Medicine
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This repository contains the published and unpublished research of the Faculty of Medicine by the staff members of the faculty
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Item Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predicting cardiovascular risk among Sri Lankans: a cohort study(BMJ Publishing Group Ltd, 2025-01) Mettananda, C.; Solangaarachchige, M.; Haddela , P.; Dassanayake , A.S.; Kasturiratne , A.; Wickremasinghe , A.; Kato, N.; De Silva, H.J.INTRODUCTION Models derived from non-Sri Lankan cohorts are used for cardiovascular (CV) risk stratification of Sri Lankans. OBJECTIVE To develop a CV risk prediction model using machine learning (ML) based on data from a Sri Lankan cohort followed up for 10 years, and to compare the predictions with WHO risk charts. DESIGN Cohort study. SETTING The Ragama Health Study (RHS), an ongoing, prospective, population-based cohort study of patients randomly selected from the Ragama Medical Office of Heath area, Sri Lanka, focusing on the epidemiology of non-communicable diseases, was used to develop the model. The external validation cohort included patients admitted to Colombo North Teaching Hospital (CNTH), a tertiary care hospital in Sri Lanka, from January 2019 through August 2020. PARTICIPANTS All RHS participants, aged 40-64 years in 2007, without cardiovascular disease (CVD) at baseline, who had complete data of 10-year outcome by 2017, were used for model development. Patients aged 40-74 years admitted to CNTH during the study period with incident CV events or a disease other than an acute CV event (CVE) with complete data for CVD risk calculation were used for external validation of the model. METHODS Using the follow-up data of the cohort, we developed two ML models for predicting 10-year CV risk using six conventional CV risk variables (age, gender, smoking status, systolic blood pressure, history of diabetes, and total cholesterol level) and all available variables (n=75). The ML models were derived using classification algorithms of the supervised learning technique. We compared the predictive performance of our ML models with WHO risk charts (2019, Southeast Asia) using area under the receiver operating characteristic curves (AUC-ROC) and calibration plots. We validated the 6-variable model in an external hospital-based cohort. RESULTS Of the 2596 participants in the baseline cohort, 179 incident CVEs were observed over 10 years. WHO risk charts predicted only 10 CVEs (AUC-ROC: 0.51, 95% CI 0.42 to 0.60), while the new 6-variable ML model predicted 125 CVEs (AUC-ROC: 0.72, 95% CI 0.66 to 0.78) and the 75-variable ML model predicted 124 CVEs (AUC-ROC: 0.74, 95% CI 0.68 to 0.80). Calibration results (Hosmer-Lemeshow test) for the 6-variable ML model and the WHO risk charts were χ2=12.85 (p=0.12) and χ2=15.58 (p=0.05), respectively. In the external validation cohort, the sensitivity, specificity, positive predictive value, negative predictive value, and calibration of the 6-variable ML model and the WHO risk charts, respectively, were: 70.3%, 94.9%, 87.3%, 86.6%, χ2=8.22, p=0.41 and 23.7%, 79.0%, 35.8%, 67.7%, χ2=81.94, p<0.0001. CONCLUSIONS ML-based models derived from a cohort of Sri Lankans improved the overall accuracy of CV-risk prediction compared with the WHO risk charts for this cohort of Southeast Asians.Item Undetected falls among older adults attending medical clinics in four tertiary care centres in Sri Lanka; the need of a comprehensive geriatric assessment(BioMed Central, 2024-10) De Zoysa, W.; Rathnayake, N.; Palangasinghe, D.; Silva, S.; Jayasekera, P.; Mettananda, C.; Abeygunasekara, T.; Lekamwasam. S.OBJECTIVE Falls take a high priority among the prevalent medical conditions in old age. Despite this, a history of falls or the risk of future falls is not routinely assessed or properly managed in medical clinics in Sri Lanka. This study was done to evaluate the prevalence and factors associated with falls and recurrent falls among older adults attending medical clinics in four selected tertiary care centres in the country.METHODS A cross-sectional study was carried out at four centres (Teaching Hospital Karapitiya, Colombo South Teaching Hospital, Colombo North Teaching Hospital and University Hospital-Kotelawala Defence University) with 704 older adults, aged 65 years and above, attending medical clinics for more than six consecutive months. Information related to falls and possible associated factors (socio-demographic, behavioural, environmental and biological) were collected using an interviewer-administered questionnaire.Results: The Mean (SD) age of the participants was 72.5(5.5) years and 58.7% were females. Of the 704 total sample, 220 (31.3%, 95% CI 28-35%) participants experienced at least one fall after the age of 65, and 12.8% (95% CI 10-15%) (n = 90) experienced recurrent falls (two or more falls within the last 12 months). Falls were associated with gender, level of education, marital status, and physical dependence (p < 0.01). For those who had at least one fall, multiple logistic regression (MLR) revealed being single (p = 0.03, OR = 2.12, 95% CI; 1.052-4.304), being widowed/divorced/separated (p = 0.03, OR = 1.47, 95% CI; 1.039-2.093) compared to living with a spouse, presence of moderate (p = 0.007, OR = 1.72, 95% CI; 1.160-2.577) and severe (p = 0.001, OR = 2.98, 95% CI; 1.563-5.688) physical dependency compared to mild physical dependency as risk factors for falls. Having secondary education (p = 0.01, OR = 0.55, 0.350-0.876) was a protective factor for falls. For those with recurrent falls, MLR showed moderate physical dependency (p = 0.001, OR = 2.34, 95% CI; 1.442-3.821) compared to slight physical dependency as a risk factor.CONCLUSIONS Approximately one-third of the older adults attending medical clinics had experienced at least a single fall, and one-eighth have had recurrent falls, which were mostly unrecorded and not clinically assessed. Physical dependency was the major contributing factor to falls and recurrent falls. Falls assessment should be included in the routine clinical assessment of older adults attending outdoor medical clinics. Health professionals should be educated to detect and assess those at risk of falling and take appropriate measures to prevent or minimize falls.Item Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning.(Public Library of Science, 2024-10) Mettananda, C.; Sanjeewa, I.; Arachchi, T.B.; Wijesooriya, A.; Chandrasena, C.; Weerasinghe, T.; Solangaarachchige, M.; Ranasinghe, A.; Elpitiya, I.; Sammandapperuma, R.; Kurukulasooriya, S.; Ranawaka, U.; Pathmeswaran, A.; Kasturiratne, A.; Kato, N.; Wickramasinghe, R.; Haddela, P.; De Silva, J.INTRODUCTION AND OBJECTIVES Sri Lankans do not have a specific cardiovascular (CV) risk prediction model and therefore, World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used. We aimed to develop a CV risk prediction model specific for Sri Lankans using machine learning (ML) of data of a population-based, randomly selected cohort of Sri Lankans followed up for 10 years and to validate it in an external cohort.MATERIAL AND METHODS The cohort consisted of 2596 individuals between 40-65 years of age in 2007, who were followed up for 10 years. Of them, 179 developed hard CV diseases (CVD) by 2017. We developed three CV risk prediction models named model 1, 2 and 3 using ML. We compared predictive performances between models and the WHO risk charts using receiver operating characteristic curves (ROC). The most predictive and practical model for use in primary care, model 3 was named "SLCVD score" which used age, sex, smoking status, systolic blood pressure, history of diabetes, and total cholesterol level in the calculation. We developed an online platform to calculate the SLCVD score. Predictions of SLCVD score were validated in an external hospital-based cohort.RESULTS Model 1, 2, SLCVD score and the WHO risk charts predicted 173, 162, 169 and 10 of 179 observed events and the area under the ROC (AUC) were 0.98, 0.98, 0.98 and 0.52 respectively. During external validation, the SLCVD score and WHO risk charts predicted 56 and 18 respectively of 119 total events and AUCs were 0.64 and 0.54 respectively.CONCLUSIONS SLCVD score is the first and only CV risk prediction model specific for Sri Lankans. It predicts the 10-year risk of developing a hard CVD in Sri Lankans. SLCVD score was more effective in predicting Sri Lankans at high CV risk than WHO risk charts.Item Persistent, poorly responsive immune thrombocytopenia secondary to asymptomatic COVID-19 infection in a child(Hindawi, 2023) Mettananda, C.; Williams, S.Immune thrombocytopenic purpura (ITP) secondary to asymptomatic COVID-19 infection, especially in children, is not reported. Furthermore, persistent, treatment-resistant ITP secondary to COVID-19 is not reported. We report a previously healthy 14-year-old Asian boy who developed secondary ITP following an asymptomatic COVID-19 infection and is having a relapsing and remitting cause with poor response to immunosuppressants even after 21 months following the diagnosis. This case emphasizes the importance of testing for COVID-19 in newly diagnosed ITP patients and the need for follow-up platelet counts in patients who recover from COVID-19 as it may follow into developing secondary ITP yet being asymptomatic until you present with a bleeding complication of ITP. The poor response to standard immunosuppression warrants more understanding of the pathophysiology of persistently low platelets following COVID-19 infection. Long-term sequelae of the disease highlight the importance of getting vaccinated for COVID-19 despite COVID-19 being no longer a global emergency.Item Inhaled beclomethasone in the treatment of early COVID-19: a double-blind, placebo-controlled, randomised, hospital-based trial in Sri Lanka(BMJ Publishing Group Ltd, 2023) Mettananda, C.; Peiris, C.; Abeyrathna, D.; Gunasekara, A.; Egodage, T.; Dantanarayana, C.; Pathmeswaran, A.; Ranasinha, C.OBJECTIVES: To study if early initiation of inhaled beclomethasone 1200 mcg in patients with asymptomatic, mild or moderate COVID-19 reduces disease progression to severe COVID-19. DESIGN: Double-blinded, parallel-groups, randomised, placebo-controlled trial. SETTING: A hospital-based study in Sri Lanka. PARTICIPANTS: Adults with asymptomatic, mild or moderate COVID-19, presenting within the first 7 days of symptom onset or laboratory diagnosis of COVID-19, admitted to a COVID-19 intermediate treatment centre in Sri Lanka between July and November 2021. INTERVENTIONS: All participants received inhaled beclomethasone 600 mcg or placebo two times per day, for 10 days from onset of symptoms/COVID-19 test becoming positive if asymptomatic or until reaching primary endpoint, whichever is earlier. PRIMARY OUTCOME MEASURE: Progression of asymptomatic, mild or moderate COVID-19 to severe COVID-19. SECONDARY OUTCOME MEASURES: The number of days with a temperature of 38°C or more and the time to self-reported clinical recovery. RESULTS: A total of 385 participants were randomised to receive beclomethasone(n=193) or placebo(n=192) stratified by age (≤60 or >60 years) and sex. One participant from each arm withdrew from the study. All participants were included in final analysis. Primary outcome occurred in 24 participants in the beclomethasone group and 26 participants in the placebo group (RR 0.90 ; p=0.763). The median time for self-reported clinical recovery in all participants was 5 days (95% CI 3 to 7) in the beclomethasone group and 5 days (95% CI 3 to 8) in the placebo group (p=0.5). The median time for self-reported clinical recovery in patients with moderate COVID-19 was 5 days (95% CI 3 to 7) in the beclomethasone group and 6 days (95% CI 4 to 9) in the placebo group (p=0.05). There were no adverse events. CONCLUSIONS: Early initiation of inhaled beclomethasone in patients with asymptomatic, mild or moderate COVID-19 did not reduce disease progression to severe COVID-19. TRIAL REGISTRATION NUMBER: Sri Lanka Clinical Trials Registry; SLCTR/2021/017.Item Novel therapies in clinical use for the management of hyperlipidaemia(Sri Lanka College of Internal Medicine, 2023) Mettananda, C.Optimal control of low-density lipoprotein cholesterol (LDLc) is identified as a major target in reducing cardiovascular disease burden globally. However, existing lipid-lowering therapies have not been able to achieve LDLc targets even in developed countries. Therefore, novel therapies for the management of hyperlipidaemia are being trialled. Currently, there are three main groups of newer medicines; bempedoic acid, PCSK9 inhibitors and inclisiran, in addition to statins and ezetimibe in use for the management of hyperlipidaemia. This article aims to introduce these newer medicines and their clinical use in the treatment of hyperlipidaemia.Item Knowledge and adherence to National Institute of Clinical Excellence 2020, dyslipidaemia management guidelines and its associations among medical officers in Gampaha district, Sri Lanka: a descriptive study(Sri Lanka College of Internal Medicine, 2023) Ansy, A.; Angunawala, A.; Anushika, E.; Ariyasena, S.; Aryachandra, S.; Fernando, K.; Mettananda, C.INTRODUCTION: Dyslipidaemia is an important risk factor for cardiovascular diseases(CVD) and optimal management helps prevent CVD burden in a country. Knowledge of medical officers(MOs) on dyslipidaemia management is critical in this regard. We assessed knowledge and adherence of MOs of Gampaha district to the National Institute of Clinical Excellence (NICE) guideline 2020 on management of dyslipidaemia. METHODS: We conducted a cross-sectional study at five secondary/tertiary-care hospitals in Gampaha District in January 2022. Knowledge and adherence were studied using a self-administered questionnaire consisting of 25 multiple-choice questions. Each question was scored "1" and the cumulative score was converted to 100. A score >80 was considered "good knowledge and adherence" and its associations were studied using logistic regression. RESULTS: A total of 413 MOs (63.4% females, mean age 45±7.6 years) participated in the study. Of them, 73.1% had worked in a medical ward previously. The mean knowledge and adherence score was 77±9.3. Only 30% had a score >80. Good knowledge and adherence was significantly associated with being <45 years (p .004) in age, having work experience in a medical ward (p<.001), having post-graduate training (p<.001), working in a tertiary care hospital(p=.007), and involved in private practice(p=.002). There was no significant association with attendance at continuing medical education programmes (p=.320) or the duration of service(p=.120). CONCLUSIONS: Only a third of MOs of Gampaha district had good Knowledge and adherence to NICE-2020 dyslipidaemia guidelines. Knowledge and adherence to the guideline was better in MOs who are young, in postgraduate training, with previous experience in medical wards, working in tertiary care hospitals or engaged in private practice.Item Glycaemic control and avenues for improvement among people with type 2 diabetes mellitus from rural Sri Lanka – a retrospective cohort study(Elsevier, 2023) Mettananda, C.; Chathuranga, U.; Rathnayake, T.; Luke, N.; Meegodavidanage, N.BACKGROUND The majority of Sri Lankans and South Asians are rural dwellers but follow-up data on glycaemic control and its associations in rural communities are sparse. We followed up a cohort of hospital-based rural Sri Lankans with diabetes from diagnosis up to 24-months. METHODS We conducted a retrospective cohort study of people with type-2 diabetes (T2DM) diagnosed 24 months before enrolment who were being followed up at Medical/Endocrine clinics of five hospitals selected by stratified random sampling in Anuradhapura, a rural district of Sri Lanka from June 2018 to May 2019 and retrospectively followed them up to the diagnosis of the disease. Prescription practices, cardiovascular risk factor control and their correlates were studied using self-administered and interviewer-administered questionnaires and perusing medical records. Data were analysed using SPSS version-22. FINDINGS A total of 421 participants [mean age 58.3 ± 10.4 years, female 340 (80.8%)] were included in the study. Most participants were started on anti-diabetic medications in addition to lifestyle measures. Of them, 270 (64.1%) admitted poor dietary-control, 254 (60.3%) inadequate medication-compliance and 227 (53.9%) physical inactivity. Glycaemic control was assessed mainly on fasting plasma glucose (FPG) and glycated haemoglobin (HbA1c) data were available in only 44 (10.4%). Target achievements in FPG, blood pressure, body mass index and non-smoking at 24-months following initiation of treatment were 231/421 (54.9%), 262/365 (71.7%), 74/421 (17.6%) and 396/421 (94.1%) respectively. INTERPRETATION In this cohort of rural Sri Lankans with type-2 diabetes mellitus, all were started on anti-diabetic medications at the diagnosis, but glycaemic target achievement was inadequate at 24 months. We identified the major patient-related reasons for poor blood glucose control were poor compliance with diet/lifestyle and/or medications and misconceptions about antidiabetic medications.Item Cardiovascular risk stratification in primary prevention of non-communicable diseases(Ceylon College of Physicians, 2022) Mettananda, C.No abstract availableItem Identification of patients with type 2 diabetes with non-alcoholic fatty liver disease who are at increased risk of progressing to advanced fibrosis: a cross-sectional study(BMJ Publishing Group Ltd, 2023) Mettananda, C.; Egodage, T.; Dantanarayana, C.; Fernando, R.; Ranaweera, L.; Luke, N.; Ranawaka, C.; Kottahachchi, D.; Pathmeswaran, A.; de Silva, H.J.; Dassanayake, A.S.INTRODUCTION: Identification of advanced hepatic fibrosis in non-alcoholic fatty liver disease (NAFLD) is important as this may progress to cirrhosis and hepatocellular carcinoma. The risk of hepatic fibrosis is especially high among patients with diabetes with NAFLD. Annual screening of patients with diabetes for fatty liver and calculation of Fibrosis-4 (FIB-4) score and exclusion of significant fibrosis with vibration-controlled transient elastography (VCTE) have been recommended. However, VCTE is expensive and may not be freely available in resource-limited settings. We aim to identify predictors of significant liver fibrosis who are at increased risk of progression to advanced liver fibrosis and to develop a prediction model to prioritise referral of patients with diabetes and NAFLD for VCTE. METHODS AND ANALYSIS: This cross-sectional study is conducted among all consenting adults with type 2 diabetes mellitus with NAFLD at the Colombo North Teaching Hospital, Ragama, Sri Lanka. All patients get the FIB-4 score calculated. Those with FIB-4 ≥1.3 undergo VCTE (with FibroScan by Echosens). Risk associations for progression to advanced liver fibrosis/cirrhosis will be identified by comparing patients with significant fibrosis (liver stiffness measure (LSM) ≥8 kPa) and without significant fibrosis (LSM <8 kPa). A model to predict significant liver fibrosis will be developed using logistic regression. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Ethics Committee of the Faculty of Medicine, University of Kelaniya (P/66/07/2021). Results of the study will be disseminated as scientific publications in reputable journals.