APPLICATION OF DECAF AND BAP-65 TO PREDICT THE IN-HOSPITAL MORTALITY OF ACUTE EXACERBATION OF COPD IN SRMSIMS

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Shruti Jain ◽  
Anurag Agrawal ◽  
Lalit Singh ◽  
Rajeev Tandon
Keyword(s):  
Hospital Mortality ◽  
Sensitivity And Specificity ◽  
Area Under Curve ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Receiver Operator Characteristic Curve ◽  
Minimum Area ◽  
Prediction Of Mortality ◽  
Acute Exacerbations ◽  
Exacerbation Of Copd

INTRODUCTION: Hospitalisation due to acute exacerbations of COPD is common, and subsequent mortality high. The DECAF and BAP 65 score was derived for accurate prediction of mortality and risk stratification to inform patient care. We aimed to validate these scores, and to compare them. Comparison of DECAF, BAP-65 scores in predicting in hospital mortality in AECOPD MATERIAL AND METHODS: 106 patients of AECOPD, admitted during 6 months period were scored at admission using all 2 scores and their ability to predict in-hospital mortality was analysed. RESULTS: On receiver-operator characteristic curve analysis, the area under curve for prediction of in-hospital mortality was 0.791and 0.885 respectively for DECAF and BAP-65 scores respectively. Thus, among the two scoring systems BAP-65 had maximum area under curve while DECAF had minimum area under curve. Sensitivity and specificity values for prediction of in-hospital mortality were 83.3% and 54.3% for DECAF and 83.3% and 84.0% for BAP-65. Thus BAP-65 was the best predictor with adequate sensitivity and specificity for the in-hospital mortality.CONCLUSION: BAP-65 was most effective in prediction of in-hospital mortality

Predicting mortality in bronchiectasis using bronchiectasis severity index and FACED scores: a 19-year cohort study

2015 ◽  
Vol 47 (2) ◽  
pp. 482-489 ◽  
Author(s):  
Huw C. Ellis ◽  
Steven Cowman ◽  
Michele Fernandes ◽  
Robert Wilson ◽  
Michael R. Loebinger
Keyword(s):  
Predictive Power ◽  
Characteristic Curve ◽  
Cohort Analysis ◽  
Scoring Systems ◽  
Severity Index ◽  
Receiver Operator Characteristic Curve ◽  
Mortality Prediction ◽  
Prediction Of Mortality ◽  
Long Term Mortality

The clinical course of bronchiectasis is unpredictable, posing a challenge both in clinical practice and in research. Two mortality prediction scores, the bronchiectasis severity index (BSI) and FACED scores, have recently been developed. The aim of this study was to assess the ability of these scores to predict long-term mortality and to compare the two scores.The study was a single-centre retrospective cohort analysis consisting of 91 subjects originally recruited in 1994. BSI and FACED scores were calculated at the time of enrolment and long-term mortality ascertained. Data was available for 74 patients with a median of 18.8 years of follow-up.Both scoring systems had similar predictive power for 5-year mortality (area under receiver operator characteristic curve (AUC) 0.79 for BSI and 0.8 for FACED). Both scores were able to predict 15-year mortality with the FACED score showing slightly superior predictive power (AUC 0.82 versus 0.69, p=0.0495).This study provides further validation of the FACED and BSI scores for the prediction of mortality in bronchiectasis and demonstrates their utility over a longer period than originally described. Whilst both scores had excellent predictive power, the FACED score was superior for 15-year mortality.

scholarly journals Haemoconcentration as a predictor of severity in acute pancreatitis

2016 ◽  
Vol 4 (1) ◽  
pp. 3-7
Author(s):  
Tanka Prasad Bohara ◽  
Dimindra Karki ◽  
Anuj Parajuli ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Cost Effective ◽  
Receiver Operator Characteristic Curve ◽  
First Episode ◽  
P Value ◽  
Receiver Operator Characteristic ◽  
Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

scholarly journals A new approach to scoring systems to improve identification of acute medical admissions that will require critical care

2011 ◽  
Vol 56 (4) ◽  
pp. 195-202 ◽  
Author(s):  
H A Carmichael ◽  
E Robertson ◽  
J Austin ◽  
D Mccruden ◽  
C M Messow ◽  
...  
Keyword(s):  
Early Warning ◽  
Characteristic Curve ◽  
Medical Patient ◽  
Scoring Systems ◽  
Receiver Operator Characteristic Curve ◽  
Early Warning Score ◽  
Peripheral Oxygen Saturation ◽  
Logistic Regression Models ◽  
Prospective Application ◽  
Gas Measurements

Removal of the intensive care unit (ICU) at the Vale of Leven Hospital mandated the identification and transfer out of those acute medical admissions with a high risk of requiring ICU. The aim of the study was to develop triaging tools that identified such patients and compare them with other scoring systems. The methodology included a retrospective analysis of physiological and arterial gas measurements from 1976 acute medical admissions produced PREEMPT-1 (PRE-critical Emergency Medical Patient Triage). A simpler one for ambulance use (PREAMBLE-1 [PRE-Admission Medical Blue-Light Emergency]) was produced by the addition of peripheral oxygen saturation to a modification of MEWS (Modified Early Warning Score). Prospective application of these tools produced a larger database of 4447 acute admissions from which logistic regression models produced PREEMPT-2 and PREAMBLE-2, which were then compared with the original systems and seven other early warning scoring systems. Results showed that in patients with arterial gases, the area under the receiver operator characteristic curve was significantly higher in PREEMPT-2 (89·1%) and PREAMBLE-2 (84.4%) than all other scoring systems. Similarly, in all patients, it was higher in PREAMBLE-2 (92.4%) than PREAMBLE-1 (88.1%) and the other scoring systems. In conclusion, risk of requiring ICU can be more accurately predicted using PREEMPT-2 and PREAMBLE-2, as described here, than by other early warning scoring systems developed over recent years.

scholarly journals A STUDY OF MODIFIED DECAF SCORE IN PREDICTING HOSPITAL MORTALITY IN PATIENTS OF ACUTE EXACERBATIONS OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE

2019 ◽  
Vol 3 (9) ◽  
Author(s):  
Dr. Sumit Prakash ◽  
Dr. Shruti Jain ◽  
Dr. Lalit Singh ◽  
Dr. Rajeev Tandon
Keyword(s):  
Hospital Mortality ◽  
Acute Exacerbation ◽  
Respiratory Acidosis ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Acute Exacerbation Of Copd ◽  
Social Burden ◽  
Prediction Of Mortality ◽  
Study Population ◽  
Exacerbation Of Copd

Background:  COPD is a leading cause of morbidity and mortality worldwide and results in an economic and social burden that is both substantial and increasing .The modified DECAF score was derived for accurate prediction of mortality and risk stratification to inform patient care. Methods: Hospital based descriptive type of observational study was. After applying inclusion and exclusion criterias, study population for acute exacerbation of COPD was selected. Admission clinical data, including modified DECAF indices, and mortality were recorded. Results: In our study there was a statistically significant value (p <0.05) between grade of dyspnea, respiratory acidosis (pH < 7.30) and frequency of admission in the Modified DECAF score and in-hospital mortality of Acute Exacerbation of COPD. There was insignificant relationship between Eosinopenia & consolidation and in hospital mortality  Conclusion-We concluded that the Modified DECAF score is a powerful score to predict in hospital mortality from AECOPD. Keywords: COPD, DECAF, Exacerbations, Modified DECAF.

scholarly journals Sensitivity and Specificity of Spectral Oct in Patients with Early Glaucoma

2019 ◽  
Vol 75 (5) ◽  
pp. 260-264
Author(s):  
Magdaléna Bočková ◽  
Petr Veselý ◽  
Svatopluk Synek ◽  
Lubomír Hanák ◽  
Pavel Beneš
Keyword(s):  
Retrospective Study ◽  
Visual Field ◽  
Sensitivity And Specificity ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Test Statistics ◽  
Early Glaucoma ◽  
Spectral Oct ◽  
Operating Characteristic Curve

The study examines the sensitivity and specificity of spectral OCT in detecting early glaucoma. The aim was to evaluate data obtained by RNFL analysis in 4 observed quadrants and to compare it with the resulting diagnosis of glaucoma neuropathy determined subsequently on the basis of changes in the visual field. This concerns a retrospective study numbering 31 probands who underwent OCT examination at our centre in the period from 2008 to 2017. Test statistics demonstrated sensitivity of OCT examination (specific RNFL analysis) of 63.64% and specificity of 90%. The used ROC (receiver operating characteristic curve) test showed an AUC (area under curve) value of 0.734 on a statistically significant level of p = 0.0097. We therefore found that the instrument Spectral OCT SLO, with the aid of RNFL analysis, was effective in determining probands in whom development of glaucoma pathology was subsequently confirmed.

scholarly journals Development of a brief scoring system to predict any-cause mortality in patients hospitalized with COVID-19 infection

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254580
Author(s):  
Nasheena Jiwa ◽  
Rahul Mutneja ◽  
Lucie Henry ◽  
Garrett Fiscus ◽  
Richard Zu Wallack
Keyword(s):  
Hospital Mortality ◽  
Random Sample ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Supplemental Oxygen ◽  
Derivation Cohort ◽  
Reactive Protein ◽  
Time Period ◽  
Robust Separation

Patients hospitalized with COVID-19 infection are at a high general risk for in-hospital mortality. A simple and easy-to-use model for predicting mortality based on data readily available to clinicians in the first 24 hours of hospital admission might be useful in directing scarce medical and personnel resources toward those patients at greater risk of dying. With this goal in mind, we evaluated factors predictive of in-hospital mortality in a random sample of 100 patients (derivation cohort) hospitalized for COVID-19 at our institution in April and May, 2020 and created potential models to test in a second random sample of 148 patients (validation cohort) hospitalized for the same disease over the same time period in the same institution. Two models (Model A: two variables, presence of pneumonia and ischemia); (Model B: three variables, age > 65 years, supplemental oxygen ≥ 4 L/min, and C-reactive protein (CRP) > 10 mg/L) were selected and tested in the validation cohort. Model B appeared the better of the two, with an AUC in receiver operating characteristic curve analysis of 0.74 versus 0.65 in Model A, but the AUC differences were not significant (p = 0.24. Model B also appeared to have a more robust separation of mortality between the lowest (none of the three variables present) and highest (all three variables present) scores at 0% and 71%, respectively. These brief scoring systems may prove to be useful to clinicians in assigning mortality risk in hospitalized patients.

External Validation and Comparison of Clostridioides difficile Severity Scoring Systems

2021 ◽  
Author(s):  
D Alexander Perry ◽  
Daniel Shirley ◽  
Dejan Micic ◽  
C Pratish Patel ◽  
Rosemary Putler ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
External Validation ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Receiver Operator Characteristic Curve ◽  
Adverse Outcomes ◽  
Risk Scores ◽  
Clostridioides Difficile ◽  
Multicenter Cohort ◽  
Severity Scores

Abstract Background Many models have been developed to predict severe outcomes from Clostridioides difficile infection. These models are usually developed at a single institution and largely are not externally validated. This aim of this study was to validate previously published risk scores in a multicenter cohort of patients with CDI. Methods Retrospective study on four separate inpatient cohorts with CDI from three distinct sites: The Universities of Michigan (2010-2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive testing. Both within each cohort and combined across all cohorts, published CDI severity scores were assessed and compared to each other and the IDSA guideline definitions of severe and fulminant CDI. Results A total of 3,646 patients were included for analysis. Including the two IDSA guideline definitions, fourteen scores were assessed. Performance of scores varied within each cohort and in the combined set (mean area under the receiver operator characteristic curve(AUC 0.61, range 0.53-0.66). Only half of the scores had performance at or better than IDSA severe and fulminant definitions (AUCs 0.64 and 0.63, respectively). Most of the scoring systems had more false than true positives in the combined set (mean: 81.5%, range:0-91.5%). Conclusions No published CDI severity score showed stable, good predictive ability for adverse outcomes across multiple cohorts/institutions or in a combined multicenter cohort.

Comparison of a new predictive model with other critical scores for predicting in-hospital mortality among children with pneumonia-related bacteremia

2021 ◽  
pp. jim-2020-001688
Author(s):  
Jilei Lin ◽  
Yin Zhang ◽  
Anchao Song ◽  
Nan Yang ◽  
Linyan Ying ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Early Stage ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Early Warning Score ◽  
Decision Curve Analysis ◽  
Prediction Of Mortality ◽  
Risk Of Mortality ◽  
Clinical Benefits ◽  
Better Than

Prediction of mortality in children with pneumonia-related bacteremia is necessary for providing timely care and treatment. This study aims to develop and validate a nomogram and compare it with Pediatric Risk of Mortality III (PRISM III), Brighton Pediatric Early Warning Score (Brighton PEWS) and Pediatric Critical Illness Score (PCIS), which are widely used in predicting in-hospital mortality in children with pneumonia-related bacteremia. This retrospective study collected clinical data of hospitalized children with pneumonia-related bacteremia in Chongqing, China (January 2013–May 2019). The nomogram was built using multivariate logistic regression analysis. The nomogram was compared with PRISM III, PEWS and PCIS in accuracy and clinical benefits in predicting in-hospital mortality in children with pneumonia-related bacteremia. A total of 242 children were included. The nomogram including time to first positivity of blood cultures (TTFP), serum albumin (ALB) and lactate dehydrogenase (LDH) was established. The area under the receiver operating characteristic curve of the nomogram was 0.84 (95% CI 0.77 to 0.91) in the training set and 0.82 (95% CI 0.71 to 0.93) in the validating set. Good consistency was observed between the predictions and the actual observations, and the decision curve analysis showed that the nomogram was clinically useful. The results showed that the nomogram significantly performed better than the three critical scores. In conclusion, a nomogram-illustrated model incorporating TTFP, ALB and LDH for predicting in-hospital mortality in children with pneumonia-related bacteremia at the early stage was established and validated. It performed better than PRISM III, PEWS and PCIS.

scholarly journals Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 596 ◽  
Author(s):  
Alejandro López-Escobar ◽  
Rodrigo Madurga ◽  
José María Castellano ◽  
Sara Velázquez ◽  
Rafael Suárez del Villar ◽  
...  
Keyword(s):  
At Risk ◽  
Hospital Mortality ◽  
Validation Cohort ◽  
Health Care Systems ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Oxygen Saturation Level ◽  
Training Cohort ◽  
Care Systems ◽  
Patients At Risk

Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849–0.898), NPR 0.875 (95% CI: 0.851–0.899) in training cohort and NLR 0.856 (95% CI: 0.818–0.895), NPR 0.863 (95% CI: 0.826–0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885–0.919) for VNLR and 0.891 (95% CI: 0.861–0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes.

scholarly journals Eosinopenia and neutrophil-to-lymphocyte count ratio as prognostic factors in exacerbation of COPD

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomasz Karauda ◽  
Kamil Kornicki ◽  
Amer Jarri ◽  
Adam Antczak ◽  
Joanna Miłkowska-Dymanowska ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Sensitivity And Specificity ◽  
Lymphocyte Count ◽  
Statistical Significance ◽  
Hospital Death ◽  
Clinical Aspects ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Hospital Deaths ◽  
Exacerbation Of Copd

AbstractExacerbations of Chronic Obstructive Pulmonary Disease (AECOPDs) are one of the most important clinical aspects of the disease, and when requiring hospital admission, they significantly contribute to mortality among COPD patients. Our aim was to assess the role of eosinopenia and neutrophil-to-lymphocyte count (NLR) as markers of in-hospital mortality and length of hospitalization (LoH) among patients with ECOPD requiring hospitalization. We included 275 patients. Eosinopenia was associated with in-hospital deaths only when coexisted with lymphocytopenia, with the specificity of 84.4% (95% CI 79.6–88.6%) and the sensitivity of 100% (95% CI 35.9–100%). Also, survivors presented longer LoH (P < 0.0001). NLR ≥ 13.2 predicted in-hospital death with the sensitivity of 100% (95% CI 35.9–100%) and specificity of 92.6% (95% CI 88.8–95.4%), however, comparison of LoH among survivors did not reach statistical significance (P = 0.05). Additionally, when we assessed the presence of coexistence of eosinopenia and lymphocytopenia first, and then apply NLR, sensitivity and specificity in prediction of in-hospital death was 100% (95% CI 35.9–100) and 93.7% (95% CI 90.1–96.3), respectively. Moreover, among survivors, the occurrence of such pattern was associated with significantly longer LoH: 11 (7–14) vs 7 (5–10) days (P = 0.01). The best profile of sensitivity and specificity in the prediction of in-hospital mortality in ECOPD can be obtained by combined analysis of coexistence of eosinopenia and lymphocytopenia with elevated NLR. The occurrence of a such pattern is also associated with significantly longer LoH among survivors.

Sign in / Sign up

Export Citation Format

Share Document