Dataset Information

Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR? A Systematic Review.

ABSTRACT:

Background

One third of high- and prohibitive-risk TAVR patients remain severely symptomatic or die 1 year after treatment. There is interest in identifying individuals for whom this procedure is futile and should not be offered.

Methods

We performed a systematic review of the highest reported stratum of risk in TAVR clinical predictive models (CPMs). We explore whether currently available predictive models can identify patients for whom TAVR is futile, based on a quantitative futility definition and the observed and predicted outcomes for patients in the highest stratum of risk.

Results

17 TAVR CPMs representing 69,191 treated patients were published from 2013 to 2018. When reported, the median number of patients in the highest stratum of risk was 569 (range 1 to 1759). Observed mortality for this risk stratum ranged from 9% at 30 days to 59% at 1 year after TAVR. Statistical confidence in these observed event rates was low. The highest predicted event rates ranged from 11.0% for in-hospital mortality to 75.1% for the composite of mortality or high symptom burden 1 year after TAVR.

Conclusion

No high-risk TAVR group in currently available TAVR CPMs had an appropriate event rate and adequate statistical power to meet a quantitative definition of futility.

SUBMITTER: Wessler BS

PROVIDER: S-EPMC7469888 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR? A Systematic Review.

Wessler Benjamin S BS Weintraub Andrew R AR Udelson James E JE Kent David M DM

Structural heart : the journal of the Heart Team 20200709 4

<h4>Background</h4>One third of high- and prohibitive-risk TAVR patients remain severely symptomatic or die 1 year after treatment. There is interest in identifying individuals for whom this procedure is futile and should not be offered.<h4>Methods</h4>We performed a systematic review of the highest reported stratum of risk in TAVR clinical predictive models (CPMs). We explore whether currently available predictive models can identify patients for whom TAVR is futile, based on a quantitative fut ...[more]

PMID: 32905421

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Project description:BACKGROUND:COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. OBJECTIVE:The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. METHODS:Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. RESULTS:Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). CONCLUSIONS:Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

Project description:BackgroundSolitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.MethodsWe performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.ResultsA total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.ConclusionsThe studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.Systematic review registrationPROSPERO CRD42020161559.

Dataset Information

Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR? A Systematic Review.

Background

Methods

Results

Conclusion

Publications

Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR? A Systematic Review.

Similar Datasets

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