Abstract
In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “Sars-CoV-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.
Keywords: COVID-19, readmission, risk factors, mortality, machine learning
Introduction
Overview of COVID-19
COVID-19 is an infectious disease caused by a novel coronavirus named 2019-nCoV by WHO (1). The virus can spread from person to person through respiratory droplets and close contact. Some common symptoms that can be observed include dry cough, fever, and tiredness. Some patients may experience shortness of breath, body aches, and pains, nasal congestion, runny nose, sore throat, or diarrhea. The incubation period of COVID-19 can last for 2 weeks or longer. The disease may still be infectious even during the period of latent infection.
Till 18 December 2021, a total of more than 271 million cases of Covid 19 have been reported to World Health Organization (WHO) worldwide, with a total of more than 5.3 million of deaths (2). Of the reported cases, many of them were readmitted, and a percentage of them were dead subsequently. According to research on the nationwide Veteran Affairs healthcare system, within 60 days of discharge, 19.9% who survived COVID-19 hospitalization were readmitted with 9.1% death (3). In addition, research from Einstein Medical Center Philadelphia found that 7.6% of patients were readmitted within 1 month, while 24% were readmitted within 6 months of the initial hospitalization with 9% readmitted patient death (4). From the research statistics, the percentage of readmission due to COVID-19 is quite high with the percentage around 20%.
The ways that COVID-19 affects the body seem to get mysterious due to the continuity of mutated viral variants development. Many patients' symptoms fully disappear before they suddenly and unexpectedly begin deteriorating. Other patients have recovered and tested “negative” but later tested “positive” again. While the medical community is still struggling to fully understand the novel coronavirus that causes COVID-19, there appears to be an emerging concern that virus reinfection has been reported in recovered or even vaccinated patients. This raises new questions among scientists and healthcare authorities after the country successfully flattened the curve. There is no clear evidence of what causes the reinfection after recovery and vaccination.
Since the outbreak was in 2019, the public has mostly focused on the direct and indirect effects of COVID-19. Studies of the long-term effects on long covid and its associated rehospitalization are needed to effectively plan healthcare delivery and capacity. Despite the long-term consequences on public health becoming clearer from time to time, an investigation on factors that lead to rehospitalization is urgently needed.
Reinfection and Recurrence of COVID-19
Reinfection is the infection following recovery from or superimposed on an infection of the same type (5), in this case, if the patient is infected more than one time of COVID-19, then he is said to have reinfection.
On the other hand, recurrence is the new occurrence of something that happened or appeared before (6). To be more precise, recurrence of COVID-19 is the repeated occurrence of morbidities or symptoms due to previous SARS-CoV-2 infection.
Hospital Readmission and Its Impact on the Society
The reinfection and recurrence of COVID-19 may be directly impacting the hospital readmission rate, despite the comorbidities being an external factor contributing to the readmission as well. Looking into its significance, as readmissions may further pull resources from an already overwhelmed healthcare system, such as the issue of bed productivity. As a long-term consequence, readmission will waste a lot of public resources and it may cause new waves of the covid pandemic (7). This will lead to more secondary damage to the country economically, including an increase in job unemployment and a declination of GDP. Outcomes on long-term outcomes could be a reference for physicians and even policy makers in making decisions on discharge, hospital-capacity planning, and possibly patient monitoring after discharge for patients with COVID-19. Readmission rate is also an important parameter of patient safety and a hospital's quality performance.
Work Motivation and Objective of Systematic Review
As mentioned earlier, unplanned or unwanted hospital readmissions create a burden on society while the long-term consequences post COVID-19 infection remain uncertain to the public. In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in the mitigation of hospital readmissions due to COVID-19. At the same time, challenges faced, a gap of knowledge and potential research directions were discussed to provide insights for future research.
Search Strategy and Eligibility Criteria
Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “sars-cov-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles.
The eligibility criteria were as follows:
The title and abstract should be relevant to our objective of the review, which is to review the current studies on hospital readmission due to COVID-19.
The published date for the research should be from 2020 onwards.
The data collection must include patients diagnosed with COVID-19 via reverse transcriptase polymerase chain reaction (RT-PCR).
The research outcomes must consist of either readmission rate, odds for readmission or risk factor for readmission.
Case studies reports, news items, review papers, non-English articles are excluded for our review.
Only articles with full text access are included to ensure the quality of review.
Figure 1 is the PRISMA diagram for the screening flow of articles from the databases.
Figure 1.
PRISMA diagram for screening of articles from databases.
Risk of Bias Assessment
The risk of bias assessment has been carried out based on a few criteria in the Cochrane review, which are:
Selection Bias. The participants or patient data collected must be readmitted to the hospital due to reinfection of Covid=19.
Detection Bias. The method of diagnosis for COVID-19 should be the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test.
Reporting Bias. There should be no missing outcome data in the report.
Performance Bias and Attrition Bias are not available, as both biases are related to interventional studies, but the review was done based on observational studies.
Each paper is evaluated with the criteria and judged as “Low Risk” (denoted as “+”) if the criteria mentioned are fulfilled, “High Risk” (denoted as “-”) if else, and “Unclear Risk” (denoted as “?”) if not mentioned in the whole report. Table 1 for the risk of bias assessment.
Table 1.
Risk of bias assessment table.
| References | Selection | Detection | Reporting | Risk of bias |
|---|---|---|---|---|
| Atalla et al. (8) | + | + | + | Low |
| Donnelly et al. (3) | + | ? | + | Some concerns |
| Drewett et al. (9) | + | ? | + | Some concerns |
| Green et al. (10) | + | + | + | Low |
| Guarin et al. (4) | + | + | + | Low |
| Günster et al. (11) | + | + | + | Low |
| Gwin et al. (12) | + | ? | + | Some concerns |
| Hong et al. (13) | + | + | + | Low |
| Iheaku et al. (14) | + | + | + | Low |
| Jeon et al. (15) | + | + | + | Low |
| Kirkegaard et al. (16) | + | + | - | Some concerns |
| Lavery et al. (17) | + | - | - | High |
| Loerinc et al. (18) | + | + | + | Low |
| Mehl et al. (19) | + | ? | + | Some concerns |
| Menditto et al. (20) | + | + | + | Low |
| Mooney et al. (21) | + | ? | + | Some concerns |
| Nematshahi et al. (22) | + | + | - | Some concerns |
| Parra et al. (23) | + | + | - | Some concerns |
| Ramos-Martínez et al. (24) | + | + | + | Low |
| Robinson-Lane et al. (25) | + | + | + | Low |
| Rodriguez et al. (26) | + | + | + | Low |
| Taupin et al. (27) | + | + | + | Low |
| Uyaroglu et al. (28) | + | + | - | Some concerns |
| Verna et al. (29) | + | ? | + | Some concerns |
| Yang et al. (30) | + | + | - | Some concerns |
| Yeo et al. (31) | + | + | + | Low |
Green is for “Low Risk”, yellow is for “Unclear Risk”, and red is for “High Risk”.
Readmission and Mortality Rate Post COVID-19 Admission
The readmission and mortality rate are important parameters to the medical and healthcare sector such as the hospital administrations. In the COVID-19 pandemic, these parameters cause pressure on the decision and policy makers of healthcare sector.
In this chapter, we will review past studies on the readmission and mortality rate post COVID-19 admission. A total of 23 articles related to readmission and mortality rate post COVID-19 admission are collected for review.
According to research in Germany, the mortality rate was 24.9% (index hospitalization), 23.9% (30-day), 27.9% (90-day), and 29.6% (180-day), with a readmission rate of 26.8%. Günster et al. found that the latter was 52.3% for patients aged >80 years, and the mortality is higher among those who have been ventilated (11).
Next, according to research in England, an 11.3% of readmission rate for 274 individuals was reported. Higher readmission rates are observed among older patients compared to young age groups (17.9 vs. 14.8%, p = 0.36), as well as higher mortality in older patients (60.2 vs. 20.3%, p <0.001) (21).
Research in South Korea reveals a 4.3% readmission rate with a higher rehospitalization rate for men compared to women (5.5 vs 3.5%). Older age was also the risk factor for a higher readmission rate. Medical benefited pat.ients have a readmission rate of 15.4%, which is higher than patients with health insurance by 4.7 times. (15).
There are nine articles from the United States. Among them, two of the articles used descriptive statistics as their analysis method, six of them used various inferential statistical analysis approaches such as the Chi-square test, Student-t-test, Kruskal–Wallis H test, Wilcoxon rank-sum test, Fisher's exact test, and Mann–Whitney U test, and only 1 of them utilizes machine learning in their research. The readmission and mortality rates differ from each other, with the highest readmission rate of 19.9% by Donnelly et al. and the lowest of 4.5% by Yeo et al. For mortality rate, the highest mortality rate is 13% by Robinson-Lane et al., while according to research by Mehl et al., there was no mortality found throughout the research. For the outcomes of the research using machine learning approach, the predictive performance, area under the receiver-operating characteristic curve - readmission is 0.871 (95% CI, 0.830–0.917), while the area under the precision-recall curve – readmission is 0.504 (95% CI, 0.388–0.604) (19).
Two research works have been contributed by China, one of them reported an 8.86% of readmission rate, and 9% for the other one. Both use relatively small data size (79 and 145) and uses inferential statistical approaches in their research, thus the readmission rate has a small difference (0.14%).
There are also two articles from Georgia, with both using descriptive statistics as their analysis approach. According to research with a smaller data size (310), the post-discharge readmission rate was 5.2% with 68.8% of these attributable to COVID-19. The research with a bigger data size (2,399) reveals a 6.4% readmission rate within 30 days. Despite the difference in data size, the result does not have a difference in a big range.
Three research works have been reported from Spain, with one using descriptive statistics, and another two using the inferential statistics method. All three studies show that the readmission rate is around 5% in Spain (4.2%, 4.4%, and 5.4%), One study shows that 0.73% died during the first month post-discharge.
Another research from Iran reveals that the rate of readmission for 30 and 60 days after discharge was 7.6% and 8.1%, respectively, 14.2% readmission rates were reported by Drewett et al. from Australia (9), 9.2% by Green et al. from Israel (10), and 7.1% by Uyaroglu et al. from Turkey (28).
In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (13) use the inferential statistical approach in their studies, while only one uses the machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from multi regions.
Table 2 shows the reviews for readmission and/or mortality rate.
Table 2.
Table of review on readmission and / or mortality rate.
| References | Year | Data region | Data size | Statistical analysis | Outcomes: Readmission and / or mortality rate |
|---|---|---|---|---|---|
| Günster et al. (11) | 2021 | Germany | 8,769 | Descriptive statistics | Median age of 72 years, mortality during index hospitalization = 24.9%, 30-day all-cause mortality rate was 23.9%, the 90-day rate was 27.9%, and the 180-day rate, 29.6%. The latter was 52.3% for patients aged >80 years, 23.6% if not ventilated, during index hospitalization, but 53.0% in case of those ventilated invasively. Readmission rate within 180 days is 26.8%. |
| Mooney et al. (21) | 2021 | England | 393 | Descriptive statistics | 11.3% of readmission rate for 274 individuals were reported. Higher readmission rate are observed among older patients compared to young age groups (17.9 vs. 14.8%, p = 0.36), as well as higher mortality in older patients (60.2 vs. 20.3%, p < 0.001) |
| Jeon et al. (15) | 2020 | South Korea | 7,590 | Descriptive statistics, Chi-square test | Of the 7,590 subjects analyzed in South Korea, there are 4.3% readmission rate with higher rehospitalization rate of men compared to women (5.5 vs. 3.5%). Older age was also the risk factor to higher readmission rate. Medical benefited patients have readmission rate of 15.4%, which is higher than patients with health insurance by 4.7 times. |
| Yeo et al. (31) | 2021 | United States | 1,062 | Descriptive statistics, Chi-square test, Student t-test or Mann–Whitney–Wilcoxon nonparametric test |
At the end of the study, a total of 48 (4.5%) patients were readmitted within 30 days of discharge, and a median time to readmission was 5 days. |
| Yang et al. (30) | 2021 | China | 79 | Chi-square test, Fisher' t-test, Student's t-test, and Kruskal-Wallis H test | Readmission rate due to COVID-19 re-positive = 8.86% |
| Lavery et al. (17) | 2020 | United States | 126,137 | Descriptive statistics | Among the 106,543 (85%) surviving patients, 9% (9,504) were readmitted to the same hospital within 2 months of discharge through August 2020. |
| Rodriguez et al. (26) | 2021 | United States | 2,256 + 855 (validation) | L1-penalized logistic regression (logistic L1), elastic-net logistic regression (logistic EN), and gradient boosted trees (GBT) |
The predictive performance: area under the receiver-operating characteristic curve— readmission 0.871 (95% CI, 0.830–0.917); area under the precision-recall curve— readmission 0.504 (95% CI, 0.388–0.604). |
| Loerinc et al. (18) | 2021 | Georgia | 310 | Descriptive statistics | The post-discharge readmission rate was 5.2% with 68.8% of these attributable to COVID-19. |
| Gwin et al. (12) | 2021 | United States | 151 | Two-sided t-tests, Wilcoxon rank-sum test, Fisher's exact test |
24% hospital return rate was reported, with 11% of revisit rate, 14% of 14-day revisit, and 24% of 30-day revisit rate. |
| Nematshahi et al. (22) | 2021 | Iran | 416 | Descriptive statistics | 51 of 416 patients was readmitted. 7.6% of 30-day readmission and 8.1% of 60-day readmission were reported. |
| Mehl et al. (19) | 2021 | United States | 66 | Descriptive statistics | readmission is 6% with no mortality |
| Ramos-Martínez et al. (24) | 2021 | Spain | 7137 | Chi-square test, Fisher's exact test, Student's t-test and the Mann-Whitney U Test |
4.2% were readmitted. 35 died during hospital readmission (11.7%, p = 0.007). 50 died during the first month after discharge (0.73%). |
| Parra et al. (23) | 2020 | Spain | 1,368 | Chi-square test, Fisher's exact test, Student's t-test and the Mann-Whitney U-Test |
4.4% with 74% male who presented COVID-19 were readmitted during the 3 weeks after discharge from hospital |
| Kirkegaard et al. (16) | 2021 | Spain | 629 | Descriptive statistics | Readmission cumulative incidence =5.4% and incidence rate = 0.034 person-years. |
| Taupin et al. (27) | 2021 | United States | 576 | Chi-square test, Fisher's exact test, Wilcoxon rank sum test | 76 of 576 COVID-19 hospitalizations resulted in a 30-day revisit (13.2%), including 21 ED visits without admission (3.6%) and 55 readmissions (9.5%). |
| Robinson-Lane et al. (25) | 2021 | United States | 2,217 | Descriptive statistics, Pearson chi-squared test |
11.8% rehospitalization was observed, with 55% consists of non-white patients. |
| Donnelly et al. (3) | 2021 | United States | 1,775 | Wilcoxon rank sum tests, Pearson χ2 tests of association | Within 60 days of discharge,19.9% who survived COVID-19 hospitalization were readmitted, 9.1% died, and 27.0% were readmitted or died. |
| Atalla et al. (8) |
2021 | United States | 339 | Pearson's chi-square test, Student t-test and Mann-Whitney Wilcoxon test | 19/279 were readmitted (6.8%) after a median of 5 days. |
| Iheaku et al. (14) | 2021 | Georgia | 2,399 | Descriptive and observatory | 6.4% were readmitted within 30 days. 72% of the readmitted patients were Black or Hispanic |
| Drewett et al. (9) | 2021 | Australia | 169 | Chi-squared and rank sum tests | 14.2% were readmitted to hospital within 6 months. |
| Hong et al. (13) | 2021 | China | 145 | Mann–Whitney U-test, χ2 test | 9% of readmission rate with 7 males and 6 females due to repositivity |
| Green et al. (10) | 2021 | Israel | 618 | Descriptive statistics | Of the 544 patient who were discharged, 1.83% died following and 9.2% were re-admitted. Median post discharge follow-up was 59 days (Interquartile range, IQR, 28–161). |
| Uyaroglu et al. (28) | 2021 | Turkey | 154 | Pearson Chi-Square Test or Fisher exact Test, Mann–Whitney U-test or Kruskal Wallis Tests |
Of 154 patients, 7.1% were readmitted. The median time of readmission was 8.1 days (IQR=5.2). |
Readmission Risk Factors Post COVID-19 Admission
Hospital readmission could be due to different reasons than the main infection (COVID-19), and some clinical factors such as comorbidities may possess a higher risk to the infected person. If the person suffering from certain comorbidity, he may have a higher risk to be readmitted to the hospital post-COVID-19 admission. After a review of the readmission and mortality rate statistics across regions, we will review the readmission risk factors post COVID-19 admission. A total of 18 articles are relevant to this topic.
There are six articles from United States, with three using multivariable logistic regression, while the others use Pearson chi-squared test, Piecewise Cox proportional hazards regression Student t-test, and Mann–Whitney Wilcoxon test, respectively. Results wise, Guarin et al. found coronary artery disease and Hispanic ethnicity as the risk factor (4). Age more than, diabetes, CVD, CKD stage 1–5, and CKD stage 5 were reported by Verna et.al (29). Yeo et.al. found that a peak serum creatinine level of ≥1.29 mg/dl during index hospitalization has an odds ratio (OR) of 2.41 (31). Robinson-Lane et al. focus on the ethnicity factor and found that more than half of hospital readmissions within the 60 days following discharge were among non-white patients (55%) (25). Donnelly et al. compared the readmission rate between survivors of COVID-19, pneumonia, and heart failure, and they found that COVID-19 survivors had lower rates of 60-day readmission or death than matched survivors of pneumonia (26.1 vs. 31.7%; p = 0.006) and heart failure (27.0% vs. 37.0%; p < 0.001) (3). Atalla et al. discovered a few risk factors: hypertension, diabetes, chronic obstructive pulmonary disease (COPD), liver disease, cancer, alcohol, and drug abuse (8).
One research from Italy discovered that cognitive impairment, P/F<300 mmHg, being resident in a geriatric care facility, and neutrophilia as the risk factors contributing to readmission.
Next, according to research from Germany, the risk factors identified are BMI > 40, liver disease, metastatic cancer, and coagulopathy. For age, the OR was 1.08 per year, the odds for increase by a factor of 2.21 per additional 10 years of age (1.082449∧10) (11).
The risk factors from research in South Korea are male, 65 years of age or older, having medical benefits, living in Gyeongsangbuk-, prescribed Kaletra, patients with chest radiographs, CT took, and shorter length of stay in the first admission.
According to Nematshahi et al. from Iran, the site of lung involvement. age over 60 years, diabetes, and high creatinine level (≥1.2 mg/dl) are identified as risk factors of readmission (22).
All three study from Spain use logistic regression for their research, and the data sizes are 7,137, 1,368, and 629, respectively. Age, age-adjusted Charlson comorbidity index score, chronic obstructive pulmonary disease (COPD), asthma, hemoglobin level at admission, ground-glass opacification at admission, and glucocorticoid treatment are identified by Ramos-Martínez et al. (24), immunocompromised patients, hypertensive patients, and fever during the 48 h prior to discharge by Parra et al. (23), while prior diagnosis of heart failure, length of stay during index admission >13 days, treatment with corticosteroids and developing pulmonary thromboembolism are identified by Kirkegaard et al. (16).
Diabetes and end stage renal disease are the risk factors from the research in Georgia. Increased length of stay during the index admission, ICU admission, supplemental oxygen and high flow nasal oxygen (HFNO), and chronic obstructive respiratory disease (COPD) are the risk factors based on research from Australia. Hong et al. from China also had research and found that longer virus shedding duration, and lower IgG levels as the risk factors for readmission (13).
On the other hand, Green et al. from Israel found that older age, age above 80 years, higher CCI, and solid organ transplantation as the risk factors (10). Individual comorbidities included in CCI are the history of coronary artery disease, CVA, diabetes mellitus, severe kidney disease metastatic disease, and higher systolic blood pressure.
Lastly, research in Turkey reveals malignancy and hypertension as the risk factors for readmission (28).
A word cloud is constructed based on the findings by extracting the risk factors from all articles into the system. If the frequency of risk factors is higher, then the word size will be bigger. Figure 2 is a word cloud to summarize and visualize the risk factors mentioned in this chapter.
Figure 2.
Word cloud for the risk factors of readmission.
Overall, the logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor for readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD.
Table 3 shows the reviews for general readmission risk factors.
Table 3.
Table of review on general readmission risk factors post COVID-19 admission.
| References | Year | Data region | Data size | Statistical analysis | Outcomes: Risk factors of readmission |
|---|---|---|---|---|---|
| Guarin et al. (4) | 2021 | United States | 275 | Multivariable logistic regression |
Coronary artery disease (CAD) odds ratio (OR), 2.15 (95% confidence interval [CI]: 1.04–4.44; p = 0.039), Hispanic ethnicity OR, 3.16 (95% CI: 1.01–9.88; p = 0.048). |
| Verna et al. (29) | 2021 | United States | 29,659 | Multivariable logistic regression |
Age >60 vs. 18–40 (odds ratio [OR] = 1.92, 95% confidence interval [CI] = 1.48, 2.50), admitted in the Northeast vs. West (OR = 1.43, 95% CI = 1.14, 1.79) or South (OR = 1.28, 95% CI = 1.11, 1.49), Diabetes (OR=1.34, 95% CI = 1.12, 1.60), CVD (OR = 1.46, 95% CI = 1.23, 1.72), CKD stage 1–5 (OR = 1.51, 95% CI = 1.25,1.81) and CKD stage 5 (OR = 2.27, 95% CI = 1.81, 2.86) |
| Menditto et al. (20) | 2021 | Italy | 283 | Welch's t-test, Mann Whitney U test | Cognitive impairment (OR 17.3 [CI 4.7–63.2]), P/F <300 mmHg (OR 8.6 [CI 1.6–44.3]), Being resident in geriatric care facility (OR 7.6 [CI 2.1–26.4]) and Neutrophilia (OR 5.8 [CI 1.6–22.0]). |
| Günster et al. (11) | 2021 | Germany | 8,769 | Multivariable logistic regression, Wald test, squared Pearson correlation (R2) |
BMI > 40 (OR 2.01, 95%-CI 1.33 to 3.05), Liver disease (OR 2.45, 95%-CI 1.85–3.25), Metastatic cancer (OR 8.02, 95%-CI 3.57–18.00), and Coagulopathy (OR 2.31, 95%-CI 1.82–2.94). For age, the OR was 1.08 per year, the odds for increase by the factor 2.21 per additional 10 years of age (1.082449∧10) |
| Jeon et al. (15) | 2020 | South Korea | 7,590 | Logistic regression | Male (Odds Ratio (OR): 1.340, 95% CI: 1.055,1.706), 65 years of age or older (OR: 2.235, 95% CI: 1.111–4.497), Having medical benefits (OR: 2.757, 95% CI: 2.040–3.725), and Living in Gyeongsangbuk-do (OR: 2.876, 95% CI: 2.144–3.857), Prescribed Kaletra (OR: 1.388, 95% CI:1.056–1.827), Patients with chest radiographs (OR: 1.591, 95% CI: 1.121–2.258) and CT taken (OR: 1.330, 95% CI: 1.031–1.71), Shorter length of stay in first admission (OR: 0.945, 95% CI: 0.933–0.958). |
| Yeo et al. (31) | 2021 | United States | 1,062 | Multivariable logistic regression |
Peak serum creatinine level of ≥1.29 mg/dL during the index hospitalization, vs. creatinine of <1.29 mg/dL, (adjusted odds ratio: 2.41; 95% CI: 1.23–4.74). |
| Nematshahi et al. (22) | 2021 | Iran | 416 | Regression model | Site of Lung involvement (OR > 4). Age over 60 years (OR = 1.12), underlying disease especially Diabetes (OR = 3.43), high creatinine level (≥1.2 mg/dl) (OR = 2.15) |
| Ramos-Martínez et al. (24) | 2021 | Spain | 7,137 | Univariate and Multivariate logistic regression |
Age [odds ratio (OR): 1.02; 95% confident interval (95% CI): 1.01–1.03], age-adjusted Charlson comorbidity index score (OR: 1.13; 95% CI: 1.06–1.21), chronic obstructive pulmonary disease (OR: 1.84; 95% CI: 1.26–2.69), asthma (OR: 1.52; 95% CI: 1.04–2.22), hemoglobin level at admission (OR: 0.92; 95% CI: 0.86–0.99), Ground-glass opacification at admission (OR: 0.86; 95% CI:0.76–0.98) and Glucocorticoid treatment (OR: 1.29; 95% CI: 1.00–1.66) |
| Parra et al. (23) | 2020 | Spain | 1,368 | Univariate and Multivariate logistic regression |
Immunocompromised patients (N = 10.2%) (p = 0.04), Hypertensive patients (p = 0.07), Fever during the 48 h prior to Discharge (p < 0.001) |
| Kirkegaard et al. (16) | 2021 | Spain | 629 | Multivariate logistic regression |
Prior diagnosis of heart failure (OR 4.09; 95% CI 1.35–12.46; p = 0.013), length of stay during index admission >13 days (OR 2.72; 95% CI 1.21–6.12; p = 0.015), treatment with corticosteroids (OR 2.39; 95% CI 1.01–5.70; p = 0.049) and developing pulmonary thromboembolism (OR 11.59; 95% CI 2.89–46.48; p = 0.001) |
| Robinson-Lane et al. (25) | 2021 | United States | 2,217 | Analysis of variance and Pearson chi-squared test |
Black patients have the lowest physician follow-up post discharge (n = 65, 60.2%) and the longest delays in returning to work (average 35.5 days). More than half of hospital readmissions within the 60 days following discharge were among non-white patients (n = 144, 55%). The most post discharge deaths are white patients (n = 153, 21.5%) |
| Donnelly et al. (3) | 2021 | United States | 1,775 | Piecewise Cox proportional hazards regression |
COVID-19 survivors had lower rates of 60-day readmission or death than matched survivors of pneumonia (26.1 vs. 31.7%; p = 0.006) and heart failure (27.0 vs. 37.0%; p < 0.001). However, COVID-19 survivors had higher rates of readmission or death within the first 10 days after discharge than matched survivors of pneumonia (13.4 vs. 9.7%; p = 0.01) and heart failure (13.9 vs. 8.8%; p < 0.001) |
| Atalla et al. (8) | 2021 | United States | 339 | Pearson's chi-square test, Student t-test and Mann-Whitney Wilcoxon test | Hypertension (68.4% vs. 44.1%, p = 0.038), diabetes (57.9 vs. 32.2%, p = 0.021), chronic pulmonary disease (57.9 vs. 12.8%, p < 0.001), liver disease (15.8 vs. 2.5%, p = 0.001), cancer (21.1 vs. 7.2%, p = 0.03), alcohol (26.3 vs.4.1%, p < 0.001), and drug abuse (15.8 vs. 4.4%, p = 0.027) were more common among patients who readmitted, patients readmitted had a lower ICU utilization (10.5 vs. 34.4%, p = 0.032) and intubation rate (0 vs. 20%, p = 0.03) during index hospitalization. |
| Iheaku et al. (14) | 2021 | Georgia | 2,399 | Descriptive and observatory | Diabetes (p: 0.03) and end stage renal disease (p: 0.004) |
| Drewett et al. (9) | 2021 | Australia | 169 | Chi-squared and rank sum tests | Increased length of stay during index admission (5 vs. 7 days, p = 0.04), ICU admission (p = 0.04), supplemental oxygen (p = 0.03) and high flow nasal oxygen (HFNO) (p = 0.01), chronic respiratory disease (21.7 vs. 41.7%, p = 0.12) |
| Hong et al. (13) | 2021 | China | 145 | Covariate binary logistic regression analysis with forward conditional method | Longer virus shedding duration [odd ratio (OR) = 1.280, 95% confidence interval (CI):1.052–1.558; p = 0.013], and lower IgG levels (OR = 0.690, 95% CI: 0.528–0.901; p = 0.007] |
| Green et al. (10) | 2021 | Israel | 618 | Cox proportional hazards model; with the Fine and Gray methodology, multiple regression model | Older age (HR 1.03 for every year, 95% CI 1.01–1.04, p = 0.002); age above 80 years (HR4.93, 95% CI 1.14–21.29, p = 0.032; and higher CCI (HR for every point 1.34, 95% CI 1.24–1.44, p < 0.0001). Individual comorbidities included in CCI: history of coronary artery disease, CVA, diabetes mellitus, severe kidney disease metastatic disease and a higher systolic blood pressure. Solid organ transplantation (HR 4.97, 95% CI 2.09–11.83, p = 0.0009). In multivariate analysis: solid organ transplantation (HR 3.37, 95% CI 2.73–7.5, p = 0.0028) and CCI (HR 1.34 for every point, 95% CI 1.23–1.46, p < 0.0001) |
| Uyaroglu et al. (28) | 2021 | Turkey | 154 | Pearson Chi-Square Test or Fisher exact Test, Mann-Whitney U-test or Kruskal Wallis Tests |
Malignancy (18.7 vs. 2.1%, p = 0.04) and hypertension (45.5 vs. 14%, p = 0.02) |
Discussions
In this chapter, we will discuss the limitations, research gaps, and potential future directions of research on COVID-19 readmissions.
Most of the research involves data collection from a single center, which is focused on one specific region. As we all know, COVID-19 is a widespread disease that affects public health globally, if the study focuses on one single region, the research insights may be valuable only to the people of the same region, whereas outside institutions might not use those insights as reference. To make the research outcomes practical and applicable to most regions, comprehensive data collection should be made across regions.
Next, the study duration of research is to be questioned. COVID-19 is a rapid-evolving phenomenon, delta and omicron variants have emerged lately, and this will impact the accuracy of results if the study was done before the emergence of the variants. Research on COVID-19 should be carried out progressively and continuously in a long term to obtain updated info and insights from time to time. However, if the study duration is too long, the insights may not be relevant anymore if the publishing is after the lengthy study duration. The timeline of the research and experiments should be planned carefully to make sure the insights obtained are not outdated.
Most of the experimental designs do not include a control group for results comparison. A control group is important to allow researchers to confirm that the study results are due to the manipulation of independent variables (infection of COVID-19) rather than extraneous variables. The control group is the group of participants who are not exposed to the manipulated variable and the results are to be compared with the groups exposed to the manipulated variable.
As one of the future directions, the machine learning approach can be utilized in the statistical analysis of readmission and mortality due to COVID-19. As reviewed in Chapter 2, most of the studies only used inferential statistical analysis. Machine Learning (ML) is a subfield of computer science and artificial intelligence. ML deals with building systems (algorithms, models) that can learn from data and observations, instead of explicitly programmed instructions and rules. Machine learning finds generalizable patterns from data and can make predictions based on the data, while inferential statistics are used to formalize the relationships between variables. Machine learning can be applied in readmission studies in order to predict the readmission rate according to each patient's condition, including comorbidities, and demographic and geographic factors. Also, machine learning can be used in the prognosis of the potential rise in certain regions with available outcomes as reference. The insight from the inferential statistics is insufficient for healthcare policies and decisions, however, with predictive insights from machine learning, the healthcare sector can make and change decisions to prevent or curb the phenomenon.
Next, from Chapter 3, we can notice a few dominant risk factors of readmission being identified, such as age, diabetes, and COPD. One potential direction is to investigate the relationship and make predictions of hospital readmissions on age factors. Studying the influence of age on the risk of readmission could help healthcare personnel to give more attention to the aged patients while designing interventions, especially for old folks could tackle the dominant risk factor and reduce the readmission rates. The same goes for diabetes, COPD, and other risk factors, more research could be done specifically on the certain risk factor, or interventional studies could be designed to tackle and curb the COVID-19 readmission.
Apart from the conventional risk factors which increase the risk of readmission, interestingly, the rate of readmission increases significantly in patients with dysbiotic gastrointestinal symptoms, according to a gastrointestinal study in Istanbul (32). Also, the study found that intestinal microbiota affects disease morbidity and mortality (33). In particular, the Th17/II17-116 immune cascade is responsible for COVID-19 pathology and can increase the severity of the disease (34). From the evidence, gastrointestinal symptoms may be a new direction of research as a COVID-19 readmission risk factor. Other symptoms may also be the focus of research, for instance, respiratory symptoms, which are not yet widely explored.
Also, no study has been reported with a collection of data involving multi regions. Despite the collection, the process will be harder, and the data will be harder in synchronization, the research outcomes will be more useful and practical to the public. As we all know, COVID-19 is spreading worldwide, and study from a single center only provides useful insights for that particular region.
Conclusion
To summarize, studies on readmission and mortality of COVID-19 had been done in various regions and promising statistics have been obtained. The readmission and mortality rates of COVID-19 and readmission risk factors of COVID-19 have been reviewed and discussed. Despite a number of research work has been done, there are a few limitations and research gaps to focus on and improve in future research. A few future research directions have been proposed, including the utilization of machine learning in statistical analysis, investigation of dominant risk factors, experimental design on interventions to curb dominant risk factors and increasing the scale of data collection from single centered to multi centered. COVID-19 is a rapidly evolving phenomenon, and all research should be done progressively and continuously in a fast manner so that the research insights are relevant and updated from time to time.
Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
Author Contributions
All authors contributed equally to design, analyze, and drafting this manuscript. All authors contributed to the article and approved the submitted version.
Funding
This study was supported in parts by the 2020 APT EBC-C (Extra-Budgetary Contributions from China) Project on Promoting the Use of ICT for Achievement of Sustainable Development Goals, and Association of Commonwealth University (ACU) United Kingdom, and Universiti Malaya under Grant IF015-2021 and IF063-2021.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
- 1.World Health Organization . Coronavirus. World Health Organization. (2021). Available online at: https://www.who.int/health-topics/coronavirus#tab=tab_1 (accessed December 30, 2021).
- 2.World Health Organization . WHO Coronavirus (COVID-19) Dashboard. World Health Organization. (2021). Available online at: https://covid19.who.int/ (accessed December 18, 2021).
- 3.Donnelly JP, Wang XQ, Iwashyna TJ, Prescott HC. Readmission and death after initial hospital discharge among patients with COVID-19 in a large multihospital system. JAMA. (2021) 325:304–6. 10.1001/jama.2020.21465 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Guarin G, Lo KB, Bhargav R, Salacup G, Wattoo A, Coignet JG, et al. Factors associated with hospital readmissions among patients with COVID-19: a single-center experience. J Med Virol. (2021) 93:5582–7. 10.1002/jmv.27104 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Merriam-Webster. (n.d.). Reinfection Definition & Meaning. Merriam-Webster. Available online at: https://www.merriam-webster.com/dictionary/reinfection (accessed December 30, 2021).
- 6.Merriam-Webster. (n.d.). Recurrence Definition & Meaning. Merriam-Webster. Available online at: https://www.merriam-webster.com/dictionary/recurrence (accessed December 30, 2021).
- 7.Holt HD. The readmission difference: examining the negative impact of hospital readmissions on financial performance. Inte J Health Sci. (2017) 5:31–41. 10.15640/ijhs.v5n3a5 [DOI] [Google Scholar]
- 8.Atalla E, Kalligeros M, Giampaolo G, Mylona EK, Shehadeh F, Mylonakis E. Readmissions among patients with COVID-19. Int J Clin Pract. (2021) 75:e13700. 10.1111/ijcp.13700 [DOI] [PubMed] [Google Scholar]
- 9.Drewett GP, Chan RK, Jones N, Wimaleswaran H, Howard ME, McDonald CF, et al. Risk factors for readmission following inpatient management of COVID-19 in a low-prevalence setting. Intern Med J. (2021) 51:821–3. 10.1111/imj.15218 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Green H, Yahav D, Eliakim-Raz N, Karny-Epstein N, Kushnir S, Shochat T, et al. Risk-factors for re-admission and outcome of patients hospitalized with confirmed COVID-19. Sci Rep. (2021) 11:17416. 10.1038/s41598-021-96716-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Günster C, Busse R, Spoden M, Rombey T, Schillinger G, Hoffmann W, et al. 6-month mortality and readmissions of hospitalized COVID-19 patients: A nationwide cohort study of 8,679 patients in Germany. PLoS ONE. (2021) 16:e0255427. 10.1371/journal.pone.0255427 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Gwin M, Saleki M, Lampert H, Meo N, Bann M. Emergency department visits and readmissions after COVID-19 hospitalization: a cross-sectional analysis. Intern Emerg Med. (2021) 16:1715–8. 10.1007/s11739-021-02644-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hong LX, Liu L, Lin A, Yan WH. Risk factors for SARS-CoV-2 re-positivity in COVID-19 patients after discharge. Int Immunopharmacol. (2021) 95:107579. 10.1016/j.intimp.2021.107579 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Iheaku O, Wiley Z, Martin N, Amzat T, Kkobaidze K, Kulshreshta A, et al. Risk factors for 30-day readmission of COVID-19 patients: a retrospective - prospective study from the cross (COVID-19 characteristics of readmissions and outcomes and social determinants of health study) collaborative. J Gen Intern Med. (2021) 36(Suppl 1):S73–S74. [Google Scholar]
- 15.Jeon WH, Seon JY, Park SY, Oh IH. Analysis of Risk Factors on Readmission Cases of COVID-19 in the Republic of Korea: Using Nationwide Health Claims Data. Int J Environ Res Public Health. (2020) 17:5844. 10.3390/ijerph17165844 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kirkegaard C, Falcó-Roget A, Sánchez-Montalvá A, Valls Á, Clofent D, Campos-Varela I, et al. Incidence and risk factors for early readmission after hospitalization for SARS-CoV-2 infection: results from a retrospective cohort study. Infection. (2021) 50:371–80. 10.1007/s15010-021-01662-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lavery AM, Preston LE, Ko JY, Chevinsky JR, DeSisto CL, Pennington AF, et al. Characteristics of hospitalized COVID-19 patients discharged and experiencing same-hospital readmission - United States, March-August 2020. Morbid Mortal Wkly Rep. (2020) 69:1695–9. 10.15585/mmwr.mm6945e2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Loerinc LB, Scheel AM, Evans ST, Shabto JM, O'Keefe GA, O'Keefe JB. Discharge characteristics and care transitions of hospitalized patients with COVID-19. Healthcare. (2021) 9:100512. 10.1016/j.hjdsi.2020.100512 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Mehl SC, Loera JM, Shah SR, Vogel AM, Fallon SC, Glover CD, et al. Favorable postoperative outcomes for children with COVID-19 infection undergoing surgical intervention: Experience at a free-standing children's hospital. J Pediatr Surg. (2021) 56:2078–85. 10.1016/j.jpedsurg.2021.01.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Menditto VG, Fulgenzi F, Bonifazi M, Gnudi U, Gennarini S, Mei F, et al. Predictors of readmission requiring hospitalization after discharge from emergency departments in patients with COVID-19. Am J Emerg Med. (2021) 46:146–9. 10.1016/j.ajem.2021.04.055 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mooney CJ, Hone L, Majid M, Cai J, Mieiro L, Fink DL. A Single centre study on the thirty-day hospital reattendance and readmission of older patients during the SARS-CoV-2 pandemic. Age Ageing. (2021) 50:i12–42. 10.1093/ageing/afab030.71 [DOI] [Google Scholar]
- 22.Nematshahi M, Soroosh D, Neamatshahi M, Attarian F, Rahimi F. Factors predicting readmission in patients with COVID-19. BMC Res Notes. (2021) 14:374. 10.1186/s13104-021-05782-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Parra LM, Cantero M, Morrás I, Vallejo A, Diego I, Jiménez-Tejero E, et al. Hospital Readmissions of Discharged Patients with COVID-19. Int J Gen Med. (2020) 13:1359–66. 10.2147/IJGM.S275775 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Ramos-Martínez A, Parra-Ramírez LM, Morrás I, Carnevali M, Jiménez-Ibañez L, Rubio-Rivas M, et al. Frequency, risk factors, and outcomes of hospital readmissions of COVID-19 patients. Sci Rep. (2021) 11:13733. 10.1038/s41598-021-93076-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Robinson-Lane SG, Sutton NR, Chubb H, Yeow RY, Mazzara N, DeMarco K, et al. Race, Ethnicity, and 60-Day Outcomes After Hospitalization With COVID-19. J Am Med Dir Assoc. (2021) 22:2245–50. 10.1016/j.jamda.2021.08.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Rodriguez VA, Bhave S, Chen R, Pang C, Hripcsak G, Sengupta S, et al. Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients. J Am Med Inform Assoc. (2021) 28:1480–8. 10.1093/jamia/ocab029 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Taupin D, Anderson TS, Merchant EA, Kapoor A, Sokol-Hessner L, Yang JJ, et al. Preventability of 30-Day Hospital Revisits Following Admission with COVID-19 at an Academic Medical Center. Jt Comm J Qual Patient Saf. (2021) 47:696–703. 10.1016/j.jcjq.2021.08.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Uyaroglu OA, Basaran NÇ, Özisik L, Dizman GT, Eroglu I, Sahin TK, et al. Thirty-day readmission rate of COVID-19 patients discharged from a tertiary care university hospital in Turkey: An observational, single-center study. Int J Qual Health Care. (2021) 33:mzaa144. 10.1093/intqhc/mzaa144 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Verna EC, Landis C, Brown RS, Jr, Mospan AR, Crawford JM, Hildebrand JS, et al. Factors Associated with Readmission in the US Following Hospitalization with COVID-19. Clin Infect Dis. (2021) ciab464. 10.1093/cid/ciab464 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Yang ZQ, Chen XF, Huang RB, Li SK, Lin DY, Yang ZJ, et al. Atypical presentations of coronavirus disease 2019 (COVID-19) from onset to readmission. BMC Infect Dis. (2021) 21:127. 10.1186/s12879-020-05751-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yeo I, Baek S, Kim J, Elshakh H, Voronina A, Lou MS, et al. Assessment of thirty-day readmission rate, timing, causes and predictors after hospitalization with COVID-19. J Intern Med. (2021) 290:157–65. 10.1111/joim.13241 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Bozkurt HS, Bilen Ö. Gastrointestinal symptoms in COVID-19 could be associated with severe lung involvement and increased readmission rates. Eur J Inflamm. (2021) 19:1–8. 10.1177/20587392211048259 [DOI] [Google Scholar]
- 33.Bozkurt HS, Bilen Ö. Oral booster probiotic bifidobacteria in SARS-COV-2 patients. Int J Immunopathol Pharmacol. (2021) 35:1–8. 10.1177/20587384211059677 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Bozkurt HS, Quigley EM. The probiotic Bifidobacterium in the management of Coronavirus: a theoretical basis. Int J Immunopathol Pharmacol. (2020) 34:2058738420961304. 10.1177/2058738420961304 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.