In-hospital Mortality Prognostication for Cancer Patients with Febrile Neutropenia: A Single Center Observational Study

In-hospital Mortality Prognostication for Cancer Patients with Febrile Neutropenia: A Single Center Observational 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 Article In-hospital Mortality Prognostication for Cancer Patients with Febrile Neutropenia: A Single Center Observational Study Safee Ullah Chaudhary, Amna Tahir, Muhammad Hassan, Asim Farooq, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4730716/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Abstract Febrile neutropenia (FN) in cancer patients undergoing chemotherapy can result in life-threatening outcomes. Hence, an evaluation of associated risk factors can enable clinical surveillance as well as inform prophylactic measures. In this retrospective cohort study, we report a mortality prognostication model for chemotherapy-treated cancer patients upon a neutropenic episode.Clinical and diagnostic data of 137 febrile neutropenia patients (>18 years) was collected from a cancer hospital, with the primary endpoint of post-hospital admission mortality within 30 days. The data was integratively analyzed and machine learning techniques were applied to develop the predictive model which was then internally cross validated. Towards enabling personalized risk assessment, a nomogram was constructed and validated. Chemotherapy-treated cancer patients undergoing a neutropenic episode exhibited an overall mortality rate of 17.36%. Multivariate logistic analysis elucidated that shock, pneumonia, carboplatin, doxorubicin, antifungal and antiviral prophylaxis, and hemoglobin correctly classified cases with an overall accuracy of 92% and discriminated mortality with a specificity of 76%. Antiviral (odds ratio (OR): 0.669, p = 0.689), and antifungal prophylaxis (OR: 0.619, p = 0.5) demonstrated a protective effect. The receiver operating characteristic (ROC) curve of the nomogram exhibited an area under the curve of 0.878 (95% CI 0.778 - 0.977), Hosmer–Lemeshow test p-value = 0.635, and a high net benefit in the clinical decision curve. The proposed model offers insights into the role of clinical predictors as well as treatment characteristics that can ameliorate mortality risk in cancer patients with FN. The study highlights bacteremia-related surveillance, along with thrombocytopenia, linked to carboplatin, for reducing individualized mortality risk along with improved monitoring and informed treatment strategies. Health sciences/Health care/Public health/Epidemiology Health sciences/Risk factors Health sciences/Oncology/Cancer/Cancer epidemiology Health sciences/Medical research Febrile neutropenia Prognostication Chemotherapy Mortality Cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Febrile neutropenia (FN) is a significant complication in cancer patients undergoing chemotherapy, impacting 10%-50% of patients with solid tumors and rising to 80% in hematological malignancies 1–3 . FN entails considerable risks, including hypotension, acute kidney injury, and respiratory and heart failure, that occur in 25%-30% of cases 4 . Beyond these primary complications, FN increases susceptibility to invasive infections that can swiftly progress to conditions like septic shock and mortality 5,6 which contribute to an overall mortality rate between 2–21% 7,8 . Risk factors for FN-related complications of FN encompass age, cancer type, comorbidities, delayed antibiotics, and laboratory or vital sign abnormalities 9–11 . The Talcott, Multinational Association for Supportive Care in Cancer (MASCC), and Clinical Index of Stable Febrile Neutropenia (CISNE) risk scores have been developed using different combinations of these factors, towards enhancing FN management 10,11 . While Kuderer et al 12 identified an increased risk of hospitalization and post-FN mortality, Nordvig et al 13 explored the heightened long-term risk of infections in surviving patients who experienced FN during chemotherapy. Overall, the impact of FN on mortality remains underexplored and hinges on various factors contributive towards noninfectious-related mortality including organ damage, chemo-related dose modifications and non-compliance, patient characteristics, and type of malignancy 12,14 . This underscores the need for effective monitoring and management of cancer patients, not only during chemotherapy, but also for mitigating the long-term threat of severe infections associated with FN. FN is underpinned by a complex interplay of multiple risk factors and can lead to adverse clinical outcomes. This necessitates the development of integrative clinical decision-making models for its effective and timely management 15 . Literature furnishes evidence on the association between likelihood of developing FN and the duration and severity of neutropenia in both acute leukemia patients 1 and in those with solid tumors 16 . The risk of febrile neutropenia (FN) increases with both the severity and duration of neutropenia and is most frequently observed early in the course of chemotherapy 17–19 . Identifying risk factors associated with increased morbidity and mortality in relation to FN 20 is crucial for evidence-based clinical monitoring and prophylactic interventions. Towards this goal, we conducted a multivariate analysis to identify patient-centric risk factors associated with FN mortality and developed a prognostic model for a cohort of FN patients with different cancer types, at a single cancer center. The proposed prognostication model can be used to assess mortality risk after the initiation of a neutropenic episode amongst cancer patients. An improved quantitation of individualized mortality risk would facilitate a more informed strategy for patient monitoring and treatment. Moreover, prognostic modelling could provide substantial evidence towards improving health care systems and advancing medical research especially in countries with limited healthcare infrastructure. Patients and Methods Setting and study population This retrospective cohort study focused on patients diagnosed with FN at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC), from Aug 2014 to April 2018 (IRB EX-01-02-22-01). The study had the IRB requirement of written informed consent waived in accordance with documentation of informed consent (45 CFR 46.117) 21 , as it posed “no more than minimal risks to participants”. In conformance to the definition 22,23 , the inclusion criteria was ( 1 ) diagnosis of malignancy; ( 2 ) with a low neutrophil count (< 1,500/µL); ( 3 ) fever defined as an oral temperature (≥ 38.3°C / 101°F); ( 4 ) a history of chemotherapy administered within 15 days before their hospitalization and, ( 5 ) over the age of 18 years. The primary objective of this pilot study was to evaluate the risk factors of, and estimate the risk associated with mortality after the onset of FN episode amongst cancer patients. To ensure generalizability, clinical relevance, and practical applicability, SKMCH&RC Hospital Information System (HIS) was mined to identify FN patients and associated datasets, towards identification of multiple patient-centric risk factors associated with FN mortality and onward development of prognostic models. Initially, 1000 patients experiencing neutropenic sepsis were sampled from HIS, from which 137 patients which satisfied the inclusion criteria, were selected (see Supplementary Figure S1 ). Based on the power analysis conducted using the compromise method 24 results indicated that with a sample size of 137 and a balanced alpha and beta levels of 0.05 ensured sufficient power of 85.7% to detect an effect size of approximately 0.5 in the logistic regression model. Data collection and Prognostic factors The study involved a data collection exercise which encompassed patient-related information such as anthropometrics & demographics, oncology history, chemotherapy and prophylactic treatment, microbiology, radiology, pathology & hematology reports. For a detailed overview of the patient oriented clinical features (see Supplementary Table S1 ). Patient data was systematically curated from the HIS by a consortium of oncologists, radiologists, and physicians, who had access to both structured and unstructured information within the HIS. Each patient was identified by their unique medical record number (MRN). Admission dates were confirmed via the admission history notes. Upon hospital admission after a febrile neutropenic episode, the following baseline clinical data were collected: age; gender; demographics; BMI, comorbidities; type of malignancy; cancer stage; number of chemo cycles; presence or absence of metastasis, chemotherapeutic and antibacterial regimen; use of granulocyte colony-stimulating factor (G-CSF); use of antibacterial, antifungal, antiamoebic, antiviral and antifungal prophylaxis 2 weeks prior to FN episode. Diagnostic data was accessed from the designated report section, emphasizing both pathology and radiology reports. Cancer type information was gathered from biopsy reports and consultant notes, which provided essential histopathological details and clinical insights into cancer classification and staging. Treatment data was collected by an oncologist through examining medication histories and chemotherapy administration notes, detailing the types of chemotherapy drugs used, dosages, schedules, and any recorded side effects. Microbiological data was extracted from the microbiology section within the main pathology reports. It included information on detected pathogens, sample sources, and antibiotic resistance profiles. FN episodes were categorized into three groups based on infection type: ( 1 ) clinically documented infection (CDI) defined as fever and local inflammation without confirmation of pathogenic organisms, ( 2 ) microbiologically documented infection (MDI) defined as fever with detected infectious organisms from cultures (blood, sputum, urine, stool, throat swab), and ( 3 ) fever of unknown origin (FUO) defined as isolated fever without signs or symptoms suggestive of clinical infection and microbial documentation. In hospital case mortality outcome was labelled upon patient demise within 30 days after the diagnosis of FN. Finally, the resulting dataset was audited by a team of oncologists, radiologists and physicians. Statistical analysis Quantitative parameters were reported as median and interquartile range (25th − 75th percentile) after being tested for normality whereas categorical variables were expressed as frequency and percentages. Continuous variables were compared using the Mann-Whitney U test while qualitative variables employed Chi-square and Fisher's exact test. Linear discriminant, logistic regression, and random forest classification model with 800 trees and balanced sampling was trained on the neutropenic data set (samples provided in Supplementary Figure S10) and classification accuracies were compared. This dataset didn’t account for any missing values. Interaction terms, collinearity, goodness-of-fit, the presence of confounders or suppressors, and the models’ assumption were tested for the final model. Statistical analyses were performed using IBM SPSS, version 26.0 (IBM Corp., Armonk, N.Y., USA) and R (version 4.3.1). Power analysis was performed using G*Power version 3.1. A two-tailed p value less than 0.05 was considered as statistical significance. Logistic Regression Binary logistic regression is the generalization of linear regression to fit an additive model. Regularization was used to limit model complexity and control overfitting 25 . The simplified final model was internally validated using ten-fold cross-validation and bootstrapping to assess the stability and reliability of the estimates. We constructed a nomogram 26 based on the final fitted logistic regression model, to translate each effect within the model onto a 0 to 100 scale, maintaining proportionality with the log odds. The points were summed across predictors to yield Total Points , which subsequently transformed into a linear predictor and provided predicted probabilities 27 . The model fit employed a binomial distribution with a logit link function. Next, the distribution of potential predictors in the model, along with their aggregate regression score, was superimposed on the nomogram scales. The accuracy of the model in predicting absolute risk was assessed using the Hosmer-Lemeshow test and the calibration graphical method with a p-value < .05 indicating poor calibration. Decision curve analysis (DCA) was employed to evaluate the clinical practicality of the nomogram model. Interpretability Machine learning (ML) methods are inherently variable in their interpretability. Linear models establish explicit connections between clinical inputs and predictions. In contrast, ensemble methods, like random forests aggregate multiple individual models, thus limiting their interpretability. Notably, models lacking clinical interpretability pose considerable challenges in justifying predictions and evaluating validity, thereby impeding clinical translation 28 . Model sparsity - another aspect of interpretability, explains the number of features used for generating a prediction. For that, we split input features into 3 main branches (see Supplementary Figure S2 & S3) for better feature extraction. Results 1. Patient Characteristics A sample of 1000 hospitalized cancer patients over the period of 44 months (Aug 2014 to April 2018), recognized for neutropenia sepsis, were collected. Of these patients, 137 (13.7%) were categorized to develop febrile neutropenic sepsis using neutrophil count (< 1,500/µL), fever (≥ 38.3°C / 101°F) thresholds 22,23 (Supplementary Figure S1 ). The neutropenic cohort reported a mortality rate of 18.24% amongst patients, who suffered from Grade 3 or 4 neutropenic sepsis. Males exhibited a higher rate of deaths as compared to females (64% vs 36%). There was no contrasting difference seen amongst alive and deceased patients for age, BMI, fever, creatinine, and number of chemo cycles (p-value > 0.05). However, in case of hematological parameters including platelets, hemoglobin, and RBCs, a significant decline was observed for the deceased group, whereas a significant heightened median expression was seen for blood urea nitrogen (BUN) (p-values < 0.01). There was a significant association observed for tumor type by survival status, wherein hematological malignancies accounted for 64% of the deaths (Table 1 ). Of 137 patients, 58 (42.34%) had hematologic malignancies (HM); 30 had non-Hodgkin’s lymphoma (51.7%), while 16 had acute lymphoblastic leukemia (27.5%) (Supplementary Table S2). 79 patients (57.66%) suffered from solid tumors, of these 51.8% were Breast carcinoma occurrences (supplementary Table S3). Table 1 Febrile Neutropenic Patient characteristics at time of hospital Admission. Mean values (± SD) and frequency (%) for different parameters compared by survival status Alive (n = 112) Deceased (n = 25). Continuous variables for non-normal data were expressed as medians (interquartile ranges). While categorical variables were expressed as number and percentage. Comparisons between Alive and deceased groups were performed Mann Whitney rank sum test whereas Chi-square or Fisher’s exact test was employed for categorical variables. Alive (n = 112) Deceased (n = 25) Parameters n Column (n%) Median (IQR) n Column (n%) Median (IQR) p-value Gender Female 57 50.9% 9 36.0% 0.19 Male 55 49.1% 16 64.0% Age 36.75 (30.88, 42.45) 33.57 (31.23,43.19) 0.713 BMI 23.27 (22, 24.64) 21.83 (18.13, 26) 0.20 Fever 39 (39, 39.4) 39 (39, 39.5) 0.63 Platelets (x103 µL) 97.50 (76, 123) 13 (7.0, 54.0) 0.0001** Haemoglobin (g/dL) 8.70 (8, 9.4) 7.7 (7.00, 8.80) 0.006** Creatinine (mg/dl) 0.60 (0.55, 0.64) 0.61 (0.50, 0.93) 0.866 RBCs (x106 µl) 3.18 (2.95, 3.49) 2.72 (2.44, 3.22) 0.016* Blood Urea Nitrogen (BUN) (mg/dl) 9.37 (8.05, 10.49) 13.86 (11.17,19.70) 0.0035** No. of Chemo Cycle 2 (2, 3 ) 2 (2, 4 ) 0.76 Tumor Type ST 70 62.5% 9 36.0% 0.023* HM 42 37.5% 16 64.0% Infection Type MDI 43 38.4% 16 64.0% .003** CDI 12 10.7% 5 20.0% FUO 57 50.9% 4 16.0% Shock No 108 96.4% 15 60.0% < 0.0001*** Yes 4 3.6% 10 40.0% Pneumonia No 108 96.4% 16 64.0% < 0.0001*** Yes 4 3.6% 9 36.0% ANC 5 days No 82 73.2% 12 48.0% 0.018* Yes 30 26.8% 13 52.0% Gram (-) Infection No 92 82.1% 16 64% 0.05 Yes 20 17.9% 9 36% IQR: Inter Quartile Range, ST: Solid Tumor, HM: Hematologic Malignancy, MDI: Microbiologically Documented Infection, CDI: Clinically Documented Infection, and FUO: Fever of Unknown Origin ANC: Absolute Neutrophil Count. p-value: * < 0.05, ** < 0.001, *** < 0.0001 Of the neutropenic cohort, 59 (43.1%) had microbiologically documented infections (MDI), 17 (12.4%) clinically documented infections (CDI), and 61 (44.5%) fever of unknown origin (FUO) this infection type was significantly (p-value = 0.023) different between two groups. The highest occurrence of infection type amongst alive group was due to FUO (50.9%) in contrast to deceased group where major contribution towards mortality was in effect of MDI (64%) (Table 1 ). Out of all 24 species of bacteria identified, 16 were gram negative, while 8 were gram positive. Moreover, 2 fungal and 3 parasitic species were also identified and isolated in culture test (Supplementary table S4). 36% of deaths were attributed to gram negative infection but could not reach statistical significance (p-value = 0.058 - two tails, and 0.046 – one tail). Presence of Shock (yes = 1) (40% vs 3.6%) and pneumonia (yes = 1) (36% vs 3.6%) had a higher incidence amongst the deceased group in comparison to alive, which is indicative of a significantly strong association (p-value 5 days also exhibited a significant association (p-value = 0.018) with survival status (Table 1 ). 2. Predictive Modelling Next, we sub-categorized clinical features (Supplementary Table S1 ) into three major categories (Supplementary Figure S2) towards feature selection and multivariate modelling (Supplementary Figure S3). A. Multivariate analysis of factors underpinning FN related in-hospital mortality To investigate patient related factors in association with in-hospital mortality, we performed categorization to enable univariate analyses (Table 1 ) (Supplementary Figure S2). This helped in shortlisting the first set (Set 1) of candidate predictors. We further applied multivariate analyses on these predictors of mortality and found all models to be statistically significant (p-value < 0.001, Table 2 A). B. Evaluation of chemotherapeutics in association with mortality amongst cancer patients with febrile neutropenia (FN) Clinical guidelines indicate that certain chemotherapeutics increase the likelihood of febrile neutropenia 29 . To evaluate this, we isolated chemotherapeutic regimens that may confer a higher risk of FN and profiled it over survival status (Supplementary Table S5A). After primary evaluation of the chemotherapeutic regimens, the second set (Set 2) (Table 2 B) of candidate predictors was collated. To evaluate the strength of these chemotherapy-related predictors towards mortality, we built three classification models (Table 2 B). C. Impact of antimicrobials and GCSF-Prophylaxis on hospital mortality Cancer patients are provided with different types of prophylaxis as a part of their treatment including granulocyte colony-stimulating factor (GCSF)-prophylaxis and antimicrobials; antiamoebic, antiviral and antifungal prophylaxis (Supplementary Table S5B). These formed the third set of candidate predictors (Table 2 C) and to determine the influence of these prophylactic treatments on mortality, we again performed predictive modelling using RF, LDA and LR (Table 2 C). 3. Integrative approach towards identification of the independent predictors of hospital mortality in FN patients To develop an integrative predictive model encompassing attributes with an enhanced predictive quality, we integrated those candidate predictors which were significant, had a high mean decrease accuracy (MDA), and had improved odds from the outcomes of individual models (Tables 1 & 2 , Supplementary Figure S4 – S8, supplementary Table S6- S11). To test the effect of these selected predictors on the likelihood of patient mortality, we applied LR and found overall model to be statistically significant at, X 2 ( 12 ) = 57.036, p < 0.0001. The model explained 55.5% (Nagelkerke R2) of the variance in mortality and correctly classified 90.5% of cases (supplementary Table S12). Post-hoc tuning of model parameters and removal of non-contributing predictors, an adjusted model was developed. Upon bootstrapping with the over 1000 iterations, the components and characteristics of the adjusted model are summarized in (supplementary Table S13). The adjusted model was statistically significant at, X 2 ( 12 ) = 57.036, p < 0.0001. This integrative model explained 52.2% (Nagelkerke R 2 ) of the variance in mortality and increased accuracy of correctly classified cases to 92%. Odds ratios for the integrative logistic model are provided in Fig. 1 . To further validate the contribution of these predictors in classification of alive and dead cases, we applied RF and LDA. Outcomes from the random forest and LDA classification were in line with the logistic regression performance indices (Table 3 ). The five most important predictors of mortality were shock (mean decrease accuracy (MDA) = 34.07), pneumonia (27.98), hemoglobin (14.06), antiviral prophylaxis (9.82) and carboplatin (8.18) (Fig. 2 ). Likewise, for the LR model (Fig. 1 , Supplementary S12) only shock, pneumonia and carboplatin had a significant OR. LDA provided, Fischer’s discriminant functions (DF0 (Alive) and DF1(Dead)) based on the estimation of corresponding β-values (Supplementary Table S14) which contributed towards the total variance of 100% and canonical correlation of 0.67 (p-value < 0.0001). Internal 10-fold cross validation for the integrative logistic model was performed and as a result cross validation accuracy (CVA) of 0.8608 with a kappa value of 0.437 was achieved. Table 2 Comparative performance indices of LR, LDA and RF models when using 3 different candidate predictor sets to predict NS associated in-hospital mortality. The confusion matrix shows the classification of the cases based on their predicted survival status in the neutropenic dataset. Where performance indices are calculated according to : Accuracy = (TP + TN)/(TP + FP + TN + FN); sensitivity = TP/(TP + FN); specificity = TN/(TN + FP). (A) Predictor Set 1 (B) Predictor Set 2 (C) Predictor Set 3 Confusion Matrix Accuracy Sensitivity Specificity p-value Confusion Matrix Accuracy Sensitivity Specificity p-value Confusion Matrix Accuracy Sensitivity Specificity p-value Models Logistic Regression (LR) A D 0.883 0.938 0.64 < 0.0001 A D 0.862 0.981 0.304 0.006 A D 0.825 1 0.04 0.308 A 105 7 105 2 112 0 D 9 16 16 7 24 1 Linear Discriminant Analysis (LDA) A 105 7 0.898 0.938 0.72 < 0.0001 105 2 0.862 0.981 0.304 < 0.0001 72 40 0.645 0.643 0.64 0.325 D 7 18 16 7 9 16 Random Forest (RF) A 108 4 0.927 0.964 0.76 0.0002 105 2 0.862 0.981 0.261 0.14 112 0 0.818 1 0 0.55 D 6 19 17 6 25 0 Predictor variables Age, BMI, Platelets, Haemoglobin, Shock, Pneumonia, Gram Negative Infection Cisplatin, Cyclophosphamide, Doxorubicin, Etoposide, CP, Carboplatin, Cycle GCSF Prophylaxis, Antiamoebic Prophylaxis, Antiviral Prophylaxis, Antifungal Prophylaxis, Antibiotics 2WP, Antiamoebic 2WP, Antiviral 2WP, Antifungal 2WP A: Alive, D: Dead, CP: Carboplatin/Paclitaxel, GCSF: Granulocyte-Colony Stimulating Factor, 2WP: Prophylaxis 2 Weeks prior hospital admission Table 3 Comparative performance indices of LR, LDA and RF models for final predictor set to predict NS associated in-hospital mortality. The confusion matrix shows the classification of the cases based on their predicted survival status in the neutropenic dataset. Here, the columns denote the actual cases, and the rows denote the predicted. Where performance indices are calculated according to : Accuracy = (TP + TN)/(TP + FP + TN + FN); sensitivity = TP/(TP + FN); specificity = TN/(TN + FP). Final Predictor Set Confusion Matrix Accuracy Sensitivity Specificity p-value Models Logistic Regression (LR) A D 0.92 0.955 0.76 < 0.0001 A 105 7 D 9 16 Linear Discriminant Analysis (LDA) A 102 10 0.891 0.911 0.8 < 0.0001 D 5 25 Random Forest (RF) A 110 2 0.941 0.982 0.76 < 0.0001 D 6 19 Predictor variables Age, Haemoglobin, Shock, Pneumonia, Carboplatin, Doxorubicin, Antifungal 2WP, Antiviral Prophylaxis. A: Alive, D: Dead, 2WP: 2 weeks prior prophylaxis Nomogram for predicting in-hospital mortality Towards clinical translation of the proposed model, a normogram was developed for prediction of mortality amongst cancer patients with FN (Fig. 3 ). For that, the regression coefficients for each predictor were rescaled between 0 to 100 which were then transformed into probabilities through logit transformation. Taken together, clinicians can input a patient’s profile, comprising of 8 predictors, and map them on to the nomogram to calculate the probability of a mortality. Consequently, receiver operating characteristic curve analysis gave an area under the curve of 0.878 (95% CI 0.778–0.977) (Fig. 4 A). The calibration of the nomogram was checked by the calibration curve (Fig. 4 B) using the Hosmer–Lemeshow test (p-value = 0.635) indicating no deviation from the fit and showing agreement between the observation and prediction. DCA conducted for the nomogram (Fig. 4 C & D, Supplementary Figure S9) indicates that the proposed model may inform clinical decisions with a threshold risk probability ≥ 10%. Discussion In this work, we present a clinical model for prognosticating outcomes for patients with FN. The proposed model was developed based on a cohort of 137 cancer patients who presented febrile neutropenia at time of hospital admission. The model predictors included bacteremia (shock, pneumonia), chemotherapeutics (carboplatin, doxorubicin), prophylaxis (antifungal and antiviral), along with hematological parameters such as hemoglobin. The resultant integrative model has a classification accuracy of 92% (Table 3 ) and can identify patients with febrile neutropenia (FN) at a high risk of mortality along with providing insights for prompt interventions to improve patient care and outcomes. Cancer patients with febrile neutropenia are at a high risk of developing infections, which can rapidly overwhelm the patient and cause septic shock and death 30–32 . The current model reports shock and pneumonia to be the most important mortality predictors, with highest variable importance (Fig. 2 ), and odds (Fig. 1 ). Previously, several clinical studies have reported bacteremia and pneumonia as major causes of morbidity and mortality among patients with FN 33,34 . Bacteremic pneumonia in patients with cancer having neutropenia, is associated with a poor outcome 33 . Pseudomonas aeruginosa infection is commonly believed to primarily impact cancer patients experiencing prolonged and severe neutropenia - in particular individuals with hematologic malignancies undergoing intensive chemotherapy within hospital setting 35 . Moreover, in a large retrospective study, it was found that Pseudomonas aeruginosa (10%), Escherichia coli (7.7%), and Klebsiella pneumoniae (5.6%) are the leading cause for gram-negative pneumonia 34 . This is indicative of pneumonia being an independent predictive factor of mortality in patients 36,37 with neutropenia. Our analysis revealed that 64% of deaths were attributed to microbiologically documented infections (Table 1 ). Through various culture tests, we identified 16 types of gram-negative and 8 types of gram-positive bacterial species (Table S3). The proportion of culture positive tests were low because the patients were already on antimicrobial prophylaxis. Notably, Escherichia coli ( 6 ), Pseudomonas aeruginosa ( 2 ), and Klebsiella pneumoniae ( 1 ) infections, documented in blood culture, were associated with 9 cases of mortality. This underscores the importance of closely monitoring the clinical characteristics and risk factors associated with bacteremic pneumonia caused by these bacteria. Moreover, thrombocytopenia is highly prevalent in ICU admittees with severe sepsis and septic shock. Its onset, whether a relative or absolute decrease in platelet count, significantly and independently correlates with a doubling of the expected mortality rate during the septic episode 38–40 . Our results show platelet counts are significantly lower in deceased patients (Table 1 ) and that thrombocytopenia in the ICU acts as a risk indicator, rather than a primary cause of mortality. A prompt investigation and treatment of underlying factors contributing to this condition is, therefore, clinically employable. Chemotherapy-induced thrombocytopenia (CIT) is a common complication of cancer treatment with cytotoxic agents, with carboplatin being among the most commonly implicated agents in causing CIT 41,42 used as monotherapy or in combination with other chemo drugs. Here, we show that patients on carboplatin therapy and a neutropenic episode exhibit significant decrease in platelets (median 85 (3-125)) vs 85 (70–115) not taking carboplatin, moreover, same trend is observed for etoposide (Supplementary Table S5C). This could be indicative of drug-induced thrombocytopenia as an underlying cause that is contributing significantly towards mortality (Fig. 2 ) with odds increasing up to 3 times in carboplatin-treated patients. The elevated risks associated with thrombocytopenia linked to carboplatin, etoposide, and other medications especially penicillin 43 , can potentiate immune-mediated thrombocytopenia and warrant further investigation. The underlying mechanism can be valuable for clinicians as it holds critical implications. In particular, for immune-mediated thrombocytopenia, avoiding the drug is imperative, whereas in dose-dependent thrombocytopenia, dose reduction may be adequate. FN is a medical emergency, carrying a high mortality risk in the absence of timely and appropriate treatment. The use of doxorubicin and antiviral and antifungal prophylaxis plays a crucial role in reducing morbidity and mortality in patients with neutropenic sepsis 44 . Fungal pathogens are prevalent in high-risk patients experiencing neutropenia. Among these, Candida spp. and Aspergillus spp . are the most frequently implicated in invasive fungal infections 45 . Like these findings, our culture investigations also isolated these two species; therefore, provision of anti-fungal prophylaxis 2 weeks prior to the neutropenic episode provided patients with a protective effect. This is well in concordance with our model which also shows that antivirals and antifungals decrease the odds of mortality (Fig. 1 & Supplementary Table S12) and may warrant further investigation. Furthermore, the selection of antimicrobial agents for prophylaxis and empirical therapy should be driven by the local susceptibility and resistance patterns of microorganisms. Taken together, the current model provides a novel predictor for mortality in cancer patients with FN in light of prophylactic interventions. The primary strength of our observational report is the integration of multifactorial data from a single cancer center, ensuring consistent treatment policies and standardized data collection. This integration leverages readily available prognostic factors, many of which are previously reported with insufficient statistical adjustments. Further, we applied a rigorous method in shortlisting of the potential prognostic factors from a broader range of modalities. The main limitations of this study are the retrospective nature and small size of the data. Our future work will aim to refine this model further through a validation in a larger multicenter population for more generalizability and identification of more prognostic factors. With the secondary aim of advising the integration of a more refined model into the health care system for prompt risk stratification for mortality and better implementation of the clinical management. When evaluating which method should be implemented to aid clinical review, we should mainly consider the model’s performance index, ease of implementation and interpretability. The random forests achieved the highest accuracy (94%) in identifying the occurrence of an event, however when the level of interpretation is required RF becomes difficult to interpret for an end user and is potentially more challenging to implement in real-time clinical settings. Therefore, the nomogram model was selected with an accuracy of 92% focusing on key aspects relevant to clinical usability, which was further supplemented through DCA. The model can be easily integrated into an electronic health record (EHR) system and made available as an online tool or mobile application. The model facilitates interpretable decision-making based on risk estimation and patient consultations without significant workflow disruptions. Conclusion Identification of mortality-associated factors are imperative for prognostication of cancer patients presenting neutropenia. Towards that, the factors identified in this study may offer valuable insights in resolving the underlying cause of mortality in association with shock, pneumonia, and thrombocytopenia. In particular, shock and pneumonia are reported as critical predictors, and are associated with bacteremia linked to Pseudomonas aeruginosa , Escherichia coli , and Klebsiella pneumoniae . Moreover, elevated risk is reported in association with thrombocytopenia, induced by carboplatin, etoposide, and penicillin. Combination of anti-viral and antifungal prophylaxis may improve the survival in this population. Together, a comprehensive evaluation of patient’s treatment regimen and appropriate laboratory assessments, interpreted in the clinical context, are essential for neutropenia management and in decreasing in-hospital mortality rates. Future work will investigate the use of additional treatment modalities to improve the prognostic model and risk stratification. Declarations Conflict of interest None, the author(s) declare no material or financial competing interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript and was conducted independently. Data availability statement The dataset generated and analyzed during the current study is not publicly available due to ongoing data analysis on the same dataset as an extension of another research project but is available from the corresponding author on reasonable request. References Moon, H., Choi, Y. J. & Sim, S. H. Validation of the Clinical Index of Stable Febrile Neutropenia (CISNE) model in febrile neutropenia patients visiting the emergency department. Can it guide emergency physicians to a reasonable decision on outpatient vs. inpatient treatment? PLoS One 13 , e0210019 (2018). Carmona-Bayonas, A. et al. Prediction of serious complications in patients with seemingly stable febrile neutropenia: Validation of the clinical index of stable febrile neutropenia in a prospective cohort of patients from the FINITE study. Journal of Clinical Oncology 33 , 465–471 (2015). de Naurois, J. et al. Management of febrile neutropenia: ESMO Clinical Practice Guidelines. 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Mortality, morbidity, and cost associated with febrile neutropenia in adult cancer patients. Cancer 106 , 2258–2266 (2006). Nordvig, J. et al. Febrile Neutropenia and Long-term Risk of Infection Among Patients Treated With Chemotherapy for Malignant Diseases. Open Forum Infect Dis 5 , (2018). Lalami, Y. & Klastersky, J. Impact of chemotherapy-induced neutropenia (CIN) and febrile neutropenia (FN) on cancer treatment outcomes: An overview about well-established and recently emerging clinical data. Crit Rev Oncol Hematol 120 , 163–179 (2017). Du, X. et al. Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models. Int J Med Inform 139 , (2020). Green, M. D. et al. A randomized double-blind multicenter phase III study of fixed-dose single-administration pegfilgrastim versus daily filgrastim in patients receiving myelosuppressive chemotherapy. Annals of Oncology 14 , 29–35 (2003). Lyman, G. H. & Delgado, D. J. Risk and timing of hospitalization for febrile neutropenia in patients receiving CHOP, CHOP-R, or CNOP chemotherapy for intermediate-grade non-Hodgkin lymphoma. Cancer 98 , 2402–2409 (2003). Crawford, J. et al. Risk and timing of neutropenic events in adult cancer patients receiving chemotherapy: the results of a prospective nationwide study of oncology practice. J Natl Compr Canc Netw 6 , 109–118 (2008). Lyman, G. H., Abella, E. & Pettengell, R. Risk factors for febrile neutropenia among patients with cancer receiving chemotherapy: A systematic review. Crit Rev Oncol Hematol 90 , 190–199 (2014). Aagaard, T. et al. Mortality and admission to intensive care units after febrile neutropenia in patients with cancer. Cancer Med 9 , 3033 (2020). Requirements (2018 Common Rule) | HHS.gov. https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/revised-common-rule-regulatory-text/index.html#46.116. Mehta, H. M., Malandra, M. & Corey, S. J. G-CSF and GM-CSF in Neutropenia. The Journal of Immunology 195 , 1341–1349 (2015). Giri, R. K. & Sahoo, R. K. Febrile Neutropenia. Onco-critical Care: An Evidence-based Approach 233–250 (2023) doi:10.1007/978-981-16-9929-0_21. Erdfelder, E., FAul, F., Buchner, A. & Lang, A. G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods 41 , 1149–1160 (2009). Shipe, M. E., Deppen, S. A., Farjah, F. & Grogan, E. L. Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis 11 , S574–S584 (2019). Zhang, Z. & Kattan, M. W. Drawing Nomograms with R: applications to categorical outcome and survival data. Ann Transl Med 5 , (2017). Harrell, F. E. Regression Modeling Strategies. (2001) doi:10.1007/978-1-4757-3462-1. Towards trustable machine learning. Nat Biomed Eng 2 , 709–710 (2018). Okunaka, M., Kano, D., Matsui, R., Kawasaki, T. & Uesawa, Y. Comprehensive Analysis of Chemotherapeutic Agents That Induce Infectious Neutropenia. Pharmaceuticals 14 , (2021). Marín, M. et al. Factors influencing mortality in neutropenic patients with haematologic malignancies or solid tumours with bloodstream infection. Clinical Microbiology and Infection 21 , 583–590 (2015). Ramzi, J. et al. Predictive factors of septic shock and mortality in neutropenic patients. Hematology 12 , 543–548 (2007). Clarke, R. T., Jenyon, T., Parsons, V. V. H. & King, A. J. Neutropenic sepsis: management and complications. Clinical Medicine 13 , 185 (2013). Carratalà, J., Rosón, B., Fernández-Sevilla, A., Alcaide, F. & Gudiol, F. Bacteremic Pneumonia in Neutropenic Patients With Cancer: Causes, Empirical Antibiotic Therapy, and Outcome. Arch Intern Med 158 , 868–872 (1998). Zhao, C. et al. Risk Factors for 30-Day Mortality in Patients with Bacteremic Pneumonia Caused by Escherichia coli and Klebsiella pneumoniae: A Retrospective Study. Int J Gen Med 16 , 6163 (2023). Maschmeyer, G. & Braveny, I. Review of the incidence and prognosis of Pseudomonas aeruginosa infections in cancer patients in the 1990s. Eur J Clin Microbiol Infect Dis 19 , 915–925 (2000). Chang, H. Y. et al. Causes of death in adults with acute leukemia. Medicine (United States) 55 , 259–268 (1976). Evans, S. E. & Ost, D. E. Pneumonia in the neutropenic cancer patient. Curr Opin Pulm Med 21 , 260 (2015). Venkata, C., Kashyap, R., Christopher Farmer, J. & Afessa, B. Thrombocytopenia in adult patients with sepsis: Incidence, risk factors, and its association with clinical outcome. J Intensive Care 1 , 1–10 (2013). Larkin, C. M., Santos-Martinez, M. J., Ryan, T. & Radomski, M. W. Sepsis-associated thrombocytopenia. Thromb Res 141 , 11–16 (2016). Awad, W. B., Nazer, L., Elfarr, S., Abdullah, M. & Hawari, F. A 12-year study evaluating the outcomes and predictors of mortality in critically ill cancer patients admitted with septic shock. BMC Cancer 21 , 1–7 (2021). Wu, Y. H., Chen, H. Y., Hong, W. C., Wei, C. Y. & Pang, J. H. S. Carboplatin-Induced Thrombocytopenia through JAK2 Downregulation, S-Phase Cell Cycle Arrest and Apoptosis in Megakaryocytes. Int J Mol Sci 23 , (2022). Ten Berg, M. J. et al. Thrombocytopenia in adult cancer patients receiving cytotoxic chemotherapy: Results from a retrospective hospital-based cohort study. Drug Saf 34 , 1151–1160 (2011). Bakchoul, T. & Marini, I. Drug-associated thrombocytopenia. Hematology 2018 , 576–583 (2018). Villafuerte-Gutierrez, P., Villalon, L., Losa, J. E. & Henriquez-Camacho, C. Treatment of Febrile Neutropenia and Prophylaxis in Hematologic Malignancies: A Critical Review and Update. Adv Hematol 2014 , (2014). Pfaller, M. A., Pappas, P. G. & Wingard, J. R. Invasive Fungal Pathogens: Current Epidemiological Trends. Clinical Infectious Diseases 43 , S3–S14 (2006). Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4730716","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328881893,"identity":"d1511b29-1293-41ab-9d92-162481d068c7","order_by":0,"name":"Safee Ullah 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Centre","correspondingAuthor":false,"prefix":"","firstName":"Kashif","middleName":"","lastName":"Asghar","suffix":""},{"id":328881907,"identity":"4050708a-36e1-4f96-8a9c-1322dff706eb","order_by":14,"name":"Ahsun Khan","email":"","orcid":"","institution":"Shaukat Khanum Memorial Cancer Hospital and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Ahsun","middleName":"","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-07-12 13:45:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4730716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4730716/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43856-025-01142-9","type":"published","date":"2025-11-07T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66528241,"identity":"20f75fa8-9b8a-47a1-b1ed-b16e756fb234","added_by":"auto","created_at":"2024-10-14 05:26:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of adjusted odds ratio (OR) as an output of logistic regression associated with in-hospital mortality of patients with febrile neutropenia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach square represents point estimate of OR with horizontal line representing 95% CI. Red dotted line no effect point (OR=1) and significant predictors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4730716/v1/d0bce64be281bc5870b45f07.png"},{"id":66528298,"identity":"6897912f-74ad-4a2a-960e-8cdcc729404e","added_by":"auto","created_at":"2024-10-14 05:26:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative variable importance by mean decrease accuracy (MDA) plot for classification random forest, for mortality predictive model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePredictors shown in the plot are ranked by their MDA values. Predictors that display a pronounced increase in relative importance (MDA) compared to other predictors are influentialpredictors for the mortality outcome. The x-axis indicates the MDA after variable permutation and the values are shown at the end of bar. 2W: 2 weeks prior hospital admission\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4730716/v1/d9456ca1ee0a8ac87e21175d.png"},{"id":66528301,"identity":"a336ac2b-1e3d-4390-b196-9e716d11f677","added_by":"auto","created_at":"2024-10-14 05:26:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for the in-hospital mortality prediction using logistic regression model. This is described as a series of straight lines with a common linear scale on the plot.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere the scale factors of the individual lines for each covariate are given by their beta co-efficients, in the estimated model. The red dot in each line, represents the value of each of the 8 predictors for the patient. The illustrated example used the following hypothetical measures: Antiviral Prophylaxis= \"No\"(12.5), Antifungal 2WP= \"No\" (15), Carboplatin= \"Yes\"(62.5), Doxorubicin = \"Yes\"(0), Pneumonia= \"yes\"(87.5), Age =75 (7), Hemoglobin= 10.2 (40) , Shock= \"No\" (0). Risk score = 224.5, corresponding to the probability of 0.84 for Risk of death and the estimated calculated probability = 0.843 (0.172, 0.992).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4730716/v1/3b687099964ece4059b78ec5.png"},{"id":66528354,"identity":"3aa52af6-95dd-4bf2-bd35-a295be6ef05e","added_by":"auto","created_at":"2024-10-14 05:26:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155185,"visible":true,"origin":"","legend":"\u003cp\u003e(A)The area under the receiver operating characteristic (ROC) curve (AUC) for the validation set was 0.878 (95% CI: 0.778–9.77), showing the good predictive power of the model with good discrimination. (B) A calibration curve of the mortality prediction model in patients with febrile neutropenia. Note: The x-axis is the risk of death. And y-axis is the actual incidence of death. In the ideal situation, the dashed line is on the diagonal and the solid line is reflective of the predictive power of the model, good superimposition provides better predictive power. (C) \u0026amp; (D) The decision curves analysis (DCA) curve of the prognostic nomogram. Note: The DCA curve for mortality model where the horizontal axis represents the high-risk threshold value with the cost: benefit ratio scale. This is representative of the reference probability of whether a patient receives treatment, and the vertical y-axis illustrates the net benefit rate, (subtracting the proportion of false positives from the proportion of true positives), weighted by the odds of the threshold probability. Under the same threshold probability, a higher net benefit indicates that patients can derive maximal advantage from employing this model for diagnosis. The closer the proximity of DCA curve on to the top region is directly associated with the high diagnostic performance of the model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4730716/v1/f184436400afc0526cbb3295.png"},{"id":95430180,"identity":"192dd337-8110-467f-9b0d-3622dc80c78e","added_by":"auto","created_at":"2025-11-08 08:06:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2042030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4730716/v1/c1e75d44-a596-4c1f-9367-5c8050e88e55.pdf"},{"id":66528291,"identity":"fe69087e-d7c0-444d-adaf-e71f72cd7786","added_by":"auto","created_at":"2024-10-14 05:26:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":485057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementary02.docx","url":"https://assets-eu.researchsquare.com/files/rs-4730716/v1/2cb1a03964f434bd85435d28.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"In-hospital Mortality Prognostication for Cancer Patients with Febrile Neutropenia: A Single Center Observational Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFebrile neutropenia (FN) is a significant complication in cancer patients undergoing chemotherapy, impacting 10%-50% of patients with solid tumors and rising to 80% in hematological malignancies\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. FN entails considerable risks, including hypotension, acute kidney injury, and respiratory and heart failure, that occur in 25%-30% of cases \u003csup\u003e4\u003c/sup\u003e. Beyond these primary complications, FN increases susceptibility to invasive infections that can swiftly progress to conditions like septic shock and mortality\u003csup\u003e5,6\u003c/sup\u003e which contribute to an overall mortality rate between 2\u0026ndash;21% \u003csup\u003e7,8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRisk factors for FN-related complications of FN encompass age, cancer type, comorbidities, delayed antibiotics, and laboratory or vital sign abnormalities \u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. The Talcott, Multinational Association for Supportive Care in Cancer (MASCC), and Clinical Index of Stable Febrile Neutropenia (CISNE) risk scores have been developed using different combinations of these factors, towards enhancing FN management \u003csup\u003e10,11\u003c/sup\u003e. While Kuderer \u003cem\u003eet al\u003c/em\u003e \u003csup\u003e12\u003c/sup\u003e identified an increased risk of hospitalization and post-FN mortality, Nordvig \u003cem\u003eet al\u003c/em\u003e \u003csup\u003e13\u003c/sup\u003e explored the heightened long-term risk of infections in surviving patients who experienced FN during chemotherapy. Overall, the impact of FN on mortality remains underexplored and hinges on various factors contributive towards noninfectious-related mortality including organ damage, chemo-related dose modifications and non-compliance, patient characteristics, and type of malignancy \u003csup\u003e12,14\u003c/sup\u003e. This underscores the need for effective monitoring and management of cancer patients, not only during chemotherapy, but also for mitigating the long-term threat of severe infections associated with FN.\u003c/p\u003e \u003cp\u003eFN is underpinned by a complex interplay of multiple risk factors and can lead to adverse clinical outcomes. This necessitates the development of integrative clinical decision-making models for its effective and timely management\u003csup\u003e15\u003c/sup\u003e. Literature furnishes evidence on the association between likelihood of developing FN and the duration and severity of neutropenia in both acute leukemia patients\u003csup\u003e1\u003c/sup\u003e and in those with solid tumors\u003csup\u003e16\u003c/sup\u003e. The risk of febrile neutropenia (FN) increases with both the severity and duration of neutropenia and is most frequently observed early in the course of chemotherapy\u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. Identifying risk factors associated with increased morbidity and mortality in relation to FN\u003csup\u003e20\u003c/sup\u003e is crucial for evidence-based clinical monitoring and prophylactic interventions. Towards this goal, we conducted a multivariate analysis to identify patient-centric risk factors associated with FN mortality and developed a prognostic model for a cohort of FN patients with different cancer types, at a single cancer center. The proposed prognostication model can be used to assess mortality risk after the initiation of a neutropenic episode amongst cancer patients. An improved quantitation of individualized mortality risk would facilitate a more informed strategy for patient monitoring and treatment. Moreover, prognostic modelling could provide substantial evidence towards improving health care systems and advancing medical research especially in countries with limited healthcare infrastructure.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting and study population\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study focused on patients diagnosed with FN at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH\u0026amp;RC), from Aug 2014 to April 2018 (IRB EX-01-02-22-01). The study had the IRB requirement of written informed consent waived in accordance with \u003cem\u003edocumentation of informed consent (45 CFR 46.117)\u003c/em\u003e \u003csup\u003e21\u003c/sup\u003e, as it posed \u0026ldquo;no more than minimal risks to participants\u0026rdquo;. In conformance to the definition\u003csup\u003e22,23\u003c/sup\u003e, the inclusion criteria was (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) diagnosis of malignancy; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) with a low neutrophil count (\u0026lt;\u0026thinsp;1,500/\u0026micro;L); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) fever defined as an oral temperature (\u0026ge;\u0026thinsp;38.3\u0026deg;C / 101\u0026deg;F); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) a history of chemotherapy administered within 15 days before their hospitalization and, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) over the age of 18 years.\u003c/p\u003e \u003cp\u003eThe primary objective of this pilot study was to evaluate the risk factors of, and estimate the risk associated with mortality after the onset of FN episode amongst cancer patients. To ensure generalizability, clinical relevance, and practical applicability, SKMCH\u0026amp;RC Hospital Information System (HIS) was mined to identify FN patients and associated datasets, towards identification of multiple patient-centric risk factors associated with FN mortality and onward development of prognostic models. Initially, 1000 patients experiencing neutropenic sepsis were sampled from HIS, from which 137 patients which satisfied the inclusion criteria, were selected (see Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the power analysis conducted using the compromise method\u003csup\u003e24\u003c/sup\u003e results indicated that with a sample size of 137 and a balanced alpha and beta levels of 0.05 ensured sufficient power of 85.7% to detect an effect size of approximately 0.5 in the logistic regression model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and Prognostic factors\u003c/h2\u003e \u003cp\u003eThe study involved a data collection exercise which encompassed patient-related information such as anthropometrics \u0026amp; demographics, oncology history, chemotherapy and prophylactic treatment, microbiology, radiology, pathology \u0026amp; hematology reports. For a detailed overview of the patient oriented clinical features (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Patient data was systematically curated from the HIS by a consortium of oncologists, radiologists, and physicians, who had access to both structured and unstructured information within the HIS. Each patient was identified by their unique medical record number (MRN). Admission dates were confirmed via the admission history notes. Upon hospital admission after a febrile neutropenic episode, the following baseline clinical data were collected: age; gender; demographics; BMI, comorbidities; type of malignancy; cancer stage; number of chemo cycles; presence or absence of metastasis, chemotherapeutic and antibacterial regimen; use of granulocyte colony-stimulating factor (G-CSF); use of antibacterial, antifungal, antiamoebic, antiviral and antifungal prophylaxis 2 weeks prior to FN episode. Diagnostic data was accessed from the designated report section, emphasizing both pathology and radiology reports. Cancer type information was gathered from biopsy reports and consultant notes, which provided essential histopathological details and clinical insights into cancer classification and staging. Treatment data was collected by an oncologist through examining medication histories and chemotherapy administration notes, detailing the types of chemotherapy drugs used, dosages, schedules, and any recorded side effects. Microbiological data was extracted from the microbiology section within the main pathology reports. It included information on detected pathogens, sample sources, and antibiotic resistance profiles. FN episodes were categorized into three groups based on infection type: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) clinically documented infection (CDI) defined as fever and local inflammation without confirmation of pathogenic organisms, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) microbiologically documented infection (MDI) defined as fever with detected infectious organisms from cultures (blood, sputum, urine, stool, throat swab), and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) fever of unknown origin (FUO) defined as isolated fever without signs or symptoms suggestive of clinical infection and microbial documentation. In hospital case mortality outcome was labelled upon patient demise within 30 days after the diagnosis of FN. Finally, the resulting dataset was audited by a team of oncologists, radiologists and physicians.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eQuantitative parameters were reported as median and interquartile range (25th \u0026minus;\u0026thinsp;75th percentile) after being tested for normality whereas categorical variables were expressed as frequency and percentages. Continuous variables were compared using the Mann-Whitney U test while qualitative variables employed Chi-square and Fisher's exact test. Linear discriminant, logistic regression, and random forest classification model with 800 trees and balanced sampling was trained on the neutropenic data set (samples provided in Supplementary Figure S10) and classification accuracies were compared. This dataset didn\u0026rsquo;t account for any missing values. Interaction terms, collinearity, goodness-of-fit, the presence of confounders or suppressors, and the models\u0026rsquo; assumption were tested for the final model. Statistical analyses were performed using IBM SPSS, version 26.0 (IBM Corp., Armonk, N.Y., USA) and R (version 4.3.1). Power analysis was performed using G*Power version 3.1. A two-tailed p value less than 0.05 was considered as statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLogistic Regression\u003c/h2\u003e \u003cp\u003eBinary logistic regression is the generalization of linear regression to fit an additive model. Regularization was used to limit model complexity and control overfitting\u003csup\u003e25\u003c/sup\u003e. The simplified final model was internally validated using ten-fold cross-validation and bootstrapping to assess the stability and reliability of the estimates. We constructed a nomogram\u003csup\u003e26\u003c/sup\u003e based on the final fitted logistic regression model, to translate each effect within the model onto a 0 to 100 scale, maintaining proportionality with the log odds. The points were summed across predictors to yield \u003cem\u003eTotal Points\u003c/em\u003e, which subsequently transformed into a linear predictor and provided predicted probabilities\u003csup\u003e27\u003c/sup\u003e. The model fit employed a binomial distribution with a logit link function. Next, the distribution of potential predictors in the model, along with their aggregate regression score, was superimposed on the nomogram scales. The accuracy of the model in predicting absolute risk was assessed using the Hosmer-Lemeshow test and the calibration graphical method with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.05 indicating poor calibration. Decision curve analysis (DCA) was employed to evaluate the clinical practicality of the nomogram model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInterpretability\u003c/h2\u003e \u003cp\u003eMachine learning (ML) methods are inherently variable in their interpretability. Linear models establish explicit connections between clinical inputs and predictions. In contrast, ensemble methods, like random forests aggregate multiple individual models, thus limiting their interpretability. Notably, models lacking clinical interpretability pose considerable challenges in justifying predictions and evaluating validity, thereby impeding clinical translation\u003csup\u003e28\u003c/sup\u003e. Model sparsity - another aspect of interpretability, explains the number of features used for generating a prediction. For that, we split input features into 3 main branches (see Supplementary Figure S2 \u0026amp; S3) for better feature extraction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1. Patient Characteristics\u003c/h2\u003e \u003cp\u003eA sample of 1000 hospitalized cancer patients over the period of 44 months (Aug 2014 to April 2018), recognized for neutropenia sepsis, were collected. Of these patients, 137 (13.7%) were categorized to develop febrile neutropenic sepsis using neutrophil count (\u0026lt;\u0026thinsp;1,500/\u0026micro;L), fever (\u0026ge;\u0026thinsp;38.3\u0026deg;C / 101\u0026deg;F) thresholds\u003csup\u003e22,23\u003c/sup\u003e (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The neutropenic cohort reported a mortality rate of 18.24% amongst patients, who suffered from Grade 3 or 4 neutropenic sepsis. Males exhibited a higher rate of deaths as compared to females (64% vs 36%). There was no contrasting difference seen amongst alive and deceased patients for age, BMI, fever, creatinine, and number of chemo cycles (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, in case of hematological parameters including platelets, hemoglobin, and RBCs, a significant decline was observed for the deceased group, whereas a significant heightened median expression was seen for blood urea nitrogen (BUN) (p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There was a significant association observed for tumor type by survival status, wherein hematological malignancies accounted for 64% of the deaths (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of 137 patients, 58 (42.34%) had hematologic malignancies (HM); 30 had non-Hodgkin\u0026rsquo;s lymphoma (51.7%), while 16 had acute lymphoblastic leukemia (27.5%) (Supplementary Table S2). 79 patients (57.66%) suffered from solid tumors, of these 51.8% were Breast carcinoma occurrences (supplementary Table S3).\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\u003e\u003cb\u003eFebrile Neutropenic Patient characteristics at time of hospital Admission.\u003c/b\u003e Mean values (\u0026plusmn;\u0026thinsp;SD) and frequency (%) for different parameters compared by survival status Alive (n\u0026thinsp;=\u0026thinsp;112) Deceased (n\u0026thinsp;=\u0026thinsp;25). Continuous variables for non-normal data were expressed as medians (interquartile ranges). While categorical variables were expressed as number and percentage. Comparisons between Alive and deceased groups were performed Mann Whitney rank sum test whereas Chi-square or Fisher\u0026rsquo;s exact test was employed for categorical variables.\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAlive (n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eDeceased (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eColumn (n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eColumn (n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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\u003e36.75 (30.88, 42.45)\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 \u003cp\u003e33.57 (31.23,43.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\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\u003e23.27 (22, 24.64)\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 \u003cp\u003e21.83 (18.13, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFever\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\u003e39 (39, 39.4)\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 \u003cp\u003e39 (39, 39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelets (x103 \u0026micro;L)\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\u003e97.50 (76, 123)\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 \u003cp\u003e13 (7.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0001**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaemoglobin (g/dL)\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\u003e8.70 (8, 9.4)\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 \u003cp\u003e7.7 (7.00, 8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.006**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dl)\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\u003e0.60 (0.55, 0.64)\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 \u003cp\u003e0.61 (0.50, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRBCs (x106 \u0026micro;l)\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\u003e3.18 (2.95, 3.49)\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 \u003cp\u003e2.72 (2.44, 3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.016*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Urea Nitrogen (BUN) (mg/dl)\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\u003e9.37 (8.05, 10.49)\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 \u003cp\u003e13.86 (11.17,19.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0035**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo. of Chemo Cycle\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\u003e2 (2, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\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 \u003cp\u003e2 (2, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTumor Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.023*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eInfection Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e.003**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFUO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eShock\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePneumonia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eANC\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHospital Stay\u0026thinsp;\u0026gt;\u0026thinsp;5 days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.018*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGram (-) Infection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eIQR: Inter Quartile Range, ST: Solid Tumor, HM: Hematologic Malignancy, MDI: Microbiologically Documented Infection, CDI: Clinically Documented Infection, and FUO: Fever of Unknown Origin \u003c/p\u003e \u003cp\u003eANC: Absolute Neutrophil Count. p-value: * \u0026lt; 0.05, ** \u0026lt; 0.001, *** \u0026lt; 0.0001\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\u003eOf the neutropenic cohort, 59 (43.1%) had microbiologically documented infections (MDI), 17 (12.4%) clinically documented infections (CDI), and 61 (44.5%) fever of unknown origin (FUO) this infection type was significantly (p-value\u0026thinsp;=\u0026thinsp;0.023) different between two groups. The highest occurrence of infection type amongst alive group was due to FUO (50.9%) in contrast to deceased group where major contribution towards mortality was in effect of MDI (64%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Out of all 24 species of bacteria identified, 16 were gram negative, while 8 were gram positive. Moreover, 2 fungal and 3 parasitic species were also identified and isolated in culture test (Supplementary table S4). 36% of deaths were attributed to gram negative infection but could not reach statistical significance (p-value\u0026thinsp;=\u0026thinsp;0.058 - two tails, and 0.046 \u0026ndash; one tail). Presence of Shock (yes\u0026thinsp;=\u0026thinsp;1) (40% vs 3.6%) and pneumonia (yes\u0026thinsp;=\u0026thinsp;1) (36% vs 3.6%) had a higher incidence amongst the deceased group in comparison to alive, which is indicative of a significantly strong association (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) for mortality indexing. Hospital stay\u0026thinsp;\u0026gt;\u0026thinsp;5 days also exhibited a significant association (p-value\u0026thinsp;=\u0026thinsp;0.018) with survival status (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2. Predictive Modelling\u003c/h2\u003e \u003cp\u003eNext, we sub-categorized clinical features (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) into three major categories (Supplementary Figure S2) towards feature selection and multivariate modelling (Supplementary Figure S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eA. Multivariate analysis of factors underpinning FN related in-hospital mortality\u003c/h2\u003e \u003cp\u003eTo investigate patient related factors in association with in-hospital mortality, we performed categorization to enable univariate analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Supplementary Figure S2). This helped in shortlisting the first set (Set 1) of candidate predictors. We further applied multivariate analyses on these predictors of mortality and found all models to be statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eB. Evaluation of chemotherapeutics in association with mortality amongst cancer patients with febrile neutropenia (FN)\u003c/h2\u003e \u003cp\u003eClinical guidelines indicate that certain chemotherapeutics increase the likelihood of febrile neutropenia \u003csup\u003e29\u003c/sup\u003e. To evaluate this, we isolated chemotherapeutic regimens that may confer a higher risk of FN and profiled it over survival status (Supplementary Table S5A). After primary evaluation of the chemotherapeutic regimens, the second set (Set 2) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) of candidate predictors was collated. To evaluate the strength of these chemotherapy-related predictors towards mortality, we built three classification models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eC. Impact of antimicrobials and GCSF-Prophylaxis on hospital mortality\u003c/h2\u003e \u003cp\u003eCancer patients are provided with different types of prophylaxis as a part of their treatment including granulocyte colony-stimulating factor (GCSF)-prophylaxis and antimicrobials; antiamoebic, antiviral and antifungal prophylaxis (Supplementary Table S5B). These formed the third set of candidate predictors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) and to determine the influence of these prophylactic treatments on mortality, we again performed predictive modelling using RF, LDA and LR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3. Integrative approach towards identification of the independent predictors of hospital mortality in FN patients\u003c/h2\u003e \u003cp\u003eTo develop an integrative predictive model encompassing attributes with an enhanced predictive quality, we integrated those candidate predictors which were significant, had a high mean decrease accuracy (MDA), and had improved odds from the outcomes of individual models (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Figure S4 \u0026ndash; S8, supplementary Table S6- S11). To test the effect of these selected predictors on the likelihood of patient mortality, we applied LR and found overall model to be statistically significant at, X\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;57.036, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. The model explained 55.5% (Nagelkerke R2) of the variance in mortality and correctly classified 90.5% of cases (supplementary Table S12). Post-hoc tuning of model parameters and removal of non-contributing predictors, an adjusted model was developed. Upon bootstrapping with the over 1000 iterations, the components and characteristics of the adjusted model are summarized in (supplementary Table S13). The adjusted model was statistically significant at, X\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;57.036, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. This integrative model explained 52.2% (Nagelkerke R\u003csup\u003e2\u003c/sup\u003e) of the variance in mortality and increased accuracy of correctly classified cases to 92%. Odds ratios for the integrative logistic model are provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To further validate the contribution of these predictors in classification of alive and dead cases, we applied RF and LDA. Outcomes from the random forest and LDA classification were in line with the logistic regression performance indices (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The five most important predictors of mortality were shock (mean decrease accuracy (MDA)\u0026thinsp;=\u0026thinsp;34.07), pneumonia (27.98), hemoglobin (14.06), antiviral prophylaxis (9.82) and carboplatin (8.18) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Likewise, for the LR model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary S12) only shock, pneumonia and carboplatin had a significant OR. LDA provided, Fischer\u0026rsquo;s discriminant functions (DF0 (Alive) and DF1(Dead)) based on the estimation of corresponding β-values (Supplementary Table S14) which contributed towards the total variance of 100% and canonical correlation of 0.67 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Internal 10-fold cross validation for the integrative logistic model was performed and as a result cross validation accuracy (CVA) of 0.8608 with a kappa value of 0.437 was achieved.\u003c/p\u003e \u003cp\u003e \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\u003e\u003cb\u003eComparative performance indices of LR, LDA and RF models when using 3 different candidate predictor sets to predict NS associated in-hospital mortality.\u003c/b\u003e The confusion matrix shows the classification of the cases based on their predicted survival status in the neutropenic dataset. Where performance indices are calculated according to : Accuracy = (TP\u0026thinsp;+\u0026thinsp;TN)/(TP\u0026thinsp;+\u0026thinsp;FP\u0026thinsp;+\u0026thinsp;TN\u0026thinsp;+\u0026thinsp;FN); sensitivity\u0026thinsp;=\u0026thinsp;TP/(TP\u0026thinsp;+\u0026thinsp;FN); specificity\u0026thinsp;=\u0026thinsp;TN/(TN\u0026thinsp;+\u0026thinsp;FP).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e(A) Predictor Set 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e \u003cp\u003e(B) Predictor Set 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c20\" namest=\"c15\"\u003e \u003cp\u003e(C) Predictor Set 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c4\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eConfusion Matrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c10\" namest=\"c9\" rowspan=\"2\"\u003e \u003cp\u003eConfusion Matrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c16\" namest=\"c15\" rowspan=\"2\"\u003e \u003cp\u003eConfusion Matrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLogistic Regression (LR)\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 \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLinear Discriminant Analysis (LDA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eRandom Forest (RF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictor variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eAge, BMI, Platelets, Haemoglobin, Shock, Pneumonia, Gram Negative Infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e \u003cp\u003eCisplatin, Cyclophosphamide, Doxorubicin, Etoposide, CP, Carboplatin, Cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c20\" namest=\"c15\"\u003e \u003cp\u003eGCSF Prophylaxis, Antiamoebic Prophylaxis, Antiviral Prophylaxis, Antifungal Prophylaxis, Antibiotics 2WP, Antiamoebic 2WP, Antiviral 2WP, Antifungal 2WP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"20\" nameend=\"c20\" namest=\"c1\"\u003e \u003cp\u003eA: Alive, D: Dead, CP: Carboplatin/Paclitaxel, GCSF: Granulocyte-Colony Stimulating Factor, 2WP: Prophylaxis 2 Weeks prior hospital admission\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\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\u003e\u003cb\u003eComparative performance indices of LR, LDA and RF models for final predictor set to predict NS associated in-hospital mortality.\u003c/b\u003e The confusion matrix shows the classification of the cases based on their predicted survival status in the neutropenic dataset. Here, the columns denote the actual cases, and the rows denote the predicted. Where performance indices are calculated according to : Accuracy = (TP\u0026thinsp;+\u0026thinsp;TN)/(TP\u0026thinsp;+\u0026thinsp;FP\u0026thinsp;+\u0026thinsp;TN\u0026thinsp;+\u0026thinsp;FN); sensitivity\u0026thinsp;=\u0026thinsp;TP/(TP\u0026thinsp;+\u0026thinsp;FN); specificity\u0026thinsp;=\u0026thinsp;TN/(TN\u0026thinsp;+\u0026thinsp;FP).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eFinal Predictor Set\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c4\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eConfusion Matrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLogistic Regression (LR)\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 \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLinear Discriminant Analysis (LDA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eRandom Forest (RF)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictor variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eAge, Haemoglobin, Shock, Pneumonia, Carboplatin, Doxorubicin, Antifungal 2WP, Antiviral Prophylaxis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eA: Alive, D: Dead, 2WP: 2 weeks prior prophylaxis\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNomogram for predicting in-hospital mortality\u003c/h2\u003e \u003cp\u003eTowards clinical translation of the proposed model, a normogram was developed for prediction of mortality amongst cancer patients with FN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For that, the regression coefficients for each predictor were rescaled between 0 to 100 which were then transformed into probabilities through logit transformation. Taken together, clinicians can input a patient\u0026rsquo;s profile, comprising of 8 predictors, and map them on to the nomogram to calculate the probability of a mortality. Consequently, receiver operating characteristic curve analysis gave an area under the curve of 0.878 (95% CI 0.778\u0026ndash;0.977) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The calibration of the nomogram was checked by the calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) using the Hosmer\u0026ndash;Lemeshow test (p-value\u0026thinsp;=\u0026thinsp;0.635) indicating no deviation from the fit and showing agreement between the observation and prediction. DCA conducted for the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC \u0026amp; D, Supplementary Figure S9) indicates that the proposed model may inform clinical decisions with a threshold risk probability\u0026thinsp;\u0026ge;\u0026thinsp;10%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this work, we present a clinical model for prognosticating outcomes for patients with FN. The proposed model was developed based on a cohort of 137 cancer patients who presented febrile neutropenia at time of hospital admission. The model predictors included bacteremia (shock, pneumonia), chemotherapeutics (carboplatin, doxorubicin), prophylaxis (antifungal and antiviral), along with hematological parameters such as hemoglobin. The resultant integrative model has a classification accuracy of 92% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and can identify patients with febrile neutropenia (FN) at a high risk of mortality along with providing insights for prompt interventions to improve patient care and outcomes.\u003c/p\u003e \u003cp\u003eCancer patients with febrile neutropenia are at a high risk of developing infections, which can rapidly overwhelm the patient and cause septic shock and death \u003csup\u003e30\u0026ndash;32\u003c/sup\u003e. The current model reports shock and pneumonia to be the most important mortality predictors, with highest variable importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and odds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Previously, several clinical studies have reported bacteremia and pneumonia as major causes of morbidity and mortality among patients with FN \u003csup\u003e33,34\u003c/sup\u003e. Bacteremic pneumonia in patients with cancer having neutropenia, is associated with a poor outcome \u003csup\u003e33\u003c/sup\u003e. \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e infection is commonly believed to primarily impact cancer patients experiencing prolonged and severe neutropenia - in particular individuals with hematologic malignancies undergoing intensive chemotherapy within hospital setting \u003csup\u003e35\u003c/sup\u003e. Moreover, in a large retrospective study, it was found that \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (10%), \u003cem\u003eEscherichia coli\u003c/em\u003e (7.7%), and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (5.6%) are the leading cause for gram-negative pneumonia \u003csup\u003e34\u003c/sup\u003e. This is indicative of pneumonia being an independent predictive factor of mortality in patients \u003csup\u003e36,37\u003c/sup\u003e with neutropenia.\u003c/p\u003e \u003cp\u003eOur analysis revealed that 64% of deaths were attributed to microbiologically documented infections (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Through various culture tests, we identified 16 types of gram-negative and 8 types of gram-positive bacterial species (Table S3). The proportion of culture positive tests were low because the patients were already on antimicrobial prophylaxis. Notably, \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) infections, documented in blood culture, were associated with 9 cases of mortality. This underscores the importance of closely monitoring the clinical characteristics and risk factors associated with bacteremic pneumonia caused by these bacteria.\u003c/p\u003e \u003cp\u003eMoreover, thrombocytopenia is highly prevalent in ICU admittees with severe sepsis and septic shock. Its onset, whether a relative or absolute decrease in platelet count, significantly and independently correlates with a doubling of the expected mortality rate during the septic episode \u003csup\u003e38\u0026ndash;40\u003c/sup\u003e. Our results show platelet counts are significantly lower in deceased patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and that thrombocytopenia in the ICU acts as a risk indicator, rather than a primary cause of mortality. A prompt investigation and treatment of underlying factors contributing to this condition is, therefore, clinically employable. Chemotherapy-induced thrombocytopenia (CIT) is a common complication of cancer treatment with cytotoxic agents, with carboplatin being among the most commonly implicated agents in causing CIT \u003csup\u003e41,42\u003c/sup\u003e used as monotherapy or in combination with other chemo drugs. Here, we show that patients on carboplatin therapy and a neutropenic episode exhibit significant decrease in platelets (median 85 (3-125)) vs 85 (70\u0026ndash;115) not taking carboplatin, moreover, same trend is observed for etoposide (Supplementary Table S5C). This could be indicative of drug-induced thrombocytopenia as an underlying cause that is contributing significantly towards mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with odds increasing up to 3 times in carboplatin-treated patients.\u003c/p\u003e \u003cp\u003eThe elevated risks associated with thrombocytopenia linked to carboplatin, etoposide, and other medications especially penicillin \u003csup\u003e43\u003c/sup\u003e, can potentiate immune-mediated thrombocytopenia and warrant further investigation. The underlying mechanism can be valuable for clinicians as it holds critical implications. In particular, for immune-mediated thrombocytopenia, avoiding the drug is imperative, whereas in dose-dependent thrombocytopenia, dose reduction may be adequate.\u003c/p\u003e \u003cp\u003eFN is a medical emergency, carrying a high mortality risk in the absence of timely and appropriate treatment. The use of doxorubicin and antiviral and antifungal prophylaxis plays a crucial role in reducing morbidity and mortality in patients with neutropenic sepsis \u003csup\u003e44\u003c/sup\u003e. Fungal pathogens are prevalent in high-risk patients experiencing neutropenia. Among these, \u003cem\u003eCandida spp.\u003c/em\u003e and \u003cem\u003eAspergillus spp\u003c/em\u003e. are the most frequently implicated in invasive fungal infections \u003csup\u003e45\u003c/sup\u003e. Like these findings, our culture investigations also isolated these two species; therefore, provision of anti-fungal prophylaxis 2 weeks prior to the neutropenic episode provided patients with a protective effect. This is well in concordance with our model which also shows that antivirals and antifungals decrease the odds of mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Supplementary Table S12) and may warrant further investigation. Furthermore, the selection of antimicrobial agents for prophylaxis and empirical therapy should be driven by the local susceptibility and resistance patterns of microorganisms.\u003c/p\u003e \u003cp\u003eTaken together, the current model provides a novel predictor for mortality in cancer patients with FN in light of prophylactic interventions. The primary strength of our observational report is the integration of multifactorial data from a single cancer center, ensuring consistent treatment policies and standardized data collection. This integration leverages readily available prognostic factors, many of which are previously reported with insufficient statistical adjustments. Further, we applied a rigorous method in shortlisting of the potential prognostic factors from a broader range of modalities. The main limitations of this study are the retrospective nature and small size of the data. Our future work will aim to refine this model further through a validation in a larger multicenter population for more generalizability and identification of more prognostic factors. With the secondary aim of advising the integration of a more refined model into the health care system for prompt risk stratification for mortality and better implementation of the clinical management. When evaluating which method should be implemented to aid clinical review, we should mainly consider the model\u0026rsquo;s performance index, ease of implementation and interpretability. The random forests achieved the highest accuracy (94%) in identifying the occurrence of an event, however when the level of interpretation is required RF becomes difficult to interpret for an end user and is potentially more challenging to implement in real-time clinical settings. Therefore, the nomogram model was selected with an accuracy of 92% focusing on key aspects relevant to clinical usability, which was further supplemented through DCA. The model can be easily integrated into an electronic health record (EHR) system and made available as an online tool or mobile application. The model facilitates interpretable decision-making based on risk estimation and patient consultations without significant workflow disruptions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIdentification of mortality-associated factors are imperative for prognostication of cancer patients presenting neutropenia. Towards that, the factors identified in this study may offer valuable insights in resolving the underlying cause of mortality in association with shock, pneumonia, and thrombocytopenia. In particular, shock and pneumonia are reported as critical predictors, and are associated with bacteremia linked to \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. Moreover, elevated risk is reported in association with thrombocytopenia, induced by carboplatin, etoposide, and penicillin. Combination of anti-viral and antifungal prophylaxis may improve the survival in this population. Together, a comprehensive evaluation of patient\u0026rsquo;s treatment regimen and appropriate laboratory assessments, interpreted in the clinical context, are essential for neutropenia management and in decreasing in-hospital mortality rates. Future work will investigate the use of additional treatment modalities to improve the prognostic model and risk stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eNone, the author(s) declare no material or financial competing interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript and was conducted independently.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe dataset generated and analyzed during the current study is not publicly available due to ongoing data analysis on the same dataset as an extension of another research project but is available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMoon, H., Choi, Y. 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Invasive Fungal Pathogens: Current Epidemiological Trends. \u003cem\u003eClinical Infectious Diseases\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, S3\u0026ndash;S14 (2006).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Febrile neutropenia, Prognostication, Chemotherapy, Mortality, Cancer ","lastPublishedDoi":"10.21203/rs.3.rs-4730716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4730716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFebrile neutropenia (FN) in cancer patients undergoing chemotherapy can result in life-threatening outcomes. Hence, an evaluation of associated risk factors can enable clinical surveillance as well as inform prophylactic measures. In this retrospective cohort study, we report a mortality prognostication model for chemotherapy-treated cancer patients upon a neutropenic episode.Clinical and diagnostic data of 137 febrile neutropenia patients (\u0026gt;18 years) was collected from a cancer hospital, with the primary endpoint of post-hospital admission mortality within 30 days. The data was integratively analyzed and machine learning techniques were applied to develop the predictive model which was then internally cross validated. Towards enabling personalized risk assessment, a nomogram was constructed and validated. Chemotherapy-treated cancer patients undergoing a neutropenic episode exhibited an overall mortality rate of 17.36%. Multivariate logistic analysis elucidated that shock, pneumonia, carboplatin, doxorubicin, antifungal and antiviral prophylaxis, and hemoglobin correctly classified cases with an overall accuracy of 92% and discriminated mortality with a specificity of 76%. Antiviral (odds ratio (OR): 0.669, p = 0.689), and antifungal prophylaxis (OR: 0.619, p = 0.5) demonstrated a protective effect. The receiver operating characteristic (ROC) curve of the nomogram exhibited an area under the curve of 0.878 (95% CI 0.778 - 0.977), Hosmer–Lemeshow test p-value = 0.635, and a high net benefit in the clinical decision curve. The proposed model offers insights into the role of clinical predictors as well as treatment characteristics that can ameliorate mortality risk in cancer patients with FN. The study highlights bacteremia-related surveillance, along with thrombocytopenia, linked to carboplatin, for reducing individualized mortality risk along with improved monitoring and informed treatment strategies.\u003c/p\u003e","manuscriptTitle":"In-hospital Mortality Prognostication for Cancer Patients with Febrile Neutropenia: A Single Center Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 05:25:24","doi":"10.21203/rs.3.rs-4730716/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d1b9207-4f59-4c0d-80ca-38fa043e49cf","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34813930,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":34813931,"name":"Health sciences/Risk factors"},{"id":34813932,"name":"Health sciences/Oncology/Cancer/Cancer epidemiology"},{"id":34813933,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-11-08T08:06:18+00:00","versionOfRecord":{"articleIdentity":"rs-4730716","link":"https://doi.org/10.1038/s43856-025-01142-9","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2025-11-07 05:00:00","publishedOnDateReadable":"November 7th, 2025"},"versionCreatedAt":"2024-10-14 05:25:24","video":"","vorDoi":"10.1038/s43856-025-01142-9","vorDoiUrl":"https://doi.org/10.1038/s43856-025-01142-9","workflowStages":[]},"version":"v1","identity":"rs-4730716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4730716","identity":"rs-4730716","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}