Introduction: Propensity score matching (PSM) is a method to reduce the impact of essential and confounders. When the number of confounders is high, there may be a problem of matching, in which, finding matched pairs for the case group is difficult, or impossible. The propensity score (PS) minimizes the effect of the confounders, and it is reduced to one dimension. There are various algorithms in the field of PSM. This study aimed to compared the nearest neighbor and caliper algorithms. Methods: Data obtained in this study were from patients undergoing angiography at Ghaem Hospital in Mashhad, between 2011-12. The study was a retrospective case-control using PSM. In total, 604 patients were included in the case and control groups. A logistic regression model was used to calculate the propensity score and adjust the variables, such as age, gender, Body Mass Index (BMI), systolic blood pressure, smoking status, and triglyceride. Then, the Odds Ratios (ORs) with 95% Confidence Intervals (CIs) for the raw data and two matching algorithms were determined to examine the relationship between type 2 diabetes and coronary artery disease (CAD). Results: Propensity score in the nearest neighbor and caliper algorithms matched the total number of 604 samples, 200 and 178 pairs, respectively. All variables were significantly different between the two groups before matching (P<0.05). The gender was significantly different between the two groups after matching using the nearest neighbor algorithm (P=0.002). No variables created a significant difference between the two groups after matching with the caliper algorithm. Conclusion: Bias reduction in the caliper algorithm was greater than for the nearest neighbor algorithm for all variables except the triglyceride variable.
Background: Poisoning is a medical emergency, and is considered as a common cause of morbidity and mortality worldwide. In this study, the extended Cox model was used to determine the factors affecting the length of hospitalization in those with drug poisoning. Methods: The sample size included 2408 patients with opioids poisoning referring to the Emergency Department of Imam Reza Hospital in Mashhad, Iran from March 21, 2018 to March 20, 2019. Extended Cox model was fitted to determine the effect of five covariates (age, gender, marital status, type of poisoning, and type of opioids). In survival analysis, the length of hospitalization was considered as a time covariate (T). Patients’ recovery was also regarded as an event. Results: Of 2408 patients, 399 (16.6%) were censored and 2009 (83.4%) were uncensored. The risk of failure in complete recovery from poisoning in males was 1.189 times more compared to females. The risk of failure in complete recovery for the 15-24, 25-44, 45-64, and >65 years age groups were 0.277, 0.241, 0.289, and 0.481 times lower, respectively compared to the <2 years age group. For the married patients, the risk was 0.291 times lower compared to the divorced patients. For those poisoned accidentally, the risk was 0.490 times lower than compared to those poisoned intentionally. For those used methadone, morphine, opium, and tramadol, the risk was 1.195, 1.243, 1.193, and 1.147 times more, respectively compared to those used marijuana. By increasing the time (day) of hospital stay, the risk of failure for the 25-44, 45-64, and >65 years age groups were 1.024, 1.028, and 1.040 times more, respectively compared to the <2 years age group. Moreover, for those poisoned accidentally, the risk was 1.197 times more compared to those poisoned intentionally by the time (day) of hospital stay. Conclusion: The factors affecting the length of hospitalization in those poisoned by drugs are gender, marital status, and type of opioids covariate as time-independent covariate, and age and type of poisoning as time-dependent covariates. Since the complications of drug poisoning impose many costs on the health system, knowledge of these covariates can help take some measures for complete recovery of poisoned patients in a shorter length of hospital stay.
Background: Poisoning is a medical emergency, and is considered as a common cause of morbidity and mortality worldwide. In this study, the extended Cox model was used to determine the factors affecting the length of hospitalization in those with drug poisoning. Methods: The sample size included 2408 patients with opioids poisoning referring to the Emergency Department of Imam Reza Hospital in Mashhad, Iran from March 21, 2018 to March 20, 2019. Extended Cox model was fitted to determine the effect of five covariates (age, gender, marital status, type of poisoning, and type of opioids). In survival analysis, the length of hospitalization was considered as a time covariate (T). Patients’ recovery was also regarded as an event. Results: Of 2408 patients, 399 (16.6%) were censored and 2009 (83.4%) were uncensored. The risk of failure in complete recovery from poisoning in males was 1.189 times more compared to females. The risk of failure in complete recovery for the 15-24, 25-44, 45-64, and >65 years age groups were 0.277, 0.241, 0.289, and 0.481 times lower, respectively compared to the <2 years age group. For the married patients, the risk was 0.291 times lower compared to the divorced patients. For those poisoned accidentally, the risk was 0.490 times lower than compared to those poisoned intentionally. For those used methadone, morphine, opium, and tramadol, the risk was 1.195, 1.243, 1.193, and 1.147 times more, respectively compared to those used marijuana. By increasing the time (day) of hospital stay, the risk of failure for the 25-44, 45-64, and >65 years age groups were 1.024, 1.028, and 1.040 times more, respectively compared to the <2 years age group. Moreover, for those poisoned accidentally, the risk was 1.197 times more compared to those poisoned intentionally by the time (day) of hospital stay. Conclusion: The factors affecting the length of hospitalization in those poisoned by drugs are gender, marital status, and type of opioids covariate as time-independent covariate, and age and type of poisoning as time-dependent covariates. Since the complications of drug poisoning impose many costs on the health system, knowledge of these covariates can help take some measures for complete recovery of poisoned patients in a shorter length of hospital stay.
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