scholarly journals A Web Based Dynamic MANA Nomogram for Predicting the Malignant Cerebral Edema 

2020 ◽  
Author(s):  
Wenzhe Sun ◽  
Guo Li ◽  
Yang Song ◽  
Zhou Zhu ◽  
Zhaoxia Yang ◽  
...  
Keyword(s):  
Cerebral Edema ◽  
Clinical Decision Making ◽  
Binary Logistic Regression ◽  
Mass Effect ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Chinese Patients ◽  
Midline Shift ◽  
Patient Death ◽  
Exact Probability

Abstract Background: For large hemispheric infarction (LHI), malignant cerebral edema (MCE) is a life-threatening complication with mortality approaching 80%. Establishing a convenient prediction model of MCE after LHI is vital for rapid identification of high-risk patients and understanding of the potential mechanism of MCE.Methods: 142 consecutive patients with LHI within 24h of onset from January 1, 2016 to August 31, 2019 were retrospectively collected. MCE was defined as patient death or received DHC with obvious mass effect (≥ 5mm midline shift or Basal cistern effacement). Binary logistic regression was performed to evaluated the independent predictors of MCE. Independent prognostic factors were incorporated to build dynamic MANA nomogram to predict MCE.Results: After adjustment for confounders, four independent factors were identified, including previously known atrial fibrillation (KAF), midline shift (MLS), National Institutes of Health Stroke Scale (NIHSS) and anterior cerebral artery (ACA) territory involvement. Furthermore, to facilitate the use of the nomogram for clinicians, we use “Dynnom” package to build dynamic MANA (acronym for MLS, ACA territory involvement, NIHSS and KAF) nomogram on web page (http://www.MANA-nom.com) to calculate the exact probability of developing MCE. The c-statistic of MANA nomogram was up to 0.887 ± 0.041 and AUC-ROC value in this cohort was 0.887 (95%CI, 0.828~0.934).Conclusions: Independent predictors of MCE included KAF, MLS, NIHSS, and ACA territory involvement. The dynamic MANA nomogram is a convenient, practical and effective clinical decision-making tool for predicting MCE after LHI in Chinese patients.

scholarly journals A Web Based Dynamic MANA Nomogram for Predicting the Malignant Cerebral Edema in patients with large hemispheric infarction

2020 ◽  
Author(s):  
Wenzhe Sun ◽  
Guo Li ◽  
Yang Song ◽  
Zhou Zhu ◽  
Zhaoxia Yang ◽  
...  
Keyword(s):  
Cerebral Edema ◽  
Clinical Decision Making ◽  
Mass Effect ◽  
Clinical Decision ◽  
Chinese Patients ◽  
Midline Shift ◽  
Patient Death ◽  
Exact Probability ◽  
Decompressive Hemicraniectomy ◽  
Hemispheric Infarction

Abstract Background: For large hemispheric infarction (LHI), malignant cerebral edema (MCE) is a life-threatening complication with a mortality rate approaching 80%. Establishing a convenient prediction model of MCE after LHI is vital for the rapid identification of high-risk patients as well as for a better understanding of the potential mechanism underlying MCE.Methods: 142 consecutive patients with LHI within 24h of onset between January 1, 2016 and August 31, 2019 were retrospectively reviewed. MCE was defined as patient death or received decompressive hemicraniectomy (DHC) with obvious mass effect (≥ 5mm midline shift or Basal cistern effacement). Binary logistic regression was performed to identify independent predictors of MCE. Independent prognostic factors were incorporated to build a dynamic nomogram for MCE prediction.Results: After adjusting for confounders, four independent factors were identified, including previously known atrial fibrillation (KAF), midline shift (MLS), National Institutes of Health Stroke Scale (NIHSS) and anterior cerebral artery (ACA) territory involvement. To facilitate the nomogram use for clinicians, we used the “Dynnom” package to build a dynamic MANA (acronym for MLS, ACA territory involvement, NIHSS and KAF) nomogram on web (http://www.MANA-nom.com) to calculate the exact probability of developing MCE. The MANA nomogram’s C-statistic was up to 0.887 ± 0.041 and the AUC-ROC value in this cohort was 0.887 (95%CI, 0.828~0.934).Conclusions: Independent MCE predictors included KAF, MLS, NIHSS, and ACA territory involvement. The dynamic MANA nomogram is a convenient, practical and effective clinical decision-making tool for predicting MCE after LHI in Chinese patients.

scholarly journals A Web Based Dynamic MANA Nomogram for Predicting the Malignant Cerebral Edema in patients with large hemispheric infarction

2020 ◽  
Author(s):  
Wenzhe Sun ◽  
Guo Li ◽  
Yang Song ◽  
Zhou Zhu ◽  
Zhaoxia Yang ◽  
...  
Keyword(s):  
Cerebral Edema ◽  
Clinical Decision Making ◽  
Mass Effect ◽  
Clinical Decision ◽  
Chinese Patients ◽  
Midline Shift ◽  
Patient Death ◽  
Exact Probability ◽  
Decompressive Hemicraniectomy ◽  
Hemispheric Infarction

Abstract Background: For large hemispheric infarction (LHI), malignant cerebral edema (MCE) is a life-threatening complication with a mortality rate approaching 80%. Establishing a convenient prediction model of MCE after LHI is vital for the rapid identification of high-risk patients as well as for a better understanding of the potential mechanism underlying MCE. Methods: 142 consecutive patients with LHI within 24h of onset from January 1, 2016 to August 31, 2019 were retrospectively collected. MCE was defined as patient death or received decompressive hemicraniectomy (DHC) with obvious mass effect (≥ 5mm midline shift or Basal cistern effacement). Binary logistic regression was performed to identify independent predictors of MCE. Independent prognostic factors were incorporated to build a dynamic nomogram for MCE prediction. Results : After adjustment for confounders, four independent factors were identified, including previously known atrial fibrillation (KAF), midline shift (MLS), National Institutes of Health Stroke Scale (NIHSS) and anterior cerebral artery (ACA) territory involvement. To facilitate the use of nomogram for clinicians, we use “Dynnom” package to build dynamic MANA (acronym for MLS, ACA territory involvement, NIHSS and KAF) nomogram on web page ( http://www.MANA-nom.com ) to calculate the exact probability of developing MCE. The c-statistic of MANA nomogram was up to 0.887 ± 0.041 and AUC-ROC value in this cohort was 0.887 (95%CI, 0.828~0.934). Conclusions: Independent predictors of MCE included KAF, MLS, NIHSS, and ACA territory involvement. The dynamic MANA nomogram is a convenient, practical and effective clinical decision-making tool for predicting MCE after LHI in Chinese patients.

scholarly journals A web based dynamic MANA Nomogram for predicting the malignant cerebral edema in patients with large hemispheric infarction

BMC Neurology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Wenzhe Sun ◽  
Guo Li ◽  
Yang Song ◽  
Zhou Zhu ◽  
Zhaoxia Yang ◽  
...  
Keyword(s):  
Cerebral Edema ◽  
Clinical Decision Making ◽  
Mass Effect ◽  
Clinical Decision ◽  
Chinese Patients ◽  
Midline Shift ◽  
Patient Death ◽  
Exact Probability ◽  
Decompressive Hemicraniectomy ◽  
Hemispheric Infarction

Abstract Background For large hemispheric infarction (LHI), malignant cerebral edema (MCE) is a life-threatening complication with a mortality rate approaching 80%. Establishing a convenient prediction model of MCE after LHI is vital for the rapid identification of high-risk patients as well as for a better understanding of the potential mechanism underlying MCE. Methods One hundred forty-two consecutive patients with LHI within 24 h of onset between January 1, 2016 and August 31, 2019 were retrospectively reviewed. MCE was defined as patient death or received decompressive hemicraniectomy (DHC) with obvious mass effect (≥ 5 mm midline shift or Basal cistern effacement). Binary logistic regression was performed to identify independent predictors of MCE. Independent prognostic factors were incorporated to build a dynamic nomogram for MCE prediction. Results After adjusting for confounders, four independent factors were identified, including previously known atrial fibrillation (KAF), midline shift (MLS), National Institutes of Health Stroke Scale (NIHSS) and anterior cerebral artery (ACA) territory involvement. To facilitate the nomogram use for clinicians, we used the “Dynnom” package to build a dynamic MANA (acronym for MLS, ACA territory involvement, NIHSS and KAF) nomogram on web (http://www.MANA-nom.com) to calculate the exact probability of developing MCE. The MANA nomogram’s C-statistic was up to 0.887 ± 0.041 and the AUC-ROC value in this cohort was 0.887 (95%CI, 0.828 ~ 0.934). Conclusions Independent MCE predictors included KAF, MLS, NIHSS, and ACA territory involvement. The dynamic MANA nomogram is a convenient, practical and effective clinical decision-making tool for predicting MCE after LHI in Chinese patients.

scholarly journals A Web Based Dynamic MANA Nomogram for Predicting the Malignant Cerebral Edema in patients with large hemispheric infarction

2020 ◽  
Author(s):  
Wenzhe Sun ◽  
Guo Li ◽  
Yang Song ◽  
Zhou Zhu ◽  
Zhaoxia Yang ◽  
...  
Keyword(s):  
Cerebral Edema ◽  
Clinical Decision Making ◽  
Mass Effect ◽  
Clinical Decision ◽  
Chinese Patients ◽  
Midline Shift ◽  
Patient Death ◽  
Exact Probability ◽  
Decompressive Hemicraniectomy ◽  
Hemispheric Infarction

Abstract Background: For large hemispheric infarction (LHI), malignant cerebral edema (MCE) is a life-threatening complication with a mortality rate approaching 80%. Establishing a convenient prediction model of MCE after LHI is vital for the rapid identification of high-risk patients as well as for a better understanding of the potential mechanism underlying MCE.Methods: 142 consecutive patients with LHI within 24h of onset from January 1, 2016 to August 31, 2019 were retrospectively collected. MCE was defined as patient death or received decompressive hemicraniectomy (DHC) with obvious mass effect (≥ 5mm midline shift or Basal cistern effacement). Binary logistic regression was performed to identify independent predictors of MCE. Independent prognostic factors were incorporated to build a dynamic nomogram for MCE prediction. Results: After adjustment for confounders, four independent factors were identified, including previously known atrial fibrillation (KAF), midline shift (MLS), National Institutes of Health Stroke Scale (NIHSS) and anterior cerebral artery (ACA) territory involvement. To facilitate the use of nomogram for clinicians, we use “Dynnom” package to build dynamic MANA (acronym for MLS, ACA territory involvement, NIHSS and KAF) nomogram on web page (http://www.MANA-nom.com) to calculate the exact probability of developing MCE. The c-statistic of MANA nomogram was up to 0.887 ± 0.041 and AUC-ROC value in this cohort was 0.887 (95%CI, 0.828~0.934).Conclusions: Independent predictors of MCE included KAF, MLS, NIHSS, and ACA territory involvement. The dynamic MANA nomogram is a convenient, practical and effective clinical decision-making tool for predicting MCE after LHI in Chinese patients.

A Woman With Headaches and a Tumefactive Brain Lesion

2021 ◽  
pp. 202-203
Author(s):  
Andrew McKeon
Keyword(s):  
Corpus Callosum ◽  
Clinical Decision Making ◽  
Demyelinating Disease ◽  
Brain Lesion ◽  
Brain Magnetic Resonance Imaging ◽  
Corticosteroid Treatment ◽  
Mass Effect ◽  
Clinical Decision ◽  
Contrast Imaging ◽  
Language Deficit

A 65-year-old woman sought care for a 6-month history of confusion and emotional disturbance that was initially ascribed to stress. She then had development of headaches over several weeks, which prompted brain magnetic resonance imaging with contrast. Imaging showed a mass emanating bilaterally from the splenium of the corpus callosum with heterogeneous T1 postgadolinium enhancement. Neurologic examination indicated left homonymous hemianopia, but she was otherwise normal. She had neither alexia nor other language deficit that may appear with a splenial corpus callosum lesion. A biopsy of the brain mass was performed. Histologic analysis of the biopsy specimen revealed glioblastoma multiforme. Corticosteroid treatment was prescribed, which relieved her headache. Radiation therapy and chemotherapy (temozolomide) were recommended. No further follow-up information was available. In neurologic clinical practice, a large corpus callosum–based lesion is sometimes encountered. The localization of such lesions is not specific for any one diagnosis, but radiologic characteristics can aid clinical decision making. Although the radiologic appearance of a lesion spreading out into both hemispheres from the corpus callosum can indicate butterfly glioma, the differential diagnosis also includes tumefactive demyelinating disease and lymphoma, which can also have a callosal localization and produce mass effect.

scholarly journals Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elza Rechtman ◽  
Paul Curtin ◽  
Esmeralda Navarro ◽  
Sharon Nirenberg ◽  
Megan K. Horton
Keyword(s):  
Machine Learning ◽  
New York ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Vital Signs ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Gradient Boosting ◽  
Hospital System ◽  
Response Strategies

AbstractTimely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

Can artificial intelligence support the clinical decision making for Barcelona clinic liver cancer stage 0/a hepatocellular carcinoma in China?

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15634-e15634
Author(s):  
Ze-bin Chen ◽  
Shu-Ling Chen ◽  
Rui-Ming Liang ◽  
Zhen-Wei Peng ◽  
Jing-Xian Shen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Hepatocellular Carcinoma ◽  
Decision Making ◽  
Clinical Decision Support System ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Concordance Rate ◽  
Chinese Patients ◽  
Cancer Types ◽  
Ibm Watson

e15634 Background: Artificial intelligence (AI) is emerging as a revolutionary technology with the power to transform healthcare. IBM Watson for Oncology (WFO), as an AI clinical decision support system (CDSS), has been investigated about its impact on clinical decision making in some cancer types and shown potential to be an effective CDSS in cancer care. However, the feasibility of WFO in Chinese patients with hepatocellular carcinoma (HCC) has not been reported. Methods: Artificial intelligence (AI) is emerging as a revolutionary technology with the power to transform healthcare. IBM Watson for Oncology (WFO), as an AI clinical decision support system (CDSS), has been investigated about its impact on clinical decision making in some cancer types and shown potential to be an effective CDSS in cancer care. However, the feasibility of WFO in Chinese patients with hepatocellular carcinoma (HCC) has not been reported. Results: The overall concordance rate was 60.5%, with 53.7% and 61.4% in BCLC stage 0 and A respectively. After the MDT re-review, the overall, BCLC stage 0 and A concordance rate increased to 67.3%, 65.9% and 67.3%. The main discordance was that MDT recommended more aggressive treatment options (eg. hepatectomy) than WFO did. The increase in concordance rate may be due to the progress of treatment of HCC in the past 5 years. Conclusions: With the concordance and reasonability verified by MDT in this study, WFO may provide practical reference in BCLC stage 0/A HCC. Localization is required to cover the disparity in guideline and patient characteristics between China and the US.

scholarly journals Back to Basics: Recognition of Sepsis with New Definition

2019 ◽  
Vol 8 (11) ◽  
pp. 1838 ◽  
Author(s):  
Horak ◽  
Martinkova ◽  
Radej ◽  
Matejovič
Keyword(s):  
Decision Making ◽  
At Risk ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Junior Doctors ◽  
Serious Infections ◽  
Sepsis Definition ◽  
Definition Of

Patients with serious infections at risk of deterioration represent highly challenging clinical situations, and in particular for junior doctors. A comprehensive clinical examination that integrates the assessment of vital signs, hemodynamics, and peripheral perfusion into clinical decision making is key to responding promptly and effectively to evolving acute medical illnesses, such as sepsis or septic shock. Against this background, the new concept of sepsis definition may provide a useful link between junior doctors and consultant decision making. The purpose of this article is to introduce the updated definition of sepsis and suggest its practical implications, with particular emphasis on integrative clinical assessment, allowing for the rapid identification of patients who are at risk of further deterioration.

scholarly journals Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

2020 ◽  
Author(s):  
Elza Rechtman ◽  
Paul Curtin ◽  
Esmeralda Navarro ◽  
Sharon Nirenberg ◽  
Megan K Horton
Keyword(s):  
Machine Learning ◽  
New York ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Vital Signs ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Gradient Boosting ◽  
Hospital System ◽  
Response Strategies

Abstract Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95%CI, 1.66-1.92]), male sex (OR, 1.57 [95%CI, 1.30-1.90]), higher BMI (OR, 1.03 [95%CI, 1.102-1.05]), higher heart rate (OR, 1.01 [95%CI, 1.00-1.01]), higher respiratory rate (OR, 1.05 [95%CI, 1.03-1.07]), lower oxygen saturation (OR, 0.94 [95%CI, 0.93-0.96]), and chronic kidney disease (OR, 1.53 [95%CI, 1.20-1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC=0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

Preliminary Findings on the Use of Session Questionnaires to Improve Clinician-Client Alliance With Special Consideration to Clinical Education

2015 ◽  
Vol 25 (1) ◽  
pp. 50-60
Author(s):  
Anu Subramanian
Keyword(s):  
Graduate Students ◽  
Clinical Education ◽  
Clinical Decision Making ◽  
Rating Scale ◽  
Treatment Effectiveness ◽  
Clinical Decision ◽  
Added Value ◽  
Therapeutic Outcomes ◽  
Specific Feedback ◽  
Stakeholder Perspective

ASHA's focus on evidence-based practice (EBP) includes the family/stakeholder perspective as an important tenet in clinical decision making. The common factors model for treatment effectiveness postulates that clinician-client alliance positively impacts therapeutic outcomes and may be the most important factor for success. One strategy to improve alliance between a client and clinician is the use of outcome questionnaires. In the current study, eight parents of toddlers who attended therapy sessions at a university clinic responded to a session outcome questionnaire that included both rating scale and descriptive questions. Six graduate students completed a survey that included a question about the utility of the questionnaire. Results indicated that the descriptive questions added value and information compared to using only the rating scale. The students were varied in their responses regarding the effectiveness of the questionnaire to increase their comfort with parents. Information gathered from the questionnaire allowed for specific feedback to graduate students to change behaviors and created opportunities for general discussions regarding effective therapy techniques. In addition, the responses generated conversations between the client and clinician focused on clients' concerns. Involving the stakeholder in identifying both effective and ineffective aspects of therapy has advantages for clinical practice and education.

Sign in / Sign up

Export Citation Format

Share Document