The characteristics of patients, overall and stratified per readmission status, are detailed in table 1.

Further descriptive characteristics specific to the group of readmitted patients are given in table 2.

Figure 2 shows the overall cumulative incidence functions of readmission. Stratified cumulative incidence functions of readmission within 60 days can be found in Appendix A.2. Overall, 50% of the readmissions occurred within four days and 80% of the readmissions within ten days after index discharge.

Figure 3 shows the distribution of time to hospital readmission, stratified per index hospitalisation duration. Among the readmitted patients, the median time to readmission was shorter for those with an index hospitalisation duration of <5 days (median of 3 days, IQR 2–4 days) than for those with an index hospitalisation duration of 5 days or longer.

Overall 17.2% of the readmitted patients spent time in the IMCU/ICU during the subsequent hospitalisation, compared to 16.6% for the index hospitalisations among all patients. In total, 87.0% of the readmitted patients showed at least one complication during the readmission stay, and the most common complications were: respiratory complications (63.2%), deconditioning syndrome (21.3%) and thrombosis complications (13.4%) (table 2). The case fatality ratio was 9.2% (95% CI 8.6–9.7%) during the index hospitalisation and 7.1% (95% CI 3.9–10.4%) during rehospitalisation (p = 0.72).

Among the 151 patients who were readmitted and had respiratory complications at readmission, 95 (62.9%) had the same CURB-65 score at the index hospitalisation and at readmission, 37 (24.5%) had a higher CURB-65 score at readmission and 19 (12.6%) had a lower CURB-65 score at readmission (figure 4).

Risk factors for hospital readmission within CH-SUR

Table 3 shows the odds ratios (ORs) for hospital readmission within 60 days in CH-SUR, with 95% confidence intervals (95% CIs), from the univariable logistic regression.

The model with the same eight covariables (oncological pathology, obesity, immunosuppression and any treatment, in addition to age, sex, discharge to home and ICU/IMCU stay) produced the lowest values for both AIC and SBIC and was therefore selected for the multivariable analysis. Table 4 shows the ORs and 95% CIs for hospital readmission within 60 days, from the multivariable logistic regression models using either the imputed dataset or the complete cases dataset. Of note, when limiting to cases with complete follow-up information in the multivariable analysis, the number of included patients dropped from 8039 to 5161, with 163 (3.2%) readmissions within 60 days after discharge of index hospitalisation.

Based on our multivariable logistic regression models, factors increasing the odds of hospital readmission were age of 65 years or older (OR 1.63, 95% CI 1.24–2.15, p <0.001), being discharged to home at the end of the index hospitalisation (OR 1.77, 95% CI 1.19–2.62, p = 0.004), presence of oncological pathology (OR 1.82, 95% CI 1.27–2.61, p = 0.001) and immunosuppression (OR 2.34, 95% CI 1.67–3.29, p <0.00001). Females had lower odds of hospital readmission than males (OR 0.68, 95% CI 0.52–0.89, p = 0.005). Being admitted at least once to the IMCU/ICU during the index hospitalisation, obesity and receiving any treatment during the index hospitalisation were found to have no significant impact on the risk of hospital readmission.

The collinearity check revealed some collinearity particularly between age and other variables, but the results of the model omitting age did not essentially differ from those of the main analysis (Appendix A.3). We also conducted an analysis stratified by age (<65 vs ≥65 years) to test whether the lower readmission rate among patients who stayed at the IMCU or ICU during the index hospitalisation could be related to the younger age of these patients. The odds ratios did however not essentially differ between the two age groups, although there were some minor differences: discharge was no longer significantly associated with rehospitalisation in the younger age group, and immunosuppression was not associated with rehospitalisation in the older age group (Appendix A.3).

Discussion

In this study using Swiss COVID-19 Hospital-Based Surveillance (CH-SUR) data, we assessed the risk of hospital readmission within 60 days among COVID-19 patients discharged alive from the hospital, and investigated the risk factors for hospital readmission. Between December 2020 and December 2021, the rate of hospital readmission within 60 days among COVID-19 patients was 3.2%, with little variation observed over that time period (ranging from 1.6% to 4.6%).

Due to the growing recognition of COVID-19’s medium- and long-term health consequences, there has been significant interest in studying the post-discharge trajectory of COVID-19 patients. Reported readmission rates range between 4% [21] and 9% [13] from large sample studies in Spain and the United States, respectively, with readmissions occurring typically within the first week post-discharge. Respiratory distress was identified as the most common reason for readmission [22]. Other reasons for readmission included: pain, altered mental status, fall, fever, soft tissue infection, thrombotic event and gastrointestinal symptoms [7, 21–25]. We observed lower readmission rates than those reported previously in a systematic review [26]. However, most of the studies included were based on data collected in 2020, during the first months of the COVID-19 pandemic, when the understanding of COVID-19 and its management were still limited. Case management of hospitalised patients drastically changed with the results of the first large clinical trials during summer 2020 and the broader use of immunomodulatory drugs in patients with severe disease [27]. The disease itself and its management evolved from the beginning of the pandemic. New variants of COVID-19 appeared and co-existed, changing disease severity. The vaccine policy kept evolving, with new vaccine doses being administered to preserve immunity, leading to a beneficial impact on recovery.

The time from index hospitalisation discharge to readmission was relatively short, with a median time of four days. This may indicate that hospital readmissions within CH-SUR generally occur early enough to avoid a severe degradation of the status of the patient. We found that most of the readmitted patients had similar severity scores at index admission and at readmission. The in-hospital case fatality ratio was found to be slightly lower, although not significantly, after hospital readmission than at the end of index hospitalisation. Post-discharge, this short time span between index hospitalisation discharge and readmission could justify short-term medical surveillance for at-risk patients (males, with comorbidities, older than 65) in order to identify as quickly as possible any complication which would require further hospitalisation.

As previously reported by others [13, 14, 28], male sex and age of 65 years or over were found to increase the risk of hospital readmission within CH-SUR. Differences in the effects of COVID-19 by sex and age have been evidenced by several studies, with older patients as well as male patients being generally associated with a worse outcome [18]. This unbalanced sex-associated risk could relate to the difference in immune response to the SARS-CoV-2 virus between men and women [29,30], but it could also be associated with the higher prevalence among men than women of chronic diseases known to be COVID-19 risk factors. Likewise, the elderly population is known to have a reduced immune response, and to more likely present comorbidities that are risk factors for COVID-19 adverse events [18].

Patients with chronic cardiovascular diseases as well as immunocompromised patients had a significantly higher risk of hospital readmission compared to those without such comorbidities. Similar results have been demonstrated in other studies [21,31]. Immunosuppression, as defined in CH-SUR and in other studies, encompasses a wide range of immune dysfunctions and the risk may vary within this group. It is particularly worth noting that our definition considered all HIV-infected individuals as immunocompromised, although at present most individuals diagnosed with HIV in Switzerland are virally suppressed and with good immunological response. The increased risk of hospital readmission among patients with oncological diseases may partly be related to their immunosuppressive treatments. Another study on patients in the United States revealed significantly higher rates of cancer among readmitted compared with non-readmitted patients [32]. According to the univariable analysis, the risk of readmission was also not different among patients suffering from chronic respiratory diseases and chronic renal diseases compared to those without, similarly to other studies [33]. However, these variables were left out from the multivariable analysis as they were not considered to add information to the model.

Patients with an initially short index hospital stay had a significantly higher risk of being readmitted [14, 22]. This seems to be a common observation once sufficient sample size is considered [34]. Others describe the disease course as a biphasic phenomenon starting with a mild clinical presentation, allowing for discharge, and later evolving into a worsened state [32]. Early discharge can also be frequent in moments of hospital saturation when resources are insufficient to handle the number of patients [14]. It is known that hasty discharges before the severity peak or complete recovery favour readmissions [35]. Readmissions could also relate to post-discharge care systems. In some health facilities, management of patients after discharge can be poorer than preventive hospital measures [14]. Although administrative prioritisations aim to reduce both length of stay and readmissions, these two goals seem inversely related [22].

Discharge to home at the end of the index hospitalisation was a risk for readmission compared to discharge to medical facilities (long-term care, rehabilitation, etc.), as opposed to other findings [9, 13, 36], all based in the United States, where readmission was less frequent among patients discharged to home. In the Netherlands [6], discharge to long-term care facilities increased the risk of hospital readmission. Reasons for such differences can reside in the low-level individual differences between hospitals’ discharge decisions [37] or in the high-level differences between countries’ medical care systems. In an analysis of the health systems for Switzerland and the Netherlands [38], Switzerland’s system was described as more successful in its adaptation to changing societal constraints. Within Switzerland, quality of rehabilitation and intermediate-care facilities is ensured through an association, SwissREHA, accounting for more than 50% of all rehabilitation beds in the country [39]. As a possible proxy for COVID-19 rehabilitation, stroke rehabilitation was shown [40] to be conducted more efficiently in Switzerland than in the United States. Further studies accounting for insurance coverage of patients [14], governance and financing of healthcare systems and societal factors are necessary to learn more about this aspect.

Patients who were admitted to the IMCU or ICU during the index hospitalisation had a slightly lower risk of readmission, although the association was not statistically significant. Several other studies also found no significant association [14]. The first justification resides in a statistical bias. Due to a higher in-hospital death rate for those patients admitted to IMCU/ICU (20.2% compared to 9.1% for patients not admitted to IMCU/ICU), the readmission population is biased towards having fewer IMCU/ICU-admitted patients because a substantial proportion of them have already died during the index hospitalisation. The second explanation is that ICU admission is related to illness severity and cautions practitioners against a preemptive discharge [22]. In our data, more than 80% of patients who did not spend time in the IMCU/ICU during the index hospitalisation were discharged to home, whereas it reduced to about 40% for patients who were admitted to the IMCU/ICU.

This study investigated the effect of the immunisation status on readmission odds and concluded that there was no significant effect. From a data perspective, the proportion of patients with unknown outcome for their index hospitalisation was 6.0% overall, but the missing data rose to 10.5% when focusing on patients fully immunised. The dataset was biased towards reflecting the outcomes of patients not or partially immunised, possibly because incomplete immunisation is viewed by the practitioners as a risk factor for COVID-19 severity or recurrence. High-risk, unvaccinated patients were more likely to die in hospital and therefore would be excluded from readmission analysis, while high-risk, vaccinated individuals were more likely to survive and contribute to the analysis [43]. It is important to take into consideration the specific vaccination context of Switzerland over the year 2021. In the first six months of 2021, people at high risk of severe COVID-19 (elderly, vulnerable and immunosuppressed individuals) were vaccinated in priority: a non-negligible part of them were less prone to respond to the vaccine. Then it became clear during the last six months of the year that elderly people who did not get their booster doses were also at greater risk of severe disease caused by the Delta variant (the Delta variant became dominant in Switzerland in July 2021). Considering the previously demonstrated vaccine efficacy and the described vaccination strategy in Switzerland during the study period, the study tends to report risk factors for readmission for lower-risk unvaccinated patients and higher-risk vaccinated patients.

Limitations

The limitations of this analysis resided in its observational nature and its imputed data. The reason for readmission was not recorded in our database, limiting the understanding of risk factors. Data on competing events such as death after discharge was not available for most patients: therefore, we were unable to perform a time-to-event analysis, and the probability of rehospitalisation may be slightly biased. The study may in general be subject to some selection bias: for example, certain patients such as those admitted to the ICU or IMCU, and those without proper immunisation, were at higher risk of death and thus exclusion, which may impact the association of these variables with rehospitalisation. There may also be differences in patient characteristics between the centres that the analysis did not control for. The definitions of some variables were not optimal, for example immunocompromised patients also include HIV-infected individuals under successful antiretroviral therapy. We had to restrict the study period to one year given that the collection and definition of variables evolved substantially over time. Therefore we were not able to include the early stages of the pandemic, nor the later waves dominated by the Omicron variant. Age was identified as having strong collinearity with many other risk factors; however the analysis removing the age variable did not change the significance of the risk factors presented above (see Appendix A.3).

Conclusion

We showed that older age, male sex, immunosuppression, discharge to home instead of a facility and oncological pathology were the main risk factors for hospital readmission identified within the CH-SUR Swiss-based surveillance system. Our findings can support the development and implementation of efficient patient management strategies for discharge, as well as support services and coordinated care networks such as post-discharge telehealth monitoring, to mitigate the risk of readmission. Moreover, further studies including immunisation status for the high-risk groups and other socioeconomic factors of patients could bring further elements to fully understand the hospital readmission dynamics among COVID-19 patients.

Acknowledgments

To everyone (former and current) involved in the CH-SUR project, especially to all the study nurses for data entry and administrative coordination.

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Appendix

The appendix is available in the pdf version of the article at https://doi.org/10.57187/s.3773.