Dataset Information

Survival stratification in childhood rhabdomyosarcoma of the extremities: a derivation and validation study.

ABSTRACT: The objective of this study was to estimate overall survival in children with extremity rhabdomyosarcoma (RMS). In addition, we attempted to construct a nomogram to predict the prognosis in such patients using a population-based cohort. The national Surveillance, Epidemiology, and End Results (SEER) registry was used to identify a cohort of childhood RMS patients. A total of 197 patients with RMS were ultimately included. Multivariable analysis identified age group, N classification, M classification, and treatment combinations as independent predictive factors for patient overall survival. Candidate variables such as age group, N classification, M classification, and treatment combinations were used to fit the model. For overall survival, the bootstrap-adjusted c-index was 0.76 (95% CI, 0.73-0.80) for the nomogram. Furthermore, we performed recursive partitioning analysis for risk stratification according to overall survival, and 3 prognostic subgroups were generated (low, intermediate and high risk). Finally, we evaluated multimodal treatment based on the risk stratification according to the nomogram and IRSG prognostic stratification model. With regard to the entire cohort, overall survival in patients who received surgery and radiation was superior to that in patients who received surgery or radiation (p?=?0.001). Regarding RPA and IRSG prognostic stratification, we found that the differences remained significant (p??0.05). We performed a population-based analysis of data from the SEER registry in an effort to identify prognostic factors and develop a nomogram in children with extremity RMS. The nomogram appears to be suitable for the survival stratification of children with RMS and will help clinicians identify patients who may be at a reduced probability of survival and assist them in making treatment and surveillance decisions. More studies concerning overall survival in children with RMS are needed to confirm and update our findings.

SUBMITTER: Zhu L

PROVIDER: S-EPMC7105456 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Survival stratification in childhood rhabdomyosarcoma of the extremities: a derivation and validation study.

Zhu Linchao L Sun Ying Y Wang Xuhui X Wang Lin L Zhang Shufeng S Meng Qinglei Q Wang Xiaohui X

Scientific reports 20200330 1

The objective of this study was to estimate overall survival in children with extremity rhabdomyosarcoma (RMS). In addition, we attempted to construct a nomogram to predict the prognosis in such patients using a population-based cohort. The national Surveillance, Epidemiology, and End Results (SEER) registry was used to identify a cohort of childhood RMS patients. A total of 197 patients with RMS were ultimately included. Multivariable analysis identified age group, N classification, M classific ...[more]

PMID: 32231229

Similar Datasets

Project description:Patients with critical limb ischemia (CLI) are a heterogeneous population with respect to risk for mortality and limb loss, complicating clinical decision-making. Endovascular options, compared with bypass, offer a tradeoff between reduced procedural risk and inferior durability. Risk stratified data predictive of amputation-free survival (AFS) may improve clinical decision making and allow for better assessment of new technology in the CLI population.This was a retrospective analysis of prospectively collected data from patients who underwent infrainguinal vein bypass surgery for CLI. Two datasets were used: the PREVENT III randomized trial (n = 1404) and a multicenter registry (n = 716) from three distinct vascular centers (two academic, one community-based). The PREVENT III cohort was randomly assigned to a derivation set (n = 953) and to a validation set (n = 451). The primary endpoint was AFS. Predictors of AFS identified on univariate screen (inclusion threshold, P < .20) were included in a stepwise selection Cox model. The resulting five significant predictors were assigned an integer score to stratify patients into three risk groups. The prediction rule was internally validated in the PREVENT III validation set and externally validated in the multicenter cohort.The estimated 1-year AFS in the derivation, internal validation, and external validation sets were 76.3%, 72.5%, and 77.0%, respectively. In the derivation set, dialysis (hazard ratio [HR] 2.81, P < .0001), tissue loss (HR 2.22, P =.0004), age >or=75 (HR 1.64, P = .001), hematocrit <or=30 (HR 1.61, P = .012), and advanced CAD (HR 1.41, P = .021) were significant predictors for AFS in the multivariable model. An integer score, derived from the ss coefficients, was used to generate three risk categories (low <or= 3 [44.4% of cohort], medium 4-7 [46.7% of cohort], high >or=8 [8.8% of cohort]). Stratification of the patients, in each dataset, according to risk category yielded three significantly different Kaplan-Meier estimates for 1-year AFS (86%, 73%, and 45% for low, medium, and high risk groups, respectively). For a given risk category, the AFS estimate was consistent between the derivation and validation sets.Among patients selected to undergo surgical bypass for infrainguinal disease, this parsimonious risk stratification model reliably identified a category of CLI patients with a >50% chance of death or major amputation at 1 year. Calculation of a "PIII risk score" may be useful for surgical decision making and for clinical trial designs in the CLI population.

Project description:BackgroundPostoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.MethodsThe risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale [NuDESC] or Confusion Assessment Method for the Intensive Care Unit [CAM-ICU]) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts ("validation" and "sustained performance") and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.ResultsThe logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67-0.75), compared with 0.65 (0.58-0.72) in the validation (n = 908) and 0.75 (0.71-0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67-0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%-36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.ConclusionsThe AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record-based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.

Project description:Children with severe pneumonia in low- and middle-income countries (LMICs) suffer from high rates of treatment failure despite appropriate World Health Organization (WHO)-directed antibiotic treatment. Developing a clinical prediction rule for treatment failure may allow early identification of high-risk patients and timely intervention to decrease mortality. We used data from two separate studies conducted at the Dhaka Hospital of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) to derive and externally validate a clinical prediction rule for treatment failure of children hospitalized with severe pneumonia. The derivation dataset was from a randomized clinical trial conducted from 2018 to 2019, studying children aged 2 to 59 months hospitalized with severe pneumonia as defined by WHO. Treatment failure was defined by the persistence of danger signs at the end of 48 hours of antibiotic treatment or the appearance of any new danger signs within 24 hours of enrollment. We built a random forest model to identify the top predictors. The top six predictors were the presence of grunting, room air saturation, temperature, the presence of lower chest wall indrawing, the presence of respiratory distress, and central cyanosis. Using these six predictors, we created a parsimonious model with a discriminatory performance of 0.691, as measured by area under the receiving operating curve (AUC). We performed external validation using a temporally distinct dataset from a cohort study of 191 similarly aged children with severe acute malnutrition and pneumonia. In external validation, discriminatory performance was maintained with an improved AUC of 0.718. In conclusion, we developed and externally validated a parsimonious six-predictor model using random forest methods to predict treatment failure in young children with severe pneumonia in Bangladesh. These findings can be used to further develop and validate parsimonious and pragmatic prognostic clinical prediction rules for pediatric pneumonia, particularly in LMICs.

Dataset Information

Survival stratification in childhood rhabdomyosarcoma of the extremities: a derivation and validation study.

Publications

Survival stratification in childhood rhabdomyosarcoma of the extremities: a derivation and validation study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets