Frequency of Cardiovascular Complications in Coronavirus-19 infected Patients during the second wave in a tertiary care Hospital of Lahore, Pakistan Cardiovascular Complications in COVID-19 patients

Coronavirus 2019 (COVID-19) affects the cardiorespiratory systems by causing inflammation of the circulatory systems, hence, causing more cardiac complications in COVID-19 patients Objective: To observe the frequency of cardiac complications in COVID-19 patients during the second wave Methods: 235 COVID-19 patients presenting with cardiac complication to the Cardiology Department of Mayo Hospital, Lahore during 6 month periods of second wave of COVID-19 were included. Data regarding the medical history, age, gender was taken and the observed cardiac complication were noted Results: There were 76.6% male patients and 23.4% female patients. 56.2 % patients were already hypertensive, 37.9% were diabetic, 18.3% had familial history of cardiac diseases. The cardiovascular manifestations observed were, myocardial infarction (57.82%), 7.3% had angina, 21.3% had acute coronary syndrome and 12.3% had non ST elevation myocardial infarction. Conclusions: Our study suggests that there is a prevalence of cardiovascular diseases in COVID-patients which is a major concern as they are at increased risk for severe infection.

New Generalizations of Power Cauchy Distribution

Four new generalizations of power-Cauchy distribution are proposed in this thesis. Firstly, the Poisson-X family is proposed and its important mathematical properties are obtained. Then, a member of the Poisson-X family namely the Poisson power-Cauchy)distributionisdefinedanditsstructuralpropertiesareinvestigated. The proposed model is fitted to three real-life data sets to illustrate its flexibility. Secondly, the log-odd normal generalized family of distributions is introduced. Then, a special model of this family, the log-odd normal power-Cauchy is defined and its mathematical properties are obtained. A real-life data set is used to prove the superiority of the proposed model. Thirdly, the Weibull-Power-Cauchy distribution is proposed and its mathematical properties are obtained. Two useful characterizations based on truncated moments are also presented. The proposed model is applied to three real-life data sets to investigate its flexibility. Lastly, a new extensionofpower-Cauchymodelisproposedbycompoundingthepower-Cauchyand negative-binomial distribution called the power-Cauchy negative-binomial distribution. Some mathematical properties of the proposed model are obtained and the model parameters are estimated using the maximum likelihood method. A simulation study is carried out to investigate the performance of maximum likelihood method. The flexibility of the proposed model is illustrated through three real-life data sets. Then, a new class of regression model is introduced for location and scale based on the logarithm of the proposed random variable and, estimation and inference on the regression coefficients are discussed.