- Email: [email protected]
Development and Validation of a Necrotizing Soft-Tissue Infection Mortality Risk Calculator Using NSQIP
Development and Validation of a Necrotizing Soft-Tissue Infection Mortality Risk Calculator Using NSQIP Iris Faraklas, RN, BSN, Gregory J Stoddard, MPH, Leigh A Neumayer, MD, FACS, Amalia Cochran, MD, FACS Necrotizing soft-tissue infections (NSTI) are a group of uncommon, rapidly progressive infections requiring prompt surgical debridement and systemic support. A previous attempt to define risk factors for mortality from NSTI had multiple limitations. The objective of this study was to develop and validate a 30-day postoperative mortality risk calculator for patients with NSTI using NSQIP. STUDY DESIGN: The NSQIP Participant Use Files (2005e2010) were used as the primary data source. Patients diagnosed with NSTI were identified by ICD-9 codes. Multiple logistic regression analysis identified key preoperative variables predicting mortality. Bootstrap analysis was used to validate the model. RESULTS: In 1,392 identified NSTI cases, demographics were as follows: 42% were female, median age was 55 years (interquartile range 46 to 63 years), and median body mass index was 32 kg/m2 (interquartile range 26 to 40 kg/m2). Thirty-day mortality was 13%. Seven independent variables were identified that correlated with mortality: age older than 60 years (odds ratio [OR] ¼ 2.5; 95% CI 1.7e3.6), functional status (partially dependent: OR ¼ 1.6; 95% CI 1.0e2.7; totally dependent: OR ¼ 2.3; 95% CI 1.4e3.8), requiring dialysis (OR ¼ 1.9; 95% CI 1.2e3.1), American Society of Anesthesiologists class 4 or higher (OR ¼ 3.6; 95% CI 2.3e5.6), emergent surgery (OR ¼ 1.6; 95% CI 1.0e2.3), septic shock (OR ¼ 2.4; 95% CI 1.6e3.6), and low platelet count (<50K/mL: OR ¼ 3.5; 95% CI 1.6e7.4; <150K/mL but >50K/mL: OR ¼ 1.9; 95% CI 1.2e2.9). The receiver operating characteristic area was 0.85 (95% CI 0.82e0.87), which indicated a strong predictive model. Using bootstrap validation, the optimism-corrected receiver operating characteristic area was 0.83 (95% CI 0.81e0.86), which represents the model performance in future patients. The model was used to develop an interactive risk calculator. CONCLUSIONS: This risk calculator has excellent predictive ability for mortality in patients with NSTI. This simple interactive tool can aid physicians and patients in the decision-making process. (J Am Coll Surg 2013;217:153e161. 2013 by the American College of Surgeons)
BACKGROUND:
Necrotizing soft-tissue infections (NSTI) are a group of uncommon fulminant infections associated with high rates of both morbidity and mortality that require prompt surgical debridement as well as systemic support.1-3 The
reported annual incidence of NSTI cases ranges from 500 to 1,500 in the United States.3,4 This number appears to be rising, possibly reflecting better identification and reporting of cases, or due to better standardization of definitions used by practitioners.5,6 These infections can be caused by external trauma, but often arise with no obvious signs of trauma, especially in patients with predisposing factors.5,6 Several scoring systems, of which the Laboratory Risk Indicator for Necrotizing Fasciitis score is the best known, have been created to distinguish NSTI from other soft-tissue infections. However, none of these scoring systems are prognostic of mortality in NSTI patients.3,7-10 Reported mortality rates consequent to NSTI during the last 2 decades have ranged from 10% to >30%.5,6,10,11 Lengthy and variable lists of factors influencing mortality have been generated by many publications. However, the
CME questions for this article available at http://jacscme.facs.org Disclosure Information: Authors have nothing to disclose. Timothy J Eberlein, Editor-in-Chief, has nothing to disclose. Presented at the Western Surgical Association, 120th Scientific Session, Colorado Springs, CO, November 2012. Received December 29, 2012; Revised February 14, 2013; Accepted February 14, 2013. From the Departments of Surgery (Faraklas, Neumayer, Cochran) and Internal Medicine (Stoddard), University of Utah, Salt Lake City, UT. Correspondence address: Amalia Cochran, MD, FACS, Department of Surgery, University of Utah, 30 North 1900 East, 3B110 SOM, Salt Lake City, UT 84132. email: [email protected]
ª 2013 by the American College of Surgeons Published by Elsevier Inc.
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Abbreviations and Acronyms
CPT IQR NSTI OR PUF ROC
¼ ¼ ¼ ¼ ¼ ¼
Current Procedural Terminology interquartile range necrotizing soft-tissue infection odds ratio Participant Use Data File receiver operating characteristic
only published risk scoring system for NSTI mortality to date is that of Anaya and colleagues.10 The objective of our study was to use NSQIP to develop and validate a 30-day postoperative mortality risk calculator for NSTI patients.
METHODS Dataset After receiving institutional review board approval, data were extracted from 2005e2010 NSQIP Participant Use Data Files (PUF). The design of NSQIP has been described in detail by other authors.12-14 The PUF contain prospectively collected data using standardized variable definitions on surgical patients at participating academic and community hospitals. The PUF include >135 variables for each patient, including preoperative risk factors, intraoperative variables, and 30-day postoperative morbidity and mortality for patients who undergo major surgical procedures in both the inpatient and outpatient settings. Data from each participating hospital are collected, validated, and submitted by a trained surgical clinical reviewer through medical chart abstraction. To ensure inter-rater reliability, NSQIP requires standardized initial and ongoing reviewer training and conducts periodic inter-rater reliability audits of participating sites. A sample of all surgical cases submitted by each participating institution is selected for analysis. This system allows the data to remain stratified, prevents bias, and eliminates the potential for over- or underrepresentation by individual centers. The NSQIP normalizes a subset of cases that are representative of all surgical cases nationwide. The PUF do not identify hospitals, health care providers, or patients. The files are Health Insurance Portability and Accountability Act compliant. Every surgical procedure is recorded in NSQIP using ICD-9-CM codes. Patients discharged with an ICD-9-CM diagnosis of gas gangrene (040.0); necrotizing fasciitis (728.86); or Fournier’s gangrene (608.83) were included in this analysis. Outpatients with a length of stay <1 day were excluded because this clinical course would not be consistent with an NSTI. Current Procedural Terminology (CPT) codes were available for all patients and were used to cross-check ICD-9-CM codes.
J Am Coll Surg
Data evaluated from NSQIP included basic demographics (age, sex, race, height, and weight), medical comorbidities, preoperative laboratory values, surgical, and outcomes data. The NSQIP does not delineate between those symptoms present at time of admission and those symptoms that occurred later in the hospital stay but before surgery. However, NSQIP does delineate between complications occurring before, during, and after surgery. A list of important variables analyzed in this review and their definitions is included in Appendix 1 (online only). Readers who have additional questions about NSQIP can contact the program at http://www. acsnsqip.org. Appendix 2 (online only) lists preoperative laboratory tests collected in NSQIP and included in the PUF. Only preoperative laboratory variables that were collected within 2 days before surgery were included in analysis. Missing laboratory variables were not assumed to be normal and these missing variables were excluded from analysis. Abnormal cutoffs for laboratory values were based on the guidelines from ARUP Laboratories, which serves as a national reference laboratory. The primary outcomes variable for our analysis was surgical mortality. Outcomes are tracked for 30 days postoperatively in NSQIP. If the patient was still an inpatient at 30 days, they were tracked until discharge or until NSQIP locked the data file at 120 days after the first surgery. Statistical analysis Statistical analysis was performed using Stata version 12.1 (StataCorp). For comparison of patient demographics, Pearson chi-square or Fisher’s exact test was used for categorical variables; Wilcoxon rank sum was used for continuous variables. All reported p values are from a 2-sided comparison. Univariate exploratory analysis of pre- and intraoperative variables was performed to establish relationships with 30-day postoperative mortality. All significant independent variables were included in the stepwise multiple logistic regression analysis to evaluate mortality in NSTI patients; however, preoperative laboratory values with <400 observations were excluded in the interest of maintaining a robust model. The included laboratory values were the following: hematocrit, BUN, creatinine, sodium, aspartate aminotransferase, alkaline phosphatase, partial thromboplastin time, prothrombin time, white blood cell count, and platelet count. Statistical models are frequently validated using the concordance statistic (c-statistic).15 In measuring discrimination of a model for binary outcomes, as in our study with mortality, the c-statistic is the same as the area under
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the receiver operating characteristic (ROC) curve.16 An ROC curve is a plot of the true-positive against the false-positive rate for the different possible thresholds of a diagnostic test. The ROC curve demonstrates the tradeoff between sensitivity and specificity. The closer the curve follows the left-hand border and the top border, the more accurate the test. The ROC area refers to the ability of the risk estimate to discriminate cases (nonsurvivors) from noncases (survivors).17,18 The ROC area equals 1 if discrimination is perfect and 0.50 if it is no better than chance. Statisticians generally agree that a ROC >0.70 shows acceptable discrimination, and an ROC >0.80 is considered excellent discrimination for use in clinical practice.19 An optimism-corrected ROC area is reduced by the estimated deterioration that the model will have when applied to new subjects. When a sample size is limited, the favored method for validation of a statistical model is the bootstrap method.15 Unlike the traditional split-sample approach in which half of the sample is used to develop the model and the other half to validate the model, bootstrapping permits the use of the entire dataset for both model development and validation. Numerous studies have demonstrated that bootstrapping outperforms split-sample validation.15,20,21 If the bootstrap optimism-corrected ROC area shows acceptable predictive accuracy, then the model is validated.15 In multivariable logistic regression for the identified outcomes (mortality), with the independent variables Xi (ie, age, functional status), a is the intercept of the model, and bi the slope of each predictor variable. Predicted probability of mortality is calculated from the following prediction equation: Pobability of mortality ð%Þ ¼
1 1þe
P
ð aþ
bi X i Þ
100%
Once the model was developed and validated, an interactive spreadsheet was created using the risk factors from the validated model.
RESULTS A total of 1,392 patients with NSTI were identified in NSQIP from 2005 through 2010, 82% (n ¼ 1,142) with a principal discharge diagnosis of necrotizing fasciitis, 8.6% (n ¼ 119) with gas gangrene, and 9.4% (n ¼ 131) with Fournier’s gangrene. Five patients included in the analysis were coded as outpatients, but had lengths of stay longer than 1 day; we assumed that the outpatient coding was in error for the purposes of this study. As shown in Table 1, the number of NSTI admissions documented in NSQIP increased progressively from 47 patients
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Table 1. Number of Necrotizing Soft-Tissue Infections Cases vs Total NSQIP Cases per Year Year No. of NSTI cases (% NSQIP cases) Total NSQIP cases, n
2005 2006 2007 2008 2009 2010
47 140 212 281 352 360
(0.14) (0.14) (0.10) (0.10) (0.10) (0.09)
34,099 118,391 211,407 271,368 336,190 363,431
NSTI, necrotizing soft-tissue infections.
in 2005 to 360 in 2010. Total NSQIP cases grew at a rate comparable with NSTI cases, from slightly >34,000 in 2005 to >360,000 in 2010. Each patient had a corresponding principal CPT code documented, with 221 different CPT codes reported. The most frequent CPT codes were debridement of NSTI to external genitalia, perineum, and abdominal wall (11004-11006); these three codes accounted for 43% of all codes reported. Basic demographics and preoperative characteristics for survivors and nonsurvivors are shown in Table 2. Most patients were male, with a median age of 55 years (interquartile range [IQR] 46 to 63 years) and median body mass index of 32 (IQR 26 to 40). Median length of stay was 16 days (IQR 9 to 30 days); however, there were 9 patients who were still in the hospital when NSQIP locked their data file (>120 days from first surgery). Thirty-day mortality was 13% (n ¼ 181). Median length of stay for survivors was 17 days (IQR 10 to 32 days) and median length of stay for nonsurvivors was 9 days (IQR 3 to 18 days; p < 0.001, Wilcoxon rank sum). Multivariable logistic regression (Table 3) identified the following 7 independent variables that affected mortality: patients older than 60 years, totally dependent functional status, requiring dialysis before surgery, low platelet count (<150,000/mm3), septic shock, American Society of Anesthesiologists class 4 or higher, and emergent surgery (<12 hours post admission). Partially dependent functional status was marginally significant (p ¼ 0.07), but when it was removed from analysis the discriminatory ability of the model decreased from an ROC area of 0.85 to 0.82; and the variable was kept in the model. The ROC area was 0.85 (95% CI 0.82e0.87), which indicated a strong predictive model. Using bootstrap validation, the optimism-corrected ROC area was 0.83 (95% CI 0.81e0.86), which represents the predictive ability of the model in future patients. The variables identified in model creation were used to develop an interactive risk calculator. This risk calculator
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Table 2.
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Patient Demographics and Preoperative Status: Survivors vs Nonsurvivors
Variable
Age, y, median (IQR) Male, n (%) Caucasian, n (%) Body mass index, median (IQR) Diabetic, n (%) Smoker, n (%) >2 Alcoholic drinks/day for 2 weeks before operation, n (%) ASA class, n (%) 1 2 3 4 5 Functional status, n (%) Partially dependent Totally dependent Transfer from, n (%) Home Chronic care Acute or emergent care Ventilatory support Pneumonia Dyspnea, n (%) At rest With moderate exertion History of COPD, n (%) History of angina, n (%) Hypertension, n (%) History of CHF, n (%) MI within 6 months, n (%) Previous PCI, n (%) Previous cardiac surgery, n (%) Dialysis, n (%) Ascites, n (%) Chronic steroid use, n (%) History of surgery for PVD, n (%) Rest pain, n (%) Cancer, n (%) Preoperative weight loss, n (%) Bleeding disorder, n (%) >4 blood transfusions before surgery, n (%) Preoperative SIRS, sepsis or septic shock, n (%) SIRS before surgery Sepsis before surgery Septic shock before surgery Impaired sensorium Stroke Surgery in past 30 days, n (%)
Total (n ¼ 1,392)
55 801 271/399 32 681 402
(46e63) (58) (68) (26e40) (49) (29)
72 (5) 15 149 630 546 52
(1) (11) (45) (39) (4)
Survivors (n ¼ 1,211)
54 704 237/322 32 606 363
(45e62) (58) (74) (26e40) (50) (30)
65 (5) 14 149 600 418 30
Nonsurvivors (n ¼ 181)
62 97 34/48 32 75 39
(51e73) (54) (71) (25e40) (41) (22)
7 (4)
(1) (12) (50) (35) (2)
1 (<1) 0 30 (17) 128 (71) 22 (12)
331 (24) 365 (26)
293 (24) 260 (21)
38 (21) 105 (58)
985 44 351 273 56
(71) (3) (25) (20) (4)
869 34 299 200 39
(72) (3) (25) (17) (3)
116 10 52 73 17
(65) (6) (29) (40) (9)
156 121 125 13 753 68 37 91 103 127 47 93 111 163 62 48 216 65
(11) (9) (9) (<1) (54) (5) (3) (7) (7) (9) (3) (7) (8) (12) (4) (3) (16) (5)
110 98 97 8 648 51 26 75 84 88 31 70 102 150 44 39 158 56
(9) (8) (8) (<1) (54) (4) (2) (6) (7) (7) (3) (6) (8) (12) (4) (3) (13) (5)
46 23 28 5 105 17 11 16 19 39 16 23 9 13 18 9 58 9
(25) (13) (15) (3) (58) (9) (6) (9) (11) (22) (9) (13) (5) (7) (10) (5) (32) (5)
177 586 354 166 53 434
(13) (42) (25) (12) (4) (31)
164 547 245 120 42 387
(14) (45) (20) (10) (3) (32)
13 39 109 46 11 47
(7) (22) (60) (25) (6) (26)
P value
<0.001 0.356 0.516 0.624 0.090 0.093 0.259 <0.001
<0.001
0.049
<0.001 <0.001 <0.001
0.002 0.019 0.264 0.005 0.005 0.196 0.094 <0.001 <0.001 0.001 0.140 0.047 0.001 0.270 <0.001 0.850 <0.001
<0.001 0.095 0.188 (Continued)
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Continued Total (n ¼ 1,392)
Variable
Emergent surgery, n (%) Abnormal hematocrit, n (%) Abnormal albumin, n (%) Abnormal white blood cell count, n (%) Abnormal white blood cell count and abnormal sodium, n (%) Abnormal sodium, n (%) Abnormal BUN, n (%) Abnormal creatinine, n (%) Abnormal bilirubin, n (%) Abnormal AST, n (%) Abnormal ALP, n (%) Abnormal platelet count <50,000/mm3, n (%) <150,000/mm3 but >50,000/mm3, n (%) Abnormal PTT, n (%) Abnormal PT, n (%) Abnormal INR, n (%)
Survivors (n ¼ 1,211)
Nonsurvivors (n ¼ 181)
P value
862 30/1,329 428/748 721/1,392
(62) (2) (57) (52)
726 24/1,151 339/612 633/1,211
(60) (2) (55) (52)
136 6/178 89/136 88/181
(75) (3) (65) (49)
<0.001 0.350 0.035 0.381
327/1,392 577/1,392 712/1,314 585/1,311 302/744 325/749 252/754
(23) (41) (54) (45) (41) (43) (33)
285/1,211 496/1,211 561/1,138 464/1,136 232/613 247/617 203/620
(24) (41) (49) (41) (38) (40) (33)
42/181 81/181 151/176 121/175 70/131 78/132 49/134
(23) (45) (86) (69) (53) (59) (37)
0.548 0.333 <0.001 <0.001 <0.001 <0.001 0.442 <0.001
42/1,332 186/1,332 297/834 548/820 74/907
(3) (14) (36) (67) (8)
24/1,154 137/1,154 240/707 451/699 43/771
(2) (12) (34) (65) (6)
18/178 49/178 57/127 97/121 31/136
(10) (28) (45) (80) (23)
0.014 0.001 <0.001
ALP, alkaline phosphatase; ASA, American Society of Anesthesiologists; AST, aspartate amino transferase; CHF, congestive heart failure; INR, international normalized ratio; IQR, interquartile range; PCI, percutaneous coronary intervention; PT, prothrombin time; PTT, partial thromboplastin time; PVD, peripheral vascular disease; SIRS, systemic inflammatory response syndrome.
is freely available on request, and an example of how the calculator is used is shown in Figure 1 and in Appendix 3 (online only). Patient cases in Table 4 are examples of calculated postoperative mortality using the risk calculator and corresponding variables.
DISCUSSION Because of the rapidly progressive nature of NSTI, the potential for mortality is a looming concern for practitioners who treat these patients. Fortunately, studies published in the last decade demonstrate lower mortality rates Table 3. Multivariable Logistic Regression Model for 30-Day Postoperative Mortality Variable
Older than 60 years Dependence level Partially dependent Completely dependent Dialysis before operation ASA class 4 Emergent operation Preoperative septic shock Platelet count <50,000/mm3 <150,000/mm3 but >50,000/mm3
Odds ratio
95% CI
p Value
2.47
1.72e3.55
<0.001
1.61 2.33 1.89 3.55 1.56 2.35
0.95e2.69 1.43e3.80 1.15e3.10 2.25e5.59 1.03e2.34 1.55e3.56
0.072 0.001 0.012 <0.001 0.035 <0.001
3.48
1.65e7.37
0.001
1.67
1.21e2.87
0.005
ASA, American Society of Anesthesiologists.
than those historically reported, now ranging from 11% to 17%.5,6,10,11,22,23 Although mortality rates appear to have improved, the identified risk factors for mortality remain protean. Age has been described as a risk for mortality in multiple studies, including our own earlier work.6,10 Other described risk factors have included specific microbial pathogens,3,24 absence of hyperbaric oxygen therapy,25,26 leukocytosis,10,27 and multiple organ failure.5,11,25,28 However, the patients included in the studies and the definitions used for NSTI in identifying these factors have been heterogeneous. Although Anaya and colleagues created the first clinical scoring system to estimate patient mortality from NSTI, our model stands in some contrast to theirs.10 First, their work had the explicit goal of generating a clinical score that could be used at the time of admission to estimate mortality. Although this was not a primary focus of our analysis, our resulting model is created from data that are available at or near the time of admission for an NSTI. Second, although similarity exists between identified variables in our 2 models, NSQIP does not include tachycardia and hypothermia, precluding a definitive direct comparison of the 2 models’ predictive ability. Finally, the Anaya model was developed using administrative data from 2 centers, then was internally validated using data-splitting techniques. The use of NSQIP data for development of our model provides an advantage because of the multicenter nature, and our use of
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Figure 1. Screen shot, interactive necrotizing soft-tissue infections mortality risk calculator.
bootstrapping instead of data-splitting uses the preferred methodology for statistical model validation. Large national databases with validated data obtained by trained collectors have emerged as valuable sources of high-quality data on large numbers of surgical patients, creating a unique opportunity to evaluate mortality risk factors for NSTI. The NSQIP is the best established of these databases. Originally developed to evaluate surgical outcomes in the Veterans Affairs medical system, it has since become the leading nationally validated risk-adjusted outcomes-based program to measure and improve the Table 4.
Sample Mortality Calculations for Necrotizing Soft-Tissue Infections Patients Using Interactive Calculator
Sample Age older patient than 60 y
1 2 3 4 5 6 7 8
quality of surgical care in the private sector.14 Our group previously used NSQIP data to examine outcomes from NSTI in a review including 688 patients with a 12% mortality rate. Using logistic regression, we identified that risk factors for mortality included age, transfer from another facility, emergent surgery, and “sepsis spectrum.”6 NSQIP has been successfully used to model mortality risk in bariatric surgery with predictive abilities that are similarly robust to our results.18 The optimism-corrected ROC for our bootstrap model of 0.83 is consistent with excellent discrimination for
No No Yes Yes No No Yes Yes
Functional status
Independent Partially dependent Partially dependent Totally dependent Totally dependent Totally dependent Totally dependent Totally dependent
ASA, American Society of Anesthesiologists.
Dialysis within last Emergent Septic Preoperative platelet Estimated risk of 30 days before surgery ASA class surgery shock count/mm3 mortality, %
No No No No Yes Yes Yes Yes
3 3 3 3 4 5 5 5
Yes Yes Yes Yes Yes Yes Yes Yes
No Yes Yes Yes Yes Yes Yes Yes
>150,000 >150,000 >150,000 >150,000 >150,000 135,000 135,000 <50,000
1.8 6.5 14.8 20.1 40.6 55.9 75.8 85.5
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a model that can be used in clinical practice.19 In our mortality risk calculator generated from the predictive model, variables are entered as age older than 60 years, partial or complete functional dependence, dialysis dependence, American Society of Anesthesiologists classification, need for emergent surgery, platelet count <150,000/mm3, and presence of septic shock. Data from a patient can be entered into this calculator to return a probability of mortality expressed as a percentage. It is also worth noting that although hyponatremia and leukocytosis are commonly used to identify NSTI patients, neither of these significantly influenced mortality in our analysis and are therefore not components of our risk calculator.9,22,23 Our findings are not without limitations. Although our sample size is larger than any other study to date, our study suffers from the relative clinical rarity of NSTIs. Inclusion in our analysis was dependent on ICD-9 coding, and the use of ICD-9 codes associated with NSTI can vary among facilities in spite of NSQIP data collection being a verified and validated process. Another limitation are the data points included in the PUF, which excludes variables such as number of operations per admission, preoperative glucose levels, if a symptom was present at admission or occurred during admission but before surgery, and microbiology. Institutional variability can occur in the microbial profiles and in the processes of patient care, particularly surgical management. With >500 facilities currently contributing data to NSQIP, this should help mitigate those sources of institutional variability. The primary disadvantage of using NSQIP for the model creation is that there might be a selection bias toward tertiary care facilities because of the limited number of community hospitals that are enrolled in NSQIP; therefore, the relevance of the model to patients treated at smaller facilities is not certain. A weakness of the calculator itself is that the maximal risk of mortality is 85.5%; not all patients with every risk factor died, and therefore no 100% risk of mortality can be calculated using our existing model. This model for mortality risk in patients with NSTI provides an important advance in providing care to these patients and counseling to their families. The model is both relevant and broadly applicable and all variables are available at or near the time of admission. The creation of a simple interactive calculator assures ease of use. Most importantly, it has excellent discrimination, allowing clinicians to have better informed discussions with patients and families about mortality risk in this particular set of complex critically ill patients. Although risk models should not dictate management, when they are available they provide an important tool in the clinician’s toolbox for effective communication.
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Author Contributions Study conception and design: Faraklas, Cochran Acquisition of data: Faraklas, Stoddard Analysis and interpretation of data: Faraklas, Stoddard, Neumayer, Cochran Drafting of manuscript: Faraklas, Stoddard Critical revision: Neumayer, Cochran
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Discussion
surgical care. National VA Surgical Quality Improvement Program. Ann Surg 1998;228:491e507. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361e387. Hanley J, McNeil B. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29e36. Cohen ME, Dimick JB, Bilimoria KY, et al. Risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program: a comparison of logistic versus hierarchical modeling. J Am Coll Surg 2009;209: 687e693. Ramanan B, Gupta PK, Gupta H, et al. Development and validation of a bariatric surgery mortality risk calculator. J Am Coll Surg 2012;214:892e900. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York: John Wiley & Sons; 2000. Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med 2000;19:453e473. Steyerberg EW, Harrell FE Jr, Borsboom GJ, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001;54: 774e781. Su Y, Chen H, Hong Y, et al. Laboratory Risk Indicator for Necrotizing Fasciitis Score and the Outcomes. ANZ J Surg 2008;78:968e972. Yaghoubian A, deVirgilio C, Dauphine C, et al. Use of admission serum lactate and sodium levels to predict mortality in necrotizing soft-tissue infections. Arch Surg 2007;142: 840e846. Ustin JS, Malangoni MA. Necrotizing soft-tissue infections. Crit Care Med 2011;39:2156e2162. Elliott D, Kufera J, Myers R. Necrotizing soft tissue infections: risk factors for mortality and strategies for management. Ann Surg 1996;224:672e683. Riseman JA, Zamboni WA, Curtis A, et al. Hyperbaric oxygen therapy for necrotizing fasciitis reduces mortality and the need for debridements. Surgery 1990;108:847e850. Dworkin MS, Westercamp MD, Park L, McIntyre A. The epidemiology of necrotizing fasciitis including factors associated with death and amputation. Epidemiol Infect 2009;137: 1609e1614. Howell GM, Rosengart MR. Necrotizing soft tissue infections. Surg Infect (Larchmt) 2011;12:185e190.
Discussion INVITED DISCUSSANT: DR KAREN BRASEL (Milwaukee, WI): The authors have used National Surgical Quality Improvement Program (NSQIP) data to develop a prediction model for mortality for patients with necrotizing soft tissue infections. The diagnosis of soft tissue infection was made by ICD-9 code, including the codes for gas gangrene, Fournier’s gangrene, and necrotizing fasciitis. The focus of this model was torso and perineal wounds; patients with necrotizing soft tissue infections of the extremity were excluded. The authors correctly point out that
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previous investigations of patients with necrotizing soft tissue infections suffer from small numbers and/or are from single institutions. What we have learned from these studies is that the single most important factor associated with potentially preventable mortality is early operative intervention, or time to operative intervention. This study differs from others in several ways: A much larger population is included, using a national database, and all patients underwent operation. Although the advantages of a large database are many, one of the disadvantages is that the analysis is limited to the available data. The authors used multivariate regression analysis to develop a risk model, validating it with bootstrapping, a statistical technique used to estimate sampling distributions. Factors included in the risk calculator include age (older than 60 years), American Society of Anesthesiologists (ASA) class, need for dialysis, functional status, septic shock, preoperative platelet count, and need for emergent surgery. 1. Are you confident that the ICD-9 codes include all relevant necrotizing soft tissue infections? Many necrotizing soft tissue infections that I treat involve the subcutaneous tissues, without gas or involvement of the fascia. 2. Over 5 years, the number of annual patients recorded in NSQIP rose from 47 to 360. Do you think that the incidence actually rose that much, or do you have another explanation? 3. Can you comment on the time to operative intervention in relation to mortality? 4. The predictors identified by the authors are not surprising in terms of being associated with mortality. Older, sicker patients do not do as well. Unlike time to operative intervention, which is under at least partial control of the medical system in which we play an important part, these predictors are not modifiable. The unique aspect of the work is the accuracy with which the model predicts mortality for an individual patient. You concluded that the interactive tool developed will help physicians and patients in the decision-making process. How do you envision this tool aiding in the care of these patients? DR AMALIA COCHRAN: In terms of the question that Dr Brasel raised regarding the ICD-9 codes, we did not include necrotizing cellulitis in here. One of the reasons that we chose not to do that is because there’s essentially nothing in the literature about mortality risk associated with necrotizing cellulitis. And, therefore, it was not clear to us that it would be specifically relevant to our goal with this particular study. In terms of the time to operation and how that affects mortality risk, indeed, that is something that has been shown to be an independent risk factor in many studies. As Dr Brasel also noted, one of the limitations of large databases like NSQIP is that you are constrained by the data points that are collected. When the data are collected regarding emergent operations for these patients, it is not at a granular level. So we can’t look at 2 hours vs 4 hours vs 8 hours to the operating room with this dataset. We are simply able to say that an emergent operation, defined by NSQIP as 12 hours or less from admission to the operating room, is a risk factor for mortality. Last but not least, with regard to the question about the benefit and care, we do not visualize this as a tool that would enable someone to rapidly move to a discussion about palliation or comfort care measures for a patient without offering them
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Appendix 1. Variables Analyzed and NSQIP Definitions Variable
Sex (M/F) Ethnicity Type of admission Age, y Height, in Weight, lb Functionally dependent Diabetes Smoker Alcohol Dyspnea Ventilator dependent History of severe COPD
Current pneumonia Ascites Esophageal varices History of CHF History of MI Previous cardiac surgery History of angina Hypertension History of PVD Rest pain Renal failure Dialysis Impaired sensorium Coma Severe neurological deficit Paralysis Cancer Steroid Weight loss Bleeding disorder Transfusion Chemotherapy or radiation Pregnancy Earlier operation Emergent surgery
Definition
Reported as either male or female Categories include: American Indian or Alaskan Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, Hispanic, Unknown, Other Direct admissions were those not transferred from a health care facility. Transfers were patients transferred from an acute care hospital to the NSQIP participating hospital Age of patient with patients older than 89 years coded as 90þ, no patients younger than 15 are included (per HIPAA) Patient’s reported height Patient’s reported weight Patient requiring partial or totally dependent assistance from another person for activities of daily living before surgery Reported as either diabetes mellitus requiring therapy with oral-hypoglycemic agents or insulin therapy If the patient has smoked cigarettes in the year before admission, excludes cigars or pipes If patient endorses drinking >2 drinks/day for the 2 weeks before admission Multiple options reported (at rest or with moderate exertion) if patient has difficult, painful, or labored breathing If patient requires ventilator-assisted respiration anytime in the 48 hours before surgery, excludes treatment of sleep apnea with CPAP If patient has severe functional disability from COPD, previous hospitalization of COPD, excludes patient whose only pulmonary disease is asthma, acute or chronic inflammatory disease resulting in bronchospasm or patients with diffuse interstitial fibrosis or sarcoidosis Patient being treated for current pneumonia with positive cultures Patient with the presence of fluid accumulation in peritoneal cavity within 30 days before operation Patient with esophageal varices present preoperatively and documented on an EGD or CT scan Only patients with newly diagnosed or with new symptoms of congestive heart failure within 30 days of surgery Patient with a history of MI within 6 months Patient that has had major cardiac surgery, excludes pacemaker or defibrillator insertions Patient with new onset or worsening angina in past 30 days before surgery Patient with elevated hypertension requiring medication Patient that has had any angioplasty or revascularization for PVD Patient with rest pain, ulceration or gangrene due to PVD; Fournier’s gangrene is excluded Patient has clinical condition associated with rapid, steady increasing azotemia and a rising creatinine >3 mg/dL Patient requiring has acute or chronic renal failure requiring dialysis within 2 weeks of surgery Patient is acutely confused or delirious within 48 hours of surgery Patient unconscious for >24 hours before surgery, excludes drug-induced coma Patient with a history of hemiplegia, TIA, CVA, or tumor involving the central nervous system Patient that is either paraplegic or quadriplegic Patient being treated for disseminated cancer Patient who requires regular administration of steroids, excludes short course of steroids in past 30 days >10% of body weight in previous 6 months, intentional weight loss is excluded Patient with any condition that places the patient at risk for excessive bleeding requiring hospitalization Patient requiring 5 U blood transfused within 72 hours of surgery Patient receiving chemotherapy or radiotherapy within 30 days before surgery If patient was pregnant at time of surgery If patient had any major surgery in 30 days before surgery If surgeon and anesthesiologist reported case as emergent, usually performed within 12 hours of admission or after the onset of related preoperative symptomatology (Continued)
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Appendix 1. Continued Variable
SIRS
Sepsis Septic shock ASA classification
Return to OR
Definition
Systemic inflammatory response syndrome: includes the presence of two or more of the following: temperature >38 C or <36 C, heart rate >90 bpm, respiratory rate >20 breaths/min or PaCO2 <32 mm/Hg, WBC >12,000/mm3 or <4,000/mm3, or >10% immature (band) forms, anion gap acidosis Includes SIRS plus one of the following: positive blood cultures, clinical documentation of purulence, or positive culture from any site thought to be causative Includes sepsis and documented organ and/or circulatory dysfunction ASA classification: 1 ¼ normal healthy patient 2 ¼ patient with mild systemic disease 3 ¼ patient with severe systemic disease 4 ¼ patient with severe systemic disease that is a constant threat to life 5 ¼ moribund patient who is not expected to survive without the operation If patient returned to the OR (specific number of surgeries that involve return to OR are not documented)
ASA, American Society of Anesthesiologists; CHF, congestive heart failure; CPAP, continuous positive airway pressure; F, female; HIPAA, Health Insurance Portability and Accountability Act; M, male; MI, myocardial infarction; OR, operating room; PVD, peripheral vascular disease; SIRS, systemic inflammatory response syndrome; TIA, transient ischemic event.
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Appendix 2. Laboratory Tests Analyzed with Corresponding ARUP Laboratories Threshholds Laboratory test
White blood cell count Sodium Blood urea nitrogen Creatinine, serum Albumin, serum Total bilirubin, serum Aspartate aminotransferase Alkaline phosphatase Hematocrit Platelets Partial prothrombin time Prothrombin time International normalized ratio
Abnormal cutoff point
15.4 K/mL <135 mg/dL >40 mg/dL 2.5 mg/dL <2.4 mg/dL >1.0 mg/dL >35 U/L >136 U/L <21% <150,000/mm3 >35 s >14 s 2
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BACKGROUND:
Necrotizing soft-tissue infections (NSTI) are a group of uncommon fulminant infections associated with high rates of both morbidity and mortality that require prompt surgical debridement as well as systemic support.1-3 The
reported annual incidence of NSTI cases ranges from 500 to 1,500 in the United States.3,4 This number appears to be rising, possibly reflecting better identification and reporting of cases, or due to better standardization of definitions used by practitioners.5,6 These infections can be caused by external trauma, but often arise with no obvious signs of trauma, especially in patients with predisposing factors.5,6 Several scoring systems, of which the Laboratory Risk Indicator for Necrotizing Fasciitis score is the best known, have been created to distinguish NSTI from other soft-tissue infections. However, none of these scoring systems are prognostic of mortality in NSTI patients.3,7-10 Reported mortality rates consequent to NSTI during the last 2 decades have ranged from 10% to >30%.5,6,10,11 Lengthy and variable lists of factors influencing mortality have been generated by many publications. However, the
CME questions for this article available at http://jacscme.facs.org Disclosure Information: Authors have nothing to disclose. Timothy J Eberlein, Editor-in-Chief, has nothing to disclose. Presented at the Western Surgical Association, 120th Scientific Session, Colorado Springs, CO, November 2012. Received December 29, 2012; Revised February 14, 2013; Accepted February 14, 2013. From the Departments of Surgery (Faraklas, Neumayer, Cochran) and Internal Medicine (Stoddard), University of Utah, Salt Lake City, UT. Correspondence address: Amalia Cochran, MD, FACS, Department of Surgery, University of Utah, 30 North 1900 East, 3B110 SOM, Salt Lake City, UT 84132. email: [email protected]
ª 2013 by the American College of Surgeons Published by Elsevier Inc.
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ISSN 1072-7515/13/$36.00 http://dx.doi.org/10.1016/j.jamcollsurg.2013.02.029
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Abbreviations and Acronyms
CPT IQR NSTI OR PUF ROC
¼ ¼ ¼ ¼ ¼ ¼
Current Procedural Terminology interquartile range necrotizing soft-tissue infection odds ratio Participant Use Data File receiver operating characteristic
only published risk scoring system for NSTI mortality to date is that of Anaya and colleagues.10 The objective of our study was to use NSQIP to develop and validate a 30-day postoperative mortality risk calculator for NSTI patients.
METHODS Dataset After receiving institutional review board approval, data were extracted from 2005e2010 NSQIP Participant Use Data Files (PUF). The design of NSQIP has been described in detail by other authors.12-14 The PUF contain prospectively collected data using standardized variable definitions on surgical patients at participating academic and community hospitals. The PUF include >135 variables for each patient, including preoperative risk factors, intraoperative variables, and 30-day postoperative morbidity and mortality for patients who undergo major surgical procedures in both the inpatient and outpatient settings. Data from each participating hospital are collected, validated, and submitted by a trained surgical clinical reviewer through medical chart abstraction. To ensure inter-rater reliability, NSQIP requires standardized initial and ongoing reviewer training and conducts periodic inter-rater reliability audits of participating sites. A sample of all surgical cases submitted by each participating institution is selected for analysis. This system allows the data to remain stratified, prevents bias, and eliminates the potential for over- or underrepresentation by individual centers. The NSQIP normalizes a subset of cases that are representative of all surgical cases nationwide. The PUF do not identify hospitals, health care providers, or patients. The files are Health Insurance Portability and Accountability Act compliant. Every surgical procedure is recorded in NSQIP using ICD-9-CM codes. Patients discharged with an ICD-9-CM diagnosis of gas gangrene (040.0); necrotizing fasciitis (728.86); or Fournier’s gangrene (608.83) were included in this analysis. Outpatients with a length of stay <1 day were excluded because this clinical course would not be consistent with an NSTI. Current Procedural Terminology (CPT) codes were available for all patients and were used to cross-check ICD-9-CM codes.
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Data evaluated from NSQIP included basic demographics (age, sex, race, height, and weight), medical comorbidities, preoperative laboratory values, surgical, and outcomes data. The NSQIP does not delineate between those symptoms present at time of admission and those symptoms that occurred later in the hospital stay but before surgery. However, NSQIP does delineate between complications occurring before, during, and after surgery. A list of important variables analyzed in this review and their definitions is included in Appendix 1 (online only). Readers who have additional questions about NSQIP can contact the program at http://www. acsnsqip.org. Appendix 2 (online only) lists preoperative laboratory tests collected in NSQIP and included in the PUF. Only preoperative laboratory variables that were collected within 2 days before surgery were included in analysis. Missing laboratory variables were not assumed to be normal and these missing variables were excluded from analysis. Abnormal cutoffs for laboratory values were based on the guidelines from ARUP Laboratories, which serves as a national reference laboratory. The primary outcomes variable for our analysis was surgical mortality. Outcomes are tracked for 30 days postoperatively in NSQIP. If the patient was still an inpatient at 30 days, they were tracked until discharge or until NSQIP locked the data file at 120 days after the first surgery. Statistical analysis Statistical analysis was performed using Stata version 12.1 (StataCorp). For comparison of patient demographics, Pearson chi-square or Fisher’s exact test was used for categorical variables; Wilcoxon rank sum was used for continuous variables. All reported p values are from a 2-sided comparison. Univariate exploratory analysis of pre- and intraoperative variables was performed to establish relationships with 30-day postoperative mortality. All significant independent variables were included in the stepwise multiple logistic regression analysis to evaluate mortality in NSTI patients; however, preoperative laboratory values with <400 observations were excluded in the interest of maintaining a robust model. The included laboratory values were the following: hematocrit, BUN, creatinine, sodium, aspartate aminotransferase, alkaline phosphatase, partial thromboplastin time, prothrombin time, white blood cell count, and platelet count. Statistical models are frequently validated using the concordance statistic (c-statistic).15 In measuring discrimination of a model for binary outcomes, as in our study with mortality, the c-statistic is the same as the area under
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the receiver operating characteristic (ROC) curve.16 An ROC curve is a plot of the true-positive against the false-positive rate for the different possible thresholds of a diagnostic test. The ROC curve demonstrates the tradeoff between sensitivity and specificity. The closer the curve follows the left-hand border and the top border, the more accurate the test. The ROC area refers to the ability of the risk estimate to discriminate cases (nonsurvivors) from noncases (survivors).17,18 The ROC area equals 1 if discrimination is perfect and 0.50 if it is no better than chance. Statisticians generally agree that a ROC >0.70 shows acceptable discrimination, and an ROC >0.80 is considered excellent discrimination for use in clinical practice.19 An optimism-corrected ROC area is reduced by the estimated deterioration that the model will have when applied to new subjects. When a sample size is limited, the favored method for validation of a statistical model is the bootstrap method.15 Unlike the traditional split-sample approach in which half of the sample is used to develop the model and the other half to validate the model, bootstrapping permits the use of the entire dataset for both model development and validation. Numerous studies have demonstrated that bootstrapping outperforms split-sample validation.15,20,21 If the bootstrap optimism-corrected ROC area shows acceptable predictive accuracy, then the model is validated.15 In multivariable logistic regression for the identified outcomes (mortality), with the independent variables Xi (ie, age, functional status), a is the intercept of the model, and bi the slope of each predictor variable. Predicted probability of mortality is calculated from the following prediction equation: Pobability of mortality ð%Þ ¼
1 1þe
P
ð aþ
bi X i Þ
100%
Once the model was developed and validated, an interactive spreadsheet was created using the risk factors from the validated model.
RESULTS A total of 1,392 patients with NSTI were identified in NSQIP from 2005 through 2010, 82% (n ¼ 1,142) with a principal discharge diagnosis of necrotizing fasciitis, 8.6% (n ¼ 119) with gas gangrene, and 9.4% (n ¼ 131) with Fournier’s gangrene. Five patients included in the analysis were coded as outpatients, but had lengths of stay longer than 1 day; we assumed that the outpatient coding was in error for the purposes of this study. As shown in Table 1, the number of NSTI admissions documented in NSQIP increased progressively from 47 patients
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Table 1. Number of Necrotizing Soft-Tissue Infections Cases vs Total NSQIP Cases per Year Year No. of NSTI cases (% NSQIP cases) Total NSQIP cases, n
2005 2006 2007 2008 2009 2010
47 140 212 281 352 360
(0.14) (0.14) (0.10) (0.10) (0.10) (0.09)
34,099 118,391 211,407 271,368 336,190 363,431
NSTI, necrotizing soft-tissue infections.
in 2005 to 360 in 2010. Total NSQIP cases grew at a rate comparable with NSTI cases, from slightly >34,000 in 2005 to >360,000 in 2010. Each patient had a corresponding principal CPT code documented, with 221 different CPT codes reported. The most frequent CPT codes were debridement of NSTI to external genitalia, perineum, and abdominal wall (11004-11006); these three codes accounted for 43% of all codes reported. Basic demographics and preoperative characteristics for survivors and nonsurvivors are shown in Table 2. Most patients were male, with a median age of 55 years (interquartile range [IQR] 46 to 63 years) and median body mass index of 32 (IQR 26 to 40). Median length of stay was 16 days (IQR 9 to 30 days); however, there were 9 patients who were still in the hospital when NSQIP locked their data file (>120 days from first surgery). Thirty-day mortality was 13% (n ¼ 181). Median length of stay for survivors was 17 days (IQR 10 to 32 days) and median length of stay for nonsurvivors was 9 days (IQR 3 to 18 days; p < 0.001, Wilcoxon rank sum). Multivariable logistic regression (Table 3) identified the following 7 independent variables that affected mortality: patients older than 60 years, totally dependent functional status, requiring dialysis before surgery, low platelet count (<150,000/mm3), septic shock, American Society of Anesthesiologists class 4 or higher, and emergent surgery (<12 hours post admission). Partially dependent functional status was marginally significant (p ¼ 0.07), but when it was removed from analysis the discriminatory ability of the model decreased from an ROC area of 0.85 to 0.82; and the variable was kept in the model. The ROC area was 0.85 (95% CI 0.82e0.87), which indicated a strong predictive model. Using bootstrap validation, the optimism-corrected ROC area was 0.83 (95% CI 0.81e0.86), which represents the predictive ability of the model in future patients. The variables identified in model creation were used to develop an interactive risk calculator. This risk calculator
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Table 2.
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Patient Demographics and Preoperative Status: Survivors vs Nonsurvivors
Variable
Age, y, median (IQR) Male, n (%) Caucasian, n (%) Body mass index, median (IQR) Diabetic, n (%) Smoker, n (%) >2 Alcoholic drinks/day for 2 weeks before operation, n (%) ASA class, n (%) 1 2 3 4 5 Functional status, n (%) Partially dependent Totally dependent Transfer from, n (%) Home Chronic care Acute or emergent care Ventilatory support Pneumonia Dyspnea, n (%) At rest With moderate exertion History of COPD, n (%) History of angina, n (%) Hypertension, n (%) History of CHF, n (%) MI within 6 months, n (%) Previous PCI, n (%) Previous cardiac surgery, n (%) Dialysis, n (%) Ascites, n (%) Chronic steroid use, n (%) History of surgery for PVD, n (%) Rest pain, n (%) Cancer, n (%) Preoperative weight loss, n (%) Bleeding disorder, n (%) >4 blood transfusions before surgery, n (%) Preoperative SIRS, sepsis or septic shock, n (%) SIRS before surgery Sepsis before surgery Septic shock before surgery Impaired sensorium Stroke Surgery in past 30 days, n (%)
Total (n ¼ 1,392)
55 801 271/399 32 681 402
(46e63) (58) (68) (26e40) (49) (29)
72 (5) 15 149 630 546 52
(1) (11) (45) (39) (4)
Survivors (n ¼ 1,211)
54 704 237/322 32 606 363
(45e62) (58) (74) (26e40) (50) (30)
65 (5) 14 149 600 418 30
Nonsurvivors (n ¼ 181)
62 97 34/48 32 75 39
(51e73) (54) (71) (25e40) (41) (22)
7 (4)
(1) (12) (50) (35) (2)
1 (<1) 0 30 (17) 128 (71) 22 (12)
331 (24) 365 (26)
293 (24) 260 (21)
38 (21) 105 (58)
985 44 351 273 56
(71) (3) (25) (20) (4)
869 34 299 200 39
(72) (3) (25) (17) (3)
116 10 52 73 17
(65) (6) (29) (40) (9)
156 121 125 13 753 68 37 91 103 127 47 93 111 163 62 48 216 65
(11) (9) (9) (<1) (54) (5) (3) (7) (7) (9) (3) (7) (8) (12) (4) (3) (16) (5)
110 98 97 8 648 51 26 75 84 88 31 70 102 150 44 39 158 56
(9) (8) (8) (<1) (54) (4) (2) (6) (7) (7) (3) (6) (8) (12) (4) (3) (13) (5)
46 23 28 5 105 17 11 16 19 39 16 23 9 13 18 9 58 9
(25) (13) (15) (3) (58) (9) (6) (9) (11) (22) (9) (13) (5) (7) (10) (5) (32) (5)
177 586 354 166 53 434
(13) (42) (25) (12) (4) (31)
164 547 245 120 42 387
(14) (45) (20) (10) (3) (32)
13 39 109 46 11 47
(7) (22) (60) (25) (6) (26)
P value
<0.001 0.356 0.516 0.624 0.090 0.093 0.259 <0.001
<0.001
0.049
<0.001 <0.001 <0.001
0.002 0.019 0.264 0.005 0.005 0.196 0.094 <0.001 <0.001 0.001 0.140 0.047 0.001 0.270 <0.001 0.850 <0.001
<0.001 0.095 0.188 (Continued)
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Continued Total (n ¼ 1,392)
Variable
Emergent surgery, n (%) Abnormal hematocrit, n (%) Abnormal albumin, n (%) Abnormal white blood cell count, n (%) Abnormal white blood cell count and abnormal sodium, n (%) Abnormal sodium, n (%) Abnormal BUN, n (%) Abnormal creatinine, n (%) Abnormal bilirubin, n (%) Abnormal AST, n (%) Abnormal ALP, n (%) Abnormal platelet count <50,000/mm3, n (%) <150,000/mm3 but >50,000/mm3, n (%) Abnormal PTT, n (%) Abnormal PT, n (%) Abnormal INR, n (%)
Survivors (n ¼ 1,211)
Nonsurvivors (n ¼ 181)
P value
862 30/1,329 428/748 721/1,392
(62) (2) (57) (52)
726 24/1,151 339/612 633/1,211
(60) (2) (55) (52)
136 6/178 89/136 88/181
(75) (3) (65) (49)
<0.001 0.350 0.035 0.381
327/1,392 577/1,392 712/1,314 585/1,311 302/744 325/749 252/754
(23) (41) (54) (45) (41) (43) (33)
285/1,211 496/1,211 561/1,138 464/1,136 232/613 247/617 203/620
(24) (41) (49) (41) (38) (40) (33)
42/181 81/181 151/176 121/175 70/131 78/132 49/134
(23) (45) (86) (69) (53) (59) (37)
0.548 0.333 <0.001 <0.001 <0.001 <0.001 0.442 <0.001
42/1,332 186/1,332 297/834 548/820 74/907
(3) (14) (36) (67) (8)
24/1,154 137/1,154 240/707 451/699 43/771
(2) (12) (34) (65) (6)
18/178 49/178 57/127 97/121 31/136
(10) (28) (45) (80) (23)
0.014 0.001 <0.001
ALP, alkaline phosphatase; ASA, American Society of Anesthesiologists; AST, aspartate amino transferase; CHF, congestive heart failure; INR, international normalized ratio; IQR, interquartile range; PCI, percutaneous coronary intervention; PT, prothrombin time; PTT, partial thromboplastin time; PVD, peripheral vascular disease; SIRS, systemic inflammatory response syndrome.
is freely available on request, and an example of how the calculator is used is shown in Figure 1 and in Appendix 3 (online only). Patient cases in Table 4 are examples of calculated postoperative mortality using the risk calculator and corresponding variables.
DISCUSSION Because of the rapidly progressive nature of NSTI, the potential for mortality is a looming concern for practitioners who treat these patients. Fortunately, studies published in the last decade demonstrate lower mortality rates Table 3. Multivariable Logistic Regression Model for 30-Day Postoperative Mortality Variable
Older than 60 years Dependence level Partially dependent Completely dependent Dialysis before operation ASA class 4 Emergent operation Preoperative septic shock Platelet count <50,000/mm3 <150,000/mm3 but >50,000/mm3
Odds ratio
95% CI
p Value
2.47
1.72e3.55
<0.001
1.61 2.33 1.89 3.55 1.56 2.35
0.95e2.69 1.43e3.80 1.15e3.10 2.25e5.59 1.03e2.34 1.55e3.56
0.072 0.001 0.012 <0.001 0.035 <0.001
3.48
1.65e7.37
0.001
1.67
1.21e2.87
0.005
ASA, American Society of Anesthesiologists.
than those historically reported, now ranging from 11% to 17%.5,6,10,11,22,23 Although mortality rates appear to have improved, the identified risk factors for mortality remain protean. Age has been described as a risk for mortality in multiple studies, including our own earlier work.6,10 Other described risk factors have included specific microbial pathogens,3,24 absence of hyperbaric oxygen therapy,25,26 leukocytosis,10,27 and multiple organ failure.5,11,25,28 However, the patients included in the studies and the definitions used for NSTI in identifying these factors have been heterogeneous. Although Anaya and colleagues created the first clinical scoring system to estimate patient mortality from NSTI, our model stands in some contrast to theirs.10 First, their work had the explicit goal of generating a clinical score that could be used at the time of admission to estimate mortality. Although this was not a primary focus of our analysis, our resulting model is created from data that are available at or near the time of admission for an NSTI. Second, although similarity exists between identified variables in our 2 models, NSQIP does not include tachycardia and hypothermia, precluding a definitive direct comparison of the 2 models’ predictive ability. Finally, the Anaya model was developed using administrative data from 2 centers, then was internally validated using data-splitting techniques. The use of NSQIP data for development of our model provides an advantage because of the multicenter nature, and our use of
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Figure 1. Screen shot, interactive necrotizing soft-tissue infections mortality risk calculator.
bootstrapping instead of data-splitting uses the preferred methodology for statistical model validation. Large national databases with validated data obtained by trained collectors have emerged as valuable sources of high-quality data on large numbers of surgical patients, creating a unique opportunity to evaluate mortality risk factors for NSTI. The NSQIP is the best established of these databases. Originally developed to evaluate surgical outcomes in the Veterans Affairs medical system, it has since become the leading nationally validated risk-adjusted outcomes-based program to measure and improve the Table 4.
Sample Mortality Calculations for Necrotizing Soft-Tissue Infections Patients Using Interactive Calculator
Sample Age older patient than 60 y
1 2 3 4 5 6 7 8
quality of surgical care in the private sector.14 Our group previously used NSQIP data to examine outcomes from NSTI in a review including 688 patients with a 12% mortality rate. Using logistic regression, we identified that risk factors for mortality included age, transfer from another facility, emergent surgery, and “sepsis spectrum.”6 NSQIP has been successfully used to model mortality risk in bariatric surgery with predictive abilities that are similarly robust to our results.18 The optimism-corrected ROC for our bootstrap model of 0.83 is consistent with excellent discrimination for
No No Yes Yes No No Yes Yes
Functional status
Independent Partially dependent Partially dependent Totally dependent Totally dependent Totally dependent Totally dependent Totally dependent
ASA, American Society of Anesthesiologists.
Dialysis within last Emergent Septic Preoperative platelet Estimated risk of 30 days before surgery ASA class surgery shock count/mm3 mortality, %
No No No No Yes Yes Yes Yes
3 3 3 3 4 5 5 5
Yes Yes Yes Yes Yes Yes Yes Yes
No Yes Yes Yes Yes Yes Yes Yes
>150,000 >150,000 >150,000 >150,000 >150,000 135,000 135,000 <50,000
1.8 6.5 14.8 20.1 40.6 55.9 75.8 85.5
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a model that can be used in clinical practice.19 In our mortality risk calculator generated from the predictive model, variables are entered as age older than 60 years, partial or complete functional dependence, dialysis dependence, American Society of Anesthesiologists classification, need for emergent surgery, platelet count <150,000/mm3, and presence of septic shock. Data from a patient can be entered into this calculator to return a probability of mortality expressed as a percentage. It is also worth noting that although hyponatremia and leukocytosis are commonly used to identify NSTI patients, neither of these significantly influenced mortality in our analysis and are therefore not components of our risk calculator.9,22,23 Our findings are not without limitations. Although our sample size is larger than any other study to date, our study suffers from the relative clinical rarity of NSTIs. Inclusion in our analysis was dependent on ICD-9 coding, and the use of ICD-9 codes associated with NSTI can vary among facilities in spite of NSQIP data collection being a verified and validated process. Another limitation are the data points included in the PUF, which excludes variables such as number of operations per admission, preoperative glucose levels, if a symptom was present at admission or occurred during admission but before surgery, and microbiology. Institutional variability can occur in the microbial profiles and in the processes of patient care, particularly surgical management. With >500 facilities currently contributing data to NSQIP, this should help mitigate those sources of institutional variability. The primary disadvantage of using NSQIP for the model creation is that there might be a selection bias toward tertiary care facilities because of the limited number of community hospitals that are enrolled in NSQIP; therefore, the relevance of the model to patients treated at smaller facilities is not certain. A weakness of the calculator itself is that the maximal risk of mortality is 85.5%; not all patients with every risk factor died, and therefore no 100% risk of mortality can be calculated using our existing model. This model for mortality risk in patients with NSTI provides an important advance in providing care to these patients and counseling to their families. The model is both relevant and broadly applicable and all variables are available at or near the time of admission. The creation of a simple interactive calculator assures ease of use. Most importantly, it has excellent discrimination, allowing clinicians to have better informed discussions with patients and families about mortality risk in this particular set of complex critically ill patients. Although risk models should not dictate management, when they are available they provide an important tool in the clinician’s toolbox for effective communication.
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Author Contributions Study conception and design: Faraklas, Cochran Acquisition of data: Faraklas, Stoddard Analysis and interpretation of data: Faraklas, Stoddard, Neumayer, Cochran Drafting of manuscript: Faraklas, Stoddard Critical revision: Neumayer, Cochran
REFERENCES 1. Kuncir EJ, Tillou A, St Hill CR, et al. Necrotizing soft-tissue infections. Emerg Med Clin North Am 2003;21:1075e1087. 2. Salcido RS. Necrotizing fasciitis: reviewing the causes and treatment strategies. Adv Skin Wound Care 2007;20: 288e293; quiz 294285. 3. Sarani B, Strong M, Pascual J, Schwab CW. Necrotizing fasciitis: current concepts and review of the literature. J Am Coll Surg 2009;208:279e288. 4. Faucher LD, Morris SE, Edelman LS, Saffle JR. Burn center management of necrotizing soft-tissue surgical infections in unburned patients. Am J Surg 2001;182:563e569. 5. Kao LS, Lew DF, Arab SN, et al. Local variations in the epidemiology, microbiology, and outcome of necrotizing soft-tissue infections: a multicenter study. Am J Surg 2011;202: 139e145. 6. Mills MK, Faraklas I, Davis C, et al. Outcomes from treatment of necrotizing soft-tissue infections: results from the National Surgical Quality Improvement Program database. Am J Surg 2010;200:790e796; discussion 796797. 7. Wall DB, Klein SR, Black S, de Virgilio C. A simple model to help distinguish necrotizing fasciitis from nonnecrotizing soft tissue infection. J Am Coll Surg 2000;191:227e231. 8. Wong CH, Khin LW. Clinical relevance of the LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) score for assessment of early necrotizing fasciitis. Crit Care Med 2005; 33:1677. 9. Wong CH, Khin LW, Heng KS, et al. The LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) score: a tool for distinguishing necrotizing fasciitis from other soft tissue infections. Crit Care Med 2004;32:1535e1541. 10. Anaya D, Bulger E, Kwon Y, et al. Predicting death in necrotizing soft tissue infections: a clinical score. Surg Infect 2009; 10:517e522. 11. Endorf FW, Klein MB, Mack CD, et al. Necrotizing softtissue infections: differences in patients treated at burn centers and non-burn centers. J Burn Care Res 2008;29:933e938. 12. American College of Surgeons. National Surgical Quality Improvement Program Participant User Data Profile. Available at: http://www.acsnsqip.org/puf/docs/ACS_NSQIP_ Participant_User_Data_File_User_Guide.pdf. Accessed April 20, 2012. 13. Daley J, Khuri SF, Henderson W, et al. Risk adjustment of the postoperative morbidity rate for the comparative assessment of the quality of surgical care: results of the National Veterans Affairs Surgical Risk Study. J Am Coll Surg 1997;185: 328e340. 14. Khuri SF, Daley J, Henderson W, et al. The Department of Veterans Affairs’ NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of
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surgical care. National VA Surgical Quality Improvement Program. Ann Surg 1998;228:491e507. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361e387. Hanley J, McNeil B. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29e36. Cohen ME, Dimick JB, Bilimoria KY, et al. Risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program: a comparison of logistic versus hierarchical modeling. J Am Coll Surg 2009;209: 687e693. Ramanan B, Gupta PK, Gupta H, et al. Development and validation of a bariatric surgery mortality risk calculator. J Am Coll Surg 2012;214:892e900. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York: John Wiley & Sons; 2000. Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med 2000;19:453e473. Steyerberg EW, Harrell FE Jr, Borsboom GJ, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001;54: 774e781. Su Y, Chen H, Hong Y, et al. Laboratory Risk Indicator for Necrotizing Fasciitis Score and the Outcomes. ANZ J Surg 2008;78:968e972. Yaghoubian A, deVirgilio C, Dauphine C, et al. Use of admission serum lactate and sodium levels to predict mortality in necrotizing soft-tissue infections. Arch Surg 2007;142: 840e846. Ustin JS, Malangoni MA. Necrotizing soft-tissue infections. Crit Care Med 2011;39:2156e2162. Elliott D, Kufera J, Myers R. Necrotizing soft tissue infections: risk factors for mortality and strategies for management. Ann Surg 1996;224:672e683. Riseman JA, Zamboni WA, Curtis A, et al. Hyperbaric oxygen therapy for necrotizing fasciitis reduces mortality and the need for debridements. Surgery 1990;108:847e850. Dworkin MS, Westercamp MD, Park L, McIntyre A. The epidemiology of necrotizing fasciitis including factors associated with death and amputation. Epidemiol Infect 2009;137: 1609e1614. Howell GM, Rosengart MR. Necrotizing soft tissue infections. Surg Infect (Larchmt) 2011;12:185e190.
Discussion INVITED DISCUSSANT: DR KAREN BRASEL (Milwaukee, WI): The authors have used National Surgical Quality Improvement Program (NSQIP) data to develop a prediction model for mortality for patients with necrotizing soft tissue infections. The diagnosis of soft tissue infection was made by ICD-9 code, including the codes for gas gangrene, Fournier’s gangrene, and necrotizing fasciitis. The focus of this model was torso and perineal wounds; patients with necrotizing soft tissue infections of the extremity were excluded. The authors correctly point out that
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previous investigations of patients with necrotizing soft tissue infections suffer from small numbers and/or are from single institutions. What we have learned from these studies is that the single most important factor associated with potentially preventable mortality is early operative intervention, or time to operative intervention. This study differs from others in several ways: A much larger population is included, using a national database, and all patients underwent operation. Although the advantages of a large database are many, one of the disadvantages is that the analysis is limited to the available data. The authors used multivariate regression analysis to develop a risk model, validating it with bootstrapping, a statistical technique used to estimate sampling distributions. Factors included in the risk calculator include age (older than 60 years), American Society of Anesthesiologists (ASA) class, need for dialysis, functional status, septic shock, preoperative platelet count, and need for emergent surgery. 1. Are you confident that the ICD-9 codes include all relevant necrotizing soft tissue infections? Many necrotizing soft tissue infections that I treat involve the subcutaneous tissues, without gas or involvement of the fascia. 2. Over 5 years, the number of annual patients recorded in NSQIP rose from 47 to 360. Do you think that the incidence actually rose that much, or do you have another explanation? 3. Can you comment on the time to operative intervention in relation to mortality? 4. The predictors identified by the authors are not surprising in terms of being associated with mortality. Older, sicker patients do not do as well. Unlike time to operative intervention, which is under at least partial control of the medical system in which we play an important part, these predictors are not modifiable. The unique aspect of the work is the accuracy with which the model predicts mortality for an individual patient. You concluded that the interactive tool developed will help physicians and patients in the decision-making process. How do you envision this tool aiding in the care of these patients? DR AMALIA COCHRAN: In terms of the question that Dr Brasel raised regarding the ICD-9 codes, we did not include necrotizing cellulitis in here. One of the reasons that we chose not to do that is because there’s essentially nothing in the literature about mortality risk associated with necrotizing cellulitis. And, therefore, it was not clear to us that it would be specifically relevant to our goal with this particular study. In terms of the time to operation and how that affects mortality risk, indeed, that is something that has been shown to be an independent risk factor in many studies. As Dr Brasel also noted, one of the limitations of large databases like NSQIP is that you are constrained by the data points that are collected. When the data are collected regarding emergent operations for these patients, it is not at a granular level. So we can’t look at 2 hours vs 4 hours vs 8 hours to the operating room with this dataset. We are simply able to say that an emergent operation, defined by NSQIP as 12 hours or less from admission to the operating room, is a risk factor for mortality. Last but not least, with regard to the question about the benefit and care, we do not visualize this as a tool that would enable someone to rapidly move to a discussion about palliation or comfort care measures for a patient without offering them
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Appendix 1. Variables Analyzed and NSQIP Definitions Variable
Sex (M/F) Ethnicity Type of admission Age, y Height, in Weight, lb Functionally dependent Diabetes Smoker Alcohol Dyspnea Ventilator dependent History of severe COPD
Current pneumonia Ascites Esophageal varices History of CHF History of MI Previous cardiac surgery History of angina Hypertension History of PVD Rest pain Renal failure Dialysis Impaired sensorium Coma Severe neurological deficit Paralysis Cancer Steroid Weight loss Bleeding disorder Transfusion Chemotherapy or radiation Pregnancy Earlier operation Emergent surgery
Definition
Reported as either male or female Categories include: American Indian or Alaskan Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, Hispanic, Unknown, Other Direct admissions were those not transferred from a health care facility. Transfers were patients transferred from an acute care hospital to the NSQIP participating hospital Age of patient with patients older than 89 years coded as 90þ, no patients younger than 15 are included (per HIPAA) Patient’s reported height Patient’s reported weight Patient requiring partial or totally dependent assistance from another person for activities of daily living before surgery Reported as either diabetes mellitus requiring therapy with oral-hypoglycemic agents or insulin therapy If the patient has smoked cigarettes in the year before admission, excludes cigars or pipes If patient endorses drinking >2 drinks/day for the 2 weeks before admission Multiple options reported (at rest or with moderate exertion) if patient has difficult, painful, or labored breathing If patient requires ventilator-assisted respiration anytime in the 48 hours before surgery, excludes treatment of sleep apnea with CPAP If patient has severe functional disability from COPD, previous hospitalization of COPD, excludes patient whose only pulmonary disease is asthma, acute or chronic inflammatory disease resulting in bronchospasm or patients with diffuse interstitial fibrosis or sarcoidosis Patient being treated for current pneumonia with positive cultures Patient with the presence of fluid accumulation in peritoneal cavity within 30 days before operation Patient with esophageal varices present preoperatively and documented on an EGD or CT scan Only patients with newly diagnosed or with new symptoms of congestive heart failure within 30 days of surgery Patient with a history of MI within 6 months Patient that has had major cardiac surgery, excludes pacemaker or defibrillator insertions Patient with new onset or worsening angina in past 30 days before surgery Patient with elevated hypertension requiring medication Patient that has had any angioplasty or revascularization for PVD Patient with rest pain, ulceration or gangrene due to PVD; Fournier’s gangrene is excluded Patient has clinical condition associated with rapid, steady increasing azotemia and a rising creatinine >3 mg/dL Patient requiring has acute or chronic renal failure requiring dialysis within 2 weeks of surgery Patient is acutely confused or delirious within 48 hours of surgery Patient unconscious for >24 hours before surgery, excludes drug-induced coma Patient with a history of hemiplegia, TIA, CVA, or tumor involving the central nervous system Patient that is either paraplegic or quadriplegic Patient being treated for disseminated cancer Patient who requires regular administration of steroids, excludes short course of steroids in past 30 days >10% of body weight in previous 6 months, intentional weight loss is excluded Patient with any condition that places the patient at risk for excessive bleeding requiring hospitalization Patient requiring 5 U blood transfused within 72 hours of surgery Patient receiving chemotherapy or radiotherapy within 30 days before surgery If patient was pregnant at time of surgery If patient had any major surgery in 30 days before surgery If surgeon and anesthesiologist reported case as emergent, usually performed within 12 hours of admission or after the onset of related preoperative symptomatology (Continued)
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Appendix 1. Continued Variable
SIRS
Sepsis Septic shock ASA classification
Return to OR
Definition
Systemic inflammatory response syndrome: includes the presence of two or more of the following: temperature >38 C or <36 C, heart rate >90 bpm, respiratory rate >20 breaths/min or PaCO2 <32 mm/Hg, WBC >12,000/mm3 or <4,000/mm3, or >10% immature (band) forms, anion gap acidosis Includes SIRS plus one of the following: positive blood cultures, clinical documentation of purulence, or positive culture from any site thought to be causative Includes sepsis and documented organ and/or circulatory dysfunction ASA classification: 1 ¼ normal healthy patient 2 ¼ patient with mild systemic disease 3 ¼ patient with severe systemic disease 4 ¼ patient with severe systemic disease that is a constant threat to life 5 ¼ moribund patient who is not expected to survive without the operation If patient returned to the OR (specific number of surgeries that involve return to OR are not documented)
ASA, American Society of Anesthesiologists; CHF, congestive heart failure; CPAP, continuous positive airway pressure; F, female; HIPAA, Health Insurance Portability and Accountability Act; M, male; MI, myocardial infarction; OR, operating room; PVD, peripheral vascular disease; SIRS, systemic inflammatory response syndrome; TIA, transient ischemic event.
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Appendix 2. Laboratory Tests Analyzed with Corresponding ARUP Laboratories Threshholds Laboratory test
White blood cell count Sodium Blood urea nitrogen Creatinine, serum Albumin, serum Total bilirubin, serum Aspartate aminotransferase Alkaline phosphatase Hematocrit Platelets Partial prothrombin time Prothrombin time International normalized ratio
Abnormal cutoff point
15.4 K/mL <135 mg/dL >40 mg/dL 2.5 mg/dL <2.4 mg/dL >1.0 mg/dL >35 U/L >136 U/L <21% <150,000/mm3 >35 s >14 s 2
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