© 2015 Wiley Periodicals, Inc.
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A Predictive Model and Risk Score for Unplanned Cardiac Surgery Intensive Care Unit Readmissions J. Trent Magruder, M.D.,* Markos Kashiouris, M.D.,y Joshua C. Grimm, M.D.,* Damon Duquaine, B.S., M.P.H.,* Barbara McGuinness, R.N.,* Sara Russell, R.N.,* Megan Orlando, B.S.,* Marc Sussman, M.D.,* and Glenn J.R. Whitman, M.D.* *Division of Cardiac Surgery, The Johns Hopkins Hospital, Baltimore, MD; Division of Cardiology, The Johns Hopkins Hospital, Baltimore, MD; and yDivision of Pulmonary and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA ABSTRACT Background: Readmissions or ‘‘bounce back’’ to the intensive care unit (ICU) following cardiac surgery is associated with an increased risk of morbidity and mortality. We sought to identify clinical and system-based factors associated with ICU bounce backs in order to generate a Bounce Back After Transfer (BATS) prediction score. Methods: We prospectively collected the clinical and financial records of all patients undergoing coronary artery bypass grafting (CABG) or surgical aortic valve replacement (AVR) between May 2013 and March 2014. Multivariable logistic regression was used to identify independent predictors of bounce backs to the ICU which served as the basis for our BATS score. Results: Of the 532 patients that underwent CABG or AVR during the study period, 35 (6.6%) were readmitted to the ICU. After risk adjustment, female sex, NYHA class III/IV, urgent or emergent operative status, and postoperative renal failure were the predictors of ICU bounce backs utilized to create the BATS score. Patients in the low (10) score cohorts experienced bounce back rates of 3.0%, 10.4%, and 42%, respectively. After adjusting for preoperative patient risk, ICU bounce back resulted in an increase in $68,030 to a patient’s total hospital charges. Conclusions: A predictive model (BATS) can determine the risk of a bounce back to the ICU after transfer to the floor. We speculate that determination of a patient’s BATS upon ICU transfer would allow targeted floor care and decrease bounce back rates, along with postoperative morbidity, mortality, and cost of care. doi: 10.1111/jocs.12589 (J Card Surg 2015;30:685–690) A readmission to the intensive care unit (ICU) or ‘‘bounce back’’ following cardiac surgery is associated with increased morbidity and mortality as well as significant resource consumption.1–4 Unplanned bounce back rates range from 2% to 9% depending on the patient population analyzed. A number of patientspecific independent risk factors for bounce backs have been identified, such as age, heart failure, renal failure, and prolonged mechanical ventilation.3–5 While these retrospective studies have identified high-risk characteristics, few, if any, have successfully stratified patients based on these factors, and none have examined resource and system-based risk factors.
Conflict of interest: The authors acknowledge no conflict of interest in the submission. Address for correspondence: Glenn J.R. Whitman, M.D., Division of Cardiac Surgery, The Johns Hopkins Hospital, Zayed Tower 7107, 1800 Orleans St, Baltimore, MD 21287. Fax: þ1-443-287-4226, e-mail: [email protected]
Furthermore, while readmissions are undoubtedly costly, the economic impact of ICU bounce backs has not been previously quantified. Accordingly, we sought to identify risk factors in a prospectively collected population of cardiac surgical ICU patients in order to derive the Bounce Back After Transfer (BATS) score. Furthermore, we attempted to determine the independent effect of a cardiac surgical ICU bounce back on hospital charges. PATIENTS AND METHODS Patient population and study design After obtaining appropriate institutional review board approval from our institution, we prospectively collected clinical and financial records of all patients who underwent coronary artery bypass grafting or surgical aortic valve replacement at our institution between May 2013 and March 2014. Patient comorbidity and operative data abstracted from our institutional Society
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MAGRUDER, ET AL. READMISSIONS AFTER CARDIAC SURGERY
of Thoracic Surgeons (STS) database, as well as anesthesia operative records, hospital bed census data, hospital charge data, and cardiac surgical floor data were all entered prospectively into a single database designed to track cardiac surgery ICU bounce backs. An intensive care unit bounce back was defined as the transfer of a patient from the cardiac surgical floor back to the intensive care unit following his or her initial postoperative ICU admission. To understand the effect of clinical and administrative pressure to ‘‘open’’ ICU beds for pending cases, daily ICU bed census and expected operative volume were collected. Additionally, overnight floor transfers, defined as occurring between 1800 and 0600 hours, were identified within the cohort. Financial data collected included the total hospital charges incurred by each patient in our series. Charge data, as opposed to cost data, were utilized because the state of Maryland holds a Medicare waiver to operate the nation’s only all-payer hospital rate regulation system. Under the waiver, all third parties pay the same rate for the same procedures, and so charges are equivalent to costs for these patients. Transfer practices and floor care At our institution, the decision to transfer a patient out of the ICU is made jointly by the attending surgeon and the attending intensivist for the ICU. Though no formal protocol exists for transfer, we use the following guidelines: a patient must be extubated, hemodynamically stable, oriented or with a ‘‘sitter’’ for supervision. Additionally, patients may not transfer to the floor if they are pacer-dependent, requiring suctioning greater than every four hours, requiring titration of any vasoactive drip, or exhibit signs or symptoms of active bleeding. Our step-down floor, known as the Cardiovacular Progressive Care Unit (CVPCU), is a 32-bed monitored inpatient unit specializing in the care of postoperative cardiac and vascular surgery patients. Two levels of care are available: intermediate care (IMC, up to 12 of the 32 beds), and general surgical floor care (including telemetry). The CVPCU also provides care to heart and lung transplant patients, as well as ventricular assist device patients. Though ventilators are not available on the CVPCU, the unit does have certain specialized capabilities, such as the ability to maintain patients on some inotropic infusions (including dobutamine, dopamine, milrinone, and isoproterenol). Nursing staff ratios are 1:3 patients during the day and 1:4 at night. Provider coverage is provided by a nurse practicioner/physician assistant team during the day and cardiothoracic surgery residents at night. Statistical analysis Statistical analysis was performed using Stata 12 (StataCorp, College Station, TX, USA). Continuous variables were analyzed with Student’s t-test or the Wilcoxon rank-sum test, while categorical variables were analyzed with Fisher’s exact or chi-squared tests, as appropriate. To identify independent predictors of
J CARD SURG 2015;30:685–690
Figure 1. Bubble plot of predicted versus actual ICU bounce back rates. Circles denote weighted patient scores.
ICU readmission, covariates with an explanatory p-value
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PERIOPERATIVE MANAGEMENT_____________________________________________________
A Predictive Model and Risk Score for Unplanned Cardiac Surgery Intensive Care Unit Readmissions J. Trent Magruder, M.D.,* Markos Kashiouris, M.D.,y Joshua C. Grimm, M.D.,* Damon Duquaine, B.S., M.P.H.,* Barbara McGuinness, R.N.,* Sara Russell, R.N.,* Megan Orlando, B.S.,* Marc Sussman, M.D.,* and Glenn J.R. Whitman, M.D.* *Division of Cardiac Surgery, The Johns Hopkins Hospital, Baltimore, MD; Division of Cardiology, The Johns Hopkins Hospital, Baltimore, MD; and yDivision of Pulmonary and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA ABSTRACT Background: Readmissions or ‘‘bounce back’’ to the intensive care unit (ICU) following cardiac surgery is associated with an increased risk of morbidity and mortality. We sought to identify clinical and system-based factors associated with ICU bounce backs in order to generate a Bounce Back After Transfer (BATS) prediction score. Methods: We prospectively collected the clinical and financial records of all patients undergoing coronary artery bypass grafting (CABG) or surgical aortic valve replacement (AVR) between May 2013 and March 2014. Multivariable logistic regression was used to identify independent predictors of bounce backs to the ICU which served as the basis for our BATS score. Results: Of the 532 patients that underwent CABG or AVR during the study period, 35 (6.6%) were readmitted to the ICU. After risk adjustment, female sex, NYHA class III/IV, urgent or emergent operative status, and postoperative renal failure were the predictors of ICU bounce backs utilized to create the BATS score. Patients in the low (10) score cohorts experienced bounce back rates of 3.0%, 10.4%, and 42%, respectively. After adjusting for preoperative patient risk, ICU bounce back resulted in an increase in $68,030 to a patient’s total hospital charges. Conclusions: A predictive model (BATS) can determine the risk of a bounce back to the ICU after transfer to the floor. We speculate that determination of a patient’s BATS upon ICU transfer would allow targeted floor care and decrease bounce back rates, along with postoperative morbidity, mortality, and cost of care. doi: 10.1111/jocs.12589 (J Card Surg 2015;30:685–690) A readmission to the intensive care unit (ICU) or ‘‘bounce back’’ following cardiac surgery is associated with increased morbidity and mortality as well as significant resource consumption.1–4 Unplanned bounce back rates range from 2% to 9% depending on the patient population analyzed. A number of patientspecific independent risk factors for bounce backs have been identified, such as age, heart failure, renal failure, and prolonged mechanical ventilation.3–5 While these retrospective studies have identified high-risk characteristics, few, if any, have successfully stratified patients based on these factors, and none have examined resource and system-based risk factors.
Conflict of interest: The authors acknowledge no conflict of interest in the submission. Address for correspondence: Glenn J.R. Whitman, M.D., Division of Cardiac Surgery, The Johns Hopkins Hospital, Zayed Tower 7107, 1800 Orleans St, Baltimore, MD 21287. Fax: þ1-443-287-4226, e-mail: [email protected]
Furthermore, while readmissions are undoubtedly costly, the economic impact of ICU bounce backs has not been previously quantified. Accordingly, we sought to identify risk factors in a prospectively collected population of cardiac surgical ICU patients in order to derive the Bounce Back After Transfer (BATS) score. Furthermore, we attempted to determine the independent effect of a cardiac surgical ICU bounce back on hospital charges. PATIENTS AND METHODS Patient population and study design After obtaining appropriate institutional review board approval from our institution, we prospectively collected clinical and financial records of all patients who underwent coronary artery bypass grafting or surgical aortic valve replacement at our institution between May 2013 and March 2014. Patient comorbidity and operative data abstracted from our institutional Society
686
MAGRUDER, ET AL. READMISSIONS AFTER CARDIAC SURGERY
of Thoracic Surgeons (STS) database, as well as anesthesia operative records, hospital bed census data, hospital charge data, and cardiac surgical floor data were all entered prospectively into a single database designed to track cardiac surgery ICU bounce backs. An intensive care unit bounce back was defined as the transfer of a patient from the cardiac surgical floor back to the intensive care unit following his or her initial postoperative ICU admission. To understand the effect of clinical and administrative pressure to ‘‘open’’ ICU beds for pending cases, daily ICU bed census and expected operative volume were collected. Additionally, overnight floor transfers, defined as occurring between 1800 and 0600 hours, were identified within the cohort. Financial data collected included the total hospital charges incurred by each patient in our series. Charge data, as opposed to cost data, were utilized because the state of Maryland holds a Medicare waiver to operate the nation’s only all-payer hospital rate regulation system. Under the waiver, all third parties pay the same rate for the same procedures, and so charges are equivalent to costs for these patients. Transfer practices and floor care At our institution, the decision to transfer a patient out of the ICU is made jointly by the attending surgeon and the attending intensivist for the ICU. Though no formal protocol exists for transfer, we use the following guidelines: a patient must be extubated, hemodynamically stable, oriented or with a ‘‘sitter’’ for supervision. Additionally, patients may not transfer to the floor if they are pacer-dependent, requiring suctioning greater than every four hours, requiring titration of any vasoactive drip, or exhibit signs or symptoms of active bleeding. Our step-down floor, known as the Cardiovacular Progressive Care Unit (CVPCU), is a 32-bed monitored inpatient unit specializing in the care of postoperative cardiac and vascular surgery patients. Two levels of care are available: intermediate care (IMC, up to 12 of the 32 beds), and general surgical floor care (including telemetry). The CVPCU also provides care to heart and lung transplant patients, as well as ventricular assist device patients. Though ventilators are not available on the CVPCU, the unit does have certain specialized capabilities, such as the ability to maintain patients on some inotropic infusions (including dobutamine, dopamine, milrinone, and isoproterenol). Nursing staff ratios are 1:3 patients during the day and 1:4 at night. Provider coverage is provided by a nurse practicioner/physician assistant team during the day and cardiothoracic surgery residents at night. Statistical analysis Statistical analysis was performed using Stata 12 (StataCorp, College Station, TX, USA). Continuous variables were analyzed with Student’s t-test or the Wilcoxon rank-sum test, while categorical variables were analyzed with Fisher’s exact or chi-squared tests, as appropriate. To identify independent predictors of
J CARD SURG 2015;30:685–690
Figure 1. Bubble plot of predicted versus actual ICU bounce back rates. Circles denote weighted patient scores.
ICU readmission, covariates with an explanatory p-value
