Aims
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To determine whether integration of the hospital palliative care team at the end of life can prevent the use of burdensome diagnostic and therapeutic procedures.
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To prove that if the palliative care team is involved, it gives clearer context to the end of life, as the dying phase is documented.
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To prove that integration of hospital palliative care teams at the end of life can prevent transitions to intensive care and lower healthcare costs.
Design
We used a case-control observational retrospective study design. The differences in the end-of-life between the two groups of patients with (palliative care group) and without (nonpalliative care group) the support of the hospital palliative care team were compared. We retrospectively analysed and documented data from paper and electronic medical records of the terminal hospitalization.
Setting
The Faculty Hospital Kralovske Vinohrady is a tertiary university hospital in Prague, with 1,200 beds serving a catchment area of approximately 300,000 inhabitants. Patients in the catchment area are the oldest of all Prague inhabitants.
The hospital palliative care team was established in 2016, and with 748 new patients per year (in 2020), it is the most efficient and one of the most advanced teams in the Czech Republic. This has led to the implementation and development of specialist hospital palliative care in the country. Nearly one-third of the patients indicated to the team die during their terminal hospitalization. There were approximately 1,100 deaths in the hospital, with 15–19% of dying patients supported by the palliative care team.
Participants
All in-patients who died between January 2019 and April 2020 were eligible for the study. Cases were supported by the hospital palliative care team during the dying phase. They were matched with similar controls from all deceased patients not supported by the team. Routine big data from the National Death Registry and the National Registry of Hospital Activity were used for matching. Controls had similar diagnoses on the death certificate, sex, age group, and Charlson comorbidity index. Power analysis was performed to calculate the sample size large enough to demonstrate the presence of an economically relevant financial difference between the two groups.
Data collection
Data were collected from paper and electronic medical records. Three researchers, all physicians (2 internists and 1 oncologist), analysed the records of the deceased patients. They inserted the variables into a prepared template. When the semiqualitative data were analysed, content analysis of the written data was carried out according to an approved mechanism about which a consensus was reached by all researchers and the project manager. Economic analysis was performed by counting all hospital costs of health care procedures billed to the insurance company, including medications and materials.
Variables
Estimation of the total daily costs of a terminal hospitalization and the determination of their difference between the control group and cases were the primary outcomes.
The secondary outcomes were the length of the terminal hospitalization, days in the intensive care unit, the use of IV antibiotics, chemotherapy and radiotherapy in the last month, and the number of costly diagnostic procedures (CT/MRI scans).
Another secondary outcome was the difference between the groups in documenting the fact that the patient was dying. Content analysis of the words referencing the dying phase was approved before data collection by consensus of all three researchers and the project leader.
Time spent in the care of the palliative team was a potential confounder; therefore, it was included in the dataset. Demographic differences were decreased by case-control matching using age, diagnosis, comorbidities, and sex.
The results could have been biased by the data collection methodology and analysis of medical records. For this reason, regular monthly meetings of the researchers and the project manager were scheduled to assure clarification of potential uncertainties and to approve a unified model of data collection. All data were inserted into an Excel chart made for this purpose.
Reporting
The STROBE checklist for case-control studies was used to report the study results. The study protocol was registered with the Technology Agency of the Czech Republic programme ÉTA 3 grant called Dying Matters [TL03000709].
Data analysis and statistical methods
The geometric mean and logarithmic transformation of hospital costs were used for power analysis and sample size calculation because of the asymmetric distribution of data. A total of 195 patients in each group were needed to prove the cost difference of 10,000.00 CZK between the groups with a 0.05 level of significance and a power of 0.8. We used PS Power and Sample Size Calculations (version 3.0).
Standard descriptive statistics were adopted for the description of the data. Numerical variables were described using the mean, standard deviation and 95% confidence interval. Categorical variables were described using absolute and relative frequencies of categories (percentages).
The statistical significance of differences between the clinical and control groups was tested by Fisher’s exact test for categorical variables and by the Mann-Whitney U test for numerical data.
The results were considered statistically significant at the level of alpha < 0.05 in all applied analyses. Analyses were performed using IBM SPSS Statistics 25.0.0 (IBM Corporation, 2017).