Data mining validation of EUCAST Fluconazole

Transcription

1 AAC Accepts, published online ahead of print on 11 May 2009 Antimicrob. Agents Chemother. doi: /aac Copyright 2009, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved. 1 2 Data mining validation of EUCAST Fluconazole Breakpoints Isabel Cuesta 1, Concha Bielza 2, Pedro Larrañaga 2, Manuel Cuenca-Estrella 1, Fernando Laguna 3, Dolors Rodriguez-Pardo 4, Benito Almirante 4, Albert Pahissa 4, Juan L. Rodríguez-Tudela 1 * 1 Servicio de Micologia. Centro Nacional de Microbiologia. Instituto de Salud Carlos III. Majadahonda. Madrid. 2 Departamento de Inteligencia Artificial. Facultad de Informática. Universidad Politécnica. Madrid. 3 Servicio de Medicina Interna. Hospital Carlos III. Madrid. 4 Servicio de Enfermedades Infecciosas, Hospital Universitario Valle de Hebrón. Barcelona. *Corresponding author: Juan Luis Rodríguez Tudela. Servicio de Micología. Centro Nacional de Microbiología. Instituto de Salud Carlos III. Ctra Majadahonda Pozuelo Km Majadahonda. Spain. Phone: Fax: : jlrtudela@isciiii.es Page 1 of 22

2 19 Abstract EUCAST fluconazole breakpoints considers susceptible a strain of Candida spp with a MIC 2 mg/l, intermediate with a MIC of 4 mg/l and resistant with a MIC > 4 mg/l. Machine learning models are supported by a complex statistical analysis to assess if the results have statistical relevance or they are chosen at random. The aim of this work is to use supervised classification algorithms to analyze the clinical data used to produce EUCAST fluconazole breakpoints. Five supervised classifiers (J48, CART, OneR, Naïve Bayes and simply logistic) were used to analyze two cohorts of patients with oropharyngeal candidosis and candidaemia. The target variable was the outcome of the infections and predictor variables were MIC or Dose/MIC. The statistical power was assessed by means of sensitivity, specificity, false positive rate, area under ROC curve and Matthews Correlation Coefficient (MCC). CART obtained the best statistical power for a MIC >4 mg/l for detecting failures (sensitivity 87%, false positive rate 8%, area under ROC curve 0.89 and MCC 0.80). For Dose/MIC the target was >75 with a sensitivity of 91%, a false positive rate of 10%, an area under ROC curve of 0.90 and a MCC of Other classifiers obtained similar breakpoints with a lower statistical power. EUCAST fluconazole breakpoints have been validated by means of machine learning methods. These computer tools must be incorporated to the process for developing breakpoints because it avoids researcher s bias providing the statistical power of the model. Abstract words: 242 Body text words: 2942 Page 2 of 22

3 Introduction The Antifungal Susceptibility Testing Subcommittee (AFST) of European Committee on Antimicrobial Susceptibility Testing (EUCAST) has been recently established the breakpoints for fluconazole and Candida 14. EUCAST considers susceptible a strain of Candida spp with a MIC 2 mg/l, intermediate with a MIC of 4 mg/l and resistant with a MIC > 4 mg/l. These breakpoints do not apply to Candida glabrata and Candida krusei 14. The process for establishing breakpoints designed by EUCAST takes into account pharmacokinetic (PK)-pharmacodynamic (PD) data and other factors, such as dosing regimens, toxicology, resistance mechanisms, wild type MIC distributions, and clinical outcome data. The clinical data used in the process for setting breakpoints for fluconazole was reported in a previous study 12. That study analyzed, in the classical way, the correlation of MICs and the Dose/MIC to the clinical outcome of patients with candidaemia and oropharyngeal candidosis (OPC) who had been treated with fluconazole. The results showed that 93.7% (136 of 145 episodes) of infections due to isolates with fluconazole MICs 2 mg/l responded to fluconazole treatment. A response of 66% (8 of 12 episodes) was observed when the infections were caused by isolates with an MIC of 4 mg/l and to 11.8% (12 of 101 episodes) when the infection was caused by isolates with a fluconazole MIC > 4 mg/l. Clinical outcome data used for setting breakpoints has usually been analyzed following the rule 10. This rule states that infections due to susceptible isolates respond to therapy approximately 90% of the time, whereas infections due to resistant isolates respond approximately 60% of the time. However, data mining tools have been developed allowing a better analysis and Page 3 of 22

4 interpretation of the data. An extraordinary development in contemporary computer science is the introduction and application of methods of machine learning. These enable a computer program to analyze automatically a large body of data and decide what information is most relevant. This information can then be used to make decisions faster and more accurately. Machine learning tools for data mining tasks contain implementations of most of the algorithms for supervised classification such as decision trees, rule sets, Bayesian classifiers, support vector machines, logistic and linear regression, multilayer perceptrons and nearest-neighbour methods, as well as metalearners like bagging, boosting and stacking. Machine learning models are supported by a complex statistical analysis including performance measures like sensitivity, specificity, false positive rate and area under the ROC curve which enable to assess whether the results have statistical relevance or they are chosen at random. The aim of this work is to use supervised classification algorithms to analyze the clinical data used to produce EUCAST fluconazole breakpoints in order to know whether the breakpoints chosen are similar or not to those produced by these tools. Methods Patients Candidaemia. One hundred and twenty six candidemia patients treated with fluconazole were recruited from a population-based surveillance study performed in Barcelona during Characteristics of the patients and analysis of the correlation of the MIC with the outcome of the patients have been published previously 12. Briefly, a case was defined by recovery of any Page 4 of 22

5 Candida species from blood cultures. Candidemia that occurred > 30 days after the initial case was considered a new case. Cure was defined by eradication of candidemia and resolution of the associated signs and symptoms. Failure was defined as persistent candidemia despite 4 days of fluconazole treatment. The recommended dose of fluconazole for candidemia is 400 mg/day, but the dose was adjusted to 200 mg/day when the creatinine clearance was between 10 and 50 ml/min and to 100 mg/day when the creatinine clearance <10 ml/min. Four episodes of candidemia were treated with 100 mg/day fluconazole, 25 with 200 mg/day, 92 with 400 mg/day, 2 with 600 mg/day, and 3 with 800 mg/day 12. Oropharyngeal candidiasis (OPC). One hundred and ten patients with HIV infection had been treated in another study with fluconazole for a total of 132 episodes of oral thrush caused by Candida albicans 6. Sixty-five episodes of OPC were treated with 100 mg/day fluconazole, 44 with a dose of 200 mg/day, and 23 with 400 mg/day 12. Characteristics of the patients and analysis of the correlation of the MIC with the outcome of the patients have been published previously 12. Briefly, clinical resolution was defined as the absence of lesions compatible with oral thrush after 10 days of therapy. Mycological cure was not evaluated. All episodes were used to evaluate clinical outcome irrespective of the dose of fluconazole given. Hence a total of 258 episodes of Candida infection were available for analysis. Antifungal susceptibility testing Antifungal susceptibility testing was performed following the guidelines of the subcommittee for antifungal susceptibility testing of EUCAST for fermentative yeasts. 11. Briefly, fluconazole was distributed in RPMI supplemented with 2% glucose in flat-bottomed microtitration trays which were inoculated with 10 5 Page 5 of 22

6 CFU/ml of yeast, incubated at 35 C and then read after 24h at 530 nm using a spectrophotometer. The endpoint was defined as the concentration that resulted in 50% inhibition when compared with the growth of the control well. C. krusei ATCC 6258 and Candida parapsilosis ATCC were included for quality control. Computational methods The following information of each patient was entered in an Excel database (Microsoft Iberica, Spain): MIC of the isolate, proportion between the dose administered and the MIC of the isolate (Dose/MIC) and outcome of the patients following the definitions stated above as success or failures. The database contains 156 successes (60.5%) and 102 failures (39.5%). When required, data were transformed to log 2 to approximate a normal distribution. To build up the models, the target variable was the outcome of the infections and predictor variables were MIC or Dose/MIC. The models were built up using the WEKA version and CART v. 6.0 (Correlation and Regression Trees, Salford Systems, San Diego, CA). Five classifiers were used to analyze the database: J48 and CART decision trees, One R decision rule, Naïve Bayes and Simple Logistic regression. They cover a wide spectrum of methodologies (trees, rules, probabilistic classifiers) and have been chosen because of their sound theoretical basis and their intuitive interpretation. The main characteristics of the classifiers are as follows. J48 and CART decision trees. A decision tree basically defines at its nodes a series of tests on predictor variables organized in a tree-like structure. Each terminal node called leaf gives a classification that applies to all instances that reach the leaf, after being routed down the tree according to the values of the Page 6 of 22

7 predictors tested in successive nodes. The tree is constructed by recursively splitting the data into smaller and smaller subsets so that after each split the new data subset is purer (less entropy) than the old data subset. The J48 algorithm is the Weka implementation of C4.5 decision tree 9, which is an outgrowth of the basic original ID3 algorithm. It incorporates a method for postpruning the tree to avoid over-fitting. A default of 25% for the confidence level on the error rate was kept. CART system was proposed by Breiman et al. 2. CART is characterized by binary-split searches, automatic self-validation procedures and surrogate splitters. The CART method used to build up the model was Gini with the minimum cost tree regardless of its size. OneR. 4 is a simple classification rule. It is a one-level decision tree expressed as a set of rules only testing one particular predictor variable. The classification given to each branch is the class label that occurs most often in the data. The predictor variable chosen is the one that produces rules with the smallest number of errors, computed as the number of instances that do not have the majority class. The Naïve Bayes classifier 3 is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions. A Naïve Bayes classifier assumes that given the class variable, the predictor variables are conditionally independent. Simple logistic builds logistic regression models, i.e. a linear model based on a transformed target variable using the logit transformation 5. The weights in the linear combination of the predictor variables are calculated from the training data and need to maximize the likelihood function. Page 7 of 22

8 Every classifier develops a model when it searches for the MIC or the Dose/MIC value that better splits the populations of successes and failures. Ten-fold cross validation was used as the method to estimate the performance of each classifier. This was assessed by means of: (i) Sensitivity, (ii) Specificity, (iii) False positive rate or 1-specificity, (iv) Area under ROC curve and (v) Matthews Correlation Coefficient (MCC). This coefficient is a measure of the quality of two-class classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. It returns a value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. Results As stated previously, the cohort with oral thrush and candidemia have been previously described in detail 6,12. Therefore the results will only describe the performance of the classifiers and their statistical power. Table 1 shows the MIC value predicting failure for each classifier and the sensitivity, specificity, false positive rate, area under the ROC curve and MCC for the cohort of patients with OPC and candidemia. All classifiers yielded good results with OPC dataset as MCC value shows. J48 and Naïve Bayes selected a MIC value >2 mg/l and CART, One R and Simply Logistic > 4 mg/l for predicting failures. The best areas under ROC curves were those obtained with Naïve Bayes (0.94) and Simple Logistic (0.95) but they had a higher false positive rate than CART (Table 1). However, for candidemia the results were poor. Three classifiers, J48, One R and Simple Logistic were Page 8 of 22

9 unable to find out a MIC value splitting the populations of successes and failures (Table 1). CART and Naïve Bayes found >4 mg/l and >2 mg/l respectively, as the best value for predicting failures although the statistical power was limited. The area under ROC curve for Naïve Bayes was 0.48 and for CART was 0.63 (Table 1). When the whole set was analyzed, similar MIC values to those obtained with OPC predictive model were obtained. CART and Naïve Bayes were the classifiers showing the same results for both OPC and candidaemia. The areas under ROC curves were 0.89 and 0.91 respectively but the false positive rate was slightly lower for CART (8% vs. 13%). Table 2 shows the Dose/MIC value predicting treatment success for each classifier and the sensitivity, specificity, false positive rate, area under the ROC curve and MCC for the cohort of patients with OPC and candidemia All classifiers yielded a Dose/MIC value that predicts treatment success for OPC dataset. In all cases, Dose/MIC value was >37.5 and the area under the ROC curve 0.85 (Table 2). The classification tress generated by CART and J48 for OPC dataset were rather complex (Figure 1 shows CART tree). Area under ROC curves were 0.98 and 0.95 with a false positive rate of 0 and 28% respectively (Table 2). Figure 1 shows the tree produced by CART for OPC dataset, and both datasets OPC and Candidaemia. For OPC none of the patients responded with a Dose/MIC With a Dose/MIC > there were 14 (24.6%) failures and 43 (75.4%) successes. All patients with a Dose/MIC >75 were successes. The percentage of failures with a Dose/MIC between >18.75 and <37.5 were 57.9% (11 patients) and with a Dose/MIC between 37.5 and 75 were 42.9% (3 patients) Page 9 of 22

10 (Figure 1A). Dose/MIC CART tree of the candidaemia cohort also showed a value of >75 as the splitter. In this case, it was a two leaf tree with a 4% false positive rate but a limited sensitivity of 31%. The area under the ROC curve was Similar values were obtained with J48 tree for OPC dataset. No response was obtained when the Dose/MIC was 12.5 and no failures were detected when the Dose/MIC value was >50. For the whole dataset, only results of CART were considered because the breakpoint for OPC and candidemia cohorts was identical. The tree is displayed in figure 1B. Only 6% of failures were detected with a Dose/MIC value > 75 whereas 85.3% of failures were observed when the Dose/MIC was 75. The area under ROC curve was 0.90 and the false positive rate was 10% (Table 2). Discussion Machine learning methods are being used in genomics, proteomics, microarrays, system biology evolution and text mining 7. However, this is the first time that they have been applied to validate antifungal breakpoints. The rule has defined the accuracy of antimicrobial susceptibility testing for predicting the outcome of bacterial infections. This rule was inherited by mycologists because the clinical correlation studies analyzed showed a similar predictive pattern 10. However, the correlation of MICs values or pharmacodynamic parameters with the outcome of patients should include a similar statistical evaluation that any diagnostic tool. Machine learning methods give the opportunity to use statistical theory for building a model using a dataset. However, it is crucial to find the optimal solution and for this, several classifiers must be run comparing the models obtained with each one. In this Page 10 of 22

11 work, 5 classifiers have been compared (J48, CART, One R, Naïve Bayes and Simple Logistic) in order to identify which MIC or Dose/MIC values split the populations of successes and failures. The statistical power of every model has been evaluated by means of sensitivity, specificity, false positive rate, area under the ROC curve and MCC. The classifiers choose the MIC value that better splits the populations of successes and failures. This value is presented as X mg/l for successes and >X mg/l for failures. However, breakpoints usually have three categories, susceptible, intermediate (susceptible dependent on dose) and resistant, presented as Susceptible x mg/l; Intermediate >x, <y mg/l; and Resistant >y mg/l. The definition of intermediate category implies that an infection due to the isolate may be appropriately treated in body sites where the drugs are physically concentrated or when a high dosage of drug can be used. It also indicates a buffer zone that should prevent small, uncontrolled, technical factors from causing major discrepancies in interpretations. The main target of any susceptibility testing is to identify resistant strains or, in other words, the drugs that are less likely to eradicate the infection 13. Thus, a very major error is considered when a resistant strain is characterized as susceptible because the patient can be treated with a drug lacking of activity against the microorganism causing the infection. On the other hand, a major error is defined when a susceptible strain is classified as resistant. In summary, the aim is to minimize very major errors and in consequence, the rule for any analysis trying to predict the outcome of patients should be focused in failures. Crude mortality rate of Candida blood stream infections is 40% 8, and by any means inappropriate treatments must be avoided. Page 11 of 22

12 All classifiers were able to yield a predictive model for OPC dataset. CART obtained the best statistical power for a MIC >4 mg/l for detecting failures although the rest of classifiers have also a high statistical power (Table 1). However, for candidaemia the scenario was completely different. Just, CART and Naïve Bayes were able to find out a MIC value splitting the population but the statistical power was limited but slightly better for CART. Regarding Dose/MIC target, results were similar for OPC dataset. All classifiers were able to find out a Dose/MIC value splitting the populations of successes and failures with areas under ROC curves above However, for candidaemia, only CART produced same Dose/MIC value as for OPC but with little statistical power (Table 2). Both cohorts have different statistical power and this merits an explanation. For OPC cohort 85 cases had a MIC > 4 mg/l but there were only 4 cases for candidaemia. The lack of strains with MIC >4 mg/l in candidaemia cohort explains the limited statistical power of the models. However, the models for OPC and candidaemia datasets obtained the same values for MIC and Dose/MIC for at least one classifier. Therefore, the results of the whole dataset can be considered if the models produce the same MIC or Dose/MIC when the datasets are analyzed separately despite candidemia and OPC are quite different clinical situations. If several models satisfy this circumstance the statistical power must be taken into account. CART model produced identical targets for both datasets and highest statistical power satisfying both circumstances. CART model proposed a MIC >4 mg/l as the breakpoint of resistance with a sensitivity of 87%, a false positive rate of 8%, area under ROC curve of 0.89 and MCC 0.80 (Table 1). In addition, a Page 12 of 22

13 Dose/MIC value >75 is proposed as the target to get treatment success. This means that a fluconazole dose of 400 mg/day will cover all strains with a MIC of 4 mg/l or less. The sensitivity of this target is 91% with a false positive rate of 10%, area under the ROC curve of 0.90 and a MCC index of 0.80 (Table 2). In summary, fluconazole breakpoints (Susceptible 2 mg/l; Intermediate 4 mg/l; Resistant >4 mg/l) raised by EUCAST antifungal susceptibility testing subcommittee have been validated by means of machine learning methods. This proves that these computer tools must be incorporated to the process for developing breakpoints because it avoids completely researcher s bias providing the statistical power of the model. The use of machine learning tools allows the evaluation of antimicrobial susceptibility results in a similar way than the other diagnostic tools. Acknowledgements This work has been supported in part by Ministerio de Sanidad y Consumo, Instituto de Salud Carlos III, Spanish Network of Infection in Transplantation (RESITRA G03/075) and Spanish Network for the Research in Infectious Diseases (REIPI RD06/0008). Potential conflicts of interest: In the past 5 years, M.C.E. has received grant support from Astellas Pharma, BioMerieux, Gilead Sciences, Merck Sharp and Dohme, Pfizer, Schering Plough, Soria Melguizo SA, the European Union, the ALBAN program, the Spanish Agency for International Cooperation, the Spanish Ministry of Culture and Education, The Spanish Health Research Fund, The Instituto de Salud Page 13 of 22

14 Carlos III, The Ramon Areces Foundation, The Mutua Madrileña Foundation. He has been an advisor/consultant to the Panamerican Health Organization, Gilead Sciences, Merck Sharp and Dohme, Pfizer, and Schering Plough. He has been paid for talks on behalf of Gilead Sciences, Merck Sharp and Dohme, Pfizer, and Schering Plough. In the past 5 years, J.L.R.T. has received grant support from Astellas Pharma, Gilead Sciences, Merck Sharp and Dohme, Pfizer, Schering Plough, Soria Melguizo SA, the European Union, the Spanish Agency for International Cooperation, the Spanish Ministry of Culture and Education, The Spanish Health Research Fund, The Instituto de Salud Carlos III, The Ramon Areces Foundation, The Mutua Madrileña Foundation. He has been an advisor/consultant to the Panamerican Health Organization, Gilead Sciences, Merck Sharp and Dohme, Myconostica, Pfizer, and Schering Plough. He has been paid for talks on behalf of Gilead Sciences, Merck Sharp and Dohme, Pfizer, and Schering Plough. In the past 5 years, B.A. has received grant support from Gilead Sciences, Pfizer, and the Instituto de Salud Carlos III. He has been paid for talks on behalf of Gilead Sciences, Merck Sharp and Dohme, Pfizer, and Novartis. Other authors: no conflict Page 14 of 22

15 Reference List 1. Almirante, B., D. Rodriguez, B. J. Park, M. Cuenca-Estrella, A. M. Planes, M. Almela, J. Mensa, F. Sanchez, J. Ayats, M. Gimenez, P. Saballs, S. K. Fridkin, J. Morgan, J. L. Rodriguez-Tudela, D. W. Warnock, and A. Pahissa Epidemiology and predictors of mortality in cases of Candida bloodstream infection: results from population-based surveillance, barcelona, Spain, from 2002 to J.Clin.Microbiol. 43: Breiman L., Friedman J.H., Olshen J.H., and Stone C.G Classification and regression trees. Wadsworth International, Belmont. 3. Cestnik B., Kononenko I., and Bratko I ASSISTANT-86: a knowledge elicitation tool for sophisticated users, p In: Progress in Machine Learning. Sigma Press. 4. Holte, R. C Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning 11: Hosmer D.W. and Lemeshow S Applied logistic regression. Wiley Interscience, John Wiley & Sons, Inc, New York. 6. Laguna, F., J. L. Rodriguez-Tudela, J. V. Martinez-Suarez, R. Polo, E. Valencia, T. M. Diaz-Guerra, F. Dronda, and F. Pulido Patterns of fluconazole susceptibility in isolates from human immunodeficiency virusinfected patients with oropharyngeal candidiasis due to Candida albicans. Clin.Infect.Dis. 24: Larranaga, P., B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armananzas, G. Santafe, A. Perez, and V. Robles Machine learning in bioinformatics. Briefings in Bioinformatics 7: Pfaller, M. A. and D. J. Diekema Epidemiology of Invasive Candidiasis: a Persistent Public Health Problem. Clin.Microbiol.Rev. 20: Quinlan J.R C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. 10. Rex, J. H. and M. A. Pfaller Has antifungal susceptibility testing come of age? Clin.Infect.Dis. 35: Rodriguez-Tudela, J. L., M. C. Arendrup, F. Barchiesi, J. Bille, E. Chryssanthou, M. Cuenca-Estrella, E. Dannaoui, D. W. Denning, J. P. Donnelly, F. Dromer, W. Fegeler, C. Lass-Florl, C. Moore, M. Richardson, P. Sandven, A. Velegraki, and P. Verweij EUCAST Definitive Document EDef 7.1: method for the determination of broth dilution MICs of antifungal agents for fermentative yeasts. Clinical Microbiology and Infection 14: Page 15 of 22

16 Rodriguez-Tudela, J. L., B. Almirante, D. Rodriguez-Pardo, F. Laguna, J. P. Donnelly, J. W. Mouton, A. Pahissa, and M. Cuenca-Estrella Correlation of the MIC and Dose/MIC Ratio of Fluconazole to the Therapeutic Response of Patients with Mucosal Candidiasis and Candidemia. Antimicrob.Agents Chemother. 51: Sanders, C. C Arts Versus Asts - Where Are We Going. J.Antimicrob.Chemother. 28: Tudela, J. L. R., J. P. Donnelly, M. C. Arendrup, S. Arikan, F. Barchiesi, J. Bille, E. Chryssanthou, M. Cuenca-Estrella, E. Dannaoui, D. Denning, W. Fegeler, P. Gaustad, N. Klimko, C. Lass-Florl, C. Moore, M. Richardson, A. Schmalreck, J. Stenderup, A. Velegraki, and P. Verweij EUCAST Technical Note on fluconazole. Clinical Microbiology and Infection 14: Witten, I. H. and Frank E Data Mining: Practical machine learning tools and techniques. Elsevier, San Francisco. Downloaded from on November 11, 2018 by guest Page 16 of 22

17 Table 1. Sensitivity, specificity, false positive rate, area under the receiver operator curve (ROC) curve and Matthews Correlation Coefficient (MCC) for MIC value as a predictor of failures. MIC value for Classifiers failures in mg/l Sensitivity in % Specificity in % False positive rate in % Area under ROC curve MCC J48 > CART > OPC one R > Naïve Bayes > Simple Logistic > J48 NC CART > Candidemia one R NC Naïve Bayes > Simple Logistic NC J48 > CART > All one R > Naïve Bayes > Simple Logistic > NC: Not calculated. The classifier did not find a value splitting the populations of successes and failures Page 17 of 22

18 Table 2. Sensitivity, specificity, false positive rate, area under the receiver operator curve (ROC) and Matthews Correlation Coefficient (MCC) for Dose/MIC as a predictor of treatment success. Dose/MIC value for Classifiers success Sensitivity in % Specificity in % False positive rate in % Area under ROC curve MCC J48 >50.0* CART >75.0* OPC one R > Naïve Bayes > Simple Logistic > J48 NC CART > Candidemia one R NC Naïve Bayes > Simple Logistic NC J48 > CART > All one R > Naïve Bayes > Simple Logistic NC NC: Not calculated. The classifier did not find a value splitting the populations of successes and failures Page 18 of 22

19 *J48 and CART classifiers produce complex trees for Dose/MIC versus outcome (Figure 1 and 2). The initial value splitting the tree for successes and failures was 12.5 and respectively. Therefore, the table shows the Dose/MIC value predicting absence of failures. Page 19 of 22

20 Figure 1. CART tree of Dose/MIC versus outcome of patients with OPC (A), and CART tree for both datasets (OPC and Candidaemia) (B) Page 20 of 22

21 402 A DOSE_MIC <= Terminal Node 1 Class = Failure Cla ss Cases % Failure Success W = N = 75 Node 1 Clas s = Success DOSE_ MIC <= Class Cases % Failur e Succ ess W = N = 13 2 DOSE_MIC <= Terminal Node 2 Clas s = Success Class Cases % Failur e Succ ess W = N = 19 DOSE_MIC <= No de 3 Class = Succe ss DOSE_MIC <= Class Ca ses % Failu re Success W = N = 26 DOSE_MIC > Node 2 Clas s = Success DOSE_ MIC <= Class Cases % F ailur e Succ ess W = N = 57 DOSE_MIC > Te rmin al Node 3 Class = Success Class Cases % Failure Succes s W = 7.00 N = 7 DOSE_MIC > Ter minal Node 4 Class = Suc cess Class Cases % Failur e Suc cess W = N = 31 B DOSE_MIC <= Terminal Node 1 Class = Failure Class Cases % Failure Success W = N = 109 Node 1 Class = Success DOSE_MIC <= Class Cases % Failure Success W = N = 258 DOSE_MIC > Terminal Node 2 Class = Success Class Cases % Failure Success W = N = 149 Page 21 of 22