Comorbidities Matter as Distressed Communities Index Fails to Predict Mortality in Necrotizing Soft Tissue Infection.

Background: Necrotizing soft tissue infections (NSTIs) are life-threatening infections requiring prompt intervention. The Distressed Communities Index (DCI) is a comprehensive ranking of socioeconomic well-being based on zip code. We sought to identify the role of DCI in predicting mortality in NSTI, because it remains unknown. Patients and Methods: A retrospective, single-institution analysis of patients diagnosed with NSTI (2011-2020) requiring surgical intervention. The DCI is a composite score based on community-level factors: unemployment, education level, poverty rate, median income, business growth, and housing vacancies. The DCI scores were matched to the patient's zip code and stratification was performed using quintiles. Parametric and non-parametric analyses were performed to evaluate both the demographic and clinical characteristics. Multivariable regression analyses were performed to identify independent variables associated with outcomes. Results: Six hundred twenty patients met inclusion criteria. Ninety-day mortality was 12.4% (n = 77). Patients who died were more likely to be female (58.4%), older (median age 60.5 ± 11.3 years), have a body mass index (BMI) ≥30 (61.5%), have a higher Charlson Comorbidity Index (3; interquartile range [IQR], 2-7). After regression analysis, neither the composite DCI by quintile, nor the individual component scores, were found to correlate with mortality. Interestingly, underlying heart disease, hepatic dysfunction, and renal disease at baseline were found to significantly correlate with mortality from NSTI with p values <0.05. Conclusions: Socioeconomic status and insurance payer are championed for inclusion when constructing risk models, evaluating resource utilization, comparing hospitals, and determining patient management. The severity of community distress measured by DCI did not correlate with mortality for NSTI, despite contrasting evidence in other diseases. This finding is likely caused by a combination of both individual and community-level resources. This is highlighted by the recognition that comorbidities did correlate with mortality. The absence of DCI-related associations observed in this study warrants further investigation, as do mechanisms for the prevention of further organ dysfunction.

Characterization of Necrotizing Soft Tissue Infections in Obese and Non-Obese Patients.

Background: The impact of obesity on the pathogenesis and prognosis of necrotizing soft tissue infections (NSTIs) is unclear. The goal of this study was to characterize differences in NSTI presentation and outcomes by obesity status. Patients and Methods: A retrospective analysis of institutional data for patients diagnosed with NSTIs were identified (n = 619; 2011-2020). Patients were divided based on obesity (body mass index [BMI] ≥ 30 kg/m2) and non-obese (BMI <30 kg/m2). Primary outcomes included NSTI location, micro-organisms, and index hospitalization data. Multiple logistic regression was used to model predictors of in-hospital and 90-day mortality. Results: The obese cohort (n = 390; 63%) had higher rates of congestive heart failure and type 2 diabetes mellitus. There were no differences in length of stay, mortality, or discharge disposition between groups. A higher rate of respiratory failure was observed in the obese versus non-obese group (36.7% vs. 20.9%; p < 0.0005). The obese cohort was associated with perineal (40.8% vs. 27.0%) and torso NSTIs (20.9% vs. 15.8%; p < 0.005) but reduced staphylococcal (19.2% vs. 27.4%; p = 0.02) and group A streptococcal (2.6% vs. 6.5%; p = 0.03) infections, and increased polymicrobial infections. Class 2 obesity was a negative predictor for in-hospital mortality (odds ratio [OR], 0.1; 95% confidence interval [CI], 0.03-0.5) and 90-day mortality (OR, 0.3; 95% CI, 0.1-0.8), when adjusting for demographic data, type of infection, and baseline comorbidities. Conclusions: Necrotizing soft tissue infections in obesity may present with unique distributions and microbial characteristics. Class 2 obesity may exhibit a survival benefit compared with non-obese patients, suggestive of an obesity paradox.

Outcomes of Patients with Necrotizing Soft Tissue Infections: A Propensity-Matched Analysis Using the National Inpatient Sample.

Background: Necrotizing soft tissue infections (NSTIs) are severe, rapidly spreading infections with high morbidity and mortality. Attempts to identify risk factors for mortality and morbidity have produced variable results. We hope to determine which factors across the NSTI population impact mortality, morbidities, and discharge disposition. Patients and Methods: Retrospective data from the National Inpatient Sample from 2012-2018 of patients with primary diagnosis of NSTI (gas gangrene, necrotizing faciitis, cutaneous gangrene, or Fournier gangrene) were identified for analysis. A 1:4 greedy match was performed and risk factors for in-hospital mortality and discharge disposition were examined. Continuous variables were assessed using t-tests and Wilcoxon rank sum tests. Categorical variables were assessed using χ2 and Fisher exact tests. Statistical significance was defined as p < 0.05. Results: A total of 6,608 patients were identified. Weighted, this represents 33,040 patients; 32,390 are in the no-mortality cohort and 650 in the mortality cohort. Advanced age group was a risk factor for both in-hospital mortality and morbidity, but not for discharge to a skilled nursing or rehabilitation facility. Having two or more comorbidities was a risk factor for mortality, morbidity, and discharge to skilled nursing or rehabilitation facility. Cancer, liver disease, and kidney disease were predictors of in-hospital mortality. Diabetes mellitus and kidney disease were predictors of experiencing an in-hospital complication. Diabetes mellitus, heart disease, and kidney disease were predictors for discharge to skilled nursing or rehabilitation facility. Conclusions: Necrotizing soft tissue infections are associated with substantial morbidity and mortality. Identifying patients at higher risk for mortality, morbidity, and higher level of care at discharge can help providers properly allocate resources to improve patient outcomes and reduce the financial burden on patients and healthcare facilities. Special attention should be paid to those with existing or acute kidney dysfunction because this was the only comorbidity associated with increased risk mortality, morbidity, and discharge to higher level of care.

Leaving Against Medical Advice After Emergency General Surgery: Avoiding a Two-Hit Effect.

BACKGROUND: Emergency general surgery has higher adverse outcomes than elective surgery. Patients leaving the hospital against medical advice (AMA) have a greater risk for readmission and complications. We sought to identify clinical and demographic characteristics along with hospital factors associated with leaving AMA after EGS operations. METHODS: A retrospective review of the Nationwide Inpatient Sample was performed. All patients who underwent an EGS procedure accounting for >80% of the burden of EGS-related inpatient resources were identified. 4:1 propensity score analysis was conducted. Regression analyses determined predictive factors for leaving AMA. RESULTS: 546,856 patients were identified. 1085 (0.2%) patients who underwent EGS left AMA. They were more likely to be men (59% versus 42%), younger (median age 51 y, IQR [37.61] versus 54, IQR [38.69]), qualify for Medicaid (26% versus 13%) or be self-pay (17% versus 9%), and be within the lowest quartile median household income (40% versus 28%) (all P < 0.05). After applying 4:1 propensity score matching, individuals who were self-pay (OR 3.15, 95% CI 2.44-4.06) or insured through Medicare (OR 2.75, 95% CI 2.11-3.57) and Medicaid (OR 3.58, 95% CI 2.83-4.52) had increased odds of leaving AMA compared with privately insured patients. In addition, history of alcohol (OR 2.21, 95% CI 1.65-2.98), drug abuse (OR 4.54, 95% CI 3.23-6.38), and psychosis (OR 2.31, 95% CI 1.65-3.23) were associated with higher likelihood for leaving AMA. CONCLUSIONS: Patients undergoing EGS have a high risk of complications, and leaving AMA further increases this risk. Interventions to encourage safe discharge encompassing surgical, psychiatric, and socioeconomic factors are warranted to prevent a two-hit effect and compound postoperative risk.

Computed Tomography-Measured Psoas Muscle Density as a Predictive Factor for Hypophosphatemia Associated With Refeeding.

BACKGROUND: Initiation of parenteral nutrition (PN) after a period of starvation can be complicated by refeeding syndrome (RFS). RFS is associated with electrolyte abnormalities including hypomagnesemia, hyponatremia, and hypophosphatemia. Risk factors include recent weight loss, low body mass index, and electrolyte deficiencies; however, these associations are not strong. We hypothesized that a validated measure of nutrition risk, computed tomography (CT)-measured psoas muscle density, can be used to predict the development of hypophosphatemia associated with RFS. METHODS: A retrospective analysis of surgical patients initiated on PN with an abdominal CT scan within the past 3 months was conducted. CT-measured psoas muscle density was assessed as a predictive variable for the development of electrolyte abnormalities. Daily electrolyte and clinical outcome measures were recorded. RESULTS: One hundred nine patients were stratified based on Hounsfield unit average calculation (HUAC). The lowest 25th percentile of patients had HUAC <25. Low HUAC was associated with a significant percent decrease in phosphate levels from baseline to PN day 3 (P < .01) and significant difference in serum phosphate value on PN day 3 (P < .01). The low muscle density quartile also experienced longer days on the mechanical ventilator (P = .01) compared with patients with a higher psoas muscle density. CONCLUSION: Psoas muscle density predicted the development of hypophosphatemia in patients initiated on PN. This measurement may aid in identifying patients at highest risk of experiencing RFS. A mean psoas HU <25 may prompt additional precautions, including additional phosphate replacement and slower initiation of PN.

Analyzing Outcomes Among Older Adults With Necrotizing Soft-Tissue Infections in the United States.

BACKGROUND: Necrotizing soft-tissue infections (NSTIs) encompass a group of severe, life-threatening diseases with high morbidity and mortality. Evidence suggests advanced age is associated with worse outcomes. To date, no large data sets exist describing outcomes in older individuals, and risk factor identification is lacking. METHODS: Retrospective data were obtained from the 2015 Medicare 100% sample. Included in the analysis were those aged ≥65 y with a primary diagnosis of an NSTI (gas gangrene, necrotizing fasciitis, cutaneous gangrene, or Fournier's gangrene). Risk factors for in-hospital mortality and discharge disposition were examined. Continuous variables were assessed using central tendency, t-tests, and Wilcoxon rank-sum tests. Categorical variables were assessed using the chi-squared and Fisher's exact tests. Statistical significance was defined as P < 0.05. RESULTS: 1427 patient records were reviewed. 59% of patients were male, and the overall mean age was 75.4±8.6 y. 1385 (97.0%) patients required emergency surgery for their NSTI diagnosis. The overall mortality was 5.3%. Several underlying comorbidities were associated with higher rates of mortality including cancer (OR: 3.50, P = 0.0009), liver disease (OR: 2.97, P = 0.03), and kidney disease (OR: 2.15, P = 0.01). While associated with high in-hospital mortality, these diagnoses were not associated with a difference in the rate of discharge to home compared with skilled nursing or rehab. Overall, patients discharged to skilled nursing facilities or rehab had higher rates of underlying comorbidities than patients who were discharged home (3 or more comorbid illness 84.3% versus 68.6%, P < 0.0001); however, no individual comorbid illness was associated with discharge location. CONCLUSIONS: In our Medicare data set, we identified several medical comorbidities that are associated with increased rates of in-hospital mortality. Patients with underlying cancers had the highest odds of increased mortality. The effect on outcomes of the potentially immunosuppressive cancer treatments in these patients is unknown. These data suggest that patients with underlying illnesses, especially cancer, kidney disease, or liver disease have higher mortalities and are more likely to be discharged to skilled nursing facilities or rehab. It is unclear why these illnesses were associated with these worse outcomes while others including diabetes and heart disease were not. These data suggest that these particular comorbid illnesses may have special prognostic implications, although further analysis is necessary to identify the causative factors.