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Risk Factors for Developing Pneumonia after Elective Surgical Procedures
Vol. 223, No. 4S2, October 2016
repairs. Twelve patients had concurrent midline hernias. Nine surgical site events occurred, including 1 urine leak and 1 hematoma, all of which resolved without lasting effect. There were no mesh related complications. With an average follow-up of 13 months, 4 (21%) recurrences have been identified, 3 of which were subsequently repaired with an open onlay approach. CONCLUSIONS: Hernias around ileal conduit represent a unique surgical challenge. We demonstrated that retromuscular mesh repair of parastomal hernias secondary to ileal conduit creation is safe and effective. Risk Factors for Developing Pneumonia after Elective Surgical Procedures Randi Lassiter, MD, Colville H Ferdinand, MD, FACS Augusta University, Augusta, GA INTRODUCTION: Pneumonia represents a significant source of unanticipated morbidity and costs for postoperative patients. In an era of increased focus on healthcare quality on the part of patients and payers alike, it is imperative that institutions remain cognizant of opportunities to minimize preventable complications. We hypothesized that there are independent risk factors for postoperative pneumonia which can be used to identify high-risk patients prior to surgery. METHODS: We performed a retrospective study of elective surgical cases performed between March 1, 2014 and November 1, 2015 at a single tertiary care medical center using data from NSQIP. All elective cases across 10 surgical subspecialties, which were captured in the database during the period of study were included. Bivariate and multivariable analysis were used to predict development of pneumonia within 30 days after surgery. RESULTS: A total of 1,887 surgical cases were captured in NSQIP over the 20 month study period, and 64 patients (3.39%) developed postoperative pneumonia. Based upon a backward elimination logistic regression model, independent risk factors for development of postoperative pneumonia following elective surgical procedures included shortness of breath, American Society of Anesthesiologists physical status classification of 4 or more, urinary tract infection present at the time of surgery, and inpatient admission; area under receiver operator curve: 0.7825. CONCLUSIONS: There are multiple independent risk factors for postoperative pneumonia following elective surgery. Interestingly, smoking was not significantly associated with postoperative pneumonia in the population of study. Efforts to improve postoperative pulmonary outcomes should be focused on patients with the characteristics listed above.
Risk Factors for Unplanned Readmissions to a Large Urban Safety Net Hospital Erica I Hodgman, Madhu Subramanian, MD, Herb A Phelan, MD, FACS, Steven E Wolf, MD, FACS University of Texas-Southwestern Medical Center, Dallas, TX
Scientific Poster Presentations: 2016 Clinical Congress
e111
INTRODUCTION: Most studies evaluating risk factors for readmission are based on billing or administrative databases, which are limited in the depth and detail of information available. We sought to evaluate our experience at a large, urban safety net hospital using hand-collected data. METHODS: The charts of patients discharged over 6 months from May 2013 through April 2014 were reviewed by 2 physicians. To evaluate the effect of resident experience, 3 time periods during the year were examined: May-June, July-August, and January-February. Data collected included variables thought to influence unplanned readmission, including mental health and medical history, diagnosis and procedure type, complications, and readmission details. RESULTS: A total of 1,853 unique patients were admitted 2,062 times during the observed periods. The most common diagnoses were cholelithiasis/cholecystitis (14.2%), burns (9.9%), appendicitis (7.0%), and abscess (3.2%). Most patients were discharged home (n¼1,869, 90.6%). The 30-day unplanned readmission rate was 10.8% (n¼222). Univariate predictors of unplanned readmission are in the Table. Notably, time of year did not influence readmission risk. Backwards-stepwise logistic regression using these variables yielded a model had poor predictive ability (r2 ¼0.12). The strongest predictors of readmission were transfusion (odds ratio [OR] 1.89, 95% CI 1.20-2.95), discharge with a drain in place (OR 1.72, 95% CI 1.07 e 1.72), and an American Society of Anesthesiologists physical status classification score of 4 (OR 3.49, 95% CI 1.56 e 8.20). Table. Not readmitted Readmitted (n¼1,789) (n¼272)
Deep/organ space infection, n (%) Urinary tract infection, n (%) Small bowel obstruction, n (%) Transfusion, n (%) Discharged with drain, n (%) Discharged with ostomy, n (%) Discharged on coumadin, n (%) Discharged on low molecular weight heparin, n (%) History of depression or anxiety, n (%)
p Value
215 (8.9)
75 (15.5)
<0.0001
59 (3.2)
19 (8.6)
<0.0001
39 (2.1) 168 (9.1)
13 (5.9) 43 (19.4)
0.0008 <0.0001
150 (8.2)
37 (16.7)
<0.0001
50 (2.7)
24 (10.8)
<0.0001
39 (2.9)
11 (6.6)
0.01
33 (2.4)
13 (7.2)
0.0005
321 (17.5)
61 (27.5)
0.0002
CONCLUSIONS: In this large, heterogenous population, many factors appear to predict 30-day readmission. However, even in combination, these variables which are often thought to influence readmission, explain only a minority of the readmissions to our hospital.
repairs. Twelve patients had concurrent midline hernias. Nine surgical site events occurred, including 1 urine leak and 1 hematoma, all of which resolved without lasting effect. There were no mesh related complications. With an average follow-up of 13 months, 4 (21%) recurrences have been identified, 3 of which were subsequently repaired with an open onlay approach. CONCLUSIONS: Hernias around ileal conduit represent a unique surgical challenge. We demonstrated that retromuscular mesh repair of parastomal hernias secondary to ileal conduit creation is safe and effective. Risk Factors for Developing Pneumonia after Elective Surgical Procedures Randi Lassiter, MD, Colville H Ferdinand, MD, FACS Augusta University, Augusta, GA INTRODUCTION: Pneumonia represents a significant source of unanticipated morbidity and costs for postoperative patients. In an era of increased focus on healthcare quality on the part of patients and payers alike, it is imperative that institutions remain cognizant of opportunities to minimize preventable complications. We hypothesized that there are independent risk factors for postoperative pneumonia which can be used to identify high-risk patients prior to surgery. METHODS: We performed a retrospective study of elective surgical cases performed between March 1, 2014 and November 1, 2015 at a single tertiary care medical center using data from NSQIP. All elective cases across 10 surgical subspecialties, which were captured in the database during the period of study were included. Bivariate and multivariable analysis were used to predict development of pneumonia within 30 days after surgery. RESULTS: A total of 1,887 surgical cases were captured in NSQIP over the 20 month study period, and 64 patients (3.39%) developed postoperative pneumonia. Based upon a backward elimination logistic regression model, independent risk factors for development of postoperative pneumonia following elective surgical procedures included shortness of breath, American Society of Anesthesiologists physical status classification of 4 or more, urinary tract infection present at the time of surgery, and inpatient admission; area under receiver operator curve: 0.7825. CONCLUSIONS: There are multiple independent risk factors for postoperative pneumonia following elective surgery. Interestingly, smoking was not significantly associated with postoperative pneumonia in the population of study. Efforts to improve postoperative pulmonary outcomes should be focused on patients with the characteristics listed above.
Risk Factors for Unplanned Readmissions to a Large Urban Safety Net Hospital Erica I Hodgman, Madhu Subramanian, MD, Herb A Phelan, MD, FACS, Steven E Wolf, MD, FACS University of Texas-Southwestern Medical Center, Dallas, TX
Scientific Poster Presentations: 2016 Clinical Congress
e111
INTRODUCTION: Most studies evaluating risk factors for readmission are based on billing or administrative databases, which are limited in the depth and detail of information available. We sought to evaluate our experience at a large, urban safety net hospital using hand-collected data. METHODS: The charts of patients discharged over 6 months from May 2013 through April 2014 were reviewed by 2 physicians. To evaluate the effect of resident experience, 3 time periods during the year were examined: May-June, July-August, and January-February. Data collected included variables thought to influence unplanned readmission, including mental health and medical history, diagnosis and procedure type, complications, and readmission details. RESULTS: A total of 1,853 unique patients were admitted 2,062 times during the observed periods. The most common diagnoses were cholelithiasis/cholecystitis (14.2%), burns (9.9%), appendicitis (7.0%), and abscess (3.2%). Most patients were discharged home (n¼1,869, 90.6%). The 30-day unplanned readmission rate was 10.8% (n¼222). Univariate predictors of unplanned readmission are in the Table. Notably, time of year did not influence readmission risk. Backwards-stepwise logistic regression using these variables yielded a model had poor predictive ability (r2 ¼0.12). The strongest predictors of readmission were transfusion (odds ratio [OR] 1.89, 95% CI 1.20-2.95), discharge with a drain in place (OR 1.72, 95% CI 1.07 e 1.72), and an American Society of Anesthesiologists physical status classification score of 4 (OR 3.49, 95% CI 1.56 e 8.20). Table. Not readmitted Readmitted (n¼1,789) (n¼272)
Deep/organ space infection, n (%) Urinary tract infection, n (%) Small bowel obstruction, n (%) Transfusion, n (%) Discharged with drain, n (%) Discharged with ostomy, n (%) Discharged on coumadin, n (%) Discharged on low molecular weight heparin, n (%) History of depression or anxiety, n (%)
p Value
215 (8.9)
75 (15.5)
<0.0001
59 (3.2)
19 (8.6)
<0.0001
39 (2.1) 168 (9.1)
13 (5.9) 43 (19.4)
0.0008 <0.0001
150 (8.2)
37 (16.7)
<0.0001
50 (2.7)
24 (10.8)
<0.0001
39 (2.9)
11 (6.6)
0.01
33 (2.4)
13 (7.2)
0.0005
321 (17.5)
61 (27.5)
0.0002
CONCLUSIONS: In this large, heterogenous population, many factors appear to predict 30-day readmission. However, even in combination, these variables which are often thought to influence readmission, explain only a minority of the readmissions to our hospital.