scholarly journals Predicting In-hospital Death in Pneumonic COPD exacerbation via BAP-65, CURB-65, and Machine Learning

2021 ◽  
pp. 00452-2021
Author(s):  
Akihiro Shiroshita ◽  
Yuya Kimura ◽  
Hiroshi Shiba ◽  
Chigusa Shirakawa ◽  
Kenya Sato ◽  
...  

IntroductionThere is no established clinical prediction model for in-hospital death among patients with pneumonic chronic obstructive pulmonary disease (COPD) exacerbation. We aimed to externally validate BAP-65 and CURB-65 and to develop a new model based on the eXtreme Gradient Boosting (XGBoost) algorithm.MethodsThis multicentre cohort study included patients aged ≥40 years with pneumonic COPD exacerbation. The input data were age, sex, activities of daily living, mental status, systolic and diastolic blood pressure, respiratory rate, heart rate, peripheral blood eosinophil count, and blood urea nitrogen. The primary outcome was in-hospital death. BAP-65 and CURB-65 underwent external validation using the area under the receiver operating characteristic curve (AUROC) in the whole dataset. We used XGBoost to develop a new prediction model. We compared the AUROCs of XGBoost with that of BAP-65 and CURB-65 in the test dataset using bootstrap sampling.ResultsWe included 1190 patients with pneumonic COPD exacerbation. The in-hospital mortality was 7% (88/1190). In the external validation of BAP-65 and CURB-65, the AUROCs (95% confidence interval [CI]) of BAP-65 and CURB-65 were 0.69 (0.66–0.72, and 0.69 (0.66–0.72), respectively. XGBoost showed an AUROC of 0.71 (0.62–0.81) in the test dataset. There was no significant difference in the AUROCs of XGBoost versus BAP-65 (absolute difference, 0.054; 95% CI, −0.057–0.16) or versus CURB-65 (absolute difference, 0.0021; 95% CI, −0.091–0.088).ConclusionBAP-65, CURB-65, and XGBoost showed low predictive performance for in-hospital death in pneumonic COPD exacerbation. Further large-scale studies including more variables are warranted.

2021 ◽  
Author(s):  
Akihiro Shiroshita ◽  
Yuya Kimura ◽  
Hiroshi Shiba ◽  
Chigusa Shirakawa ◽  
Kenya Sato ◽  
...  

Abstract IntroductionThere is no established clinical prediction model for in-hospital death among patients with pneumonic chronic obstructive pulmonary disease (COPD) exacerbation. We aimed to externally validate BAP-65 and CURB-65 and to develop a new model based on the eXtreme Gradient Boosting (XGBoost) algorithm.MethodsThis multicentre cohort study included patients aged ≥40 years with pneumonic COPD exacerbation. The input data were age, sex, activities of daily living, mental status, systolic and diastolic blood pressure, respiratory rate, and heart rate, peripheral blood eosinophil count, and blood urea nitrogen. The primary outcome was in-hospital death. BAP-65 and CURB-65 underwent external validation using the area under the receiver operating characteristic curve (AUROC) in the whole dataset. We used XGBoost to develop a new prediction model. We compared the AUROCs of XGBoost with that of BAP-65 and CURB-65 in the test dataset using bootstrap sampling.ResultsWe included 1190 patients with pneumonic COPD exacerbation. The in-hospital mortality was 7% (88/1190). In the external validation of BAP-65 and CURB-65, the AUROCs [95% confidence interval (CI)] of BAP-65 and CURB-65 were 0.69 [0.66–0.72], and 0.69 [0.66–0.72], respectively. XGBoost showed an AUROC of 0.71 [0.62–0.81] in the test dataset. There was no significant difference in the AUROCs of XGBoost vs BAP-65 (absolute difference, 0.054; 95% CI, -0.057–0.16) or vs CURB-65 (absolute difference, 0.0021; 95% CI, -0.091–0.088). ConclusionBAP-65, CURB-65, and XGBoost showed low predictive performance for in-hospital death in pneumonic COPD exacerbation. Further large-scale studies including more variables are warranted.


QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Samar El Sharkawy ◽  
Riham Hazem Raafat ◽  
Reem Osama Mohamed Ahmed Qassem

Abstract Background The Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines define COPD as a disease state characterized by airflow limitation that is not fully reversible, is usually progressive, and is associated with an abnormal inflammatory response of the lungs to inhaled noxious particles or gases. Objective To identify outcomes of patients with eosinophilic COPD exacerbations requiring hospital admission. Patients and Methods This study is a prospective cohort study that was conducted on two groups of total 60 patients recruited from Ain Shams University hospitals between October 2019 and July 2020. Group 1: Eosinophilic COPD exacerbation if the peripheral blood eosinophil on admission is ≥ 200 cells/µL and/or ≥2% of the total leukocyte count Group 2: Non-eosinophilic COPD exacerbation if the peripheral blood eosinophil on admission is < 200 cells/µL and/or < 2% of the total leukocyte count. Results There was significant high diagnostic performance in predicting readmission at 6-month among eosinophilic group. Eosinophils count, percent (%) and NLR cutoff points had high characteristics (highest in NLR ≥3.1 at discharge) in predicting readmission at 6-month among eosinophilic group. Diagnostic performance of Eosinophils count, percent (%) and NLR were assessed. Eosinophils count, percent (%) and NLR had significant high diagnostic performance in predicting readmission at 6-month among eosinophilic group. Eosinophils count, % and NLR cutoff points had high characteristics (highest in NLR ≥2.1 at discharge) in predicting readmission at 6month among non-eosinophilic group. Conclusion Eosinophils can be used as a prognostic marker in non-infective COPD exacerbations. Validity of eosinophil count and percent as a prognostic parameter in COPD exacerbation can be increased by combining with other parameters for example NLR.


ORL ◽  
2021 ◽  
pp. 1-8
Author(s):  
Mingjie Wang ◽  
Bing Zhou ◽  
Yunchuan Li ◽  
Shunjiu Cui ◽  
Qian Huang

Introduction: Osteitis in chronic rhinosinusitis (CRS) is a predictive factor of disease severity and an important potential reason for disease recalcitrance. Other than medical treatment, transnasal endoscopic surgery could be another choice to deal with osteitis in CRS. Objective: In this study, 2 different surgical outcomes and influence in patients with osteitis in CRS were discussed. Methods: A retrospective analysis of 51 cases was carried out. Osteitis in CRS was confirmed by sinus computed tomography (CT). According to surgical management, patients were divided into the radical endoscopic sinus surgery (RESS) group (n = 24) and functional endoscopic sinus surgery (FESS) group (n = 27). Baseline measures and postoperative outcomes were evaluated by symptom visual analog scale (VAS), peripheral blood eosinophil percentage, serum total IgE, skin prick test, endoscopy Lund-Kennedy score, CT scan Lund-Mackay score, and global osteitis scoring scale (GOSS) in 2 groups. Results and Conclusions: There was no significant difference between the 2 groups in age, gender, and complicated with allergic rhinitis and asthma. The preoperative symptom VAS score and endoscopy Lund-Kennedy score were higher in the RESS group than in the FESS group, and the Lund-Mackay score and GOSS score were similar in the 2 groups. One year after surgery, symptom VAS scores, endoscopy Lund-Kennedy score, and Lund-Mackay score were significantly lower in the 2 groups. The endoscopy Lund-Kennedy score and Lund-Mackay score were lower in the RESS group than in the FESS group 1 year after surgery. RESS was more effective in reducing inflammatory load of sinuses in patients with osteitis in CRS.


2020 ◽  
Vol 2 (Supplement_3) ◽  
pp. ii14-ii14
Author(s):  
Toru Umehara ◽  
Manabu Kinoshita ◽  
Takahiro Sasaki ◽  
Hideyuki Arita ◽  
Ema Yoshioka ◽  
...  

Abstract Introduction: Clinical application of survival prediction of primary glioblastoma (pGBM) using preoperative images remains challenging due to a lack of robustness and standardization of the method. This research focused on validating a machine learning-based texture analysis model for this purpose using internal and external cohorts. Method: We included all cases of IDH wild-type pGBM available of preoperative MRI (T1WI, T2WI, and Gd-T1WI) from the databases of Kansai Molecular Diagnosis Network for CNS tumors (KN) and The Cancer Genome Atlas (TCGA). Of 242 cases from KN, we assigned 137 cases as a training dataset (D1), and the remaining 105 cases as an internal validation dataset (D2). Furthermore, we extracted 96 cases from TCGA as an external validation dataset (D3). Preoperative MRI scans were semi-quantitatively analyzed, leading to the acquisition of 489 texture features as explanatory variables. Dichotomous overall survival (OS) with a 16.6 months cutoff was regarded as the response variable (short/long OS). We employed Lasso regression for feature selection, and a survival prediction model constructed for D1 via cross-validation (M1) was applied to D2 and D3 to ensure the model robustness. Results: The population of predicted short OS by M1 significantly showed poorer prognosis in D2 (median OS 11.1 vs. 19.4 months; log-rank test, p=0.03), while there was no significant difference in D3 (median OS 14.2 vs. 11.9 months; p=0.61). In the comparative analysis using t-SNE, there was little variation in the feature distribution among three datasets. Conclusion: We were able to validate the prediction model in the internal but not in the external cohort. The presented result supports the use of machine learning-based texture analysis for survival prediction of pGBM in a localized population or country. However, further consideration is required to achieve a universal prediction model for pGBM, irrespective of regional difference.


2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  

2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  

2021 ◽  
pp. 1-10
Author(s):  
Weichen Zhang ◽  
Qiuna Du ◽  
Jing Xiao ◽  
Zhaori Bi ◽  
Chen Yu ◽  
...  

<b><i>Background:</i></b> Our research group has previously reported a noninvasive model that estimates phosphate removal within a 4-h hemodialysis (HD) treatment. The aim of this study was to modify the original model and validate the accuracy of the new model of phosphate removal for HD and hemodiafiltration (HDF) treatment. <b><i>Methods:</i></b> A total of 109 HD patients from 3 HD centers were enrolled. The actual phosphate removal amount was calculated using the area under the dialysate phosphate concentration time curve. Model modification was executed using second-order multivariable polynomial regression analysis to obtain a new parameter for dialyzer phosphate clearance. Bias, precision, and accuracy were measured in the internal and external validation to determine the performance of the modified model. <b><i>Results:</i></b> Mean age of the enrolled patients was 63 ± 12 years, and 67 (61.5%) were male. Phosphate removal was 19.06 ± 8.12 mmol and 17.38 ± 6.75 mmol in 4-h HD and HDF treatments, respectively, with no significant difference. The modified phosphate removal model was expressed as Tpo<sub>4</sub> = 80.3 × <i>C</i><sub>45</sub> − 0.024 × age + 0.07 × weight + β × clearance − 8.14 (β = 6.231 × 10<sup>−3</sup> × clearance − 1.886 × 10<sup>−5</sup> × clearance<sup>2</sup> – 0.467), where <i>C</i><sub>45</sub> was the phosphate concentration in the spent dialysate measured at the 45th minute of HD and clearance was the phosphate clearance of the dialyzer. Internal validation indicated that the new model was superior to the original model with a significantly smaller bias and higher accuracy. External validation showed that <i>R</i><sup>2</sup>, bias, and accuracy were not significantly different than those of internal validation. <b><i>Conclusions:</i></b> A new model was generated to quantify phosphate removal by 4-h HD and HDF with a dialyzer surface area of 1.3–1.8 m<sup>2</sup>. This modified model would contribute to the evaluation of phosphate balance and individualized therapy of hyperphosphatemia.


2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zhong Xin ◽  
Lin Hua ◽  
Xu-Hong Wang ◽  
Dong Zhao ◽  
Cai-Guo Yu ◽  
...  

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.


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