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PubMed2000

Mathematical models for predicting G-duration tolerances.

Mathematical models that predict fatigue-based G-duration tolerances for relaxed and straining subjects are developed and validated using published data. These models are based on regression analysis calculations using published G-duration tolerance data of relaxed subjects exposed to 3-5 G and subjects exposed to 6-9 G using an anti-G suit and performing the anti-G straining maneuver. These G-duration models are derived from published G-level tolerance models based on intravascular hydrostatic pressures and physiologic responses to maximum voluntary contractions (MVC%). Included in the validation of these models are the baroreceptor and muscle contraction cardiovascular reflexes that support arterial BP. A basic energy pool that supports a G-duration of 140 s for G exposures > 5 G is theorized. Because of the long duration of sustained G exposures in these models, the physiologic dynamics involved in predicting straining G-duration tolerances, are identified and validated using different time periods, i.e., Phases I and II. These models, based on sustained G exposures to a constant G level are also applicable to exposures of variable G levels known as simulated aerial combat maneuver (SACM) G-profile tolerances. G-duration tolerances > 9 G are predicted using these models for subjects using reclined-seat backs and positive pressure breathing.

Aerospace Medicine↗

PubMed2000

Application of artificial neural networks to modeling and prediction of ambient ozone concentrations.

The deterministic modeling of ambient O3 concentrations is difficult because of the complexity of the atmospheric system in terms of the number of chemical species; the availability of accurate, time-resolved emissions data; and the required rate constants. However, other complex systems have been successfully approximated using artificial neural networks (ANNs). In this paper, ANNs are used to model and predict ambient O3 concentrations based on a limited number of measured hydrocarbon species, NOx compounds, temperature, and radiant energy. In order to examine the utility of these approaches, data from the Coastal Oxidant Assessment for Southeast Texas (COAST) program in Houston, TX, have been used. In this study, 53 hydrocarbon compounds, along with O3, nitrogen oxides, and meteorological data were continuously measured during summer 1993. Steady-state ANN models were developed to examine the ability of these models to predict current O3 concentrations from measured VOC and NOx concentrations. To predict the future concentrations of O3, dynamic models were also explored and were used for extraction of chemical information such as reactivity estimations for the VOC species. The steady-state model produced an approximation of O3 data and demonstrated the functional relationship between O3 and VOC-NOx concentrations. The dynamic models were able to the adequately predict the O3 concentration and behavior of VOC-NOx-O3 system a number of hourly intervals into the future. For 3 hr into the future, O3 concentration could be predicted with a root-mean squared error (RMSE) of 8.21 ppb. Extending the models further in time led to an RMSE of 11.46 ppb for 5-hr-ahead values. This prediction capability could be useful in determining when control actions are needed to maintain measured concentrations within acceptable value ranges.

Air Pollution↗

PubMed2006

Radionuclides in fruit systems: model prediction-experimental data intercomparison study.

This paper presents results from an international exercise undertaken to test model predictions against an independent data set for the transfer of radioactivity to fruit. Six models with various structures and complexity participated in this exercise. Predictions from these models were compared against independent experimental measurements on the transfer of 134Cs and 85Sr via leaf-to-fruit and soil-to-fruit in strawberry plants after an acute release. Foliar contamination was carried out through wet deposition on the plant at two different growing stages, anthesis and ripening, while soil contamination was effected at anthesis only. In the case of foliar contamination, predicted values are within the same order of magnitude as the measured values for both radionuclides, while in the case of soil contamination models tend to under-predict by up to three orders of magnitude for 134Cs, while differences for 85Sr are lower. Performance of models against experimental data is discussed together with the lessons learned from this exercise.

Cesium Radioisotopes↗

PubMed2005

Effect of patient location on the performance of clinical models to predict pulmonary embolism.

INTRODUCTION: Current clinical likelihood models for predicting pulmonary embolism (PE) are used to categorize outpatients into low, intermediate and high clinical pre-test likelihood of PE. Since these clinical prediction rules were developed using outpatients it is not known if they can be applied universally to both inpatients and outpatients with suspected PE. Thus, the purpose of this study was to determine the effect of patient location on the performance of clinical models to predict PE. MATERIALS AND METHODS: Two clinical models (Wells and Wicki) were applied to data from the multi-centered PIOPED study. The Wells score was applied to 1359 patients and the Wicki score was applied to 998 patients. 361 patients (27%) from the PIOPED study did not have arterial gas measurement and were excluded from the Wicki score patient group. Patients were stratified by their location at the time of entry into the PIOPED study as follows: outpatient/emergency, surgical ward, medicine/coronary care unit or intensive care unit. The diagnostic performance of the two clinical models was applied to the various patient locations and the performance was evaluated using the area under a fitted receiver operating characteristic curve (AUC). RESULTS: The prevalence of PE in the three clinical probability categories were similar for the two scoring methods. Both clinical models yielded the lowest diagnostic performance in patients referred from surgical wards. The AUC for both clinical prediction rules decreased significantly when applied to inpatients in comparison to outpatients. CONCLUSIONS: Current clinical prediction rules for determining the pre-test likelihood of PE yielded different diagnostic performances depending upon patient location. The performance of the clinical prediction rules decreased significantly when applied to inpatients. In particular, the rules performed least well when applied to patients referred from surgical wards suggesting these rules should not be used in this patient group. As expected the clinical prediction rules performed best in outpatients with the optimum diagnostic performance in patients referred from emergency and outpatient wards.

Adult↗

PubMed2005

Clinical model to predict survival in chemonaive patients with advanced non-small-cell lung cancer treated with third-generation chemotherapy regimens based on eastern cooperative oncology group data.

PURPOSE: (1) Identify clinical factors that can be used to predict survival in chemotherapy-naive patients with advanced non-small-cell lung cancer (NSCLC) treated with third-generation chemotherapy regimens, and (2) build a clinical model to predict survival in this patient population. PATIENTS AND METHODS: Using data from two randomized, phase III Eastern Cooperative Oncology Group (ECOG) trials (E5592/E1594), we performed univariate and multivariate stepwise Cox regression analyses to identify survival prognostic factors. We used 75% of randomly sampled data to build a prediction model for survival, and the remaining 25% of data to validate the model. RESULTS: From 1993 to 1999, 1,436 patients with stage IV or IIIB NSCLC with effusion were treated with platinum-based doublets (involving either paclitaxel, docetaxel, or gemcitabine). The response rate and median survival time were 20% and 8.2 months, respectively. One- and 2-year survivals were 33% and 11%, respectively. In multivariate analysis, six independent poor prognostic factors were identified: skin metastasis (hazard ratio [HR], 1.88), lower performance status (ECOG 1 or 2; HR, 1.46), loss of appetite (HR, 1.62), liver metastasis (HR, 1.32), >/= four metastatic sites (HR, 1.20), and no prior surgery (HR, 1.16). A nomogram using six pretreatment prognostic factors was built to predict 1- and 2-year survival. CONCLUSION: Six pretreatment factors can be used to predict survival in chemotherapy-naive NSCLC patients treated with standard chemotherapy. Using our prognostic nomogram, 1- and 2-year survival probability of NSCLC patients can be estimated before treatment. This prognostic model may help clinicians and patients in clinical decision making, as well as investigators in research planning.

Aged↗

PubMed2001

Cheminformatic models to predict binding affinities to human serum albumin.

Models to predict binding affinities to human serum albumin (HSA) should be very useful in the pharmaceutical industry to speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We have experimentally determined through high-performance affinity chromatography the binding affinities to HSA of 95 diverse drugs and druglike compounds. These data have allowed us the derivation of quantitative structure-activity relationship models to predict binding affinities to HSA of new compounds on the basis of their structure. Simple linear, one-variable models have been derived for specific families of compounds (r(2) > or = 0.80; q(2) > or = 0.62): beta-adrenergic antagonists, steroids, COX inhibitors, and tricyclic antidepressants. Also, global models have been derived to be applicable to the whole medicinal chemical space by using the full database of HSA binding constants described above. For this aim, a genetic algorithm has been used to exhaustively search and select for multivariate and nonlinear equations, starting from a large pool of molecular descriptors. The resulting models display good fits to the experimental data (r(2) > or = 0.78; LOF < or = 0.12). In addition, both internal (cross validation and randomization) and external validation tests have demonstrated that these models have good predictive power (q(2) > or = 0.73; PRESS/SSY < or = 0.23; r(2) > or = 0.82 for the external set). Statistical analysis of the equation populations indicates that hydrophobicity (as measured by the ClogP) is the most important variable determining the binding extent to HSA. In addition, structural factors (especially the topological (6)chi(ring) index and some Jurs descriptors) also frequently appear as descriptors in the best equations. Therefore, binding to HSA turns out to be determined by a combination of hydrophobic forces together with some modulating shape factors. This agrees with X-ray structures of HSA alone or bound to ligands, where the binding pockets of both sites I and II are composed mainly of hydrophobic residues.

Adrenergic beta-Antagonists↗

PubMed1995

A model for predicting transfusion requirements in head and neck surgery.

INTRODUCTION: Although allogeneic blood transfusions have allowed surgeons increased latitude in resecting advanced cancers, they can cause significant morbidity or even death in rare instances. Potential side effects may include transmission of infection and immunosuppression leading to an increased risk of cancer recurrence. Because patients have become more reluctant to receive transfusions, they frequently request preoperative autologous blood donation (PABD). In practice, however, only 50% or less of the donated blood is ultimately transfused while the remainder is discarded. PURPOSE: The purpose of this study was to develop a transfusion prediction and risk assessment (TPRA) model for predicting the need for perioperative blood transfusions in patients undergoing major head and neck oncologic surgical procedures. By knowing the probability for blood transfusion, the physician and patient can make an educated decision regarding the need for PABD. PATIENTS AND METHODS: Over a 4-year period, 436 patients underwent major head and neck surgical procedures for neoplasms of the upper aerodigestive tract, the thyroid gland, and the salivary glands. Data obtained prospectively on each patient included age and gender, the TNM stage, primary disease site, type of prior treatment, estimated intraoperative blood loss, duration of surgery, transfusion requirements, preoperative and postoperative hemoglobin and hematocrit levels, type of procedure and method of reconstruction. These variables were examined singly and in combination both for descriptive purposes and to evaluate their interrelationships. In order to develop the TPRA model, only the 12 variables available prior to the surgical procedure were examined. Variables associated with transfusion need were evaluated further in a multivariate analysis. The logistic regression model allowed a linear expression of patient characteristics to be related to a function of the probability of transfusion need. Analyses of association between categorical variables and transfusion status were based on chi-squared, Fisher's Exact, and Mann-Whitney U tests. RESULTS: Overall, 51 (11.7%) patients required blood transfusions. The median number of units transfused was 2.0 (range, 1 to 13 U). Univariate analysis demonstrated a higher probability for blood replacement in patients with oropharyngeal or hypopharyngeal primary tumor sites, a preoperative hemoglobin level below normal, prior chemotherapy, composite resection, flap reconstruction, between 50 and 59 years of age, and T3 or T4 tumor stage. Logistic regression analysis demonstrated that the need for flap reconstruction, a preoperative hemoglobin below the normal level, and T3 or T4 primary stage were the three factors most significantly associated with the need for transfusion (P < .03). Based on eight combinations of these three variables, transfusion risk predictions were obtained. The TPRA model predicted that patients with a normal hemoglobin level who did not require flap reconstruction and did not have either a T3 or T4 primary stage tumor had the lowest probability (.02) for requiring blood transfusion. Patients at highest risk (.65) were those with less than a normal hemoglobin level, who required flap reconstruction, and had T3 or T4 primary tumor stage. Based on the TPRA model, an algorithm was developed which could serve as a guideline for preoperative transfusion planning. CONCLUSION: By using the TPRA model to change guidelines for preoperative transfusion planning, costs can theoretically be reduced by 50% without significantly increasing the risk of exposing patients to allogeneic blood transfusion. If the TPRA model proves accurate in a follow-up study to test its validity, it may have clinical utility for aiding the surgeon in more cost-effective transfusion planning.

Adolescent↗

PubMed1997

Prediction of worst case migration from packaging to food using mathematical models.

Prediction of migration from packaging to food is often made using equations which are not always designed specifically for the problem. At least, these equations should overestimate migration, in order to be on the safe side. Integration of Fick's equation under the assumption of 'infinite packaging' provides an equation which is very practical since it requires only a few experimental data. It is shown here that, unfortunately, the use of this equation leads to a systematic underestimation of the diffusivity, by the square of the percentage of migration at steady state. In contrast to widely accepted opinion, this model is not conservative. A conservative approach requires that the diffusivity is determined under 'finite packaging' assumptions, associated with very large volumes of food and with long term experiments. These equations are applied to the migration of a phenolic antioxidant from polypropylene.

Food Contamination↗

PubMed2006

"Blind" testing of models for predicting the 90Sr activity concentration in river systems using post-Chernobyl monitoring data.

Two different models for predicting the time-dependent mobility of (90)Sr in river systems have been evaluated using post-Chernobyl monitoring data for five large Belarusian rivers (Dnieper, Pripyat, Sozh, Besed and Iput) in the period between 1990 and 2004. The results of model predictions are shown to be in good agreement (within a factor of 5) with the measurements of (90)Sr activity concentration in river waters over a long period of time after the accident. This verifies the relatively good accuracy of the generalised input parameters of these models which were derived primarily from measurements of (90)Sr deposited after atmospheric nuclear weapons testing (NWT). For the cases studied here, the simpler AQUASCOPE model performed just as well as the more complex "Global" model which used GIS-based catchment data as an input. The reasons for this are discussed. Exponential decay equations were also curve-fitted to the data for each river to help assess the uncertainties in the predictive models.

Chernobyl Nuclear Accident↗

PubMed2000

Hydrophilic matrices for controlled drug delivery: an improved mathematical model to predict the resulting drug release kinetics (the "sequential layer" model).

PURPOSE: The aims of this study were (i) to elucidate the transport mechanisms involved in drug release from hydrophilic matrices; and (ii) to develop an improved mathematical model allowing quantitative predictions of the resulting release kinetics. METHODS: Our previously presented model has been substantially modified, by adding: (i) inhomogeneous swelling; (ii) poorly water-soluble drugs; and (iii) high initial drug loadings. The validity of the improved model has been tested experimentally using hydroxypropyl methylcellulose (HPMC)-matrices, containing either a poorly or a freely water-soluble drug (theophylline or chlorpheniramine maleate) at various initial loadings in phosphate buffer pH 7.4 and 0.1 N HCl, respectively. RESULTS: By overcoming the assumption of homogeneous swelling we show that the agreement between theory and experiment could be significantly improved. Among others, the model could describe quantitatively even the very complex effect on the resulting relative release rates (first slowing down, then accelerating drug release) observed when increasing the initial loading of poorly water-soluble drugs. CONCLUSIONS: The practical benefit of this work is an improved design model that can be used to predict accurately the required composition and dimensions of drug-loaded hydrophilic matrices in order to achieve desired release profiles, thus facilitating the development of new pharmaceutical products.

Chemistry, Pharmaceutical↗

PubMed1991

Using the health belief model to predict safer sex intentions among adolescents.

The purpose of this study was to use Health Belief Model (HBM) concepts to predict adolescents intentions to adopt safer sex behaviors. A stratified, random sampling technique was used to generate a sample of seventh-, ninth-, and eleventh-grade students representative of a southeastern state. A predictive model was constructed of the HBM variables. The model accounted for 43% of the variance in safer sex intentions among the seventh grade, 27% of the variance for the ninth grade and 17% of the variance for the eleventh grade. For the eleventh grade sample gender was the only significant predictor. The results of this study suggest that educational programs to promote safer sex intentions should focus on health related motivations among younger students. With older adolescents it may be more effective to expand education efforts to address factors directly relevant to their motivational schema and social environment. This study underscores the importance of using theory-based models in conducting health education research.

Adolescent↗

PubMed2005

A predictive mathematical model for the calculation of the final mass of Graves' disease thyroids treated with 131I.

Substantial reductions in thyroid volume (up to 70-80%) after radioiodine therapy of Graves' hyperthyroidism are common and have been reported in the literature. A relationship between thyroid volume reduction and outcome of 131I therapy of Graves' disease has been reported by some authors. This important result could be used to decide individually the optimal radioiodine activity A0 (MBq) to administer to the patient, but a predictive model relating the change in gland volume to A0 is required. Recently, a mathematical model of thyroid mass reduction during the clearance phase (30-35 days) after 131I administration to patients with Graves' disease has been published and used as the basis for prescribing the therapeutic thyroid absorbed dose. It is well known that the thyroid volume reduction goes on until 1 year after therapy. In this paper, a mathematical model to predict the final mass of Graves' diseased thyroids submitted to 131I therapy is presented. This model represents a tentative explanation of what occurs macroscopically after the end of the clearance phase of radioiodine in the gland (the so-called second-order effects). It is shown that the final thyroid mass depends on its basal mass, on the radiation dose absorbed by the gland and on a constant value alpha typical of thyroid tissue. Alpha has been evaluated based on a set of measurements made in 15 reference patients affected by Graves' disease and submitted to 131I therapy. A predictive equation for the calculation of the final mass of thyroid is presented. It is based on macroscopic parameters measurable after a diagnostic 131I capsule administration (0.37-1.85 MBq), before giving the therapy. The final mass calculated using this equation is compared to the final mass of thyroid measured 1 year after therapy administration in 22 Graves' diseased patients. The final masses calculated and measured 1 year after therapy are in fairly good agreement (R = 0.81). The possibility, for the physician, to decide a therapeutic activity based on the desired decrease of thyroid mass instead of on a fixed thyroid absorbed dose could be a new opportunity to cure Graves' disease.

Computer Simulation↗

PubMed2000

Survival characteristics and the applicability of predictive mathematical modelling to Listeria monocytogenes growth in sous vide products.

Survival and growth of Listeria monocytogenes isolates during sous vide processing and storage, and the applicability of predictive modelling in determining the potential for growth of L. monocytogenes in broth models and in sous vide products was investigated. L. monocytogenes grew in anaerobic tryptose phosphate broth and in chicken and beef samples by 2 log cycles in 8 days at 3 degrees C and 4-5 log cycles in 6 days at 8 degrees C. However, heating to an internal temperature of 70 degrees C resulted in a 4-5 log reduction and 70 degrees C/2 min resulted in a reduction greater than 7 log cycles. Lowering the product pH to 5.0 was effective in inhibiting L. monocytogenes growth, whereas a sodium chloride concentration of 2% had a negligible effect on growth rates. The square root model (Ratkowsky et al., 1983) predicted L. monocytogenes growth rates at 0-25 degrees C with a coefficient of determination (R2 value) of 98.36-99.63% and a bias factor of 1.08 to 1.21 in beef, chicken and broth substrates of unmodified pH. In addition, the Response Surface Polynomial Model (Version 3.1, Buchanan et al., 1989) predicted generation times at 5-25 degrees C with a 0-17.4% difference between observed and expected generation times in tryptose phosphate broth at pH 7.3. There were however, large differences (25.5 vs. 5.3 h) between observed generation times at pH 5.6 (8 degrees C) and those predicted by the Pathogen Modelling Program in tryptose phosphate broth. A divergence from predicted values was also noted at lower temperatures (0-3.5 degrees C) in the square root model.

Anaerobiosis↗

PubMed1996

A prognostic model for predicting 10-year survival in patients with primary melanoma. The Pigmented Lesion Group.

OBJECTIVE: To develop a prognostic model, based on clinical and pathologic data that are routinely available to the clinician, that would estimate the chance for survival of a patient with primary cutaneous melanoma after definitive surgical therapy. DESIGN: Cohort analytical study. SETTING: University medical center. PATIENTS: 488 patients with primary cutaneous melanoma who had no apparent metastatic disease. Patients were followed prospectively for at least 10 years. An independent validation sample of 142 patients was used to assess the stability of the model. MEASUREMENTS: Six clinical and pathologic variables that predict survival and are readily available to the clinician were used to develop a prediction model. The variables were tested for their association with death by using a univariate logistic regression model. Point estimates were generated for the probability of surviving melanoma at 10 years. Variables that were statistically significantly associated with survival were retained for testing in a logistic regression model. RESULTS: 488 patients were followed prospectively for a median of 13.5 years (minimum, 10.0 years; maximum, 20.5 years). The overall 10-year survival of the study group was 78%. Four variables were found to be independent predictors of survival. Presented as adjusted odds ratios, from strongest to weakest relative predictive strength, these variables were tumor thickness (odds ratio, 50.8), site of primary melanoma (odds ratio, 4.4), age of the patient (odds ratio, 3.0), and sex of the patient (odds ratio, 2.0). The four-variable model was significantly more accurate than tumor thickness alone, particularly for predicting death. Overall, use of the model reduced the error rate of the prediction of death by 50%. CONCLUSIONS: A prognostic model that uses four readily accessible variables more accurately predicts outcome in patients with primary melanoma than does tumor thickness alone. This four-variable model can identify patients at high risk for the recurrence of disease, an identification that becomes increasingly important as adjuvant therapies are developed for treatment of melanoma.

Adult↗

PubMed1996

Decrease in the size of ovarian endometriomas during ovarian suppression in stage IV endometriosis. Role of preoperative medical treatment.

OBJECTIVE: To evaluate the effect of hormonal suppression on the size of ovarian endometriomas and to develop a predictive model for changes in the size of these lesions. STUDY DESIGN: The study consisted of 80 women of reproductive age with the diagnosis of stage IV pelvic endometriosis, according to the revised American Fertility Society (rAFS) classification, and included 48 women with endometriomas > or = 3 cm. After the initial laparoscopic and sonographic evaluation, ovarian suppression was achieved with either danazol or a gonadotropin-releasing hormone agonist (GnRH-a) for six months. In all patients, pretreatment and posttreatment pelvic sonograms were performed, and at the end of treatment residual disease was evaluated and resected by laparotomy or laparoscopy. Seven of 80 women with endometriomas > or = 3 cm had serial sonograms during the course of therapy. Serial pelvic sonograms in this subgroup were used to develop a statistical model for predicting the size of endometriomas after treatment. The model was then tested in another subgroup of 41 women with endometriomas > or = 3 cm. RESULTS: At the end of treatment, there was a significant decrease in the r-AFS score in both the danazol and GnRH-a groups. Medical treatment facilitated surgical resection of residual disease and preservation of ovarian tissue. There was no difference in this respect between danazol and GnRH-a. Endometriomas decreased by 51% in both treatment groups. The predictive model, when tested on 41 patients, underestimated the actual change by 11%, but the difference was within the 95% confidence limits. CONCLUSION: This study documented, for the first time, that ovarian endometriomas decrease in size during hormonal suppression. Both danazol and GnRH-a were equally effective.

Adult↗

PubMed1999

A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks.

The aim of this study was to compare the ability of artificial neural networks and the Acute Physiology and Chronic Health Evaluation II score to predict mortality in adult intensive care units. The same physiological variables were used in both predictive models to predict hospital mortality from a data set of 8796 patients collected from 26 adult intensive care units in the United Kingdom and Ireland as part of the Intensive Care Society study. The results from the two models were compared with the actual outcome. The overall prediction accuracy and the overall goodness-of-fit of all the models were assessed. Both predictive models showed similar goodness-of-fit and prediction discrimination. The overall predictive and classification performance of the artificial neural network developed matched and in some aspects was better than that of Acute Physiology and Chronic Health Evaluation II.

APACHE↗

PubMed2006

Analysis of 275 patients with sarcoidosis over a 38 year period; a single-institution experience.

BACKGROUND: Sarcoidosis is a systemic granulomatous disease with unknown etiology. OBJECTIVES: We evaluated seasonal variation, demographic, clinical and diagnostic features of sarcoidosis in recently diagnosed symptomatic patients in the whole cohort (275 patients) and in the subgroups according to the estimated disease course (subacute course group vs. chronic course group). We also developed a prediction model to predict the course of sarcoidosis using simple clinical and demographic variables. MATERIAL AND METHODS: Two hundred and seventy-five patients with sarcoidosis. MEASUREMENTS AND STATISTICS: Roger's test, chi-square, t-test and multiple logistic regression were used. RESULTS: The distribution of cumulative monthly diagnosis was the lowest in November (fall) (p<0.01). Seasonal pattern was influenced by age and gender. Constitutional symptoms, stages 2 and 3 diseases and the absence of erythema nodosum were highly significant parameters for chronic course. Using these variables, the developed model had a specificity of 93.1% and its positive predictive value was 89.5%. Progression of the disease was documented 6.4% in subacute group vs. 32.1% in chronic group (p=0.00001). Preventive effect of smoking was more pronounced in females than in males in our cohort. CONCLUSIONS: Further well-designed and large prospective studies are required to better understand the importance of these findings, and to validate the prediction model presented here.

Acute Disease↗

PubMed1996

A model for predicting transfusion after coronary artery bypass grafting.

BACKGROUND: Blood conservation has become an important issue in cardiac surgery. This study was undertaken to determine if the need of blood transfusion could be predicted from preoperative patient variables. METHODS: From January 1, 1992, to December 31, 1993, 2,033 patients having isolated coronary artery bypass grafting procedures were studied; 1,446 (71%) were male and 587 (29%), female. The mean age was 65.1 +/- 9.9 years (range, 31 to 88 years). Emergency operation, urgent operation, and reoperations were done in 78 (4%), 188 (9%), and 189 (9%) patients, respectively. In the entire group, 1,245 (61%) received transfusion during hospitalization, and 788 (39%) did not. Logistic regression analysis was used to construct a model that predicted the need of transfusion of packed red blood cells after coronary artery bypass grafting. A transfusion risk score was constructed by assigning points to independent predictive factors on the basis of the logistic regression coefficient and the odds ratio. Preoperative predictors of transfusion were emergency operation, urgent operation, cardiogenic shock, catheterization-induced coronary occlusion, low body mass index, left ventricular ejection fraction lower than 0.30, age greater than 74 years, female sex, low red cell mass, peripheral vascular disease, insulin-dependent diabetes, creatinine level greater than 1.8 mg/dL, albumin value lower than 4 g/dL, and redo operation. RESULTS: The mean transfusion risk score for patients receiving 0, 1 to 4, and greater than 4 units of packed red blood cells was 2.3 +/- 0.9, 5.2 +/- 3.0, and 9.6 +/- 3.5, respectively (p = 0.001). Patients with a score higher than 6 had a 95% transfusion incidence. The predictive model was validated on 422 patients having coronary artery bypass grafting from January 1 to May 31, 1994. The observed rates of the validation group fell within the 95% confidence intervals of the predicted rates. CONCLUSIONS: These data demonstrate that readily available patient variables can predict patients at risk for transfusion. Routine use of aprotinin and other adjustments of cardiopulmonary bypass should be considered to reduce transfusion in high-risk patients.

Adult↗