BACKGROUND

Multiple sclerosis (MS) is an inflammatory central nervous system demyelinating disorder resulting in neurologic decline. Patients predominantly have a relapsing and remitting disease course requiring multiple hospitalizations and, occasionally, rehospitalizations. Hospitalization readmission rates are important metrics that have direct financial implications for hospitals and serve as an indicator of disease burden on patients and society. We sought to analyze hospital readmissions of patients with MS and identify the subsequent predictive characteristics/comorbidities for readmission.

METHODS

All hospital admissions due to MS were queried using the 2017 Nationwide Readmissions Database. All patients with nonelective rehospitalization within 30 days of discharge were examined.

RESULTS

The 30-day readmission rate for MS is 10.6% (range, 10.4%–10.8%). Female sex has a protective role in readmission rates, and age has no effect. Comorbidities, including heart failure, acute kidney injury, chronic obstructive pulmonary disease, chronic kidney disease, respiratory failure, substance abuse, diabetes, hypertension, peripheral artery disease, liver failure, anemia, coagulation disorders, cancer, depression, and infections, are predictive of readmissions, whereas sleep apnea is protective. No effect is seen with neurologic blindness, plasma exchange, or intravenous immunoglobulin treatment.

CONCLUSIONS

Several medical comorbidities are predictive of hospital readmission of patients with MS. Most rehospitalizations are due to infectious and neurologic etiologies; thus, targeted interventions may lead to lower readmission rates.

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system.1  In most patients with MS, the disease follows a relapsing and remitting course resulting in neurologic disability. The prevalence of MS has increased in both developed and developing countries in part due to improvements in diagnosis and the development of novel disease-modifying agents.2  Multiple sclerosis is the leading cause of nontraumatic neurologic disability in younger adults.3  Medical costs of MS rank second only to those of congestive heart failure,4  and hospitalizations remain a significant portion of these costs: patients with MS are 3.5 times more likely to be hospitalized relative to the general population.5  With the growing prevalence of MS, it is imperative to decrease unnecessary readmissions to alleviate the health care system burden.

Hospital readmissions are also associated with negative patient outcomes, including hospital-acquired infections, deconditioning, and negative psychological effects.6  Therefore, readmission rates are an important quality measure, and understanding these rates will lead to identification of preventable adverse outcomes. The 30-day readmission rate is widely used as the indicator for hospital performance associated with financial implications.7  The identification of patients at high risk for readmission is the initial step to decrease readmissions and improve overall disease control.

Many studies have been published on hospital readmission rates,8,9  but there is minimal readmission data regarding MS. One current study is analyzing the trends in a European cohort.10  The only US study, published by Patel et al,11  analyzed MS readmissions stratified by age and sex. To date, the present study is the largest to analyze MS readmission rates in the general US population and the first study of MS readmission rates to use International Classification of Diseases, Tenth Revision (ICD-10) codes. Compared with previous versions, the ICD-10 provides more specific diagnosis and procedural codes and minimizes misclassification of patients and procedures.12  The purpose of this study was to identify patients with MS at high risk for readmission within 30 days of their initial discharge.

Nationwide Readmissions Database

This study examined MS hospital readmissions using 2017 discharge data from the Nationwide Readmissions Database (NRD) of the Healthcare Cost and Utilization Project (HCUP) developed by the Agency for Healthcare Research and Quality (AHRQ). The data provided a national representative sample of all-age, all-payer discharges from US nonfederal hospitals across 28 states.13  This was 18 million discharges (weighted estimate ~36 million discharges). All hospitalizations and rehospitalizations were determined using a deidentified unique patient linkage number. The data used represented 58.2% of all 2017 US hospitalizations.

Patient Determination

The MS definition was determined by codes from Clinical Classifications Software Refined (CCSR) for the ICD-10 (TABLE S1, which is published in the online version of this article at IJMSC.org). The primary outcome was the first unplanned readmission, defined as a nonelective rehospitalization within 30 days of discharge from the patient’s index hospitalization. If a patient had multiple such readmissions, only the first encounter was used for analysis. Exclusion criteria included admission in December, death during the initial hospitalization, and missing discharge data. The study was approved for exempt status by the University of South Alabama’s institutional review board. Similar methods have been used in previous studies.14,15 

Clinical Variables

The ICD-10 codes were used to define pertinent clinical variables. Data on teaching status, metropolitan/non-metropolitan location, control/ownership, and bed size were collected for each hospital (TABLE S2). A cost analysis covered index admission length of stay, index admission cost, discharge destination, primary expected payer, and quartile of median household income (Table S2). We multiplied the hospital charges with the AHRQ’s all-payer cost-to-charge ratios to determine total cost. Severity of MS at the index hospitalization was measured by risk of mortality and loss of function subclasses (Table S2). Baseline patient comorbidities and procedures during the index hospitalization were determined (Table S2). The first CCSR diagnosis code determined the cause of readmission (TABLE 1). All CCSR codes used in this study are presented (Table S1).

TABLE 1.

Etiologies of 30-Day Readmissions in Patients With Multiple Sclerosis

Etiologies of 30-Day Readmissions in Patients With Multiple Sclerosis
Etiologies of 30-Day Readmissions in Patients With Multiple Sclerosis

Statistical Analysis

Statistical analysis was performed using IBM SPSS Statistics for Windows, version 1.0.0.118 (IBM Corp). To reduce bias, the data were weighted as recommended by the NRD. Two-sided t tests with a significance level of .05 were used for statistical analysis. Statistical differences in participants’ baseline characteristics were determined using the Pearson χ2 test for categorical variables and the Mann-Whitney U test for continuous variables; the reference group was participants with no readmissions. Multivariable logistic regression analysis was performed to determine the predictors of readmission, with adjustments for age group, sex, hospital status, and comorbidities (TABLE 2).

TABLE 2.

Multivariable Logistic Regression Analysis for Predicting Readmission in Patients With Multiple Sclerosis

Multivariable Logistic Regression Analysis for Predicting Readmission in Patients With Multiple Sclerosis
Multivariable Logistic Regression Analysis for Predicting Readmission in Patients With Multiple Sclerosis

Readmission Rate for MS

Of 95,537 weighted admissions in the 2017 NRD with MS as the primary diagnosis, 10,102 patients were readmitted within 30 days of discharge. Patients with a primary diagnosis of MS were defined as those admitted for MS flare-ups and those admitted for MS workups and subsequently diagnosed as having MS. The 30-day readmission rate for the general population of patients with MS was 10.6% (range, 10.4%–10.8%) (FIGURE S1). The readmission frequency is highest in the first days after discharge, with decreasing frequency over the course of 30 days. Half of all readmissions occurred within 12 days of discharge from the index hospitalization.

Baseline Characteristics

The baseline characteristics for the index hospitalization are shown in Table S2. Most of the patients in the study were women aged 46 to 67 years, consistent with the epidemiology of MS. Readmission was less frequent for women than for men (10.2% vs 11.7%; P < .001). Patients with a weekend admission (P = .002), a longer index hospitalization (P < .001), or a high-cost hospital stay (P < .001) were statistically significantly more likely to be readmitted. Patients with private insurance were less likely to be readmitted than were patients with Medicare. Patients in the top or bottom quartile of household income were more likely to be readmitted than those in the middle 2 quartiles. Patients with a higher risk of mortality and a loss of function were more likely to be readmitted in a severity-dependent manner. Interestingly, patients discharged to their homes with no services were less likely to be readmitted than those discharged with home health care or those transferred to a short- or long-term care facility.

Patient Comorbidities and Procedures During Index Hospitalization

Table S2 shows the effects of patient comorbidities and intravenous immunoglobulin (IVIg) and plasma exchange (PLEX) treatments on readmission rates. Higher readmission rates were seen in patients with the comorbidities analyzed except for alcohol abuse, hypertension, and obesity. Statistically significantly lower readmission rates were seen with neurologic blindness. There was no significant difference in readmission rates after PLEX or IVIg treatment.

Etiology of Readmissions

Readmission diagnoses are shown in Table 1. The 3 most common causes of readmission were infectious complications (24.8%), neurologic complications (14.5%), and respiratory complications (6.9%), which correspond to 46.2% of all readmissions. Septicemia was the most frequent diagnosis for readmission (15.4%). The most frequent neurologic diagnosis of readmission was MS (9.0%). Other infrequent causes of readmission include complication of the genitourinary device (5.7%), urinary tract infection (4.1%), acute respiratory failure (2.2%), acute renal failure (2.1%), heart failure (2.1%), and pneumonia (2.0%).

Predictors of Readmissions

Multivariable logistic regression analysis was preformed to identify the characteristics of patients at high risk for 30-day readmission (Table 2). Age was not predictive of readmission; all age groups studied had similar readmission rates. Relative to the Medicaid population, patients with private insurance (odds ratio [OR] = 0.679; P < .001), self-pay patients (OR = 0.776; P = .008), no-charge patients (OR = 0.537; P = .022), and other (insurance options, eg, workers’ compensation and other government programs) (OR = 0.651; P < .001) were protective against readmission (Table 2). Nonmetropolitan hospitals had decreased odds of readmission relative to nonteaching metropolitan hospitals (OR = 0.811, P < .001) (Table 2). There was no significance between metropolitan teaching hospitals and metropolitan non-teaching hospitals (OR = 0.999, P = .970). Several comorbidities, including heart failure (P < .001), acute kidney injury (P < .001), chronic obstructive pulmonary disease (P < .001), chronic kidney disease (P < .001), respiratory failure (P = .003), substance abuse (P < .001), diabetes (P < .001), hypertension (P = .010), peripheral artery disease (P < .001), liver failure (P < 0.001), anemia (P < .001), coagulation disorders (P < .001), cancer (P < .001), depression (P = .003), and infections (P < .001), are predictive of readmissions, whereas sleep apnea (P = .038) is protective. Neither PLEX (P = .596) nor IVIg (P = .241) treatment during the index hospitalization affected the odds of readmission.

This retrospective study analyzed 30-day readmission rates in patients hospitalized with a primary diagnosis of MS. Approximately 1 in 10 of these patients (10.6%) had a readmission within 30 days of discharge. This rate is relatively better than the overall readmission rates in the general population (13.9%) and in patients with nervous system diseases (14.0%).16  This finding is likely multifactorial and may be due to the broadening treatment landscape for MS, including more efficacious disease-modifying therapies. The readmission rate in this study is comparable with those in the previous European10  and US11  studies.

Relapsing-remitting MS predominantly affects women, with a prevalence ratio of approximately 3:1.17  A similar sex ratio was seen in MS index hospitalizations, with women being 73.7% of all hospitalizations. However, this study found that female sex protected against readmission (Table 2). Previous studies have found that MS disease activity is more aggressive in men, specifically with earlier disability accumulation. This may be in part due to delays in seeking care, hormonal differences, or the increased percentage of men with primary progressive MS.18  Neither of the previous studies found a statistically significant sex difference for readmission.10,11  It is possible that the sex difference was discovered only because of the larger sample size in this study. In the general population, age is a factor in readmission rates, with readmission rates increasing as age increases.16  However, although readmitted patients tended to be older (Table S2), age was not predictive of readmissions after accounting for comorbidities (Table 2). This finding suggests that the comorbidities associated with older age play a larger role than age itself, as supported by the findings in earlier studies.10,11  Patients with Medicaid and patients with Medicare had similar odds of readmission, and all other types of insurance were protective (Table 2). A similar trend was also identified in a previous study11  and is seen in the general population, as private insurance is obtained through employment and employed people have a higher baseline functional status.16  Only a small proportion (8.6%) of patients in this study were admitted to nonmetropolitan hospitals, perhaps because patients with severe MS relapses go to or are transferred to a metropolitan hospital with an affiliated MS specialist. Therefore, the disease severity is likely higher at metropolitan hospitals vs nonmetropolitan hospitals, accounting for the increased readmissions. This difference was not observed in the earlier study, possibly due to the smaller sample size.11 

Several studies have shown an increase in MS disease burden and hospitalizations over time,19,20  thought to be due to the progressive nature of the disease leading to higher disease burden in older patients. However, our data showed that, accounting for comorbidities, age is not a factor in hospital readmissions for patients with MS. Therefore, the increased comorbidities associated with aging are the likely cause of hospital readmissions rather than the progressive nature of MS. This is also shown by the stepwise increase in hospitalizations, the loss of function, and likelihood of dying in the All Patient Refined Diagnosis Related Groups (Table S2). These scales represent disease severity and preexisting conditions. Therefore, the higher the number of comorbidities, the higher the risk of loss of function or the likelihood of dying. Other studies reached the same conclusion.21 

The comorbidities that increased the odds of readmission are shown in Table 2. Many of these comorbidities have been previously identified as factors in MS rehospitalization, including heart failure,22  infections,23  and psychiatric conditions.21  Several comorbidities, including hypertension, diabetes, and peripheral artery disease, lead to vascular damage due to oxidative radicals. The increased oxidative stress in the brain parenchyma has been shown to be a mediator of demyelination.24  However, in another study, hyperlipidemia, also a contributor to vessel damage and plaque buildup, was not shown to be predictive of readmission, although hypertension, diabetes, and obstructive lung disease were shown to worsen functional status in MS.25  The reproducibility of this effect may suggest that the oxidative damage of hyperlipidemia has less of an impact in MS than do diabetes and hypertension. In our study, optic neuritis did not affect readmission rates, likely as it is commonly seen in younger females with milder forms of the disease. In a previous study, optic neuritis was found to be protective of readmission.11  An unexpected finding was smoking’s lack of effect on readmission. Smoking has been shown to be a risk factor for MS and is associated with a poorer prognosis.26  However, the present data showed that smoking did not increase readmission odds. Sleep apnea was the only comorbidity protective against readmission in the present study. Further research is needed to determine whether this is an incidental finding or whether sleep apnea has a protective effect in MS.

Two treatment options for acute MS relapses are PLEX and IVIg, which circulate antibodies to decrease the host immune response. However, these treatments are generally reserved for persistent flare-ups that do not improve with intravenous corticosteroid use.27  Previous studies have suggested better outcomes with PLEX28  than with IVIg.29  Treatment with neither PLEX nor IVIg was seen to affect readmission rates (Table 2). Because IVIg and PLEX treatments are reserved for resistant relapses, these patients may already have a higher likelihood of readmission. These second-line treatments may decrease readmissions such that no measurable effect can be identified on statistical analysis. Furthermore, few patients (approximately 0.5%) received PLEX or IVIg during their index hospitalization. These results are in accordance with the previous study in which no association was seen between PLEX/IVIg treatment and readmission in most of the subgroups analyzed, and in the few subgroups that showed statistical significance, PLEX/IVIg treatment increased readmission rates.11 

Analysis of readmission etiologies showed that the most common were infections (24.8%), predominantly nonspecific septicemia (15.4%) (Table 1). Patients with MS are at increased risk for infections: diaphragmatic weakness can lead to pneumonia, and neurogenic bladder can lead to urinary tract infections. Immunosuppressive drug use also contributes to the overall risk of infection. The second most common readmission cause was neurologic diagnoses (14.5%). This contrast with the previous analysis, which showed that half of all readmissions were due to neurologic complications.11  This difference may be explained by the improved treatment modalities developed for MS during the past several years. Other etiologies of readmissions are respiratory complications (6.9%), surgical device complications (6.7%), and cardiac complications (5.3%). Future research could use artificial intelligence and machine learning to develop algorithms to predict the patients at high risk for rehospitalization, and this could help physicians improve patient care and health care outcomes.

This study successfully used the 2017 NRD to identify characteristics of patients with MS with an increased likelihood of hospital readmission within 30 days and the specific etiologies of those readmissions. Many other studies have been conducted using the same methods.8,9  This study’s limitations follow. The NRD stores ICD-10 codes for every encounter. Patients admitted through the emergency department and patients admitted for observation are not included in the database. Another constraint is the elements captured by the database. Race and ethnicity data are not available, limiting insight. Geographic information also is not captured by the database,30  although MS has a strong geographic distribution pattern. Importantly, no information is provided about a patient’s current medications or discharge medications, precluding the ability to distinguish patients who are taking disease-modifying agents that help control the disease. These factors likely play a role in readmission rates.

Misclassification bias is also present in this study. Although ICD-10 coding is designed to improve accuracy and decrease coding errors, inaccuracies are likely still present. These are expected drawbacks when using a large database containing approximately 60% of inpatient admissions in the United States. Finally, because this is a retrospective, observational study, no statement about causation can be made from the data. The strength of the study as the largest readmission analysis of patients with MS to date remains.

During the past several decades, significant progress has been made in the management of MS. The disease burden, both financial and patient quality of life, has decreased due to a better understanding of the disease and increased treatment options. Hospital readmissions are a significant portion of the remaining disease burden of MS. This study identified several characteristics of patients at high risk for readmission. The most common causes of readmission are sepsis, MS, and respiratory failure. Future efforts to equip these patients with additional resources for better outpatient transitions might lead to decreased readmission rates, lessening the disease burden on patients with MS and on society at large.

PRACTICE POINTS
  • » Patients hospitalized for multiple sclerosis (MS) are readmitted to the hospital within 30 days of discharge at a rate of 10.6% (range, 10.4%–10.8%).

  • » The 3 most frequent etiologies of readmission are sepsis, MS, and acute respiratory failure. By targeting interventions at these etiologies, physicians can decrease the overall hospital readmission rate for patients with MS.

  • » Several comorbidities and patient characteristics are identified as potential predictors that can help distinguish patients who may require more resources to prevent unnecessary readmissions.

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FINANCIAL DISCLOSURES: Dr Kilgo is on the speakers’ bureaus of Biogen and Genentech; serves as a consultant for Genentech, Biogen, and Alexion Pharmaceuticals; and is a principal investigator for industry-sponsored clinical trials in association with Genentech; EMD Serono, Inc; and Bristol Meyers Squibb. The other authors have disclosed no relevant financial relationships.

FUNDING/SUPPORT: None.

Author notes

*AP and AA contributed equally to this work.

Note: Supplementary material for this article is available at IJMSC.org.

Supplementary Material