Access to optimal treatment of acute myeloid leukemia patients is affected by sociodemographic factors: a French population-based study.

Access to optimal treatment of acute myeloid leukemia patients is affected by sociodemographic factors: a French population-based study. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Access to optimal treatment of acute myeloid leukemia patients is affected by sociodemographic factors: a French population-based study. Kueshivi Midodji ATSOU, Bernard RACHET, Camille MARINGE, Edouard CORNET, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4968151/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background During their care pathway, AML patients not admitted to Specialized Hematology Units (SHU) have less access to curative treatment. We aim to determine whether access to optimal curative treatment is affected by sociodemographic factors. Methods We included 1,033 incidents AML-cases diagnosed between 2012–2016 from three French “départements”. We considered patients managed in reference hospitals SHU within 5 days(n = 297) received “gold-standard” treatment. Treatment was "curative-treatment” if intensive chemotherapy and “non-curative” otherwise. Firstly, we trained a Gradian Boosting Machine (GBM) algorithm on 80%(n = 238) of "gold-standard" cases to learn how they were treated and validated the model on the remaining 20%(n = 59). Next, GBM predictions were contrasted with actual treatment. Using multivariable logistic regression, we examined how non-optimal treatment (discrepancy between predicted curative and observed non-curative treatment) was associated with sociodemographic factors. Patients with predicted non-curative treatment were excluded as uninformative on access to curative treatment (n = 471). Results The rate of “curative treatment” was 84.8% (252/297) for gold-standard patients vs. 33.5% (247/736) for others. The three most influential predictive factors in gold-standard patients were age (68.3%-influence), t-AML/MDS (15.8%), and the AML-others subtypes (5.4%). A total of n = 102(9.9%) patients were in non-optimal treatments. Living in Basse-Normandie (0.65-times;95%CI [0.5,0.8]) and over 30minutes from a reference hospital were strongly associated with a non-optimal treatment. Conclusion There are geographical disparities in access to optimal treatment, potentially linked to medical desert situations or medical system organization which must be addressed. Acute Myeloid Leukemia Curative treatment Inequalities in treatment access Care pathway Socio-demographic factors Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 I. Background Acute Myeloid Leukemia (AML) is a very serious disease that affects the elderly ( 1 ). The last decades have seen considerable advances in the diagnosis and care of AML patients. These include an enhanced comprehension of the disease, resulting in improved classification, better cytogenetic risk profiling, the advent of new therapies for patients unfit for intensive chemotherapy and the introduction of therapy targeting mutations such as FLT3 or NPM1 ( 2 – 4 ). However, these advances have not fundamentally changed the induction phase of the intensive chemotherapy, which is still based on the 3 + 7 regimen (3 days of anthracyclines and 7 days of cytarabine), and remains the standard reference treatment for non-promyelocytic AML ( 1 – 6 ). The eligibility for the 3 + 7 chemotherapy, is complex, especially among elderly patients, as it results from a combination of the patient’s clinical and biological characteristics, but also influenced by their individual care pathway. In addition, the lack of standardization of treatment eligibility criteria means that the subjectivity of clinical assessment continues to play an important role ( 7 ), leading to persistent disparities in treatment. These disparities are influenced by potentially avoidable factors that need to be identified and their effects quantified ( 8 – 12 ) to allow all patients to be offered the best treatment. Although the five-year cancer survival of AML patients improved considerably between 1990 and 2015, this improvement varies unevenly between age groups. Patients under 60 gained at least 22% (up to 32%), which contrasts with an increase of less than 15% in older patients ( 13 ). These disparities in survival have been partially attributed to unequal access to intensive chemotherapy (curative treatment), especially among the elderly, who experienced a significantly higher incidence ( 14 ) but who are the least curatively treated ( 15 ). Furthermore, the positive impact of curative treatment is well established and aims at a complete remission ( 16 , 17 ), with largely improved survival compared with other therapeutic modalities ( 18 , 19 ), shown to be also beneficial in elderly patients ( 20 – 22 ). In our previous study, we showed that factors such as age or comorbidities played an upstream role, especially in access to specialized hematology units (SHU) and consequently in access to curative treatment. More specifically, patients not admitted to SHU had more limited access to diagnostic tools, resulting in a poorer assessment of the criteria used to decide their eligibility for curative treatment ( 23 ). It suggests that there may be a subjective interpretation of the patient's clinical condition, and therefore a difference in the treatment between SHU and non-SHU patients. This hypothesis has been reinforced by studies, including a recent survey of French hematologists ( 24 ), which highlighted the impact of practitioners’ subjective assessment in the choice of treatment modalities and in particular access to transplantation, which remains the main treatment ( 25 , 26 ). Based on these observations, we argue that this could also explain the observed difference in access to curative treatment according to age. In the literature, studies describing the role of the non-standardization of treatment eligibility criteria and comparing the type of AML patients' treatment are quite rare. To this end, we conducted a population-based study to answer the questions left open in the previous article, namely: (i) what is the influence and the contribution of clinico-biological criteria in the choice of therapeutic modalities in SHU and non-SHU patients; (ii) how treatment differs between SHU and non-SHU patients; (ii) whether sociodemographic factors are associated with treatment. II. Methods 2.1 AML cohort We included all AML subtypes in patients of all ages (0 to 101) with recorded treatment modalities (n = 1033/1039). Patients represent all AML incident cases diagnosed between 2012 and 2016 in the three French “départements” covered by population-based registries, specialized in hematological malignancy (Basse-Normandie, Gironde and Côte d’Or, covering a population of 3 625 400 people). The “départements” in which cases were diagnosed (and registered) correspond to administrative subdivisions of the French territory as defined by INSEE (French National Institute for Statistics and Economic Studies). The patient’s town of residence was classified as rural or urban according to the INSEE classification of towns ( 27 ). University centers and anti-cancer centers, in which patients were treated, were defined as reference hospitals (one anti-cancer center and one university center per “département”). 2.2 Tumor and patient characteristics We grouped AML cases into 6 subtypes based on the ICD-0-3 morphology codes: ( 1 ) AML with Recurrent Cytogenetic Abnormalities (AML-RCA: 9865-3, 9869-3, 9871-3, 9896-3, 9897-3, 9898-3, 9877-3); ( 2 ) Acute Promyelocytic Leukemia (PML-RARA:9866-3); ( 3 ) AML with Multilineage-Related Changes (AML-MRC: 9895-3, 9984-3); ( 4 ) treatment-related AML/Myelodysplasia Syndrome (t-AML/MDS : 9920-3, 9987-3); ( 5 ) AML Not-Otherwise Specified (AML-NOS: (9861-3) and ( 6 ) AML-others (9931-3, 9805-3, 9806-3, 9808-3, 9809-3, 9807-3, 9872-3, 9873-3, 9874-3, 9867-3, 9891-3, 9840-3, 9910-3, 9870-3, 9931-3, 9930-3). To approximate patients' socio-economic level and their degree of comorbidity, we used the quintiles of the European Deprivation Index (EDI) ( 28 ) and the Charlson Comorbidity Index ( 29 ), respectively. The EDI quintiles range from the least (EDI = 1) to the most deprived (EDI = 5). The Charlson Comorbidity Index (CCI) was divided into 3 categories (no comorbidity for CCI = 0, low-mild comorbidity for CCI between 1 and 3, and severe comorbidities for patients with CCI > 3). Patients were categorized according to the cytogenetic and biomolecular prognostic score published in the ELN 2017 guidelines (Favorable, Intermediate and Adverse) ( 3 , 30 ). 2.3 Treatment modalities The different treatment modalities for AML patients include intensive chemotherapy, non-intensive chemotherapy treatment, supportive care, abstention from treatment or refusal of treatment. Intensive chemotherapy was defined as first-line treatment with a 3 + 7 protocol (3 days of anthracyclines and 7 days of cytarabine) in combination or not with molecules targeting key cytogenetic mutations. For patients diagnosed with PML-RARA, intensive chemotherapy is based on All-trans Retinoic Acid (ATRA) with trioxide. Non-intensive chemotherapy refers to patients treated with drugs such as Aracytin or Vidaza ( 3 , 7 , 29 ). For this study, patients were grouped into two treatment modalities: “curative treatment” for intensive chemotherapy and “non-curative treatment” for any other treatment modalities. We also assumed that the 297 patients treated in reference hospitals by hematologists within 5 days of diagnosis had received the best possible care, curative or not, and were considered as ‘gold-standard patients’, the others being ‘non-gold standard patients’ (N = 736) (Fig. 1). For all patients, the date of diagnosis corresponds to the date of collection of the biological sample that was used to establish the diagnosis. 2.4 Identification of patients with optimal treatment and modelling of the associated effect of sociodemographic factors This modelling was carried out in three stages. The first step was to develop a predictive model using machine learning techniques. Next, this model was used to identify patients receiving ‘optimal’ and ‘non-optimal’ treatment. Finally, we used a regression model to determine the socio-demographic factors associated with not receiving optimal curative treatment (Fig. 2). Step1 – Building a predictive model for receipt of treatment in gold-standard patients We first aimed to identify the clinico-biological characteristics that predict curative treatment. To achieve this, we used data from gold-standard patients only, 80% being used to train various machine learning models (training data) and 20% to validate the final models (test data). Three machine learning algorithms (GLM: Generalized Linear Model, GBM: Gradian Boosted Model and RF: Random Forest) were employed. To maximize the performance of the training phase, a 10-fold cross-validation sampling was applied during this phase. The variables initially included to train the machine learning models were patient’s age at diagnosis, comorbidities and biological characteristics of the AML (AML subtype, secondary AML profile and cytogenetic prognosis). The performance of the final models was then evaluated on the test data based on model accuracy, i.e. the proportion of patients for whom treatment was correctly predicted by the model. Step 2 – Predicting receipt of optimal treatment We retain the predictions of the best performing model (GBM) obtain among the gold-standard patients and then apply it to the remaining patients, i.e. the non-gold standard patients, to predict the treatment they would have received, had they been seen by a hematologist within five days of diagnosis. We then compared the predictions with the actual treatment received for both gold and non-gold standard patients. We focused on two groups of patients for the next step of the analysis by defining a binary outcome. (i) “Optimal treatment” (outcome = 1): those with both predicted and actual curative treatment and (ii) “non-optimal treatment” (outcome = 0) those predicted to receive curative treatment but who underwent non-curative treatment. The remaining 471 patients predicted to receive non-curative treatment were excluded for the next step because they did not provide any additional information to help understand the observed disparities in receiving optimal treatment. Step 3 – socio-demographic factors associated with receipt of optimal treatment We then applied a logistic regression model to estimate the association between the probability of receiving optimal treatment (as defined in step 2) and the following sociodemographic characteristics: sex, “département” of diagnosis, EDI quintile, distance and travel time between town of residence and the nearest reference hospital, and type of residence (urban or rural). We estimated confidence intervals using a 1,000 bootstrap repetitions of the analyses (bootstrap of step 3 after each step 1 and 2) to account for the uncertainty in the outcome, derived from model-based predictions (step 2). We then tested the normality of the bootstrapped distribution of the logistic coefficients from which we derived the 95% confidence interval and its associated p-value (Fig. 2). All statistical analyses were performed using RStudio version 4.2.2 (2022-10-31 ucrt). The machine learning models were built using the “caret” packages version 6.0–93. III. Results 3.1 Description of gold and non-gold standard groups of patients A total of 85% (252/297) of gold standard patients received curative treatment compared to 34% (247/736) of non-gold standard patients (Table 1 ). Gold standard patients who received curative treatment were younger (median age at 49 vs. 59 years), however, with a wider age range (1–95 years vs. 1–82 years in non-gold standards). More men were treated curatively than women in non-gold-standard patients (38% vs. 29%), a disparity which was not seen in gold standard patients. There was no difference in curative treatment in both groups of patients by social deprivation or by commune of residence. The proportion of patients receiving curative treatment was low in Basse-Normandie (183/473 or 38.7%), and higher in Gironde (225/413 or 54.5%) and Côte-d'Or (91/147 or 61.9%). A similar pattern was seen in both groups of patients. Table 1 Patient’s characteristics according to treatment Gold-standard patients Non-gold standard patients Characteristic Overall, N = 297 1 Non-curative treatment, N = 45 1 Curative treatment, N = 252 1 p-value 2 Overall, N = 736 1 Non-curative treatment, N = 489 1 Curative treatment, N = 247 1 p-value 2 Age at diagnosis 53 (1, 91) 75 (49, 83) 49 (1, 91) < 0.001 72 (1, 95) 77 (1, 95) 59 (1, 82) 0.9 0.008 Men 155 (52%) 24 (53%) 131 (52%) 371 (50%) 229 (47%) 142 (57%) Women 142 (48%) 21 (47%) 121 (48%) 365 (50%) 260 (53%) 105 (43%) EDI quintile > 0.9 0.2 1 (least deprived) 44 (15%) 7 (16%) 37 (15%) 118 (16%) 70 (14%) 48 (19%) 2 54 (18%) 10 (22%) 44 (17%) 123 (17%) 76 (16%) 47 (19%) 3 66 (22%) 10 (22%) 56 (22%) 158 (21%) 106 (22%) 52 (21%) 4 84 (28%) 11 (24%) 73 (29%) 197 (27%) 140 (29%) 57 (23%) 5 (Most deprived) 49 (16%) 7 (16%) 42 (17%) 140 (19%) 97 (20%) 43 (17%) City of residence 0.3 0.3 Rural 86 (29%) 16 (36%) 70 (28%) 185 (25%) 117 (24%) 68 (28%) Urban 210 (71%) 29 (64%) 181 (72%) 549 (75%) 370 (76%) 179 (72%) Unknown 1 0 1 2 2 0 Diagnostic department < 0.001 < 0.001 Basse-Normandie 142 (48%) 35 (78%) 107 (42%) 331 (45%) 255 (52%) 76 (31%) Côte-d'Or 55 (19%) 5 (11%) 50 (20%) 92 (12%) 51 (10%) 41 (17%) Gironde 100 (34%) 5 (11%) 95 (38%) 313 (43%) 183 (37%) 130 (53%) Charlson comorbidities index < 0.001 < 0.001 No comorbidities 198 (67%) 15 (33%) 183 (73%) 299 (41%) 164 (34%) 135 (55%) Low-mild comorbidities 74 (25%) 18 (40%) 56 (22%) 293 (40%) 203 (42%) 90 (36%) Severe comorbidities 25 (8.4%) 12 (27%) 13 (5.2%) 144 (20%) 122 (25%) 22 (8.9%) AML subtype < 0.001 < 0.001 AML-MRC 12 (4.0%) 5 (11%) 7 (2.8%) 108 (15%) 72 (15%) 36 (15%) AML-NOS 11 (3.7%) 5 (11%) 6 (2.4%) 117 (16%) 100 (20%) 17 (6.9%) AML-RCA 36 (12%) 2 (4.4%) 34 (13%) 27 (3.7%) 8 (1.6%) 19 (7.7%) AML others 161 (54%) 12 (27%) 149 (59%) 264 (36%) 131 (27%) 133 (54%) PML-RARA 39 (13%) 0 (0%) 39 (15%) 9 (1.2%) 2 (0.4%) 7 (2.8%) Therapy related AML/MDS 38 (13%) 21 (47%) 17 (6.7%) 211 (29%) 176 (36%) 35 (14%) AML type < 0.001 < 0.001 de novo AML 252 (85%) 24 (53%) 228 (90%) 509 (69%) 303 (62%) 206 (83%) t-AML 33 (11%) 9 (20%) 24 (9.5%) 92 (12%) 68 (14%) 24 (9.7%) t-MDS 12 (4.0%) 12 (27%) 0 (0%) 135 (18%) 118 (24%) 17 (6.9%) Cytogenetic initial prognosis < 0.001 < 0.001 Favorable 96 (32%) 2 (4.4%) 94 (37%) 59 (8.0%) 9 (1.8%) 50 (20%) Intermediate 106 (36%) 21 (47%) 85 (34%) 315 (43%) 190 (39%) 125 (51%) Adverse 83 (28%) 14 (31%) 69 (27%) 145 (20%) 88 (18%) 57 (23%) Missing (Karyotype/ FISH not done) 12 (4.0%) 8 (18%) 4 (1.6%) 217 (29%) 202 (41%) 15 (6.1%) Distance city & academic hospital (kms) 0.021 0.018 [0,10) 88 (30%) 6 (13%) 82 (33%) 175 (24%) 112 (23%) 63 (26%) [10,30) 59 (20%) 10 (22%) 49 (20%) 158 (22%) 96 (20%) 62 (25%) [30,50) 38 (13%) 4 (8.9%) 34 (14%) 99 (13%) 62 (13%) 37 (15%) [50,100) 63 (21%) 12 (27%) 51 (20%) 190 (26%) 129 (26%) 61 (25%) > 100 48 (16%) 13 (29%) 35 (14%) 112 (15%) 88 (18%) 24 (9.7%) Unknown 1 0 1 2 2 0 Travel time in minute 0.072 0.13 [0,15) 79 (27%) 8 (18%) 71 (28%) 146 (20%) 94 (19%) 52 (21%) [15,30) 68 (23%) 8 (18%) 60 (24%) 171 (23%) 104 (21%) 67 (27%) [30,60) 77 (26%) 11 (24%) 66 (26%) 229 (31%) 153 (31%) 76 (31%) > 60 72 (24%) 18 (40%) 54 (22%) 188 (26%) 136 (28%) 52 (21%) Unknown 1 0 1 2 2 0 Survival time (days) 1012 (2, 3050) 249 (2, 2425) 1289 (3, 3050) < 0.001 199 (0, 3223) 90 (0, 3223) 782 (1, 2917) < 0.001 1 n (%); Median (Range) 2 Fisher's Exact Test for Count Data; Wilcoxon rank sum test; Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates) Higher proportion of gold-standard patients had no comorbidities (67% vs. 41% in non-gold-standard). Furthermore, 52% (13/25) of gold standard patients with severe comorbidities had received curative treatment, contrasting with 15% (22/144) in non-gold standard patients (Table 1 ). AML-others was the most represented sub-type, overall and in both groups of patients. All patients diagnosed with PML-RARA (n = 39) were treated among gold-standard patients, while 2 of the 9 non-gold-standard patients with the same subtypes were not treated. In total, 85% (n = 252/297) of gold-standard patients had de novo AML vs 69% (n = 509/736). All gold-standard t-MDS patients were not treated, while 13% (17/135) received curative treatment among non-gold standard patients. The proportion of cytogenetic risk was favorable, intermediate and adverse in 32%, 36% and 28%, respectively, among gold-standard patients (vs 8%, 43% and 20% in non-gold-standard patients). A total of 83% of patients with an adverse cytogenetic risk had received curative treatment in gold-standard patients (vs. 39% in non-gold patients) (Table 1 ). Finally, the proportion of curative treatment decreased, overall and in both groups of patients, as the distance or travel time between the town of residence and the nearest reference hospital increased (Table 1 ). 3.2 Performance of the machine learning predictive model The GBM algorithm was the most accurate when used to predict the treatment modalities on the test data, with an accuracy of 91.5% (ROC = 0.97, Sensitivity = 1, Specificity = 0.91). The performances of the RF and GLM algorithms were poorer, with accuracies of 89.8% (ROC = 0.95, Sensitivity = 0.7, Specificity = 0.97) and 86.4% (ROC = 0.93, Sensitivity = 0.78, Specificity = 0.96), respectively (Fig. 3/ Appendix-Table 5). 3.3 Relative importance of patient and tumor characteristics in receiving curative treatment Among gold-standard patients, age was the most important factor influencing the delivery of curative treatment with a relative influence (RI) of 68.3%. The other three most influential factors were respectively the presence of t-AML/MDS (15.8%), AML-others (5.4%) and PML-RARA (RI = 3.4%) subtypes. All other clinical and biological factors used to train the GBM model had a relative influence below 2% (Table 2 ). We also calculated the ICEs (Individual Conditional Expectations) to measure the effect of each age level on the prediction of treatment outcome adjusted on the average effect of the remaining predictors (Fig. 3). Age influence was null or minimal in patients under 50, moderate between 50 and 75, and very strong above 75. Table 2 Relative influence of patient clinical characteristics in the predictive model (GBM) Clinic -biologic characteristic Relative influence (RI) Age at diagnosis 68.35 Therapy related AML/MDS subtype 15.80 AML others subtype 5.37 PML-RARA subtype 3.36 t-AML 1.94 Severe comorbidities 1.86 Low-mild comorbidities 1.85 Adverse Cytogenetic prognosis 0.68 Intermediate Cytogenetic prognosis 0.57 AML-RCA subtype 0.22 AML-NOS subtype 0.00 t-MDS 0.00 Karyotype/FISH not done (Missing Cytogenetic initial prognosis) 0.00 We trained and tested a GBM model specifically in the non-gold standard patients and modelled the importance of their most predictive factor. According to this model, age was also the most important predictive factor. However, the importance given to each age level was globally higher than that observed among gold-standard patients (Fig. 4). 3.4 Optimal vs. non-optimal treatment In total, we identified 460 patients who received optimal (curative) treatment and 102 patients with non-optimal treatment. Curative treatment receipt differed only according to the “département” of residence ( p = 0.011) and was reduced with increasing distance ( p = 0.008) and travel time ( p = 0.007) between the municipality of residence and the nearest reference hospital (Table 3 ). Table 3 Treatment quality description and the associated sociodemographic factors Descriptive Univariate Multivariate Characteristic Overall, N = 562 1 Non-optimal treatment, N = 102 1 Optimal treatment, N = 460 1 p-value 2 OR 3 95% CI 3 p-value OR 3 95% CI 3 p-value Sex > 0.9 0.90 — Men 311 (55%) 57 (56%) 254 (55%) — — 1.0 1, 1 Women 251 (45%) 45 (44%) 206 (45%) 1.03 0.67, 1.59 0.96 0.78, 1.17 0.7 Diagnostic department 0.011 0.007 — Basse-Normandie 237 (42%) 56 (55%) 181 (39%) — — 0.61 0.49, 0.77 < 0.001 Côte-d'Or 80 (14%) 8 (7.8%) 72 (16%) 2.78 1.33, 6.58 1.6 1.15, 2.22 < 0.001 Gironde 245 (44%) 38 (37%) 207 (45%) 1.69 1.07, 2.68 1.0 0.83, 1.27 0.7 EDI quintile 0.2 0.21 — 1 99 (18%) 20 (20%) 79 (17%) — — 1.0 1, 1 2 108 (19%) 24 (24%) 84 (18%) 0.89 0.45, 1.73 1.1 0.92, 1.42 0.14 3 124 (22%) 23 (23%) 101 (22%) 1.11 0.57, 2.17 1.3 0.89, 1.75 0.2 4 136 (24%) 16 (16%) 120 (26%) 1.90 0.93, 3.93 2.0 1.5, 2.79 < 0.001 5 95 (17%) 19 (19%) 76 (17%) 1.01 0.50, 2.05 1.1 0.88, 1.4 0.5 Travel time in minute 0.007 0.007 — [0,15) 122 (22%) 13 (13%) 109 (24%) — — 1.0 1, 1 [15,30) 138 (25%) 19 (19%) 119 (26%) 0.75 0.35, 1.57 0.93 0.61, 1.42 0.8 [30,60) 173 (31%) 42 (42%) 131 (29%) 0.37 0.18, 0.71 0.46 0.35, 0.61 60 127 (23%) 27 (27%) 100 (22%) 0.44 0.21, 0.89 0.59 0.44, 0.79 < 0.001 Unknown 2 1 1 Distance city & academic hospital (kms) 0.008 0.010 [0,10) 148 (26%) 15 (15%) 133 (29%) — — [10,30) 122 (22%) 19 (19%) 103 (22%) 0.61 0.29, 1.26 [30,50) 77 (14%) 15 (15%) 62 (14%) 0.47 0.21, 1.02 [50,100) 135 (24%) 32 (32%) 103 (22%) 0.36 0.18, 0.70 > 100 78 (14%) 20 (20%) 58 (13%) 0.33 0.15, 0.68 Unknown 2 1 1 Town type 0.093 0.094 Rural 166 (30%) 37 (37%) 129 (28%) — — Urban 394 (70%) 64 (63%) 330 (72%) 1.48 0.93, 2.32 Unknown 2 1 1 1 n (%); Median (IQR) 2 Fisher's Exact Test for Count Data; Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates); Wilcoxon rank sum test 3 OR = Odds Ratio, CI = Confidence Interval After adjusted for all sociodemographic covariables, patients living in Côte-d'Or had 1.6 (95%CI 1.15–2.22) times the odds of receiving optimal treatment compared to the adjusted grand mean, whereas the ORs were 1.00 (95%CI 0.83–1.27) and 0.61 (95%CI 0.49–0.77) in patients living in Gironde and Basse-Normandie, respectively. Similarly, patients living in a community more than 30 minutes from a reference hospital were fewer to receive optimal treatment compared to those living closer. Delivery of optimal treatment was comparable between the different levels of deprivation, except for EDI = 4 (OR = 2.0, 95%CI 1.5–2.8) (Table 3 ). We then examined to which extent more patients could have been treated under optimal circumstances: we derived and contrasted model-based marginal predictions for each variable level. We found that, on average, 12.5% (95% CI + 3.4% +21.6%; p = 0.007) more patients in Basse-Normandie would have received optimal treatment if they had lived in Côte-d'Or (+ 7.7% if they had lived in Gironde). In other words, of the 236 patients in Basse-Normandie for whom we examined the type of treatment, 29 additional patients would have received curative treatment if they had lived in Côte-d'Or (Table 4). Likewise, + 12.2% of patients living between 30 to 60 minutes from a reference hospital would have been treated if they were less than 15 minutes away. IV. Discussion Main line The non-standardization of therapeutic eligibility guidelines is a major factor in unequal access to curative treatment, particularly for patients medically less fit, for whom decision criteria are not clear and depend very subjectively on the attending physician ( 24 ). We described that age; the secondary aspect and the AML subtype are major eligibility criteria for curative treatment in patients seen by hematologists within 5 days of their diagnosis. We highlighted that geographical variability (between and within “départements”, such as distance to reference hospitals) are strong factors leading to unequal access to curative treatment. Variable’s importance In the treatment of AML patients, age is the most important factor, and young patients are treated almost systematically, regardless of their comorbidity and cytogenetic prognosis. However, as age increases, cytogenetic risk, co-morbidities ( 31 , 32 ) and social frailty ( 33 ) are more likely to be worse ( 34 , 35 ). These frailty conditions strongly impact on the age limit at which patients can be treated. Therefore, there is a subjective impact of the physician personal experience and the practices of the hospital in the choice of therapeutic modalities for the elderly patients ( 24 – 26 , 36 , 37 ). In this study, we showed that in academic SHU, the age of patients receiving curative treatments was extended to the maximum limit (91 years vs. 82). After adjusting for other predictive factors, we compared the impact of age in the two groups of our population (gold vs. non-gold standard patients) and found that the effect of age was highly significant in the non-gold standard patients (Fig. 4). These findings suggest that for patients admitted to non-SHU units, chronological age is used to assess eligibility to treatment, whereas in SHU, biological age (e.g. age coupled with clinical status) is considered instead. We suggest that these results should guide non-SHU units to look beyond age when treating patients. Impact of sociodemographic factors The main hypothesis in this study was that regardless of their’ sociodemographic conditions, patients should have access to the same therapeutic options if their clinical and biological characteristics were equivalent. However, we showed that reception of optimal treatment depends on geographical location. More specifically, approximately 12% and 7% more patients from Basse-Normandie could have received curative treatment if they had lived in Côte-d’Or or Gironde, respectively. A few hypotheses can be formulated in the light of our results. Given the younger age of patients treated in Basse-Normandie (53 years) compared to Gironde (56) and Côte-d'Or (57), we suspected the influence of non-standardized practices (Table 7-Appendix). We therefore hypothesize a strong age-based selectivity of patients treated by non-hematologists, especially in Basse-Normandie( 20 ). In addition, the access rate to cytogenetic analysis (60% of patients without a karyotype) and the median time between diagnosis and treatment in Basse-Normandie were smaller (2 days) than in Côte-d'Or and Gironde (7% and 31%, 3 and 5 days, respectively). These results support a differential approach to patient management in Basse-Normandie, potentially based on a strict and rapid selection of patients to be treated, particularly based on age. In contrast, the other “départements” take the time to carry out a more precise clinical and biological assessment (karyotype and cytogenetics) to treat as many patients as possible. An earlier study comparing the outcomes of non-Hodgkin's lymphoma in the same three "départements”" highlighted a lower cancer survival for patients residing in Basse-Normandie, potentially reflecting similar regional patient management approach ( 38 ). The presence of medical deserts, in addition to the larger size of the Basse-Normandie “département” (17,589 km 2 vs 10,000 km 2 and 8,763 km 2 for Gironde and Côte-d'Or, respectively) may also explain the role of distance and travel time between patient’s residence and reference hospitals in the observed disparities in optimal treatment, since both the distance and travel time are significantly greater in Basse-Normandie. These bigger distances and travel time may contribute to referring the patient to the nearest hospital rather than to the reference hospital. To complement these points, we examined the Potential Localized Accessibility (PLA), a measure of accessibility to general practitioners, for each patient’s city of residence ( 39 – 42 ) across the three “départements”. Patients in Basse-Normandie lived in areas where access to primary care was the poorest (Table 7-Appendix). Based on these different arguments, we argue that the disparities in curative treatment could be the result of the non-standardization of clinical practices, particularly in non-reference hospitals, which was worsened by the policy of rapid care and screening in Basse-Normandie, and the geographical disparities in health care provision. Study limitations and strengths The major limitation of this study is the small number of patients (N = 297) on which the machine learning algorithms were trained, which may have prevented the algorithm from identifying some of the rarer features as important ( 43 ). We minimized the overfitting bias by focusing on feature classes with at least 10 patients. We are continuing to collect data on gold-standard patients in French reference hospitals to improve our predictive model. However, our study included all patients diagnosed in the three selected “départements”, as in the real world. Another strength is that we were able to identify and measure modifiable factors that affect the type of treatment received by patients. V. Conclusion There are geographical disparities in receipt of optimal treatment. We have linked these differences to the quality of care and medical desert situations and to different institutional policies concerning access to curative treatments. However, policies and guidelines aimed at implementing standardized clinical practices, replicating what is performed in specialized care facilities, could greatly reduce the discrepancies observed in treatment. Finally, an assessment of the impact of the quality of treatment on complete remission and cancer survival in AML patients under real conditions would provide a convincing argument for standardizing institutional policies for treating these patients. Declarations Ethics approval and consent to participate: This study was authorised by the CNIL (Commission Nationale Informatique & Libertés) and received a favourable opinion from the ethics committee of the CESRESS (Comité d'Éthique et Scientifique pour les Recherches, les études et Évaluations dans le domaine de Santé) under the reference number MLD/CBO/AR2111097. Availability of data and materials’: The data sets reported in this study are available on reasonable request from the corresponding author. Competing interests: The authors declare no competing financial interests. Funding: This study was supported by research funding from Fonds Européen de développement regional (FEDER: programme opérationnel FEDER-FSE Bourgogne 2014-2020) and from Institut National du Cancer (Projet INCa-SHS-ESP, n°2018-124) Authors’ Contributions: MM designed and supervised the execution of the S-LAM project. SG, SGB, HR, LB and JMP collected the data. 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IRDES [Internet]. 2016. Radke J, Mu L. Spatial Decompositions, Modeling and Mapping Service Regions to Predict Access to Social Programs. Geographic Inform Sci 1 déc. 2000;6(2):105–12. Higgs G, Zahnow R, Corcoran J, Langford M, Fry R. Modelling spatial access to General Practitioner surgeries: Does public transport availability matter? J Transp Health sept. 2017;6:143–54. Clemmensen LH, Kjærsgaard RD. Data Representativity for Machine Learning and AI Systems. arXiv preprint arXiv:220304706. 2022. Additional Declarations No competing interests reported. Supplementary Files 4SupplementarymaterialIJEH.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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01:19:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3621009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4968151/v1/93ec269d-1967-49cf-9d82-3825d962d18f.pdf"},{"id":65305189,"identity":"1ae95987-ff12-4b5b-b7f1-7c0e1841864f","added_by":"auto","created_at":"2024-09-26 01:03:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26669,"visible":true,"origin":"","legend":"","description":"","filename":"4SupplementarymaterialIJEH.docx","url":"https://assets-eu.researchsquare.com/files/rs-4968151/v1/2f51bb94f9515441f6be16fa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAccess to optimal treatment of acute myeloid leukemia patients is affected by sociodemographic factors: a French population-based study.\u003c/p\u003e","fulltext":[{"header":"I. Background","content":"\u003cp\u003eAcute Myeloid Leukemia (AML) is a very serious disease that affects the elderly (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The last decades have seen considerable advances in the diagnosis and care of AML patients. These include an enhanced comprehension of the disease, resulting in improved classification, better cytogenetic risk profiling, the advent of new therapies for patients unfit for intensive chemotherapy and the introduction of therapy targeting mutations such as FLT3 or NPM1 (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, these advances have not fundamentally changed the induction phase of the intensive chemotherapy, which is still based on the 3\u0026thinsp;+\u0026thinsp;7 regimen (3 days of anthracyclines and 7 days of cytarabine), and remains the standard reference treatment for non-promyelocytic AML (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The eligibility for the 3\u0026thinsp;+\u0026thinsp;7 chemotherapy, is complex, especially among elderly patients, as it results from a combination of the patient\u0026rsquo;s clinical and biological characteristics, but also influenced by their individual care pathway. In addition, the lack of standardization of treatment eligibility criteria means that the subjectivity of clinical assessment continues to play an important role (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), leading to persistent disparities in treatment. These disparities are influenced by potentially avoidable factors that need to be identified and their effects quantified (\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) to allow all patients to be offered the best treatment.\u003c/p\u003e \u003cp\u003eAlthough the five-year cancer survival of AML patients improved considerably between 1990 and 2015, this improvement varies unevenly between age groups. Patients under 60 gained at least 22% (up to 32%), which contrasts with an increase of less than 15% in older patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These disparities in survival have been partially attributed to unequal access to intensive chemotherapy (curative treatment), especially among the elderly, who experienced a significantly higher incidence (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) but who are the least curatively treated (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Furthermore, the positive impact of curative treatment is well established and aims at a complete remission (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), with largely improved survival compared with other therapeutic modalities (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), shown to be also beneficial in elderly patients (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our previous study, we showed that factors such as age or comorbidities played an upstream role, especially in access to specialized hematology units (SHU) and consequently in access to curative treatment. More specifically, patients not admitted to SHU had more limited access to diagnostic tools, resulting in a poorer assessment of the criteria used to decide their eligibility for curative treatment (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). It suggests that there may be a subjective interpretation of the patient's clinical condition, and therefore a difference in the treatment between SHU and non-SHU patients. This hypothesis has been reinforced by studies, including a recent survey of French hematologists (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), which highlighted the impact of practitioners\u0026rsquo; subjective assessment in the choice of treatment modalities and in particular access to transplantation, which remains the main treatment (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Based on these observations, we argue that this could also explain the observed difference in access to curative treatment according to age. In the literature, studies describing the role of the non-standardization of treatment eligibility criteria and comparing the type of AML patients' treatment are quite rare.\u003c/p\u003e \u003cp\u003eTo this end, we conducted a population-based study to answer the questions left open in the previous article, namely: (i) what is the influence and the contribution of clinico-biological criteria in the choice of therapeutic modalities in SHU and non-SHU patients; (ii) how treatment differs between SHU and non-SHU patients; (ii) whether sociodemographic factors are associated with treatment.\u003c/p\u003e"},{"header":"II. Methods","content":"\u003cp\u003e2.1 AML cohort\u003c/p\u003e\u003cp\u003eWe included all AML subtypes in patients of all ages (0 to 101) with recorded treatment modalities (n\u0026thinsp;=\u0026thinsp;1033/1039). Patients represent all AML incident cases diagnosed between 2012 and 2016 in the three French \u0026ldquo;d\u0026eacute;partements\u0026rdquo; covered by population-based registries, specialized in hematological malignancy (Basse-Normandie, Gironde and C\u0026ocirc;te d\u0026rsquo;Or, covering a population of 3 625 400 people). The \u0026ldquo;d\u0026eacute;partements\u0026rdquo; in which cases were diagnosed (and registered) correspond to administrative subdivisions of the French territory as defined by INSEE (French National Institute for Statistics and Economic Studies). The patient\u0026rsquo;s town of residence was classified as rural or urban according to the INSEE classification of towns (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). University centers and anti-cancer centers, in which patients were treated, were defined as reference hospitals (one anti-cancer center and one university center per \u0026ldquo;d\u0026eacute;partement\u0026rdquo;).\u003c/p\u003e \u003cp\u003e2.2 Tumor and patient characteristics\u003c/p\u003e \u003cp\u003eWe grouped AML cases into 6 subtypes based on the ICD-0-3 morphology codes: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) AML with Recurrent Cytogenetic Abnormalities (AML-RCA: 9865-3, 9869-3, 9871-3, 9896-3, 9897-3, 9898-3, 9877-3); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Acute Promyelocytic Leukemia (PML-RARA:9866-3); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) AML with Multilineage-Related Changes (AML-MRC: 9895-3, 9984-3); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) treatment-related AML/Myelodysplasia Syndrome (t-AML/MDS : 9920-3, 9987-3); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) AML Not-Otherwise Specified (AML-NOS: (9861-3) and (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) AML-others (9931-3, 9805-3, 9806-3, 9808-3, 9809-3, 9807-3, 9872-3, 9873-3, 9874-3, 9867-3, 9891-3, 9840-3, 9910-3, 9870-3, 9931-3, 9930-3).\u003c/p\u003e \u003cp\u003eTo approximate patients' socio-economic level and their degree of comorbidity, we used the quintiles of the European Deprivation Index (EDI) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and the Charlson Comorbidity Index (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), respectively. The EDI quintiles range from the least (EDI\u0026thinsp;=\u0026thinsp;1) to the most deprived (EDI\u0026thinsp;=\u0026thinsp;5). The Charlson Comorbidity Index (CCI) was divided into 3 categories (no comorbidity for CCI\u0026thinsp;=\u0026thinsp;0, low-mild comorbidity for CCI between 1 and 3, and severe comorbidities for patients with CCI\u0026thinsp;\u0026gt;\u0026thinsp;3). Patients were categorized according to the cytogenetic and biomolecular prognostic score published in the ELN 2017 guidelines (Favorable, Intermediate and Adverse) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e2.3 Treatment modalities\u003c/p\u003e \u003cp\u003eThe different treatment modalities for AML patients include intensive chemotherapy, non-intensive chemotherapy treatment, supportive care, abstention from treatment or refusal of treatment. Intensive chemotherapy was defined as first-line treatment with a 3\u0026thinsp;+\u0026thinsp;7 protocol (3 days of anthracyclines and 7 days of cytarabine) in combination or not with molecules targeting key cytogenetic mutations. For patients diagnosed with PML-RARA, intensive chemotherapy is based on All-trans Retinoic Acid (ATRA) with trioxide. Non-intensive chemotherapy refers to patients treated with drugs such as Aracytin or Vidaza (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). For this study, patients were grouped into two treatment modalities: \u0026ldquo;curative treatment\u0026rdquo; for intensive chemotherapy and \u0026ldquo;non-curative treatment\u0026rdquo; for any other treatment modalities.\u003c/p\u003e \u003cp\u003eWe also assumed that the 297 patients treated in reference hospitals by hematologists within 5 days of diagnosis had received the best possible care, curative or not, and were considered as \u0026lsquo;gold-standard patients\u0026rsquo;, the others being \u0026lsquo;non-gold standard patients\u0026rsquo; (N\u0026thinsp;=\u0026thinsp;736) (Fig.\u0026nbsp;1). For all patients, the date of diagnosis corresponds to the date of collection of the biological sample that was used to establish the diagnosis.\u003c/p\u003e\u003cp\u003e2.4 Identification of patients with optimal treatment and modelling of the associated effect of sociodemographic factors\u003c/p\u003e \u003cp\u003eThis modelling was carried out in three stages. The first step was to develop a predictive model using machine learning techniques. Next, this model was used to identify patients receiving \u0026lsquo;optimal\u0026rsquo; and \u0026lsquo;non-optimal\u0026rsquo; treatment. Finally, we used a regression model to determine the socio-demographic factors associated with not receiving optimal curative treatment (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eStep1 \u0026ndash; Building a predictive model for receipt of treatment in gold-standard patients\u003c/p\u003e \u003cp\u003eWe first aimed to identify the clinico-biological characteristics that predict curative treatment. To achieve this, we used data from gold-standard patients only, 80% being used to train various machine learning models (training data) and 20% to validate the final models (test data). Three machine learning algorithms (GLM: Generalized Linear Model, GBM: Gradian Boosted Model and RF: Random Forest) were employed. To maximize the performance of the training phase, a 10-fold cross-validation sampling was applied during this phase. The variables initially included to train the machine learning models were patient\u0026rsquo;s age at diagnosis, comorbidities and biological characteristics of the AML (AML subtype, secondary AML profile and cytogenetic prognosis). The performance of the final models was then evaluated on the test data based on model accuracy, i.e. the proportion of patients for whom treatment was correctly predicted by the model.\u003c/p\u003e \u003cp\u003eStep 2 \u0026ndash; Predicting receipt of optimal treatment\u003c/p\u003e \u003cp\u003eWe retain the predictions of the best performing model (GBM) obtain among the gold-standard patients and then apply it to the remaining patients, i.e. the non-gold standard patients, to predict the treatment they would have received, had they been seen by a hematologist within five days of diagnosis. We then compared the predictions with the actual treatment received for both gold and non-gold standard patients. We focused on two groups of patients for the next step of the analysis by defining a binary outcome. (i) \u0026ldquo;Optimal treatment\u0026rdquo; (outcome\u0026thinsp;=\u0026thinsp;1): those with both predicted and actual curative treatment and (ii) \u0026ldquo;non-optimal treatment\u0026rdquo; (outcome\u0026thinsp;=\u0026thinsp;0) those predicted to receive curative treatment but who underwent non-curative treatment. The remaining 471 patients predicted to receive non-curative treatment were excluded for the next step because they did not provide any additional information to help understand the observed disparities in receiving optimal treatment.\u003c/p\u003e \u003cp\u003eStep 3 \u0026ndash; socio-demographic factors associated with receipt of optimal treatment\u003c/p\u003e \u003cp\u003eWe then applied a logistic regression model to estimate the association between the probability of receiving optimal treatment (as defined in step 2) and the following sociodemographic characteristics: sex, \u0026ldquo;d\u0026eacute;partement\u0026rdquo; of diagnosis, EDI quintile, distance and travel time between town of residence and the nearest reference hospital, and type of residence (urban or rural). We estimated confidence intervals using a 1,000 bootstrap repetitions of the analyses (bootstrap of step 3 after each step 1 and 2) to account for the uncertainty in the outcome, derived from model-based predictions (step 2). We then tested the normality of the bootstrapped distribution of the logistic coefficients from which we derived the 95% confidence interval and its associated p-value (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using RStudio version 4.2.2 (2022-10-31 ucrt). The machine learning models were built using the \u0026ldquo;caret\u0026rdquo; packages version 6.0\u0026ndash;93.\u003c/p\u003e"},{"header":"III. Results","content":"\u003cp\u003e3.1 Description of gold and non-gold standard groups of patients\u003c/p\u003e \u003cp\u003eA total of 85% (252/297) of gold standard patients received curative treatment compared to 34% (247/736) of non-gold standard patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Gold standard patients who received curative treatment were younger (median age at 49 vs. 59 years), however, with a wider age range (1\u0026ndash;95 years vs. 1\u0026ndash;82 years in non-gold standards). More men were treated curatively than women in non-gold-standard patients (38% vs. 29%), a disparity which was not seen in gold standard patients. There was no difference in curative treatment in both groups of patients by social deprivation or by commune of residence. The proportion of patients receiving curative treatment was low in Basse-Normandie (183/473 or 38.7%), and higher in Gironde (225/413 or 54.5%) and C\u0026ocirc;te-d'Or (91/147 or 61.9%). A similar pattern was seen in both groups of patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient\u0026rsquo;s characteristics according to treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eGold-standard patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eNon-gold standard patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;297\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-curative treatment,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;45\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCurative treatment,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;252\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;736\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-curative treatment,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;489\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCurative treatment,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;247\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (1, 91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (49, 83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (1, 91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72 (1, 95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77 (1, 95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59 (1, 82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e371 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e229 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e142 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e365 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e260 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDI quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (least deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e140 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 (Most deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCity of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e117 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e549 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e370 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e179 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnostic department\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasse-Normandie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e331 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e255 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026ocirc;te-d'Or\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGironde\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e313 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e183 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e130 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson comorbidities index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e299 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e164 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e135 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-mild comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e293 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e203 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e122 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAML subtype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML-MRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML-NOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML-RCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e264 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e133 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePML-RARA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapy related AML/MDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e211 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e176 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAML type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ede novo AML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e509 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e206 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-AML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-MDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e118 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCytogenetic initial prognosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e315 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e190 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing (Karyotype/ FISH not done)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e217 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e202 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance city \u0026amp; academic hospital (kms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[10,30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[30,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[50,100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e129 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTravel time in minute\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0,15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e146 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[15,30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[30,60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e229 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e153 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e188 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e136 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurvival time (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1012 (2, 3050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (2, 2425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1289 (3, 3050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199 (0, 3223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90 (0, 3223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e782 (1, 2917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%); Median (Range)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eFisher's Exact Test for Count Data; Wilcoxon rank sum test; Fisher's Exact Test for Count Data with simulated p-value\u003c/p\u003e \u003cp\u003e (based on 2000 replicates)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHigher proportion of gold-standard patients had no comorbidities (67% vs. 41% in non-gold-standard). Furthermore, 52% (13/25) of gold standard patients with severe comorbidities had received curative treatment, contrasting with 15% (22/144) in non-gold standard patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAML-others was the most represented sub-type, overall and in both groups of patients. All patients diagnosed with PML-RARA (n\u0026thinsp;=\u0026thinsp;39) were treated among gold-standard patients, while 2 of the 9 non-gold-standard patients with the same subtypes were not treated. In total, 85% (n\u0026thinsp;=\u0026thinsp;252/297) of gold-standard patients had de novo AML vs 69% (n\u0026thinsp;=\u0026thinsp;509/736). All gold-standard t-MDS patients were not treated, while 13% (17/135) received curative treatment among non-gold standard patients. The proportion of cytogenetic risk was favorable, intermediate and adverse in 32%, 36% and 28%, respectively, among gold-standard patients (vs 8%, 43% and 20% in non-gold-standard patients). A total of 83% of patients with an adverse cytogenetic risk had received curative treatment in gold-standard patients (vs. 39% in non-gold patients) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the proportion of curative treatment decreased, overall and in both groups of patients, as the distance or travel time between the town of residence and the nearest reference hospital increased (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e3.2 Performance of the machine learning predictive model\u003c/p\u003e \u003cp\u003eThe GBM algorithm was the most accurate when used to predict the treatment modalities on the test data, with an accuracy of 91.5% (ROC\u0026thinsp;=\u0026thinsp;0.97, Sensitivity\u0026thinsp;=\u0026thinsp;1, Specificity\u0026thinsp;=\u0026thinsp;0.91). The performances of the RF and GLM algorithms were poorer, with accuracies of 89.8% (ROC\u0026thinsp;=\u0026thinsp;0.95, Sensitivity\u0026thinsp;=\u0026thinsp;0.7, Specificity\u0026thinsp;=\u0026thinsp;0.97) and 86.4% (ROC\u0026thinsp;=\u0026thinsp;0.93, Sensitivity\u0026thinsp;=\u0026thinsp;0.78, Specificity\u0026thinsp;=\u0026thinsp;0.96), respectively (Fig.\u0026nbsp;3/ Appendix-Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e3.3 Relative importance of patient and tumor characteristics in receiving curative treatment\u003c/p\u003e\u003cp\u003eAmong gold-standard patients, age was the most important factor influencing the delivery of curative treatment with a relative influence (RI) of 68.3%. The other three most influential factors were respectively the presence of t-AML/MDS (15.8%), AML-others (5.4%) and PML-RARA (RI\u0026thinsp;=\u0026thinsp;3.4%) subtypes. All other clinical and biological factors used to train the GBM model had a relative influence below 2% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We also calculated the ICEs (Individual Conditional Expectations) to measure the effect of each age level on the prediction of treatment outcome adjusted on the average effect of the remaining predictors (Fig.\u0026nbsp;3). Age influence was null or minimal in patients under 50, moderate between 50 and 75, and very strong above 75.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative influence of patient clinical characteristics in the predictive model (GBM)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinic -biologic characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative influence (RI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapy related AML/MDS subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML others subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePML-RARA subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-AML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-mild comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse Cytogenetic prognosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate Cytogenetic prognosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML-RCA subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML-NOS subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-MDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaryotype/FISH not done (Missing Cytogenetic initial prognosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe trained and tested a GBM model specifically in the non-gold standard patients and modelled the importance of their most predictive factor. According to this model, age was also the most important predictive factor. However, the importance given to each age level was globally higher than that observed among gold-standard patients (Fig.\u0026nbsp;4).\u003c/p\u003e\u003cp\u003e3.4 Optimal vs. non-optimal treatment\u003c/p\u003e \u003cp\u003eIn total, we identified 460 patients who received optimal (curative) treatment and 102 patients with non-optimal treatment. Curative treatment receipt differed only according to the \u0026ldquo;d\u0026eacute;partement\u0026rdquo; of residence (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and was reduced with increasing distance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) and travel time (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) between the municipality of residence and the nearest reference hospital (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment quality description and the associated sociodemographic factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDescriptive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;562\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-optimal treatment,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;102\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOptimal treatment,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;460\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOR\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1, 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e251 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67, 1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.78, 1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnostic department\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasse-Normandie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.49, 0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026ocirc;te-d'Or\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33, 6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.15, 2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGironde\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07, 2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.83, 1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDI quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1, 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45, 1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.92, 1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57, 2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.89, 1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93, 3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.5, 2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50, 2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.88, 1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTravel time in minute\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0,15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1, 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[15,30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.35, 1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.61, 1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[30,60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18, 0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.35, 0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21, 0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.44, 0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance city \u0026amp; academic hospital (kms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[10,30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29, 1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[30,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21, 1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[50,100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18, 0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15, 0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTown type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e394 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93, 2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%); Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eFisher's Exact Test for Count Data; Fisher's Exact Test for Count Data with simulated p-value\u003c/p\u003e \u003cp\u003e (based on 2000 replicates); Wilcoxon rank sum test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eOR = Odds Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003e\u003cimg 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\" width=\"686\" height=\"401\"\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjusted for all sociodemographic covariables, patients living in C\u0026ocirc;te-d'Or had 1.6 (95%CI 1.15\u0026ndash;2.22) times the odds of receiving optimal treatment compared to the adjusted grand mean, whereas the ORs were 1.00 (95%CI 0.83\u0026ndash;1.27) and 0.61 (95%CI 0.49\u0026ndash;0.77) in patients living in Gironde and Basse-Normandie, respectively. Similarly, patients living in a community more than 30 minutes from a reference hospital were fewer to receive optimal treatment compared to those living closer. Delivery of optimal treatment was comparable between the different levels of deprivation, except for EDI\u0026thinsp;=\u0026thinsp;4 (OR\u0026thinsp;=\u0026thinsp;2.0, 95%CI 1.5\u0026ndash;2.8) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe then examined to which extent more patients could have been treated under optimal circumstances: we derived and contrasted model-based marginal predictions for each variable level. We found that, on average, 12.5% (95% CI\u0026thinsp;+\u0026thinsp;3.4% +21.6%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) more patients in Basse-Normandie would have received optimal treatment if they had lived in C\u0026ocirc;te-d'Or (+\u0026thinsp;7.7% if they had lived in Gironde). In other words, of the 236 patients in Basse-Normandie for whom we examined the type of treatment, 29 additional patients would have received curative treatment if they had lived in C\u0026ocirc;te-d'Or (Table\u0026nbsp;4). Likewise, +\u0026thinsp;12.2% of patients living between 30 to 60 minutes from a reference hospital would have been treated if they were less than 15 minutes away.\u003c/p\u003e"},{"header":"IV. Discussion","content":"\u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMain line\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe non-standardization of therapeutic eligibility guidelines is a major factor in unequal access to curative treatment, particularly for patients medically less fit, for whom decision criteria are not clear and depend very subjectively on the attending physician (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). We described that age; the secondary aspect and the AML subtype are major eligibility criteria for curative treatment in patients seen by hematologists within 5 days of their diagnosis. We highlighted that geographical variability (between and within \u0026ldquo;d\u0026eacute;partements\u0026rdquo;, such as distance to reference hospitals) are strong factors leading to unequal access to curative treatment.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eVariable\u0026rsquo;s importance\u003c/span\u003e \u003c/p\u003e \u003cp\u003eIn the treatment of AML patients, age is the most important factor, and young patients are treated almost systematically, regardless of their comorbidity and cytogenetic prognosis. However, as age increases, cytogenetic risk, co-morbidities (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and social frailty (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) are more likely to be worse (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These frailty conditions strongly impact on the age limit at which patients can be treated. Therefore, there is a subjective impact of the physician personal experience and the practices of the hospital in the choice of therapeutic modalities for the elderly patients (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In this study, we showed that in academic SHU, the age of patients receiving curative treatments was extended to the maximum limit (91 years vs. 82). After adjusting for other predictive factors, we compared the impact of age in the two groups of our population (gold vs. non-gold standard patients) and found that the effect of age was highly significant in the non-gold standard patients (Fig.\u0026nbsp;4). These findings suggest that for patients admitted to non-SHU units, chronological age is used to assess eligibility to treatment, whereas in SHU, biological age (e.g. age coupled with clinical status) is considered instead. We suggest that these results should guide non-SHU units to look beyond age when treating patients.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eImpact of sociodemographic factors\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe main hypothesis in this study was that regardless of their\u0026rsquo; sociodemographic conditions, patients should have access to the same therapeutic options if their clinical and biological characteristics were equivalent. However, we showed that reception of optimal treatment depends on geographical location.\u003c/p\u003e \u003cp\u003eMore specifically, approximately 12% and 7% more patients from Basse-Normandie could have received curative treatment if they had lived in C\u0026ocirc;te-d\u0026rsquo;Or or Gironde, respectively. A few hypotheses can be formulated in the light of our results. Given the younger age of patients treated in Basse-Normandie (53 years) compared to Gironde (56) and C\u0026ocirc;te-d'Or (57), we suspected the influence of non-standardized practices (Table\u0026nbsp;7-Appendix). We therefore hypothesize a strong age-based selectivity of patients treated by non-hematologists, especially in Basse-Normandie(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In addition, the access rate to cytogenetic analysis (60% of patients without a karyotype) and the median time between diagnosis and treatment in Basse-Normandie were smaller (2 days) than in C\u0026ocirc;te-d'Or and Gironde (7% and 31%, 3 and 5 days, respectively). These results support a differential approach to patient management in Basse-Normandie, potentially based on a strict and rapid selection of patients to be treated, particularly based on age. In contrast, the other \u0026ldquo;d\u0026eacute;partements\u0026rdquo; take the time to carry out a more precise clinical and biological assessment (karyotype and cytogenetics) to treat as many patients as possible. An earlier study comparing the outcomes of non-Hodgkin's lymphoma in the same three \"d\u0026eacute;partements\u0026rdquo;\" highlighted a lower cancer survival for patients residing in Basse-Normandie, potentially reflecting similar regional patient management approach (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The presence of medical deserts, in addition to the larger size of the Basse-Normandie \u0026ldquo;d\u0026eacute;partement\u0026rdquo; (17,589 km\u003csup\u003e2\u003c/sup\u003e vs 10,000 km\u003csup\u003e2\u003c/sup\u003e and 8,763 km\u003csup\u003e2\u003c/sup\u003e for Gironde and C\u0026ocirc;te-d'Or, respectively) may also explain the role of distance and travel time between patient\u0026rsquo;s residence and reference hospitals in the observed disparities in optimal treatment, since both the distance and travel time are significantly greater in Basse-Normandie. These bigger distances and travel time may contribute to referring the patient to the nearest hospital rather than to the reference hospital. To complement these points, we examined the Potential Localized Accessibility (PLA), a measure of accessibility to general practitioners, for each patient\u0026rsquo;s city of residence (\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) across the three \u0026ldquo;d\u0026eacute;partements\u0026rdquo;. Patients in Basse-Normandie lived in areas where access to primary care was the poorest (Table\u0026nbsp;7-Appendix). Based on these different arguments, we argue that the disparities in curative treatment could be the result of the non-standardization of clinical practices, particularly in non-reference hospitals, which was worsened by the policy of rapid care and screening in Basse-Normandie, and the geographical disparities in health care provision.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eStudy limitations and strengths\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe major limitation of this study is the small number of patients (N\u0026thinsp;=\u0026thinsp;297) on which the machine learning algorithms were trained, which may have prevented the algorithm from identifying some of the rarer features as important (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). We minimized the overfitting bias by focusing on feature classes with at least 10 patients. We are continuing to collect data on gold-standard patients in French reference hospitals to improve our predictive model.\u003c/p\u003e \u003cp\u003eHowever, our study included all patients diagnosed in the three selected \u0026ldquo;d\u0026eacute;partements\u0026rdquo;, as in the real world. Another strength is that we were able to identify and measure modifiable factors that affect the type of treatment received by patients.\u003c/p\u003e"},{"header":"V. Conclusion","content":"\u003cp\u003eThere are geographical disparities in receipt of optimal treatment. We have linked these differences to the quality of care and medical desert situations and to different institutional policies concerning access to curative treatments. However, policies and guidelines aimed at implementing standardized clinical practices, replicating what is performed in specialized care facilities, could greatly reduce the discrepancies observed in treatment. Finally, an assessment of the impact of the quality of treatment on complete remission and cancer survival in AML patients under real conditions would provide a convincing argument for standardizing institutional policies for treating these patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This study was authorised by the CNIL (Commission Nationale Informatique \u0026amp; Libert\u0026eacute;s) and received a favourable opinion from the ethics committee of the CESRESS\u0026nbsp;(Comit\u0026eacute; d\u0026apos;\u0026Eacute;thique et Scientifique pour les Recherches, les \u0026eacute;tudes et \u0026Eacute;valuations dans le domaine de Sant\u0026eacute;) under the\u0026nbsp;reference number MLD/CBO/AR2111097.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026rsquo;:\u0026nbsp;\u003c/strong\u003eThe data sets reported in this study are available on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by research funding from Fonds Europ\u0026eacute;en de d\u0026eacute;veloppement regional (FEDER: programme op\u0026eacute;rationnel FEDER-FSE Bourgogne 2014-2020) and from Institut National du Cancer (Projet INCa-SHS-ESP, n\u0026deg;2018-124)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eMM designed and supervised the execution of the S-LAM project. SG, SGB, HR, LB and JMP collected the data. EC, SO supervised the data collections and ensure verifications. Data management by JB. Statistic method design by BR, CM. RG, LR, and KMA. Hematological advice for the discussion: MLC, CR, AM, XT and SKW. Data analysis and manuscript redaction by KMA. Final approval of manuscript: All authors. Accountable for all aspects of the work: All authors. The work reported in the paper has been per-formed by all authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge Morgane MOUNIER for the design of the S-LAM project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEstey E, D\u0026ouml;hner H. Acute myeloid leukaemia. Lancet nov. 2006;368(9550):1894\u0026ndash;907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026ouml;hner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. 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Spatial Decompositions, Modeling and Mapping Service Regions to Predict Access to Social Programs. Geographic Inform Sci 1 d\u0026eacute;c. 2000;6(2):105\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggs G, Zahnow R, Corcoran J, Langford M, Fry R. Modelling spatial access to General Practitioner surgeries: Does public transport availability matter? J Transp Health sept. 2017;6:143\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClemmensen LH, Kj\u0026aelig;rsgaard RD. Data Representativity for Machine Learning and AI Systems. arXiv preprint arXiv:220304706. 2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute Myeloid Leukemia, Curative treatment, Inequalities in treatment access, Care pathway, Socio-demographic factors, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4968151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4968151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDuring their care pathway, AML patients not admitted to Specialized Hematology Units (SHU) have less access to curative treatment. We aim to determine whether access to optimal curative treatment is affected by sociodemographic factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe included 1,033 incidents AML-cases diagnosed between 2012\u0026ndash;2016 from three French \u0026ldquo;d\u0026eacute;partements\u0026rdquo;. We considered patients managed in reference hospitals SHU within 5 days(n\u0026thinsp;=\u0026thinsp;297) received \u0026ldquo;gold-standard\u0026rdquo; treatment. Treatment was \"curative-treatment\u0026rdquo; if intensive chemotherapy and \u0026ldquo;non-curative\u0026rdquo; otherwise. Firstly, we trained a Gradian Boosting Machine (GBM) algorithm on 80%(n\u0026thinsp;=\u0026thinsp;238) of \"gold-standard\" cases to learn how they were treated and validated the model on the remaining 20%(n\u0026thinsp;=\u0026thinsp;59). Next, GBM predictions were contrasted with actual treatment. Using multivariable logistic regression, we examined how non-optimal treatment (discrepancy between predicted curative and observed non-curative treatment) was associated with sociodemographic factors. Patients with predicted non-curative treatment were excluded as uninformative on access to curative treatment (n\u0026thinsp;=\u0026thinsp;471).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe rate of \u0026ldquo;curative treatment\u0026rdquo; was 84.8% (252/297) for gold-standard patients vs. 33.5% (247/736) for others. The three most influential predictive factors in gold-standard patients were age (68.3%-influence), t-AML/MDS (15.8%), and the AML-others subtypes (5.4%). A total of n\u0026thinsp;=\u0026thinsp;102(9.9%) patients were in non-optimal treatments. Living in Basse-Normandie (0.65-times;95%CI [0.5,0.8]) and over 30minutes from a reference hospital were strongly associated with a non-optimal treatment.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere are geographical disparities in access to optimal treatment, potentially linked to medical desert situations or medical system organization which must be addressed.\u003c/p\u003e","manuscriptTitle":"Access to optimal treatment of acute myeloid leukemia patients is affected by sociodemographic factors: a French population-based study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-26 01:03:35","doi":"10.21203/rs.3.rs-4968151/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c57cea7-cd5d-4c3d-bf09-b719443fb552","owner":[],"postedDate":"September 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-26T01:03:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-26 01:03:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4968151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4968151","identity":"rs-4968151","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}