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Ann Thorac Surg 2008;86:213-218. doi:10.1016/j.athoracsur.2008.03.063
© 2008 The Society of Thoracic Surgeons
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Original Articles: General Thoracic

A Scoring System Predicting the Risk for Intensive Care Unit Admission for Complications After Major Lung Resection: A Multicenter Analysis

Alessandro Brunelli, MDa,*, Mark K. Ferguson, MDb, Gaetano Rocco, MDc, Paola Pieretti, MDd, Wickii T. Vigneswaran, MDb, Nicholas J. Morgan-Hughes, MDc, Marco Zanello, MDd,e, Michele Salati, MDa

a Umberto First Regional Hospital, Ancona, Italy
b University of Chicago, Chicago, Illinois
c Sheffield Teaching Hospital, Sheffield, United Kingdom
d Bellaria Hospital, Bologna, Italy
e University of Bologna, Bologna, Italy

Accepted for publication March 26, 2008.

* Address correspondence to Dr Brunelli, Via S. Margherita 23, 60124 Ancona, Italy (Email: alexit_2000{at}yahoo.com).

Presented at the Poster Session of the Forty-fourth Annual Meeting of The Society of Thoracic Surgeons, Fort Lauderdale, FL, Jan 28–30, 2008.


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Comment
 References
 
Background: We aimed to develop and validate a scoring system to predict intensive care unit (ICU) admission for complications after major lung resection for purposes of optimizing planning of resources for patient care.

Methods: Patients undergoing major lung resections performed between 2000 and 2006 at three thoracic surgery units were analyzed for unplanned admission to the ICU for complications. Variables were initially screened by univariate analysis. Selected variables were used in a stepwise logistic regression analysis that was validated by bootstrap analysis. The scoring system was developed by proportional weighting of the significant and reliable predictors estimates and validated on patients operated on in a different center.

Results: In the derivation set of 1297 patients, 82 (6.3%) had ICU admission for complications, and 30 died (associated mortality rate, 36.5%). Predictive variables and their scores were pneumonectomy, 2 points; and 1 point each for age older than 65, predicted postoperative forced expiratory volume in 1 second below 65%, predicted postoperative carbon monoxide lung diffusion capacity below 50%, and cardiac comorbidity. Patients were grouped into three risk classes by their scores, which were significantly associated with incremental risk of ICU admission in the validation set of 349 patients.

Conclusions: This scoring system predicts incremental risk of ICU admission for complications after major lung resection. This system may help in assessing the need for additional postoperative resources and in modifying indicators used to determine the appropriateness of initial transfer of postoperative patients from ICU or stepdown status and in developing criteria for future cost-effectiveness trials.


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Comment
 References
 
Despite the current financial climate with limited resources allocated to health care, the demand for services such as intensive care units (ICUs) is rapidly increasing [1]. The increased frequency of elderly patients with more underlying comorbidities, greater patient expectations, and introduction of advanced technology has contributed to an increased demand for expensive care [2, 3]. For this reason, the economic costs of ICU care are also increasing, amounting to approximately 20% to 30% of the total in-patient expenditures [4–6].

High levels of demand lead to ICUs being perpetually at or near a crisis point [3]. The response to the crisis of ICU bed shortages has concentrated largely on the supply side of the problem. However, financial constraints mean that the supply of ICU services cannot increase indefinitely, and resources will indeed remain more or less fixed for the foreseeable future. The alternative to increasing supply is to tackle the problem of demand and implement some organized form of rationing.

Thoracic surgery is one of the specialties that uses ICU resources more often [3, 7], either electively for monitoring high-risk patients in the early postoperative period or emergently for major cardiorespiratory complications requiring active life-supporting treatments.

So far, no reliable system has been developed that may assist in stratifying the risk of thoracic patients requiring postoperative intensive care. Thus, the purpose of this multicenter investigation was to develop and validate a risk score to predict the need for emergency ICU admission for cardiorespiratory complications after major lung resection, with the ultimate intent to provide an instrument for optimizing planning of resources for patient care and developing criteria for future cost-effectiveness trials on postoperative advanced care management.


   Patients and Methods
Top
 Abstract
 Introduction
 Patients and Methods
Results
 Comment
 References
 
This article reports a retrospective analysis performed on prospectively collected data. The study included 1297 consecutive patients who underwent major lung resections (1115 lobectomies or bilobectomies, 182 pneumonectomies) at three international dedicated thoracic surgery units (unit A, 700 patients; unit B, 224 patients; unit C, 373 patients) during a 7-year period (2000 to 2006). These patients were used to develop the predictive model of ICU admission caused by postoperative complications. An additional 349 patients operated on in another unit (285 lobectomies, 64 pneumonectomies) during the same period were used to validate the model.

In all centers, data of the patients were entered prospectively into electronic databases by trained clinical staff physicians and periodically audited by designated Clinical Audit Leads responsible for assessing the completeness and accuracy of variables. Clinical risk factors and end points were defined before the database project in each unit was started and were deemed consistent across the four centers. The local Institutional Review Boards (IRBs) approved the databases. All patients provided informed consent to be included in the data sets, or informed consent was waived by the appropriate IRB.

All participating centers are tertiary referral hospitals whose main characteristics are summarized in Table 1.


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  		Table 1 Structural Characteristics of Participating Centers
 
Operability criteria were similar in all participating units. Operations were contraindicated in those patients with a predicted postoperative forced expiratory volume in 1 second (ppoFEV1) and predicted postoperative carbon monoxide lung diffusion capacity (ppoDLCO) of less than 30% of predicted, in association with a poor exercise capacity, defined as height at maximal stair climbing test lower than 12 m or maximum oxygen consumption at cycle-ergospirometry of less than 10 mL/kg/min, or both. The value for DLCO was systematically measured in all patients.

Lung resections were normally performed through a muscle-sparing thoracotomy. Postoperative treatment was standardized and consisted of judicious fluid infusion (usually 1 mL/kg/h), antibiotic and antithrombotic prophylaxis, chest physiotherapy and physical rehabilitation, and adequate epidural or intravenous analgesic therapy, which was titrated to keep the pain visual analog score below 4 (scale, 0 to 10) during the first 48 to 72 hours. Patients were mobilized as soon as possible, and bronchodilators were administered only in case of an objective evidence of reversible obstruction after bronchodilator administration at the preoperative pulmonary function tests).

The postoperative management policies differed among the participating units. In units A and B, patients were usually admitted to a dedicated general thoracic surgery ward immediately after operation. The ICU was used only in case of major cardiorespiratory complications that required active life-supporting treatment [7]. In units C and D, patients were initially admitted to ICU for a period of 24 to 48 hours and then transferred to the thoracic ward when deemed to be in stable cardiorespiratory condition.

All centers have dedicated general thoracic surgery wards staffed 24 hours a day, 365 days a year, with dedicated nurses and chest physiotherapists. In the early postoperative period, the patients are monitored by means of electrocardiograph and pulse oximeter. Noninvasive systemic blood pressure, respiratory rate, and body temperature are recorded every other hour, or more frequently if indicated, on a special chart. Every bed has oxygen and aspiration points, volumetric infusion pump, infusion administration, and syringe pumps available. The nurse-to-patient ratio is 1:4 in every section of the ward. The surgical team includes certified thoracic surgeons and residents. At least one thoracic surgeon is always present in the ward during the day, and one is always on-call at home during the night.

Statistical Analysis
To construct the model of emergency ICU admission for complications, a series of preoperative and operative variables were initially screened by univariate analysis.

Preoperative and Operative Variables
The following spirometric variables were tested for possible association with postoperative emergency ICU admission: FEV1, DLCO, predicted postoperative FEV1 (ppoFEV1) calculated by the formula [(preoperative FEV1/number of preoperative functioning segments) x number of postoperative functioning segments], and predicted postoperative DLCO (ppoDLCO) calculated by the formula [preoperative DLCO/number of preoperative functioning segments] x number of postoperative functioning segments].

Pulmonary function tests were performed according to the American Thoracic Society criteria. The DLCO was measured in all patients by the single-breath method. Results of spirometry were collected after bronchodilator administration and were expressed as percentage of predicted for age, sex, and height.

The number of functioning segments was estimated by means of computed tomography scan, bronchoscopy, and quantitative perfusion lung scan.

For the purpose of the present study, a concomitant cardiac disease (cardiac comorbidity) was defined as previous cardiac operation, previous myocardial infarction, history of coronary artery disease, or current treatment for hypertension, arrhythmia, or cardiac failure. Additional variables used in the analysis were age, sex, and type of operation (lobectomy vs pneumonectomy).

Outcome Variables
Postoperative emergency ICU admission, cardiopulmonary complications, and death were considered as those occurring within 30 days from operation or during a longer period if the patient was still in the hospital. Emergency ICU admissions were considered as those occurring in patients admitted or readmitted to the ICU for major cardiopulmonary complications and receiving active life-supporting treatments according to the definition of Zimmerman and colleagues [7]. These include controlled ventilation, intermittent mandatory ventilation or assisted ventilation, spontaneous positive end-expiratory pressure or continuous positive airway pressure, nasal or oral intubation, fresh tracheostomy, atrial or ventricular pacing, multiple vasoactive drugs, postarrest, cardioversion, hemodialysis, and complex metabolic balance.

For the purpose of this study, patients admitted to ICU only for postoperative monitoring were not considered as having an emergency ICU admission.

Only high-quality, at least 95% complete, variables that were judged to be reliable and consistent across the participating centers were used for the analysis. Sporadic or missing data were imputed by averaging the nonmissing values for continuous variables and taking the most frequent category response for categoric variables. Normality of data was assessed by the Shapiro-Wilk normality test. Continuous variables were compared by means of the unpaired Student t test or the Mann-Whitney test. Categoric variables were compared by the {chi}2 test or the Fisher exact test, whenever appropriate. For the purpose of building the scoring system, numeric continuous variables were dichotomized by selecting the best threshold value by using receiver operator curve (ROC) analysis.

Variables with a value of p < 0.1 on univariate analysis were then used as independent variables in a stepwise logistic regression analysis with emergency ICU admission for complications as the dependent variable. For the purpose of this study, emergency ICU admissions were considered as those occurring in patients admitted or readmitted to the ICU for major cardiopulmonary complications and receiving active life-supporting treatment [7]. The patients admitted to the ICU only for postoperative monitoring were not considered as having an emergency ICU admission.

A significance level of 0.05 was selected for variable retention in the final model. To avoid multicolinearity, only one variable in a set of variables with a correlation coefficient greater than 0.5 was selected by bootstrap analysis and used in the regression analysis. Calibration and discrimination of the model were assessed by Hosmer-Lemeshow goodness of fit and c-index statistics, respectively. The model was then validated by bootstrap bagging simulation by using 1000 samples with the same number of observations as the original data set [8–10]. For each sample, stepwise logistic regression was performed entering the variables with p < 0.1 at univariate analysis. The stability of the final stepwise model was 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 occurred in more than 50% of the bootstrap models, the original final stepwise regression model was judged to be stable [8–10].

The final ICU scoring system was then developed by proportional weighting of the logistic regression coefficients, assigning a value of 1 point to the variable with the smallest coefficient. For each patient, an aggregate score was obtained by summing the corresponding points of each variable in the final model. Scores ranged from 0 to 6 (Figure 1). Patients were then grouped in classes of incremental ICU admission risk according to their aggregate score. Scores with similar ICU admission rates were grouped. This aggregate scoring system was then validated in an external sample of 349 patients operated on in another center (unit D). The bootstrap technique was used to create 1000 simulated samples of 349 patients drawn with replacement from this external center. The scoring system was then applied in each of these samples to assess its discriminatory capacity.


Figure 1
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  		Fig 1. Distribution of risk scores in the analyzed patients (derivation set). (ICU = intensive care unit.)

 
All tests were two-tailed with a significant level of 0.05. The analysis was performed by using Stata 8.2 software (StataCorp, College Station, TX).


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Comment
 References
 
The characteristics of the patients in the derivation and validation sets are reported in Table 2. In the derivation set (units A, B, and C), 82 of 1297 patients (6.3%) were admitted to an ICU for complications requiring active life-supporting treatment. The median time for emergency ICU admission was postoperative day 2 (range, 0 to 5 days), and the median length of stay in ICU was 5 days (range, 1 to 51 days). The main reasons for admitting patients in to ICU are reported in Table 3. The most frequent diagnosis (72%) was respiratory failure requiring mechanical assisted ventilation. The mortality rate among patients admitted to ICU for complications was 36.5% (30 patients).


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  		Table 2 Characteristics of the Patients in Each Unit
 

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  		Table 3 Reasons for Emergency Intensive Care Unit Admission (82 Patients)
 
ROC analysis was used to categorize continuous variables. The best cutoff values discriminating patients who required ICU admission from those who did not were an age older than 65 years (8.0% vs 4.3%, p = 0.007), ppoFEV1 of less than 65% (9.4% vs 3%, p < 0.0001), and ppoDLCO of less than 50% (12.0% vs 4.2%, p < 0.0001). Categoric variables associated with an increased incidence of ICU admission at univariate analysis were cardiac comorbidity (8.7% vs 5.0%, p = 0.009) and pneumonectomy (16.5% vs 4.7%, p < 0.0001).

Logistic regression and bootstrap analyses confirmed that all these factors were independently and reliably associated with ICU admission (Table 4). The regression coefficients were used to construct the following proportionally weighed scores: age older than 65 years (1 point), pneumonectomy (2 points), ppoFEV1 of less than 65% (1 point), ppoDLCO of less than 50% (1 point), and cardiac comorbidity (1 point). The distribution of ICU scores is shown in Figure 1. Patients were grouped into three risk classes of incremental risk of ICU admission—class A, B, and C—according to their scores.


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  		Table 4 Results of the Regression Analysis a
 
These ICU risk classes were significantly associated with incremental risk of ICU admission in the validation set of 349 patients operated on in external unit D (Fig 2). Bootstrap was performed in this external sample of patients to obtain 1000 simulated external samples in which to test the risk score. The three ICU risk classes showed a significant incremental risk of ICU admission in 98% of the bootstrap samples. The ICU risk of patients in class A was less than 5% in 93% of the bootstrap samples, whereas the risk in patients of class C exceeded 20% in 73% of the samples (Table 5).


Figure 2
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  		Fig 2. Intensive care unit (ICU) admission rates in the derivation (gray bars) and validation sets (black bars). The three risk classes were significantly associated with an incremental risk of ICU admission either in both the derivation and validation sets (p < 0.0001).

 

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  		Table 5 Frequency of Emergency Intensive Care Unit Admissions for Complications in 1000 Bootstrap Samples Drawn From Unit D (Validation Set) and Grouped by Class of Risk
 
Even when the analysis was restricted to lobectomy patients only, the score had a significant discrimination in the validation set, with ICU admission rates of 1.7% for class A, 8% for class B, and 43% for class C (Fisher exact test, p < 0.0001).


   Comment
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Comment
References
 
The increased number and survival of patients with disproportionately high medical needs (elderly and chronically ill), the increased use of new and expensive diagnostic and therapeutic modalities, and the increased expectations of the public and health care professionals have resulted in increasingly expensive care. An estimated 7% of hospital beds that account for 20% to 30% of hospital costs are attributed to ICU cost centers [1]. These factors have led physicians and administrators to place increasing emphasis on fiscal restraint and utilization review for ICU admission and discharge decisions [11], particularly in light of the mounting pressure of managed care and the difficulty of recovering costs under closed financial systems.

General thoracic surgery accounts for approximately 10% of all ICU admissions [3], either electively for monitoring high-risk patients in the early postoperative period or emergently for major cardiorespiratory complications requiring active life-supporting treatments or more invasive monitoring. The objective of this study was to develop and validate a risk score stratifying the risk of an unplanned admission to ICU for cardiorespiratory complications after major lung resection, with the ultimate intent to provide an instrument for optimizing pathways of care and use of resources for advanced care management.

Our analysis yielded a risk-adjusted scoring system with good clinical face and content validities. Classes of risk were generated and proved to be significantly associated with incremental ICU admission rates when tested on an external unit different from the ones from which the model was derived. The discrimination of the ICU risk score was further assessed on 1000 bootstrapped samples drawn from this external unit. By this technique we demonstrated the stability of the model across multiple simulated external samples. Patients with a score of 0 (lowest risk) showed an ICU admission rate lower than 5% in 93% of the samples, whereas those with a risk score 4 or more (highest risk) had an ICU admission rate greater than 20% in 73% of the samples.

As ever, caution is required in interpreting the prediction of a risk model in an individual patient. However, the model can be reliably applied to the whole population of lung resection candidates in order to identify subsets of patients at higher risk for emergency ICU admission. This may serve multiple purposes. In times of persistent shortage of ICU beds, surgical procedures on patients with high risk scores may be restricted or postponed to reduce the potential ICU demand, in line with position 9 of the American Thoracic Society statement on the fair allocation of ICU resources [12]. It may be used as an instrument for the continuous evaluation of the cost-effectiveness of programs such as thoracic surgery, which uses a large amount of ICU resources, by trending the different risk groups over time. At the same time it may represent a fair and common tool to plan and negotiate allocation of institutional resources or structural expansion at an interinstitutional level.

From a more clinical point of view, the score can be used for establishing inclusion criteria for future cost-effectiveness prospective trials about policies of advanced care management (ICU/intermediate care) in patients after lung resection, as an additional diagnosis-specific tool to assist in defining criteria of step-up/step-down care in addition to more generic systems (ie, Acute Physiology and Chronic Health Evaluation III), and for counseling patients before operation about their perioperative risk. Preventative measures may be implemented for those patients identified to be at increased risk of postoperative emergency ICU admission in the attempt to improve their outcome. These patients, for instance, may benefit from a direct admission to ICU rather than to a step-down unit or may be kept in the ICU longer if that is where they spend their first day.

Finally, the risk score may be used as an additional outcome indicator of thoracic ward/ICU quality of care. Emergency ICU admission and readmissions for complications may signal a substandard process of care while in the ward or ICU [2]. Yet there has been no theoretic or experiential evaluation of the factors that might separate appropriate ICU admission or readmission from those resulting from poor quality of care, and this measure should be always complementary to other more traditional clinical performance indicators such as risk-adjusted mortality and morbidity [13, 14]. In fact, several confounders, such as structural characteristics of the thoracic unit, changing admission/discharge criteria in times of ICU shortage of beds, presence of other nearby or adjacent ICU or intermediate care facilities, and triage pressure may play a substantial role in the decision to admit a patient to ICU [15].

This study has potential limitations. This was an observational analysis, and although performed on prospectively compiled databases, it may have inherent selection biases.

In addition, the multicenter and retrospective nature of the study implies that the criteria of ICU admission may have been different at different centers and influenced not only by clinical factors but also by structural conditions and triage pressure. To minimize this confounding factor, we considered as complicated ICU admissions only those patients who were admitted or readmitted to ICU and who required active life-supporting treatments. Admissions for monitoring only were not considered as complicated ICU admissions.

In conclusion, we were able to develop a scoring system that predicts incremental risk of ICU admission for complications after major lung resection. The use of this system may help in assessing and planning the need for additional postoperative resources and in modifying indicators used to determine the appropriateness of the initial transfer of postoperative patients from ICU or stepdown status and in developing criteria for future cost-effectiveness trials. Although the score was validated on an external sample of patients, future independent studies are needed to verify its applicability in other centers.


   References
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Comment
 References
 

  1. Barnato AE, McClellan MB, Kagay CR, et al. Trends in inpatient treatment intensity among Medicare beneficiaries at the end of life Health Serv Res 2004;39:363-375.[Medline]
  2. Rosenberg AL, Watts C. Patients readmitted to ICUs. A systematic review of risk factors and outcomes. Chest 2000;118:492-502.[Medline]
  3. Parker A, Wyatt R, Ridley S. Intensive care services; a crisis of increasing expressed demand Anaesthesia 1998;53:113-120.[Medline]
  4. Halpern NA, Bettes L, Greenstein R. Federal and nationwide intensive care units and healthcare costs: 1986–1992 Crit Care Med 1994;22:2001-2007.[Medline]
  5. Jacobs P, Noseworthy TW. National estimates of intensive care utilization and costs: Canada and United States Crit Care Med 1990;18:1282-1286.[Medline]
  6. Berenson R. Health technology case study 28: intensive care units (ICUs); clinical outcomes, costs, and decisionmakingWashington, DC: Congress of the United States, Office of Technology Assessment; 1984.
  7. Zimmerman JE, Wagner DP, Knaus WA, Williams JF, Kolakowski D, Draper EA. The use of risk predictions to identify candidates for intermediate care units. Implications for intensive care utilization and cost. Chest 1995;108:490-499.[Medline]
  8. Blackstone EH. Breaking down barriers: helpful breakthrough statistical methods you need to understand better J Thorac Cardiovasc Surg 2001;122:430-439.[Free Full Text]
  9. Grunkemeier GL, Wu YX. Bootstrap resampling method: something for nothing? Ann Thorac Surg 2004:1142-1144.
  10. Brunelli A, Rocco G. Internal validation of risk models in lung resection surgery: bootstrap versus training and test sampling J Thorac Cardiovasc Surg 2006;131:1243-1247.[Abstract/Free Full Text]
  11. Brook RH, Lohr KN. Monitoring quality of care in the Medicare program: two proposed systems JAMA 1987;258:3138-3141.[Abstract/Free Full Text]
  12. Lanken PN, Terry PB, Adler DC, et al. Fair allocation of intensive care unit resources Am J Respir Crit Care Med 1997;156:1282-1301.[Free Full Text]
  13. Cooper GS, Sirio CA, Rotondi AJ, et al. Are readmissions to the intensive care unit a useful measure of hospital performance? Med Care 1999;37:399-408.[Medline]
  14. Brunelli A, Rocco G. The comparison of performance between thoracic surgical units Thorac Surg Clin 2007;17:413-424.[Medline]
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Invited Commentary
Arie Pieter Kappetein
Ann. Thorac. Surg. 2008 86: 219. [Extract] [Full Text] [PDF]



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Ann. Thorac. Surg., July 1, 2008; 86(1): 219 - 219.
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