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J Thorac Cardiovasc Surg 1999;117:969-979
© 1999 Mosby, Inc.

GENERAL THORACIC SURGERY

PROGNOSTIC MODELS OF THIRTY-DAY MORTALITY AND MORBIDITY AFTER MAJOR PULMONARY RESECTION

From the Veterans Affairs Medical Center/Harvard Medical School, Brockton/West Roxbury, Mass; the Veterans Affairs Medical Center/Duke University Medical Center, Durham, NC; and the Cooperative Studies Program Coordinating Center, Veterans Affairs Medical Center, Hines, Ill.

Supported by the Department of Veterans Affairs Office of Patient Care Services, the Health Services Research and Development Service, and the Cooperative Studies Program. Jennifer Daley, MD, is a Senior Research Associate in the Career Development Award Program of the Health Services Research and Development of the Department of Veterans Affairs.

Presented in part at the Thirtieth Annual Session of The Society of Thoracic Surgeons, Palm Springs, Calif, 1995.

Received for publication May 27, 1998. Revisions requested Sept 10, 1998. Revisions received Dec 10, 1998. Accepted for publication Jan 12, 1999. Address for reprints: David H. Harpole, Jr, MD, Chief, Cardiothoracic Surgery, Durham VA Medical Center, 10 Fulton St, Durham, NC 27705.

	Abstract

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

 
Background:A part of the prospective, multi-institutional National Veterans Affairs Surgical Quality Improvement Program was developed to predict 30-day mortality and morbidity for patients undergoing a major pulmonary resection. Methods: Perioperative data were acquired from 194,319 noncardiac surgical operations at 123 Veterans Affairs Medical Centers between October 1, 1991, and August 31, 1995. Current Procedural Terminology code-based analysis was undertaken for major pulmonary resections (lobectomy and pneumonectomy). Preoperative, intraoperative, and outcome variables were collected. The 30-day mortality and morbidity models were developed by means of multivariable stepwise logistic regression with the preoperative and intraoperative variables used as independent predictors of outcome. Results: A total of 3516 patients (mean age 64 ± 9 years) underwent either lobectomy (n = 2949) or pneumonectomy (n = 567). Thirty-day mortality was 4.0% for lobectomy (119/2949) and 11.5% for pneumonectomy (65/567). The preoperative predictors of 30-day mortality were albumin, do not resuscitate status, transfusion of more than 4 units, age, disseminated cancer, impaired sensorium, prothrombin time more than 12 seconds, type of operation, and dyspnea. When the intraoperative variables were considered, intraoperative blood loss was added to the preoperative model. In the presence of these intraoperative variables in the model, do not resuscitate status and prothrombin time more than 12 seconds were only marginally significant. Thirty-day morbidity, defined as the presence of 1 or more of the 21 predefined complications, was 23.8% for lobectomy (703/2949) and 25.7% for pneumonectomy (146/567). In multivariable models, independent preoperative predictors (P < .05) of 30-day morbidity were age, weight loss greater than 10% in the 6 months before surgery, history of chronic obstructive pulmonary disease, transfusion of more than 4 units, albumin, hemiplegia, smoking, and dyspnea. When intraoperative variables were added to the preoperative model, the duration of operation time and intraoperative transfusions were included in the model and albumin became marginally significant. Conclusions: This analysis identifies independent patient risk factors that are associated with 30-day mortality and morbidity for patients undergoing a major pulmonary resection. This series provides an initial risk-adjustment model for major pulmonary resections. Future refinements will allow comparative assessment of surgical outcomes and quality of care at many institutions.

	Introduction

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

 
Several single-institution, retrospective series have been published that examine patient characteristics associated with an increased risk of mortality and morbidity after major pulmonary resection. Because series span several decades and are of limited size, multivariable analyses were not undertaken. ^1-12 The National Veterans Affairs (VA) Surgical Quality Improvement Program was designed to prospectively collect historical, laboratory, operative, and postoperative patient characteristics and clinical outcomes for many operative procedures from several surgical subspecialties. The goal of the study was to develop tools for future risk-adjusted models of mortality and morbidity that would allow for the comparative assessment of the quality of surgical care at various institutions. ^13-18 This project examined the subset of patients who had undergone a major pulmonary resection (lobectomy or pneumonectomy).

	Methods

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

 
The methods of the National VA Surgical Quality Improvement Program have been described in detail elsewhere. ¹³

Population
Data were acquired prospectively on 194,319 consecutive patients undergoing noncardiac surgical procedures from October 1, 1991, until August 31, 1995, in 123 Department of VA Medical Centers. The database included 10,008 patients who had undergone a lung operation, of which 3516 (35%) were for a major pulmonary resection (Current Procedural Terminology [CPT] codes 32440-32445 and 32480-32488). International Classification of Diseases—ninth revision—Clinical Modification (ICD-9-CM) diagnoses for these pulmonary resections included 3251 for cancer (non–small cell lung cancer = 3105, pulmonary metastatic cancer = 146), 34 benign lung masses (hamartoma or typical carcinoid), 156 infectious processes (Mycobacterium tuberculosis = 27, fungal = 36, bronchiectasis = 25, infectious massive hemoptysis = 19, inflammatory lung masses with pneumonia = 49), and 51 other lung masses (not otherwise stated).

Patient characteristics and clinical outcomes
A total of 122 separate variables were recorded for each operation by a dedicated surgical nurse reviewer who was independent of the various surgical teams, allowing unbiased data collection. Each reviewer was educated on the set of standard criteria for coding all collected variables. These variables were selected by a committee of expert advisors to the VA National Surgical Quality Improvement Program, and the data management manual was made available on the Internet in late 1998. A system of data audits was performed throughout the study interval to ensure data quality and completeness. In brief, after the data manager education process was completed, each center underwent periodic internal and external random audits for data quality. Whenever possible, data were directly downloaded to the statistical center from each VA Medical Center's computer (ie, laboratory results). A more complete description of the data collection and auditing process is given in a manuscript presented at the 1998 meeting of the American Surgical Association. ¹⁴

Preoperative patient characteristics included 4 demographic, 43 historical (divided among anatomic systems), and 17 preoperative laboratory values. Thirteen intraoperative and operation-specific variables were recorded. Follow-up 30-day postoperative mortality statistics and the presence of any one of 21 pre-defined postoperative complications occurring within 30 days of the index operation were recorded (Appendix 1).

Statistical analysis
Univariate analyses were performed on the preoperative, demographic, and laboratory variables, relating them to operative mortality and morbidity. In the case of continuous variables, an unpaired t test was used. For the categoric variables, a ² test was used. The variables with a prevalence of greater than 0.5% that were univariately significant at P < .20 were considered as possible independent variables in a multivariable logistic regression. Stepwise logistic regression analysis with entry and exit criteria set at the P = .05 level was used with 30-day mortality as the dependent variable. For morbidity, the presence or absence of one or more complications was used as the dependent variable.

Subsequent mortality and morbidity models were developed to incorporate the type of operation (pneumonectomy or lobectomy) and then the intraoperative variables such as operation time and amount of blood loss. The modeling was done in a 2-stage process. The first-stage model consisted of preoperative variables and the type of operation. In the second stage the variables from the preoperative model were fixed and the intraoperative variables were added to these for possible inclusion in the stage 2 model. The second-stage model then consists of the first-stage model plus the intraoperative variables that were significant in the second model. Variables in the models are listed in Appendix 2.

All data were more than 99% complete, including the preoperative laboratory variables, except for serum albumin (78%), serum bilirubin (79%), alkaline phosphatase (77%), and serum aspartate aminotransferase (75%). A regression procedure was used to impute missing laboratory values. ^19,20 Some of the laboratory variables were not linearly related to the postoperative outcomes, but rather had U shapes or inverted J shapes. In these cases, the categorized laboratory variables based on clinical cutoff points were entered into the models.

To investigate the stability of the models, we used a bootstrap technique. ^21,22 In the bootstrap procedure, repeated samples of n observations are selected with replacement from the original set of n observations. For each sample, forward stepwise logistic regression was performed simultaneously entering the preoperative and intraoperative variables listed in Appendix 1. The stability of the final stepwise model can be assessed by identifying the variables that enter most frequently in the repeated bootstrap models and comparing those variables with the variables in the final stepwise model. If the final stepwise model variables occur in a majority (>50%) of the bootstrap models, the original final stepwise regression model can be judged to be stable.

	Results

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

 
Patient characteristics and outcomes
The population for this project consisted of 3516 patients who underwent either an anatomic lobectomy (n = 2949) or a pneumonectomy (n = 567). The mean age for the study sample is 64 years with a standard deviation of 9 years and a range from 22 to 91 years. The majority of the population was male (98.7%). Other selected patient characteristics are listed in Table I. There were 119 deaths within 30 days for the patients undergoing lobectomy (4.0%) and 65 deaths for those undergoing pneumonectomy (11.5%), for an overall 30-day mortality of 5.2% in the 3516 patients. The postoperative morbidity for the lobectomy patients was 23.8% (703/2949) and for the pneumonectomy patients was 25.7% (146/567). The overall 30-day morbidity was 24.1% (849/3516). The most frequently occurring complications were pneumonia (11.7% for lobectomy and 9.7% for pneumonectomy) followed by unplanned reintubation (7.9% and 10.6%). Other individual complication rates are shown in Table II.

Multivariable analyses
All factors with a prevalence of greater than 0.5% of the sample and a univariate significance of P < .2 were placed into a multivariable stepwise logistic regression analysis to identify risk factors associated with mortality and morbidity. In the multivariable logistic regression model for mortality, the preoperative independent predictors (P < .05) were serum albumin, do not resuscitate status before the operation, transfusion of more than 4 units of red blood cells before the operation, age, disseminated cancer (defined as a resection for a pulmonary metastasis or for patients with clinical evidence of mediastinal lymph nodes in non–small cell lung cancer), impaired sensorium, prothrombin time of more than 12 seconds, operation type, and dyspnea. The ß Coefficients for these variables along with their P values and associated odds ratios are contained in Table III-a. When the intraoperative variables are added for possible entry into this logistic model for mortality, the combined model contains the preoperative predictors and the intraoperative blood loss. This combined model is described in Table III-b.

In addition, the stability of the mortality and morbidity models was verified with a bootstrap resampling method. ^26,27 The bootstrap resampling technique was performed on 200 random samples for both mortality and morbidity with the use of the preoperative and intraoperative variables that were univariately significant. All the variables in the final mortality model were included in a majority of the 200 bootstrap replications except for dyspnea and prothrombin time. For morbidity, all variables in the final model were included in at least 50% of the bootstrap replications, except for smoking and albumin (Table V).

	Discussion

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

The purpose of this project was to use these data to construct a model of preoperative patient risk for mortality and morbidity within 30 days of major pulmonary resections. Data collection was prospective and recent (1991-1995), eliminating any temporal bias that existed in previous single-institution reports that spanned decades and may not reflect current clinical practice. Because a dedicated surgical nurse reviewer acquired all of the data prospectively, its reproducibility and accuracy were assured. Interpretive test results, such as electrocardiography and spirometry, were not included in this study.

The 30-day mortality and morbidity rates (5.2% and 24.1%, respectively) from this multi-institution series are in the range of those published from single-institution retrospective series (mortality 3%-14%; morbidity 10%-25%). ^1-9 The largest pneumonectomy series (n = 244) from the Mayo Clinic reported 11% deaths and 24% major morbidity within 30 days. These procedures were performed for cancer and infection. ¹⁰ Deslauriers and associates ¹¹ reported a series of 1076 patients from several centers in Canada who had undergone elective lobectomy or pneumonectomy for cancer from 1978 until 1984. They observed an overall 3.2% mortality and a 19% morbidity rate, which are similar to our overall mortality and morbidity rates.

Multi-institution, prospective data are available from the Lung Cancer Study Group (1979-1981). Ginsberg and colleagues ¹² reported a 2.6% mortality for a lobectomy (n = 1058) and a 6.2% mortality for a pneumonectomy (n = 569) for cancer. Silvestri and colleagues ²⁸ published data from hospital discharges in the state of South Carolina from 1991 until 1995. This study observed a 30-day mortality of 11.8% for pneumonectomy and 3.0% for lobectomy performed by board-certified thoracic surgeons. Both of these series included elective operations performed for cancer. Our VA data included 3516 consecutive patients who underwent a lobectomy or pneumonectomy for any cause, elective or emergency. There were 19 emergency procedures for infection or massive hemoptysis, and 11 urgent or emergency procedures were performed on patients who had previously asked not to be resuscitated in the event of a cardiac arrest. Significant comorbidity is common in this veteran population, including 39% of patients with chronic obstructive lung disease, 33% with moderate or severe dyspnea, 19% with chronic alcoholism, 11% with diabetes, and 10% with a history of a stroke or transient ischemic attack. These factors may explain the higher mortality rates observed in this population of male veterans. In fact, if the mortality models for lobectomy and pneumonectomy were used to a define a "low risk" subset of patients (expected mortality 5.0%), observed mortality was actually 2.5% for lobectomy and 7.0% for pneumonectomy.

Limitations of study
The study population is predominantly male. The 1997 cancer statistics demonstrate that 40% of newly diagnosed cases of lung cancer occur in women. ²⁹ Clearly, the generalizability of this VA study will need to be verified in a more heterogeneous population. Early efforts by the Society of Thoracic Surgeons may accomplish this goal, and this VA study can act as a model for future analytical risk models. In an effort for data consistency and completeness, only easily quantifiable data were included and no subspecialty procedure-specific morbidity (ie, bronchopleural fistula instead of pneumonia) was collected, allowing consistent data collection throughout the VA system. Future studies are being designed to include procedure-specific variable collection, such as exercise oximetry, spirometry, and predicted postresection forced expiratory volume in 1 second.

Clinical relevance of models
Two separate analyses were undertaken after univariate analyses identified potential variables associated with mortality and morbidity. The first model consisted of preoperative variables that would be readily available to clinicians, as well as the type of resection planned (lobectomy or pneumonectomy). The mortality model demonstrated the significance of patient age, pulmonary reserve (dyspnea class), level of cognition, nutritional status (serum albumin), liver function (serum prothrombin time), clinical cancer stage, and need for a pneumonectomy on perioperative survival. If one added the intraoperative variables to this model, only the operative blood loss (a measure of the difficulty of the operation) added independent prognostic information.

The morbidity models included different variables. In addition to patient age, serum albumin, and dyspnea class, preoperative smoking, weight loss, and hemiplegia were significant predictors of a complicated postoperative course. Intraoperative variables that significantly predicted a complication included operative time and blood loss, both markers of a more difficult operation, whereas there was no difference in the risk of a complication between the two procedures once the model accounted for these other significant variables. Figs. 1 and 2 demonstrate the results of two continuous variables (age in years and serum albumin level before operation) on mortality and morbidity.

View larger version (22K): [in this window] [in a new window]   Fig 1. Statistical nomograms were constructed with 95% confidence intervals to demonstrate the results. Top, Probability of death for age in years. Bottom, Probability of a complication for patient by age. Note that risk increases with age.

View larger version (20K): [in this window] [in a new window]   Fig 2. Statistical nomograms were constructed with 95% confidence intervals to demonstrate the results. Top, Probability of death for serum albumin. Bottom, Probability of a complication for patient by serum albumin. Note that risk decreases with improved nutritional status.

	Conclusion

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

	Appendix 1

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

 
National Surgical Quality Improvement Program
Program Worksheet

1. PREOPERATIVE INFORMATION
   
1. GENERAL
   
1. Diabetes mellitus
   
1. No
   
2. Oral
   
3. Insulin
   
2. Current smoker within 2 weeks before admission
   
1. Yes
   
2. No
   
3. Alcohol > 2 drinks/day within 2 weeks before admission
   
1. Yes
   
2. No
   
4. Drug addiction
   
1. Yes
   
2. No
   
5. Dyspnea
   
1. No
   
2. Minimal exertion
   
3. Rest
   
6. Do not resuscitate status—this admission
   
1. Yes
   
2. No
   
7. Functional status
   
1. Independent
   
2. Partially dependent
   
3. Total dependent
   
2. PULMONARY
   
1. Ventilator dependent > 48 hours
   
1. Yes
   
2. No
   
2. History of severe chronic obstructive pulmonary disease
   
1. Yes
   
2. No
   
3. Current pneumonia
   
1. Yes
   
2. No
   
3. HEPATOBILIARY
   
1. Hepatomegaly
   
1. Yes
   
2. No
   
2. Ascites
   
1. Yes
   
2. No
   
3. Esophageal varices
   
1. Yes
   
2. No
   
4. Active hepatitis
   
1. Yes
   
2. No
   
4. CARDIAC
   
1. History of myocardial infarction within past 6 months
   
1. Yes
   
2. No
   
2. Previous percutaneous transluminal coronary angioplasty
   
1. Yes
   
2. No
   
3. Previous operation with cardiopulmonary bypass
   
1. Yes
   
2. No
   
4. History of angina within month before the operation
   
1. Yes
   
2. No
   
5. Congestive heart failure within 1 month before the operation
   
1. Yes
   
2. No
   
6. Hypertension requiring medication
   
1. Yes
   
2. No
   
5. RENAL
   
1. Acute renal failure (change in creatinine > 2.0 mg/dL)
   
1. Yes
   
2. No
   
2. Currently requiring dialysis
   
1. Yes
   
2. No
   
6. VASCULAR
   
1. History of revascularization/amputation for peripheral vascular disease
   
1. Yes
   
2. No
   
2. Rest pain/gangrene
   
1. Yes
   
2. No
   
3. Absent peripheral pulses
   
1. Yes
   
2. No
   
7. CENTRAL NERVOUS SYSTEM
   
1. Impaired sensorium
   
1. Yes
   
2. No
   
2. Severe head trauma
   
1. Yes
   
2. No
   
3. History of coma
   
1. Yes
   
2. No
   
4. Quadriplegia
   
1. Yes
   
2. No
   
5. Paraplegia
   
1. Yes
   
2. No
   
6. Hemiplegia
   
1. Yes
   
2. No
   
7. History of transient ischemic attacks
   
1. Yes
   
2. No
   
8. Cerebrovascular accident residual neurologic deficit
   
1. Yes
   
2. No
   
9. Cerebrovascular accident/no neurologic deficit
   
1. Yes
   
2. No
   
10. Neuromuscular degenerative disease
    
1. Yes
   
2. No
   
11. Tumor involving the central nervous system
    
1. Yes
   
2. No
   
8. NUTRITIONAL/IMMUNE/OTHER
   
1. Disseminated cancer (stage IV)
   
1. Yes
   
2. No
   
2. Open wound or infection
   
1. Yes
   
2. No
   
3. Chronic steroid use
   
1. Yes
   
2. No
   
4. >10% loss of body weight within last 6 months
   
1. Yes
   
2. No
   
5. Bleeding disorders
   
1. Yes
   
2. No
   
6. Transfusion > 4 red blood cell units this admission
   
1. Yes
   
2. No
   
7. Number of prior operations this admission
   
2. OPERATIVE INFORMATION
   
1. Surgical specialty: (code)
   
2. Principal operation: (CPT code)
   
3. Other procedures: (CPT code)
   
4. Concomitant procedures: (code)
   
5. Postgraduate-year primary surgeon
   
6. Emergency case
   
1. Yes
   
2. No
   
7. Wound classification
   
1. Clean
   
2. Clean/contaminated
   
3. Contaminated
   
4. Infected
   
8. Redo
   
1. Yes
   
2. No
   
9. American Society of Anesthesiology class
   
10. Principal anesthesia technique
    
1. General
   
2. Spinal
   
3. Epidural
   
4. Regular/local
   
11. Time operation began (24-hour clock)_____
    
12. Intraoperative blood loss (mL) (minimal = 100)_____
    
13. Number of red blood cell units transfused_____
    
3. OUTCOME INFORMATION
   
1. Postoperative diagnosis (ICD-9-Code)
   
2. Length of postoperative hospital stay (days)_____
   
3. Thirty-day postoperative status
   
4. Date of death _____
   
5. Return to operating room within 30 days after index procedure
   
6. Yes
   
7. No
   
8. Number of returns related to index procedure _____
   
9. CPT code _____
   
10. CPT code _____
    
11. CPT code _____
    
12. Number of returns unrelated to index procedure
    
13. CPT code _____
    
14. CPT code _____
    
15. CPT code _____
    
4. COMPLICATIONS (See Table II)
   
5. LABORATORY VALUES
   
1. Preoperative (nearest to operation date)
   
1. Sodium______ Month____ Day____ Year____
   
2. Potassium______ Month____ Day____ Year____
   
3. Glucose______ Month____ Day____ Year____
   
4. BUN______ Month____ Day____ Year____
   
5. Creatinine______ Month____ Day____ Year____
   
6. CK______ Month____ Day____ Year____
   
7. CK-MB______ Month____ Day____ Year____
   
8. Albumin ______ Month ____Day____Year____
   
9. Total bilirubin______ Month____ Day____ Year____
   
10. ALT______ Month____ Day____ Year____
    
11. AST______ Month____ Day____ Year____
    
12. AP______ Month____ Day____ Year____
    
13. WBC______ Month____ Day____ Year____
    
14. Hct______ Month____ Day____ Year____
    
15. Platelets______ Month____ Day____ Year____
    
16. PTT______ Month____ Day____ Year____
    
17. PT______ Month ____Day____Year____
    
2. Postoperative laboratory values (record highest and lowest levels)
   
1. Sodium______ Month____ Day____ Year____
   
2. Sodium______ Month____ Day____ Year____
   
3. Potassium______ Month____ Day____ Year____
   
4. Potassium______ Month____ Day____ Year____
   
5. Glucose______ Month____ Day____ Year____
   
6. Creatinine______ Month____ Day____ Year____
   
7. CK______ Month____ Day____ Year____
   
8. CK-MB______ Month____ Day____ Year____
   
9. Albumin______ Month ____ Day____ Year____
   
10. Total bilirubin______ Month____ Day____ Year____
    
11. WBC______ Month____ Day____ Year____
    
12. Hct______ Month____ Day____ Year____
    

	Appendix 2

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

 

View this table: [in this window] [in a new window]   List of variables used in mortality and/or morbidity modeling (univariate P < = .2 and prevalence = 0.5%)

	References

Top Abstract Introduction Methods Results Discussion Conclusion Appendix 1 Appendix 2 References

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