The association between geriatric treatment and 30-day readmission risk among medical inpatients aged ≥75 years with multimorbidity.

BACKGROUND: Readmission to hospital is frequent among older patients and reported as a post-discharge adverse outcome. The effect of treatment in a geriatric ward for acutely admitted older patients on mortality and function is well established, but less is known about the possible influence of such treatment on the risk of readmission, particularly in the oldest and most vulnerable patients. Our aim was to assess the risk for early readmission for multimorbid patients > 75 years treated in a geriatric ward compared to medical wards and to identify risk factors for 30-day readmissions.
METHODS: Prospective cohort study of patients acutely admitted to a medical department at a Norwegian regional hospital. Eligible patients were community-dwelling, multimorbid, receiving home care services, and aged 75+. Patients were consecutively included in the period from 1 April to 31 October 2012. Clinical data were retrieved from the referral letter and medical records.
RESULTS: We included 227 patients with a mean (SD) age of 86.0 (5.7) years, 134 (59%) were female and 59 (26%) were readmitted within 30 days after discharge. We found no statistically significant difference in readmission rate between patients treated in a geriatric ward versus other medical wards. In adjusted Cox proportional hazards regression analyses, lower age (hazard ratio (95% confidence interval) 0.95 (0.91-0.99) per year), female gender (2.17 (1.15-4.00)) and higher MMSE score (1.03 (1.00-1.06) per point) were significant risk factors for readmission.
CONCLUSIONS: Lower age, female gender and higher cognitive function were the main risk factors for 30-day readmission to hospital among old patients with multimorbidity. We found no impact of geriatric care on the readmission rate.

Introduction

Readmission to hospital is frequent among older patients and reported as a post-discharge adverse outcome [1]. Higher readmission rates result in increased pressure on hospital beds and higher costs, and represents a strain upon the patients. Reported readmission rates vary considerably (5–35%), depending on time span, age group and patient population [2]. Research on risk factors for readmission among patients above 75 years and those with impaired activities of daily living (ADL) is scarce [3]. In two systematic reviews [1, 4], only one study including patients older than 75 years was identified [5]. In that study, risk factors for readmission were severe disability for self-feeding and the presence of frailty markers [5].

For elderly patients the effect of hospital-based geriatric care on mortality, function and nursing home placement is well established [6-8]. Considerably less is known about the possible influence of treatment in a geriatric ward on the risk of early readmission, in particular for the oldest patients with multiple diseases and poor function. Previous reviews have not distinguished between early and late readmissions [6, 7], the share of studies on early readmissions is low [9, 10] and few patients with cognitive impairment have been included [11].

In the most recent Cochrane review of geriatric treatment with models based on comprehensive geriatric assessment (CGA) [7], only two studies of early readmission were identified [9, 10]. Furthermore, the systematic reviews on readmission risks did not include treatment in a geriatric ward as a potential explanatory variable [1, 4]. Since the evidence from randomized trials is so scarce, there is a need for observational studies addressing this relationship.

Accordingly, the aim of this study was to identify independent risk factors for readmissions within 30 days for multimorbid medical in-patients aged 75 years or more and receiving home care services, and in particular to study whether treatment in an acute geriatric ward was associated with a reduced readmission rate compared with treatment in regular medical wards.

Materials and methods

This is a prospective cohort study with consecutive inclusion of patients acutely admitted to the medical department of Vestfold Hospital Trust and living in six of twelve municipalities in the Norwegian county Vestfold (with 151,300 inhabitants in the six target municipalities). The participating municipalities were similar to the others in terms of size, distance to hospital and population composition.

Patient selection

During the period from April to October 2012, a project nurse screened all emergency admissions from the target municipalities to consecutively recruit patients who met the following inclusion criteria: home-dwelling, aged 75 years or more, suffering from at least two chronic conditions and receiving municipal home care services. Patients not able to give informed consent, who were terminally ill, or who lived in a long-term care facility prior to admission were not included.

The medical department of Vestfold Hospital Trust consists of separate wards for medical subspecialties including neurology and geriatrics. The main difference between the geriatric ward and the other wards is the presence of a multidisciplinary team consisting of a physiotherapist, an occupational therapist and a pharmacist, in addition to healthcare workers, nurses and doctors, all working within the framework of a structured multiprofessional model based on the principles of CGA [12]. The characteristics of the study cohort have previously been published [13].

Data collection

Eligible patients were approached by the project nurse as soon as possible after admission, normally the next day, and the project nurse used the Norwegian Version of the Barthel Index to assess independence in personal activities of daily living [14] and the Mini-Mental State Evaluation–Norwegian Revised Version (MMSE-NR) for cognitive screening [15]. Height and weight were measured and Body Mass Index (BMI) was calculated. A physiotherapist did an assessment of handgrip strength (HGS) within the first three days of the hospital stay. The test was completed with both hands using a Jamar dynamometer, the mean value of three trials was used. The result from the strongest hand was used for the analyses.

Two physicians, one consultant specialized in geriatric medicine and old age psychiatry and one resident in geriatric medicine, scrutinized all medical records of the included patients. Complete information regarding the current acute hospital admission, prior admissions and medications at the time of admittance was retrieved from the referral letter, electronic patient records and the patient administrative system. The Cumulative Illness Rating Scale for Geriatrics (CIRS-G) was scored for each patient [16]. All readmissions occurring within 30 days of discharge were retrieved from the patient administrative system. All readmissions to any hospital department, including psychiatric and palliative wards were counted. Deaths in the observation period was retrieved from the Norwegian Death Registry.

Laboratory measurements

Blood samples were drawn as part of the clinical routine, and blood tests chosen based on previous reports [17, 18]. Haemoglobin (Hgb) was measured on either Sysmex XE 2100 or Sysmex XE 5000 (Sysmex Europe) with reagents from the supplier. Serum creatinine was analysed on Vitros 5.1 (Ortho-Clinical Diagnostics, USA) with reagents from the supplier. Glomerular filtration rate (GFR) was estimated using the Modification of Diet in Renal Disease equation [19].

Statistical analyses

Demographic and clinical characteristics are presented as means and standard deviations (SD) or frequencies and percentages. Groups of patients were compared by independent samples t-test or χ2-test. Unadjusted and adjusted proportional hazards Cox regression models were estimated to assess the association between pre-selected, relevant patient characteristics and the rate of readmission. Relevant variables were selected based on clinical judgement, previous reports, correlations between the variables in the dataset, and number of missing cases for some variables. Death was considered as a competing risk factor for readmission and was included in the model as such. Schoenfeld residuals for continuous characteristics and survival curves for categorical characteristics were used to assess the assumption of proportional hazards. Martingale residuals were used to assess the functional form of continuous characteristics. The results are presented as hazard ratios (HR) and 95% confidence intervals (CI). Eight patients were excluded from regression analyses because of one or more missing values. All tests were two-sided and results with p-values below 0.05 were considered statistically significant. For illustrative purposes, we also created Kaplan-Meier plots for treatment in the geriatric ward versus other wards as well as for all statistically significant risk factors. In these plots, age and MMSE score were dichotomized at the median.

The analyses were carried out using SPSS version 26 and STATA version 16.

Ethics

Written informed consent was obtained from all the participants in accordance with Norwegian legal regulation. The study was presented to the Regional Committee for Medical Research Ethics and approved by the Norwegian Social Science Data Services.

Results

A flowchart of the study sample is given in Fig 1.

Fig 1

Patient flow.

The mean age of the participants was 86.0 years (SD 5.7), 134 (59%) were female and 59 (26%) were readmitted within 30 days of discharge. Geriatric ward patients were older (p = 0.009), stayed shorter at hospital (p = 0.017), had delirium more often (p = 0.022), had lower Barthel score (p = 0.031), lower BMI (p = 0.003), and higher eGFR (p = 0.026) than patients from other wards. Among the patients in geriatric ward, 3 of 52 (5.8%) died within 30 days of discharge, versus 17 of 175 (9.7%) of the patients in the other wards (p = 0.378). Patient characteristics are presented in Table 1. The readmission rate for patients treated in the geriatric ward was 16.9%, and for those treated in medical wards 25.0% (p = 0.206). A higher proportion (67%) of patients admitted to medical wards was discharged back home (as opposed to a community based rehabilitation unit) than of patients admitted to the geriatric ward (50%) (p = 0.027). Among patients discharged to a community based rehabilitation unit, 21% were readmitted, compared to 29% among those discharged home, (p = 0.130).

Table 1

Characteristics of the participants, N = 227.

	Patients readmitted n = 59	Patients not readmitted n = 168	Patients admitted to geriatric ward n = 52	Patients admitted to medical wards n = 175
Age, years. Mean (SD)	84.0 (5.2)	86.7 (5.8)	87.8 (5.4)	85.5 (5.7)
Female gender. N (%)	42 (71)	92 (55)	31 (60)	103 (59)
Geriatric ward. N (%)	10 (16.9)	42 (25.0)	---	---
Living alone. N (%)	40 (68)	122 (73)	39 (75)	123 (70)
Length of stay. Median (IQR)	6.0 (3.0–9.0)	5.0 (3.0–8.0)	5.3 (4.0–7.8)	6.0 (3.0–8.0)
Delirium during stay. N (%)	17 (29)	54 (32)	23 (44)	48 (27)
Barthel ADL Index. Mean (SD) n = 223	13.6 (4.9)	12.9 (4.8)	11.8 (4.1)	13.5 (5.0)
BMI, kg/m2. Median (IQR) n = 217	23.8 (20.9–27.4)	22.7 (20.4–26.6)	22.0 (19.0–25.2)	23.4 (20.9–27.3)
MMSE. Mean (SD) n = 223	23.8 (5.1)	22.8 (5.2)	21.9 (5.7)	23.3 (5.0)
HGS, kg. Mean (SD). Women n = 106	11.6 (5.7)a	10.6 (4.5)a	9.7 (5.6)c	11.3 (4.7)c
HGS, kg. Mean (SD). Men n = 75	24.8 (9.7)b	20.8 (8.4)b	20.5 (8.3)d	21.9 (8.9)d
Number of daily medications. Mean (SD)	8.7 (3.6)	7.7 (3.6)	7.4 (2.7)	8.1 (3.9)
CIRS-G total score. Mean (SD)	22.2 (5.2)	21.3 (6.3)	22.0 (6.4)	21.4 (5.9)
Haemoglobin, g/100 mL. Mean (SD)	10.4 (1.9)	11.4 (1.8)	11.2 (1.6)	11.0 (1.9)
eGFR. Mean (SD) n = 226	45.6 (15.3)	48.2 (14.7)	51.2 (12.8)	46.4 (15.3)
Alive after 12 months. N (%)	27 (46)	111 (66)	34 (65)	104 (59)

ADL = Activities of Daily Living, BMI = Body Mass Index, MMSE = Mini Mental State Evaluation—Norwegian Revised Version, HGS = Hand Grip Strength, CIRS-G = Cumulative Illness Rating scale for Geriatrics, eGFR = Estimated Glomerular Filtration Rate.

*Chi-square for categorical variables. Independent sample t-test for continuous variables.

aFemales: 35 patients readmitted and 71 not readmitted.

bMales: 14 patients readmitted and 61 not readmitted.

cFemales: 22 patients geriatric ward and 84 patients medical ward.

dMales: 19 patients geriatric ward and 56 patients medical ward.

In unadjusted Cox models, presented in Table 2, lower age, higher MMSE score and increased number of daily drugs were significantly associated with a higher risk of readmission, while lower age, female gender and higher MMSE score were independent and significant risk factors for higher readmission rate in the adjusted model. According to analysis of Schoenfeld residuals and survival plots, only MMSE violated the proportional hazards assumption. This variable was therefore included as a time-dependent characteristic in the Cox model. No non-linearity issues were identified, as judged by martingale residuals.

Table 2

Results of Cox proportional hazards regression analysis of risk of readmission with death as «competing risk».

N = 219.

Variable	Unadjusted		Adjusted
	HR (95% CI)	p-value	HR (95%)	p-value
Age	0.94 (0.90; 0.98)	0.005	0.95 (0.91; 0.99)	0.019
Gender, female	1.75 (0.98; 3.13)	0.058	2.17 (1.15; 4.00)	0.016
Barthel ADL	1.03 (0.97; 1.09)	0.331	1.02 (0.96; 1.08)	0.616
CIRS-G	1.03 (0.99; 1.07)	0.159	1.03 (0.97; 1.10)	0.309
eGFR	0.99 (0.98; 1.01)	0.434	1.00 (0.98; 1.02)	0.909
Ward (Geriatrics)	0.64 (0.31; 1.34)	0.240	0.78 (0.36; 1.73)	0.545
MMSE (time dependent)	1.03 (1.01; 1.06)	0.013	1.03 (1.00; 1.06)	0.034
Number of daily drugs	1.08 (1.01; 1.16)	0.035	1.04 (0.95; 1.13)	0.421

ADL = Activities of Daily Living, MMSE = Mini Mental State Examination—Norwegian Revised Version,

CIRS-G = Cumulative Illness Rating scale for Geriatrics, eGFR = Estimated Glomerular Filtration Rate.

Significant Differences significant at 0.05 level in bold font.

Kaplan-Meier plots for the statistically significant risk factors as well as for treatment in geriatric versus other wards are displayed in Fig 2.

Fig 2

Kaplan-Meier plots for one-year survival by a) Ward, b) Age, b) Gender and d) MMSE score. a) Ward, n = 227, no cases were censored. b) Age, n = 227, no cases were censored. c) Gender, n = 227, no cases were censored. d) MMSE score, n = 223, no cases were censored.

Discussion

Treatment in a geriatric ward versus other wards

The main purpose of our study was to assess whether treatment in a geriatric ward as compared with other medical wards influenced on the readmission rate in the oldest-old patients with multimorbidity. No association between the hospital ward and readmission rate was identified.

One might expect that better discharge planning and a stronger emphasis on individual treatment plans in a geriatric ward might reduce the risk of early readmission [20]. Moreover, routines for medication reviews, with a clinical pharmacist as part of the multidisciplinary team in the geriatric ward, could be expected to prevent early readmissions. We have previously shown that patients treated in the geriatric ward had more drug changes and were discharged with fewer potentially inappropriate drugs [21]. However, the evidence so far does not support the supposition that treatment in a geriatric ward substantially decreases early readmission rate [9-11].

As expected, patients in the geriatric ward were older, had more often delirium and were more malnourished and more functionally impaired than those admitted to the other medical wards. Such known prognostic variables were accounted for in the multivariate analysis. Due to the observational design of the study, we can however not rule out the possibility of residual confounding. Patients more likely to be readmitted for some reason not measured might have been more likely to be placed in the acute geriatric ward during the index stay. For example, patients in the geriatric ward might have fewer organ-specific symptoms and more general symptoms related to a non-specific functional decline, both conditions that have been associated with an increased readmission risk [16, 22, 23]. A larger proportion of the patients in the geriatric ward was discharged to a community rehabilitation unit. Only a few earlier studies have investigated discharge destination and risk for readmission, with one study showing higher risk for readmission for patients discharged to a nursing home [24] while a few other studies had no significant results [1].

Risk factors for readmission

Lower age, female gender and higher MMSE score were independently associated with higher readmission risk. These results may be considered as surprising, since studies of younger populations among other factors have found male gender, higher age, poor overall health and functional disability to be associated with higher risk for early readmission [1, 25]. We are concerned that these results may reflect elements of ageism in the Norwegian health care system that prevent the oldest patients and those with cognitive impairment from being readmitted to hospital even when they might benefit from it. In Norway, strong financial incentives have been introduced, aiming at transferring emergency treatment of frail elderly patients to a lower level of care. Our results may indicate that older patients with signs of cognitive decline now tend to be treated acutely in nursing home beds [26] instead of being readmitted to hospital when exacerbations of their chronic disorders occur. Whereas treatment in geriatric wards is firmly evidence based, this is not the case for emergency nursing home beds. A reluctance to readmit the oldest and most severely cognitively impaired patients to hospital may thus hinder them of receiving the most effective treatment.

In younger patient groups, previous research has found higher readmission rates among men than among women [1, 25]. For older populations, previous research, like ours, identified female gender as a risk for readmission [27].

Taken together, our results indicate that vulnerable elderly patients may have different risk factors for early readmission than younger and more robust populations.

Readmission rate

The early readmission rate was high in our material (26%) compared to several previous studies [2, 25]. Most of the available literature on early readmission is based on studies of younger patients who have other characteristics and social needs.

We identified only one study reporting higher rates of early readmission than we found, and that was a study including patients at high risk of hospitalization [2]. Our high readmission rate might be explained by the characteristics of our study cohort such as a high mean age, pronounced comorbidity and inclusion of patients with mild to moderate cognitive impairment. Moreover, all patients received home care service, indicating some degree of ADL impairment. As previously reported [28], our patients had a high prevalence of sarcopenia, which is an important frailty marker. All these factors are known to increase the risk of readmission [29-32].

Strengths and limitations

Only very few studies of readmissions have comprised patients aged 75 years or more [1]. Our study thus contributes to a field where there is little research-based knowledge.

Norwegian hospitals are financed by the government. Patients in the index municipalities use Vestfold Hospital Trust regardless of disease, and follow-up of the patients regarding readmission was therefore complete. We also had access to complete data on deaths through the Norwegian Death Registry. We were therefore able to include death within 30 days after discharge as a competing risk.

An important weakness is the observational design, preventing us from drawing any firm causal inference. Unknown and unmeasured confounding factors may have been present. The study had insufficient power to include more covariates in the regression analysis, which might have been desirable. On the other hand, preliminary correlation analyses (not shown) indicated that inclusion of other variables into the adjusted model would not have provided more information. The fact that more patients from the geriatric ward were discharged to municipal rehabilitation units, and patients discharged to such units to a lesser degree were readmitted, might be considered a confounding factor. We will, however, argue that better identification of rehabilitation needs and more adequate use of post discharge rehabilitation units constitute integral parts of geriatric care, and as so is not reasonable to adjust for.

The power may have been insufficient for detecting differences in readmission rate between the wards. If the real difference is as low as 16.9% versus 25.0%, as our estimates suggest, a considerably larger sample would presumably be needed to get a statistically significant result. Moreover, we do not know whether reasons for exclusion differed between the groups (Fig 1). Different selection mechanisms may have been active in the geriatric than in then general medical group, thus potentially influencing the external validity of our findings. But since the potentially confounding variables have been measured, we have been able to adjust for them in the multivariate analyses. Accordingly, we presume that the results of the adjusted analyses have god validity. Patients with severe dementia, were excluded due to the requirement for informed consent. In Vestfold Hospital Trust these patients are treated in all medical wards, so the exclusion would not influence the results for different wards.

Another possible limitation is that we registered all-cause readmissions to the same hospital without discriminating between acute and elective cases. Other studies have had different approaches regarding this issue, with some including only readmissions for the same medical problem while others included all-cause readmissions [1]. In a study carried out to evaluate a community-based discharge scheme, most of the readmissions were emergency readmissions, and they were especially prominent among patients older than 85 years [27].

Conclusion

Lower age, female gender and higher cognitive function were the main risk factors for 30-day readmission to hospital, but no differences between geriatric and other medical wards were identified.

3 Aug 2021

PONE-D-21-13191

The association between geriatric treatment and 30-day readmission risk among elderly medical inpatients

PLOS ONE

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ACADEMIC EDITOR:

Please consider the methodological aspects raised by reviewers, including  the issue of “municipal emergency beds”, that actually sound as a different form of hospital admission, and comment on cases selection process (Figure 1), leading to the inclusion of a minority of patient: are these data generalizable?

A further aspects that I feel is lacking are is a statistical comparison of patients’ features between Geriatrics and Internal Medicine group, as the former looks, as expected, older and more cognitively impaired. Can this difference bias results? Moreover a discussion on statistical power should be included among study limitations, as the numerical difference observed in readmissions between the two groups might be non significant due to small sample size.

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Reviewer #1: introduction

line 58: It is unclear what unsatisfactory patient pathways means. this could use some clarification

line 60: what is meant by the oldest patients? What age group specifically?

line 64: There is somewhat of a disconnect between the ideas in paragraph 1 and 2. I think there is an opportunity to better link the need for looking at readmission risk factors in older individuals and CGA as a factor.

line 68: but there have been some studies that have looked at CGA and readmission. What are you contributing that they did not do?

lines 72-73: The purpose does not seem to link back to any of the old-old age group discussed in the first paragraph. need better/clearer connection between what is known, gaps, and purpose.

methods:

line 78: I am confused with how the participants were recruited. While they were admitted or from some other community database that had information on individuals who were recently admitted to the hospital. can you please clarify this in the manuscript. This also seems to be participants from a larger study. please describe more about the larger study design and who you are selecting from that larger study.

line 93: I think that participants need to be receiving home care services is lost earlier in this manuscript. That should be included when describing your population and perhaps considered in the introduction and discussion sections. what type of home care are these individuals receiving should also be clarified.

data collection and lab measures: It is a little unclear here why you are collecting the data you are collecting at specific time points. perhaps a conceptual model or other table or figure would clarify why you are collecting specific data and certain times.

results: I feel like the CGA piece is completing lost in this section. How do these findings fit with CGA versus just looking at readmission risk factors. I am very confused about the focus of this manuscript

line 146: I am confused. they needed to be receiving home care to be included, but some were also receiving care in a nursing home? Do these contradict each other?

discussion: This needs to be re-focused around what is the main purpose and variables of interest for your study. There are multiple focuses currently and it is confusing to read. is it focused on readmission, readmission risk, old-old individuals, CGA? There are a lot of ideas and many of these have been already covered in previous research. You will also want to work on what is new here? what is the gap you are addressing that we haven’t already examined?

Reviewer #2: Wang-Hansen et al. report the risk of readmission from a single Norwegian hospital following discharge, for older adults aged over 75 years old and receiving home care services. The consented study population is divided according to management in either a general medical or specialist geriatric medicine setting, the latter of which is used as a surrogate for receipt of comprehensive geriatric assessment. In adjusted Cox proportional hazards modelling accounting for the competing risk of death after discharge, geriatric medicine care was not associated with any difference in the risk of readmission. The authors found that younger, female patients with higher cognitive function were at the highest risk of 30-day readmission.

The manuscript addresses a topic of importance which is not well covered in the wider literature. However, there are some areas of the analysis that are concerning and I have raised these points below.

1. A major concern is about population generalisability to “geriatric medicine care” and by association “comprehensive geriatric assessment”. Some elements of this are limitations of a consented observational cohort, but these should still be acknowledged in the discussion. The requirement for informed consent prevents many patients with delirium and dementia from participation; this group would usually make up a large part of geriatric medicine care. It would be helpful within Figure 1 to breakdown the numbers for the loss from ineligibility by patients managed in general medicine and geriatric medicine settings – it is important for the reader to know if the group representing geriatric medicine in this analysis only account for a minority of patients in the wards.

2. Similarly for transparency, it would be helpful to know how many deaths occurred after discharge within 30 days and in which groups (general medicine or geriatric medicine) these occurred. Currently the table only provides survival to 1 year. The use of a competing risk Cox model is welcome, but given the small sample size, applicability may be questionable if many more deaths occurred within the modelling window for the small geriatric medicine group of just 52 patients.

3. I also have concerns about the patient group here as the discharge destination numbers seem very surprising. The inclusion criteria require all patients to have been admitted from home. In the geriatric medicine managed group, 50% are then discharged to a nursing home following a median length of stay of just 5 days. This seems very unlikely if such a transfer was a permanent switch of residence, particularly since 33% of general medical patients followed the same route. I am concerned that these are “discharges” to rehabilitation facilities for post-acute care with a later assessment of return home. This would clearly lower the risk of hospital readmission, as such facilities often have access to nursing and/or medical care that would not otherwise be available if the patient had been discharged to their usual residence. Can the authors explain if this is the case, and if so acknowledge this as a bias that might explain the direction of their results?

4. As well as this, the discussion raises a concern about the outcome ascertainment: “Our results may indicate that older patients with signs of cognitive decline now tend to be treated in municipal emergency beds…”. I am unfamiliar with the health service setting here, but transfer from a home to a “municipal emergency bed” sounds like it is not included in the outcome measure, but is a (re)admission to a healthcare setting? If so, this is a significant limitation to interpretation of the results. Can I suggest the authors provide some context for readers not familiar with the Norwegian system and explain this a bit clearer in the discussion?

5. In Figure 1, the majority of eligible patients excluded from the study were due to “administrative reasons” rather than lack of consent. This is not explained in the methods but is particularly important because the term “consecutive patients” is used to imply no selection bias in approach of eligible patients. Can the authors clarify what administrative reasons means?

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Reviewer #1: No

Reviewer #2: No

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21 Oct 2021

Point-by-point response to reviewer comments for manuscript PONE-D-21-13191

“The association between geriatric treatment and 30-day readmission risk among elderly medical inpatients.”

Thank you for your very thorough and relevant comments.

Below we have responded point-by-point to the comments, also describing the subsequent revisions of the manuscript.

Please consider the methodological aspects raised by reviewers, including the issue of “municipal emergency beds”, that actually sound as a different form of hospital admission, and comment on cases selection process (Figure 1), leading to the inclusion of a minority of patient: are these data generalizable?

We agree that our wording regarding “municipal emergency beds” might cause confusion, and have rephrased the relevant section (page 14-15, line 223-230) accordingly. What has been called “municipal emergency beds” in Norway are nursing home beds open for acute admittance. These facilities are sparsely fitted, and do not regularly offer investigations such as x-ray and microbiology. If seniors in need for readmission are admitted to such beds instead of hospital beds, it may explain our findings of a decreased readmission rate with increasing age. Given the low standard of medical service connected to the “municipal emergency beds” compared to regular hospital beds, we feel that it may also be pertinent to speculate whether this is indicative of ageism.

The patients recruited for this study were all regular medical patients and prospectively included after admission to hospital. Patients from other municipalities than those selected a priori, were not eligible and should not be included in Figure 1. We have redesigned figure 1 to make this clearer.

A further aspects that I feel is lacking are is a statistical comparison of patients’ features between Geriatrics and Internal Medicine group, as the former looks, as expected, older and more cognitively impaired. Can this difference bias results? Moreover a discussion on statistical power should be included among study limitations, as the numerical difference observed in readmissions between the two groups might be non significant due to small sample size.

Yes, it is correct that since the patients were not randomly allocated to the alternative wards, systematic differences between the groups do exist. We have now described this more explicitly in the Results section page 8, line 149-152. Since these potentially confounding variables have been measured, we have been able to adjust for them in the multivariate analyses. Accordingly, we presume that the results of the adjusted analyses have good validity.

Regarding the limited statistical power, we have now added a brief discussion on this important limitation (page 16, line 266-269)

In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

There are several legal restrictions to data sharing in Norway, and this is not approved by the Norwegian Data Protection Authorities The Norwegian Social Science Data service has not approved data delivery outside of Europe; consent for publication of raw data was not obtained from participants included in the study; and, importantly, complete anonymization is unachievable as the data contains potentially identifying patient information that may be trackable. For these reasons, we ask that the data are available only upon request. Data requests can be addressed to Department of Research at Vestfold Hospital Trust by Tomm Bernklev tomm.bernklev@siv.no.

Reviewer #1

line 58: It is unclear what unsatisfactory patient pathways means. this could use some clarification.

We have reformulated the sentence: Page 4, line 55.

line 60: what is meant by the oldest patients? What age group specifically?

We have clarified in the titlr that our focus is patients aged 75+: Page 1, line 3 and page 3, line 26.

line 64: There is somewhat of a disconnect between the ideas in paragraph 1 and 2. I think there is an opportunity to better link the need for looking at readmission risk factors in older individuals and CGA as a factor.

We agree, and we have clarified treatment in a geriatric ward as one of many factors that may affect readmission rate. Page 4, line 62 – 64.

line 68: but there have been some studies that have looked at CGA and readmission. What are you contributing that they did not do?

We have rephrased the entire text and used the term geriatric care instead of CGA, as we think that this is most accurate for what we have studied. CGA was an element of the treatment in our geriatric ward, but this is not a study of CGA per se. We have explained in the Introduction (page 5, line 68-70) that the most recent Cochrane review includes two studies of readmission including elements of geriatric care as a potentially explanatory factor (White et al 1994 (reference #9) and Wald et al 2011 (reference #10)). These studies were small, and did not investigate a geriatric ward but a CGA team without geriatrician.

lines 72-73: The purpose does not seem to link back to any of the old-old age group discussed in the first paragraph. need better/clearer connection between what is known, gaps, and purpose.

We have rephrased the text to better clarify our emphasis on the oldest old. Page 5, line 75.

line 78: I am confused with how the participants were recruited. While they were admitted or from some other community database that had information on individuals who were recently admitted to the hospital. can you please clarify this in the manuscript. This also seems to be participants from a larger study. please describe more about the larger study design and who you are selecting from that larger study.

We agree that our phrasing was confusing on this point, and are sorry for that! The participants were not recruited form any database or larger study, but were all prospectively included to this specific study after admission to hospital. We have redesigned figure 1 and rephrased the first part of the Materials and methods section (page 5, line 79-83) to make this clearer.

line 93: I think that participants need to be receiving home care services is lost earlier in this manuscript. That should be included when describing your population and perhaps considered in the introduction and discussion sections. what type of home care are these individuals receiving should also be clarified.

Thank you for this valuable advice! We have moved this information further up in the Methods section page 5, line 85-90.

data collection and lab measures: It is a little unclear here why you are collecting the data you are collecting at specific time points. perhaps a conceptual model or other table or figure would clarify why you are collecting specific data and certain times.

We have tried to clarify this in the text. Page 6, line 104 and page 7, line 117-118.

I feel like the CGA piece is completing lost in this section [results]. How do these findings fit with CGA versus just looking at readmission risk factors. I am very confused about the focus of this manuscript

We agree that the aim was unsatisfactorily described, and have changed this accordingly. For clarity, we have now consistently used “treatment in a geriatric ward” contrasted by “treatment in other wards” as relevant exposure variable throughout the manuscript. We have clarified in the Methods section (page 6, line 92-96) that treatment in the geriatric ward comprised an element of CGA, but the study is focused on the effect of ward allocation, not on the CGA methodology per se, in line with most of the RCTs of effects of geriatric treatment.

line 146: I am confused. they needed to be receiving home care to be included, but some were also receiving care in a nursing home? Do these contradict each other?

We have tried to clarify this se page 14, line 211-215. Patients had to stay in their own home and receive home care before the index hospital stay. After the index stay, some were discharged to nursing home based rehabilitation units.

This [the Discussion] needs to be re-focused around what is the main purpose and variables of interest for your study. There are multiple focuses currently and it is confusing to read. is it focused on readmission, readmission risk, old-old individuals, CGA? There are a lot of ideas and many of these have been already covered in previous research. You will also want to work on what is new here? what is the gap you are addressing that we haven’t already examined?

Large parts of the Discussion section have now been re-written, attempting to meet these pertinent comments from the reviewer. The discussion is now structured around i) the potential effect of geriatric care on readmission, ii) risk factors for readmission in general among patients aged 75+, and iii) readmission rate.

Reviewer #2:

The manuscript addresses a topic of importance which is not well covered in the wider literature. However, there are some areas of the analysis that are concerning and I have raised these points below.

1. A major concern is about population generalisability to “geriatric medicine care” and by association “comprehensive geriatric assessment”. Some elements of this are limitations of a consented observational cohort, but these should still be acknowledged in the discussion. The requirement for informed consent prevents many patients with delirium and dementia from participation; this group would usually make up a large part of geriatric medicine care. It would be helpful within Figure 1 to breakdown the numbers for the loss from ineligibility by patients managed in general medicine and geriatric medicine settings – it is important for the reader to know if the group representing geriatric medicine in this analysis only account for a minority of patients in the wards.

We agree that Figure 1 in its original version was confusing, as it comprised patients that were definitely ineligible. These are now removed and we have tried to make the figure clearer. However, among those who were in principle eligible, we do unfortunately not have data on the reasons for exclusion specified for each ward (geriatric versus other). This is a weakness mainly challenging external validity of the results, and we have now acknowledged this weakness in the Discussion section (page 16, line 269-271).

2.Similarly for transparency, it would be helpful to know how many deaths occurred after discharge within 30 days and in which groups (general medicine or geriatric medicine) these occurred. Currently the table only provides survival to 1 year. The use of a competing risk Cox model is welcome, but given the small sample size, applicability may be questionable if many more deaths occurred within the modelling window for the small geriatric medicine group of just 52 patients.

Data for 30-days mortality by ward type is given below:

Ward N (patients) %

Geriatric 3 (52) 5.8

Medical 17 (175) 9.7

The 30-days mortality was lower among patients treated in the Geriatric ward, but the difference was not statistically significant (p=0.378). We have added this information page 8, line 152-153.

3. I also have concerns about the patient group here as the discharge destination numbers seem very surprising. The inclusion criteria require all patients to have been admitted from home. In the geriatric medicine managed group, 50% are then discharged to a nursing home following a median length of stay of just 5 days. This seems very unlikely if such a transfer was a permanent switch of residence, particularly since 33% of general medical patients followed the same route. I am concerned that these are “discharges” to rehabilitation facilities for post-acute care with a later assessment of return home. This would clearly lower the risk of hospital readmission, as such facilities often have access to nursing and/or medical care that would not otherwise be available if the patient had been discharged to their usual residence. Can the authors explain if this is the case, and if so acknowledge this as a bias that might explain the direction of their results?

The reviewer raises a relevant point, and the suggested pathway is right. “Nursing home” as discharge destination does mainly imply a community rehabilitation unit for post-acute care. Usually the patients are admitted there for 1-2 weeks and then returned to their own homes. This discharge destination was more common in the geriatric group which is now explained on page 8, line 155-158. In the Discussion, we have elaborated on this and argued that this is not a bias but bay be an effect of better identification of rehabilitation needs in the geriatric care group.

4. As well as this, the discussion raises a concern about the outcome ascertainment: “Our results may indicate that older patients with signs of cognitive decline now tend to be treated in municipal emergency beds…”. I am unfamiliar with the health service setting here, but transfer from a home to a “municipal emergency bed” sounds like it is not included in the outcome measure, but is a (re)admission to a healthcare setting? If so, this is a significant limitation to interpretation of the results. Can I suggest the authors provide some context for readers not familiar with the Norwegian system and explain this a bit clearer in the discussion?

We understand the need for further explanation of the Norwegian context. Please see our response to the Editor above.

5. In Figure 1, the majority of eligible patients excluded from the study were due to “administrative reasons” rather than lack of consent. This is not explained in the methods but is particularly important because the term “consecutive patients” is used to imply no selection bias in approach of eligible patients. Can the authors clarify what administrative reasons means?

Figure 1 is changed in the revised manuscript. Please see our response to the Editor above.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

22 Dec 2021

The association between geriatric treatment and 30-day readmission risk among medical inpatients aged ≥75 years with multimorbidity.

PONE-D-21-13191R1

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31 Dec 2021

PONE-D-21-13191R1

The association between geriatric treatment and 30-day readmission risk among medical inpatients aged ≥75 years with multimorbidity.

Dear Dr. Wang-Hansen:

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