Illustration of missing data handling technique generated from hepatitis C induced hepatocellular carcinoma cohort study

Background and Objectives: Missing outcome data are a common occurrence for most clinical research trials. The ’complete case analysis’ is a widely adopted method to tackle with missing observations. However, it reduced the sample size of the study and thus have an impact on statistical power. Hence every effort should be made to reduce the amount of missing data. The objective of this work is to provide the application of different analytical tools to handle missing data imputation techniques through illustration.Methods: We used Imputation techniques such as EM algorithm, MCMC, Regression, and Predictive Mean matching methods and compared the results on hepatitis C virus-induced hepatocellular carcinoma (HCV-HCC) data. The statistical models by Generalized Estimating Equations, Time-dependent Cox Regression, and Joint Modeling were applied to obtain the statistical inference on imputed data. The missing data handling technique compatible with Principle Component Analysis (PCA) was found suitable to work with high dimensional data.Results: Joint modelling provides a slightly lower standard error than other analytical methods each imputation. Accordingly, to our methodology, Joint Modeling analysis with the EM algorithm imputation method has appeared to be the most appropriate method with HCV-HCC data. However, Generalized Estimating Equations and Time-dependent Cox Regression methods were relatively easy to run.Conclusion: The multiple imputation methods are efficient to provide inference with missing data. It is technically robust than any ad hoc approach to working with missing data

Illustration of missing data handling technique generated from hepatitis C induced hepatocellular carcinoma cohort study

Background and Objectives: Missing outcome data are a common occurrence for most clinical research trials. The ’complete case analysis’ is a widely adopted method to tackle with missing observations. However, it reduced the sample size of the study and thus have an impact on statistical power. Hence every effort should be made to reduce the amount of missing data. The objective of this work is to provide the application of different analytical tools to handle missing data imputation techniques through illustration.Methods: We used Imputation techniques such as EM algorithm, MCMC, Regression, and Predictive Mean matching methods and compared the results on hepatitis C virus-induced hepatocellular carcinoma (HCV-HCC) data. The statistical models by Generalized Estimating Equations, Time-dependent Cox Regression, and Joint Modeling were applied to obtain the statistical inference on imputed data. The missing data handling technique compatible with Principle Component Analysis (PCA) was found suitable to work with high dimensional data.Results: Joint modelling provides a slightly lower standard error than other analytical methods each imputation. Accordingly, to our methodology, Joint Modeling analysis with the EM algorithm imputation method has appeared to be the most appropriate method with HCV-HCC data. However, Generalized Estimating Equations and Time-dependent Cox Regression methods were relatively easy to run.Conclusion: The multiple imputation methods are efficient to provide inference with missing data. It is technically robust than any ad hoc approach to working with missing data

Cancer Patients Missing Pain Score Information:- Application with Imputation Techniques

Background: Methods for handling missing data in clinical research have been getting more attentions over last few years. Contemplation of missing data in any study is vital as they may lead to considerable biases and can have an impact on the power of the study. Objective: This manuscript is dedicated to present different techniques to handle missing observations obtained from repeatedly measured pain score data on palliative cancer. Methods: This problem caused by subjects drop out before completion of the study. The reason for dropout or withdrawal may be related to study (e.g., adverse event, death, unpleasant study procedures, lack of improvement) or unrelated to the study (e.g., moving away, unrelated disease). The dropout might be very common on studies on palliative cancer patients. The Palliative treatment is designed to relieve symptoms, and improve the quality of life and can be used at any stage of an illness if there are troubling symptoms, such as pain or sickness. Results: The mean(SD) of observed pain score was 3.638(3.156) whereas the imputed mean values were 3.615(2.980), 3.618(2.954), 3.577(2.892), 3.560(2.999) and 3.627(2.949) respectively for the imputation methods regression, predictive mean matching, propensity score, EM algorithm and MCMC methods for pain score values at visit3.  Interpretation and Conclusion: The EM algorithm shows the least percentage change from observed values in both visits followed by predictive mean matching method and MCMC methods. The multiple imputation techniques have few advantages; the imputed values are drawsfrom a distribution, so they inherently contain some variation by introducing an additional form of error in the parameter estimates across the imputation &nbsp

AN OBSERVATIONAL STUDY OF MEDICATION ERRORS AMONG PSYCHIATRIC PATIENTS IN A TERTIARY CARE HOSPITAL

Objective: The objective of this study is to evaluate the common medication error (ME), and its causes, category, and severity by using suitable questionnaire.Methods: A prospective observational study was carried out for 6 months in a psychiatric department. Demographic data, clinical history, and complete prescription were noted.Results: A total of 120 psychiatric cases were collected, among that 116 MEs were identified in which male patients were 64 (55%) and females 52 (44.8%). The number of MEs occurred due to physician was 67 (57.7%), due to nurses was 15 (12.9%), and combined was 38 (32.7%). Incomplete prescription was the main type of error that we found. About 43.1% of the error we identified was informed to the staff and and no specific action was needed for 37.1% of errors. In our study, we found that majority of 54 (46.5%) errors were categorized under category B, but there was no harm to the patient.Conclusion: The present study concluded that most of the patients admitted in the psychiatry department would experience MEs. Clinical pharmacist can play a major role in the early detection and prevention of MEs and thus can improve the quality of care to the patients

Critically ill patients with diabetes and Middle East respiratory syndrome:a multi-center observational study

Background: Diabetes is a risk factor for infection with coronaviruses. This study describes the demographic, clinical data, and outcomes of critically ill patients with diabetes and Middle East Respiratory Syndrome (MERS).Methods: This retrospective cohort study was conducted at 14 hospitals in Saudi Arabia (September 2012–January 2018). We compared the demographic characteristics, underlying medical conditions, presenting symptoms andsigns, management and clinical course, and outcomes of critically ill patients with MERS who had diabetes compared to those with no diabetes. Multivariable logistic regression analysis was performed to determine ifdiabetes was an independent predictor of 90-day mortality.Results: Of the 350 critically ill patients with MERS, 171 (48.9%) had diabetes. Patients with diabetes were more likely to be older, and have comorbid conditions, compared to patients with no diabetes. They were more likely topresent with respiratory failure requiring intubation, vasopressors, and corticosteroids. The median time to clearance of MERS-CoV RNA was similar (23 days (Q1, Q3: 17, 36) in patients with diabetes and 21.0 days (Q1, Q3: 10, 33) in patients with no diabetes). Mortality at 90 days was higher in patients with diabetes (78.9% versus 54.7%, p <0.0001). Multivariable regression analysis showed that diabetes was an independent risk factor for 90-day mortality(odds ratio, 2.09; 95% confidence interval, 1.18–3.72).Conclusions: Half of the critically ill patients with MERS have diabetes; which is associated with more severe disease. Diabetes is an independent predictor of mortality among critically patients with MERS

Critically ill patients with diabetes and Middle East respiratory syndrome:a multi-center observational study

Background: Diabetes is a risk factor for infection with coronaviruses. This study describes the demographic, clinical data, and outcomes of critically ill patients with diabetes and Middle East Respiratory Syndrome (MERS).Methods: This retrospective cohort study was conducted at 14 hospitals in Saudi Arabia (September 2012–January 2018). We compared the demographic characteristics, underlying medical conditions, presenting symptoms andsigns, management and clinical course, and outcomes of critically ill patients with MERS who had diabetes compared to those with no diabetes. Multivariable logistic regression analysis was performed to determine ifdiabetes was an independent predictor of 90-day mortality.Results: Of the 350 critically ill patients with MERS, 171 (48.9%) had diabetes. Patients with diabetes were more likely to be older, and have comorbid conditions, compared to patients with no diabetes. They were more likely topresent with respiratory failure requiring intubation, vasopressors, and corticosteroids. The median time to clearance of MERS-CoV RNA was similar (23 days (Q1, Q3: 17, 36) in patients with diabetes and 21.0 days (Q1, Q3: 10, 33) in patients with no diabetes). Mortality at 90 days was higher in patients with diabetes (78.9% versus 54.7%, p <0.0001). Multivariable regression analysis showed that diabetes was an independent risk factor for 90-day mortality(odds ratio, 2.09; 95% confidence interval, 1.18–3.72).Conclusions: Half of the critically ill patients with MERS have diabetes; which is associated with more severe disease. Diabetes is an independent predictor of mortality among critically patients with MERS

Statistical analysis plan for the Pneumatic CompREssion for PreVENting Venous Thromboembolism (PREVENT) trial: a study protocol for a randomized controlled trial

Abstract Background The Pneumatic CompREssion for Preventing VENous Thromboembolism (PREVENT) trial evaluates the effect of adjunctive intermittent pneumatic compression (IPC) with pharmacologic thromboprophylaxis compared to pharmacologic thromboprophylaxis alone on venous thromboembolism (VTE) in critically ill adults. Methods/design In this multicenter randomized trial, critically ill patients receiving pharmacologic thromboprophylaxis will be randomized to an IPC or a no IPC (control) group. The primary outcome is “incident” proximal lower-extremity deep vein thrombosis (DVT) within 28 days after randomization. Radiologists interpreting the lower-extremity ultrasonography will be blinded to intervention allocation, whereas the patients and treating team will be unblinded. The trial has 80% power to detect a 3% absolute risk reduction in the rate of proximal DVT from 7% to 4%. Discussion Consistent with international guidelines, we have developed a detailed plan to guide the analysis of the PREVENT trial. This plan specifies the statistical methods for the evaluation of primary and secondary outcomes, and defines covariates for adjusted analyses a priori. Application of this statistical analysis plan to the PREVENT trial will facilitate unbiased analyses of clinical data. Trial registration ClinicalTrials.gov, ID: NCT02040103. Registered on 3 November 2013; Current controlled trials, ID: ISRCTN44653506. Registered on 30 October 2013

Critically ill healthcare workers with the middle east respiratory syndrome (MERS): A multicenter study.

BACKGROUND:Middle East Respiratory Syndrome Coronavirus (MERS-CoV) leads to healthcare-associated transmission to patients and healthcare workers with potentially fatal outcomes. AIM:We aimed to describe the clinical course and functional outcomes of critically ill healthcare workers (HCWs) with MERS. METHODS:Data on HCWs was extracted from a multi-center retrospective cohort study on 330 critically ill patients with MERS admitted between (9/2012-9/2015). Baseline demographics, interventions and outcomes were recorded and compared between survivors and non-survivors. Survivors were approached with questionnaires to elucidate their functional outcomes using Karnofsky Performance Status Scale. FINDINGS:Thirty-Two HCWs met the inclusion criteria. Comorbidities were recorded in 34% (11/32) HCW. Death resulted in 8/32 (25%) HCWs including all 5 HCWs with chronic renal impairment at baseline. Non-surviving HCW had lower PaO2/FiO2 ratios 63.5 (57, 116.2) vs 148 (84, 194.3), p = 0.043, and received more ECMO therapy compared to survivors, 9/32 (28%) vs 4/24 (16.7%) respectively (p = 0.02).Thirteen of the surviving (13/24) HCWs responded to the questionnaire. Two HCWs confirmed functional limitations. Median number of days from hospital discharge until the questionnaires were filled was 580 (95% CI 568, 723.5) days. CONCLUSION:Approximately 10% of critically ill patients with MERS were HCWs. Hospital mortality rate was substantial (25%). Patients with chronic renal impairment represented a particularly high-risk group that should receive extra caution during suspected or confirmed MERS cases clinical care assignment and during outbreaks. Long-term repercussions of critical illness due to MERS on HCWs in particular, and patients in general, remain unknown and should be investigated in larger studies