1 Degree project, 30 ECTS May 14, 2020
Identification of risk factors associated with
unplanned readmission, palliative decision or
mortality within 30 days at the acute admissions
unit during 2019 – a retrospective cohort study.
Version 2Author: Ida Dahlgren, MB School of Medical Sciences Örebro University Örebro Sweden Supervisors: Chariklia Lempessi, MD Department of internal medicine Örebro University Hospital Örebro Sweden Michiel van Nieuwenhoven, MD, PhD Department of Gastroenterology Örebro University Hospital Örebro Sweden
Word count Abstract: 221 Manuscript: 3163
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Abstract
Introduction: A recent study at the acute admission unit (AAU), revealed that 13.5 percent of all patients discharged from the department, were readmitted within 30 days during 2018. In the group of 80 years and above, the cause for re-admission was multifactorial.
Aim: To identify factors that are associated with unplanned re-admission, palliative decision, or death within 30 days after discharge from the AAU, in patients of 80 years or above. Another aim is to examine if longer hospital stay, patient discharge planning and fast follow-up can protect against these outcomes.
Methods: A retrospective cohort study comprising 287 patients. Data on age, sex, length of stay, comorbidities (Elixhauser comorbidity index), frailty (Clinical frailty scale), National Early Warning Score (NEWS), social status, home care, lab values and outcome were collected. All variables were analyzed using Chi-square test with univariate and multivariate logistic regression, and a p-value < 0.05 was considered statistically significant.
Results: 276 patients were included. A NEWS ≥ 3 was associated with significantly increased risk for poor outcome (odds ratio 2.4). Living with someone without municipal support was associated with a significantly decreased risk for poor outcome (odds ratio 0.21).
Conclusions: The results indicate that it is crucial to stabilize patients of 80 years or above before discharge. And that living with someone without municipal support is a protective factor.
Key words: readmission, mortality, National Early Warning Score (NEWS), municipality support, clinical frailty scale (CFS)
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Abbreviations:
Activities of daily life (ADL) Acute admissions unit (AAU) Charlson Comorbidity index (CCI) Clinical Frailty Scale (CFS)
C-reactive protein (CRP)
Elixhauser Comorbidity index (ECI) Hemoglobin (Hb)
National Early Warning Score (NEWS) University hospital of Örebro (USÖ)
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Background:
In an internal quality study recently conducted at the internal medicine section of the acute admissions unit (AAU) at the University hospital of Örebro (USÖ) revealed that 13.5 percent of all patients discharged from the AAU were readmitted within 30 days during 2018 [1]. The main reasons for acute readmission were in descending order: intoxication, heart failure, pneumonia and chronic obstructive pulmonary disease [1]. Readmissions were most common in the group 30-39 years due to intoxications, followed by the groups 90 years and above, 40-49 years and 80-89 years [1]. The causes for acute readmission in the group of 80 years and above were multifactorial [1].
The AAU at USÖ serves as an independent department but is closely connected to the emergency department. Patients are usually admitted for a few days before discharged from the hospital or moved to other clinical wards. In 2018, 29% of the patients admitted to the AAU were 80 years or above [1]. People of 80 years or above comprise 5% of the population in Sweden and represent more than 20% of all hospital admissions and around 26% of the total admission time in Sweden [2]. With an increasing life expectancy in the Swedish population, this group is estimated to increase from 503 000 to 800 000 people by 2030 [2]. The healthcare system will consequently face a larger group of elderly and fragile people that stand for a high consumption of the total health- and social care [2].
Sweden has one of the highest healthy-life-expectancy in the world, where more than 70% of the population above 65 years is expected to be in good health another 15 years [3]. A recent report from the Swedish National Board of Health and Welfare demonstrates a trend that between 2010 to 2017, fewer people in the group 80 years or above are living in retirement homes or similar accommodations, as well as having home care [2]. This might be a result of a more vital elderly population in Sweden than just a few years back [2].
High comorbidity is an important predicting factor when considering acute readmission or poor outcome [4]. With an older patient group the number of comorbidities increases [5]. A helpful approach to look at disease burden in patients is by using comorbidity indexes, where diseases are assessed and converted into a score. Studies have shown that the Elixhauser Index, with 30 comorbidities, outperform the more commonly used Charlson Comorbidity Index for predicting readmission [6–9]. Another approach is looking at frailty, which has shown to be an independent risk factor for poor outcome such as readmission and mortality [10–12]. In a study examining different methods to assess frailty, the Clinical Frailty Scale (CFS) outperformed
5 two other, more objective, methods for predicting acute readmission. The correlation increased when assessing frailty in patients above 65. Frailty, according to CFS, is estimated by considering the physical condition of the patient, their use of medical devices and their need for assistance with personal activities in daily life (ADL) [13].
Since the past decades Swedish health care has gone through comprehensive transformations caused by system change towards more outpatient care and new and more effective treatments. Since 1994, the number of hospital beds per 1000 inhabitants have reduced from 5.2 to 2.2 beds in 2017 [3,14]. The length of the hospital stay for acute curative care has also decreased, from 7.1 to 5.7 days [3,14]. The median length of stay per patient at the AAU was 30 hours during 2018 [1]. A Swedish study from 2015 confirmed that a hospital stay of 10 days or less was associated with increased mortality after discharge, for patients 50 years or above that have undergone hip operations, both in short term and long-term mortality [15]. These results have been confirmed respectively contradicted in studies conducted in South Korea and the USA [12,13].
Mortality and acute readmission, defined as unplanned and within 30 days, are usually outcomes used to measure the quality of in-patient care. The AAU with its unique connection to the emergency department should consider the benefits of avoiding readmission against the need to have beds available for new patients from the emergency department.
Aim:
The aim of this study is to investigate which factors are associated with poor outcome, i.e. unplanned readmission, palliative decision or mortality within 30 days among patient of 80 years or above. Secondly, whether longer hospital stay, patient discharge planning and fast follow-up can protect against a poor outcome.
Method:
Study design: This is a retrospective cohort study conducted at the University hospital in Örebro.
Inclusion criteria:
- The patient is of 80 years or above.
- The admission to the acute admissions unit is unplanned.
6 - Discharge to their home or to a short-term accommodation.
- Not an outsourced patient (“satelitpatient”): patient admitted to a clinical ward that do not have the specific competence nor the medical responsibility for that patient.
Population: We obtained a database containing identification numbers for all patients of 80 years or above who were discharged at the AAU from the hospital or moved to other in-hospital wards during 2019. This includes patients that had been admitted during 2018 but discharged during 2019. The database contained information if the patient had a fatal outcome and to which section and clinic the patient belonged to. Patients who did not meet the inclusion criteria were excluded. The reaming patients’ medical records were reviewed and patients who did not meet the inclusion criteria or patients with inaccessible information regarding their outcome (readmission, palliative decision or mortality) were excluded.
Variables of interest: The database also contained data on age, sex, and length of stay. Further data that we extracted were based on each patient’s medical record using Klinisk Portal. These data comprised comorbidity using Elixhauser comorbidity index (ECI), frailty using the Clinical frailty scale (CFS), the last obtained c-reactive protein (CRP), hemoglobin (Hb), the National Early Warning Score (NEWS) during the hospital stay, patient discharge planning, planned “fast follow-up” as well as civil status and use of home care. We refer to appendix 1 for further information on the collection of the data.
Elixhauser comorbidity index (ECI): 30 diseases are assessed and transformed into a score
ranging from -19 to 89. A higher score is associated with an increased risk for in-hospital mortality, ranging from a minimum of 0.37% to a maximum of 99.41% [5,9,16].
Clinical frailty scale (CFS): A scale that ranges from 1 to 8, were 1 represents the most vital
patient and 8 represents a bedridden patient, who is not expected to live for another six months [17]. A patient is considered frail if a score of ≥ 5 [13].
Hemoglobin (Hb): A value beneath 120 for women respectively 130 for men was defined as
anemia [18].
National Early Warning Score (NEWS): A scoring system based on consciousness, blood
pressure, pulse, respiratory frequency, saturation, use of oxygen and temperature, with a minimum score of 0 and maximum score of 21. Respiratory frequency was excluded as a parameter since we first planned to look at each parameter separately and not to use NEWS.
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Patient discharge planning: Coordination with the municipality to either initiate support at the
patient’s home or expansion of the already established support, such as cleaning, personal hygiene, and distribution of medicines. Further, deciding whether the patient is in need of a placement at a short- or long-term care facility.
“Fast follow-up”: Includes a planned follow-up to the outpatient clinic (Medicin Mottagning
6) connected to the AAU, if standardized care process for cancer investigation is initiated (Standardiserade vårdförlopp), or follow-up by any of the two outpatients teams belonging to the Geriatric clinic of USÖ (Geriatriska Öppenvårds Teamet and Närsjukvårdsteamet).
Outcome: The outcome was unplanned readmission, palliative decision or mortality within 30 days from the day of discharge.
Statistics: The obtained data were collected and de-identified in Microsoft Excel® version 16. The collected data were then analyzed using IBM® SPSS® Statistics® version 26. Binary variables are presented in number and percentage, and ordinal or continuously variables are presented in median and interquartile range (IQR). All data were analyzed using Chi-square test with univariate logistic regression and multivariate logistic regression adjusting for all other variables. A P-value of < 0.05 is considered statistically significant. Two sensitivity analyses were conducted. The first sensitivity analysis was, for patients admitted to the AAU more than one time, to only include the first admission. We anticipated that the correlation between the variables and the outcome could become more wage when including the same patient in the study more than one time. The second sensitivity analysis was to exclude palliative decision as part of the outcome. This because we did not investigate each patient’s primary care medical journals and assumed that some palliative decisions within 30 days from discharge had gone unrecorded.
Ethical consideration: An approval from the head of the department of internal medicine was obtained on 11 February 2020. We did not apply for ethical approval. This was considered not necessary as this study is very small and can be classified as a quality study for the hospital. All the collected data were kept in a way that only authorized people had access to the information. During data analyses, all patients were de-identified.
Results:
Study population: In total 276 patient cases were included in the study, see flowchart Figure 1. From the register, 300 patients were directly excluded since they did not meet the inclusion
8 criteria. After reviewing the remaining 287 patients’ medical journals, another five patients were excluded since they did not meet the inclusion criteria. Seven patients were excluded because we were not able to obtain the necessary information on the patient’s outcome. One patient was admitted without an admission registration and was added to the data. Twenty patients were admitted twice to the AAU and 1 patient was admitted three times. The length between their admissions varied between 10 months to less than a day.
Figure 1: Flowchart of the study population.
Baseline characteristics: Se table 1. The median age of the included population was 87 years. Almost half (43%) of the population had no municipal support, 19% lived in a care facility and 38% had municipal support. The median CFS was 5, hence more patients were perceived as fragile than not fragile. When discharged from the AAU, most of the patients had a NEWS score of less than 2 and elevated level of CRP. Half of the study population had anemia according to the WHO criteria at discharge. The median length of stay was 2 days. Eight % and 26% had a “discharge planning” or a planned “fast followed up”, respectively.
Table 1 Baseline characteristics of the study population.
n = 276
Median age, years (IQR) 87 (83–90)
Female sex, no (%) 168 (60.9)
Social status pre-admission Without municipal support - living alone, no (%)
69 (25) Without municipal support
- living with someone, no (%)
50 (18.1)
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Living in a care facility, no (%) 51 (18.5)
Disease burden
ECI, median (IQR) 5 (0–11)
< 2, no (%) 90 (32.6) 2–8, no (%) 98 (35.5) > 8, no (%) 88 (31.9) CFS, median (IQR) 5 (4–5) 1–3, no (%) 44 (15.9) 4–5, no (%) 171 (62.0) 6–8, no (%) 61 (22.1)
Last obtained value during the hospital stay
NEWS, median (IQR) 1 (0–2)
0–2, no (%) 227 (82.2)
≥ 3, no (%) 49 (17.8)
C-reactive protein, median (IQR) 36 (8–77)
Anemia, no (%) 139 (50.4)
Length of stay in hours, median (IQR) 46 (23–71) Measures taken before discharge
Patient discharge planning, no (%) 21 (7.6)
Planned fast follow-up, no (%) 72 (26.1)
ECI – Elixhauser Comorbity Index CFS – Clinical Frailty Scale
NEWS – National Early Warning Score
Outcome: The outcome occurred for 51 patients (18.5%); 40 patients were readmitted, 7 patients received a palliative decision and 19 had a fatal outcome within 30 days. The results for how each variable relates to the outcome by logistic univariate and multivariate regression is shown in table 2.
Statistically significant risk factors for the outcome using univariate logistic regression comprised: age above 87 years (p=0.05), a CFS between 4-5 (p=0.05) respectively 6-8 (p=<0.01) compared to CFS 1-3, and NEWS of 3 or more (p=<0.01) compared to NEWS 1-2. Additionally, living without municipal support, alone or with someone, was protective against the outcome (p=<0.01) comparing to having municipal support.
Adjusting for all other variables using multivariate logistic regression, a NEWS of 3 or more (p=0.03) and living with someone and without municipality support (p=0.02) were statistically significant as a risk factor respectively protective factor. The odds ratio for being discharged
10 with a NEWS of 3 or more was 2.40 (1.11–5.18) and the odds ratio when having no municipality support and living with someone was 0.21 (0.05–0.80).
The odds ratio was increased with an elevated score for the ECI as well as for CFS using multivariate logistic regression, but they were not statistically significant. For the most comorbid and fragile, the odds ratio increased with 1.8 (0.80–4.25) and 3.4 (0.63–18.70) respectively, p-vale was for both 0.16.
Using univariate analysis, a length of stay longer than 46 hours increased the odds ratio with 1.5 (p=0.19) but when adjusting for other variables it decreased to 1 (p=0.96). Planned follow-up, on the other hand, was noted to protect (p=0.65) using univariate and with multivariate analysis the odds ratio increased to 1.1 (p=0.81). Patients having a discharge planning (p=0.08) had an increased risk for poor outcome, both in univariate and multivariate analysis, with an odds ratio of approximately 2.5.
Table 2: Univariate and multivariate analysis for each variable against poor outcome; unplanned readmission, palliative decision and mortality within 30 days from discharge.
Univariate analysis Multivariate analysis OR (95% Cl) p-value OR (95% Cl) p-value Above 87 years 1.87 (1.01–3.46) <0.05 1.44 (0.71–2.90) 0.31 Female sex 1.00 (0.53–1.86) 0.99 1.06 (0.42–1.84) 0.74
Social status preadmission Without municipal
support - living alone 0.28 (0.11–0.72) <0.01 0.40 (0.15–1.12) 0.08 Without municipal
support - living with someone
0.19 (0.05–0.65) <0.01 0.21 (0.05–0.80) 0.02
Municipal support Reference Reference
Living in a care facility 1.22 (0.58–2.57) 0.60 1.04 (0.42–2.57) 0.94 Disease burden ECI < 2 Reference Reference 2-8 0.98 (0.44–2.17) 0.96 1.09 (0.46–2.58) 0.84 > 8 1.70 (0.80–3.61) 0.17 1.84 (0.80–4.25) 0.16 CFS 1-3 Reference Reference 4-5 4.11 (0.94–17.98) 0.06 2.50 (0.53–11.77) 0.25
11 6-8 10.24 (2.25–46.65) <0.01 3.43 (0.63–18.70) 0.16 Last obtained value
during the hospital stay:
NEWS ≥ 3 2.75 (1.37–5.54) <0.01 2.40 (1.11–5.18) 0.03
Anemia 1.32 (0.71–2.44) 0.38 0.94 (0.46–1.93) 0.86
CRP > 36 1.62 (0.87–3.01) 0.13 1.50 (0.73–3.08) 0.27 Measures taken before
discharge Patient discharge
planning 2.47 (0.94–6.47) 0.07 2.69 (0.88–8.20) 0.08
Planned fast follow-up 0.87 (0.43–1.78) 0.71 1.11 (0.49–2.52) 0.81 Length of stay > 46 hours 1.44 (0.78–2.67) 0.25 0.98 (0.48–2.01) 0.96
ECI – Elixhauser Comorbity Index CFS – Clinical Frailty Scale
NEWS – National Early Warning Score
Result of the sensitivity analysis 1: see appendix 2, table 1 and 2. After excluding patients that were admitted a second or third time to the AAU, 254 patients were included. Almost no change in the baseline characteristics was observed. NEWS of 3 or more became the only statistically significant variable with multivariate logistic regression, with a p-value of 0.01. Living without municipality support with someone had a decrease in odds ratio 0.25 and a p-value of 0.05. Result of the sensitivity analysis 2: see appendix 3, table 1. When excluding palliative decision from the outcome, the number of patients where the outcome occurred decreased from 18.5 to 18.1 percent. Adjusting for the new outcome (acute readmission and mortality), NEWS 3 or more and living with someone and without municipality support remained the only two statistically significant variables with multivariate logistic regression, with a p-value of 0.02 respectively 0.05. Patient discharge planning almost became statistically significant p=0.06 with an odds ratio of 2.93 (0.96–8.96). All other variables remained fairly the same.
Discussion:
In this retrospective cohort study, we examined potential risk factors as well as protective factors for readmission, palliative decision and mortality within 30 days after being discharged from the AAU. The only statistically significant risk factor that we identified in our study was that a NEWS (excluding respiratory frequency) of 3 or more on discharge increased the risk for
12 poor outcome more than twice (odds ratio 2.4) compared to patients with NEWS 0-2. This was also verified when conducting sensitivity analyses; where we excluded patients, who were admitted to the AAU for a second and a third time, respectively, when analyzing all variables against only readmission and mortality. In addition to this finding, we observed that an elevated score in the ECI and CFS increased the risk exponentially for poor outcome, using multivariate logistic regression. However, the correlation could not be proved statistically. For the CFS it reached statistically significance for the highest score when analyzed with univariate logistic regression, and the patients with CFS 4-5 had a fourfold increased risk for poor outcome and CFS 6-8 showed a 10-fold increased risk compared to the patients with CFS 1-3.
A finding from our study was that the patients who had no municipality support while living with someone had a decreased risk for poor outcome, with an odds ratio of 0.21, compared with the 105 patients who had municipal support regardless if they lived with someone or not. A protective trend could also be seen in the patients without municipal support but that lived alone, and was with univariate logistic regression statistically significant, with an odds ratio of 0.28. However, living in facility care showed no statistical significance. In contrast to another study which demonstrated that living in facility care was a risk factor for acute readmissions [4]. This discrepancy might be explained by the fact that in this study only patients with diastolic heart failure were included.
Interestingly, we did not find that the factors; longer hospital stay, patient discharge planning, and fast follow-up were protective, as we anticipated it would, rather the opposite. Especially discharge planning, which had more than a 2-fold increase in odds ratio for the outcome and almost reached statistically significance. This finding is in contrast to other studies [19–21]. One reason for this finding could be that the doctor correctly judged the patient as more ill or frail, leading to a higher risk for readmission and death and for this reason the doctor initiated a discharge planning. Another reason could be that the quality of the patient discharge planning was inadequate for many of the patients. A study from the US proved that higher quality on the discharge planning gave a more effective result with fewer acute readmissions [21].
The major weakness of this study is that it is based on medical records. This means that the selection of data which is considered important is made by individual doctors and health care professionals. This is especially important in regard to the CFS, which is a collective assessment of the patient’s vitality, as well as the use of medical equipment and personal-ADL, which is not always documented, especially when admitting critically ill patients. However, if a patient used a walking stick, walker or wheelchair, it is usually documented, and gives a clue about the
13 CFS. There is a plan to implement the CFS when admitting patients from the emergency department. This will simplify potential future studies at the USÖ. The major strengths of this study are that NEWS is taken on all patients on a daily basis, and that readmission and mortality are objective outcomes. However, as for palliative decisions as an outcome, we can assume that some have been made unrecorded, since we did not investigate each patient’s primary care medical journals. For this reason, we conducted a second sensitivity analysis to exclude palliative decision as one of the outcomes which did not change the results considerably. In conclusion, a NEWS (excluding respiratory frequency) of 3 or more increased the risk for unplanned readmission and mortality within 30 days among patients 80 years or above. This indicates that it is crucial to stabilize the most elderly and fragile before discharge. As this group is estimated to increase and already includes 20% of all hospital admissions, this conclusion could be generalized to other departments. Additional studies for this group are needed, for example a comparison between the outcome for patients that are discharged from the AAU with the group that is hospitalized at another specialized department before discharge. Another interesting study would be to investigate the occupancy rate of hospital beds at both the AAU and the rest of the hospital and to establish whether a high occupancy rate forces doctors to discharge patients earlier than they otherwise would, with respect to the patient’s NEWS value at discharge.
Acknowledgement
I would like to acknowledge my supervisors Chariklia Lempessi, MD, and Michiel van Nieuwenhoven, MD, PhD, for their help with contemplating this report. Furthermore, I also want to show my gratitude to Carl Eriksson, MD, PhD, for guiding me through the statistical analysis and Åsa Andersson, PhD, for here advices on how to look at frailty and comorbidity.
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16 APPENDIX 1
Table 1. Information of what is included in each variable as well as where it was extracted from.
Variables: Register Klinisk
Portal (KP) Where in Klinisk Portal
Age X
Sex X
Social status X Admission document from doctor
and nurses. Municipal support includes:
- Home care (hemtjänst) - Home health care (hemsjukvård)
- Other private support equivalent with above
X Admission document from doctor and nurses.
Care-facility X Admission document from doctor
and nurses.
Elixhauser Comorbidity Index X 1. Admission document 2. Diagnostic list
3. Patient medical history
4. Any documentation of weight loss in the admission notes. 5. Lab list, BMI <18. one year previous from the admission.
If a patient had one of the diseases this was noted as 1 in the excel document. If no record of the disease this was noted 0. Using the weighed points for each disease we calculated the patient’s individual score [9].
17 Disclaimer: in case of uncertainty of an existing diagnose this was further investigated and if it could not be confirmed it was not taken into consideration.
Clinical Frailty Scale X 1. Nurses admission document.
2. Doctors admission document. 3. Documentation from physio-therapist during the hospital stay about the patient’s physical shape before the admission.
Disclaimer: if there was uncertainty between two adjacent numbers of the scale, the lowest (most vital) number was systematically selected. If the uncertainty was between more numbers, the most central number was selected.
To select more objectively (and not be affected by knowing the patient’s outcome) a note was written on the side column for those patients, these notes were then assessed after all data for all patients had been collected. National Early Warning Score
Disclaimer: Respiratory frequency was excluded as a
18 we first decided to look at each
individual parameter and not using NEWS.
Hemoglobin X Lab list
C-reactive protein X Lab list
Length of stay X
Patient discharge planning (vårdplanering)
X Nurses and doctors discharge document and a fast look through the daily notes.
Planed fast follow-up - Medicin Mottagning 6 - Standardiserade vårdförlopp - Geriatriska Öppenvårds Teamet
- Närsjukvårdsteamet
X Discharge document by the doctor.
Outcome - Mortality - Readmission - Palliative decision
X 1. If the patient is deceased, it is displayed with a grey note on the top of the medical record, “Patient info” to get the exact date when this occurred
2. “Patient information” to get information of readmissions
3. If readmitted or visited the emergency department within 30 days, I read the notes to see if a palliative decision has been made.
19 APPENDIX 2
Sensitivity analysis 1: If more than one admission to the AAU only the first admission to the AAU is included. In total 254 patients were included. The outcome occurred for 45 of the patients (17.7 percent).
Table 1: Illustrating the baseline characteristics of the study population, when excluding the second and third admission to the AAU.
Totalt antal 254
Median age, years (IQR) 87 (83–90)
Female sex, no (%) 156 (61.4)
Social status pre-admission Without municipal support - living alone, no (%)
65 (25.6)
Without municipal support - living with someone, no (%)
45 (17.7)
Municipal support, no (%) 96 (37.8)
Living in a care facility, no (%) 47 (18.5)
Disease burden
Elixhauser Comorbidity Index, median (IQR) 5 (0–11) < 2, no (%) 82 (32.3) 2-8, no (%) 92 (36.2) > 8, no (%) 80 (31.5)
Clinical Frailty Scale, median (IQR) 5 (4–5)
1-3, no (%) 42 (16.5) 4-5, no (%) 157 (61.8) 6-8, no (%) 55 (21.7)
Last obtained value during the hospital stay
NEWS, median (IQR) 1 (0–2)
0-2, no (%) 211 (83.1) ≥ 3, no (%) 43 (16.9)
20
C-reactive protein, median (IQR) 36 (7–77)
Anemia, no (%) 125 (49.2)
Length of stay in days, median (IQR) 46 (23–72)
Measures taken before discharge
Patient discharge planning, no (%) 18 (7.1)
Planned fast follow-up, no (%) 67 (26.4)
ECI – Elixhauser Comorbity Index CFS – Clinical Frailty Scale
NEWS – National Early Warning Score
Table 2: Univariate and multivariate analysis for each variable against the outcome: unplanned readmission, palliative decision or mortality within 30 days.
Univariate analysis OR (95% Cl) p-value Multivariate analysis OR (95% Cl) p-value Above 87 years 1.67 (0.87-3.19) 0.12 1.35 (0.65–2.80) 0.42 Female sex 1.04 (0.54-2.02) 0.90 0.90 (0.41–1.97) 0.79 Social status preadmission
Without municipal support - living alone
0.33 (0.13-0.86) 0.02 0.42 (0.15–1.20) 0.11
Without municipal support - living with someone
0.23 (0.07–0.81) 0.02 0.25 (0.06–1.00) 0.05
Municipal support Reference
Living in a care facility 1.23 (0.56–2.72) 0.61 1.05 (0.40–2.76) 0.92
Disease burden Elixhauser Comorbidty Index
21 <2 Reference
2-8 0.95 (0.42–2.17) 0.91 1.04 (0.43–2.55) 0.93 >8 1.54 (0.70–3.40) 0.28 1.60 (0.66–3.88) 0.30 Clinical Frailty Scale
1-3 Reference
4-5 4.15 (0.95–18.24) 0.06 2.34 (0.50–11.28) 0.28 6-8 8.21 (1.77–38.07) <0.01 2.91 (0.52–16.38) 0.23
Last obtained value during the hospital stay:
NEWS ≥ 3 2.80 (1.33–5.89) <0.01 2.79 (1.24–6.26) 0.01
Anemia 1.10 (0.58–2.09) 0.78 0.79 (0.38–1.67) 0.54
C- reactive protin > 36 1.47 (0.77–2.81) 0.24 1.24 (0.58–2.63) 0.59
Length of stay > 46 hours 1.87 (0.97–3.61) 0.06 1.27 (0.60–2.70) 0.54
Measures taken before discharge
Patient discharge planning 2.53 (0.89–7.13) 0.08 2.66 (0.81–8.71) 0.11 Planned fast follow-up 0.76 (0.35–1.64) 0.49 1.00 (0.42–2.38) 1.00
ECI – Elixhauser Comorbity Index CFS – Clinical Frailty Scale
22 APPENDIX 3
Sensitivity analysis 2: All variables were analyzed against the outcome unplanned readmission and mortality within 30 days, hence palliative decision is not taken into consideration as part of the outcome.
There are no changes in baseline characteristics since the study population did not change. 276 patient cases were included. The outcome occurred for 50 of the patients (18.1 percent).
Table 1: Univariate and multivariate analysis for each variable against poor outcome: unplanned readmission or mortality within 30 days.
Univariate analysis OR (95% Cl) p-value Multivariate analysis OR (95% Cl) p-value Above 87 years 1.98 (1.06–3.68) 0.03 1.52 (0.75–3.08) 0.25 Female sex 1.00 (0.56–1.99) 0.86 0.94 (0.44–1.97) 0.86 Social status preadmission
Without municipal support - living alone
0.29 (0.11–0.76) <0.01 0.43 (0.16–1.20) 0.11
Without municipal support - living with someone
0.19 (0.05–0.65) <0.01 0.23 (0.06–0.89) 0.03
Municipal support Reference
Living in a care facility 1.22 (0.58–2.57) 0.60 1.12 (0.45–2.81) 0.80 Disease burden Elixhauser Comorbidty Index <2 Reference 2-8 0.98 (0.44–2.17) 0.96 1.09 (0.46–2.58) 0.85 >8 1.70 (0.80–3.61) 0.17 1.76 (0.76–4.10) 0.19 Clinical Frailty Scale
23 4-5 4.11 (0.94–17.98) 0.06 2.35 (0.50–11.04) 0.28 6-8 10.24 (2.25–
46.65)
<0.01 3.36 (0.61–18.40) 0.16
Last obtained value during the hospital stay:
NEWS ≥ 3 2.75 (1.37–5.54) <0.01 2.49 (1.15–5.39) 0.02
Anemia 1.32 (0.71–2.44) 0.38 0.90 (0.43–1.86) 0.77
C- reactive protine > 36 1.62 (0.87–3.01) 0.13 1.47 (0.71–3.05) 0.30
Length of stay > 46 hours 1.44 (0.78–2.67) 0.25 0.93 (0.45–1.92) 0.85
Measures taken before discharge
Patient discharge planning 2.47 (0.94–6.47) 0.07 2.93 (0.96–8.96) 0.06 Planned fast follow-up 0.87 (0.43–1.78) 0.71 1.15 (0.50–2.63) 0.74
ECI – Elixhauser Comorbity Index CFS – Clinical Frailty Scale
24 Cover letter
Örebro, Sweden, 2020 May 14 .
Dear editors,
Please consider publishing our study “Identification of risk factors associated with unplanned
readmission, palliative decision or mortality within 30 days at the acute admissions unit during 2019 - a retrospective cohort study”, by Ida Dahlgren et al.
With this study we wanted to find out which variable can predict as well as protect against acute readmission, palliative decision or mortality within 30 days after discharge in patients of 80 years or above. We examined several factors, among those: National Early Warning Score at discharge, Clinical frailty Scale, Elixhauser Comorbidity, hemoglobin, c-reactive protein and social status. Our main finding was that a NEWS value of 3 or more increased the risk (odds ratio 2.4) for poor outcome. Our finding suggest that it is crucial to stabilize the most elderly patients before discharge.
We consider this finding important as people of 80 years and above is a group that is estimated to increase and already includes more than 20 percent of all admissions to hospitals in Sweden. To be able to meet this growing healtcare demand it is of importance that we can predict, and hence, prevent unnecessary readmissions.
This is an original study. We have not published it before and not sent it to any other medical journals for consideration. We look forward to hearing from you.
Sincerely, Ida Dahlgren
Bachelor of medicine Örebro University, Sweden School of Medical Sciences
Email-address: xxx@gmail.se Phone: +4670 00 00 00
25 Populärvetenskaplig sammanfattning
Hur identifierar och förhindrar vi att de mest sköra och äldsta
patienterna i vårt samhälle återinläggs?
Akuta återinläggningar (dvs oplanerade inläggningar inom 30 dagar efter att en patient blivit utskriven från en vårdavdelning) ses ofta som ett mått på kvalitén av vården. Under 2018 noterade man att gruppen 80-åringar och äldre stod för mer än 30% av alla akuta återinläggningar från akutvårdsavdelningen på Universitetssjukhuset i Örebro. Det fanns ingen tydlig orsak till dessa återinläggningar, varav vår studie genomfördes.
I vår studie inkluderades 276 patientfall, alla patienter var 80 år eller äldre. För varje patient kollade vi på en rad olika faktorer bland annat multisjuklighet, skörhet, vårtid, vitalparametrar, om patient bodde själv eller med någon, samt om de hade hemtjänst.
Vi fann att om en patient skrivs ut med suboptimala vitalparametrar (blodtryck, puls etc.) var det 2.4 gånger ökat samband för dessa patienter att återinläggas eller dö inom 30 dagar. I motsatts till vår hypotes kunde vi inte bevisa att längre vårtider, vårdplanering eller snabbuppföljning var skyddande. Däremot fann vi att bo med en partner eller anhörig var skyddande jämfört med att ha hemtjänst. Våra resultat indikerar att för de äldsta och sköraste patienterna är det viktigt att de är stabiliserade i sina vialparametrar inför hemgång. Huruvida längre vårdtid, vårdplanering och snabbuppföljning är skyddande behövs ytterligare studier på, vårt resultat talade snarare för motsatsen. Även om dessa åtgärder inte skyddar mot återinläggning eller död kan de förhoppningsvis ge en ökad trygghet hos patienten vilket inte ska underskattas.
24 april 2020
Ida Dahlgren, läkarstudent
Charilika Lempessi, överläkare och handledare
26 Etisk reflektion
Etiskreflektion
– en masterstudie baserad på granskning av patientjournaler.
Att vårdavdelningar kontinuerligt genomför kvalitetsarbeten för att utvärdera arbetet på kliniken är otroligt viktigt. Den här studien är en uppföljning av en tidigare kvalitetsstudie som baserades på patientdata från 2018. Den tidigare studien kunde inte ge tydligt svar till varför återinläggningar var så högt bland patienter 80 år och äldre och hur detta kunde undvikas, varför denna studie ansågs viktigt att genomföra.
Under studien tog jag del av 287 patienters journaler. Patientjournalerna innehåller känsligdata om patients hälsa men ibland också personlig information om sexualitet och livssituation. Att läsa någons journal kan uppfattas som en kräkning av en individs integritet vilket gör det viktigt att väga nyttan mot risker.
Risker som vi förespådde var den integritetskränkning som patient kunde uppleva. Samt läckage av patientuppgifter på grund av vårdslöst hanterande av data av mig eller dataintrång av okänd gärningsman. Vi ansåg att nyttan för studien övervägde riskerna. Vi fick ett godkännande av
klinikchefen för att genomföra kvalitetsstudien. Inget etikgodkännande söktes, då vi såg få risker för de patienter som inkluderas.
Inga obehöriga än jag själv hade tillgång till registret med personuppgifter. När all data var insamlad gjordes en kopia av Exceldokument där alla personnummer togs bort, därav vid rådfrågning av andra kollegor var patienterna avidentifierade. Originaldokumentet med personuppgifter kommer raderas när kursen är avslutad.
Med tanke på att den här patientgruppen är en av de mest resurskrävande för sjukvården och att gruppen är beräknad att öka, är det viktigt att studier genomförs kontinuerligt för att i framtiden kunna bemöta ett ökat sjukvårdsbehov.
Ida Dahlgren 24 April 2020
