External validation of life expectancy prognostic models in patients evaluated for palliative radiotherapy at the end-of-life.
Ontology highlight
ABSTRACT:
SUBMITTER: Mojica-Marquez AE
PROVIDER: S-EPMC7433812 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
External validation of life expectancy prognostic models in patients evaluated for palliative radiotherapy at the end-of-life.
Mojica-Márquez Adrianna E AE Rodríguez-López Joshua L JL Patel Ankur K AK Ling Diane C DC Rajagopalan Malolan S MS Beriwal Sushil S
Cancer medicine 20200626 16
<h4>Background</h4>The TEACHH and Chow models were developed to predict life expectancy (LE) in patients evaluated for palliative radiotherapy (PRT). We sought to validate the TEACHH and Chow models in patients who died within 90 days of PRT consultation.<h4>Methods</h4>A retrospective review was conducted on patients evaluated for PRT from 2017 to 2019 who died within 90 days of consultation. Data were collected for the TEACHH and Chow models; one point was assigned for each adverse factor. TEA ...[more]
Similar Datasets
Project description:Purpose: Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer. Materials and methods: We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV1), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data. Results: Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55-0.76) and 0.70 (95% CI 0.58-0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host. Conclusion: We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV1 that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.
| S-EPMC6927468 | biostudies-literature
Project description:To externally validate the prognostic models for predicting the time-dependent outcome in patients with locally advanced cervical cancer (LACC) who were treated with concurrent chemoradiotherapy in an independent cohort.A historical cohort of 297 women with LACC who were treated with radical concurrent chemoradiotherapy from 1999 to 2014 at the 12 de Octubre University Hospital (H12O), Madrid, Spain. The external validity of prognostic models was quantified regarding discrimination, calibration, measures of overall performance, and decision curve analyses.The review identified 8 studies containing 13 prognostic models. Different (International Federation of Gynecology and Obstetrics [FIGO] stages, parametrium involvement, hydronephrosis, location of positive nodes, and race) but related cohorts with validation cohort (5-year overall survival [OS]=70%; 5-year disease-free survival [DFS]=64%; average age of 50; and over 79% squamous cell) were evaluated. The following models exhibited good external validity in terms of discrimination and calibration but limited clinical utility: the OS model at 3 year from Kidd et al.'s study (area under the receiver operating characteristic curve [AUROC]=0.69; threshold of clinical utility [TCU] between 36% and 50%), the models of DFS at 1 year from Kidd et al.'s study (AUROC=0.64; TCU between 24% and 32%) and 2 years from Rose et al.'s study (AUROC=0.70; TCU between 19% and 58%) and the distant recurrence model at 5 years from Kang et al.'s study (AUROC=0.67; TCU between 12% and 36%).The external validation revealed the statistical and clinical usefulness of 4 prognostic models published in the literature.
| S-EPMC5540718 | biostudies-literature
Project description:BackgroundSeveral prognostic models for outcomes after chronic subdural hematoma (CSDH) treatment have been published in recent years. However, these models are not sufficiently validated for use in daily clinical practice. We aimed to assess the performance of existing prediction models for outcomes in patients diagnosed with CSDH.MethodsWe systematically searched relevant literature databases up to February 2021 to identify prognostic models for outcome prediction in patients diagnosed with CSDH. For the external validation of prognostic models, we used a retrospective database, containing data of 2384 patients from three Dutch regions. Prognostic models were included if they predicted either mortality, hematoma recurrence, functional outcome, or quality of life. Models were excluded when predictors were absent in our database or available for < 150 patients in our database. We assessed calibration, and discrimination (quantified by the concordance index C) of the included prognostic models in our retrospective database.ResultsWe identified 1680 original publications of which 1656 were excluded based on title or abstract, mostly because they did not concern CSDH or did not define a prognostic model. Out of 18 identified models, three could be externally validated in our retrospective database: a model for 30-day mortality in 1656 patients, a model for 2 months, and another for 3-month hematoma recurrence both in 1733 patients. The models overestimated the proportion of patients with these outcomes by 11% (15% predicted vs. 4% observed), 1% (10% vs. 9%), and 2% (11% vs. 9%), respectively. Their discriminative ability was poor to modest (C of 0.70 [0.63-0.77]; 0.46 [0.35-0.56]; 0.59 [0.51-0.66], respectively).ConclusionsNone of the examined models showed good predictive performance for outcomes after CSDH treatment in our dataset. This study confirms the difficulty in predicting outcomes after CSDH and emphasizes the heterogeneity of CSDH patients. The importance of developing high-quality models by using unified predictors and relevant outcome measures and appropriate modeling strategies is warranted.
| S-EPMC9519711 | biostudies-literature
Project description:BACKGROUND: The use of palliative radiotherapy (PRT) is variable in advanced cancer. Little is known about PRT utilization by end-of-life (EOL) cancer patients in Canada. This study examined the PRT utilization rates and factors associated with its use in a cohort of cancer patients who died in British Columbia (BC). METHODS: BC residents with invasive cancer who died between April 1, 2010 and March 31, 2011 were included in the study. Their cancer registry and radiotherapy treatment records were extracted from the BC Cancer Agency information systems and linked for the analysis. The PRT utilization rates by age, sex, primary cancer diagnosis, geographic region, survival time and travel time to the cancer centre were examined. Multivariable logistic regression was used to determine the factors that influenced the PRT utilization rates. RESULTS: Of the 12,300 decedents in the study 2,669 (21.7%) had received at least one course of PRT in their last year of life. The utilization rates dropped to 5.0% and 2.2% in the last 30 and 14 days of life, respectively. PRT utilization varied across diagnosis and was highest for lung cancer (45.7%) and lowest for colorectal cancer (8.9%). The rates also varied by age, survival time and travel time to the nearest radiotherapy centre. There was a greater odds of receiving PRT for those with primary lung cancer, survival time between 1.5-26 months from diagnosis or living within 2 hours from a cancer centre. The 85+ age group was least likely to receive PRT in their last year of life. CONCLUSIONS: This study found PRT utilization rates of EOL cancer decedents to be variable across the province of BC. Age, diagnosis, survival time and travel time to the nearest radiotherapy centre were found to influence the odds of PRT treatment. Further work is still needed to establish the appropriate PRT utilization rates for the EOL cancer population.
| S-EPMC4240806 | biostudies-other
Project description:BackgroundVarious prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). However, their generalisability and predictive performance in different populations remain largely unvalidated. This study aimed to externally validate several prognostic models of CKD in a T2D Thai cohort.MethodsA nationwide survey was linked with hospital databases to create a prospective cohort of patients with diabetes (n = 3416). We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. We updated prognostic models by including additional clinical parameters to optimise model performance in the Thai setting.ResultsSix relevant previously published models were identified. At baseline, C-statistics ranged from 0.585 (0.565-0.605) to 0.786 (0.765-0.806) for CKD and 0.657 (0.610-0.703) to 0.760 (0.705-0.816) for end-stage renal disease (ESRD). All original CKD models showed fair calibration with Observed/Expected (O/E) ratios ranging from 0.999 (0.975-1.024) to 1.009 (0.929-1.090). Hosmer-Lemeshow tests indicated a good fit for all models. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low's of 0.114 for CKD and Elley's of 0.025 for ESRD.ConclusionsAll models showed moderate discrimination and fair calibration. Updating models to include routine clinical factors substantially enhanced their accuracy. Low's (developed in Singapore) and Elley's model (developed in New Zealand), outperformed the other models evaluated. These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand.
| S-EPMC9300508 | biostudies-literature
Project description:BackgroundPre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting.MethodsIPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis.ResultsOf 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%.ConclusionsThe evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice.Trial registrationPROSPERO ID: CRD42015029349 .
| S-EPMC7604970 | biostudies-literature
Project description:BackgroundPrognostication can be used within a tailored decision-making process to achieve a more personalized approach to the care of patients with cancer. This prospective observational study evaluated the accuracy of the Palliative Prognostic score (PaP score) to predict survival in patients identified by oncologists as candidates for palliative radiotherapy (PRT). We also studied interrater variability for the clinical prediction of survival and PaP scores and assessed the accuracy of the Survival Prediction Score (SPS) and TEACHH score.Materials and methodsConsecutive patients were enrolled at first access to our Radiotherapy and Palliative Care Outpatient Clinic. The discriminating ability of the prognostic models was assessed using Harrell's C index, and the corresponding 95% confidence intervals (95% CI) were obtained by bootstrapping.ResultsIn total, 255 patients with metastatic cancer were evaluated, and 123 (48.2%) were selected for PRT, all of whom completed treatment without interruption. Then, 10.6% of the irradiated patients who died underwent treatment within the last 30 days of life. The PaP score showed an accuracy of 74.8 (95% CI, 69.5-80.1) for radiation oncologist (RO) and 80.7 (95% CI, 75.9-85.5) for palliative care physician (PCP) in predicting 30-day survival. The accuracy of TEACHH was 76.1 (95% CI, 70.9-81.3) and 64.7 (95% CI, 58.8-70.6) for RO and PCP, respectively, and the accuracy of SPS was 70 (95% CI, 64.4-75.6) and 72.8 (95% CI, 67.3-78.3).ConclusionAccurate prognostication can identify candidates for low-fraction PRT during the last days of life who are more likely to complete the planned treatment without interruption.All the scores showed good discriminating capacity; the PaP had the higher accuracy, especially when used in a multidisciplinary way.
| S-EPMC9425085 | biostudies-literature
Project description:Objectives backgroundTo externally validate clinical prediction models that aim to predict progression to invasive ventilation or death on the ICU in patients admitted with confirmed COVID-19 pneumonitis.DesignSingle-center retrospective external validation study.Data sourcesRoutinely collected healthcare data in the ICU electronic patient record. Curated data recorded for each ICU admission for the purposes of the U.K. Intensive Care National Audit and Research Centre (ICNARC).SettingThe ICU at Manchester Royal Infirmary, Manchester, United Kingdom.PatientsThree hundred forty-nine patients admitted to ICU with confirmed COVID-19 Pneumonitis, older than 18 years, from March 1, 2020, to February 28, 2022. Three hundred two met the inclusion criteria for at least one model. Fifty-five of the 349 patients were admitted before the widespread adoption of dexamethasone for the treatment of severe COVID-19 (pre-dexamethasone patients).OutcomesAbility to be externally validated, discriminate, and calibrate.MethodsArticles meeting the inclusion criteria were identified, and those that gave sufficient details on predictors used and methods to generate predictions were tested in our cohort of patients, which matched the original publications' inclusion/exclusion criteria and endpoint.ResultsThirteen clinical prediction articles were identified. There was insufficient information available to validate models in five of the articles; a further three contained predictors that were not routinely measured in our ICU cohort and were not validated; three had performance that was substantially lower than previously published (range C-statistic = 0.483-0.605 in pre-dexamethasone patients and C = 0.494-0.564 among all patients). One model retained its discriminative ability in our cohort compared with previously published results (C = 0.672 and 0.686), and one retained performance among pre-dexamethasone patients but was poor in all patients (C = 0.793 and 0.596). One model could be calibrated but with poor performance.ConclusionsOur findings, albeit from a single center, suggest that the published performance of COVID-19 prediction models may not be replicated when translated to other institutions. In light of this, we would encourage bedside intensivists to reflect on the role of clinical prediction models in their own clinical decision-making.
| S-EPMC10977519 | biostudies-literature
Project description:ObjectivesTo identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties.Study design and settingWe conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots).ResultsWe validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors.ConclusionThe validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use.
| S-EPMC10152752 | biostudies-literature
Project description:BackgroundExternal validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD.MethodsWe relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores.ResultsDepending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile-3rd quartile = 0.655-0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUCADO - AUCBODE = 0.015 [95% confidence interval (CI) = -0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated - AUCBODE = 0.008 [95% CI = -0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency.ConclusionsOur analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research.
| S-EPMC5833113 | biostudies-literature