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Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer
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Lung Cancer journal homepage: www.elsevier.com/locate/lungcan
Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer
T
Xiaowen Caoa,1, Apar Kishor Gantib,*,1, Thomas Stinchcombec, Melisa L. Wongd, James C. Hoe, Chen Shena, Yingzhou Liua, Jeffery Crawfordc, Herbert Pange,2, Xiaofei Wanga,2 a
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA Department of Internal Medicine, Veterans Affairs Nebraska Western Iowa Health Care System and University of Nebraska Medical Center, Omaha, NE, USA c Department of Medicine, Duke University School of Medicine, Durham, NC, USA d Department of Medicine, University of California, San Francisco, San Francisco, CA, USA e Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Lung cancer Neutropenia Predictive models
Objectives: Neutropenia is associated with the risk of life-threatening infections, chemotherapy dose reductions and delays that may compromise outcomes. This analysis was conducted to develop a prediction model for chemotherapy-induced severe neutropenia in lung cancer. Materials and Methods: Individual patient data from existing cooperative group phase II/III trials of stages III/IV non-small cell lung cancer or extensive small-cell lung cancer were included. The data were split into training and testing sets. In order to enhance the prediction accuracy and the reliability of the prediction model, lasso method was used for both variable selection and regularization on the training set. The selected variables was fit to a logistic model to obtain regression coefficients. The performance of the final prediction model was evaluated by the area under the ROC curve in both training and testing sets. Results: The dataset was randomly separated into training [7606 (67 %) patients] and testing [3746 (33 %) patients] sets. The final model included: age (> 65 years), gender (male), weight (kg), BMI, insurance status (yes/unknown), stage (IIIB/IV/ESSCLC), number of metastatic sites (1, 2 or ≥3), individual drugs (gemcitabine, taxanes), number of chemotherapy agents (2 or ≥3), planned use of growth factors, associated radiation therapy, previous therapy (chemotherapy, radiation, surgery), duration of planned treatment, pleural effusion (yes/unknown), performance status (1, ≥2) and presence of symptoms (yes/unknown). Conclusions: We have developed a relatively simple model with routinely available pre-treatment variables, to predict for neutropenia. This model should be independently validated prospectively.
1. Introduction Neutropenia is a serious chemotherapy-induced hematologic toxicity in cancer patients. It is associated with both the risk of life-threatening infections and also chemotherapy dose reductions and delays that may compromise treatment outcomes [1]. In addition, studies have shown that health related quality of life is reduced in patients who develop neutropenia, especially those needing hospitalization [2,3]. Hence, it is important to try and estimate the risk of development of neutropenia in an attempt to try and minimize the impact on the patient’s treatment plan. Previous studies have developed models to predict chemotherapy-induced neutropenia. These studies however have
significant limitations including small sample size and adjustment for different risk factors. To overcome these limitations, Lyman et al. (2011) prospectively developed and validated a clinical risk model for the occurrence of severe or febrile neutropenia with a prospective cohort study of 3760 patients [4]. The Lyman model performed well, with 90 % sensitivity and 96 % negative predictive value. However, this model is not lung cancer specific and has not been independently validated externally. The Lyman model includes planned relative dose intensity [RDI], which limits an easy use of the risk prediction model in clinical practice. The primary goal of this study is to develop a simple and easy-to-use prediction model for severe neutropenia using a large sample size pooled
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Corresponding author. E-mail address: [email protected] (A.K. Ganti). 1 Authors contributed equally. 2 Authors are co-senior authors. https://doi.org/10.1016/j.lungcan.2020.01.004 Received 22 July 2019; Received in revised form 31 December 2019; Accepted 3 January 2020 0169-5002/ © 2020 Published by Elsevier B.V.
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dataset consisting lung cancer trials from five National Cooperative Groups.
We developed a lung cancer dataset from existing phase II or III trials in the period 1991–2010 for non-small cell and small cell lung cancer from six national cancer cooperative groups (ECOG, CALGB, NCCTG, ACOSOG, RTOG and SWOG). Trial inclusion criteria were 1) first-line chemotherapy trials; 2) stage IIIA, IIIB, IV non-small cell lung cancer (NSCLC) or extensive small-cell lung cancer (SCLC) trials. The following types of trials were excluded 1) maintenance trials; 2) targeted agent trials; 3) one trial with extreme lab values in the inclusion criteria. Patients were excluded if any of the following parameters was missing: age, sex, performance status, adverse events (AE), initial stage, or initial stage conflicts with protocol requirement. Eventually, the analysis was based on the data from 11,352 patients of 67 lung cancer trials. See Consort Diagram (Fig. S1) for more details.
considered candidates for model entry; these included demographic variables, pretreatment measurements, laboratory measurements and chemotherapy agents (Supplementary Materials IV). Models were built using stepwise logistic regression and lasso regression on imputed datasets. In each regression method, we fitted the model on the 10 imputed training datasets individually to get 10 models with 10 sets of selected predictors. Next, we picked the union set and the intersection set of predictors from the 10 models as our selected models. Coefficients are the average of coefficients on the 10 fitted models. In stepwise selection, the alpha-to-entry was set at 0.10 and the alpha-to-stay was set at 0.05. Considering that some continuous variables, such weight and BMI, high approximately 20 % missing in the data, we developed imputation models to impute the missing values for these variables. The final prediction model is the average of the multiple models based on the imputed datasets. Risk model performances on the training and testing datasets were evaluated by ROC and AUC. A nomogram was developed to easily determine the predicted probability of developing severe neutropenia after chemotherapy in lung cancer patients using risk predictors from the final multivariable lasso regression model.
2.2. Outcome
2.6. Software
All trials in the dataset used NCI Common Terminology Criteria for Adverse Events (CTCAE) for measurement of adverse events in 5 grades (versions may vary): 1 (mild AE), 2 (moderate AE), 3 (severe AE), 4 (life-threatening or disabling AE), 5 (death related to AE). The primary outcome of this study was severe (grade 3 or higher; i.e. absolute neutrophil count < 1000/mm3) or febrile neutropenia (Table S1). Unfortunately, a limitation of the database is that we could not separate the incidence of severe neutropenia and febrile neutropenia. Observations with at least one episode of severe or febrile neutropenia were assigned “Yes” for outcome, while other observations are assigned “No”.
SAS 9.4 (Cary, NC) and R 3.43 (Vienna, Austria) were the software used for statistical analysis.
2. Methods 2.1. Dataset and patients
3. Results 3.1. Validation of the risk model in Lyman et al (2011) This analysis included data from 11,352 patients from 67 trials (Fig. S1). The distribution of selected risk factors, including age, diseases (NSCLC, SCLC), types of chemotherapy, planned RDI, by presence of severe neutropenia (Yes vs. No) is given in Table 1. To include as many patients as possible, the Lyman risk model was validated on our imputed datasets. All the predictors in Lyman’s model, except for SCLC/ NSCLC and platinum use, were significantly different among patients who had severe neutropenia and those who did not (Table S2). Next, a predictive score (sum of weighted coefficients) was calculated according to Lyman’s multivariate model, and was evaluated in a logistic regression analysis. Fig. S2 shows that Lyman’s model had an AUC of 0.8212 (95 %CI: 0.7799, 0.8625) in the present dataset, which is slightly better than 0.81 as reported in Lyman et al. (2011).4 However, when stratified by histologic subtype, the Lyman risk model had a much better predictive performance in SCLC with an AUC of 0.8772 (95 %CI: 0.8502, 0.9042) than NSCLC patients with an AUC of 0.6287 (95 %CI: 0.5141, 0.7433).
2.3. Validation of the risk model in Lyman et al (2011) To validate the Lyman risk model (2011), a risk score was calculated as a weighted sum of regression coefficients from the model. Then we built a logistic regression model with Lyman risk score as the only predictor and severe neutropenia was the outcome. The performance of the Lyman risk model (2011) on the lung cancer dataset was evaluated by receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) [5]. (Supplementary Materials III) 2.4. Missing data Missing data is an outstanding issue in pooled analyses, due to the differences across groups and trials. In order to reduce the impact of missing data, we 1) excluded patients with missing variables, especially variables with very low missing percentages; 2) coded missing data as a separate level “unknown” in categorical variables; 3) imputed continuous variables using linear regression model in logarithm scales. Specifically, multiple imputations were conducted by imputing continuous variables with fully conditional specification (FCS) method [6], which assumes the existence of a joint distribution for the variables. Multiple imputations were conducted 10 times to get 10 imputed datasets for model development and validation. Some continuous variables observed prior to treatment with significant missing, e.g. weight (18 %), BMI (21 %), and treatment duration (23 %), and the missing values were imputed prior to inclusion for variable selection.
3.2. Modeling Given the limitations of Lyman’s risk model in predicting the risk of severe neutropenia, especially in NSCLC patients, a new risk model for severe neutropenia on lung cancer patients was developed. Imputed datasets were used to eliminate the impact of missing data, while maintaining a large sample size. The dataset was randomly separated into two: 7606 (67 %) observations were included in the training dataset, and the other 3746 (33 %) observations were used to test model performance. All possible variables were considered candidates: demographic variables, baseline laboratory values and chemotherapy agents (Supplementary Materials IV), but planned RDI was not. In our database, planned RDI was defined as the ratio of chemotherapy cycles received to that planned (see Supplementary Materials III). It is worth noticing that the Lyman risk model had used a special definition of RDI, which depended on the published standards of different agents. We decided to exclude RDI from the list of possible predictors because the objective of this study was to build a predictive model for the risk of
2.5. Statistical method and model development We randomly selected data from two-thirds of the patients to derive the model, and used the remainder for validation. All variables were 15
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Table 1 Distribution of Severe Neutropenia by Risk Factors of the Final Prediction Model. Severe Neutropenia
(Age-65)/10 Mean (SD) Gender Male Female Weight Mean (SD) Logarithm BMI Mean (SD) Insurance No Yes Unknown Initial stage NSCLC 3A NSCLC 3B NSCLC 4 SCLC Extensive Number of metastatic sites at registration 0 1 2 > =3 Unknown Gemcitabine No Yes Taxanes No Yes Planned number of agents 1 2 > =3 Unknown G-CSF No Yes Radiology therapy No Yes Unknown Prior chemotherapy No Yes Unknown Prior radiotherapy No Yes Unknown Prior surgery No Yes Unknown Logarithm treatment duration days Mean (SD) Pleura effusion No Yes Unknown Performance status 0 1 > =2 Symptom No Yes Unknown
No (N = 8555)
Yes (N = 2797)
Total (N = 11352)
−0.3 (1.0)
−0.1 (0.9)
−0.3 (1.0)
5332 (62.3 %) 3223 (37.7 %)
1681 (60.1 %) 1116 (39.9 %)
7013 (61.8 %) 4339 (38.2 %)
75.5 (17.0)
77.0 (17.4)
75.9 (17.1)
3.2 (0.2)
3.3 (0.2)
3.2 (0.2)
746 (8.7 %) 7123 (83.3 %) 686 (8.0 %)
193 (6.9 %) 2569 (91.8 %) 35 (1.3 %)
939 (8.3 %) 9692 (85.4 %) 721 (6.4 %)
1336 2390 2631 2198
503 910 618 766
1839 3300 3249 2964
(16.2 (29.1 (28.6 (26.1
%) %) %) %)
(29.3 (10.5 (19.6 (13.8 (26.9
%) %) %) %) %)
p value < 0.0001 0.0355
0.0006 < 0.0001 < 0.0001
< 0.0001 (15.6 (27.9 (30.8 (25.7
%) %) %) %)
(18.0 (32.5 (22.1 (27.4
%) %) %) %)
< 0.0001 2259 (26.4 %) 915 (10.7 %) 1586 (18.5 %) 1436 (16.8 %) 2359 (27.6 %)
1067 (38.1 %) 275 (9.8 %) 634 (22.7 %) 127 (4.5 %) 694 (24.8 %)
3326 1190 2220 1563 3053
7775 (90.9 %) 780 (9.1 %)
2327 (83.2 %) 470 (16.8 %)
10102 (89.0 %) 1250 (11.0 %)
5445 (63.6 %) 3110 (36.4 %)
1579 (56.5 %) 1218 (43.5 %)
7024 (61.9 %) 4328 (38.1 %)
532 (6.2 %) 5471 (64.0 %) 2524 (29.5 %) 28 (0.3 %)
8 (0.3 %) 1647 (58.9 %) 1106 (39.5 %) 36 (1.3 %)
540 (4.8 %) 7118 (62.7 %) 3630 (32.0 %) 64 (0.6 %)
8121 (94.9 %) 434 (5.1 %)
2729 (97.6 %) 68 (2.4 %)
10850 (95.6 %) 502 (4.4 %)
3127 (36.6 %) 1200 (14.0 %) 4228 (49.4 %)
461 (16.5 %) 339 (12.1 %) 1997 (71.4 %)
3588 (31.6 %) 1539 (13.6 %) 6225 (54.8 %)
6695 (78.3 %) 375 (4.4 %) 1485 (17.4 %)
2285 (81.7 %) 27 (1.0 %) 485 (17.3 %)
8980 (79.1 %) 402 (3.5 %) 1970 (17.4 %)
5094 (59.5 %) 919 (10.7 %) 2542 (29.7 %)
1865 (66.7 %) 212 (7.6 %) 720 (25.7 %)
6959 (61.3 %) 1131 (10.0 %) 3262 (28.7 %)
4243 (49.6 %) 2521 (29.5 %) 1791 (20.9 %)
1702 (60.9 %) 283 (10.1 %) 812 (29.0 %)
5945 (52.4 %) 2804 (24.7 %) 2603 (22.9 %)
3.9 (1.3)
4.2 (1.1)
3.9 (1.2)
3529 (41.3 %) 1281 (15.0 %) 3745 (43.8 %)
1478 (52.8 %) 282 (10.1 %) 1037 (37.1 %)
5007 (44.1 %) 1563 (13.8 %) 4782 (42.1 %)
2875 (33.6 %) 4932 (57.7 %) 748 (8.7 %)
1049 (37.5 %) 1598 (57.1 %) 150 (5.4 %)
3924 (34.6 %) 6530 (57.5 %) 898 (7.9 %)
1539 (18.0 %) 3552 (41.5 %) 3464 (40.5 %)
330 (11.8 %) 334 (11.9 %) 2133 (76.3 %)
1869 (16.5 %) 3886 (34.2 %) 5597 (49.3 %)
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.0004 < 0.0001
< 0.0001
< 0.0001
p values: Chi-square test for categorical variables and Wilcoxon rank sum test for continuous variables. G-CSF: Granulocyte-colony stimulating factor.
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contribution of each predictor of the final prediction model and their combined effect to the risk score and the corresponding probability of developing severe neutropenia with a specific total risk score (Fig. 2). When the probability of developing severe neutropenia at approximately 30 % is close to the maximum of the sum of sensitivity and specificity, one could use a risk probability of > 30 % (or equivalently a risk score of > 290) as the cutoff to predict a patient to develop a severe neutropenia prior to treatment initiation. Sensitivity analyses were conducted to further investigate the impact of these predictors in the final prediction model. Including and excluding the following predictors one at a time. It was found that elimination of G-CSF, insurance, the number of chemotherapy agents, one at a time, from the full final model decreases the accuracy of the final model, but the impact is generally small with AUC 0.8348 (95 %CI: 0.8237, 0.8459) for the full final model to 0.8302 (95 %CI: 0.8188, 0.8416) for the model without G-CSF, 0.8318 (95 %CI: 0.8204, 0.8431) for the model without Insurance and 0.8158 (95 %CI: 0.8039, 0.8278) for the model without number of chemotherapy agents. We also found the prediction accuracy is also lower when the prediction model is only applied to the trial with low, medium or high rate of severe neutropenia. While it is not of the focus of the current paper, models with laboratory variables were developed. These included lactate dehydrogenase (LDH) (U/L), platelet count (x 103/ μL), hemoglobin (g/dL), serum creatinine (mg/dL), albumin (g/dL), white blood cell count (WBC) (x 103 / μL), glomerular filtration rate (GFR) [7], granulocytes (%) and absolute neutrophil count (ANC) (x 103/ μL). We found that laboratory variables did not contribute significantly to model performance.
Table 2 Final Lasso Logistic Model for Severe Neutropenia (beta = log odds ratio). Effect
Intercept Demographic (Age-65)/10 Gender -Male Weight (kg) Logarithm BMI Insurance -Unknown Insurance -Yes Diagnostic Initial stage -IV NSCLC Initial stage -IIIB NSCLC Initial stage -Extensive SCLC Number of metastatic sites at registration -1 Number of metastatic sites at registration -2 Number of metastatic sites at registration - > =3 Chemotherapy Gemcitabine -Yes Taxanes -Yes Planned number of agents -2 Planned number of agents - > =3 G-CSF-Yes Radiology therapy Radiotherapy -Unknown Radiotherapy -Yes Prior medications Prior chemotherapy -Unknown Prior chemotherapy -Yes Prior radiology-Unknown Prior radiology-Yes Prior surgery -Unknown Prior surgery -Yes Treatment-related variables Logarithm treatment duration days Pleura effusion -Unknown Pleura effusion -Yes Performance status -1 Performance status - > = 2 Symptom -Unknown Symptom -Yes
Regression Coefficients (log odds ratio) average −21.8008
min −22.8331
Max −21.4471
0.2164 −0.2241 0.0061 0.0274 −1.6162 0.0030
0.2113 −0.2409 0.0019 −0.1125 −1.6178 0.0022
0.2182 −0.1756 0.0075 0.4315 −1.6095 0.0057
−0.7392 0.2014 0.3560 0.3449
−0.7401 0.1999 0.3549 0.3421
−0.7352 0.2069 0.3596 0.3460
0.0872
0.0862
0.0880
0.2420
0.2417
0.2423
0.7214 0.4234 15.4471 16.1972 −1.4292
0.7208 0.4231 15.4416 16.1906 −1.4320
0.7230 0.4242 15.4514 16.2021 −1.4286
1.8230 1.5463
1.8220 1.5451
1.8274 1.5473
−0.1850 −0.8510 1.7579 0.7800 0.4249 −0.5417
−0.1854 −0.8538 1.7574 0.7799 0.4237 −0.5417
−0.1838 −0.8428 1.7607 0.7826 0.4254 −0.5408
0.4401
0.4395
0.4404
0.5763 −0.4583 0.1715 −0.0362 0.9124 −0.4596
0.5744 −0.4597 0.1704 −0.0389 0.9117 −0.4623
0.5772 −0.4578 0.1746 −0.0287 0.9129 −0.4589
4. Discussion Neutropenia is a common side effect of cancer chemotherapy and if severe, can cause life threatening infectious complications and also treatment delays that could adversely affect cancer specific outcomes. The risk of development of neutropenia seems to be the highest after the first cycle of cytotoxic chemotherapy [8]. The incidence of febrile neutropenia during lung cancer treatment ranges from 2 to 28% and is associated with the highest mortality of all solid tumors (11.2 %) [9]. Hence predicting which patients have an increased risk of developing neutropenia will help clinicians in either modifying the chemotherapy regimen or initiating primary prophylaxis to avoid severe neutropenia and its complications. Multiple previous studies have tried to predict for the development of neutropenia using patient, disease and treatment characteristics. The most comprehensive model thus far, has been the one developed by Lyman and colleagues [4].This was a prospective analysis of 3760 patients with multiple solid tumors and lymphoma. The final model performed quite well with a sensitivity of 85.0 %, specificity of 58.7 %, positive predictive value of 36.1 %, negative predictive value of 93.4 %, a positive likelihood ratio of 2.06 and a negative likelihood ratio of 0.26. However, this model was not specific for lung cancer and included variables like relative dose intensity, a parameter that is not available a priori to treating clinicians. Using a large dataset of patients with lung cancer treated on chemotherapy clinical trials over the past 20 years, we have developed a model to predict for severe chemotherapy-induced neutropenia in this specific patient population. This model is relatively simple to use as it includes variables that are routinely available prior to initiation of treatment. In this study, we developed prediction models using lasso method for variable selection and shrinkage regularization and logistic regression to estimate regression coefficients. The final model combines advantages of both logistic regression and lasso selection, and has a good AUC for both training set (AUC = 0.8348) and testing set (AUC = 0.8234). It is worth noticing that our final model compensated for the deficiency of Lyman’s risk model in NSCLC patients. Many of the variables included in the final model have been shown
neutropenia before treatment, while RDI cannot be obtained until after treatment is completed. Severe neutropenia was defined as any occurrence of neutropenia ≥ grade 3 or febrile neutropenia during treatments (see Supplementary Materials II for the details). We used lasso method to build risk models for severe neutropenia. As several candidate predictors, including weight and BMI, have significant missing values, multiple imputation was used to generate 10 imputed datasets. For each dataset, models were fitted with 10 imputed datasets to get 10 sets of selected variables. The intersection and union sets of variables constitutes the two final models individually under each modeling method; coefficients are the average of those in the 10 fitted models. We observed that in lasso modeling, the lasso intersection and lasso union sets of variables were identical. Predictors in lasso model were fit into logistic regression. The regression coefficients and the corresponding AUC based on the first imputation are presented. To increase the accessibility of the prediction model in practice when laboratory work is not available, we present the prediction model without laboratory values and carefully evaluate its performance. It turns out the 10 imputed datasets yielded the same set of variables. Table 2 shows the average regression coefficients of the final model, and the range of the 10 sets of regression coefficients. Fig. 1 displays and compares performances of final models on imputed dataset 1 using ROC curves and AUC values. Based on the coefficients of Table 2, we generated a nomogram to display visually the relative 17
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Fig. 1. ROC Curves of Final Lasso Logistic Models and by SCLC/NSCLC. The top panels denote the training set, while the bottom panels are the testing set. The panels on the left are for the entire cohort, the middle panels are the SCLC patients, while the right panels are the NSCLC patients.
patients and wide variety of the trials included in the dataset may offset these limitations. In summary, we have developed a model for prediction of neutropenia in patients undergoing cytotoxic chemotherapy for lung cancer based on clinical characteristics. This model should be tested prospectively.
to affect the development of neutropenia in previous studies. These include age [10,11], weight [9], extent of malignancy [12], chemotherapy agents [13], associated radiation therapy [14], and performance status [15]. This advantage of our model is the ability to predict the risk of severe neutropenia with only clinical measurements. All variables in our final model are related to patients’ demographics, diagnostic or treatment plan without the need for including laboratory measurements. This provides an easier way of clinical prediction of severe neutropenia. We believe that this model once validated, will help oncologists accurately identify those patients with lung cancer, who are at a high risk of developing severe neutropenia, based on simple, readily available information. This will enable oncologists to make proactive decisions regarding dose modification, use of prophylactic growth factors or close monitoring of high risk patients. However, limitations also exist. The high missing percentage in some variables discouraged us from investigating the full set of variables as potential predictors. To reduce this limitation as much as possible, we imputed partial missing variables in moderate missing percentages, which may introduce a bias in the analysis. A small proportion of patients in our analysis received prior chemotherapy, either in the adjuvant setting for lung cancer or for other previous malignancies. However, they were included in the present analysis, as they were eligible for the trials considered in the present analysis. Also with adjuvant therapy becoming standard of care in NSCLC, this may be clinically relevant. However, we believe that the large number of
Author contributions Dr Pang, Dr Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Ganti, Pang, Wang Acquisition or analysis of data: Cao, Pang, Shen, Wang Interpretation of data: Crawford, Ho, Ganti, Pang, Stinchcombe, Wang, Wong Drafting of the manuscript: Cao, Ganti Critical revision of the manuscript for important intellectual content: All authors Statistical analysis: Cao, Liu, Shen Obtained funding: Ganti, Pang, Stinchcombe, Wang Administrative, technical, or material support: Study supervision: Pang, Wang
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Fig. 2. This is the nomogram for predicting severe neutropenia risk for the lung cancer patients treated by chemotherapy developed from the final model. The nomogram can be read as follows: (1) use a straight edge to draw lines from each of the covariates of interest to the points scale at the top of the nomogram; (2) compute the sum of the points; and (3) use a straight edge to convert the total score to the risk of severe neutropenia at the bottom of the nomogram. Among all the predictors, the planned number of agents plays a dominant role in predicting the probability of having neutropenia. If the total score is < 295, the risk of having neutropenia is < 0.5. However, the probability of developing neutropenia increases dramatically if the total score increases from 290 to 310, compared with a score < 285. The probability of developing neutropenia will be larger than 0.9 if the total score ≥310. The nomogram can be a useful tool for physicians to assess the risk of neutropenia based on patient information.
Funding/Support
Appendix A. Supplementary data
This study was supported in part by the R21-AG042894 from the NIH National Institute on Aging, grant P01-CA142538 from the NIH National Cancer Institute, and Health and Medical Research Fund15162491 of Hong Kong. Dr. Wong was supported by grant KL2TR001870 from the National Center for Advancing Translational Sciences.
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.lungcan.2020.01.004. References [1] J. Crawford, D. Dale, G. Lyman, J. Crawford, D.C. Dale, G.H. Lyman, Chemotherapy-induced neutropenia: risks, consequences, and new directions for its management, Cancer 100 (9) (2004) 1993–1994, https://doi.org/10.1002/cncr. 20218. [2] B.V. Fortner, A.C. Houts, G. Johnson, L.S. Schwartzberg, A prospective investigation of chemotherapy induced neutropenia (CIN) and quality of life (QoL), J. Clin. Oncol. 23 (16) (2005) 8178, https://doi.org/10.1200/jco.2005.23.16_suppl.8178. [3] A. Kristensen, T.S. Solheim, Ø Fløtten, B.H. Grønberg, Associations between hematologic toxicity and health-related quality of life during first-line chemotherapy in advanced non-small-cell lung cancer: a pooled analysis of two randomized trials, Acta Oncol. 57 (11) (2018) 1574–1579, https://doi.org/10.1080/0284186x.2018. 1492151. [4] G.H. Lyman, N.M. Kuderer, J. Crawford, D.A. Wolff, E. Culakova, M.S. Poniewierski, et al., Predicting individual risk of neutropenic complications in patients receiving cancer chemotherapy, Cancer 117 (9) (2011) 1917–1927, https://doi.org/10.1002/cncr.25691. [5] Pepe, et al., The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, USA, 2003. [6] S. Van Buuren, K. Oudshoorn, Flexible Multivariate Imputation by MICE. Leiden, The Netherlands, TNO Prevention Center, 1999. [7] D.W. Cockcroft, M.H. Gault, Prediction of creatinine clearance from serum creatinine, Nephron 16 (1) (1976) 31–41. [8] J. Crawford, D.C. Dale, N.M. Kuderer, E. Culakova, M.S. Poniewierski, D. Wolff, et al., Risk and timing of neutropenic events in adult cancer patients receiving chemotherapy: the results of a prospective nationwide study of oncology practice, Journal of the National Comprehensive Cancer Network 6 (2) (2008) 109–118, https://doi.org/10.6004/jnccn.2008.0012. [9] J. Cupp, E. Culakova, M.S. Poniewierski, D.C. Dale, G.H. Lyman, J. Crawford, Analysis of Factors Associated With In-hospital Mortality in Lung Cancer Chemotherapy Patients With Neutropenia, Clinical Lung Cancer 19 (2) (2018) e163–e169, https://doi.org/10.1016/j.cllc.2017.10.013.
CRediT authorship contribution statement Xiaowen Cao: Formal analysis, Investigation, Data curation, Writing - original draft. Apar Kishor Ganti: Conceptualization, Writing - original draft, Visualization. Thomas Stinchcombe: Resources, Writing - review & editing. Melisa L. Wong: Visualization, Writing review & editing. James C. Ho: Supervision, Visualization, Writing review & editing. Chen Shen: Data curation, Validation, Writing - review & editing. Yingzhou Liu: Formal analysis, Validation, Investigation. Jeffery Crawford: Resources, Conceptualization, Writing - review & editing. Herbert Pang: Methodology, Funding acquisition, Project administration, Writing - review & editing. Xiaofei Wang: Conceptualization, Methodology, Funding acquisition, Supervision, Writing - review & editing.
Declaration of Competing Interest Dr. Wong has reported a conflict of interest outside of the submitted work (immediate family member is an employee of Genentech with stock ownership). The remaining authors have no conflicts to report. 19
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1200/jco.1992.10.2.316. [13] I. Ray-Coquard, C. Borg, T. Bachelot, C. Sebban, I. Philip, et al., Baseline and early lymphopenia predict for the risk of febrile neutropenia after chemotherapy, Br. J. Cancer 88 (2) (2003) 181–186, https://doi.org/10.1038/sj.bjc.6600724. [14] J.H. Silber, M. Fridman, R.S. DiPaola, M.H. Erder, M.V. Pauly, K.R. Fox, First-cycle blood counts and subsequent neutropenia, dose reduction, or delay in early-stage breast cancer therapy, J. Clin. Oncol. 16 (7) (1998), https://doi.org/10.1200/jco. 1998.16.7.2392 2392–400. [15] G.H. Lyman, N.M. Kuderer, Epidemiology of febrile neutropenia, Supportive Cancer Therapy 1 (1) (2003) 23–35, https://doi.org/10.3816/sct.2003.n.002.
[10] M. Schwenkglenks, C. Jackisch, M. Constenla, J.N. Kerger, R. Paridaens, L. Auerbach, et al., Neutropenic event risk and impaired chemotherapy delivery in six European audits of breast cancer treatment, Supportive Care in Cancer 14 (9) (2006) 901–909, https://doi.org/10.1007/s00520-006-0034-9. [11] G.H. Lyman, D.C. Dale, J. Friedberg, J. Crawford, R.I. Fisher, Incidence and predictors of low chemotherapy dose-intensity in aggressive non-hodgkin’s lymphoma: a nationwide study, J. Clin. Oncol. 22 (21) (2004) 4302–4311, https://doi.org/10. 1200/jco.2004.03.213. [12] J.A. Talcott, R.D. Siegel, R. Finberg, L. Goldman, Risk assessment in cancer patients with fever and neutropenia: a prospective, two-center validation of a prediction rule, Journal of Clinical Oncology. 10 (2) (1992) 316–322, https://doi.org/10.
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Lung Cancer journal homepage: www.elsevier.com/locate/lungcan
Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer
T
Xiaowen Caoa,1, Apar Kishor Gantib,*,1, Thomas Stinchcombec, Melisa L. Wongd, James C. Hoe, Chen Shena, Yingzhou Liua, Jeffery Crawfordc, Herbert Pange,2, Xiaofei Wanga,2 a
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA Department of Internal Medicine, Veterans Affairs Nebraska Western Iowa Health Care System and University of Nebraska Medical Center, Omaha, NE, USA c Department of Medicine, Duke University School of Medicine, Durham, NC, USA d Department of Medicine, University of California, San Francisco, San Francisco, CA, USA e Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Lung cancer Neutropenia Predictive models
Objectives: Neutropenia is associated with the risk of life-threatening infections, chemotherapy dose reductions and delays that may compromise outcomes. This analysis was conducted to develop a prediction model for chemotherapy-induced severe neutropenia in lung cancer. Materials and Methods: Individual patient data from existing cooperative group phase II/III trials of stages III/IV non-small cell lung cancer or extensive small-cell lung cancer were included. The data were split into training and testing sets. In order to enhance the prediction accuracy and the reliability of the prediction model, lasso method was used for both variable selection and regularization on the training set. The selected variables was fit to a logistic model to obtain regression coefficients. The performance of the final prediction model was evaluated by the area under the ROC curve in both training and testing sets. Results: The dataset was randomly separated into training [7606 (67 %) patients] and testing [3746 (33 %) patients] sets. The final model included: age (> 65 years), gender (male), weight (kg), BMI, insurance status (yes/unknown), stage (IIIB/IV/ESSCLC), number of metastatic sites (1, 2 or ≥3), individual drugs (gemcitabine, taxanes), number of chemotherapy agents (2 or ≥3), planned use of growth factors, associated radiation therapy, previous therapy (chemotherapy, radiation, surgery), duration of planned treatment, pleural effusion (yes/unknown), performance status (1, ≥2) and presence of symptoms (yes/unknown). Conclusions: We have developed a relatively simple model with routinely available pre-treatment variables, to predict for neutropenia. This model should be independently validated prospectively.
1. Introduction Neutropenia is a serious chemotherapy-induced hematologic toxicity in cancer patients. It is associated with both the risk of life-threatening infections and also chemotherapy dose reductions and delays that may compromise treatment outcomes [1]. In addition, studies have shown that health related quality of life is reduced in patients who develop neutropenia, especially those needing hospitalization [2,3]. Hence, it is important to try and estimate the risk of development of neutropenia in an attempt to try and minimize the impact on the patient’s treatment plan. Previous studies have developed models to predict chemotherapy-induced neutropenia. These studies however have
significant limitations including small sample size and adjustment for different risk factors. To overcome these limitations, Lyman et al. (2011) prospectively developed and validated a clinical risk model for the occurrence of severe or febrile neutropenia with a prospective cohort study of 3760 patients [4]. The Lyman model performed well, with 90 % sensitivity and 96 % negative predictive value. However, this model is not lung cancer specific and has not been independently validated externally. The Lyman model includes planned relative dose intensity [RDI], which limits an easy use of the risk prediction model in clinical practice. The primary goal of this study is to develop a simple and easy-to-use prediction model for severe neutropenia using a large sample size pooled
⁎
Corresponding author. E-mail address: [email protected] (A.K. Ganti). 1 Authors contributed equally. 2 Authors are co-senior authors. https://doi.org/10.1016/j.lungcan.2020.01.004 Received 22 July 2019; Received in revised form 31 December 2019; Accepted 3 January 2020 0169-5002/ © 2020 Published by Elsevier B.V.
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dataset consisting lung cancer trials from five National Cooperative Groups.
We developed a lung cancer dataset from existing phase II or III trials in the period 1991–2010 for non-small cell and small cell lung cancer from six national cancer cooperative groups (ECOG, CALGB, NCCTG, ACOSOG, RTOG and SWOG). Trial inclusion criteria were 1) first-line chemotherapy trials; 2) stage IIIA, IIIB, IV non-small cell lung cancer (NSCLC) or extensive small-cell lung cancer (SCLC) trials. The following types of trials were excluded 1) maintenance trials; 2) targeted agent trials; 3) one trial with extreme lab values in the inclusion criteria. Patients were excluded if any of the following parameters was missing: age, sex, performance status, adverse events (AE), initial stage, or initial stage conflicts with protocol requirement. Eventually, the analysis was based on the data from 11,352 patients of 67 lung cancer trials. See Consort Diagram (Fig. S1) for more details.
considered candidates for model entry; these included demographic variables, pretreatment measurements, laboratory measurements and chemotherapy agents (Supplementary Materials IV). Models were built using stepwise logistic regression and lasso regression on imputed datasets. In each regression method, we fitted the model on the 10 imputed training datasets individually to get 10 models with 10 sets of selected predictors. Next, we picked the union set and the intersection set of predictors from the 10 models as our selected models. Coefficients are the average of coefficients on the 10 fitted models. In stepwise selection, the alpha-to-entry was set at 0.10 and the alpha-to-stay was set at 0.05. Considering that some continuous variables, such weight and BMI, high approximately 20 % missing in the data, we developed imputation models to impute the missing values for these variables. The final prediction model is the average of the multiple models based on the imputed datasets. Risk model performances on the training and testing datasets were evaluated by ROC and AUC. A nomogram was developed to easily determine the predicted probability of developing severe neutropenia after chemotherapy in lung cancer patients using risk predictors from the final multivariable lasso regression model.
2.2. Outcome
2.6. Software
All trials in the dataset used NCI Common Terminology Criteria for Adverse Events (CTCAE) for measurement of adverse events in 5 grades (versions may vary): 1 (mild AE), 2 (moderate AE), 3 (severe AE), 4 (life-threatening or disabling AE), 5 (death related to AE). The primary outcome of this study was severe (grade 3 or higher; i.e. absolute neutrophil count < 1000/mm3) or febrile neutropenia (Table S1). Unfortunately, a limitation of the database is that we could not separate the incidence of severe neutropenia and febrile neutropenia. Observations with at least one episode of severe or febrile neutropenia were assigned “Yes” for outcome, while other observations are assigned “No”.
SAS 9.4 (Cary, NC) and R 3.43 (Vienna, Austria) were the software used for statistical analysis.
2. Methods 2.1. Dataset and patients
3. Results 3.1. Validation of the risk model in Lyman et al (2011) This analysis included data from 11,352 patients from 67 trials (Fig. S1). The distribution of selected risk factors, including age, diseases (NSCLC, SCLC), types of chemotherapy, planned RDI, by presence of severe neutropenia (Yes vs. No) is given in Table 1. To include as many patients as possible, the Lyman risk model was validated on our imputed datasets. All the predictors in Lyman’s model, except for SCLC/ NSCLC and platinum use, were significantly different among patients who had severe neutropenia and those who did not (Table S2). Next, a predictive score (sum of weighted coefficients) was calculated according to Lyman’s multivariate model, and was evaluated in a logistic regression analysis. Fig. S2 shows that Lyman’s model had an AUC of 0.8212 (95 %CI: 0.7799, 0.8625) in the present dataset, which is slightly better than 0.81 as reported in Lyman et al. (2011).4 However, when stratified by histologic subtype, the Lyman risk model had a much better predictive performance in SCLC with an AUC of 0.8772 (95 %CI: 0.8502, 0.9042) than NSCLC patients with an AUC of 0.6287 (95 %CI: 0.5141, 0.7433).
2.3. Validation of the risk model in Lyman et al (2011) To validate the Lyman risk model (2011), a risk score was calculated as a weighted sum of regression coefficients from the model. Then we built a logistic regression model with Lyman risk score as the only predictor and severe neutropenia was the outcome. The performance of the Lyman risk model (2011) on the lung cancer dataset was evaluated by receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) [5]. (Supplementary Materials III) 2.4. Missing data Missing data is an outstanding issue in pooled analyses, due to the differences across groups and trials. In order to reduce the impact of missing data, we 1) excluded patients with missing variables, especially variables with very low missing percentages; 2) coded missing data as a separate level “unknown” in categorical variables; 3) imputed continuous variables using linear regression model in logarithm scales. Specifically, multiple imputations were conducted by imputing continuous variables with fully conditional specification (FCS) method [6], which assumes the existence of a joint distribution for the variables. Multiple imputations were conducted 10 times to get 10 imputed datasets for model development and validation. Some continuous variables observed prior to treatment with significant missing, e.g. weight (18 %), BMI (21 %), and treatment duration (23 %), and the missing values were imputed prior to inclusion for variable selection.
3.2. Modeling Given the limitations of Lyman’s risk model in predicting the risk of severe neutropenia, especially in NSCLC patients, a new risk model for severe neutropenia on lung cancer patients was developed. Imputed datasets were used to eliminate the impact of missing data, while maintaining a large sample size. The dataset was randomly separated into two: 7606 (67 %) observations were included in the training dataset, and the other 3746 (33 %) observations were used to test model performance. All possible variables were considered candidates: demographic variables, baseline laboratory values and chemotherapy agents (Supplementary Materials IV), but planned RDI was not. In our database, planned RDI was defined as the ratio of chemotherapy cycles received to that planned (see Supplementary Materials III). It is worth noticing that the Lyman risk model had used a special definition of RDI, which depended on the published standards of different agents. We decided to exclude RDI from the list of possible predictors because the objective of this study was to build a predictive model for the risk of
2.5. Statistical method and model development We randomly selected data from two-thirds of the patients to derive the model, and used the remainder for validation. All variables were 15
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Table 1 Distribution of Severe Neutropenia by Risk Factors of the Final Prediction Model. Severe Neutropenia
(Age-65)/10 Mean (SD) Gender Male Female Weight Mean (SD) Logarithm BMI Mean (SD) Insurance No Yes Unknown Initial stage NSCLC 3A NSCLC 3B NSCLC 4 SCLC Extensive Number of metastatic sites at registration 0 1 2 > =3 Unknown Gemcitabine No Yes Taxanes No Yes Planned number of agents 1 2 > =3 Unknown G-CSF No Yes Radiology therapy No Yes Unknown Prior chemotherapy No Yes Unknown Prior radiotherapy No Yes Unknown Prior surgery No Yes Unknown Logarithm treatment duration days Mean (SD) Pleura effusion No Yes Unknown Performance status 0 1 > =2 Symptom No Yes Unknown
No (N = 8555)
Yes (N = 2797)
Total (N = 11352)
−0.3 (1.0)
−0.1 (0.9)
−0.3 (1.0)
5332 (62.3 %) 3223 (37.7 %)
1681 (60.1 %) 1116 (39.9 %)
7013 (61.8 %) 4339 (38.2 %)
75.5 (17.0)
77.0 (17.4)
75.9 (17.1)
3.2 (0.2)
3.3 (0.2)
3.2 (0.2)
746 (8.7 %) 7123 (83.3 %) 686 (8.0 %)
193 (6.9 %) 2569 (91.8 %) 35 (1.3 %)
939 (8.3 %) 9692 (85.4 %) 721 (6.4 %)
1336 2390 2631 2198
503 910 618 766
1839 3300 3249 2964
(16.2 (29.1 (28.6 (26.1
%) %) %) %)
(29.3 (10.5 (19.6 (13.8 (26.9
%) %) %) %) %)
p value < 0.0001 0.0355
0.0006 < 0.0001 < 0.0001
< 0.0001 (15.6 (27.9 (30.8 (25.7
%) %) %) %)
(18.0 (32.5 (22.1 (27.4
%) %) %) %)
< 0.0001 2259 (26.4 %) 915 (10.7 %) 1586 (18.5 %) 1436 (16.8 %) 2359 (27.6 %)
1067 (38.1 %) 275 (9.8 %) 634 (22.7 %) 127 (4.5 %) 694 (24.8 %)
3326 1190 2220 1563 3053
7775 (90.9 %) 780 (9.1 %)
2327 (83.2 %) 470 (16.8 %)
10102 (89.0 %) 1250 (11.0 %)
5445 (63.6 %) 3110 (36.4 %)
1579 (56.5 %) 1218 (43.5 %)
7024 (61.9 %) 4328 (38.1 %)
532 (6.2 %) 5471 (64.0 %) 2524 (29.5 %) 28 (0.3 %)
8 (0.3 %) 1647 (58.9 %) 1106 (39.5 %) 36 (1.3 %)
540 (4.8 %) 7118 (62.7 %) 3630 (32.0 %) 64 (0.6 %)
8121 (94.9 %) 434 (5.1 %)
2729 (97.6 %) 68 (2.4 %)
10850 (95.6 %) 502 (4.4 %)
3127 (36.6 %) 1200 (14.0 %) 4228 (49.4 %)
461 (16.5 %) 339 (12.1 %) 1997 (71.4 %)
3588 (31.6 %) 1539 (13.6 %) 6225 (54.8 %)
6695 (78.3 %) 375 (4.4 %) 1485 (17.4 %)
2285 (81.7 %) 27 (1.0 %) 485 (17.3 %)
8980 (79.1 %) 402 (3.5 %) 1970 (17.4 %)
5094 (59.5 %) 919 (10.7 %) 2542 (29.7 %)
1865 (66.7 %) 212 (7.6 %) 720 (25.7 %)
6959 (61.3 %) 1131 (10.0 %) 3262 (28.7 %)
4243 (49.6 %) 2521 (29.5 %) 1791 (20.9 %)
1702 (60.9 %) 283 (10.1 %) 812 (29.0 %)
5945 (52.4 %) 2804 (24.7 %) 2603 (22.9 %)
3.9 (1.3)
4.2 (1.1)
3.9 (1.2)
3529 (41.3 %) 1281 (15.0 %) 3745 (43.8 %)
1478 (52.8 %) 282 (10.1 %) 1037 (37.1 %)
5007 (44.1 %) 1563 (13.8 %) 4782 (42.1 %)
2875 (33.6 %) 4932 (57.7 %) 748 (8.7 %)
1049 (37.5 %) 1598 (57.1 %) 150 (5.4 %)
3924 (34.6 %) 6530 (57.5 %) 898 (7.9 %)
1539 (18.0 %) 3552 (41.5 %) 3464 (40.5 %)
330 (11.8 %) 334 (11.9 %) 2133 (76.3 %)
1869 (16.5 %) 3886 (34.2 %) 5597 (49.3 %)
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.0004 < 0.0001
< 0.0001
< 0.0001
p values: Chi-square test for categorical variables and Wilcoxon rank sum test for continuous variables. G-CSF: Granulocyte-colony stimulating factor.
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contribution of each predictor of the final prediction model and their combined effect to the risk score and the corresponding probability of developing severe neutropenia with a specific total risk score (Fig. 2). When the probability of developing severe neutropenia at approximately 30 % is close to the maximum of the sum of sensitivity and specificity, one could use a risk probability of > 30 % (or equivalently a risk score of > 290) as the cutoff to predict a patient to develop a severe neutropenia prior to treatment initiation. Sensitivity analyses were conducted to further investigate the impact of these predictors in the final prediction model. Including and excluding the following predictors one at a time. It was found that elimination of G-CSF, insurance, the number of chemotherapy agents, one at a time, from the full final model decreases the accuracy of the final model, but the impact is generally small with AUC 0.8348 (95 %CI: 0.8237, 0.8459) for the full final model to 0.8302 (95 %CI: 0.8188, 0.8416) for the model without G-CSF, 0.8318 (95 %CI: 0.8204, 0.8431) for the model without Insurance and 0.8158 (95 %CI: 0.8039, 0.8278) for the model without number of chemotherapy agents. We also found the prediction accuracy is also lower when the prediction model is only applied to the trial with low, medium or high rate of severe neutropenia. While it is not of the focus of the current paper, models with laboratory variables were developed. These included lactate dehydrogenase (LDH) (U/L), platelet count (x 103/ μL), hemoglobin (g/dL), serum creatinine (mg/dL), albumin (g/dL), white blood cell count (WBC) (x 103 / μL), glomerular filtration rate (GFR) [7], granulocytes (%) and absolute neutrophil count (ANC) (x 103/ μL). We found that laboratory variables did not contribute significantly to model performance.
Table 2 Final Lasso Logistic Model for Severe Neutropenia (beta = log odds ratio). Effect
Intercept Demographic (Age-65)/10 Gender -Male Weight (kg) Logarithm BMI Insurance -Unknown Insurance -Yes Diagnostic Initial stage -IV NSCLC Initial stage -IIIB NSCLC Initial stage -Extensive SCLC Number of metastatic sites at registration -1 Number of metastatic sites at registration -2 Number of metastatic sites at registration - > =3 Chemotherapy Gemcitabine -Yes Taxanes -Yes Planned number of agents -2 Planned number of agents - > =3 G-CSF-Yes Radiology therapy Radiotherapy -Unknown Radiotherapy -Yes Prior medications Prior chemotherapy -Unknown Prior chemotherapy -Yes Prior radiology-Unknown Prior radiology-Yes Prior surgery -Unknown Prior surgery -Yes Treatment-related variables Logarithm treatment duration days Pleura effusion -Unknown Pleura effusion -Yes Performance status -1 Performance status - > = 2 Symptom -Unknown Symptom -Yes
Regression Coefficients (log odds ratio) average −21.8008
min −22.8331
Max −21.4471
0.2164 −0.2241 0.0061 0.0274 −1.6162 0.0030
0.2113 −0.2409 0.0019 −0.1125 −1.6178 0.0022
0.2182 −0.1756 0.0075 0.4315 −1.6095 0.0057
−0.7392 0.2014 0.3560 0.3449
−0.7401 0.1999 0.3549 0.3421
−0.7352 0.2069 0.3596 0.3460
0.0872
0.0862
0.0880
0.2420
0.2417
0.2423
0.7214 0.4234 15.4471 16.1972 −1.4292
0.7208 0.4231 15.4416 16.1906 −1.4320
0.7230 0.4242 15.4514 16.2021 −1.4286
1.8230 1.5463
1.8220 1.5451
1.8274 1.5473
−0.1850 −0.8510 1.7579 0.7800 0.4249 −0.5417
−0.1854 −0.8538 1.7574 0.7799 0.4237 −0.5417
−0.1838 −0.8428 1.7607 0.7826 0.4254 −0.5408
0.4401
0.4395
0.4404
0.5763 −0.4583 0.1715 −0.0362 0.9124 −0.4596
0.5744 −0.4597 0.1704 −0.0389 0.9117 −0.4623
0.5772 −0.4578 0.1746 −0.0287 0.9129 −0.4589
4. Discussion Neutropenia is a common side effect of cancer chemotherapy and if severe, can cause life threatening infectious complications and also treatment delays that could adversely affect cancer specific outcomes. The risk of development of neutropenia seems to be the highest after the first cycle of cytotoxic chemotherapy [8]. The incidence of febrile neutropenia during lung cancer treatment ranges from 2 to 28% and is associated with the highest mortality of all solid tumors (11.2 %) [9]. Hence predicting which patients have an increased risk of developing neutropenia will help clinicians in either modifying the chemotherapy regimen or initiating primary prophylaxis to avoid severe neutropenia and its complications. Multiple previous studies have tried to predict for the development of neutropenia using patient, disease and treatment characteristics. The most comprehensive model thus far, has been the one developed by Lyman and colleagues [4].This was a prospective analysis of 3760 patients with multiple solid tumors and lymphoma. The final model performed quite well with a sensitivity of 85.0 %, specificity of 58.7 %, positive predictive value of 36.1 %, negative predictive value of 93.4 %, a positive likelihood ratio of 2.06 and a negative likelihood ratio of 0.26. However, this model was not specific for lung cancer and included variables like relative dose intensity, a parameter that is not available a priori to treating clinicians. Using a large dataset of patients with lung cancer treated on chemotherapy clinical trials over the past 20 years, we have developed a model to predict for severe chemotherapy-induced neutropenia in this specific patient population. This model is relatively simple to use as it includes variables that are routinely available prior to initiation of treatment. In this study, we developed prediction models using lasso method for variable selection and shrinkage regularization and logistic regression to estimate regression coefficients. The final model combines advantages of both logistic regression and lasso selection, and has a good AUC for both training set (AUC = 0.8348) and testing set (AUC = 0.8234). It is worth noticing that our final model compensated for the deficiency of Lyman’s risk model in NSCLC patients. Many of the variables included in the final model have been shown
neutropenia before treatment, while RDI cannot be obtained until after treatment is completed. Severe neutropenia was defined as any occurrence of neutropenia ≥ grade 3 or febrile neutropenia during treatments (see Supplementary Materials II for the details). We used lasso method to build risk models for severe neutropenia. As several candidate predictors, including weight and BMI, have significant missing values, multiple imputation was used to generate 10 imputed datasets. For each dataset, models were fitted with 10 imputed datasets to get 10 sets of selected variables. The intersection and union sets of variables constitutes the two final models individually under each modeling method; coefficients are the average of those in the 10 fitted models. We observed that in lasso modeling, the lasso intersection and lasso union sets of variables were identical. Predictors in lasso model were fit into logistic regression. The regression coefficients and the corresponding AUC based on the first imputation are presented. To increase the accessibility of the prediction model in practice when laboratory work is not available, we present the prediction model without laboratory values and carefully evaluate its performance. It turns out the 10 imputed datasets yielded the same set of variables. Table 2 shows the average regression coefficients of the final model, and the range of the 10 sets of regression coefficients. Fig. 1 displays and compares performances of final models on imputed dataset 1 using ROC curves and AUC values. Based on the coefficients of Table 2, we generated a nomogram to display visually the relative 17
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Fig. 1. ROC Curves of Final Lasso Logistic Models and by SCLC/NSCLC. The top panels denote the training set, while the bottom panels are the testing set. The panels on the left are for the entire cohort, the middle panels are the SCLC patients, while the right panels are the NSCLC patients.
patients and wide variety of the trials included in the dataset may offset these limitations. In summary, we have developed a model for prediction of neutropenia in patients undergoing cytotoxic chemotherapy for lung cancer based on clinical characteristics. This model should be tested prospectively.
to affect the development of neutropenia in previous studies. These include age [10,11], weight [9], extent of malignancy [12], chemotherapy agents [13], associated radiation therapy [14], and performance status [15]. This advantage of our model is the ability to predict the risk of severe neutropenia with only clinical measurements. All variables in our final model are related to patients’ demographics, diagnostic or treatment plan without the need for including laboratory measurements. This provides an easier way of clinical prediction of severe neutropenia. We believe that this model once validated, will help oncologists accurately identify those patients with lung cancer, who are at a high risk of developing severe neutropenia, based on simple, readily available information. This will enable oncologists to make proactive decisions regarding dose modification, use of prophylactic growth factors or close monitoring of high risk patients. However, limitations also exist. The high missing percentage in some variables discouraged us from investigating the full set of variables as potential predictors. To reduce this limitation as much as possible, we imputed partial missing variables in moderate missing percentages, which may introduce a bias in the analysis. A small proportion of patients in our analysis received prior chemotherapy, either in the adjuvant setting for lung cancer or for other previous malignancies. However, they were included in the present analysis, as they were eligible for the trials considered in the present analysis. Also with adjuvant therapy becoming standard of care in NSCLC, this may be clinically relevant. However, we believe that the large number of
Author contributions Dr Pang, Dr Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Ganti, Pang, Wang Acquisition or analysis of data: Cao, Pang, Shen, Wang Interpretation of data: Crawford, Ho, Ganti, Pang, Stinchcombe, Wang, Wong Drafting of the manuscript: Cao, Ganti Critical revision of the manuscript for important intellectual content: All authors Statistical analysis: Cao, Liu, Shen Obtained funding: Ganti, Pang, Stinchcombe, Wang Administrative, technical, or material support: Study supervision: Pang, Wang
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Fig. 2. This is the nomogram for predicting severe neutropenia risk for the lung cancer patients treated by chemotherapy developed from the final model. The nomogram can be read as follows: (1) use a straight edge to draw lines from each of the covariates of interest to the points scale at the top of the nomogram; (2) compute the sum of the points; and (3) use a straight edge to convert the total score to the risk of severe neutropenia at the bottom of the nomogram. Among all the predictors, the planned number of agents plays a dominant role in predicting the probability of having neutropenia. If the total score is < 295, the risk of having neutropenia is < 0.5. However, the probability of developing neutropenia increases dramatically if the total score increases from 290 to 310, compared with a score < 285. The probability of developing neutropenia will be larger than 0.9 if the total score ≥310. The nomogram can be a useful tool for physicians to assess the risk of neutropenia based on patient information.
Funding/Support
Appendix A. Supplementary data
This study was supported in part by the R21-AG042894 from the NIH National Institute on Aging, grant P01-CA142538 from the NIH National Cancer Institute, and Health and Medical Research Fund15162491 of Hong Kong. Dr. Wong was supported by grant KL2TR001870 from the National Center for Advancing Translational Sciences.
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CRediT authorship contribution statement Xiaowen Cao: Formal analysis, Investigation, Data curation, Writing - original draft. Apar Kishor Ganti: Conceptualization, Writing - original draft, Visualization. Thomas Stinchcombe: Resources, Writing - review & editing. Melisa L. Wong: Visualization, Writing review & editing. James C. Ho: Supervision, Visualization, Writing review & editing. Chen Shen: Data curation, Validation, Writing - review & editing. Yingzhou Liu: Formal analysis, Validation, Investigation. Jeffery Crawford: Resources, Conceptualization, Writing - review & editing. Herbert Pang: Methodology, Funding acquisition, Project administration, Writing - review & editing. Xiaofei Wang: Conceptualization, Methodology, Funding acquisition, Supervision, Writing - review & editing.
Declaration of Competing Interest Dr. Wong has reported a conflict of interest outside of the submitted work (immediate family member is an employee of Genentech with stock ownership). The remaining authors have no conflicts to report. 19
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