Development and validation of a risk calculator for predicting postoperative pneumonia.

OBJECTIVE: To identify preoperative factors associated with an increased risk of postoperative pneumonia and subsequently develop and validate a risk calculator. PATIENTS AND METHODS: The American College of Surgeons' National Surgical Quality Improvement Program, a multicenter, prospective data set (2007-2008) was used. Univariate and multivariate logistic regression analyses were performed. The 2007 data set (N=211,410) served as the training set, and the 2008 data set (N=257,385) served as the validation set. RESULTS: In the training set, 3825 patients (1.8%) experienced postoperative pneumonia. Patients who experienced postoperative pneumonia had a significantly higher 30-day mortality (17.0% vs 1.5%; P<.001). On multivariate logistic regression analysis, 7 preoperative predictors of postoperative pneumonia were identified: age, American Society of Anesthesiologists class, chronic obstructive pulmonary disease, dependent functional status, preoperative sepsis, smoking before operation, and type of operation. The risk model based on the training data set was subsequently validated on the validation data set, with model performance being very similar (C statistic: 0.860 and 0.855, respectively). The high C statistic indicates excellent predictive performance. The risk model was used to develop an interactive risk calculator. CONCLUSION: Preoperative variables associated with an increased risk of postoperative pneumonia include age, American Society of Anesthesiologists class, chronic obstructive pulmonary disease, dependent functional status, preoperative sepsis, smoking before operation, and type of operation. The validated risk calculator provides a risk estimate for postoperative pneumonia and is anticipated to aid in surgical decision making and informed patient consent.

Predictors of pulmonary complications after bariatric surgery.

BACKGROUND: Postoperative pneumonia (PP) and respiratory failure (PRF) are known to be the most common nonwound complications after bariatric surgery. Our objective was to identify their current prevalence after bariatric surgery and to study the preoperative factors associated with them using data from the American College of Surgeons' National Surgical Quality Improvement Program. METHODS: Patients undergoing bariatric surgery were identified from the National Surgical Quality Improvement Program (2006-2008), a multicenter, prospective database. Univariate analysis and multivariate logistic regression analysis were performed. RESULTS: Of 32,889 patients, PP was diagnosed in 187 patients (.6%) and PRF in 204 patients (.6%). The overall 30-day morbidity rate was 6.4%, with PP and PRF accounting for 18.7%. The 30-day mortality rate was greater for the patients with PP and PRF than those without (4.3% versus .16% and 13.7% versus .10%, P < .0001). The hospital length of stay was also longer in patients with PP/PRF (P < .0001). On multivariate analysis, congestive heart failure (odds ratio 5.3, 95% confidence interval 1.20-23.26) and stroke (odds ratio 4.1, 95% confidence interval 1.42-11.49) were the greatest preoperative risk factors for PP. Previous percutaneous coronary intervention (odds ratio 2.8, 95% confidence interval 1.64-4.74) and dyspnea at rest (odds ratio 2.64, 95% confidence interval 1.13-6.13) were the factors most strongly associated with PRF. Bleeding disorder, age, chronic obstructive pulmonary disease, and type of surgery were risk factors for both (P < .05). Smoking also predisposed to PP, and diabetes mellitus, anesthesia time, and increasing weight also predisposed to PRF (P < .05 for all). CONCLUSION: Although PP and PRF are infrequent, they account for one fifth of the postoperative morbidity and are associated with significantly increased 30-day mortality. They can be predicted by various risk factors, emphasizing the importance of patient optimization and careful selection before bariatric surgery.

Development and validation of a risk calculator for prediction of cardiac risk after surgery.

BACKGROUND: Perioperative myocardial infarction or cardiac arrest is associated with significant morbidity and mortality. The Revised Cardiac Risk Index is currently the most commonly used cardiac risk stratification tool; however, it has several limitations, one of which is its relatively low discriminative ability. The objective of the present study was to develop and validate a predictive cardiac risk calculator. METHODS AND RESULTS: Patients who underwent surgery were identified from the American College of Surgeons' 2007 National Surgical Quality Improvement Program database, a multicenter (>250 hospitals) prospective database. Of the 211 410 patients, 1371 (0.65%) developed perioperative myocardial infarction or cardiac arrest. On multivariate logistic regression analysis, 5 predictors of perioperative myocardial infarction or cardiac arrest were identified: type of surgery, dependent functional status, abnormal creatinine, American Society of Anesthesiologists' class, and increasing age. The risk model based on the 2007 data set was subsequently validated on the 2008 data set (n=257 385). The model performance was very similar between the 2007 and 2008 data sets, with C statistics (also known as area under the receiver operating characteristic curve) of 0.884 and 0.874, respectively. Application of the Revised Cardiac Risk Index to the 2008 National Surgical Quality Improvement Program data set yielded a relatively lower C statistic (0.747). The risk model was used to develop an interactive risk calculator. CONCLUSIONS: The cardiac risk calculator provides a risk estimate of perioperative myocardial infarction or cardiac arrest and is anticipated to simplify the informed consent process. Its predictive performance surpasses that of the Revised Cardiac Risk Index.

Development and validation of a risk calculator predicting postoperative respiratory failure.

BACKGROUND: Postoperative respiratory failure (PRF) (requiring mechanical ventilation > 48 h after surgery or unplanned intubation within 30 days of surgery) is associated with significant morbidity and mortality. The objective of this study was to identify preoperative factors associated with an increased risk of PRF and subsequently develop and validate a risk calculator. METHODS: The American College of Surgeons National Surgical Quality Improvement Program (NSQIP), a multicenter, prospective data set (2007-2008), was used. The 2007 data set (n = 211,410) served as the training set and the 2008 data set (n = 257,385) as the validation set. RESULTS: In the training set, 6,531 patients (3.1%) developed PRF. Patients who developed PRF had a significantly higher 30-day mortality (25.62% vs 0.98%, P < .0001). On multivariate logistic regression analysis, five preoperative predictors of PRF were identified: type of surgery, emergency case, dependent functional status, preoperative sepsis, and higher American Society of Anesthesiologists (ASA) class. The risk model based on the training data set was subsequently validated on the validation data set. The model performance was very similar between the training and the validation data sets (c-statistic, 0.894 and 0.897, respectively). The high c-statistics (area under the receiver operating characteristic curve) indicate excellent predictive performance. The risk model was used to develop an interactive risk calculator. CONCLUSIONS: Preoperative variables associated with increased risk of PRF include type of surgery, emergency case, dependent functional status, sepsis, and higher ASA class. The validated risk calculator provides a risk estimate of PRF and is anticipated to aid in surgical decision making and informed patient consent.

Determinants of outcomes in pancreatic surgery and use of hospital resources.

BACKGROUND AND OBJECTIVES: Outcomes for patients undergoing major pancreatic surgery have improved, but a subset of patients that significantly utilize more resources exists. Variables that can lead to an increase in resource utilization in patients undergoing pancreatic surgery were identified. METHODS: Patients undergoing pancreatic surgery for neoplasms were identified from the NSQIP database (2006-2008). Indices associated with increased resource utilization that we included were operative time (OT), length of stay (LOS), intraoperative RBC transfusion, return to operating room, and occurrence of postoperative complications. Analysis of covariance and multivariable logistic regression were performed. RESULTS: The 4,306 included patients had a median age of 66 years and 50.3% were males. The 30-day morbidity and mortality were 29.3% and 3.2%, respectively. Median OT was 362 min and median LOS was 10 days. Malignancy, neoadjuvant radiation, and medical co-morbidities were associated with increased OT (P < 0.0001 for all). Declining preoperative functional status was the most important predictor of LOS (P < 0.0001). Age, male gender, hypertension, severe COPD, and higher BMI were significantly associated with postoperative complications (P < 0.050 for all). CONCLUSIONS: Morbidity after pancreatic surgery remains high. Age, obesity, performance status, medical co-morbidities, and neoadjuvant radiation affect outcomes and may lead to increased use of hospital resources.

Determinants of resource utilization and outcomes in laparoscopic Roux-en-Y gastric bypass: a multicenter analysis of 14,251 patients.

BACKGROUND: Outcomes for patients who undergo laparoscopic Roux-en-Y gastric bypass (LRYGB) surgery have improved, but a subset of patients who significantly utilize more resources exists. We identified preoperative variables that increase resource utilization in patients who undergo LRYGB. METHODS: Patients who underwent LRYGB in 2007 and 2008 were identified from the NSQIP database. Variables that indicated resource utilization were operative time (OT), length of stay (LOS), and occurrence of postoperative complications. Analyses were performed by using multivariate analysis of variance and logistic regression. RESULTS: Of 14,251 patients with a mean age of 44.6 (± 11.1) years, 19.4% were men. The national 30-day morbidity and mortality were 4.5% and 0.17%, respectively. The median OT was 128 min (interquartile range (IQR), 100-167), and the median LOS was 2 days (IQR, 2-3). Bleeding disorder, male gender, African American race, increasing weight, and age were significantly associated with increased OT (p < 0.05 for all). Severe chronic obstructive pulmonary disease, bleeding disorder, increasing age, and anesthesia time were associated with increased length of stay (p < 0.05). Preoperative dialysis dependence (odds ratio (OR), 8.5; 95% confidence interval (CI), 2.3-32.3) and dyspnea at rest (OR, 3.3; 95% CI, 1.7-6.3) were the greatest predictors of postoperative complications. Emergency case, bleeding disorder, prior percutaneous coronary intervention, and increasing operative time also were significantly associated with increased postoperative complications on multivariate logistic regression analysis (p < 0.05 for all). CONCLUSIONS: Age, sex, race, obesity, and some medical comorbidities affect outcomes and increase resource utilization. Optimization of modifiable factors and careful patient selection are needed to facilitate further improvement in outcomes and resource utilization.

Development and validation of a bariatric surgery morbidity risk calculator using the prospective, multicenter NSQIP dataset.

BACKGROUND: Although a risk score estimating postoperative mortality for patients undergoing gastric bypass exists, there is none predicting postoperative morbidity. Our objective was to develop a validated risk calculator for 30-day postoperative morbidity of bariatric surgery patients. STUDY DESIGN: We used the American College of Surgeons' 2007 National Surgical Quality Improvement Program (NSQIP) dataset. Patients undergoing bariatric surgery for morbid obesity were studied. Multiple logistic regression analysis was performed and a risk calculator was created. The 2008 NSQIP dataset was used for its validation. RESULTS: In 11,023 patients, mean age was 44.6 years, 20% were male, 77% were Caucasian, and mean body mass index (BMI; calculated as kg/m(2)) was 48.9. Thirty-day morbidity and mortality were 4.2% and 0.2%, respectively. Risk factors associated with increased risk of postoperative morbidity included recent MI/angina (odds ratio [OR] = 3.65; 95% CI 1.23 to 10.8), dependent functional status (OR = 3.48; 95% CI 1.78 to -6.80), stroke (OR = 2.89; 95% CI 1.09 to 7.67), bleeding disorder (OR = 2.23; 95% CI 1.47 to 3.38), hypertension (OR = 1.34; 95% CI 1.10 to 1.63), BMI, and type of bariatric surgery. Patients with BMI 35 to <45 and >60 had significantly higher adjusted OR compared with patients with BMI of 45 to 60 (p < 0.05 for all). These factors were used to create the risk calculator and subsequently validate it, with the model performance very similar between the 2007 training dataset and the 2008 validation dataset (c-statistics: 0.69 and 0.66, respectively). CONCLUSIONS: NSQIP data can be used to develop and validate a risk calculator that predicts postoperative morbidity after various bariatric procedures. The risk calculator is anticipated to aid in surgical decision making, informed patient consent, and risk reduction.

Chronic mesenteric ischemia: endovascular versus open revascularization.

UNLABELLED: To review 20 years of literature on chronic mesenteric ischemia (CMI), examining its complex clinical presentation and comparing open and endovascular treatment options. METHODS: The PubMed and EBSCOHost electronic databases were queried to identify English-language articles published over the last 20 years. Scrutiny of the retrieved articles identified 1939 patients (mean age 65 years). Of these, 1163 patients underwent open surgery: 714 between 2000 and 2009 and 449 between 1990 and 1999. Of the 776 patients undergoing endovascular repairs, the majority (684) were performed between 2000 and 2009; 92 patients were treated between 1990 and 1999. Data were entered in an electronic database and were pooled for categorical analysis. RESULTS: No major differences were seen among open surgeries or among endovascular surgeries performed when comparing the 2 time periods. On comparing open and endovascular surgeries performed between 2000 and 2009, symptom improvement was 2.4 times more likely after open compared to endovascular surgery (95% CI 1.5 to 3.6, p<0.001). Five-year primary patency and 5-year assisted primary patency were 3.8 (95% CI 2.4 to 5.8, p<0.001) and 6.4 (95% CI 1.3 to 30.1, p = 0.02) times greater in the open group. Freedom from symptoms at 5 years was 4.4 times greater for open versus endovascular (95% CI 2.8 to 7.0, p<0.001). The complication rate for open versus endovascular surgery was 3.2 times greater (95% CI 2.5 to 4.2, p<0.001). The difference in mortality was not statistically significant (p = 0.75). CONCLUSION: Our data demonstrate that open revascularization surpasses endovascular procedures in long-term vessel patency and control of symptoms. Patients undergoing open procedures do, however, develop increased complications perioperatively. The preferred revascularization approach used in treating this condition should be tailored to the anatomy and physiology of each patient.

An empirically based tool for analyzing mortality associated with congenital heart surgery.

OBJECTIVE: Analysis of congenital heart surgery results requires a reliable method of estimating the risk of adverse outcomes. Two major systems in current use are based on projections of risk or complexity that were predominantly subjectively derived. Our goal was to create an objective, empirically based index that can be used to identify the statistically estimated risk of in-hospital mortality by procedure and to group procedures into risk categories. METHODS: Mortality risk was estimated for 148 types of operative procedures using data from 77,294 operations entered into the European Association for Cardiothoracic Surgery (EACTS) Congenital Heart Surgery Database (33,360 operations) and the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database (43,934 patients) between 2002 and 2007. Procedure-specific mortality rate estimates were calculated using a Bayesian model that adjusted for small denominators. Each procedure was assigned a numeric score (the STS-EACTS Congenital Heart Surgery Mortality Score [2009]) ranging from 0.1 to 5.0 based on the estimated mortality rate. Procedures were also sorted by increasing risk and grouped into 5 categories (the STS-EACTS Congenital Heart Surgery Mortality Categories [2009]) that were chosen to be optimal with respect to minimizing within-category variation and maximizing between-category variation. Model performance was subsequently assessed in an independent validation sample (n = 27,700) and compared with 2 existing methods: Risk Adjustment for Congenital Heart Surgery (RACHS-1) categories and Aristotle Basis Complexity scores. RESULTS: Estimated mortality rates ranged across procedure types from 0.3% (atrial septal defect repair with patch) to 29.8% (truncus plus interrupted aortic arch repair). The proposed STS-EACTS score and STS-EACTS categories demonstrated good discrimination for predicting mortality in the validation sample (C-index = 0.784 and 0.773, respectively). For procedures with more than 40 occurrences, the Pearson correlation coefficient between a procedure's STS-EACTS score and its actual mortality rate in the validation sample was 0.80. In the subset of procedures for which RACHS-1 and Aristotle Basic Complexity scores are defined, discrimination was highest for the STS-EACTS score (C-index = 0.787), followed by STS-EACTS categories (C-index = 0.778), RACHS-1 categories (C-index = 0.745), and Aristotle Basic Complexity scores (C-index = 0.687). When patient covariates were added to each model, the C-index improved: STS-EACTS score (C-index = 0.816), STS-EACTS categories (C-index = 0.812), RACHS-1 categories (C-index = 0.802), and Aristotle Basic Complexity scores (C-index = 0.795). CONCLUSION: The proposed risk scores and categories have a high degree of discrimination for predicting mortality and represent an improvement over existing consensus-based methods. Risk models incorporating these measures may be used to compare mortality outcomes across institutions with differing case mixes.