0.03, 0.42 ± 0.02, and 0.26 ± 0.02 millisieverts. CAC scoring with reduced tube voltage allows for accurate risk stratification if kVp-adapted thresholds for calculation of CAC scores are applied.ClinicalTrials.gov NCT03637231.
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Project description:BackgroundChronic obstructive pulmonary disease (COPD) has variable subtypes involving mixture of large airway inflammation, small airway disease, and emphysema. This study evaluated the relationship between visually assessed computed tomography (CT) subtypes and clinical/imaging characteristics.MethodsIn total, 452 participants were enrolled in this study between 2012 and 2017. Seven subtypes were defined by visual evaluation of CT images using Fleischner Society classification: normal, paraseptal emphysema (PSE), bronchial disease, and centrilobular emphysema (trace, mild, moderate and confluent/advanced destructive). The differences in several variables, including clinical, laboratory, spirometric, and quantitative CT features among CT-based visual subtypes, were compared using the chi-square tests and one-way analysis of variance.ResultsSubjects who had PSE had better forced expiratory volume in 1 second (FEV1) (P=0.03) percentage and higher lung density (P<0.05) than those with moderate to confluent/advanced destructive centrilobular emphysema. As the visual grade of centrilobular emphysema worsened, pulmonary function declined and modified Medical Research Council, COPD assessment test (CAT) score, and quantitative assessment (emphysema index and air trapping) increased. The bronchial subtype was associated with higher body mass index (BMI), better lung function and higher lung density. Participants with trace emphysema showed a rapid increase in functional small airway disease.ConclusionsClassifying subtypes using visual CT imaging features can reflect heterogeneity and pathological processes of COPD.
| S-EPMC8024830 | biostudies-literature
Project description:OBJECTIVES:Acute pancreatitis has a highly variable course. Currently there is no widely accepted method to measure disease activity in patients hospitalized for acute pancreatitis. We aimed to develop a clinical activity index that incorporates routine clinical parameters to assist in the measurement, study, and management of acute pancreatitis. METHODS:We used the UCLA/RAND appropriateness method to identify items for inclusion in the disease activity instrument. We conducted a systematic literature review followed by two sets of iterative modified Delphi meetings including a panel of international experts between November 2014 and November 2015. The final instrument was then applied to patient data obtained from five separate study cohorts across Southern California to assess profiles of disease activity. RESULTS:From a list of 35 items comprising 6 domains, we identified 5 parameters for inclusion in the final weighted clinical activity scoring system: organ failure, systemic inflammatory response syndrome, abdominal pain, requirement for opiates and ability to tolerate oral intake. We applied the weighted scoring system across the 5 study cohorts comprising 3,123 patients. We identified several distinct patterns of disease activity: (i) overall there was an elevated score at baseline relative to discharge across all study cohorts, (ii) there were distinct patterns of disease activity related to duration of illness as well as (iii) early and persistent elevation of disease activity among patients with severe acute pancreatitis defined as persistent organ failure. CONCLUSIONS:We present the development and initial validation of a clinical activity score for real-time assessment of disease activity in patients with acute pancreatitis.
| S-EPMC5519418 | biostudies-literature
Project description:Background and aimThe primary aim was to validate the Pancreatitis Activity Scoring System (PASS) in a multicenter prospectively ascertained acute pancreatitis (AP) cohort. Second, we investigated the association of early PASS trajectories with disease severity and length of hospital stay (LOS).MethodsData were prospectively collected through the APPRENTICE consortium (2015-2018). AP severity was categorized based on revised Atlanta classification. Delta PASS (ΔPASS) was calculated by subtracting activity score from baseline value. PASS trajectories were compared between severity subsets. Subsequently, the cohort was subdivided into three LOS subgroups as short (S-LOS): 2-3 days; intermediate (I-LOS): 3-7 days; and long (L-LOS): ≥7 days. The generalized estimating equations model was implemented to compare PASS trajectories.ResultsThere were 434 subjects analyzed including 322 (74%) mild, 86 (20%) moderately severe, and 26 (6%) severe AP. Severe AP subjects had the highest activity levels and the slowest rate of decline in activity (P = 0.039). Focusing on mild AP, L-LOS subjects (34%) had 28 points per day slower decline; whereas, S-LOS group (13%) showed 34 points per day sharper decrease compared with I-LOS (53%; P < 0.001). We noticed an outlier subset with a median admission-PASS of 466 compared with 140 in the rest. Morphine equivalent dose constituted 80% of the total PASS in the outliers (median morphine equivalent dose score = 392), compared with only 25% in normal-range subjects (score = 33, P value < 0.001).ConclusionsThis study highlighted that PASS can quantify AP activity. Significant differences in PASS trajectories were found both in revised Atlanta classification severity and LOS groups, which can be harnessed in AP monitoring/management (ClincialTrials.gov number, NCT03075618).
| S-EPMC9058811 | biostudies-literature
Project description:Background Early identification of severe acute pancreatitis (SAP) is key to reducing mortality and improving prognosis. We aimed to establish a radiomics model and nomogram for early prediction of acute pancreatitis (AP) severity based on contrast-enhanced computed tomography (CT) images. Methods We retrospectively analyzed 215 patients with first-episode AP, including 141 in the training cohort (87 men and 54 women, mean age 51.37±16.09 years) and 74 in the test cohort (40 men and 34 women, mean age 55.49±17.83 years). Radiomics features were extracted from portal venous phase images based on pancreatic and peripancreatic regions. The light gradient boosting machine (LightGBM) algorithm was used for feature selection, a logistic regression (LR) model was established and trained by 10-fold cross-validation, and a nomogram was established based on the best features. The model’s predictive performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. Results A total of 13 optimal radiomics features were selected by LightGBM for LR model building. The AUC of the radiomics (LR) model was 0.992 [95% confidence interval (CI): 0.963–0.996] in the training cohort, 0.965 (95% CI: 0.924–0.981) in the validation cohort, and 0.894 (95% CI: 0.789–0.966) in the test cohort. The sensitivity was 0.862 (95% CI: 0.674–0.954), the specificity was 0.800 (95% CI: 0.649–0.899), and the accuracy was 0.824 (95% CI: 0.720–0.919). The nomogram based on the 13 radiomics features showed that SAP would be predicted when the total score was greater than 124. Conclusions The radiomics model based on enhanced-CT images of pancreatic and peripancreatic regions performed well in the early prediction of AP severity. The nomogram based on selected radiomics features could provide a reference for AP clinical assessment.
| S-EPMC10006146 | biostudies-literature
Project description:Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value. We evaluated the external validity of Ali-M3 using sequential Japanese sampling data. In this retrospective cohort study, COVID-19 infection probabilities for 617 symptomatic patients were determined using Ali-M3. In 11 Japanese tertiary care facilities, these patients underwent reverse transcription-polymerase chain reaction (RT-PCR) testing. They also underwent chest CT to confirm a diagnosis of COVID-19. Of the 617 patients, 289 (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence interval: 0.762‒0.833) and the goodness-of-fit was P = 0.156. With a cut-off probability of a diagnosis of COVID-19 by Ali-M3 set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively. A cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among the 223 patients who required oxygen, the AUC was 0.825. Sensitivity at a cut-off of 0.5% and 0.2% was 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were fewer, the sensitivity increased for both cut-off values after 5 days. We evaluated Ali-M3 using external validation with symptomatic patient data from Japanese tertiary care facilities. As Ali-M3 showed sufficient sensitivity performance, despite a lower specificity performance, Ali-M3 could be useful in excluding a diagnosis of COVID-19.
| S-EPMC8568139 | biostudies-literature