International Journal of Health Care Quality Assurance Survival probability in patients with liver trauma Skender Buci Agim Kukeli
Article information:
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To cite this document: Skender Buci Agim Kukeli , (2016),"Survival probability in patients with liver trauma", International Journal of Health Care Quality Assurance, Vol. 29 Iss 7 pp. 778 - 785 Permanent link to this document: http://dx.doi.org/10.1108/IJHCQA-04-2016-0045 Downloaded on: 17 August 2016, At: 07:46 (PT) References: this document contains references to 11 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 19 times since 2016*
Users who downloaded this article also downloaded: (2002),"Self-inflicted deaths of prisoners serving life sentences 1988-2001", The British Journal of Forensic Practice, Vol. 4 Iss 4 pp. 30-38 http://dx.doi.org/10.1108/14636646200200024 (2010),"Managers, balance, and fulfilling lives", Gender in Management: An International Journal, Vol. 25 Iss 2 pp. 86-90 http://dx.doi.org/10.1108/17542411011026258 (1974),"Life skills and life styles", Industrial and Commercial Training, Vol. 6 Iss 11 pp. 515-528 http:// dx.doi.org/10.1108/eb003430
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IJHCQA 29,7
Survival probability in patients with liver trauma Skender Buci
778 Received 8 November 2015 Revised 13 April 2016 Accepted 11 May 2016
Department of Trauma, University Hospital of Trauma, Tirana, Albania, and
Agim Kukeli Department of Economics, University of Akron, Akron, Ohio, USA Abstract
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Purpose – The purpose of this paper is to assess the survival probability among patients with liver trauma injury using the anatomical and psychological scores of conditions, characteristics and treatment modes. Design/methodology/approach – A logistic model is used to estimate 173 patients’ survival probability. Data are taken from patient records. Only emergency room patients admitted to University Hospital of Trauma (former Military Hospital) in Tirana are included. Data are recorded anonymously, preserving the patients’ privacy. Findings – When correctly predicted, the logistic models show that survival probability varies from 70.5 percent up to 95.4 percent. The degree of trauma injury, trauma with liver and other organs, total days the patient was hospitalized, and treatment method (conservative vs intervention) are statistically important in explaining survival probability. Practical implications – The study gives patients, their relatives and physicians ample and sound information they can use to predict survival chances, the best treatment and resource management. Originality/value – This study, which has not been done previously, explores survival probability, success probability for conservative and non-conservative treatment, and success probability for single vs multiple injuries from liver trauma. Keywords Logit model, Liver trauma injury, Survival probability, Treatment mode Paper type Research paper
International Journal of Health Care Quality Assurance Vol. 29 No. 7, 2016 pp. 778-785 © Emerald Group Publishing Limited 0952-6862 DOI 10.1108/IJHCQA-04-2016-0045
Introduction This study was motivated by three drivers: trauma’s steady occurrence; few studies where survival among patients with liver trauma has been predicted; and understanding injured patients’ survival probability, which may help to audit hospital performance, resource allocation and improve communication between staff, patients and relatives. Predicting treatment outcome is paramount for: accuracy and success when communicating with patients and relatives about patient recovery (Signorini et al., 1999); as a tool for deciding whether to pursue a traditional or interventionist treatment; and for audit purposes (Signorini et al., 1999; Hunter et al., 2000). Patients (young and old) come to emergency rooms (ERs) with diverse injuries (ranging from single to multiple organs). Each patient’s injury requires a unique examination and care plan. Consequently, managers and physicians need to plan resource allocation and predict outcome. Patients admitted with varying injuries in the ER progress differently in the intensive care (IC) rooms and their need to stay hospitalized varies. Some patients with a minor injury can be easily cared for and the outcome is clear, but some injuries; i.e., those with multiple organ damage in addition to the liver, are more severe. Predicting treatment outcome, whether conservative or interventionist, at the ER, IC, or during the time a patient is hospitalized, will help managers, physicians and relatives plan, consider and reconsider treatments.
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Clinical data gathered from 173 patients admitted at the University Hospital of Trauma (former Military Hospital) in Tirana, was used to predict patient survival based on injury parameters and general condition. The same data pool was used to predict the success probability for conservative (no intervention) vs non-conservative treatment (surgical intervention in most cases). The last experiment was used to predict success probability when treating a single organ (liver) injury vs treating a liver trauma with multiple organ injuries. Logistic regression modeling techniques have been extensively used in the medical field for estimating success probability (Hunter et al., 2000; Gujarati and Porter, 2009). Other techniques such as Pearson’s test for group comparison are used to predict mortality in trauma injured patients (Cannon et al., 2009). New techniques such as neural networks (Eftekhar et al., 2005; Hunter et al., 2000) have been used, but for mortality or audit and performance purposes, probability prediction does not outperform logistic models when prediction accuracy is in question. Chawda et al. (2004) reviewed different scoring systems to identify their advantages and disadvantages. Popular scoring systems such as injury severity score (ISS) and trauma injury severity score are used, but there is no conclusive evidence regarding which is best. Köksal et al. (2009) uses age, gender, Glasgow coma scale (GCS) and revised trauma scale (RTS) to compare ISS and new injury severity score (NISS) performance to predict mortality. NISS is no more accurate than ISS. Survival probability relies on unique data, a modified logit model (explained below) and success probability for various liver injuries and different treatments.
Patients with liver trauma
779
Data and their description Data were collected in the University Hospital of Trauma between January 2009 through December 2012 (Table I). Patients were grouped into seven age categories. The variables were patients’ conditions; for example: •
if a patient has liver injury, then the variable Cinjur is 0;
•
if there is liver and other organ injury, then Cinjur is 1;
•
if a patient survives the trauma, the variable Surviv is 1;
•
if a patient dies, then Surviv is 0;
Surviv Age Gender Cinjur Lhospit Grade Conserv Interv Sinterv Resec ISS RTS
The patient survives injury and treatment (Yes ¼ 1; No ¼ 0) Age: 6-15 years old ¼ 1; 16-25 years old ¼ 2; 26-35 years old ¼ 3; 36-45 years old ¼ 4; 46-55 years old ¼ 5; 56-65 years old ¼ 6; 66-75 years old ¼ 7 Gender (Male ¼ 1; Female ¼ 0) If only the liver is injured, value is 0. For all other combinations, such as liver and head, liver and thorax, etc., the value is 1 Hospitalization (days) Grade (the six grading scale standard is used: minor ¼ 1; moderate ¼ 2; serious ¼ 3; severe ¼ 4; critical ¼ 5; un-survivable ¼ 6) Conservative treatment (Yes ¼ 1; No ¼ 0) Patient is treated with an intervention (Yes ¼ 1; No ¼ 0) Intervention success (Yes ¼ 1; No ¼ 0) Resection (Yes ¼ 1; No ¼ 0) Injury severity score (anatomical), higher score means a more severe injury Revised trauma score (psychological), low number means more severe conditions
Table I. Variables and their description
IJHCQA 29,7
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780
•
if a patient undergoes surgical intervention, then the variable Interv is 0;
•
for those treated conservatively (no surgery), the variable Interv is 1; and
•
if a patient undergoing surgical intervention has a partial liver resection, then Resec takes value 1 and 0 if not.
Injury severity was evaluated and coded in the ER as 1-6. The ISS score (Baker et al., 1974) measures the overall severity in patients with more than one injury and is computed as the abbreviated injury scale grade’s sum of squares in the three most severely injured areas. The RTS score (Chawda et al., 2004) measures the injured patients’ psychological conditions and combines the GCS, systemic blood pressure and respiratory rate. Of 173 patients in the study (83 percent male), 91 percent survived. In total, 64 percent of all patients had two or more injured organs. Fewer than half experienced conservative treatment, while 54.3 percent had a surgical intervention. The ISS range is 1-75 (the highest value) with a mean of 23. Table II shows the statistical variables’ details, including mean, minimum, maximum and standard deviation. Method The logit model was used to analyze various factors’ effects on survival probability and the intervention or conservative treatment’s success probability. Many studies predicting injury outcome have used the logit model (Montague and Brooks, 1989; Moon et al., 2013; Signorini et al., 1999; Wong and Leung, 2008). All equations are from Gujarati and Porter (2009): P i ¼ E ðY ¼ 1jX i Þ ¼
1 þ e
1 b0 þ
Pn i¼1
bi X i
(1)
where P is the success probability; X’s the factors that determine probability; β’s the coefficients to be estimated; and e ¼ 2.71828. This equation can be written in a simple general form, useful for further interpretation: Pi ¼
Table II. Summary statistics, using observations 1-173
1 1 þ eZ i
(2)
Variable
Mean
Minimum
Maximum
SD
Surviv Age Gender Lhospit Cinjur Grade Conserv Interv Resec ISS RTS Sinterv
0.913295 2.90751 0.832370 11.6360 0.641618 2.45665 0.445087 0.543353 0.063584 23.0000 6.96021 0.473988
0.000000 1.00000 0.000000 0.0420000 0.000000 1.00000 0.000000 0.000000 0.000000 1.00000 2.83300 0.000000
1.00000 7.00000 1.00000 70.0000 1.00000 5.00000 1.00000 1.00000 1.00000 75.0000 7.84100 1.00000
0.282219 1.58577 0.374622 11.2897 0.480917 1.10212 0.498418 0.499563 0.244718 16.8533 1.33200 0.500772
Patients with liver trauma
The Zi value is: Z i ¼ b0 þ
n X
bi X i
(3)
i¼1
L denotes the logarithmic odds ratio:
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Pi Li ¼ ln 1P i
¼ Z i ¼ b0 þ
n X
781 bi X i
(4)
i¼1
Equation 4 estimates the logit model using the binary dependent variable. Since the model is log-linear, the independent factors’ marginal effects are given by their partial derivatives. In a general form the partial derivative appears in the following equation: @2 P i ¼ bi P i ð1P i Þ @xi
(5)
The partial derivatives will be interpreted as the independent variables’ slope coefficients, which are computed at the independent variable’s mean value and their respective probability. One can interpret estimated β’s (Equation 5) as the odds ratio. The coefficients can be used to predict the change in success probability (success likelihood) for a given change in the independent variable’s value. Models Four different models are used to estimate injury patent’s survival probability. Age, gender, Lhospit, Cinjur, grade, Interv, Resec, ISS and RTS are explanatory variables. The models under investigation draw upon numerous studies (Chawda et al., 2004; Montague and Brooks, 1989; Moon et al., 2013) on trauma survival. There is consensus that all trauma cases have common features but trauma involving liver injury has been studied less thoroughly. Every ER patient with an injury is checked using the same standard procedure, whether the liver is injured or not. Therefore, common factors should explain the patient’s condition, treatment and progress (whether conservative treatment or intervention). The following general model is the quest to find factors determining the progress toward full recovery and the best treatment. Success probability depends upon or is determined by patient’s age, gender, hospital stay, injury combined with liver and other organs, injury grade, intervention, resection and trauma severity. The equation can be written, using the notation for variables considered in this study, as: Success probability ¼ L (age, gender, Lhospit, Cinjur, grade, Interv, Resec, ISS, RTS) We postulate that the function predicting success probability, L, is a logistic function. This function is generically written as: L ¼ ln
P 1P
¼ b0 þ b1 age þ b2 gender þ b3 Lhospit þ b4 Cinjur þ b5 grade þ b6 Interv þ b7 Resecþ b8 ISS þ b9 RTS
where coefficients b’s are estimated and used for explaining each variable’s contribution to success probability. The four models examined in this study (Models 1-4) differ
IJHCQA 29,7
in two ways: the predictors (dependent variables) used to estimate survival probability; and survival probability estimates for patients in general (Models 1-2) and for different treatments (Model 3): •
Model 1 estimates success probability as a function of the patient’s natural conditions (age and gender), injury severity (grade, ISS, RTS and combined injuries) and treatment received (intervention, conservative, resection and days in hospital). Therefore, the first logit model includes all variables (predictors) available. Expectations are that age, combined injury, injury grade and ISS would be negatively related to survival, while days in the hospital, treatment, resection and RTS score would diminish survival. Patient gender is expected to have no impact (Hunter et al., 2000) on survival.
•
Model 2 is a reduced Model 1. After logit regression for Model 1 is conducted, variables whose statistical significance does not improve model accuracy are excluded. Gender has no impact if trauma has common features. All injury severity variables (like ISS, RTS and grade), capturing the same measurement (injury effect on success probability), would not have the same statistical significance in explaining success. Therefore, Model 2, will include all independent variables used in Model 1 except those that are not statistically significant. The model that best predicts outcome is selected.
•
Model 3 estimates patients’ survival probability who have undergone surgery and liver resection. Model 3’s objective is to estimate factor impact (intervention treatment and resection treatment) on survival probability. This is important for communication with patients and/or their relatives before treatment is undertaken and for transparency and liability reasons.
•
Model 4 is used to estimate patient survival probability after treatment intervention. The objective is to find predictors that determine an intervention’s success. Information obtained from this model will provide crucial information when communicating with patient and/or relatives to get consent for an intervention treatment.
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782
Results and analysis For each model described, we estimate four logistic models (1-4). Each estimation’s output is summarized in Table III. Coefficients, standard errors, slopes (at the mean value), McFadden R2 and odds ratios are shown for each model. Model 1: for audit purposes, Equation (6) is suggested. It shows that the odds ratio (cases correctly predicted) is accurate in 94.8 percent of cases. The goodness of fit (McFadden’s R2) is 0.528. From all factors included in the model, age, injury grade and ISS have, as expected, negative impact on survival. Although not statistically important, the estimated model shows that male are more likely to survive compared to female patients. Hospitalization, injuries, treatment and organ resection are the factors that increase survival: LðSurvivÞ ¼ 0:990:62 age þ 0:036 gender þ 0:076 Lhospit þ 0:54 Cinjur1:634 grade þ 2:138 Interv þ 0:52 Resec0:016 ISS þ 1:019 RTS
(6)
Model 1 Coefficient (SE) Slopea
Model 2 Coefficient (SE) Slopea
Model 3 Coefficient (SE) Slopea
Model 4 Coefficient (SE)
Slopea
Const 0.999015 (5.22326) – −0.10283 (3.80112) – −0.862431 (4.47278) – 0.746643 (1.92901) – Age −0.622796 (0.318195) −0.007670 −0.585029 (0.29665) −0.008149 −0.474629 (0.27818) −0.006906 −0.335444 (0.121366) −0.0837041 Gender 0.0359078 (0.946138) 0.000447 – – 0.031614 (0.879444) 0.0004647 – – Lhospit 0.0761689 (0.038947) 0.000938 0.078475 (0.03905) 0.001093 – – 0.0432618 (0.019739) 0.0107952 Cinjur 0.542994 (0.907665) 0.007289 – – 0.32765 (0.8656) 0.0050071 – – Grade −1.62486 (0.656133) −0.020011 −1.47137 (0.56427) −0.020496 −1.25049 (0.49419) −0.0181948 −0.509712 (0.20509) −0.12719 Interv 2.13864 (1.45903) 0.034067 1.88427 (1.35612) 0.032342 2.31209 (1.37013) 0.0448273 – – Resec 0.522081 (1.22158) 0.005201 – – 0.727987 (1.09478) 0.0079330 1.096 (0.793104) 0.25803 ISS −0.0165822 (0.034834) −0.000204 – – −0.01167 (0.030962) −0.0001698 0.0470261 (0.016096) 0.0117346 RTS 1.01963 (0.501665) 0.012557 1.11013 (0.3962) 0.015464 1.18446 (0.455075) 0.0172341 −0.0374077 (0.204627) −0.0093344 Number of observ. 173 173 173 173 McFadden R2 0.527738 0.521588 0.468641 0.160297 Adjusted R2 0.331693 0.403960 0.292201 0.101808 Correct prediction 94.8% 95.4% 94.2% 70.5% Note: aEvaluated at the mean
Variables
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Patients with liver trauma
783
Table III. Four logistic models
IJHCQA 29,7
Model 2: Equation (7) shows survival probability by excluding all factors with respective coefficients having a z-score (calculated as a coefficient ratio with its respective standard error) lower than 1: LðSurvivÞ ¼ 0:1030:585 age1:47 grade
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784
þ 1:11 RTS þ 0:078 Lhospit þ 1:88 Interv
(7)
The model’s predictability power improves compared to Model 1. The odds ratio for this model is 95.4 or 0.6 percent higher than the Model 1. McFadden R2 is statistically tied. Therefore, Gender, Cinjur, Resec, ISS variables could be excluded without losing any predictability power. Model 3’s purpose is to estimate success probability (survival) among patients brought to the ER with injuries. We show that the information obtained from Equation (8) is adequate to better inform the patient or the patient’s relatives about treatment outcome. The variables used in this model are the ones easily measured at ER: Age, Gender, Cinjur, grade, Interv, Resec, ISS and RTS: LðSurvivÞ ¼ 0:860:47 age þ 0:032 gender þ 0:327 Cinjur1:25 grade þ 2:31 Interv þ 0:73 Resec0:012 ISS þ 1:18 RTS
(8)
The model’s predictability power is 94.2 percent, which gives an informative guide to the patient and others about treatment outcomes. Model 4 estimates success probability (survival) following intervention (in contrast to conservative treatment). If a patient is brought into the ER with an injury and is evaluated as a patient that needs intervention, then the following equation could be used to communicate the intervention’s survival success: LðSintervÞ ¼ 0:750:34 age0:51 grade0:037 RTS þ 0:43 Lhospit þ 1:096 Resec þ 0:047 ISS
(9)
The model’s predictability power is 70.5 percent, lower than other models we estimated because we are estimating intervention success probability; i.e., patients with grave conditions who survive intervention. Conclusions In this study, logistic models are used to estimate survival probability among patients with liver injury, using data from patient records. Models 1-2 can be used for audit purposes and to communicate to patients or their relatives. Models 3-4 can be used to communicate with patients and relatives about interventions, and about resource allocation. We found that patient survival probability after liver trauma is driven by injury severity (both anatomical and physiological), treatment, age and organs injured other than the liver. Our conclusions and recommendations can be used to improve information flow from physicians to patients or their relatives and to better allocate hospital resources. However, our study has limitations; we have not been able to externally validate our results; i.e., we have not tested the multinomial logit model on a different patient sample to see if statistically significant results can be replicated.
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References Baker, S.P., O’Neill, B., Haddon, W. and Long, W. (1974), “The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care”, Journal of Trauma, Vol. 14 No. 8, pp. 187-196. Cannon, C.M., Braxton, C.C., Kling-Smith, M., Mahnken, J.D., Carlton, E. and Moncure, M. (2009), “Utility of the shock index in predicting mortality in traumatically injured patients”, The Journal of TRAUMA® Injury, Infection, and Critical Care, Vol. 67 No. 6, pp. 1426-1430. Chawda, M.N., Hildebrand, F., Papeb, H.C. and Giannoudis, P.V. (2004), “Predicting outcome after multiple trauma: which scoring system?”, Injury, International Journal of the Care of the Injured, Vol. 35 No. 4, pp. 347-358. Eftekhar, B., Mohammad, K., Ardebili, H.E., Ghodsi, M. and Ketabchi, E. (2005), “Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data”, BMC Medical Informatics and Decision Making, Vol. 5 No. 3, pp. 1-8. Gujarati, D. and Porter, D. (2009), Basic Econometrics, 5th ed., McGraw Hill, New York, NY. Hunter, A., Kennedy, L., Henry, J. and Ferguson, R.I. (2000), “Application of neural networks and sensitivity analysis to improved prediction of trauma survival”, Computer Methods and Programs in Biomedicine, Vol. 62 No. 1, pp. 11-19. Köksal, Ö., Özdemir, F., Bulut, M., Aydin, S., Almacioğlu, M.L. and Özgüç, H. (2009), “Comparison of trauma scoring systems for predicting mortality in firearm injuries”, Turkish Journal of Trauma and Emergency Surgery, Vol. 15 No. 6, pp. 559-564. Montague, A.P. and Brooks, S.C. (1989), “One year’s trauma in a district general hospital: injury severity and survival”, Archives of Emergency Medicine, Vol. 6 No. 2, pp. 116-124. Moon, J.H., Seo, B., Jang, J., Lee, J. and Moon, H.S. (2013), “Evaluation of probability of survival using trauma and injury severity score method in severe neurotrauma patients”, Journal of Korean Neurosurgical Society, Vol. 54 No. 1, pp. 42-46. Signorini, D.F., Andrews, P.J.D., Jones, P.A., Wardlaw, J.M. and Miller, J.D. (1999), “Predicting survival using simple clinical variables: a case study in traumatic brain injury”, Journal of Neurology, Neurosurgery and Psychiatry, Vol. 66 No. 1, pp. 20-25. Wong, S.S.N. and Leung, G.K.K. (2008), “Injury severity score (ISS) vs ICD-derived injury severity score (ICISS) in a patient population treated in a designated Hong Kong trauma centre”, McGill Journal of Medicine, Vol. 11 No. 1, pp. 9-13.
Corresponding author Agim Kukeli can be contacted at: [email protected]
For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]
Patients with liver trauma
785
Article information:
Downloaded by United Arab Emirates University At 07:46 17 August 2016 (PT)
To cite this document: Skender Buci Agim Kukeli , (2016),"Survival probability in patients with liver trauma", International Journal of Health Care Quality Assurance, Vol. 29 Iss 7 pp. 778 - 785 Permanent link to this document: http://dx.doi.org/10.1108/IJHCQA-04-2016-0045 Downloaded on: 17 August 2016, At: 07:46 (PT) References: this document contains references to 11 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 19 times since 2016*
Users who downloaded this article also downloaded: (2002),"Self-inflicted deaths of prisoners serving life sentences 1988-2001", The British Journal of Forensic Practice, Vol. 4 Iss 4 pp. 30-38 http://dx.doi.org/10.1108/14636646200200024 (2010),"Managers, balance, and fulfilling lives", Gender in Management: An International Journal, Vol. 25 Iss 2 pp. 86-90 http://dx.doi.org/10.1108/17542411011026258 (1974),"Life skills and life styles", Industrial and Commercial Training, Vol. 6 Iss 11 pp. 515-528 http:// dx.doi.org/10.1108/eb003430
Access to this document was granted through an Emerald subscription provided by emeraldsrm:232579 []
For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.
Downloaded by United Arab Emirates University At 07:46 17 August 2016 (PT)
*Related content and download information correct at time of download.
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0952-6862.htm
IJHCQA 29,7
Survival probability in patients with liver trauma Skender Buci
778 Received 8 November 2015 Revised 13 April 2016 Accepted 11 May 2016
Department of Trauma, University Hospital of Trauma, Tirana, Albania, and
Agim Kukeli Department of Economics, University of Akron, Akron, Ohio, USA Abstract
Downloaded by United Arab Emirates University At 07:46 17 August 2016 (PT)
Purpose – The purpose of this paper is to assess the survival probability among patients with liver trauma injury using the anatomical and psychological scores of conditions, characteristics and treatment modes. Design/methodology/approach – A logistic model is used to estimate 173 patients’ survival probability. Data are taken from patient records. Only emergency room patients admitted to University Hospital of Trauma (former Military Hospital) in Tirana are included. Data are recorded anonymously, preserving the patients’ privacy. Findings – When correctly predicted, the logistic models show that survival probability varies from 70.5 percent up to 95.4 percent. The degree of trauma injury, trauma with liver and other organs, total days the patient was hospitalized, and treatment method (conservative vs intervention) are statistically important in explaining survival probability. Practical implications – The study gives patients, their relatives and physicians ample and sound information they can use to predict survival chances, the best treatment and resource management. Originality/value – This study, which has not been done previously, explores survival probability, success probability for conservative and non-conservative treatment, and success probability for single vs multiple injuries from liver trauma. Keywords Logit model, Liver trauma injury, Survival probability, Treatment mode Paper type Research paper
International Journal of Health Care Quality Assurance Vol. 29 No. 7, 2016 pp. 778-785 © Emerald Group Publishing Limited 0952-6862 DOI 10.1108/IJHCQA-04-2016-0045
Introduction This study was motivated by three drivers: trauma’s steady occurrence; few studies where survival among patients with liver trauma has been predicted; and understanding injured patients’ survival probability, which may help to audit hospital performance, resource allocation and improve communication between staff, patients and relatives. Predicting treatment outcome is paramount for: accuracy and success when communicating with patients and relatives about patient recovery (Signorini et al., 1999); as a tool for deciding whether to pursue a traditional or interventionist treatment; and for audit purposes (Signorini et al., 1999; Hunter et al., 2000). Patients (young and old) come to emergency rooms (ERs) with diverse injuries (ranging from single to multiple organs). Each patient’s injury requires a unique examination and care plan. Consequently, managers and physicians need to plan resource allocation and predict outcome. Patients admitted with varying injuries in the ER progress differently in the intensive care (IC) rooms and their need to stay hospitalized varies. Some patients with a minor injury can be easily cared for and the outcome is clear, but some injuries; i.e., those with multiple organ damage in addition to the liver, are more severe. Predicting treatment outcome, whether conservative or interventionist, at the ER, IC, or during the time a patient is hospitalized, will help managers, physicians and relatives plan, consider and reconsider treatments.
Downloaded by United Arab Emirates University At 07:46 17 August 2016 (PT)
Clinical data gathered from 173 patients admitted at the University Hospital of Trauma (former Military Hospital) in Tirana, was used to predict patient survival based on injury parameters and general condition. The same data pool was used to predict the success probability for conservative (no intervention) vs non-conservative treatment (surgical intervention in most cases). The last experiment was used to predict success probability when treating a single organ (liver) injury vs treating a liver trauma with multiple organ injuries. Logistic regression modeling techniques have been extensively used in the medical field for estimating success probability (Hunter et al., 2000; Gujarati and Porter, 2009). Other techniques such as Pearson’s test for group comparison are used to predict mortality in trauma injured patients (Cannon et al., 2009). New techniques such as neural networks (Eftekhar et al., 2005; Hunter et al., 2000) have been used, but for mortality or audit and performance purposes, probability prediction does not outperform logistic models when prediction accuracy is in question. Chawda et al. (2004) reviewed different scoring systems to identify their advantages and disadvantages. Popular scoring systems such as injury severity score (ISS) and trauma injury severity score are used, but there is no conclusive evidence regarding which is best. Köksal et al. (2009) uses age, gender, Glasgow coma scale (GCS) and revised trauma scale (RTS) to compare ISS and new injury severity score (NISS) performance to predict mortality. NISS is no more accurate than ISS. Survival probability relies on unique data, a modified logit model (explained below) and success probability for various liver injuries and different treatments.
Patients with liver trauma
779
Data and their description Data were collected in the University Hospital of Trauma between January 2009 through December 2012 (Table I). Patients were grouped into seven age categories. The variables were patients’ conditions; for example: •
if a patient has liver injury, then the variable Cinjur is 0;
•
if there is liver and other organ injury, then Cinjur is 1;
•
if a patient survives the trauma, the variable Surviv is 1;
•
if a patient dies, then Surviv is 0;
Surviv Age Gender Cinjur Lhospit Grade Conserv Interv Sinterv Resec ISS RTS
The patient survives injury and treatment (Yes ¼ 1; No ¼ 0) Age: 6-15 years old ¼ 1; 16-25 years old ¼ 2; 26-35 years old ¼ 3; 36-45 years old ¼ 4; 46-55 years old ¼ 5; 56-65 years old ¼ 6; 66-75 years old ¼ 7 Gender (Male ¼ 1; Female ¼ 0) If only the liver is injured, value is 0. For all other combinations, such as liver and head, liver and thorax, etc., the value is 1 Hospitalization (days) Grade (the six grading scale standard is used: minor ¼ 1; moderate ¼ 2; serious ¼ 3; severe ¼ 4; critical ¼ 5; un-survivable ¼ 6) Conservative treatment (Yes ¼ 1; No ¼ 0) Patient is treated with an intervention (Yes ¼ 1; No ¼ 0) Intervention success (Yes ¼ 1; No ¼ 0) Resection (Yes ¼ 1; No ¼ 0) Injury severity score (anatomical), higher score means a more severe injury Revised trauma score (psychological), low number means more severe conditions
Table I. Variables and their description
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•
if a patient undergoes surgical intervention, then the variable Interv is 0;
•
for those treated conservatively (no surgery), the variable Interv is 1; and
•
if a patient undergoing surgical intervention has a partial liver resection, then Resec takes value 1 and 0 if not.
Injury severity was evaluated and coded in the ER as 1-6. The ISS score (Baker et al., 1974) measures the overall severity in patients with more than one injury and is computed as the abbreviated injury scale grade’s sum of squares in the three most severely injured areas. The RTS score (Chawda et al., 2004) measures the injured patients’ psychological conditions and combines the GCS, systemic blood pressure and respiratory rate. Of 173 patients in the study (83 percent male), 91 percent survived. In total, 64 percent of all patients had two or more injured organs. Fewer than half experienced conservative treatment, while 54.3 percent had a surgical intervention. The ISS range is 1-75 (the highest value) with a mean of 23. Table II shows the statistical variables’ details, including mean, minimum, maximum and standard deviation. Method The logit model was used to analyze various factors’ effects on survival probability and the intervention or conservative treatment’s success probability. Many studies predicting injury outcome have used the logit model (Montague and Brooks, 1989; Moon et al., 2013; Signorini et al., 1999; Wong and Leung, 2008). All equations are from Gujarati and Porter (2009): P i ¼ E ðY ¼ 1jX i Þ ¼
1 þ e
1 b0 þ
Pn i¼1
bi X i
(1)
where P is the success probability; X’s the factors that determine probability; β’s the coefficients to be estimated; and e ¼ 2.71828. This equation can be written in a simple general form, useful for further interpretation: Pi ¼
Table II. Summary statistics, using observations 1-173
1 1 þ eZ i
(2)
Variable
Mean
Minimum
Maximum
SD
Surviv Age Gender Lhospit Cinjur Grade Conserv Interv Resec ISS RTS Sinterv
0.913295 2.90751 0.832370 11.6360 0.641618 2.45665 0.445087 0.543353 0.063584 23.0000 6.96021 0.473988
0.000000 1.00000 0.000000 0.0420000 0.000000 1.00000 0.000000 0.000000 0.000000 1.00000 2.83300 0.000000
1.00000 7.00000 1.00000 70.0000 1.00000 5.00000 1.00000 1.00000 1.00000 75.0000 7.84100 1.00000
0.282219 1.58577 0.374622 11.2897 0.480917 1.10212 0.498418 0.499563 0.244718 16.8533 1.33200 0.500772
Patients with liver trauma
The Zi value is: Z i ¼ b0 þ
n X
bi X i
(3)
i¼1
L denotes the logarithmic odds ratio:
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Pi Li ¼ ln 1P i
¼ Z i ¼ b0 þ
n X
781 bi X i
(4)
i¼1
Equation 4 estimates the logit model using the binary dependent variable. Since the model is log-linear, the independent factors’ marginal effects are given by their partial derivatives. In a general form the partial derivative appears in the following equation: @2 P i ¼ bi P i ð1P i Þ @xi
(5)
The partial derivatives will be interpreted as the independent variables’ slope coefficients, which are computed at the independent variable’s mean value and their respective probability. One can interpret estimated β’s (Equation 5) as the odds ratio. The coefficients can be used to predict the change in success probability (success likelihood) for a given change in the independent variable’s value. Models Four different models are used to estimate injury patent’s survival probability. Age, gender, Lhospit, Cinjur, grade, Interv, Resec, ISS and RTS are explanatory variables. The models under investigation draw upon numerous studies (Chawda et al., 2004; Montague and Brooks, 1989; Moon et al., 2013) on trauma survival. There is consensus that all trauma cases have common features but trauma involving liver injury has been studied less thoroughly. Every ER patient with an injury is checked using the same standard procedure, whether the liver is injured or not. Therefore, common factors should explain the patient’s condition, treatment and progress (whether conservative treatment or intervention). The following general model is the quest to find factors determining the progress toward full recovery and the best treatment. Success probability depends upon or is determined by patient’s age, gender, hospital stay, injury combined with liver and other organs, injury grade, intervention, resection and trauma severity. The equation can be written, using the notation for variables considered in this study, as: Success probability ¼ L (age, gender, Lhospit, Cinjur, grade, Interv, Resec, ISS, RTS) We postulate that the function predicting success probability, L, is a logistic function. This function is generically written as: L ¼ ln
P 1P
¼ b0 þ b1 age þ b2 gender þ b3 Lhospit þ b4 Cinjur þ b5 grade þ b6 Interv þ b7 Resecþ b8 ISS þ b9 RTS
where coefficients b’s are estimated and used for explaining each variable’s contribution to success probability. The four models examined in this study (Models 1-4) differ
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in two ways: the predictors (dependent variables) used to estimate survival probability; and survival probability estimates for patients in general (Models 1-2) and for different treatments (Model 3): •
Model 1 estimates success probability as a function of the patient’s natural conditions (age and gender), injury severity (grade, ISS, RTS and combined injuries) and treatment received (intervention, conservative, resection and days in hospital). Therefore, the first logit model includes all variables (predictors) available. Expectations are that age, combined injury, injury grade and ISS would be negatively related to survival, while days in the hospital, treatment, resection and RTS score would diminish survival. Patient gender is expected to have no impact (Hunter et al., 2000) on survival.
•
Model 2 is a reduced Model 1. After logit regression for Model 1 is conducted, variables whose statistical significance does not improve model accuracy are excluded. Gender has no impact if trauma has common features. All injury severity variables (like ISS, RTS and grade), capturing the same measurement (injury effect on success probability), would not have the same statistical significance in explaining success. Therefore, Model 2, will include all independent variables used in Model 1 except those that are not statistically significant. The model that best predicts outcome is selected.
•
Model 3 estimates patients’ survival probability who have undergone surgery and liver resection. Model 3’s objective is to estimate factor impact (intervention treatment and resection treatment) on survival probability. This is important for communication with patients and/or their relatives before treatment is undertaken and for transparency and liability reasons.
•
Model 4 is used to estimate patient survival probability after treatment intervention. The objective is to find predictors that determine an intervention’s success. Information obtained from this model will provide crucial information when communicating with patient and/or relatives to get consent for an intervention treatment.
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Results and analysis For each model described, we estimate four logistic models (1-4). Each estimation’s output is summarized in Table III. Coefficients, standard errors, slopes (at the mean value), McFadden R2 and odds ratios are shown for each model. Model 1: for audit purposes, Equation (6) is suggested. It shows that the odds ratio (cases correctly predicted) is accurate in 94.8 percent of cases. The goodness of fit (McFadden’s R2) is 0.528. From all factors included in the model, age, injury grade and ISS have, as expected, negative impact on survival. Although not statistically important, the estimated model shows that male are more likely to survive compared to female patients. Hospitalization, injuries, treatment and organ resection are the factors that increase survival: LðSurvivÞ ¼ 0:990:62 age þ 0:036 gender þ 0:076 Lhospit þ 0:54 Cinjur1:634 grade þ 2:138 Interv þ 0:52 Resec0:016 ISS þ 1:019 RTS
(6)
Model 1 Coefficient (SE) Slopea
Model 2 Coefficient (SE) Slopea
Model 3 Coefficient (SE) Slopea
Model 4 Coefficient (SE)
Slopea
Const 0.999015 (5.22326) – −0.10283 (3.80112) – −0.862431 (4.47278) – 0.746643 (1.92901) – Age −0.622796 (0.318195) −0.007670 −0.585029 (0.29665) −0.008149 −0.474629 (0.27818) −0.006906 −0.335444 (0.121366) −0.0837041 Gender 0.0359078 (0.946138) 0.000447 – – 0.031614 (0.879444) 0.0004647 – – Lhospit 0.0761689 (0.038947) 0.000938 0.078475 (0.03905) 0.001093 – – 0.0432618 (0.019739) 0.0107952 Cinjur 0.542994 (0.907665) 0.007289 – – 0.32765 (0.8656) 0.0050071 – – Grade −1.62486 (0.656133) −0.020011 −1.47137 (0.56427) −0.020496 −1.25049 (0.49419) −0.0181948 −0.509712 (0.20509) −0.12719 Interv 2.13864 (1.45903) 0.034067 1.88427 (1.35612) 0.032342 2.31209 (1.37013) 0.0448273 – – Resec 0.522081 (1.22158) 0.005201 – – 0.727987 (1.09478) 0.0079330 1.096 (0.793104) 0.25803 ISS −0.0165822 (0.034834) −0.000204 – – −0.01167 (0.030962) −0.0001698 0.0470261 (0.016096) 0.0117346 RTS 1.01963 (0.501665) 0.012557 1.11013 (0.3962) 0.015464 1.18446 (0.455075) 0.0172341 −0.0374077 (0.204627) −0.0093344 Number of observ. 173 173 173 173 McFadden R2 0.527738 0.521588 0.468641 0.160297 Adjusted R2 0.331693 0.403960 0.292201 0.101808 Correct prediction 94.8% 95.4% 94.2% 70.5% Note: aEvaluated at the mean
Variables
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Table III. Four logistic models
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Model 2: Equation (7) shows survival probability by excluding all factors with respective coefficients having a z-score (calculated as a coefficient ratio with its respective standard error) lower than 1: LðSurvivÞ ¼ 0:1030:585 age1:47 grade
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þ 1:11 RTS þ 0:078 Lhospit þ 1:88 Interv
(7)
The model’s predictability power improves compared to Model 1. The odds ratio for this model is 95.4 or 0.6 percent higher than the Model 1. McFadden R2 is statistically tied. Therefore, Gender, Cinjur, Resec, ISS variables could be excluded without losing any predictability power. Model 3’s purpose is to estimate success probability (survival) among patients brought to the ER with injuries. We show that the information obtained from Equation (8) is adequate to better inform the patient or the patient’s relatives about treatment outcome. The variables used in this model are the ones easily measured at ER: Age, Gender, Cinjur, grade, Interv, Resec, ISS and RTS: LðSurvivÞ ¼ 0:860:47 age þ 0:032 gender þ 0:327 Cinjur1:25 grade þ 2:31 Interv þ 0:73 Resec0:012 ISS þ 1:18 RTS
(8)
The model’s predictability power is 94.2 percent, which gives an informative guide to the patient and others about treatment outcomes. Model 4 estimates success probability (survival) following intervention (in contrast to conservative treatment). If a patient is brought into the ER with an injury and is evaluated as a patient that needs intervention, then the following equation could be used to communicate the intervention’s survival success: LðSintervÞ ¼ 0:750:34 age0:51 grade0:037 RTS þ 0:43 Lhospit þ 1:096 Resec þ 0:047 ISS
(9)
The model’s predictability power is 70.5 percent, lower than other models we estimated because we are estimating intervention success probability; i.e., patients with grave conditions who survive intervention. Conclusions In this study, logistic models are used to estimate survival probability among patients with liver injury, using data from patient records. Models 1-2 can be used for audit purposes and to communicate to patients or their relatives. Models 3-4 can be used to communicate with patients and relatives about interventions, and about resource allocation. We found that patient survival probability after liver trauma is driven by injury severity (both anatomical and physiological), treatment, age and organs injured other than the liver. Our conclusions and recommendations can be used to improve information flow from physicians to patients or their relatives and to better allocate hospital resources. However, our study has limitations; we have not been able to externally validate our results; i.e., we have not tested the multinomial logit model on a different patient sample to see if statistically significant results can be replicated.
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References Baker, S.P., O’Neill, B., Haddon, W. and Long, W. (1974), “The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care”, Journal of Trauma, Vol. 14 No. 8, pp. 187-196. Cannon, C.M., Braxton, C.C., Kling-Smith, M., Mahnken, J.D., Carlton, E. and Moncure, M. (2009), “Utility of the shock index in predicting mortality in traumatically injured patients”, The Journal of TRAUMA® Injury, Infection, and Critical Care, Vol. 67 No. 6, pp. 1426-1430. Chawda, M.N., Hildebrand, F., Papeb, H.C. and Giannoudis, P.V. (2004), “Predicting outcome after multiple trauma: which scoring system?”, Injury, International Journal of the Care of the Injured, Vol. 35 No. 4, pp. 347-358. Eftekhar, B., Mohammad, K., Ardebili, H.E., Ghodsi, M. and Ketabchi, E. (2005), “Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data”, BMC Medical Informatics and Decision Making, Vol. 5 No. 3, pp. 1-8. Gujarati, D. and Porter, D. (2009), Basic Econometrics, 5th ed., McGraw Hill, New York, NY. Hunter, A., Kennedy, L., Henry, J. and Ferguson, R.I. (2000), “Application of neural networks and sensitivity analysis to improved prediction of trauma survival”, Computer Methods and Programs in Biomedicine, Vol. 62 No. 1, pp. 11-19. Köksal, Ö., Özdemir, F., Bulut, M., Aydin, S., Almacioğlu, M.L. and Özgüç, H. (2009), “Comparison of trauma scoring systems for predicting mortality in firearm injuries”, Turkish Journal of Trauma and Emergency Surgery, Vol. 15 No. 6, pp. 559-564. Montague, A.P. and Brooks, S.C. (1989), “One year’s trauma in a district general hospital: injury severity and survival”, Archives of Emergency Medicine, Vol. 6 No. 2, pp. 116-124. Moon, J.H., Seo, B., Jang, J., Lee, J. and Moon, H.S. (2013), “Evaluation of probability of survival using trauma and injury severity score method in severe neurotrauma patients”, Journal of Korean Neurosurgical Society, Vol. 54 No. 1, pp. 42-46. Signorini, D.F., Andrews, P.J.D., Jones, P.A., Wardlaw, J.M. and Miller, J.D. (1999), “Predicting survival using simple clinical variables: a case study in traumatic brain injury”, Journal of Neurology, Neurosurgery and Psychiatry, Vol. 66 No. 1, pp. 20-25. Wong, S.S.N. and Leung, G.K.K. (2008), “Injury severity score (ISS) vs ICD-derived injury severity score (ICISS) in a patient population treated in a designated Hong Kong trauma centre”, McGill Journal of Medicine, Vol. 11 No. 1, pp. 9-13.
Corresponding author Agim Kukeli can be contacted at: [email protected]
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