| Literature DB >> 25962594 |
Wilko von Klüchtzner1, Daniel Grandt2.
Abstract
BACKGROUND: Transitions between different levels of healthcare, such as hospital admission and discharge, pose a considerable threat to the quality and continuity of drug therapy. This study aims to further explore the current role of hospitalization in prescribing error exposure and medication-related communication as patients are transferred from and back to ambulatory care.Entities:
Mesh:
Year: 2015 PMID: 25962594 PMCID: PMC4494641 DOI: 10.1186/s12913-015-0844-x
Source DB: PubMed Journal: BMC Health Serv Res ISSN: 1472-6963 Impact factor: 2.655
Calculation of study power
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| 1.0 | 3.0 | 99% |
| 3.5 | 95% | |
| 4.0 | 89% | |
| 4.5 | 81% | |
| 1.1 | 3.0 | >99% |
| 3.5 | 98% | |
| 4.0 | 94% | |
| 4.5 | 87% | |
| 1.2 | 3.0 | >99% |
| 3.5 | 99% | |
| 4.0 | 97% | |
| 4.5 | 92% | |
| 1.3 | 3.0 | >99% |
| 3.5 | >99% | |
| 4.0 | 99% | |
| 4.5 | 96% |
The primary hypothesis for power calculation implied that the mean number of prescribed drugs per patient would increase during hospital stay. Relying on preliminary investigations into the distribution pattern of prescriptions at admission to Essen University Hospital (mean number of prescriptions ± standard deviation: 7 ± 3) and assuming correlations of r = 0.2 to r = 0.7 between the mean prescription numbers at admission and discharge, the standard deviation of the difference between them was deduced to take on values between 3.0 and 4.5. The difference between the means itself was assumed to be at least 1. Based on these assumptions the primary hypothesis was tested in varying scenarios using matched-pair signed-rank test with a significance level of 5%. With a set sample size of n = 180 a study power of more than 80% was achieved in each tested scenario. To account for a drop-out rate of up to 10% the needed number of participants was determined to be 200.
Figure 1Results of patient recruitment. Reasons for patient exclusion and distribution of included patients to the different medical and surgical departments.
Overall burden of potentially inadequate prescribing at hospital admission and discharge
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| Median (IQR) of potential prescribing errors per patient | 1 (0–2) | 1 (0–3) | 0.135 |
| Number of patients with prescribing error exposure | 118 | 122 | 0.651 |
| single error | 44 | 44 | 0.454 |
| multiple errors | 74 | 78 |
IQR: interquartile range.
Figure 2Number of patients affected by specific types of potentially inadequate prescribing at admission (blue bars) versus discharge (purple bars). *: p < 0.05; **: p < 0.01.
Figure 3Percentage of renally impaired patients lacking appropriate dose adjustment at admission versus discharge. Error bars: 95% confidence intervals.
Binary logistic regression analysis
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| Gender | ||||
| male | 1.959 (1.064-3.608) | 0.031 | 2.115 (0.970-4.611) | 0.060 |
| female (reference) | − | − | − | − |
| Age (years) at discharge | 1.043 (1.022-1.064) | <0.001 | 1.017 (0.988-1.047) | 0.258 |
| GFR (ml/min/1.73 m2) at discharge | 0.973 (0.958-0.988) | 0.001 | 1.000 (0.978-1.023) | 0.988 |
| Prescribed drugs per patient at discharge | 1.551 (1.355-1.777) | < 0.001 | 1.524 (1.297-1.790) | < 0.001 |
| Length of stay (days) | 1.044 (0.988-1.104) | 0.122 | 0.980 (0.902-1.064) | 0.631 |
| Department | ||||
| surgical | 2.353 (0.840-6.594) | 0.104 | 4.069 (1.126-14.703) | 0.032 |
| medical (reference) | − | − | − | − |
(Incremental) impact of selected patient and treatment factors on the probability of exposure to potentially inadequate prescribing associated with hospitalization (CI: confidence interval).