Abstract
Background:
Necrotizing soft tissue infections (NSTIs) are rapidly progressive infections with high mortality and complication rates. The incidence of NSTIs has been increasing steadily whereas mortality has decreased; survivors have a high risk of re-hospitalization. We hypothesized that re-admission to the index hospital where the first admission occurred would be associated with better clinical outcomes compared with re-admission to a non-index hospital.
Patients and Methods:
We identified patients from the 2017 Nationwide Readmissions Database with an index admission for NSTIs and examined all-cause re-admissions within 90 days of discharge. We noted whether re-admission occurred at the index or a non-index hospital. Survey-weighted logistic regression identified factors associated with death at the first re-admission and re-admission to index hospital. We also compared patient outcomes between patients admitted to index versus non-index hospitals.
Results:
We identified 27,051 NSTI survivors, of whom 6,954 (25.7%) had an unplanned re-admission within 90 days. A large proportion of re-admission occurred at non-index hospitals (28.3%; n = 1,966). Factors associated with non-index re-admission included prolonged index length of stay, discharge to short-term hospital, and leaving against medical advice. Patients re-admitted to index hospitals had a lower mortality rate (4.7% vs. 6.7%; p = 0.003), lower admission costs (in $1000; 45 [23–88] vs. 50 [24–104]; p = 0.004) and higher discharge rate to home (55.7% vs. 48.6%; p < 0.001).
Conclusions:
More than one-quarter of re-admissions among NSTI survivors were to non-index hospitals. Continuity of care is important because re-admission to the index hospital was associated with better patient outcomes.
Keywords: care fragmentation, necrotizing soft tissue infection, readmissions
Necrotizing soft tissue infections (NSTIs) encompass a broad range of conditions that affect multiple anatomic locations with varying severity and depth of tissue involvement.1 They are traditionally associated with high mortality, amputation, and complication rates. The existing literature has primarily focused on improving short-term survival. Recent population-based studies have demonstrated that the mortality of NSTIs is down-trending both nationally and globally whereas incidence is increasing steadily.2,3 Mortality has decreased from near 30% in the 1990s to 9%–12% in the 2000s.4,5 This has been attributed largely to advancements in early recognition and intervention of NSTIs, leading to reduced mortality on index admission.6 However, with the increasing number of NSTI survivors, there is a need to characterize the long-term outcomes of NSTIs, their lasting effects on patients, and the associated impact on the healthcare system.
Survivors of NSTI have often endured extensive or serial surgical debridements, leading to challenges for recovery with prolonged periods for wound management and rehabilitation. A single-center study followed 345 survivors and showed that they had a higher risk of premature death secondary to infections with a median survival of 10 years.7 Two recent retrospective observational cohort studies using national databases demonstrated that the risk for 90-day re-admissions ranged from 24% to 30.3%; most re-admissions were unplanned and the median cost of re-admission ranged from $10,543 to $13,590 with an estimated annual financial burden of $1.4 billion dollars.4,8 Given the re-admission burden caused by NSTIs, patients are at high risk of care fragmentation, defined here as follow-up or re-admissions occurring at institutions other than the hospital at which the index hospitalization occurred. Care fragmentation is a known risk factor for increased mortality and complications at re-admissions for abdominal and other complex surgical patients. The importance of continuity of care should be emphasized in survivors of NSTI given their complex disease and care plans as well as their increased risk of long-term morbidity and mortality.
The objective of our study was to evaluate the impact of re-admissions to a non-index hospital and its associated risk factors. We hypothesized that re-admission to the same hospital where the index admission occurred (the index hospital) would be associated with better clinical outcomes compared with re-admission to a hospital different from the index admission (the non-index hospital).
Patients and Methods
We utilized the National Readmissions Database (NRD) from the calendar year 2017, which is a database developed for the Healthcare Cost and Utilization Project (HCUP) by the Agency for Healthcare Research and Quality.9 Data for the NRD are derived from state databases and include approximately 18 million discharges across 28 states per year; it is designed for application of survey weights that allow national estimations approximating 35 million discharges. These data are de-identified and are publicly available. Data are linked across admissions using a unique identifier, allowing for individuals to be followed across multiple admissions. Limitations of the NRD include the lack of Current Procedural Terminology (CPT) codes, as well as the inability to identify patient deaths that occur outside of the hospital.
We identified adult patients aged 18 years and older from the NRD with an admission for NSTIs by International Classification of Diseases, 10th Revision (ICD-10) code in the first nine months of the calendar year. The NSTI codes utilized for identification included ICD-10 with A48.0 (gas gangrene), M72.6 (necrotizing fasciitis), I96 (gangrene, not elsewhere classified), and N49.3 (Fournier gangrene). Patients who died during the index admission were excluded from further analysis.
We identified patient demographic factors (age, gender) and comorbidities using ICD-10 codes from the patient's index admission for NSTIs. Based on prior literature, we identified patients with index hospitalization length of stay of 14 days or longer as a possible risk factor for poor outcomes. Zip code income quartile as well as location were extracted. Patient location was categorized as urban (county in metro area of 250,000 or more) or non-urban. Comorbidities and diagnostic codes were extracted using standard Elixhauser comorbidity categories,10 using the Elixhauser comorbidity macro adapted for STATA based on the methodology by Quan et al.11,12 Index hospitalization mortality and discharge destination were also extracted. Ninety-day re-admission was defined as any re-admission listed as non-elective occurring at less than 90 days from the discharge date of the index hospitalization. Because some patients had more than one re-admission, we primarily analyzed a patient's first re-admission within a 90-day period.
Our primary outcome of interest was death that occurred during 90-day re-admission. Our secondary outcome of interest was re-admission to the index hospital. We also report the outcomes of re-admission including length of stay, discharge destination from the re-admission, re-admission costs, and mortality, comparing individuals admitted to the index hospital versus a different hospital.
We performed two-group comparisons to examine differences between patients who were and were not re-admitted within 90 days. We then examined factors associated with death during re-admission. We then utilized survey-weighted logistic regression to identify factors associated with mortality at re-admission. Factors examined in the bivariate and regression analysis included age, gender, index hospitalization discharge destination, re-admission to the same hospital, payer, zip code median income, whether the patient was a resident of the state in which the index hospitalization occurred, hospital location, and number of Elixhauser comorbidities. We then performed two-group comparisons and logistic regressions to examine factors associated with re-admission to the index hospital.
Categorical data are presented as n (%) and continuous data are presented as median (interquartile range). Two-group comparisons were performed using a χ2 analysis for categorical variables, and a Wilcoxon rank-sum test for continuous variables. All regressions were performed accounting for the weighting of the survey design. All analysis was performed using STATA MP 16.1 (StataCorp, College Station, TX). These data are available publicly and use of these data were deemed exempt by our Institutional Review Board.
Results
We identified 29,401 individuals who were admitted with NSTIs in the first nine months of 2017, of whom 27,038 were known to survive the index hospitalization; there was an 8% mortality rate at the index admission. Of patients who were discharged alive, 8,020 patients (27.3%) were re-admitted within 90 days of discharge and of these, 6,954 (23.7% of the entire population, 86.7% of re-admissions) were non-elective. Characteristics of patients who had a non-elective re-admission within 90 days are presented in Supplementary Table S1. Patients who were more likely to be re-admitted were more likely to be female, have more comorbidities, have a longer index hospitalization, and live in urban counties. Re-admitted patients were less likely to have private insurance and were less likely to have been discharged to home. The most common reasons for re-admission, using the first three characters of primary diagnosis ICD-10 code, were “other sepsis” (A41) in 14%, type 2 diabetes mellitus (E11) in 9%, infection following a procedure (T81) in 5%, and complications peculiar to reattachment and amputation (T87) in 4%.
Mortality at re-admission
Three hundred sixty-three patients died during the re-admission, a 5.2% mortality rate. Characteristics of these patients are presented in Table 1. Of re-admitted patients, factors associated with death were similar to risk factors for re-admission; in bivariable analysis, older age, female gender, urban location, Medicare insurance, and higher comorbidities were all associated with death. Admission to the index hospital was associated with survival. In adjusted regression analysis, the index hospitalization length of stay, index discharge disposition, and number of comorbidities were associated with mortality at re-admission (Table 2). Re-admission to the index hospital was associated with lower mortality (odds ratio [OR], 0.72; 95% confidence interval [CI], 0.56–0.92; p = 0.01).
Table 1.
Factors for In-Hospital Mortality at the First Re-Admission
| Independent factors | Did not die n = 6,500 | Died n = 363 | p |
|---|---|---|---|
| Gender, female | 2,656 (40.3) | 162 (44.6) | 0.1 |
| Age, y | 61 [51, 72] | 70 [60, 79] | < 0.0001 |
| Length of stay, index admission | 10 [6,18] | 11 [6,21] | 0.02 |
| Index admission >2 wk | 2,386 (36.2) | 149 (41.1) | 0.06 |
| Zip code median household income | 0.07 | ||
| $1–$43,999 | 2,336 (36.0) | 125 (34.7) | |
| $44,000–$55,999 | 1,703 (26.2) | 82 (22.8) | |
| $56,000–$73,999 | 1,438 (22.2) | 79 (21.9) | |
| $74,000+ | 1,013 (15.6) | 74 (20.6) | |
| Resident of state, yes | 6,391 (97.0) | 356 (98.1) | 0.23 |
| Patient location | |||
| Urban counties, >250,000 people | 6,500 (73.9) | 303 (83.5) | 0.04 |
| index discharge disposition | < 0.0001 | ||
| Home | 1,596 (24.2) | 40 (11.0) | |
| Home health care | 1,777 (27.0) | 81 (22.3) | |
| Skilled nursing facility | 2,876 (43.7) | 225 (62.0) | |
| Primary payer | < 0.0001 | ||
| Medicare | 3,757 (57.1) | 261 (71.9) | |
| Medicaid | 1,464 (22.3) | 56 (14.6) | |
| Private insurance | 975 (14.8) | 38 (10.5) | |
| Number of Elixhauser xomorbidities | 4 [2,5] | 5 [3,6] | < 0.0001 |
| Admitted to index hospital | 4,755 (72.2) | 232 (63.9) | 0.001 |
Shown as n (%) for categorical variables or median [interquartile range] for continuous variables. Not all categories sum to 100%; some categories with small groups are not shown. p values calculated as χ2 for categorical variables and Wilcoxon rank-sum tests for continuous variables.
Table 2.
Logistic Regression Examining Risk Factors for In-Hospital Mortality after Re-Admission
| Independent factors | OR | 95% CI | p |
|---|---|---|---|
| Gender, female | 1.18 | 0.93–1.49 | 0.17 |
| Age, y | 1.03 | 1.02–1.04 | < 0.0001 |
| Length of stay, index admission | 1.01 | 1.00–1.01 | 0.04 |
| Zip code median household income | |||
| $1–$43,999 | Ref | ||
| $44,000–$55,999 | 0.83 | 0.61–1.12 | 0.22 |
| $56,000–$73,999 | 0.89 | 0.65–1.22 | 0.48 |
| $74,000+ | 1.06 | 0.76–1.49 | 0.72 |
| Resident of state, yes | 1.11 | 0.46–2.72 | 0.82 |
| Patient location | |||
| Urban counties, >250,000 people | 1.22 | 0.85–1.73 | 0.26 |
| Discharge disposition | |||
| Home | Ref | ||
| Home health care | 1.39 | 0.92–2.11 | 0.12 |
| Skilled nursing facility | 1.67 | 1.15–2.42 | 0.007 |
| Acute-care hospital | 2.86 | 1.46–5.60 | 0.002 |
| Against medical advice | 0.94 | 0.32–2.81 | 0.92 |
| Re-admission to index hospital | 0.72 | 0.56–,0.92 | 0.01 |
| Primary payer | |||
| Medicare | Ref | ||
| Medicaid | 1.08 | 0.75–1.55 | 0.69 |
| Private insurance | 0.99 | 0.66–1.46 | 0.94 |
| Self-pay | 0.89 | 0.30–2.67 | 0.84 |
| Elixhauser comorbidities, count | 1.17 | 1.11–1.22 | < 0.0001 |
OR = odds ratio; CI = confidence interval.
Index hospital re-admission
Of patients who had a non-elective re-admission, 1,966 (28.3%) were admitted to a non-index hospital. Characteristics of these patients are presented in Table 3. In bivariable analysis, admission to a non-index hospital was associated with longer index hospitalizations, being a resident of the state, discharge disposition, and insurance payer. In adjusted regression analysis (Table 4), longer index hospitalization length of stay, being a resident of the state, and index discharge disposition (discharge to a short-term hospital and leaving against medical advice) were associated with lower odds of re-admission to the index hospital, whereas private insurance was associated with higher odds of re-admission to the index hospital (OR, 1.36; 95% CI, 1.11–1.65; p = 0.002).
Table 3.
Characteristics of Patients Re-Admitted to the Index Hospital
| Independent factors | Index hospital n = 4,988 | Non-index n = 1,966 | p |
|---|---|---|---|
| Gender, female | 2,042 (40.9) | 776 (39.5) | 0.26 |
| Age, y | 61 [51, 72] | 62 [52, 72] | 0.15 |
| Length of stay, index admission | 10 [6,17] | 11 [6,20] | 0.02 |
| Index admission >2 wk | 1,765 (35.4) | 770 (39.2) | 0.003 |
| Zip code median household income | 0.84 | ||
| $1–$43,999 | 1,767 (36.0) | 694 (35.8) | |
| $44,000–$55,999 | 1,266 (25.8) | 520 (26.8) | |
| $56,000–$73,999 | 1,095 (22.3) | 422 (21.8) | |
| $74,000+ | 783 (15.9) | 304 (15.7) | |
| Resident of state, yes | 3,925 (96.6) | 1,931 (98.2) | < 0.0001 |
| Patient location | |||
| Urban counties, >250,000 people | 3,925 (79.6) | 1,516 (78.6) | 0.36 |
| Discharge disposition | < 0.0001 | ||
| Home | 1,189 (23.9) | 447 (22.8) | |
| Home health care | 1,406 (28.2) | 453 (23.1) | |
| Skilled nursing facility | 2,189 (43.9) | 912 (46.4) | |
| Acute-care hospital | 76 (1.5) | 77 (3.9) | |
| Against medical advide | 125 (2.5) | 75 (3.8) | |
| Primary payer | 0.001 | ||
| Medicare | 2,866 (57.6) | 1,152 (58.7) | |
| Medicaid | 1,050 (21.1) | 468 (23.8) | |
| Private insurance | 776 (15.6) | 237 (12.1) | |
| Self-pay | 156 (3.1) | 48 (2.4) | |
| Number of Elixhauser comorbidities | 5 [3,6] | 5 [3,6] | 0.04 |
Shown as n (%) for categorical variables or median [interquartile range] for continuous variables. Not all categories sum to 100%; some categories with small groups are not shown. p values calculated as χ2 for categorical variables and Wilcoxon rank-sum tests for continuous variables.
Table 4.
Logistic Regression of Factors Associated with Index Hospital Re-Admission
| Independent factors | OR | 95% CI | p |
|---|---|---|---|
| Gender, female | 1.04 | 0.92–1.16 | 0.55 |
| Age, y | 1.01 | 1.00–1.01 | 0.009 |
| Length of stay, index admission | 0.996 | 0.99–1.00 | 0.012 |
| Zip code median household income | |||
| $1–$43,999 | Ref | ||
| $44,000–$55,999 | 0.93 | 0.78–1.10 | 0.41 |
| $56,000–$73,999 | 0.93 | 0.78–1.10 | 0.38 |
| $74,000+ | 0.95 | 0.78–1.14 | 0.58 |
| Resident of state, yes | 0.47 | 0.32–0.71 | <0.0001 |
| Patient location | |||
| Urban counties, >250,000 people | 1.12 | 0.95–1.31 | 0.17 |
| Discharge disposition | |||
| Home | Ref | ||
| Home health care | 1.18 | 0.99–1.41 | 0.067 |
| Skilled nursing facility | 0.88 | 0.75–1.03 | 0.12 |
| Acute-care hospital | 0.34 | 0.23–0.49 | <0.0001 |
| Against medical advice | 0.56 | 0.40–0.79 | 0.001 |
| Primary payer | |||
| Medicare | Ref | ||
| Medicaid | 1.03 | 0.87–1.23 | 0.71 |
| Private insurance | 1.36 | 1.11–1.65 | 0.002 |
| Self-pay | 1.24 | 0.83–1.87 | 0.28 |
| Elixhauser comorbidities, count | 0.98 | 0.86–1.01 | 0.23 |
OR = odds ratio; CI = confidence interval.
Table 5 shows the clinical outcomes of re-admission, grouped by index versus non-index hospitalization. Patients re-admitted to the index hospital had better clinical outcomes, including not only a lower mortality rate (4.7% vs. 6.7%; p = 0.003) but also lower costs costs (in $1000; 45 [23–88] vs. 50 [24–104]; p = 0.004) and higher discharges to home with or without home health care (55.7% vs. 48.6%; p < 0.001), with lower discharges to skilled nursing facilities or other hospitals. There was a trend toward shorter length of stay, but this was not significantly different (p = 0.06).
Table 5.
Outcomes of Patients with NSTI at First Re-Admission
| Variable | Index hospital n = 4,988 | Non-index n = 1,966 | p |
|---|---|---|---|
| Mortality on re-admission, n (%) | 232 (4.7%) | 131 (6.7%) | 0.003 |
| Subsequent re-admissions, median (IQR) | 2,382 (47.8%) | 961 (48.8%) | 0.397 |
| Re-admissions LOS, d, median (IQR) | 6 [3, 9] | 6 [3, 11] | 0.063 |
| Re-admissions costs, $, median (IQR) | 45,436 [23,225, 88,435] | 50,121 [24,640, 103,759] | 0.004 |
| Re-admission discharge destinationa | 0.000 | ||
| Home | 1,392 (27.9%) | 538 (27.4%) | |
| Home health care | 1,385 (27.8%) | 417 (21.2%) | |
| Skilled nursing facility | 1,819 (36.5%) | 737 (39.0%) | |
| Acute-care hospital | 52 (1.0%) | 37 (1.9%) | |
| Against medical advice | 104 (2.1%) | 76 (3.8%) |
NSTI = necrotizing soft tissue infection; IQR = interquartile range; LOS = length of stay.
Discharge destination categories included in the χ2 analysis also includes death, alive-unknown (n < 10).
Discussion
In this retrospective cohort study using a national database, we found a 25.7% 90-day non-elective re-admission rate in patients with NSTI, with more than one in four being re-admitted to a non-index hospital. Unplanned re-admission was associated with a 5.2% mortality rate. Re-admission to the index hospital was protective against mortality when compared with re-admission to a non-index hospital. Re-admission to the index hospital was also associated with decreased costs and an increased rate of home discharge. Our study identified factors that affect the likelihood of re-admission to index hospital to aid future research to improve continuity of care in NSTI patients. While it appears that clinical factors, such as severity of the initial disease and patient comorbidities, are factors that affect the likelihood of re-admission, non-clinical factors such as insurance and state residency of the patient were related to whether the patient is re-admitted to the index hospital.
The re-admission rate among patients with NSTI is higher than the existing literature in emergency general surgery (EGS). Two separate studies using National Surgical Quality Improvement Program and NRD data estimated a re-admission rate of 8.1% to 13% within 30 days.13,14 Established risk factors associated with unplanned re-admissions included higher comorbidity status, prolonged length of hospital stay, having public insurance, and being discharged to a post-hospitalization care facility.15,16 Similar risks factors have been noted in patients with NSTIs. Among re-admitted patients, age, comorbidity status, and being discharged to skilled nursing facility and short-term hospital are risk factors for mortality at re-admission. This may represent a cohort of patients who have reduced physiologic reserve and are more susceptible to post-operative complications or infections.
We showed that re-admission to the index hospital was the only protective factor for mortality at re-admission; older age, initial length of stay, and comorbidity burden were also associated with mortality. The protective effect of re-admission to index hospital has been observed in patients undergoing major surgeries across different surgical specialities with a 26% lower risk of 90-day mortality.17
Our study identifies factors that could be targeted to improve index hospital re-admission. We demonstrated that longer length of stay of the index hospitalizations, leaving against medical advice (AMA), discharge to short-term hospital, being resident of the same state as the index hospital were associated with decreased likelihood of re-admission to the index hospital. An AMA discharge from the index hospitalization may indicate patient dissatisfaction with the initial admitting hospital and therefore may explain increased re-admissions to non-index hospitals. While being a resident of the same state of the index hospital is a statistically significant predictor of non-index re-admission, the absolute difference between the actual percentages for index versus non-index re-admission rates is minimal. This is likely influenced by the relatively small proportion of out-of-state patients included in this study and possible difficulty with the NRD capturing transitions out of state; this result is not consistent with current literature and should be interpreted with caution. Previous studies showed that postoperative patients living farther from the index hospital and those living in rural areas are more likely to be re-admitted to a different hospital.18,19
Having private insurance as the primary payer increased the likelihood of re-admission to index hospital. Our findings may be explained by the fact that private insurance is often linked to a network hospital. Previous studies have demonstrated that insurance status can influence outcomes in EGS and trauma.20,21 Having private insurance may also imply that the members of this patient population have job security and higher income status. Although our study did not demonstrate differences between zip code median household income and re-admission destinations, socioeconomic status has been consistently reported in the literature as a predictor of non-index re-admission. For example, higher household income is associated with increased likelihood of non-index re-admissions in cardiac surgery.22 In our patient population, insurance status may be more reflective of socioeconomic status than zip code income quartile.
Fragmentation of care is widely observed across different specialties. It is a known risk factor for mortality and poor outcome in postoperative patients in both emergent and elective surgeries.19,23,24 Our study shows a positive impact of continuity of care on patients with NSTIs. We demonstrated that re-admission to index hospital is associated with decreased risk of mortality, reduced costs, and increased discharge to home. The most common diagnoses associated with NSTI re-admissions were infection.4 Easy access to comprehensive patient information including previous blood tests, microbiology culture data, imaging, and operative reports may allow for earlier recognition of severe infections and wound complications, and prompt initiation of appropriate therapy. These findings call for further studies to evaluate the possible reasons behind the benefit of continuity of care in patients with NSTI. A robust post-operative care pathway is crucial in patients with NSTI for multidisciplinary rehabilitation and long-term wound care.25 This is especially crucial for patients with NSTI with complex medical history and other comorbidities. Fragmented care can lead to increased unnecessary radiology and other diagnostic tests, thereby delaying care, and increasing the total cost of care.
Discharge to a skilled nursing facility has been shown to be an independent risk factor for death, re-admission, and post-discharge complications among EGS patients.26 A similar trend has been observed in our study in patients with NSTI regarding re-admission to a non-index hospital and increased risk of mortality at re-admission. This indicates a need to strengthen connections between index hospitals and discharge facilities to improve continuity of care and patient outcomes. Increased hospital-skilled nursing facility linkages has been demonstrated to reduce the likelihood of re-admission in surgical patients.27 There are modifiable skilled nursing facility-associated factors such as staffing level and specialization that can influence surgery patient outcomes.28 This may present a target for intervention to enhance post-acute care and rehabilitation in patients with NSTI and reduce re-admission rates. Hospital and surgery departments can consider strengthening partnerships with local skilled nursing facilities and offer training in specialized wound care. Further studies are required to show how the standard of care in skilled nursing facilities may affect the post-operative care of complex surgical patients such as patients with NSTI.
This study had several limitations. First, the retrospective review of administrative data limited our ability to understand the time course and clinical details of NSTI patient re-admission. Second, we were unable to account for the impact of clinical factors such as treatment and interventions performed during re-admission on patient outcome. Specifically, the association between the severity of the original illness, re-admission destination, and outcome were not examined in this study. We were also unable to study effects of specific hospital resources or the impact of the admitting service on our cohort. Because illness severity has been shown to be the strongest risk factor of mortality during re-admission after emergency general surgery,29 our findings were likely to have been affected by unmeasured or residual confounding factors.
Conclusions
Our study demonstrated that continuity of care matters in patients with NSTI. Re-admission to index hospital was associated not only with decreased mortality but also decreased costs and increased discharge to home. These initial findings provide a basis for future research to examine the specific factors and, eventually, build robust postoperative care pathways aimed at reducing hospital re-admissions and improving continuity of care for complex survivors of NSTI.
Supplementary Material
Authors' Contributions
Conceptualization: Lai, Brown, Claridge, Ho. Methodology: Lai, Towe, Ho. Software: Ho. Formal analysis: Towe, Ho. Data curation: Towe, Ho. Visualization: Lai. Writing–original draft: Lai, Ho. Writing–review and editing: Lai, Towe, Brown, Ho. Supervision: Claridge. Project administration: Ho. Funding acquisition: Ho.
Funding Information
Dr. Ho is supported by the Clinical and Translational Science Collaborative of Cleveland (KL2TR002457) from the National Center for Advancing Translational Sciences.
Author Disclosure Statement
Dr. Ho's spouse is a consultant for Zimmer Biomet, Atricure, Medtronic, and Astra Zeneca. Dr. Towe is a consultant for Zimmer Biomet, Atricure, Medtronic, and Astra Zeneca. No other authors have funding to disclose.
Supplementary Material
References
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