Prognosis in Equine Colic Patients Using Multivariable Analysis Mathew J. Reeves, Charles R. Curtis, Mo D. Salman and Bryan J. Hilbert
ABSTRACT Multiple logistic regression was used to investigate prognosis in 308 horses referred to the University of Minnesota veterinary teaching hospital with colic. Bivariate results identified the following significant individual parameters: absent or hypomotile abdominal sounds, medical or surgical classification, peritoneal fluid total protein, anion gap, serum glucose, capillary refill time, blood pH, heart rate, packed cell volume, base excess, serum chloride, plasma bicarbonate, serum urinary nitrogen and age. Two multivariable prognostic models were developed using logistic regression. Model I (based on 257 cases with a mortality rate of 39%) included age, sex, medical or surgical classification, capillary refill time, packed cell volume and heart rate. Model II (based on 138 cases with a mortality rate of 48%) included age, sex, medical or surgical classification, capillary refill time, serum bicarbonate, serum chloride and respiratory rate. Predictive performance of the models was evaluated by treating the calculated probability of death for each horse as a continuous test result. The influence of varying the probability cutoff point for death on test characteristics (sensitivity, specificity and positive and negative predictive values) was determined. These models have not
been validated and thus their performance in a different population is uncertain.
RESUME Les auteurs ont utilise la regression logistique multiple, pour analyser le pronostic relatif a 308 chevaux referes a l'hopital veterinaire d'enseignement de l'universite du Minnesota, a cause de coliques. Des resultats a deux variables indentifierent les parametres individuels significatifs suivants: bruits abdominaux absents ou amenuises, classification medicale ou chirurgicale, proteines totales du liquide abdominal, trou anionique, glucose serique, temps de remplissage des capillaires, pH sanguin, frequence cardiaque, hematocrite, exces basique, chlorures seriques, bicarbonate plasmatique, azote ureique serique et age. Les auteurs developperent aussi deux profils de pronostic a plusieurs variables, par la regression logistique. Le profil I se basait sur 257 cas qui afficherent un taux de mortalite de 39%; il incluait l'age, le sexe, la classification medicale ou chirurgicale, le temps de remplissage des capillaires, l'hematocrite et le rythme cardiaque. Le profil II se basait sur 138 cas qui afficherent un taux de mortalite de 48%; il incluait l'age, le sexe, la classification medicale ou chirurgicale, le temps de remplissage
des capillaires, le bicarbonate serique, les chlorures seriques et la frequence respiratoire. L'evaluation de la performance de prediction des profils se fit en traitant la probabilite de mort calculee pour chaque cheval comme un resultat de test continu. Les auteurs determinerent aussi l'influence de la variation du point limite de probabilite relatif a la mortalite, d'apres les caracteristiques suivantes du test: sensibilite, specificite, valeurs de prediction positives et negatives. Comme ces profils n'ont pas ete valides, leur performance avec un groupe different de chevaux demeure incertaine.
INTRODUCTION Central to any discussion of equine colic is the prognosis. Such information is important to the owner or trainer as it often plays an integral role when deciding between conservative and surgical management of colic patients. In an attempt to make more valid prognostic assessments in colic cases, many authors (1-27) (Table I) have attempted to define individual parameters which are useful. Variables which assess cardiovascular function are considered good prognostic guides (6), but serial estimates of these parameters are important (4) and these individual parameters should not be interpreted alone (28).
Department of Clinical Sciences (Reeves, Curtis), Department of Environmental Health (Curtis, Salman), College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, Colorado 80523 and Department of Large Animal Clinical Sciences, Veterinary Teaching Hospitals, 1365 Gortner Avenue, St. Paul, Minnesota 55108 (Hilbert). Present address of Dr. Hilbert: Epsom Equine Centre, 47 Epsom Avenue, Belmont, 6104 WA, Australia. This project was undertaken while the senior author was an intern and resident in the Department of Large Animal Clinical Sciences, College of Veterinary Medicine, University of Minnesota. Submitted March 22, 1988.
Can J Vet Res 1989; 53: 87-94
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TABLE I. Previously Reported Individual Parameters Determined to be Prognostically Useful in Equine Colic Parameter Author Clinical Heart rate Kalsbeek (1) White (2) Shideler (3) Svendson et al (4) Blood et al (5) Parry et al (6) Pulse amplitude Svendson et al(4) Greatorex (7) Parry et al (6) Blood pressure Gay et al (8) Parry et al (6) Jugular filling Parry et al (6) Svendson et al (4) Oral mucous membrane color/capillary refill time Svendson et al (4) Parry et al (6) Mental depression Kalsbeek (I) Svendson et al (4) Parry et al (6) Abdominal sounds Coffman (9) Kalsbeek (1) Stashak (10) Svendson et al (4) Reeves et al (I 1)
Clinicopathological Packed cell volume
Total and differential leukocyte count Lactate
Glucose
pH BUN Total protein Anion gap
Adams et al (12) Kalsbeek (1) Svendson et al (4) Parry et al (6) Pascoe et al ( 13) Schalm et al (14) White (2) Hutchins (15) Moore et al (I 6) Donawick (17) Kalsbeek (1) Svendson et al (4) Parry et al (6) Kalsbeek (1) Svendson et al (4) Coffman (18) Reeves et al (I 1) Kalsbeek (1) Parry et al (6) Pascoe et al ( 13) Bristol ( 19) Gosset et al (20)
S.I. intraluminal hydrostatic pressure Hemostatic abnormalities
Peritoneal Fluid Total protein Gross appearance
Cytology
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Allen et a! (21)
Byars et al (22) Johnstone et al (23) Allen et al (21) Reeves et al (I 1) Swanwick et al (24) Brownlow (25) Coffman et al (26) Browlow (25) Adams et al (12)
The methods by which some of these parameters have been defined as ";prognostically useful" are not always clear. Some assessments seem to be primarily based on clinical experience (7,26), while others have analyzed arbitrarily the distributions of the variables between the two populations of survivors and nonsurvivors (1,8). Other approaches have included the calculation of survival rates for several different cutoff points within the range of values of a parameter (6,29), or the demonstration of statistical significance of means between two populations of survivors and nonsurvivors (17,21). However, the demonstration of statistical significance between two groups is not an axiom for predictive ability. In fact all of these methods described are often unreliable in determining prognosis based on the assessment of one individual parameter, because no one individual parameter can reliably discriminate between those cases which survive and those that do not (30). Several authors have attempted to compare the "prognostic merit" of these individual parameters. The approaches by which this has been done have varied from subjective analyses (4,8,17) to approaches based on objective, multivariable statistical modelling (29,30) which are able to control some of the inherent problems associated with the use of individual parameters in assessing prognosis (31). In particular, the effect of each variable can be adjusted for the presence of all other variables in the model and any interactions between the independent variables can be included. A ranking order of the variables' ability to discriminate between survivors and nonsurvivors can also be obtained (30). The aims of this study were to use multiple logistic regression to: 1) evaluate the usefulness of a wide range of individual clinical signs and clinicopathological parameters in formulating prognosis for equine colic patients and 2) formulate prediction models to evaluate prognosis in equine colic cases using the individual variables defined in the first stage of the study. Validation of these models is being undertaken in a separate prospective study.
MATERIALS AND METHODS DATA COLLECTION
The medical case records for all the horses suffering from acute abdominal pain of digestive origin (coded with a "6" as the first digit in the topographic code in the computer-based recording system, American Veterinary Medical Data Program (AVMDP, South Campus Courts, Bldg C, Purdue University, West Lafayette, Indiana 47907) presented to the veterinary teaching hospital at the University of Minnesota during a ten year period (July 1974 to June 1984) were examined. If the attending clinician had not noted in the case record that colic was a significant problem or if the medical, surgical or postmortem findings were not consistent with colic, the case was eliminated from the study. Each colic admission to the hospital was regarded as a separate case. Twelve cases which either were euthanized for economic reasons or were dead on arrival at the hospital were excluded from the study. The following information recorded at the time of initial examination was retrieved from the case record: A) Signalment: age, sex and breed. B) History: duration in hours since the onset of colic was first observed by the owner or trainer. C) Clinical signs: heart rate, respiration rate, rectal temperature and oral mucous membrane capillary refill time (CRT). Intestinal sounds were classified into four categories according to the description in the medical record: 1) normal, 2) increased, 3) decreased or 4) absent. D) Clinicopathological data included: (i) Hematology: packed cell volume (PCV), total plasma protein (TPP) and total and differential leukocyte counts. The latter were used to classify the differential leukocyte count of each sample into one of 11 groups, based on the system used by Parry et al
(6).
(ii) Serum chemistry: sodium (Na+), potassium (K+), chloride (Cl-), glucose and serum urea nitrogen (SUN). (iii) Venous blood gases: pH, partial pressure of oxygen (PO2), partial pressure of carbon dioxide (pCO2), plasma bicarbonate (HCO3 ) and base excess.
E) Peritoneal fluid analyses: description of the gross appearance (including color, clarity, turbidity and presence of bowel contents or purulent material), total and differential leukocyte count and total protein. When the serum Na+, Cl- and HCO3 were measured and recorded, the anion gap (AG) was calculated based on the equation (AG = Na + - (Cr+ HCO3)) used by Feldman and Rosenburg (32). Two possible outcomes were defined: alive at time of discharge from the hospital, or dead prior to discharge. Some horses in the latter group were euthanized to prevent unnecessary suffering if the outcome was considered hopeless. Horses were discharged only after they had made a satisfactory recovery as indicated by vital signs and a return to normal intestinal function. Cases were classified as either medical or surgical cases based on the clinician's final diagnosis and/ or the postmortem findings. All the cases defined as "medical" were treated with medical therapy only, e.g. intestinal lubricants, intravenous fluids, analgesics and controlled exercise. All horses operated upon were considered "surgical" cases. In addition horses that died or were euthanized prior to surgical exploration that at postmortem had intestinal lesions which could only have been corrected at surgery were also included in this group. STATISTICAL ANALYSIS
variable; the unit of measure will affect the magnitude of the regression coefficient. Therefore standardized regression coefficients (SRC; regression coefficient multiplied by the standard deviation of the independent variable) (33) were calculated for continuous independent variables. The SRC are expressed in standard deviation units, i.e. the change in the log-odds of death for a change of one standard deviation of the independent variable. Thus a relative ranking of contribution to the probability of death can be obtained by ranking the SRC (33). Significance of the regression coefficients was assessed by comparing the regression coefficient divided by its standard error with a Z distribution (31). Exponentiation of the regression coefficient for categorical variables yields an odds ratio (OR) which is a quantitative measure of association between an outcome and a potential risk factor (31). Odds ratios range between 0 and infinity and the null value (no association) is 1. Odds ratios > I are predisposing and imply a direct association. Odds ratios < 1 are preventive and imply an inverse association. Therefore, OR of 2 and 0.5 have the same magnitude but imply a different association. Ninetyfive percent confidence intervals for OR were calculated (31). The multivariable models were constructed using a stepwise algorithm which assesses potential interactions and multicollinearity at each
step. The model-building algorithm is similar to a forward-backward stepwise procedure (33), with the exception of the assessment of possible interactions and multicollinearities at each step. The assessment was based on examining changes in the regression coefficients of variables already in the model when a new variable was added. When a regression coefficient changed by more than one standard error with the addition of the new variable interaction or multicollinearity was suspected. At this point in the algorithm an interaction term between the variable whose regression coefficient changed and the new variable was tested. If there was no significant (p > 0.10) interaction the change in the regression coefficient was considered to have occurred because of multicollinearity (a complete description of the algorithm is available upon request from Dr. Curtis). This approach is called the "change in estimate criterion" (31). The algorithm was simple, although time consuming, to implement because the computer program used (EGRET) is interactive and allows inspection of the parameter estimates and attributes at each step of the modelling process (35). (In contrast, most stepwise procedures either examine all possible interactions or those specified initially.) The level of significance for a variable to be entered or to be removed from the model was p < 0.10 and p > 0.10 respectively. This was based on the test for the regression
An initial screening to reduce the number of independent variables was done by examining differences in the TABLE II. Bivariate Odds Ratios for Death from Colic for Categorical Variablesa distributions of the independent Variable Nb N that Died ORc 95% CI (OR)c variables between survivors and Abdominal sounds nonsurvivors. Student's t-tests were Normal 36 5 1.0 41 7 Hypermotile 1.3 0.4, 4.4 used for continuous variables and chiHypomotile 128 46 3.5* 1.3, 9.6 square analysis for categorical variaAbsent 67 45 12.7** 4.3, 37.1 bles. Only independent variables with Treatment classification p < 0.05 were considered further. Medical treatment 134 8 1.0 The effect of single risk factors (only 174 110 Surgical treatment 27.1** 12.4, 59.0 those significant in the initial screen- Sex ing) on death were examined further Mare 157 57 1.0 with logistic regression. Logistic Stallion 71 22 0.8 0.4, 1.4 80 39 1.7+ Gelding 1.0, 2.9 regression is the appropriate method when the outcome is dichotomous aN = 190 horses survived to discharge, n = 1 8 died or were euthanized prior to discharge. + p < 0.10; ** p 50 Model Ijb missing values were not considered for Y -6.70+0.03 x AGE + -0.68 x SEXI +0.50x SEX2+ 2.86 xSURG +1.15 x CRT+ -0.12 x HC03the multivariable models. Model I was + -0.09 x Cl- + 0.04 x RESP. constructed from the complete data (N = 138; GOFCSc = X2 6.1, 0.5
0.5 would be considered more likely to die. This is a positive test result. The probability of death given a positive test result (the positive predictive value) is then obtained from Fig. 2 in a similar fashion. One should be clear about the interpretation of the results. A positive predictive value of 75% means that given 100 horses that had the same Pd result, 75 of them would die and 25 would live. The odds of mortality for such cases would be 3 to 1. The predictive value of a test is not only a property of the test's sensitivity and specificity but also the prevalence of the condition in the population being
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tested (36). In this study, the prevalence 3. SHIDELER RK, BENNETT DG. Diagnosis in equine colic. Proc Am Assoc Equine represents the proportion of cases Pract 1975: 197-201. which die. In general, as the prevalence 4. SVENDSON CK, HJANTKJAER RK, increases the positive predictive value HESSELHOLT M. Colic in the horse. A increases, while the negative predictive clinical and clinical chemistry study of 42 value decreases (37). The prevalence of cases. Nord Vet Med 1979; 31(S)1: 1-32. cases which died is relatively high in this 5. BLOOD DC, HENDERSON JA, RADOSTITS OM. Diseases of the alimentary tract. 1. study, because of the referral nature of In: Veterinary Medicine, 6th ed. London: the caseload (the mortality rates of the Bailliere Tindall, 1979: 153-164. horses used in models I and II were 39% 6. PARRY BW, ANDERSON GA, GAY CC. and 48%, respectively). The perforPrognosis in equine colic: a study of individual variables used in case assessment. mance of these models, in particular the Vet J 1983; 15: 337-344. positive predictive value, when used on 7. Equine GREATOREX JC. The clinical diagnosis of a general population of horses with colic in the horse. Equine Vet J 1972; 4: 182colic will probably be lower than 187. presented in this study, due to the lower 8. GAY CC, CARTER J, McCARTHY M, MASON TA, CHRISTIE BA, REYprevalence of mortality in such cases. NOLDS WT, SMYTH B. The value of The difference in prevalence of death arterial blood pressure measurement in between the two models may explain assessing the prognosis in equine colic. why model II had a higher positive Equine Vet J 1977; 9: 202-204. predictive value and a lower negative 9. COFFMAN JR. Diagnosis and management of acute abdominal disease in the horse predictive value than model I (Fig. 2). (part 1). Vet Med Small Anim Clin 1970; 65: Hence, the interpretation of the positive and negative predictive values will vary 10. 669-673. STASHAK TS. Clinical evaluation of the from setting to setting, according to the equine colic patient. Vet Clin North Am prevalence in the population being [Large Anim Pract] 1979; 1: 275-287. tested. Predictive values can be 11. REEVES MJ, HILBERT BJ, MORRIS RS. A retrospective study of 320 colic cases calculated for a given prevalence using referred to a veterinary teaching hospital. 2nd the sensitivity and specificity of the test Proc Equine Colic Res Symp, University of (37). Georgia, 1986. Finally, before these models can be 12. ADAMS SB, McILWRAITH CW. Abdominal crisis in the horse: a comparison of relied upon to predict the outcome of presurgical evaluation with surgical findings future colic cases they need to be results. Vet Surg 1978; 7: 63-69. validated on a separate colic popula- 13. and PASCOE PJ, McDONELL WN, TRIM tion. The evaluation of the models as a CM, VAN GORDER J. Mortality rates and continuous test is presented only to associated factors in equine colic operations - a retrospective study of 341 operations. demonstrate how such models can be Can Vet J 1983; 24: 76-85. used in the clinical setting once SCHALM OW, JAIN NC, CARROLL EJ. validation has occurred and the 14. Normal values in blood morphology with usefulness of such models has been fully comments on species characteristics in evaluated. Such validation is presently response to disease. In: Veterinary Hematolbeing undertaken. ogy, 3rd ed. Philadelphia: Lea & Febiger,
ACKNOWLEDGMENTS The first author is indebted to Drs. R.S. Morris and J. Gay for their assistance in the initiation of this study. The authors are grateful to Drs. N. Ducharme, H.N. Erb, T.S. Stashak and J. Traub-Dargatz for reviewing the
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