scholarly journals Comparison of the Main Staging Systems for Assessing the Severity of Lung Injury in Patients with COVID-19 and Evaluation of Their Predictive Value

2021 ◽  
Vol 102 (5) ◽  
pp. 296-303
Author(s):  
Y. S. Kudryavtsev ◽  
M. M. Beregov ◽  
A. B. Berdalin ◽  
V. G. Lelyuk
Keyword(s):  
Lung Tissue ◽  
Predictive Value ◽  
Prognostic Value ◽  
Time Course ◽  
Fatal Outcome ◽  
Chest Ct ◽  
Viral Pneumonia ◽  
Maximum Magnitude ◽  
Predictive Values ◽  
Staging Systems

Objective: to compare the results of staging the severity of viral pneumonia in patients with COVID-19 based on the results of chest computed tomography (CT) using the empirical visual scale CT 0–4 and chest CT severity score (CT-SS) point scale, as well as to assess their prognostic value.Material and methods. Chest CT scans and anamnestic data in patients hospitalized to a non-specialized center repurposed for the treatment of new coronavirus infection, were analyzed. Chest CT analysis was performed by two radiologists using CT 0–4 and CT-SS scales.Results. The time course of changes in the severity of lung parenchymal lesions, by using both scales, was found to be similar: the maximum magnitude of lung tissue changes was recorded on day 5 of the disease. In cases of death, there was a significantly more extensive lung parenchymal involvement at admission to the center than in recovered patients, which was also true for both CT data assessment systems. Bothscales demonstrated comparable diagnostic and prognostic value: there were no statistically significant differences in sensitivity, specificity, and predictive value of a fatal outcome. Both the CT 0–4 scales and the CT-SS are based on the estimation of the volume of the affected lung tissue, but when the CT 0–4 scale was employed, additional criteria were used in some cases: the presence of hydrothorax and the determination of the maximum score for the most affected lung. Not all patients with a pronounced CT picture of viral pneumonia had a fatal outcome, which may indicate the presence of other factors that increase its risk.Conclusion. Both CT 0–4 and CT-SS scales have similar predictive values. The greater severity of parenchymal damage assessed by these CT scales was associated with the higher mortality rate.

scholarly journals Agreement between low-dose and ultra-low-dose chest CT for the diagnosis of viral pneumonia imaging patterns during the COVID-19 pandemic

2022 ◽  
Vol 53 (1) ◽  
Author(s):  
Hooman Bahrami-Motlagh ◽  
Yashar Moharamzad ◽  
Golnaz Izadi Amoli ◽  
Sahar Abbasi ◽  
Alireza Abrishami ◽  
...  
Keyword(s):  
Radiation Dose ◽  
Ct Scan ◽  
Predictive Value ◽  
Low Dose ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Viral Pneumonia ◽  
Ct Scans ◽  
Low Dose Ct ◽  
Chest Ct Scan

Abstract Background Chest CT scan has an important role in the diagnosis and management of COVID-19 infection. A major concern in radiologic assessment of the patients is the radiation dose. Research has been done to evaluate low-dose chest CT in the diagnosis of pulmonary lesions with promising findings. We decided to determine diagnostic performance of ultra-low-dose chest CT in comparison to low-dose CT for viral pneumonia during the COVID-19 pandemic. Results 167 patients underwent both low-dose and ultra-low-dose chest CT scans. Two radiologists blinded to the diagnosis independently examined ultra-low-dose chest CT scans for findings consistent with COVID-19 pneumonia. In case of any disagreement, a third senior radiologist made the final diagnosis. Agreement between two CT protocols regarding ground-glass opacity, consolidation, reticulation, and nodular infiltration were recorded. On low-dose chest CT, 44 patients had findings consistent with COVID-19 infection. Ultra-low-dose chest CT had sensitivity and specificity values of 100% and 98.4%, respectively for diagnosis of viral pneumonia. Two patients were falsely categorized to have pneumonia on ultra-low-dose CT scan. Positive predictive value and negative predictive value of ultra-low-dose CT scan were respectively 95.7% and 100%. There was good agreement between low-dose and ultra-low-dose methods (kappa = 0.97; P < 0.001). Perfect agreement between low-dose and ultra-low-dose scans was found regarding diagnosis of ground-glass opacity (kappa = 0.83, P < 0.001), consolidation (kappa = 0.88, P < 0.001), reticulation (kappa = 0.82, P < 0.001), and nodular infiltration (kappa = 0.87, P < 0.001). Conclusion Ultra-low-dose chest CT scan is comparable to low-dose chest CT for detection of lung infiltration during the COVID-19 outbreak while maintaining less radiation dose. It can also be used instead of low-dose chest CT scan for patient triage in circumstances where rapid-abundant PCR tests are not available.

scholarly journals Precision oncology in AML: validation of the prognostic value of the knowledge bank approach and suggestions for improvement

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Marius Bill ◽  
Krzysztof Mrózek ◽  
Brian Giacopelli ◽  
Jessica Kohlschmidt ◽  
Deedra Nicolet ◽  
...  
Keyword(s):  
Predictive Value ◽  
Prognostic Value ◽  
De Novo ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Precision Oncology ◽  
Predictive Values ◽  
Mutational Status ◽  
Balanced Chromosome Rearrangements ◽  
Group B

AbstractRecently, a novel knowledge bank (KB) approach to predict outcomes of individual patients with acute myeloid leukemia (AML) was developed using unbiased machine learning. To validate its prognostic value, we analyzed 1612 adults with de novo AML treated on Cancer and Leukemia Group B front-line trials who had pretreatment clinical, cytogenetics, and mutation data on 81 leukemia/cancer-associated genes available. We used receiver operating characteristic (ROC) curves and the area under the curve (AUC) to evaluate the predictive values of the KB algorithm and other risk classifications. The KB algorithm predicted 3-year overall survival (OS) probability in the entire patient cohort (AUCKB = 0.799), and both younger (< 60 years) (AUCKB = 0.747) and older patients (AUCKB = 0.770). The KB algorithm predicted non-remission death (AUCKB = 0.860) well but was less accurate in predicting relapse death (AUCKB = 0.695) and death in first complete remission (AUCKB = 0.603). The KB algorithm’s 3-year OS predictive value was higher than that of the 2017 European LeukemiaNet (ELN) classification (AUC2017ELN = 0.707, p < 0.001) and 2010 ELN classification (AUC2010ELN = 0.721, p < 0.001) but did not differ significantly from that of the 17-gene stemness score (AUC17-gene = 0.732, p = 0.10). Analysis of additional cytogenetic and molecular markers not included in the KB algorithm revealed that taking into account atypical complex karyotype, infrequent recurrent balanced chromosome rearrangements and mutational status of the SAMHD1, AXL and NOTCH1 genes may improve the KB algorithm. We conclude that the KB algorithm has a high predictive value that is higher than those of the 2017 and 2010 ELN classifications. Inclusion of additional genetic features might refine the KB algorithm.

scholarly journals Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

10.2196/23026 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23026
Author(s):  
Shengtian Sang ◽  
Ran Sun ◽  
Jean Coquet ◽  
Harris Carmichael ◽  
Tina Seto ◽  
...  
Keyword(s):  
Clinical Judgment ◽  
Predictive Value ◽  
Clinical Care ◽  
Academic Medical Center ◽  
Invasive Mechanical Ventilation ◽  
Viral Pneumonia ◽  
Smoking History ◽  
Predictive Values ◽  
Data Limitations ◽  
Address Data

Background For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Objective This study aimed to develop and test the feasibility of a “patients-like-me” framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Methods Our framework used COVID-19–like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19–like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19–like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. Results Compared to the COVID-19–like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19–like patients. In the COVID-19–like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19–like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. Conclusions We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

scholarly journals Learning from past respiratory infections to predict COVID-19 Outcomes: A retrospective study (Preprint)

2020 ◽  
Author(s):  
Shengtian Sang ◽  
Ran Sun ◽  
Jean Coquet ◽  
Haris Carmichael ◽  
Tina Seto ◽  
...  
Keyword(s):  
Clinical Judgment ◽  
Predictive Value ◽  
Invasive Mechanical Ventilation ◽  
Viral Pneumonia ◽  
Smoking History ◽  
White Blood Count ◽  
Predictive Values ◽  
Patient Deterioration ◽  
Data Limitations ◽  
Address Data

BACKGROUND In the clinical care of well-established diseases, randomized trials, literature and research are supplemented by clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, Artificial Intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, lack of clinical data restricts the design and development of such AI tools, particularly in preparation of an impending crisis or pandemic. OBJECTIVE This study aimed to develop and test the feasibility of a ‘patients-like-me’ framework to predict COVID-19 patient deterioration using a retrospective cohort of similar respiratory diseases. METHODS Our framework used COVID-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) from an academic medical center, 2008-2019. Fifteen training cohorts were created using different combinations of the COVID-like cohorts with the ARDS cohort for exploratory purpose. Two machine learning (ML) models were developed, one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS Compared to the COVID-like cohorts (n=16,509), the COVID-19 hospitalized patients (n=125) were significantly younger, with a higher proportion of Hispanic ethnicity, lower proportion of smoking history and fewer comorbidities (P <0.001). COVID-19 patients had a lower IMV rate (15.1 vs 23.2, P=0.016) and shorter time to IMV (2.9 vs 4.1, P <0.001) compared to the COVID-like patients. In the COVID-like training data, the top models achieved excellent performance (AUV > 0.90). Validating in the COVID-19 cohort, the best performing model of predicting IMV was the XGBoost model (AUC: 0.831) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all four COVID-like cohorts without ARDS achieved the best performance (AUC: 0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood count, cardiac troponin, albumin, etc.). Our models suffered from class imbalance, that resulted in high negative predictive values and low positive predictive values. CONCLUSIONS We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

scholarly journals Prognostic value of inflammatory markers for detecting anastomotic leakage after esophageal resection

BMC Surgery ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lukas F. Liesenfeld ◽  
Peter Sauer ◽  
Markus K. Diener ◽  
Ulf Hinz ◽  
Thomas Schmidt ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Anastomotic Leakage ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Inflammatory Markers ◽  
Prognostic Value ◽  
Predictive Marker ◽  
Esophageal Resection ◽  
Predictive Values ◽  
Sensitivity Specificity

Abstract Background Early diagnosis of anastomotic leakage (AL) after esophageal resection is crucial for the successful management of this complication. Inflammatory serological markers are indicators of complications during the postoperative course. The aim of the present study was to evaluate the prognostic value of routine inflammatory markers to predict anastomotic leakage after transthoracic esophageal resection. Methods Data from all consecutive patients undergoing transthoracic esophageal resection between January 2010 and December 2016 were analyzed from a prospective database. Besides clinicodemographic parameters, C-reactive protein, white blood cell count and albumin were analyzed and the Noble/Underwood (NUn) score was calculated to evaluate their predictive value for postoperative anastomotic leakage. Diagnostic accuracy was measured by sensitivity, specificity, and negative and positive predictive values using area under the receiver operator characteristics curve. Results Overall, 233 patients with transthoracic esophageal resection were analyzed, 30-day mortality in this group was 3.4%. 57 patients (24.5%) suffered from AL, 176 patients were in the AL negative group. We found significant differences in WBCC, CRP and NUn scores between patients with and without AL, but the analyzed markers did not show an independent relevant prognostic value. For CRP levels below 155 mg/dl from POD3 to POD 7 the negative predictive value for absence of AI was > 80%. Highest diagnostic accuracy was detected for CRP levels on 4th POD with a cut-off value of 145 mg/l reaching negative predictive value of 87%. Conclusions In contrast to their prognostic value in other surgical procedures, CRP, WBCC and NUn score cannot be recommended as independent markers for the prediction of anastomotic leakage after transthoracic esophageal resection. CRP is an accurate negative predictive marker and discrimination of AL and no-AL may be helpful for postoperative clinical management. Trial registration The study was approved by the local ethical committee (S635-2013).

scholarly journals Prognostic value of chest computed tomography in community-acquired pneumonia patients

2020 ◽  
Vol 6 (4) ◽  
pp. 00079-2020
Author(s):  
Masahiro Nemoto ◽  
Kei Nakashima ◽  
Satoshi Noma ◽  
Yuya Matsue ◽  
Kazuki Yoshida ◽  
...  
Keyword(s):  
Heart Failure ◽  
Computed Tomography ◽  
Chronic Heart Failure ◽  
Chest Radiography ◽  
Predictive Value ◽  
Prognostic Value ◽  
Community Acquired Pneumonia ◽  
Chest Ct ◽  
Physical Status ◽  
Chest Computed Tomography

BackgroundChest computed tomography (CT) is commonly used to diagnose pneumonia in Japan, but its usability in terms of prognostic predictability is not obvious. We modified CURB-65 (confusion, urea >7 mmol·L−1, respiratory rate ≥30 breaths·min−1, blood pressure <90 mmHg (systolic) ≤60 mmHg (diastolic), age ≥65 years) and A-DROP scores with CT information and evaluated their ability to predict mortality in community-acquired pneumonia patients.MethodsThis study was conducted using a prospective registry of the Adult Pneumonia Study Group – Japan. Of the 791 registry patients, 265 hospitalised patients with chest CT were evaluated. Chest CT-modified CURB-65 scores were developed with the first 30 study patients. The 30-day mortality predictability of CT-modified, chest radiography-modified and original CURB-65 scores were validated.ResultsIn score development, infiltrates over four lobes and pleural effusion on CT added extra points to CURB-65 scores. The area under the curve for CT-modified CURB-65 scores was significantly higher than that of chest radiography-modified or original CURB-65 scores (both p<0.001). The optimal cut-off CT-modified CURB-65 score was ≥4 (positive-predictive value 80.8%; negative-predictive value 78.6%, for 30-day mortality). For sensitivity analyses, chest CT-modified A-DROP scores also demonstrated better prognostic value than did chest radiography-modified and original A-DROP scores. Poor physical status, chronic heart failure and multiple infiltration hampered chest radiography evaluation.ConclusionChest CT modification of CURB-65 or A-DROP scores improved the prognostic predictability relative to the unmodified scores. In particular, in patients with poor physical status or chronic heart failure, CT findings have a significant advantage. Therefore, CT can be used to enhance prognosis prediction.

Predictive Value of the Pretreatment Extent of Disease System in Hepatoblastoma: Results From the International Society of Pediatric Oncology Liver Tumor Study Group SIOPEL-1 Study

2005 ◽  
Vol 23 (6) ◽  
pp. 1245-1252 ◽  
Author(s):  
Daniël C. Aronson ◽  
J. Marco Schnater ◽  
Chris R. Staalman ◽  
Gerrit J. Weverling ◽  
Jack Plaschkes ◽  
...  
Keyword(s):  
International Society ◽  
Pediatric Oncology ◽  
Predictive Value ◽  
Interobserver Agreement ◽  
Preoperative Staging ◽  
Study Group ◽  
Predictive Values ◽  
Tumor Study ◽  
Staging Systems ◽  
Extent Of Disease

Purpose Preoperative staging (pretreatment extent of disease [PRETEXT]) was developed for the first prospective liver tumor study by the International Society of Pediatric Oncology (SIOPEL-1 study; preoperative chemotherapy and delayed surgery). Study aims were to analyze the accuracy and interobserver agreement of PRETEXT and to compare the predictive impact of three currently used staging systems. Patients and Methods Hepatoblastoma (HB) patients younger than 16 years who underwent surgical resection (128 of 154 patients) were analyzed. The centrally reviewed preoperative staging was compared with postoperative pathology (accuracy) in 91 patients (81%), and the local center staging was compared with the central review (interobserver agreement) in 97 patients (86%), using the agreement beyond change method (weighted κ). The predictive values of the three staging systems were compared in 110 patients (97%) using survival curves and Cox proportional hazard ratio estimates. Results Preoperative PRETEXT staging compared with pathology was correct in 51%, overstaged in 37%, and understaged in 12% of patients (weighted κ = 0.44; 95% CI, 0.26 to 0.62). The weighted κ value of the interobserver agreement was 0.76 (95% CI, 0.64 to 0.88). The Children's Cancer Study Group/Pediatric Oncology Group–based staging system showed no predictive value for survival (P = .516), but the tumor-node-metastasis–based system and PRETEXT system showed good predictive values (P = .0021 and P = .0006, respectively). PRETEXT seemed to be superior in the statistical fit. Conclusion PRETEXT has moderate accuracy with a tendency to overstage patients, shows good interobserver agreement (reproducibility), shows superior predictive value for survival, offers the opportunity to monitor the effect of preoperative therapy, and can also be applied in patients who have not had operations. For comparability reasons, we recommend that all HB patients included in trials also be staged according to PRETEXT.

scholarly journals Radiological Society of North America (RSNA) Expert Consensus Statement Related to Chest CT Findings in COVID-19 Versus CO-RADS: Comparison of Reporting System Performance Among Chest Radiologists and End-User Preference

2020 ◽  
pp. 084653712096891
Author(s):  
Siobhan B. O’ Neill ◽  
Danielle Byrne ◽  
Nestor L. Müller ◽  
Sabeena Jalal ◽  
William Parker ◽  
...  
Keyword(s):  
Predictive Value ◽  
Consensus Statement ◽  
Reporting System ◽  
Chest Ct ◽  
Observer Agreement ◽  
Expert Consensus ◽  
User Preference ◽  
End User ◽  
Predictive Values ◽  
Reporting Systems

Purpose: The RSNA expert consensus statement and CO-RADS reporting system assist radiologists in describing lung imaging findings in a standardized manner in patients under investigation for COVID-19 pneumonia and provide clarity in communication with other healthcare providers. We aim to compare diagnostic performance and inter-/intra-observer among chest radiologists in the interpretation of RSNA and CO-RADS reporting systems and assess clinician preference. Methods: Chest CT scans of 279 patients with suspected COVID-19 who underwent RT-PCR testing were retrospectively and independently examined by 3 chest radiologists who assigned interpretation according to the RSNA and CO-RADS reporting systems. Inter-/intra-observer analysis was performed. Diagnostic accuracy of both reporting systems was calculated. 60 clinicians participated in a survey to assess end-user preference of the reporting systems. Results: Both systems demonstrated almost perfect inter-observer agreement (Fleiss kappa 0.871, P < 0.0001 for RSNA; 0.876, P < 0.0001 for CO-RADS impressions). Intra-observer agreement between the 2 scoring systems using the equivalent categories was almost perfect (Fleiss kappa 0.90-0.92, P < 0.001). Positive predictive values were high, 0.798-0.818 for RSNA and 0.891-0.903 CO-RADS. Negative predictive value were similar, 0.573-0.585 for RSNA and 0.573-0.58 for CO-RADS. Specificity differed between the 2 systems, 68-73% for CO-RADS and 52-58% for RSNA with superior specificity of CO-RADS. Of 60 survey participants, the majority preferred the RSNA reporting system rather than CO-RADS for all options provided (66.7-76.7%; P < 0.05). Conclusions: RSNA and CO-RADS reporting systems are consistent and reproducible with near perfect inter-/intra-observer agreement and excellent positive predictive value. End-users preferred the reporting language in the RSNA system.

Predictive Value of Decreasing Antithrombin III in Deep-Vein Thrombosis

1980 ◽  
Vol 44 (03) ◽  
pp. 135-137 ◽  
Author(s):  
Thorkild Lund Andreasen
Keyword(s):  
Deep Vein Thrombosis ◽  
Predictive Value ◽  
Coronary Care Unit ◽  
Antithrombin Iii ◽  
Predictive Values ◽  
Vein Thrombosis ◽  
At Iii ◽  
Coronary Care ◽  
Time Of Admission ◽  
Deep Vein

SummaryAntithrombin III (At-III) was measured at the time of admission and two days later in 131 patients laid up in a coronary care unit. The patients were examined for deep-vein thrombosis (DVT) clinically and by means of 125I-fibrinogen scanning. 19 patients developed DVT. In 11 subjects with and 25 without DVT At-III decreased more than 10%. And in 7 with and 17 without DVT At-III decreased more than 15%. One person with DVT had subnormal At-III. By using decrease of At-III or subnormal initial At-III to predict DVT the following predictive value (PV) were found. Decrease ≤ 10%, PV pos.= 0.32 and PV neg. = 0.93. Decrease ≤ 15%, PV pos. = 0.32 and PV neg. = 0.90. The positive predictive values obtained were too low to let decreasing At-III give occasion for prophylactic anticoagulant treatment.

VALUE OF COMBINATION OF HAP SCORE AND BISAP SCORE IN PREDICTING SEVERITY IN PATIENTS OF ACUTE PANCREATITIS

2019 ◽  
pp. 96-100
Author(s):  
Thi Ngoc Suong Le ◽  
Pham Chi Tran ◽  
Van Huy Tran
Keyword(s):  
Acute Pancreatitis ◽  
Positive Predictive Value ◽  
Roc Curve ◽  
Predictive Value ◽  
Prognostic Value ◽  
Acute Inflammation ◽  
Clinical Symptoms ◽  
Mortality Rates ◽  
Multiple Organ ◽  
Central Hospital

Acute pancreatitis (AP) is an acute inflammation of the pancreas, usually occurs suddenly with a variety of clinical symptoms, complications of multiple organ failure and high mortality rates. Objectives: To determine the value of combination of HAP score and BISAP score in predicting the severity of acute pancreatitis of the Atlanta 2012 Classification. Patients and Methods: 75 patients of acute pancreatitis hospitalized at Hue Central Hospital between March 2017 and July 2018; HAP and BISHAP score is calculated within the first 24 hours. The severity of AP was classified by the revised Atlanta criteria 2012. Results: When combining the HAP and BISAP scores in predicting the severity of acute pancreatitis, the area under the ROC curve was 0,923 with sensitivity value was 66.7%, specificity value was 97.1%; positive predictive value was 66.7%, negative predictive value was 97.1%. Conclusion: The combination of HAP and BISAP scores increased the sensitivity, predictive value, and prognostic value in predicting the severity of acute pancreatitis of the revised Atlanta 2012 classification in compare to each single scores. Key words: HAPscore, BiSAP score, acute pancreatitis, predicting severity

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