Publications by authors named "N L Abraham"

862 Publications

A minimal clinically important difference measured by the Cambridge Pulmonary Hypertension Outcome Review for patients with idiopathic pulmonary arterial hypertension.

Pulm Circ 2021 Apr-Jun;11(2):2045894021995055. Epub 2021 May 21.

Pulmonary Vascular Diseases Unit, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK.

Several patient-reported outcome measures have been developed to assess health status in pulmonary arterial hypertension. The required change in instrument scores needed, to be seen as meaningful to the individual, however remain unknown. We sought to identify minimal clinically important differences in the Cambridge Pulmonary Hypertension Outcome Review (CAMPHOR) and to validate these against objective markers of functional capacity. Minimal clinically important differences were established from a discovery cohort ( = 129) of consecutive incident cases of idiopathic pulmonary arterial hypertension with CAMPHOR scores recorded at treatment-naïve baseline and 4-12 months following pulmonary arterial hypertension therapy. An independent validation cohort ( = 87) was used to verify minimal clinically important differences. Concurrent measures of functional capacity relative to CAMPHOR scores were collected. Minimal clinically important differences were derived using anchor- and distributional-based approaches. In the discovery cohort, mean (SD) was 54.4 (16.4) years and 64% were female. Most patients (63%) were treated with sequential pulmonary arterial hypertension therapy. Baseline CAMPHOR scores were: Symptoms, 12 (7); Activity, 12 (7) and quality of life, 10 (7). Pulmonary arterial hypertension treatment resulted in significant improvements in CAMPHOR scores ( < 0.05). CAMPHOR minimal clinically important differences averaged across methods for health status improvement were: Symptoms, -4 points; Activity, -4 points and quality of life -3 points. CAMPHOR Activity score change ≥minimal clinically important difference was associated with significantly greater improvement in six-minute walk distance, in both discovery and validation populations. In conclusion, CAMPHOR scores are responsive to pulmonary arterial hypertension treatment. Minimal clinically important differences in pulmonary hypertension-specific scales may provide useful insights into treatment response in future clinical trials.
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http://dx.doi.org/10.1177/2045894021995055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8149778PMC
May 2021

Pharmacological and Clinical Significance of Heme Oxygenase-1.

Antioxidants (Basel) 2021 May 27;10(6). Epub 2021 May 27.

Department of Medicine, New York Medical College, Valhalla, NY 10595, USA.

This Special Issue collates and updates the current knowledge of the pharmacology and clinical applications concerning the enzyme heme oxygenase (HO) [...].
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http://dx.doi.org/10.3390/antiox10060854DOI Listing
May 2021

The intergenerational project: creating space for play in health care.

Lancet 2021 May;397(10288):1876-1877

Royal Central School of Speech and Drama, London, UK.

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http://dx.doi.org/10.1016/S0140-6736(21)01103-XDOI Listing
May 2021

Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

JAMA Netw Open 2021 May 3;4(5):e2110703. Epub 2021 May 3.

Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota.

Importance: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior predictive models to aid in clinical decision-making.

Objective: To compare the performance of 3 machine learning approaches with the commonly used HAS-BLED (hypertension, abnormal kidney and liver function, stroke, bleeding, labile international normalized ratio, older age, and drug or alcohol use) risk score in predicting antithrombotic-related GIB.

Design, Setting, And Participants: This retrospective cross-sectional study used data from the OptumLabs Data Warehouse, which contains medical and pharmacy claims on privately insured patients and Medicare Advantage enrollees in the US. The study cohort included patients 18 years or older with a history of atrial fibrillation, ischemic heart disease, or venous thromboembolism who were prescribed oral anticoagulant and/or thienopyridine antiplatelet agents between January 1, 2016, and December 31, 2019.

Exposures: A cohort of patients prescribed oral anticoagulant and thienopyridine antiplatelet agents was divided into development and validation cohorts based on date of index prescription. The development cohort was used to train 3 machine learning models to predict GIB at 6 and 12 months: regularized Cox proportional hazards regression (RegCox), random survival forests (RSF), and extreme gradient boosting (XGBoost).

Main Outcomes And Measures: The performance of the models for predicting GIB in the validation cohort, evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, and prediction density plots. Relative importance scores were used to identify the variables that were most influential in the top-performing machine learning model.

Results: In the entire study cohort of 306 463 patients, 166 177 (54.2%) were male, 193 648 (63.2%) were White, the mean (SD) age was 69.0 (12.6) years, and 12 322 (4.0%) had experienced a GIB. In the validation data set, the HAS-BLED model had an AUC of 0.60 for predicting GIB at 6 months and 0.59 at 12 months. The RegCox model performed the best in the validation set, with an AUC of 0.67 at 6 months and 0.66 at 12 months. XGBoost was similar, with AUCs of 0.67 at 6 months and 0.66 at 12 months, whereas for RSF, AUCs were 0.62 at 6 months and 0.60 at 12 months. The variables with the highest importance scores in the RegCox model were prior GI bleed (importance score, 0.72); atrial fibrillation, ischemic heart disease, and venous thromboembolism combined (importance score, 0.38); and use of gastroprotective agents (importance score, 0.32).

Conclusions And Relevance: In this cross-sectional study, the machine learning models examined showed similar performance in identifying patients at high risk for GIB after being prescribed antithrombotic agents. Two models (RegCox and XGBoost) performed modestly better than the HAS-BLED score. A prospective evaluation of the RegCox model compared with HAS-BLED may provide a better understanding of the clinical impact of improved performance.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.10703DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140376PMC
May 2021

Knowledge, Attitude and Practice on Covid-19 among Clinical Healthcare Workers in Bingham University Teaching Hospital (BHUTH) Jos, Plateau State, Nigeria.

West Afr J Med 2021 Apr;38(4):321-327

Department of Paediatrics, College of Medicine and Health Sciences, Bingham University/Bingham University Teaching Hospital, Jos, Plateau State, Nigeria.

Background: The ongoing Covid-19 pandemic is now a global health emergency with significant morbidity and mortality among different populations of the world. Healthcare professionals are the people in the front line of situations like this. Their perspectives on the pandemic is critical to their safety and the outcomes in patients they manage. The aim of this study is to assess the knowledge, attitude and practice regarding COVID-19 among clinical healthcare professionals in Bingham University Teaching Hospital (BHUTH) Jos.

Materials And Methods: A total of 138 clinical health workers from BHUTH completed a questionnaire- based study on the knowledge, attitude and practice on COVID-19 from the April 1st to 30th May 2020. Consecutive sampling method was used for data collection and the distribution of responses was presented as frequencies and percentages. Analysis of Variance (ANOVA) test was used to investigate the level of association among variables at the significance level of p<0.05.

Results: The highest mean of correct responses for knowledge were from doctors, pharmacists and nurses with 19.1±2.35, 19.4±1.52 and 18.9±1.73 respectively. The lowest mean was from pharmacist assistant and nurse aids with 17.1±1.81. The difference was statistically significant with Anaysis of Variance (F) of 5.75 and p value of 0.001. The attitude and practice mean were good between the different clinical cadre; however, the difference was not significant.

Conclusion: There is good knowledge, attitude and practice among the doctors and pharmacists, nurses, nurse assistants and pharmacist assistants. The doctors and pharmacist had better scores. There is the need for regular training and update.
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April 2021