Predicting infectious complications in neutropenic children and young people with cancer (IPD protocol)

Characteristics of breast cancer patients at dr. Sardjito Hospital for early anticipation of neutropenia: Cross-sectional study

The highest prevalence of breast cancer in Indonesia is in the Province of Yogyakarta. dr. Sardjito General Hospital has quite complete clinical data on breast cancer patients. Characteristics of the population in various regions in Indonesia are different from one another. This problem is the basis for doing this research. Statistical data analysis needs to be done in each area for better diagnosis and treatment of cancer. Data recording is carried out continuously during outpatient treatment at dr. Sardjito General Hospital. Data for breast cancer patients was taken from July 2018 to June 2020. The data obtained were grouped into four categories: laboratory investigation, socio-demographic, clinical examination, and pathology. Descriptive and correlation analysis aims to determine the characteristics of breast cancer patients seeking treatment at dr. Sardjito General Hospital and anticipate their possibility of developing neutropenia after chemotherapy. The results of the descriptive analysis are significant to determine patient characteristics and treatment steps that can be taken. Correlation analysis variables closely related to neutrophils included leucocyte count, lymphocyte, monocyte, albumin, age at first diagnosis, and height. These variables can be a severe concern of medical personnel before undergoing chemotherapy, especially lymphocytes, which have the largest (negative) correlation and can be an early sign of neutropenia.

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Characteristics of the population in various regions in Indonesia are different from one another.

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Statistical data analysis needs to be done in each area for better diagnosis and treatment of cancer.

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The results of the descriptive analysis are essential to determine patient statistics and the treatment steps taken.

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The strong correlation with neutrophils are leukocytes, lymphocytes, monocytes, albumin, age, and height.

Short-course antibiotics for chemotherapy-induced febrile neutropaenia: retrospective cohort study

BACKGROUND

Recent research in febrile neutropaenia (FN) has focused on reducing the intensity of treatment for those thought to be at low risk of significant morbidity or mortality. This has not led to a reduced burden of treatment for either families or healthcare systems. An alternative approach is to discharge all patients who remain well after 48 hours of inpatient treatment, either with no ongoing treatment or with appropriate antibiotics if the cultures are positive. This paper aimed to demonstrate that this approach is safe.

METHODS

Patients treated according to this approach in a single centre were reviewed retrospectively, with a random selection of patients from a 4-year period. Data were collected according to the Predicting Infectious Complications of Neutropenic sepsis in Children with Cancer dataset. In addition, all septic deaths over a 10-year period were reviewed in the same manner.

RESULTS

179 episodes of FN were reviewed from 47 patients. In 70% (125/179) of episodes, patients were discharged safely once 48-hour microbiology results were available, with only 5.6% (7/125) resulting in readmission in the 48 hours following discharge. There were no septic deaths in this cohort.There were 11 deaths due to FN over the 10-year study period. Almost all patients were identified as severely unwell in the early stages of their final presentation or had a prolonged final illness.

CONCLUSION

This paper indicates that the policy described provides a balance between safety and acceptability. Further work is needed to demonstrate non-inferiority and cost-benefit.

Individual participant data validation of the PICNICC prediction model for febrile neutropenia

BACKGROUND

Risk-stratified approaches to managing cancer therapies and their consequent complications rely on accurate predictions to work effectively. The risk-stratified management of fever with neutropenia is one such very common area of management in paediatric practice. Such rules are frequently produced and promoted without adequate confirmation of their accuracy.

METHODS

An individual participant data meta-analytic validation of the 'Predicting Infectious ComplicatioNs In Children with Cancer' (PICNICC) prediction model for microbiologically documented infection in paediatric fever with neutropenia was undertaken. Pooled estimates were produced using random-effects meta-analysis of the area under the curve-receiver operating characteristic curve (AUC-ROC), calibration slope and ratios of expected versus observed cases (E/O).

RESULTS

The PICNICC model was poorly predictive of microbiologically documented infection (MDI) in these validation cohorts. The pooled AUC-ROC was 0.59, 95% CI 0.41 to 0.78, tau²=0, compared with derivation value of 0.72, 95% CI 0.71 to 0.76. There was poor discrimination (pooled slope estimate 0.03, 95% CI -0.19 to 0.26) and calibration in the large (pooled E/O ratio 1.48, 95% CI 0.87 to 2.1). Three different simple recalibration approaches failed to improve performance meaningfully.

CONCLUSION

This meta-analysis shows the PICNICC model should not be used at admission to predict MDI. Further work should focus on validating alternative prediction models. Validation across multiple cohorts from diverse locations is essential before widespread clinical adoption of such rules to avoid overtreating or undertreating children with fever with neutropenia.

Updated systematic review and meta-analysis of the predictive value of serum biomarkers in the assessment and management of fever during neutropenia in children with cancer

Routinely measurable biomarkers as predictors for adverse outcomes in febrile neutropenia could improve management through risk stratification. This systematic review assesses the predictive role of biomarkers in identifying events such as bacteraemia, clinically documented infections, microbiologically documented infection, severe sepsis requiring intensive care or high dependency care and death. This review collates 8319 episodes from 4843 patients. C-reactive protein (CRP), interleukin (IL)-6, IL-8 and procalcitonin (PCT) consistently predict bacteraemia and severe sepsis; other outcomes have highly heterogeneous results. Performance of the biomarkers at admission using different thresholds demonstrates that PCT > 0.5 ng/mL offers the best compromise between sensitivity and specificity: sensitivity 0.67 (confidence interval [CI] 0.53-0.79) specificity 0.73 (CI 0.66-0.77). Seventeen studies describe the use of serial biomarkers, with PCT having the greatest discriminatory role. Biomarkers, potentially with serial measurements, may predict adverse outcomes in paediatric febrile neutropenia and their role in risk stratification is promising.

Episodes of fever in neutropenia in pediatric patients with cancer in Bern, Switzerland, 1993-2012

Fever in neutropenia (FN) is the most frequent potentially life threatening complication of chemotherapy for cancer. Prediction of the risk to develop complications, integrated into clinical decision rules, would allow for risk-stratified treatment of FN. This retrospective, single center cohort study in pediatric patients diagnosed with cancer before 17 years, covered two decades, 1993 to 2012. In total, 703 FN episodes in 291 patients with chemotherapy (maximum per patient, 9) were reported here. Twenty-nine characteristics of FN were collected: 6 were patient- and cancer-related, 8 were characteristics of history, 8 of clinical examination, and 7 laboratory results in peripheral blood, all known at FN diagnosis. In total 28 FN outcomes were assessed: 8 described treatment of FN, 6 described microbiologically defined infections (MDI), 4 clinically defined infections, 4 were additional clinical composite outcomes, and 6 outcomes were related to discharge. These data can mainly be used to study FN characteristics and their association with outcomes over time and between centers, and for derivation and external validation of clinical decision rules.

Severe Sepsis-Associated Morbidity and Mortality among Critically Ill Children with Cancer

Severe sepsis (SS) in pediatric oncology patients is a leading cause of morbidity and mortality. We investigated the incidence of and risk factors for morbidity and mortality among children diagnosed with cancer from 2008 to 2012, and admitted with SS during the 3 years following cancer diagnosis. A total of 1,002 children with cancer were included, 8% of whom required pediatric intensive care unit (PICU) admission with SS. Death and/or multiple organ dysfunction syndrome occurred in 34 out of 99 PICU encounters (34%). Lactate level and history of stem-cell transplantation were significantly associated with the development of death and/or organ dysfunction ( p  < 0.05).

Predicting bacterial infections among pediatric cancer patients with febrile neutropenia: External validation of the PICNICC model

INTRODUCTION

The Predicting Infectious Complications in Neutropenic Children and Young People with Cancer (PICNICC) model was recently developed for antibiotic stewardship among pediatric cancer patients, but limited information is available about its clinical usefulness. We aimed to assess the performance of the PICNICC model for predicting microbiologically documented bacterial infections among pediatric cancer patients with febrile neutropenia.

MATERIALS AND METHODS

We used data for febrile neutropenia episodes at a pediatric cancer center in Aarhus, Denmark between 2000 and 2016. We assessed the area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness (i.e., net benefit). We also recalibrated the model using statistical updating methods.

RESULTS

We observed 306 microbiologically documented bacterial infections among 1,892 episodes of febrile neutropenia. The AUC of the model was 0.73 (95% confidence limits [CL]: 0.71-0.75). The calibration intercept (calibration-in-the-large) was -0.69 (95% CL: -0.86 to -0.51) and the slope was 0.77 (95% CL: 0.65-0.89). Modest net benefit was observed at a decision threshold of 5%. Recalibration improved calibration but did not improve net benefit.

CONCLUSIONS

The PICNICC model has potential for reducing unnecessary antibiotic exposure for pediatric cancer patients with febrile neutropenia, but continued validation and refinement is necessary to optimize clinical usefulness.

Predicting Infectious ComplicatioNs in Children with Cancer: an external validation study

BACKGROUND

The aim of this study was to validate the 'Predicting Infectious ComplicatioNs in Children with Cancer' (PICNICC) clinical decision rule (CDR) that predicts microbiologically documented infection (MDI) in children with cancer and fever and neutropenia (FN). We also investigated costs associated with current FN management strategies in Australia.

METHODS

Demographic, episode, outcome and cost data were retrospectively collected on 650 episodes of FN. We assessed the discrimination, calibration, sensitivity and specificity of the PICNICC CDR in our cohort compared with the derivation data set.

RESULTS

Using the original variable coefficients, the CDR performed poorly. After recalibration the PICNICC CDR had an area under the receiver operating characteristic (AUC-ROC) curve of 0.638 (95% CI 0.590-0.685) and calibration slope of 0.24. The sensitivity, specificity, positive predictive value and negative predictive value of the PICNICC CDR at presentation was 78.4%, 39.8%, 28.6% and 85.7%, respectively. For bacteraemia, the sensitivity improved to 85.2% and AUC-ROC to 0.71. Application at day 2, taking into consideration the proportion of MDI known (43%), further improved the sensitivity to 87.7%. Length of stay is the main contributor to cost of FN treatment, with an average cost per day of AUD 2183 in the low-risk group.

CONCLUSIONS

For prediction of any MDI, the PICNICC rule did not perform as well at presentation in our cohort as compared with the derivation study. However, for bacteraemia, the predictive ability was similar to that of the derivation study, highlighting the importance of recalibration using local data. Performance also improved after an overnight period of observation. Implementation of a low-risk pathway, using the PICNICC CDR after a short period of inpatient observation, is likely to be safe and has the potential to reduce health-care expenditure.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

BACKGROUND

Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one.

METHODS

The 'Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation.

RESULTS

Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically 'severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711-0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses.

CONCLUSIONS

This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making.

Antibiotic susceptibility of Gram-negatives isolated from bacteremia in children with cancer. Implications for empirical therapy of febrile neutropenia

BACKGROUND

Monotherapy is recommended as the first choice for initial empirical therapy of febrile neutropenia, but local epidemiological and antibiotic susceptibility data are now considered pivotal to design a correct management strategy.

AIM

To evaluate the proportion of Gram-negative rods isolated in bloodstream infections in children with cancer resistant to antibiotics recommended for this indication.

MATERIALS & METHODS

The in vitro susceptibility to ceftazidime, piperacillin-tazobactam, meropenem and amikacin of Gram-negatives isolated in bacteremic episodes in children with cancer followed at the Istituto "Giannina Gaslini", Genoa, Italy in the period of 2001-2013 was retrospectively analyzed using the definitions recommended by EUCAST in 2014. Data were analyzed for any single drug and to the combination of amikacin with each β-lactam. The combination was considered effective in absence of concomitant resistance to both drugs, and not evaluated by means of in vitro analysis of antibiotic combinations (e.g., checkerboard).

RESULTS

A total of 263 strains were evaluated: 27% were resistant to piperacillin-tazobactam, 23% to ceftazidime, 12% to meropenem and 13% to amikacin. Concomitant resistance to β-lactam and amikacin was detected in 6% of strains for piperacillin-tazobactam, 5% for ceftazidime and 5% for meropenem. During the study period there was a nonsignificant increase in the proportions of strains resistant to β-lactams indicated for monotherapy, and also increase in the resistance to combined therapies.

CONCLUSION

in an era of increasing resistance to antibiotics guideline-recommended monotherapy could be not appropriate for initial empirical therapy of febrile neutropenia. Strict local survey on etiology and antibiotic susceptibility is mandatory for a correct management of this complication in cancer patients.

Imaging of acute and subacute toxicities of cancer therapy in children

Effective cancer therapies have resulted in significant improvement in survival. However, treatment-related acute and subacute complications are a cause of significant morbidity and mortality. Effects of cancer therapy in children can be seen early in the survival period or later in life in almost all organ systems of the body. Many of these conditions are evaluated by imaging and some are diagnosed based on characteristic imaging features. This article aims to discuss acute and subacute toxicities of cancer therapy in children involving multiple organ systems, pulmonary, gastrointestinal, hepatobiliary, genitourinary and musculoskeletal systems with emphasis on those in which imaging plays a role in diagnosis or management. We also discuss the role of imaging and choice of imaging modalities in these conditions.

Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE

Individual participant data meta-analyses (IPD-MA) are increasingly used for developing and validating multivariable (diagnostic or prognostic) risk prediction models. Unfortunately, some predictors or even outcomes may not have been measured in each study and are thus systematically missing in some individual studies of the IPD-MA. As a consequence, it is no longer possible to evaluate between-study heterogeneity and to estimate study-specific predictor effects, or to include all individual studies, which severely hampers the development and validation of prediction models. Here, we describe a novel approach for imputing systematically missing data and adopt a generalized linear mixed model to allow for between-study heterogeneity. This approach can be viewed as an extension of Resche-Rigon's method (Stat Med 2013), relaxing their assumptions regarding variance components and allowing imputation of linear and nonlinear predictors. We illustrate our approach using a case study with IPD-MA of 13 studies to develop and validate a diagnostic prediction model for the presence of deep venous thrombosis. We compare the results after applying four methods for dealing with systematically missing predictors in one or more individual studies: complete case analysis where studies with systematically missing predictors are removed, traditional multiple imputation ignoring heterogeneity across studies, stratified multiple imputation accounting for heterogeneity in predictor prevalence, and multilevel multiple imputation (MLMI) fully accounting for between-study heterogeneity. We conclude that MLMI may substantially improve the estimation of between-study heterogeneity parameters and allow for imputation of systematically missing predictors in IPD-MA aimed at the development and validation of prediction models.

Predictors of 7- and 30-day mortality in pediatric intensive care unit patients with cancer and hematologic malignancy infected with Gram-negative bacteria

Background

Infection with Gram-negative bacteria is associated with increased morbidity and mortality. The aim of this study was to evaluate the predictors of 7- and 30-day mortality in pediatric patients in an intensive care unit with cancer and/or hematologic diseases and Gram-negative bacteria infection.

Methods

Data were collected relating to all episodes of Gram-negative bacteria infection that occurred in a pediatric intensive care unit between January 2009 and December 2012, and these cases were divided into two groups: those who were deceased seven and 30 days after the date of a positive culture and those who survived the same time frames. Variables of interest included age, gender, presence of solid tumor or hematologic disease, cancer status, central venous catheter use, previous Pseudomonas aeruginosa infection, infection by multidrug resistant-Gram-negative bacteria, colonization by multidrug resistant-Gram-negative bacteria, neutropenia in the preceding seven days, neutropenia duration ≥3 days, healthcare-associated infection, length of stay before intensive care unit admission, length of intensive care unit stay >3 days, appropriate empirical antimicrobial treatment, definitive inadequate antimicrobial treatment, time to initiate adequate antibiotic therapy, appropriate antibiotic duration ≤3 days, and shock. In addition, use of antimicrobial agents, corticosteroids, chemotherapy, or radiation therapy in the previous 30 days was noted.

Results

Multivariate logistic regression analysis resulted in significant relationship between shock and both 7-day mortality (odds ratio 12.397; 95% confidence interval 1.291–119.016; p = 0.029) and 30-day mortality (odds ratio 6.174; 95% confidence interval 1.760–21.664; p = 0.004), between antibiotic duration ≤3 days and 7-day mortality (odds ratio 21.328; 95% confidence interval 2.834-160.536; p = 0.003), and between colonization by multidrug resistant-Gram-negative bacteria and 30-day mortality (odds ratio 12.002; 95% confidence interval 1.578–91.286; p = 0.016).

Conclusions

Shock was a predictor of 7- and 30-day mortality, and colonization by multidrug resistant-Gram-negative bacteria was an important risk factor for 30-day mortality.

An updated systematic review and meta-analysis of the predictive value of serum biomarkers in the assessment of fever during neutropenia in children with cancer

BACKGROUND

Fever during neutropenia (FN) is a frequent and potentially life-threatening complication of the treatment of childhood cancer. The role of biomarkers in predicting morbidity and mortality associated with FN in children has been explored with varying results. This systematic review identified, critically appraised and synthesized information on the use of biomarkers for the prediction of outcome of FN in children/young adults, updating a review of initial assessment and adding further analysis of their value at reassessment.

METHODS

This review was conducted in accordance with the Centre for Reviews and Dissemination Methods, using 3 different random effects meta-analysis models.

RESULTS

Thirty-seven studies involving over 4689 episodes of FN in children were assessed, including an additional 13 studies investigating 18 biomarkers in 1670 FN episodes since the original review. Meta-analysis was possible for admission C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 and interleukin-8 in their ability to detect significant infection. Marked heterogeneity exists, precluding clear clinical interpretation of the results. Qualitative synthesis of the role of serial biomarkers suggests their predictive ability may be more pronounced at 24 to 48 hours compared with admission. Direct comparisons of the discriminatory power of admission values of PCT and CRP showed PCT generally had a better discriminatory estimate of serious infection than CRP.

CONCLUSIONS

There remains a paucity of robust and reproducible data on the use of biomarkers in prediction of serious infection in children with FN. Available evidence suggests PCT has better discriminatory ability than CRP and that the role of serial biomarkers warrants further study.

Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS): rationale and design for meta-analyses of individual patient data of randomized controlled trials that evaluate the effect of physical activity and psychosocial interventions on health-related quality of life in cancer survivors

BACKGROUND

Effective interventions to improve quality of life of cancer survivors are essential. Numerous randomized controlled trials have evaluated the effects of physical activity or psychosocial interventions on health-related quality of life of cancer survivors, with generally small sample sizes and modest effects. Better targeted interventions may result in larger effects. To realize such targeted interventions, we must determine which interventions that are presently available work for which patients, and what the underlying mechanisms are (that is, the moderators and mediators of physical activity and psychosocial interventions). Individual patient data meta-analysis has been described as the 'gold standard' of systematic review methodology. Instead of extracting aggregate data from study reports or from authors, the original research data are sought directly from the investigators. Individual patient data meta-analyses allow for adequate statistical analysis of intervention effects and moderators of such effects.Here, we report the rationale and design of the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) Consortium. The primary aim of POLARIS is 1) to conduct meta-analyses based on individual patient data to evaluate the effect of physical activity and psychosocial interventions on the health-related quality of life of cancer survivors; 2) to identify important demographic, clinical, personal, or intervention-related moderators of the effect; and 3) to build and validate clinical prediction models identifying the most relevant predictors of intervention success.

METHODS/DESIGN

We will invite investigators of randomized controlled trials that evaluate the effects of physical activity and/or psychosocial interventions on health-related quality of life compared with a wait-list, usual care or attention control group among adult cancer survivors to join the POLARIS consortium and share their data for use in pooled analyses that will address the proposed aims. We are in the process of identifying eligible randomized controlled trials through literature searches in four databases. To date, we have identified 132 eligible and unique trials.

DISCUSSION

The POLARIS consortium will conduct the first individual patient data meta-analyses in order to generate evidence essential to targeting physical activity and psychosocial programs to the individual survivor's characteristics, capabilities, and preferences.

REGISTRATION PROSPERO

International prospective register of systematic reviews, CRD42013003805.

A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis

The use of individual participant data (IPD) from multiple studies is an increasingly popular approach when developing a multivariable risk prediction model. Corresponding datasets, however, typically differ in important aspects, such as baseline risk. This has driven the adoption of meta-analytical approaches for appropriately dealing with heterogeneity between study populations. Although these approaches provide an averaged prediction model across all studies, little guidance exists about how to apply or validate this model to new individuals or study populations outside the derivation data. We consider several approaches to develop a multivariable logistic regression model from an IPD meta-analysis (IPD-MA) with potential between-study heterogeneity. We also propose strategies for choosing a valid model intercept for when the model is to be validated or applied to new individuals or study populations. These strategies can be implemented by the IPD-MA developers or future model validators. Finally, we show how model generalizability can be evaluated when external validation data are lacking using internal-external cross-validation and extend our framework to count and time-to-event data. In an empirical evaluation, our results show how stratified estimation allows study-specific model intercepts, which can then inform the intercept to be used when applying the model in practice, even to a population not represented by included studies. In summary, our framework allows the development (through stratified estimation), implementation in new individuals (through focused intercept choice), and evaluation (through internal-external validation) of a single, integrated prediction model from an IPD-MA in order to achieve improved model performance and generalizability.

Complications of sepsis: the role of risk prediction rules, biomarkers and host genetics

The subtitle of the Australasian Society for Infectious Diseases Annual Scientific Meeting was 'Sailing into the Future', and speakers from both adult and pediatric infectious diseases explored this theme in relation to the management of sepsis. The future will entail better risk prediction tools for patients at risk for sepsis. Such risk prediction tools are likely to incorporate genetic profiling of the host to identify the groups at highest risk for disease and death. Focused diagnostic testing in these patients will include molecular diagnostics for early detection of infection.