Abstract
Background
Hospital readmissions are core indicators of the quality of health care provision.
Objective
To understand factors associated with 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database.
Methods
This retrospective study characterized the 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database.
Results
The 30-day, all-cause hospital readmission rate in this population was 3.2%. We found the most common diagnoses at readmission to be sepsis, acute kidney injury, and pneumonia. Chronic alcoholic liver cirrhosis and congestive heart failure were prominent predictors of readmission among patients with COVID-19. Moreover, we found that younger patients and patients from economically disadvantaged backgrounds were at higher risk of 30-day readmission. Acute complications during index hospitalization, including acute coronary syndrome, congestive heart failure, acute kidney injury, mechanical ventilation, and renal replacement therapy, also increased the risk of 30-day readmission for patients with COVID-19.
Conclusion
Based on the results of our study, we advise clinicians to promptly recognize patients with COVID-19 who are at high risk of readmission, and to subsequently manage their underlying comorbidities, to institute timely discharge planning, and to allocate resources to underprivileged patients in order to decrease the risk of 30-day hospital readmissions.
Keywords: COVID-19, 30-day readmission, Nationwide Readmissions Database
Statements of clinical significance.
• The 30-day hospital readmission rate for COVID-19 patients in the United States during 2020 was 3.2%; sepsis, acute kidney injury, pneumonia, congestive heart failure, and acute pulmonary embolism were the most common diagnoses at readmission.
• Demographic factors associated with higher readmission rates among COVID-19 patients included younger age, lower median incomes, Medicaid payor status, and higher Elixhauser Comorbidity scores - specifically, ischemic stroke, hypertension, malignancy, alcoholic cirrhosis, and non-alcoholic steatohepatitis were strongly associated with increased rates of readmission.
• Acute complications during index hospitalization including acute coronary syndrome, congestive heart failure, acute kidney injury, mechanical ventilation, and renal replacement therapy were all associated with increased 30-day hospital readmission for COVID-19 patients.
• Discharge to skilled nursing facility as well as discharges against medical advice strongly correlated with 30-day readmission among COVID-19 patients.
Alt-text: Unlabelled box
Introduction
In December of 2019, SARS-CoV2 was identified to have caused acute respiratory illness in a cluster of patients in Wuhan, China that has culminated in a global pandemic characterized by significant morbidity, mortality, psychosocial stress, and healthcare burden.1 According to the Centers for Disease Control and Prevention (CDC), as of December 2, 2022 there have been a total of 98,777,270 cases in the United States with 1,077,303 reported deaths.2 Hospital readmissions are a core quality metric of health care provision and are defined as an unplanned return of a patient to the hospital.3 With respect to coronavirus disease-19 (COVID-19) patients, readmissions have been linked to worsening of active infection, reinfection, or secondary complications such as superimposed bacterial infection, acute kidney injury, myocarditis, and thromboembolism.4 , 5 To date, there is limited literature describing the risk factors associated with hospital readmission for patients with COVID-19.4 , 6 A meta-analysis by Akbari et al. reported an overall 30-day readmission rate of 9.3% for patients with COVID-19, while a review by Atalla et al. found a 30-day readmission rate of 6.8%.6 , 7 These studies have limited sample sizes; the aim of our study was to identify risk factors associated with 30-day readmissions for patients in patients with COVID-19 in the United States during the early pandemic by utilizing a large national inpatient sample – the Nationwide Readmissions Database.
Material and methods
Data source: the Nationwide Readmissions Database (NRD) for the year 2020 was utilized for retrospective analysis. The NRD is provided by the Healthcare Cost and Utilization Project (HCUP) which is sponsored by the Agency for Healthcare Research and Quality (AHRQ). Unweighted, the annual NRD contains approximately 18 million discharges, with a weighted sample that estimates around 32 million US hospital discharges. The NRD draws its sample from 31 states, which represents 62.2% of the total US population and 60.8% of all US hospitalizations.
Study population: patients ages 18 years and older admitted to the hospital with COVID-19 were included in our study. The population of interest were those patients who were readmitted to the hospital within 30 days. December 2020 discharges were excluded as 30-day readmission rates could not be calculated for these patients due to a lack of data from January 2021. ICD-10 Clinical Modification (CM) codes were used to identify patient diagnoses.
Readmission risk factors: The study examined various risk factors for readmission, which were categorized into three types. The first type is demographic factors, including age, sex, median household income based on zip code, insurance status, medical comorbidities, and Elixhauser comorbidity score. Obesity was further classified into three classes: class 1, defined as a BMI of 30 to < 35; class 2, defined as a BMI of 35 to < 40; and class 3, defined as a BMI of 40 or higher. The second type of risk factors relates to illness severity, encompassing mechanical ventilatory dependence, vasopressor use, length of stay, discharge to an acute rehabilitation center, and secondary in-hospital medical complications. Lastly, the third type of risk factors pertains to hospital-related aspects, such as geographic location, teaching status, and size.
Study outcomes: The primary outcome of this study was all cause 30-day hospital readmission, which was defined as any non-traumatic admission within 30 days of discharge from the index hospital admission. Secondary outcomes were (a) in-hospital mortality during index admission and (b) readmission diagnoses.
Statistical analysis: All statistical analyses were performed using the STATA 17.0 version (STATA CORP, College Station, Tx). Categorical variables were compared using Chi-square test and linear regression was used for continuous variables. A two-tailed p value of ≤ 0.05 was considered statistically significant. All p-values of ≤ 0.2 on univariate cox regression were used to build the multivariate cox regression model in order to identify independent predictors of readmission.
Results
Demographics and clinical characteristics of readmitted COVID-19 patients
From January 1, 2020 to November 31, 2020, there were 1,120,393 index hospitalizations for COVID-19 reported in the US. Of these, 965,231 patients were discharged from the hospital, and 30,651 went on to be readmitted within 30 days. The mean age of readmitted COVID-19 patients was 63.1 years and 62.6 years for females and males respectively; patients over the age of 70 years also comprised the highest percentage (39.9%) of readmissions. Class 3 obesity was common among readmitted COVID-19 patients, with 37.2% of patients having a BMI greater than 40 kg/m2. More than one-third (34.7%) of readmitted COVID-19 patients had a median income less than $49,999 (3.9 times the 2020 US federal poverty line of $12,760). Medicare was the most common payor (34.7%) during the index hospitalization for readmitted COVID-19 patients. The majority of readmitted COVID-19 patients were initially discharged from urban hospitals (56.9%), teaching hospitals (71.4%), and large hospitals (53.1%). The most prevalent acute COVID-19 complications during the index hospitalization for readmitted COVID-19 patients were acute kidney injury (29.3%) and acute pulmonary embolism (2.6%). Similarly, 16.2% of readmitted COVID-19 patients required mechanical ventilation during their index hospitalizations and 2.7% required vasopressors. The primary dispositions for these patients were home (62.5%) and skilled nursing facilities (20.4%). Table 1 describes the clinical characteristics of readmitted COVID-19 patients.
Table 1.
Characteristics of readmitted COVID-19 patients.
| Age group (years) | Number (n) | Prevalence (%) |
|---|---|---|
| ≥18-29 | 1591 | 5.19% |
| 30-49 | 5238 | 17.09% |
| 50-69 | 11580 | 37.78% |
| ≥70 | 12236 | 39.92% |
| Female sex | 14936 | 48.73% |
| Median income by zip code ($) | ||
| <49,999$ | 10639 | 34.71% |
| 50,000 – 64,999$ | 8714 | 28.43% |
| 65,000 – 85,999$ | 6587 | 21.49% |
| >86,000$ | 4702 | 15.34% |
| Insurance | ||
| Medicare | 16619 | 54.22% |
| Medicaid | 4567 | 14.90% |
| Private | 8392 | 27.38% |
| Uninsured | 1067 | 3.48% |
| Hospital location | ||
| Large metropolitan area | 17440 | 56.9% |
| Small metropolitan area | 9894 | 32.28% |
| Micropolitan area | 2345 | 7.65% |
| Other | 966 | 3.15% |
| Teaching hospital | 21869 | 71.35% |
| Hospital size | ||
| Small | 6378 | 20.81% |
| Medium | 7982 | 26.04% |
| Large | 16288 | 53.14% |
| Medical comorbidity | ||
| History of ischemic stroke | 392 | 1.28% |
| Alcoholic liver cirrhosis | 147 | 0.48% |
| NASH | 325 | 1.06% |
| BMI | ||
| 19-24.9 | 3773 | 12.31% |
| 25-29.9 | 2749 | 8.97% |
| 30-34.9 | 6694 | 21.84% |
| 35-39.9 | 6029 | 19.67% |
| ≥40 | 11396 | 37.18% |
| Acute complication | ||
| STEMI | 113 | 0.37% |
| NSTEMI | 460 | 1.50% |
| Myocarditis | 126 | 0.41% |
| Acute systolic heart failure | 503 | 1.64% |
| Acute diastolic heart failure | 634 | 2.07% |
| Acute pulmonary embolism | 803 | 2.62% |
| Acute kidney injury | 8984 | 29.31% |
| Renal replacement therapy | 825 | 2.69% |
| Mechanical ventilation | 4975 | 16.23% |
| Vasopressor use | 828 | 2.70% |
| Disposition | ||
| Regular | 19157 | 62.50% |
| Skilled nursing facility | 6253 | 20.40% |
| Home health | 4834 | 15.77% |
| Against medical advice | 402 | 1.31% |
Hospital readmission rate and diagnoses
We report a 3.2% 30-day, all-cause readmission rate for all patients initially hospitalized with COVID-19 in the US during 2020. The most common diagnoses at readmission were as follows: sepsis (10.3%), acute kidney injury (3.2%), pneumonia (3.2%), congestive heart failure (2.5%), and pulmonary embolism without cor pulmonale (2.3%). Table 2 outlines the most common diagnoses for 30-day readmissions among patients initially hospitalized with COVID-19.
Table 2.
Most common diagnoses at readmission for COVID-19 patients.
| Diagnoses | Number (n) and Prevalence (%) |
|---|---|
| Sepsis | 3165 (10.32%) |
| Acute Kidney Injury | 996 (3.24%) |
| Pneumonia | 971 (3.16%) |
| Congestive Heart Failure | 1866 (6.07%) |
| Pulmonary Embolism | 694 (2.26%) |
| Acute Hypoxic Respiratory Failure | 980 (3.19%) |
| Urinary Tract Infection | 534 (1.74%) |
Risk factors for readmission among patients initially hospitalized with COVID-19
After multivariate regression, independent demographic predictors of 30-day, all-cause readmissions for patients hospitalized with COVID-19 included younger age, specifically patients ages 18 to 29 years (HR: 1.7, 95% CI: 1.6-1.9) and 30 to 49 years (HR: 1.3, 95% CI, 1.3-1.4), median annual income less than $49,999 (HR: 1.2, 95% CI: 1.1-1.3), Medicaid payor status (HR: 1.6, 95% CI: 1.5-1.7), and index admission to a teaching hospital (HR: 1.08, 95% CI: 1.03-1.14). With respect to disposition, COVID-19 patients who were discharged against medical advice (HR: 1.6, 95% CI: 1.4-1.8) and to skilled nursing facilities (HR: 1.5, 95% CI: 1.5-1.6) both had higher rates of 30-day, all-cause hospital readmission.
Based on the index hospitalization, clinical risk factors associated with 30-day, all-cause hospital readmissions for patients hospitalized with COVID-19 included a higher Elixhauser Comorbidity Index score (greater than 6) at index hospitalization (HR: 2.1, 95% CI: 2.0-2.3) as well as previous diagnoses of ischemic stroke (HR: 1.2, 95% CI: 1.1-1.4), hypertension (HR: 1.2, 95% CI: 1.2-1.3), cancer (HR: 1.5, 95% CI: 1.4-1.7), alcoholic liver cirrhosis (HR: 2.2, 95% CI: 1.9-2.5), and nonalcoholic steatohepatitis (HR 1.5, 95% CI: 1.3-1.7). Notably, diabetes mellitus (HR: 1.0, 95% CI: 0.9-1.0), HIV (HR: 1.2, 95% CI: 0.9-1.5), and collagen vascular disease (HR: 1.0, 95% CI: 0.9-1.1) were not predictive of readmission.
Acute complications during index hospitalization that were associated with higher rates of 30-day, all-cause readmissions for COVID-19 patients included acute kidney injury (HR: 1.1, 95% CI: 1.1-1.2), ST segment elevation myocardial infarction (HR: 1.4, 95% CI: 1.0-1.8), non-ST segment elevation myocardial infarction (HR: 1.2, 95% CI: 1.1-1.4), acute systolic heart failure (HR: 1.4, 95% CI: 1.3-1.5), acute diastolic heart failure (HR: 1.5, 95% CI: 1.3-1.6), utilization of renal replacement therapy (HR: 1.2, 95% CI: 1.1-1.4), and mechanical ventilation (HR: 1.2, 95% CI: 1.2-1.3). The development of myocarditis (HR: 1.1, 95% CI: 0.9-1.5), acute pulmonary embolism (HR: 1.0, 95% CI: 0.9-1.1), and utilization of vasopressors (HR: 1.0, 95% CI: 0.9-1.2) were not predictive of 30-day readmission. Table 3 outlines predictors of 30-day readmission among COVID-19 patients.
Table 3.
Predictors of 30-day readmission for COVID-19 patients.
| Risk Factors | Multivariate Hazard Ratio (95% Confidence Interval) | P-Value |
|---|---|---|
| Age (years) | ||
| ≥70 | Reference | |
| 50-69 | 1.14 (1.09-1.2) | <0.001 |
| 30-49 | 1.34 (1.25-1.44) | <0.001 |
| ≥18-29 | 1.74 (1.58-1.91) | 0.001 |
| Female | 0.96 (0.93-1.004) | 0.09 |
| Median income ($) | ||
| >86,000$ | Reference | |
| 65,000 – 85,999$ | 1.06 (1.01-1.12) | 0.02 |
| 50,000 – 64,999$ | 1.11 (1.04-1.17) | <0.001 |
| <49,999$ | 1.20 (1.14-1.28) | <0.001 |
| Insurance | ||
| Private | Reference | |
| Medicare | 1.53 (1.44-1.63) | <0.001 |
| Medicaid | 1.58 (1.49-1.68) | <0.001 |
| Uninsured | 1.15 (1.01-1.31) | 0.03 |
| Hospital location | ||
| Large metropolitan area | Reference | |
| Small metropolitan area | 1.01 (0.94-1.05) | 0.97 |
| Micropolitan area | 1.01 (0.91-1.12) | 0.77 |
| Not metropolitan or micropolitan area | 1.01 (0.88-1.15) | 0.92 |
| Teaching hospital | 1.08 (1.03-1.14) | 0.004 |
| Hospital size | ||
| Small | Reference | |
| Medium | 1.06 (0.99-1.13) | 0.052 |
| Large | 1.10 (1.03-1.17) | 0.001 |
| Elixhauser comorbidity index | ||
| ≤3 | Reference | |
| >3 to ≤6 | 1.67 (1.58-1.75) | <0.001 |
| >6 | 2.14 (2.01-2.3) | <0.001 |
| Medical comorbidity | ||
| Hypertension | 1.22 (1.17-1.28) | |
| Diabetes | 0.96 (0.93-1.004) | 0.08 |
| Cancer | 1.54 (1.44-1.65) | <0.001 |
| AIDS | 1.15 (0.87-1.51) | 0.30 |
| Obesity | 0.78 (0.74-0.82) | <0.001 |
| Collagen vascular disorders | 1.01 (0.91-1.11) | 0.81 |
| Alcoholic liver cirrhosis | 2.16 (1.86-2.52) | <0.001 |
| NASH1 | 1.48 (1.30-1.68) | <0.001 |
| Ischemic stroke | 1.20 (1.07-1.35) | 0.002 |
| Acute complication | ||
| STEMI2 | 1.36 (1.02-1.8) | 0.03 |
| NSTEMI3 | 1.23 (1.08-1.4) | 0.001 |
| Myocarditis | 1.15 (0.89-1.47) | 0.26 |
| Acute systolic heart failure | 1.39 (1.26-1.53) | <0.001 |
| Acute diastolic heart failure | 1.47 (1.34-1.61) | <0.001 |
| Acute pulmonary embolism | 1.01 (0.91-1.12) | 0.71 |
| Acute kidney injury | 1.11 (1.07-1.16) | <0.001 |
| Renal replacement therapy | 1.23 (1.11-1.38) | <0.001 |
| Mechanical ventilation | 1.22 (1.16-1.3) | <0.001 |
| Vasopressor use | 1.05 (0.92-1.21) | 0.42 |
| Disposition | ||
| Home | Reference | <0.001 |
| Skilled nursing facility | 1.53 (1.45-1.61) | <0.001 |
| Home health | 1.32 (1.24-1.39) | <0.001 |
| Left against medical advice | 1.60 (1.41-1.82) | <0.001 |
Nonalcoholic Steatohepatitis;.
ST elevation myocardial infarction.
In-hospital mortality and palliative care consultation during index hospitalization
We also examined mortality and palliative care consultation during the index hospitalization to account for potential confounding variables in our dataset that may significantly affect 30-day readmission rates. We found that mortality increased with age; only 1.4% of patients ages 18 to 29 years had in-hospital mortality in comparison to a 22.3% in-hospital mortality for patients ages 70 years and older. Of all palliative care consultations during the index hospitalization, 73.7% were consultations for COVID-19 patients ages 70 years and older in comparison to 0.4% of consultations for COVID-19 patients ages 18 to 29 years. Table 4 illustrates a detailed analysis of in-hospital mortality and palliative care consultation during index hospitalization.
Table 4.
Detailed analysis of in-hospital mortality and palliative care consultation.
| (1) In-hospital mortality and palliative care consultation for each age group of COVID-19 patients during index hospitalization. | ||||
|---|---|---|---|---|
| Variable | Prevalence (%) | P-value | ||
| In-hospital mortality | <0.001 | |||
| ≥18-29 | 0.51% | |||
| 30-49 | 4.59% | |||
| 50-69 | 30.24% | |||
| ≥70 | 64.65% | |||
| Palliative care consultation | <0.001 | |||
| ≥18-29 | 0.38% | |||
| 30-49 | 2.96% | |||
| 50-69 | 22.94% | |||
| ≥70 | 73.72% | |||
| (2) In-hospital mortality analysis for each age group based on total number of admissions. | ||||
|---|---|---|---|---|
| In-Hospital Mortality | Number of Admissions | Number of Deaths (n) | Prevalence (%) | P-Value |
| ≥18-29 | 58,148 | 785 | 1.35% | <0.001 |
| 30-49 | 191,486 | 7085 | 3.70% | |
| 50-69 | 405,136 | 46646 | 11.02% | |
| ≥70 | 447,262 | 99650 | 22.28% | |
Discussion
To our knowledge, this is the largest study examining 30-day, all-cause readmissions for patients initially hospitalized with COVID-19.The most important findings of our study were: 1) the 30-day, all-cause readmission rate for patients initially hospitalized with COVID-19 in the US during 2020 was 3.2%; 2) the most common diagnoses at readmission were sepsis, acute kidney injury, pneumonia, congestive heart failure, and acute pulmonary embolism; 3) COVID-19 patients who were younger (18-29 years), had lower median incomes, held Medicaid payor status, had higher Elixhauser Comorbidity scores, or who were admitted to teaching hospitals or large hospitals all had higher 30-day readmission rates; 4) medical comorbidities including ischemic stroke, hypertension, history of cancer, alcoholic cirrhosis, non-alcoholic steatohepatitis as well as complications during index hospitalization including acute coronary syndrome, congestive heart failure, acute kidney injury, mechanical ventilation, and renal replacement therapy were all associated with 30 day readmission for COVID-19 patients; 5) discharge to skilled nursing facility as well as discharges against medical advice strongly correlated with 30 day readmission.
In contrast to prior literature, our study utilized the largest real-world sample of hospitalized COVID-19 patients, incorporating more than one million patients, to determine a 3.2% 30-day, all-cause readmission rate for patients initially hospitalized with COVID-19. In the existing literature, the 30-day readmission rate for COVID-19 hospitalizations has been reported to be 4.5% to 19.9%.7., 8., 9., 10., 11. Our readmission rate was significantly lower than previously reported rates and is likely reflective of our larger sample size, which captures more patients hospitalized with COVID-19 thereby increasing the size of our denominator. A meta-analysis by Akbari et al. reported a pooled 30-day readmission rate of 9.3% with a sample of 68,236 index hospitalizations.6
The most common diagnoses at readmission, including sepsis, acute kidney injury, pneumonia, congestive heart failure, and pulmonary embolism, all represent known complications of COVID-19, are not unexpected, and are consistent with prior literature.5 , 6 Martinez et al. found that the most common readmission diagnoses were pneumonia (54%), unspecified bacterial infection (13%), venous thromboembolism (5%), and congestive heart failure (5%).12 These results can likely be explained by COVID-19 induced systemic inflammatory response with multiorgan dysfunction and prothrombotic state.13., 14., 15. Moreover, COVID-19 infection as well as glucocorticoid treatment results in impaired pulmonary immune response and may account for the increased incidence of superimposed bacterial pneumonia.16 On a similar note, we discovered that urinary tract infection was a readmission diagnosis for 1.7% of patients with COVID-19, which may be explained by foley catheter placement in critically ill patients during index hospitalization.17 In summary, the in-hospital complications of COVID-19 likely underly the commonly observed readmission diagnoses.
One of the less-expected predictors of 30-day, all-cause readmissions for COVID-19 patients was younger age, as patients 18 to 29 years of age were at higher risk for readmission when compared to patients over the age of 70 years. This is departure from prior literature and is likely reflective of the lower mortality rate among younger patients.18 , 19 In simpler terms, patients who were at highest risk of re-admission may have died during the index hospitalization at higher rates thus decreasing the number of readmissions – a type of attrition bias. While attrition remains the most likely explanation, adherence to masking mandates among younger patients remains a concern and a possible driver of readmission.20 A third contributing factor may be that palliative care involvement among elderly COVID-19 patients may have altered the ultimate disposition and goals of care of these patients thereby reducing hospital readmissions.
A higher Elixhauser Comorbidity Index was associated with higher risk for readmission; alcoholic liver cirrhosis, non-alcoholic steatohepatitis, and congestive heart failure were particularly strong predictors of 30-day readmission.18 , 21 The association between increased COVID-19 severity and cirrhosis has been well-characterized.22., 23., 24. Similarly, congestive heart failure has been associated with prolonged hospitalization, increased mechanical ventilatory dependence, in-hospital mortality, and higher readmission rates for hospitalized COVID-19 patients.18 , 25
Interestingly, we found no relationship between diabetes mellitus and risk of readmission among COVID-19 patients, which is also a significant departure from previous literature.7 , 26 , 27 One potential explanation is that patients with poorly controlled diabetes may have had higher mortality during the index hospitalization and thus may not be represented in the readmission cohort.28 We also did not find an association between increased risk of readmission among COVID-19 patients with a history of HIV or collagen vascular disease, a novel observation that has not been reported in the literature to date.
Similarly, we found that obesity was associated with a decreased risk of 30-day readmissions. We suspect that this may be due to higher acuity and mortality of COVID-19 patients with comorbid obesity during the index hospitalization, which may have resulted in a selection bias favoring healthier individuals who were more likely to survive and be discharged home. It is well-known that obesity increases the risks of long COVID.29 Thus, it is also plausible that obese patients had lingering symptoms of COVID-19 (chronic hypoxia or severe deconditioning) requiring discharge to higher levels of care, which may be protective with respect to hospital readmissions. There is no evidence to suggest that obesity itself is protective with respect to hospital readmissions.
With respect to acute in-hospital complications, acute kidney injury during index hospitalization was associated with increased rate of readmission. Renal dysfunction in COVID-19 may be caused by direct renal tubular cytotoxicity, thrombo-embolic disease, free radical injury, and hypovolemia.30 Yeo et al. reported that acute kidney dysfunction during index hospitalization significantly increases the odds of readmission (OR 2.41, CI 1.23-4.74) at 30 days, which is consistent with our study.8
Acute cardiac complications during index hospitalization were also associated with an increased risk for readmission among COVID-19 patients which was likely driven by the risk of post-discharge complications. For instance, COVID-19 patients with STEMI are more likely to develop congestive heart failure and in-stent thrombosis31 , 32 while COVID-19 patients with NSTEMI typically have prolonged hospitalizations with complications – all factors that likely contribute to readmission.33
We report that vasopressor use during index hospitalization did not predict 30-day, all-cause readmission for patients hospitalized with COVID-19; this finding should be interpreted with caution as COVID-19 infection can result in variety of shock states. Also, there is significant in-hospital mortality associated with circulatory shock and vasopressor dependence among COVID-19 patients.34 , 35
Lastly, we examined the association between patient disposition and the risk of 30-day, all-cause readmission. As expected, patients who required skilled nursing placement had a higher risk of readmission. We believe that COVID-19 patients discharged to skilled nursing facilities are more likely to be frail and to have multiple chronic conditions, which increases the probability of readmissions. Our sub-group analysis lends support to this theory as we found that 21.5% of COVID-19 patients who were discharged to skilled nursing facilities after their index hospitalizations had Elixhauser Comorbidity Indices of greater than 6 in comparison to only 4.8% of COVID-19 patients [with Elixhauser Comorbidity Indices of greater than 6] who were discharged to home after their index hospitalizations (Table 5 ). COVID-19 patients who left against medical advice were also at increased risk of 30-day readmission. Weaver et al. reported a higher likelihood of 30-day readmission in COVID-19 patients who left against medical advice (OR: 1.58, CI: 1.20-2.09). Thus, we believe it is paramount to prevent unplanned discharges and to address the reasons underlying these irregular discharges in order to prevent adverse outcomes.
Table 5.
Distribution of Elixhauser comorbidity indices for COVID-19 patients discharged to home versus skilled nursing facilities after index hospitalization.
| Elixhauser Comorbidity Index | Home | Skilled Nursing Facility | P Value |
|---|---|---|---|
| ≤3 | 65.56% | 27.51% | <0.001 |
| >3 to ≤6 | 29.69% | 50.98% | <0.001 |
| >6 | 4.75% | 21.51% | <0.001 |
Our analysis relied on the NRD which has inherent limitations. The most conspicuous limitation is potential errors in coding the primary diagnosis and the under-reporting of secondary diagnoses. Furthermore, the NRD does not contain data on race or ethnicity, which limits our ability to draw conclusions about the role of these variables in readmission rates. Additionally, the absence of results from diagnostic tests in the database precludes us from confirming diagnoses and scrutinizing more nuanced outcomes that could provide deeper insights into the predictors of readmissions.
Another limitation is the timeframe of our data collection, which spanned from January 2020 to November 2020. During this period, treatment options for COVID-19 were limited and vaccines were not available. The exclusion of data from the month of December due to the lack of January 2021 readmission data may have impacted our results. While this was a necessary decision to ensure the accuracy of 30-day readmission rates, it does introduce potential seasonal bias and limits the completeness of our dataset. Lastly, the NRD is designed to focus on in-hospital events and readmissions, which restricts our ability to evaluate prospective outcomes such as 30-day all-cause mortality.
Conclusion
Hospital readmissions are core indicators of the quality of health care provision. Our study characterized the 30-day, all-cause hospital readmission rate for COVID-19 patients in the United States during the early pandemic, which we found to be 3.2%. We also described the most common diagnoses at readmission, which included sepsis, acute kidney injury, and pneumonia. The most prominent chronic comorbidity predictors of readmission for COVID-19 patients were alcoholic liver cirrhosis and congestive heart failure. Moreover, we found that younger patients and patients from economically disadvantaged backgrounds were at higher risk of 30-day readmission. Acute complications during index hospitalization including acute coronary syndrome, congestive heart failure, acute kidney injury, mechanical ventilation, and renal replacement therapy were associated with increased rates of 30-day readmission for COVID-19 patients.
The data presented set the stage for several important avenues of future research. Firstly, we believe it is worth evaluating race and ethnicity as predictors of readmission. It is also essential to assess changes in 30-day readmission rates with vaccine uptake. Additionally, a comparative study juxtaposing readmission risks for COVID-19 populations with non-COVID populations may provide a more comprehensive understanding of the unique factors influencing COVID-19 readmissions. Further investigation into potential intervention strategies, such as remote patient monitoring and telehealth, as well as different discharge planning strategies and post-discharge support for patients from economically disadvantaged backgrounds are important next steps to decrease COVID-19 related hospital readmissions.
In conclusion, we advise clinicians to promptly recognize COVID-19 patients at high-risk of hospital readmission and to subsequently optimize their underlying comorbidities, institute early discharge planning, and allocate resources to underprivileged patients to decrease the risk of 30-day readmissions.
Ethical approval statement
This research did not contain any studies involving animal participants, nor did it utilize any protected health information of patients or take place on any private areas. No specific permissions were required for this study.
CRediT authorship contribution statement
Taimur Sohail Muzammil: Writing – original draft. Karthik Gangu: Conceptualization, Validation, Formal analysis, Data curation. Adeel Nasrullah: Writing – original draft. Harris Majeed: Writing – review & editing. Prabal Chourasia: Writing – review & editing, Visualization. Aneish Bobba: Methodology, Software, Data curation. Rahul Shekhar: Conceptualization. Christopher Bartlett: Supervision, Writing – review & editing. Abu Baker Sheikh: Conceptualization, Methodology, Resources, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Footnotes
The authors have no disclosures or conflict of interests to report. All authors participated in the research and preparation of the manuscript. This manuscript did not receive any funding support.
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