Observational Research Using Propensity Scores

Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes

Purpose

While the beneficial effects of medications are numerous, drug–drug interactions may lead to adverse drug reactions that are preventable causes of morbidity and mortality. Our goal was to quantify the prevalence of potential drug–drug interactions in drug prescriptions at Danish hospitals, estimate the risk of adverse outcomes associated with discouraged drug combinations, and highlight the patient types (defined by the primary diagnosis of the admission) that appear to be more affected.

Methods

This cross‐sectional (descriptive part) and cohort study (adverse outcomes part) used hospital electronic health records from two Danish regions (~2.5 million people) from January 2008 through June 2016. We included all inpatients receiving two or more medications during their admission and considered concomitant prescriptions of potentially interacting drugs as per the Danish Drug Interaction Database. We measured the prevalence of potential drug–drug interactions in general and discouraged drug pairs in particular during admissions and associations with adverse outcomes: post‐discharge all‐cause mortality rate, readmission rate and length‐of‐stay.

Results

Among 2 886 227 hospital admissions (945 475 patients; median age 62 years [IQR: 41–74]; 54% female; median number of drugs 7 [IQR: 4–11]), patients in 1 836 170 admissions were exposed to at least one potential drug–drug interaction (659 525 patients; median age 65 years [IQR: 49–77]; 54% female; median number of drugs 9 [IQR: 6–13]) and in 27 605 admissions to a discouraged drug pair (18 192 patients; median age 68 years [IQR: 58–77]; female 46%; median number of drugs 16 [IQR: 11–22]). Meropenem‐valproic acid (HR: 1.5, 95% CI: 1.1–1.9), domperidone‐fluconazole (HR: 2.5, 95% CI: 2.1–3.1), imipramine‐terbinafine (HR: 3.8, 95% CI: 1.2–12), agomelatine‐ciprofloxacin (HR: 2.6, 95% CI: 1.3–5.5), clarithromycin‐quetiapine (HR: 1.7, 95% CI: 1.1–2.7) and piroxicam‐warfarin (HR: 3.4, 95% CI: 1–11.4) were associated with elevated mortality. Confidence interval bounds of pairs associated with readmission were close to 1; length‐of‐stay results were inconclusive.

Conclusions

Well‐described potential drug–drug interactions are still missed and alerts at point of prescription may reduce the risk of harming patients; prescribing clinicians should be alert when using strong inhibitor/inducer drugs (i.e. clarithromycin, valproic acid, terbinafine) and prevalent anticoagulants (i.e. warfarin and non‐steroidal anti‐inflammatory drugs ‐ NSAIDs) due to their great potential for dangerous interactions. The most prominent CYP isoenzyme involved in mortality and readmission rates was 3A4.

Polypharmacy and medication use by cancer history in a nationally representative group of adults in the USA, 2003-2014

PURPOSE

This study examines polypharmacy and prescription drug use patterns in cancer survivors, a growing population at risk for cancer sequelae and side effects from treatment, which can arise months or even years following diagnosis. Survivors may experience greater medication burden than the general population, increasing concerns for polypharmacy and subsequent risks of drug interactions and non-adherence.

METHODS

Using the National Health and Nutrition Examination Survey (NHANES) data from 2003 to 2014, we examined the association between a cancer history and presence of polypharmacy (5+ medications). We estimated prevalence ratios and prevalence differences for polypharmacy comparing those with and without a cancer history using binomial regression models and propensity score (PS) weighting to account for baseline differences between groups.

RESULTS

We identified 32,238 adults aged 20 years or older; 1899 had cancer (excluding non-melanoma skin) at least 1 year before the survey. Overall, polypharmacy prevalence was 13% and 35% in those with and without a cancer history, respectively. After PS weighting, the polypharmacy prevalence was 1.26 times higher among those with versus without a cancer history (weighted prevalence ratio, 1.26; 95% CI, 1.18, 1.35). In sub-group analyses, the weighted prevalence ratio was largest for those 20-39 years old at survey (2.78; 95% CI, 1.71, 4.53), and the weighted prevalence difference was largest for those 40-64 years old at survey (9.35%; 95% CI, 5.70%, 13.01%).

CONCLUSIONS/IMPLICATIONS FOR CANCER SURVIVORS

Cancer survivors of all ages take more medications than those without cancer history and may benefit from discussions with providers about age-tailored medication use management.

Hyperkalemia and its Association With Mortality, Cardiovascular Events, Hospitalizations, and Intensive Care Unit Admissions in a Population-Based Retrospective Cohort

INTRODUCTION

Hyperkalemia is a common, potentially life-threatening condition in patients with chronic kidney disease (CKD). We studied the association between hyperkalemia and mortality, cardiovascular events, hospitalizations, and intensive care unit (ICU) admissions.

METHODS

We performed a retrospective cohort study using administrative databases in Manitoba, Canada. All adults (≥18 years of age) with potassium tests between January 2007 and December 2016 were included, with follow-up until March 31, 2017. Propensity score matching was performed among patients with de novo hyperkalemia (serum potassium ≥ 5.0 mmol/l) and patients who were nonhyperkalemic. The association between hyperkalemia and normokalemia and mortality was assessed using multivariate Cox proportional hazards regression models, adjusting for patient characteristics in a 1:1 propensity score-matched sample. Secondary outcomes included cardiovascular events, hospitalizations, and ICU admissions. A sensitivity analysis was performed with hyperkalemia defined as serum potassium ≥ 5.5 mmol/l.

RESULTS

Of 93,667 patients with de novo hyperkalemia, 36% had diabetes mellitus (DM), 28% had CKD, and 21% had heart failure (HF). In the propensity score-matched sample of 177,082 individuals, hyperkalemia was associated with an increased risk for all-cause mortality (hazard ratio [HR] 1.15 [95% confidence interval {CI} 1.13-1.18], P < 0.001), cardiovascular events (HR 1.20 [95% CI 1.14-1.26], P < 0.001), short-term mortality (odds ratio [OR] 1.29 [95% CI 1.24-1.34], P < 0.001), hospitalizations (OR 1.71 [95% CI 1.68-1.74]), and ICU admissions (OR 3.48 [95% CI 3.34-3.62], P < 0.001). Findings were unchanged when a threshold of serum potassium ≥ 5.5 mmol/l was used.

CONCLUSION

Hyperkalemia was an independent risk factor for all-cause mortality, cardiovascular events, hospitalizations, and ICU admissions. This finding expands our understanding of important clinical outcomes associated with hyperkalemia.

Based on biomedical index data: Risk prediction model for prostate cancer

ABSTRACT

To explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.

Use of matching methods in observational studies with critical patients and renal outcomes. Scoping review

Introduction: The use of matching techniques in observational studies has been increasing and is not always used appropriately. Clinical experiments are not always feasible in critical patients with renal outcomes, and observational studies are an important alternative. Objective: Through a scoping review, determine the available evidence on the use of matching methods in studies involving critically ill patients and assessing renal outcomes. Methods: Medline, Embase, and Cochrane databases were used to identify articles published between 1992 and 2020 up to week 10, which studied different exposures in the critically ill patient with renal outcomes and used propensity matching methods. Results: Most publications are cohort studies 94 (94. 9 %), five studies (5. 1 %) were cross-sectional. The main pharmacological intervention was the use of antibiotics in seven studies (7. 1%) and the main risk factor studied was renal injury prior to ICU admission in 10 studies (10. 1%). The balance between the baseline characteristics assessed by standardized means, in only 28 studies (28. 2%). Most studies 95 (96 %) used logistic regression to calculate the propensity index. Conclusion: Major inconsistencies were observed in the use of methods and in the reporting of findings. A summary is made of the aspects to be considered in the use of the methods and reporting of the findings with the matching by propensity index.