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Handing over the care of a patient from one anesthesiologist to another occurs during some surgeries. A study was conducted to determine if this transfer of care might increase the risk of adverse outcomes (Jones et al. 2018). Read the excerpt from the study abstract published in JAMA below and answer the questions that follow. Methods: A retrospective population-based cohort study was conducted of adult patients undergoing major surgeries expected to last at least two hours and requiring a hospital stay of at least one night. The primary outcome measured was a composite of all-cause death, hospital readmission or major postoperative complications all within 30 postoperative days. Results: A total of 5941 patients underwent surgery with complete handover of anesthesia care. The primary outcome (death, readmission, or major postoperative complications) occurred in 2614 of these patients. A total of 307,125 patients underwent surgery without complete handover of anesthesia care. Of these, the primary outcome occurred in 89,066 patients. The complete handovers were statistically significantly associated with an increased risk of the primary outcome ([95\% CI, 4.5\% to \(9.1 \%] ; P<0.001\) ), all-cause death ([95\% CI, 0.5\% to \(2 \%] ; P=0.002\) ), and major complications ([95\% CI, 3.6\% to 7.9\%]; \(P<0.001\) ), but not with hospital readmission within 30 days of surgery \(([95 \% \mathrm{CI},-0.3 \%\) to \(2.7 \%] ; P=0.11) .\) a. Compare the percentage of each group who experienced the primary outcome (death, readmission, or major postoperative complications). Based on the abstract, can you reject the null hypothesis that there is no difference in the rates of primary outcome? b. If you were a hospital administrator, would you recommend that complete handover of anesthesia care during operations be limited? Why or why not? c. A difference between the two groups was found for all of the primary care outcomes except hospital readmission within 30 days of surgery. How do the confidence interval and p-values provided support this conclusion?

Step by step solution

01

Comparison and Null Hypothesis

They should compare the percentage of patients experiencing the primary outcome in both groups. In the handover group, 2614 out of 5941 patients experienced the primary outcome, that's approximately 44%. In the non-handover group, 89066 out of 307125 patients experienced the primary outcome, around 29%. The p-value is less than 0.001, which means there is strong evidence against the null hypothesis. It suggests that the difference in the rates of primary outcomes between the two groups is statistically significant and not due to random chance.

02

Recommendation as Hospital Administrator

As a hospital administrator, considering these results would be important. The complete handover of anesthesia care is associated with a higher rate of adverse outcomes. However, before making a final recommendation, they should also consider other factors such as the reasons behind the handovers, the seriousness of complications, and the benefits of having fresh anesthesiologists for long surgeries.

03

Interpretation of Confidence Interval and p-values

The confidence intervals for all-cause death and major complications do not contain 0, and the corresponding p-values are less than 0.05. This means that the increased risks associated with handovers are statistically significant. However, the confidence interval for hospital readmission does include 0, and the p-value is more than 0.05, meaning that the increased risk of hospital readmission associated with handovers is not statistically significant.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Null Hypothesis

The null hypothesis is a foundational concept in statistical analysis. It is a statement or proposition that there is no effect or no difference in a particular situation. In our exercise, the null hypothesis would assert that the transfer of care between anesthesiologists during surgery does not result in a difference in the rates of adverse outcomes, such as death, hospital readmission, or major complications, when compared to surgeries without such handovers.
Researchers aim to test the validity of the null hypothesis by analyzing data collected during studies. The goal is to determine whether any observed differences between groups are due to random chance or if they are statistically significant. If the results show a statistically significant difference, the null hypothesis can be rejected. For example, the p-value in our study was less than 0.001, indicating that the null hypothesis can be confidently rejected, suggesting a strong link between handovers and adverse outcomes.

Confidence Interval

A confidence interval provides a range of values that is believed to encompass the true value of an unknown parameter. It is a crucial concept in interpreting statistical data. In the context of our study, confidence intervals guide understanding of how certain we are about the difference in outcomes between the two groups.
For instance, let's consider the confidence interval for the increase in risk of the primary outcome due to complete handovers being between 4.5% and 9.1%. This range provides a clearer picture of the relative increase in risk, beyond a single percentage figure. The key question answered by the confidence interval is whether zero (indicating no effect) falls within this range. If not, as in this case, it supports the claim of a significant effect between conditions in the study. The absence of zero in the interval confirms that the risk increase is statistically significant.

P-value

The p-value is a statistical measure used to determine the significance of results in hypothesis testing. It indicates the probability of observing the given data, or something more extreme, if the null hypothesis is true. A lower p-value suggests that the observed data is unlikely to have occurred under the null hypothesis.
In the study about anesthesia handovers, p-values were provided to support or refute the outcome of adverse effects. The results showed a p-value of less than 0.001 for the primary adverse outcomes (deaths, major complications), indicating a less than 0.1% probability of these results occurring by chance if in fact handovers had no effect. This low p-value supports rejecting the null hypothesis, affirming the presence of an actual effect rather than chance variability. Therefore, the study concludes that handovers are associated with adverse outcomes.

Retrospective Study

A retrospective study is a type of observational study where researchers look back at existing data to find correlations or associations. These studies are invaluable for examining relationships in a large cohort of people, often using data collected over a long period.
The example study was retrospective, involving the examination of past data on patient outcomes from surgeries with and without anesthesiologist handovers. Retrospective studies have the benefit of utilizing pre-existing data, making them more feasible and less costly than prospective studies. However, they can be limited by the quality and scope of the data collected previously and are often susceptible to bias. Despite these limitations, they often provide strong preliminary evidence that can validate the need for further research, such as prospective controlled trials, to establish causality.