scholarly journals Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia

2021 ◽  
Vol 8 (1) ◽  
pp. e001045
Author(s):  
Jessica Quah ◽  
Charlene Jin Yee Liew ◽  
Lin Zou ◽  
Xuan Han Koh ◽  
Rayan Alsuwaigh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chest Radiograph ◽  
Risk Score ◽  
Mortality Risk ◽  
Characteristic Curve ◽  
Pneumonia Severity Index ◽  
Model Performance ◽  
Community Acquired Pneumonia ◽  
Pneumonia Severity ◽  
Severity Scores

BackgroundChest radiograph (CXR) is a basic diagnostic test in community-acquired pneumonia (CAP) with prognostic value. We developed a CXR-based artificial intelligence (AI) model (CAP AI predictive Engine: CAPE) and prospectively evaluated its discrimination for 30-day mortality.MethodsDeep-learning model using convolutional neural network (CNN) was trained with a retrospective cohort of 2235 CXRs from 1966 unique adult patients admitted for CAP from 1 January 2019 to 31 December 2019. A single-centre prospective cohort between 11 May 2020 and 15 June 2020 was analysed for model performance. CAPE mortality risk score based on CNN analysis of the first CXR performed for CAP was used to determine the area under the receiver operating characteristic curve (AUC) for 30-day mortality.Results315 inpatient episodes for CAP occurred, with 30-day mortality of 19.4% (n=61/315). Non-survivors were older than survivors (mean (SD)age, 80.4 (10.3) vs 69.2 (18.7)); more likely to have dementia (n=27/61 vs n=58/254) and malignancies (n=16/61 vs n=18/254); demonstrate higher serum C reactive protein (mean (SD), 109 mg/L (98.6) vs 59.3 mg/L (69.7)) and serum procalcitonin (mean (SD), 11.3 (27.8) μg/L vs 1.4 (5.9) μg/L). The AUC for CAPE mortality risk score for 30-day mortality was 0.79 (95% CI 0.73 to 0.85, p<0.001); Pneumonia Severity Index (PSI) 0.80 (95% CI 0.74 to 0.86, p<0.001); Confusion of new onset, blood Urea nitrogen, Respiratory rate, Blood pressure, 65 (CURB-65) score 0.76 (95% CI 0.70 to 0.81, p<0.001), respectively. CAPE combined with CURB-65 model has an AUC of 0.83 (95% CI 0.77 to 0.88, p<0.001). The best performing model was CAPE incorporated with PSI, with an AUC of 0.84 (95% CI 0.79 to 0.89, p<0.001).ConclusionCXR-based CAPE mortality risk score was comparable to traditional pneumonia severity scores and improved its discrimination when combined.

CURB-65 PNEUMONIA SEVERITY INDEX AND APACHE II SCORES TO ASSESS MORTALITY RISK IN COMMUNITY-ACQUIRED PNEUMONIA PATIENTS FROM PROWESS

CHEST Journal ◽  
2007 ◽  
Vol 132 (4) ◽  
pp. 559A ◽  
Author(s):  
Guy Richards ◽  
Howard Levy ◽  
Pierre-Francois Laterre ◽  
Charles Feldman ◽  
Becky M. Bates ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Pneumonia Severity Index ◽  
Severity Index ◽  
Community Acquired Pneumonia ◽  
Apache Ii ◽  
Pneumonia Severity

scholarly journals The role of qSOFA score and biomarkers in assessing severity of community-acquired pneumonia in adults

2021 ◽  
Vol 29 (1) ◽  
pp. 65-75
Author(s):  
Raluca-Elena Tripon ◽  
Victor Cristea ◽  
Mihaela-Sorina Lupse
Keyword(s):  
Adverse Outcome ◽  
Pneumonia Severity Index ◽  
Severity Index ◽  
Community Acquired Pneumonia ◽  
Pneumonia Severity ◽  
Assessment Scores ◽  
Reactive Protein ◽  
Severity Scores ◽  
The Mean ◽  
Qsofa Score

Abstract Introduction: Community-acquired pneumonia (CAP) is the primary cause of severe sepsis. Severity assessment scores have been created, in order to help physicians decide the proper management of CAP. The purpose of this study was to examine the correlations between different CAP severity scores, including qSOFA, several biomarkers and their predictive value in the 30 day follow-up period, regarding adverse outcome. Materials and methods: One hundred and thirty nine adult patients with CAP, admitted in the Teaching Hospital of Infectious Diseases, Cluj-Napoca, Romania from December 2015 to February 2017, were enrolled in this study. Pneumonia Severity Index (PSI), CURB-65, SMART-COP and the qSOFA scores were calculated at admittance. Also, C-reactive protein (CRP), procalcitonin (PCT) and albumin levels were used to determine severity. Results: The mean PSI of all patients was 93.30±41.135 points, for CURB-65 it was 1.91±0.928 points, for SMART-COP it was 1.69±1.937 points. The mean qSOFA was 1.06±0.522 points, 21 (14.9%) were at high risk of in-hospital mortality. In the group of patients with qSOFA of ≥2, all pneumonia severity scores and all biomarkers tested were higher than those with scores <2. We found significant correlations between biomarkers and severity scores, but none regarding adverse outcome. Conclusion: The qSOFA score is easier to use and it is able to accurately evaluate the severity of CAP, similar to other scores. Biomarkers are useful in determining the severity of the CAP. Several studies are needed to assess the prediction of these biomarkers and severity scores in pneumonia regarding adverse outcome.

Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia

2021 ◽  
pp. 153537022110271
Author(s):  
Yifeng Zeng ◽  
Mingshan Xue ◽  
Teng Zhang ◽  
Shixue Sun ◽  
Runpei Lin ◽  
...  
Keyword(s):  
Regression Model ◽  
Predictive Value ◽  
Cox Regression ◽  
Pneumonia Severity Index ◽  
Severity Index ◽  
Community Acquired Pneumonia ◽  
Risk Classification ◽  
Soluble Form ◽  
Pneumonia Severity ◽  
Clinical Stability

The soluble form of the suppression of tumorigenicity-2 (sST2) is a biomarker for risk classification and prognosis of heart failure, and its production and secretion in the alveolar epithelium are significantly correlated with the inflammation-inducing in pulmonary diseases. However, the predictive value of sST2 in pulmonary disease had not been widely studied. This study investigated the potential value in prognosis and risk classification of sST2 in patients with community-acquired pneumonia. Clinical data of ninety-three CAP inpatients were retrieved and their sST2 and other clinical indices were studied. Cox regression models were constructed to probe the sST2’s predictive value for patients’ restoring clinical stability and its additive effect on pneumonia severity index and CURB-65 scores. Patients who did not reach clinical stability within the defined time (30 days from hospitalization) have had significantly higher levels of sST2 at admission ( P <  0.05). In univariate and multivariate Cox regression analysis, a high sST2 level (≥72.8 ng/mL) was an independent reverse predictor of clinical stability ( P < 0.05). The Cox regression model combined with sST2 and CURB-65 (AUC: 0.96) provided a more accurate risk classification than CURB-65 (AUC:0.89) alone (NRI: 1.18, IDI: 0.16, P < 0.05). The Cox regression model combined with sST2 and pneumonia severity index (AUC: 0.96) also provided a more accurate risk classification than pneumonia severity index (AUC:0.93) alone (NRI: 0.06; IDI: 0.06, P < 0.05). sST2 at admission can be used as an independent early prognostic indicator for CAP patients. Moreover, it can improve the predictive power of CURB-65 and pneumonia severity index score.

scholarly journals Expanded CURB-65: a new score system predicts severity of community-acquired pneumonia with superior efficiency

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Jin-liang Liu ◽  
Feng Xu ◽  
Hui Zhou ◽  
Xue-jie Wu ◽  
Ling-xian Shi ◽  
...  
Keyword(s):  
Severity Score ◽  
Community Acquired Pneumonia ◽  
Score System ◽  
Pneumonia Severity ◽  
Increased Risk ◽  
Severity Scores ◽  
Low Blood Pressure ◽  
Health Care Associated Pneumonia ◽  
Score Systems ◽  
Better Than

Abstract Aim of this study was to develop a new simpler and more effective severity score for community-acquired pneumonia (CAP) patients. A total of 1640 consecutive hospitalized CAP patients in Second Affiliated Hospital of Zhejiang University were included. The effectiveness of different pneumonia severity scores to predict mortality was compared, and the performance of the new score was validated on an external cohort of 1164 patients with pneumonia admitted to a teaching hospital in Italy. Using age ≥ 65 years, LDH > 230 u/L, albumin < 3.5 g/dL, platelet count < 100 × 109/L, confusion, urea > 7 mmol/L, respiratory rate ≥ 30/min, low blood pressure, we assembled a new severity score named as expanded-CURB-65. The 30-day mortality and length of stay were increased along with increased risk score. The AUCs in the prediction of 30-day mortality in the main cohort were 0.826 (95% CI, 0.807–0.844), 0.801 (95% CI, 0.781–0.820), 0.756 (95% CI, 0.735–0.777), 0.793 (95% CI, 0.773–0.813) and 0.759 (95% CI, 0.737–0.779) for the expanded-CURB-65, PSI, CURB-65, SMART-COP and A-DROP, respectively. The performance of this bedside score was confirmed in CAP patients of the validation cohort although calibration was not successful in patients with health care-associated pneumonia (HCAP). The expanded CURB-65 is objective, simpler and more accurate scoring system for evaluation of CAP severity, and the predictive efficiency was better than other score systems.

scholarly journals Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Cheng Jin ◽  
Weixiang Chen ◽  
Yukun Cao ◽  
Zhanwei Xu ◽  
Zimeng Tan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Influenza A ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Intelligence System ◽  
Challenging Tasks ◽  
Chest X Ray ◽  
Detailed Interpretation ◽  
Timely Treatment

Abstract Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

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