Early Colectomy Saves Lives in Toxic Megacolon due to Clostridium difficile Infection

Nasim Ahmed, MD, FACS; Yen-Hong Kuo, PhD

Disclosures

South Med J. 2020;113(7):345-349. 

In This Article

Methods

Data of all of the patients who underwent emergency colectomy for toxic megacolon resulting from C. difficile colitis were extracted from the 2012–2016 NISQIP database. C. difficile infection was diagnosed based on stool sample tests. Toxic megacolon is defined as the extreme dilatation of the colon, edematous bowel wall, and loss of haustra on radiological evaluation. All elective colectomies and emergency colectomies for other reasons were excluded from the study. Patients younger than 18 years and 90 years old and older also were excluded. The study included patient demography, clinical information (principal operation; presence and absence of shock; respiratory failure before the operation requiring mechanical ventilation; and comorbidities, including history of diabetes mellitus, hypertension, chronic obstructive pulmonary disease, ascites, congestive heart failure, chronic renal failure [CKD], CKD with dialysis, disseminated cancer, and bleeding disorders), weight loss, use of steroids, use of blood transfusions before surgery, and history of smoking. The patients were then divided into two groups based on the timing of the colectomy. Patients who underwent colectomy before the presentation of septic shock were placed in the early colectomy group. Patients who underwent colectomy after the presentation of septic shock were placed in the late colectomy group. Septic shock was defined as signs of sepsis (temperature >38°C or <36°C, heart rate >90 beats per minute, white blood cell count [WBC] >1200 cells/mm3 or <4000 cells/mm3, acidosis) with confirmation of a diagnosis of ischemic/infarcted bowel, purulence or enteric content in the operative field, or positive intraoperative culture that requires bowel resection along with organ dysfunction and/or circulatory collapse. These septic shock characteristics must be present for 48 hours before the operation. The primary outcome is 30-day mortality. Secondary outcomes are total length of hospital stay, discharge disposition, and 30-day complications.

Patient characteristics including comorbidities and outcomes were summarized using mean with standard deviation, median with interquartile range (first-third quartile) for continuous variables, and frequency and percentage for categorical variables, as described previously.[16] The normality of data was tested using the Shapiro-Wilk test. Univariate analyses were performed to compare the two groups using the Wilcoxon rank-sum test and the Student t test for the continuous variables, and the χ 2 test was performed for the categorical variables.

When significant differences in patient baseline characteristics were identified on univariate analyses, a propensity score was calculated for each patient. One-to-one matching was performed using the "nearest neighbor" as the matching method to pair patients who had an early colectomy (early group) and late colectomy (late group).[16] The propensity score matching was performed using the R package MatchIt (R Foundation, Vienna, Austria).[17] The variables used for calculating the propensity score were age; race (white); sex; use of mechanical ventilation before surgery (vent-support); blood transfusion before surgery; histories of diabetes mellitus, smoking, chronic obstructive pulmonary disease, ascites, congestive heart failure, hypertension, CKD, CKD with dialysis, disseminated cancer, steroid use, bleeding disorder, and weight loss; wound class (W-class); and American Society of Anesthesiologists class (ASA-class). Numeric and graphical diagnostics were used to evaluate improvement after propensity matching. The Wilcoxon signed-rank test was used to compare the continuous variables between matched groups, depending on the normality of data. McNemar's test and the Stuart-Maxwell test were used to compare the categorical variables between the matched groups, depending upon the level of categories.[16] The risk difference and odds ratio with their respective 95% confidence intervals (CIs) were calculated for mortality. For the total length of hospital stay, the Kaplan-Meier procedure was used to estimate the median time, and the standard error was estimated using Greenwood's formula.[16] Kaplan-Meier curves were generated. The log-rank test was used to compare the time (Kaplan-Meier curves) between groups.[16] The two-sided P value was reported for each test. P < 0.05 was considered an indication of statistical significance. Statistical analysis was performed using the R language

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