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Print version ISSN 0034-7094
Rev. Bras. Anestesiol. vol.56 no.1 Campinas Jan./Feb. 2006
Mortalidade e o tempo de internação em uma unidade de terapia intensiva cirúrgica*
Mortalidad y tiempo de internación en una unidad de terapia intensiva quirúrgica
Fernando José AbelhaI; Maria Ana CastroI; Nuno Miguel LandeiroI; Aida Maria NevesI; Cristina Costa SantosII
IConsultant in Anesthesiology, Surgical
Intensive Care Unit, Department of Anesthesiology and Intensive Care
IIBiostatistics and Medical Informatics Department, Faculty of Medicine at the University of Porto
BACKGROUND AND OBJECTIVES:
Outcome in intensive care can be categorized as mortality related or morbidity
related. Mortality is an insufficient measure of ICU outcome when measured alone
and length of stay may be seen as an indirect measure of morbidity related outcome.
The aim of the present study was to estimate the incidence and predictive factors
for intrahospitalar outcome measured by mortality and LOS in patients admitted
to a surgical ICU.
METHODS: In this prospective study all 185 patients, who underwent scheduled or emergency surgery admitted to a surgical ICU in a large tertiary university medical center performed during April and July 2004, were eligible to the study. The following variables were recorded: age, sex, body weight and height, core temperature (Tc), ASA physical status, emergency or scheduled surgery, magnitude of surgical procedure, anesthesia technique, amount of fluids during anesthesia, use of temperature monitoring and warming techniques, duration of the anesthesia, length of stay in ICU and in the hospital and SAPS II score.
RESULTS: The mean length of stay in the ICU was 4.09 ± 10.23 days. Significant risk factors for staying longer in ICU were SAPS II, ASA physical status, amount of colloids, fresh frozen plasma units and packed erythrocytes units used during surgery. Fourteen (7.60%) patients died in ICU and 29 (15.70%) died during their hospitalization. Statistically significant independent risk factors for mortality were emergency surgery, major surgery, high SAPS II scores, longer stay in ICU and in the hospital. Statistically significant protective factors against the probability of dying in the hospital were low body weight and low BMI.
CONCLUSIONS: In conclusion, prolonged ICU stay is more frequent in more severely ill patients at admission and it is associated with higher hospital mortality. Hospital mortality is also more frequent in patients submitted to emergent and major surgery.
Key Words: COMPLICATIONS: morbidity, mortality, postoperative; INTENSIVE CARE: mortality, stay length; POSTOPERATIVE PERIOD: emergency surgery, major surgery
JUSTIFICATIVA E OBJETIVOS:
En cuidados intensivos los resultados pueden ser relacionados con índices
de mortalidad o morbilidad. Cuando se evalúa de forma aislada, la mortalidad
es una medida insuficiente de los resultados de una Unidad de Terapia Intensiva
(UTI); el tiempo de internación puede ser una medida indirecta de resultados
relacionados con la morbilidad. El objetivo del presente estudio fue evaluar
la incidencia y los factores predictivos para mortalidad y tiempo de internación
de los pacientes admitidos en una UTI quirúrgica.
MÉTODO: Participaron en este estudio prospectivo, realizado entre abril y julio de 2004, todos los 185 pacientes sometidos a procedimientos programados o de emergencia, admitidos en la UTI quirúrgica. Fueron registrados los siguientes parámetros: edad, sexo, altura y peso, temperatura central, estado físico según la ASA, tipo de intervención quirúrgica, porte quirúrgico, técnica anestésica, cantidad y calidad de fluidos administrados durante la anestesia, monitorización de la temperatura o la técnica de calentamiento corporal perioperatorio, duración de la anestesia, tiempo de permanencia en la UTI y en el hospital y escore SAPS II.
RESULTADOS: El tiempo medio de internación en la UTI fue de 4,09 ± 10,23 días. Factores de riesgo significativos para permanencia más prolongada en la UTI fueron el valor del escore SAPS II, estado físico ASA, cantidad de coloides administrada durante la intervención quirúrgica, unidades de plasma fresco y unidades de concentrados de hematíes. Catorce pacientes (7,60%) murieron durante la internación en la UTI y otros 29 (15,70%) murieron durante la internación hospitalar. Factores de riesgo independientes de mortalidad con diferencia estadística significativa fueron intervenciones quirúrgicas de emergencia, de gran porte, escores altos SAPS II, permanencia prolongada en la UTI y en el hospital . Factores protectores con diferencia estadística significativa para riesgo de muerte hospitalar fueron bajo peso corporal y bajo índice de masa corporal (IMC).
CONCLUSIONES: Las internaciones prolongadas en UTI son más frecuentes en los pacientes más graves en el momento de la admisión y están asociadas a mayor mortalidad hospitalar. La mortalidad hospitalar es también más frecuente en pacientes sometidos a intervenciones quirúrgicas de emergencia o de porte mayor.
Outcome in intensive care have primarily been focused on hospital survival and resource utilization adjusted for severity of illness. Many outcome prediction systems for ICU patients have been developed1-3 and are routinely used in many ICU all over the world measuring severity of illness as mortality prediction models. They have been widely used and their performance well studied in large international data set4. Predicted outcomes may be used both for clinical decision making in individual patients and for assessing quality of care.
Cost analysis studies have found that the ICU cost per day per patient is remarkably consistent across most diagnoses5 and therefore, ICU length of stay (LOS) has been also used as a measure of resource utilization in the ICU6,7. Despite refinements in perioperative management, prolonged intensive care unit stay is still associated with poor patient outcome and increased costs8-10. Risk factors, which predispose toward prolonged stay in ICU after surgery have been found and widely studied and are associated with poor patient outcome and increased costs11-13.
Although LOS in ICU may be affected by discharge policies, variable practice patterns and bed management14 prolonged ICU stay can adversely affect the health status by increasing the risk of infection, complications, and, possibly, mortality15. It have also impact upon bed availability and could result in cancellation of elective surgeries, leading to long waiting times and time spent on the ward before ICU admission.
The likely LOS of a patient may also influence therapeutic decisions. Several recent studies have indicated that some therapeutic strategies that impact on patient outcome may only have an effect on patients with longer ICU stays16,17.
The aim of the present study was to estimate the incidence and predictive factors for intrahospitalar outcome measured by mortality and LOS in patients admitted to a surgical ICU.
The protocol was approved by our institutional review board. This prospective study was performed during a three-month period between April and July 2004. All postoperative adult patients (> 18 years old) who underwent scheduled or emergency noncardiac surgery admitted to a nine bed surgical ICU of a tertiary cares hospital was eligible for the study.
The following clinical variables were recorded on admission to the ICU: age, sex, body weight and height, preoperative body temperature, ASA physical status, emergency or scheduled surgery, magnitude of surgical procedure as described by Kongsayreepong24 and classified in major (surgery in which body cavities or major vessels are exposed to ambient temperature such as major abdominal, thoracic, major vascular, thoracic spine surgery with instrumentation, or hip arthroplasty), medium (surgery in which body cavities are exposed to a lesser degree such as appendectomy), and minor surgery (superficial surgery), anesthesia technique, amount of crystalloids, colloids, packed erythrocytes and fresh frozen plasma during anesthesia, use of temperature monitoring and warming techniques, and duration of the anesthesia. Core temperature (Tc) measured by an infrared tympanic membrane thermometer was evaluated before surgery in the ward, on arrival at the ICU and every two hours until 6 hours after.
For all patients we also recorded the LOS and the mortality in the ICU and in the hospital and the Simplified Acute Physiology Score II (SAPS II) was calculated18.
Postoperative prolonged ICU stay was defined as intensive care lasting for three days and longer. Two groups of patients were created: patients with prolonged ICU stay and patients with no prolonged ICU stay. For mortality analysis we also created two groups of patients: patients who died during their stay in the hospital and patients who survived. Groups were compared to assess the relationship between each clinical variable and long ICU stay or mortality using univariate analysis performed by simple binary logistic regression with an odds ratio (OR) and its 95% CI and independent sample t test, Chi-square or Fishers Exact tests. Multivariate predictors were determined for staying longer in ICU and mortality by multiple regression binary logistic with forward conditional elimination to examine covariate effects of each and calculate OR and their 95% CI. A two-sided significance level of 0.05 was used for all analysis.
Quantitative variables are presented as mean ± SD. All analyses were performed using SPSS for Windows (version 12.0, Chicago, IL).
All 185 patients were admitted in the study. The LOS in the ICU varied from 1 to 82 days (Table II) with a mean ± SD of 4.09 ± 10.23 days (Table I). The percentage of patients who stayed in ICU longer than 3 days were 20% (n = 37) (Table I).
According to univariate analysis significant risk factors for staying longer in ICU were SAPS II (OR 1.126, 95% CI 1.083-1.171, p < 0.001), ASA physical status (OR 4.764, 95% CI 1.876-12.096, p < 0.001 for ASA III/IV patients), emergency surgery (OR 6.526, 95% CI 2.771-15.370, p < 0.001), amount of colloids, fresh frozen plasma and packed erythrocytes used during surgery (OR 4.954, 95% CI 1.514-16.214, p = 0.008, OR 1.836, 95% CI 1.163 - 2.899, p = 0.009, OR 1.309 95% CI 1.014 - 1.689, p = 0.039, respectively). Temperatures measured at 2, 4 and 6 hours after admission were protective factors against staying longer in ICU (OR 0.602, 95% CI 0.392 - 0.923, p = 0.020, OR 0.622, 95% CI 0.390 - 0.992, p = 0.046 and OR 0.454, 95% CI 0.262 -0.885, p = 0.005, respectively). Patients who stayed longer in ICU significantly have a higher mortality in the ICU (OR 79.6, 95% CI 10.0 - 636.9, p < 0.001) and in the hospital (OR 14.6, 95% CI 5.9 - 36.2, p < 0.001) (Table II).
Multiple regression binary logistic with forward conditional elimination to examine all covariate effects of each factor showed considerably significant factor predicting longer staying in the ICU to be higher SAPS scores (OR 1.101, 95% CI 1.053-1.151, p < 0.001). This analysis showed that SAPS score was the factor that more significantly predicts longer LOS in ICU (Table III).
Fourteen (7.60%) patients died in ICU and 29 (15.70%) died during their hospitalization.
According to univariate analysis (Table III), age, sex, anesthesia technique, use of a perioperative warming technique, temperature monitoring, duration of anesthesia or surgery were not independent risk factors for mortality in the hospital. In the same analysis, temperature was not a risk factor at admission, neither at two, four and six hours after arrival in ICU.
Statistically significant independent protective factors for mortality were low body weight (OR 0.970, 95% CI 0.941-0.999, p = 0.044) and low Body Mass Index (BMI) (OR 0.895, 95% CI 0.815-0.982, p = 0.019), and statistically significant independent risk factors were emergency surgery (OR 7.109, 95% CI 2.902-17.419, p < 0.001), major surgery (OR 5.500, 95% CI 1.133-26.690, p = 0.034), high SAPS II scores (OR 1.105, 95% CI 1.067-1.144, p < 0.001), longer stay in ICU (OR 14.47, 95% CI 5.87-36.18, p < 0.001 for LOS longer than 3 days) and in the hospital (OR 1.024, 95% CI 1.011-1.038, p < 0.001).
The multiple logistic regression analysis (Table IV) showed that considerably significant factors predicting death in the hospital were higher SAPS scores (OR 1.101, 95% CI 1.053-1.151, p < 0.001), age (OR 6.541, 95% CI 1.685-25.388, p = 0.007 for patients older than 65 years) and LOS in the ICU (OR 3.56, 95% CI 1.13-11.27 p < 0.001). The analysis showed that these were the factors that more significantly predicted death in the intrahospitalar setting.
In this study, severity of illness of patients as measured by ASA physical status and SAPS II scores were predictors of prolonged stay in ICU and the same was true for emergent surgical patients and those with greater amount of intraoperative intravenous fluid administration other than crystalloids. This probably reflects the assumption that more severely ill patients stay longer in ICU.
Long ICU LOS has been defined as stay of more than 7 days18,19. As a result of high cost of ICU stay, and because in our study only 20% of patients stayed longer than 3 days in ICU we thought it was of interest to find clinical predictors for more restricted LOS in ICU and in fact those were the reasons to choose 3 days as the cut-off point to consider long ICU stay.
During this study, all patients were managed using the same standard of care and with ICU habitual protocols, including that of hypothermia treatment being applied according to the patients evolution and so this did not influence outcome in the different groups.
Hypothermia at ICU admission was not predictor of staying longer in the ICU but higher Tc measured at 2, 4 and 6 hours after arrival were found to be protective factors for longer ICU LOS.
In our study patients with prolonged ICU stay included almost all patients that had postoperative complications that lead them to increased requirements for intensive care treatment and those who were more severely ill at admission.
Overall mortality in our study was 7,6% during ICU stay and 15,7% during hospital stay. These values are in the range of what was expected by the correspondent standard mortality ratios of SAPS II.
In our institution, there are few beds providing intermediate care and that could explain longer LOS in ICU and the fact that patients who stay longer in ICU are more prone to die.
Statistically significant preadmission clinical predictors of death were ASA physical status; emergency surgery and magnitude of surgery being higher BMI and body weight were protective factors. SAPS II score and LOS in ICU and in the hospital were also statistically independent factors for dying during hospital stay.
The ASA classification has established itself as the most widely used patient risk assessment tool in anesthesia. Although developed in 1941 by Saklad20 it remains accepted as a standard for assessing preoperative fitness.
Several retrospective studies have demonstrated a correlation between ASA classification and perioperative mortality1-24 and have suggested its usefulness as a predictor of patient outcome25.
The importance of the type of surgery has been emphasized previously21,24,26,27. Elective surgery and minor surgery reduce operative risk and there is an effect on poor outcome attributable to emergency surgery.
Studies addressing the association of high BMI in patients admitted to the ICU and the hospital have demonstrated conflicting results. Our study agree with Choban retrospective study28 but not with a recently published study by Finkielman et al.29 and with other recent studies30,31 that have investigated the impact of BMI on ICU outcome and have showed that high BMI was not associated with high mortality in post-operative patients.
In conclusion, prolonged ICU stay is more frequent in more severely ill patients at admission and is associated with higher hospital mortality. Hospital mortality is also more frequent in patients submitted to emergent surgery and major surgery.
01. Le Gall JR, Loirat P, Alperovitch A et al - A simplified acute physiology score for ICU patients. Crit Care Med, 1984;12:975-977. [ Links ]
02. Knaus WA, Draper EA, Wagner DP et al - APACHE II: a severity of disease classification system. Crit Care Med, 1985;13:818-829. [ Links ]
03. Knaus WA, Wagner DP, Draper EA et al - The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest, 1991;100:1619-1636. [ Links ]
04. Castella X, Artigas A, Bion J et al - A comparison of severity of illness scoring systems for intensive care unit patients: results of a multicenter, multinational study. Crit Care Med, 1995;23: 1327-1335. [ Links ]
05. Noseworthy TW, Konopad E, Shustack A et al - Cost accounting of adult intensive care: methods and human and capital inputs. Crit Care Med, 1996;24:1168-1172. [ Links ]
06. Wong DT, Gomez M, McGuire GP et al - Utilization of intensive care unit days in a Canadian medical-surgical intensive care unit. Crit Care Med, 1999;27:1319-1324. [ Links ]
07. Knaus WA, Wagner DP, Zimmerman JE et al - Variations in mortality and length of stay in intensive care units. Ann Intern Med, 1993;118:753-761. [ Links ]
08. Ryan TA, Rady MY, Bashour CA et al - Predictors of outcome in cardiac surgical patients with prolonged intensive care stay. Chest, 1997;112:1035-1042. [ Links ]
09. Tuman KJ, McCarthy RJ, March RJ et al - Morbidity and duration of ICU stay after cardiac surgery. A model for preoperative risk assessment. Chest, 1992;102:36-44. [ Links ]
10. Rosenberg AL, Watts C - Patients readmitted to ICUs: a systemic review of risk factors and outcomes. Chest, 2000;118: 492-502. [ Links ]
11. Hammermeister KE - Risk, predicting outcomes, and improving care. Circulation, 1995;91:899-900. [ Links ]
12. Bucerius J, Gummert JF, Walther T et al - Predictors of prolonged ICU stay after on-pump versus off-pump coronary artery bypass grafting. Intensive Care Med, 2004;30:88-95. [ Links ]
13. Tu JV, Jaglal SB, Naylor CD - Multicenter validation of a risk index for mortality, intensive care unit stay, and overall hospital length of stay after cardiac surgery. Steering Committee of the Provincial Adult Cardiac Care Network of Ontario. Circulation, 1995;91:677-684. [ Links ]
14. Predicting outcome in ICU patients. 2nd European Consensus Conference in Intensive Care Medicine. Intensive Care Med, 1994;20:390-397. [ Links ]
15. Gilio AE, Stape A, Pereira CR et al - Risk factors for nosocomial infections in a critically ill pediatric population: a 25-month prospective cohort study. Infect Control Hosp Epidemiol, 2000;21:340-342. [ Links ]
16. van den Berghe G, Wouters P, Weekers F et al - Intensive insulin therapy in the critically ill patients. N Eng J Méd, 2001;345:1359-1367. [ Links ]
17. Corwin HL, Gettinger A, Rodriguez RM et al - Efficacy of recombinant human erythropoietin in the critically ill patient: a randomized, double-blind, placebo-controlled trial. Crit Care Med, 1999;27:2346-2350. [ Links ]
18. Le Gall JR, Lemeshow S, Saulnier F - A new Simplified Acute Physiology Score (SAPSII) based on a European/North American multicenter study. JAMA, 1993;270:2957-2963. [ Links ]
19. Stricker K, Rothen HU, Takala J - Resource use in the ICU: short- vs. long-term patients. Acta Anaesthesiol Scand, 2003;47: 508-515. [ Links ]
20. Saklad M - Grading of patients for surgical procedures. Anesthesiology, 1941;2:281-284. [ Links ]
21. Cook TM, Day CJ - Hospital mortality after urgent and emergency laparotomy in patients aged 65 yr and over. Risk and prediction of risk using multiple logistic regression analysis. Br J Anaesth, 1998;80;776-781. [ Links ]
22. Wolters U, Wolf T, Stutzer H et al - ASA classification and perioperative variables as predictors of postoperative outcome. Br J Anaesth, 1996;77:217-222. [ Links ]
23. Pedersen T, Eliasen K, Ravnborg M et al - Risk factors, complications and outcome in anaesthesia. A pilot study. Eur J Anaesthesiol, 1986;3:225-239. [ Links ]
24. Kongsayreepong S, Chaibundit C, Chadpaibool J et al - Predictor of core hypothermia and surgical intensive care unit, Anesth Analg, 2003;96:826-833. [ Links ]
25. Donati A, Ruzzi M, Adrario E et al - A new and feasible model for predicting operative risk. Br J Anaesth, 2004;93:393-399. [ Links ]
26. Arvidsson S, Ouchterlony J, Sjostedt L et al - Predicting postoperative adverse events. Clinical efficiency of four general classification systems. The project perioperative risk Acta Anaesthesiol Scand, 1996;40:783-791. [ Links ]
27. Tiret L, Hatton F, Desmonts JM et al - Prediction of outcome of anaesthesia in patients over 40 years: a multifactorial risk index. Stat Med, 1988;7:947-954. [ Links ]
28. Choban PS, Weireter LJ, Maynes C - Obesity and increased mortality in blunt trauma. J Trauma, 1991;31:1253-1257. [ Links ]
29. Finkielman JD, Gajic O, Afessa B - Underweight is independently associated with mortality in post-operative and non-operative patients admitted to the intensive care unit: a retrospective study. BMC Emerg Med, 2004;4:3. [ Links ]
30. Tremblay A, Bandi V - Impact of body mass index on outcomes following critical care. Chest, 2003;123:1202-1207. [ Links ]
31. Garrouste-Orgeas M, Troche G, Azoulay E et al - Body mass index. An additional prognostic factor in ICU patients. Intensive Care Med, 2004;30:437-443. [ Links ]
Dr. Fernando José Abelha
Address: Hospital de São João
Alameda Professor Hernâni Monteiro
ZIP: 4100 319 City: Porto, Portugal
Submitted for publication March 11, 2005
Accepted for publication October 21, 2005
* Received from Departamento de Anestesiologia e Unidade de Terapia Intensiva da Universidade do Porto, Portugal