scholarly journals Machine learning-based clinical outcome prediction in surgery for acromegaly

Endocrine ◽  
2021 ◽  
Author(s):  
Olivier Zanier ◽  
Matteo Zoli ◽  
Victor E. Staartjes ◽  
Federica Guaraldi ◽  
Sofia Asioli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transsphenoidal Surgery ◽  
Prediction Models ◽  
External Validation ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Total Resection ◽  
Prior Surgery ◽  
Using Data ◽  
Csf Leaks

Abstract Purpose Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly. Methods Using data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed. Results The derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59–0.88) for GTR, 0.63 (0.40–0.82) for BR, as well as 0.77 (0.62–0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively. Conclusions Gross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization.

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

2021 ◽  
pp. postgradmedj-2020-139352
Author(s):  
Simon Allan ◽  
Raphael Olaiya ◽  
Rasan Burhan
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Prediction Models ◽  
Bone Fractures ◽  
Statin Treatment ◽  
Computer Algorithms ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Heart Attacks

Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

scholarly journals MO1031FROM MARCELLO MALPIGHI  THROUGH  JEAN REDMAN OLIVER AND JOSEP TRUETA: A CONTINUING CONTRIBUTION TO THE “BLACK BOX”  CLINICAL PREDICTION MODELS IN NEPHROLOGY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Enrico Favaro ◽  
Roberta Lazzarin ◽  
Daniela Cremasco ◽  
Erika Pierobon ◽  
Marta Guizzo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Prediction Models ◽  
Kidney Diseases ◽  
Kidney Injury ◽  
Black Box ◽  
Renal Physiology ◽  
Clinical Prediction ◽  
Renal Circulation ◽  
Clinical Prediction Models

Abstract Background and Aims The modern development of the black box approach in clinical nephrology is inconceivable without a logical theory of renal function and a comprehension of anatomical architecture of the kidney, in health and disease: this is the undisputed contribution offered by Malpighi, Oliver and Trueta starting from the seventeenth century. The machine learning model for the prediction of acute kidney injury, progression of renal failure and tubulointerstitial nephritis is a good example of how different knowledge about kidney are an indispensable tool for the interpretation of model itself. Method Historical data were collected from literature, textbooks, encyclopedias, scientific periodicals and laboratory experimental data concerning these three authors. Results The Italian Marcello Malpighi (1628-1694), born in Crevalcore near Bologna, was Professor of anatomy at Bologna, Pisa and Messina. The historic description of the pulmonary capillaries was made in his second epistle to Borelli published in 1661 and intitled De pulmonibus, by means of the frog as “the microscope of nature” (Fig. 1). It is the first description of capillaries in any circulation. William Harvey in De motu cordis in 1628 (year of publication the same of date of birth of Italian anatomist!) could not see the capillary vessels. This thriumphant discovery will serve for the next reconnaissance of characteristic renal rete mirabile.in the corpuscle of Malpighi, lying within the capsule of Bowman. Jean Redman Oliver (1889-1976), a pathologist born and raised in Northern California, was able to bridge the gap between the nephron and collecting system through meticulous dissections, hand drawn illustrations and experiments which underpin our current understanding of renal anatomy and physiology. In the skillful lecture “When is the kidney not a kidney?” (1949) Oliver summarizes his far-sighted vision on renal physiology and disease in the following sentence: the Kidney in health, if you will, but the Nephrons in disease. Because, the “nephron” like the “kidney” is an abstraction that must be qualified in terms of its various parts, its cellular components and the molecular mechanisms involved in each discrete activity (Fig. 2). The Catalan surgeon Josep Trueta I Raspall (1897-1977) was born in the Poblenou neighborhood of Barcelona. His impact of pioneering and visionary contribution to the changes in renal circulation for the pathogenesis of acute kidney injury was pivotal for history of renal physiology. “The kidney has two potential circulatory circulations. Blood may pass either almost exclusively through one or other of two pathways, or to a varying degree through both”. (Studies of the Renal Circulation, published in 1947). Now this diversion of blood from cortex to the less resistant medullary circulation is known with the eponym Trueta shunt. Conclusion The black box approach to the kidney diseases should be considered by practitioners as a further tool to help to inform model update in many clinical setting. The number of machine learning clinical prediction models being published is rising, as new fields of application are being explored in medicine (Fig. 3). A challenge in the clinical nephrology is to explore the “kidney machine” during each therapeutic diagnostic procedure. Always, the intriguing relationship between the set of nephrological syndromes and kidney diseases cannot disregard the precious notions the specific organization of kidney microcirculation, fruit of many scientific contributions of the work by Malpighi, Oliver and Trueta (Fig. 3).

scholarly journals A systematic review of the quality of clinical prediction models in in vitro fertilisation

2020 ◽  
Vol 35 (1) ◽  
pp. 100-116 ◽  
Author(s):  
M B Ratna ◽  
S Bhattacharya ◽  
B Abdulrahim ◽  
D J McLernon
Keyword(s):  
Systematic Review ◽  
Live Birth ◽  
Prediction Models ◽  
Conflicts Of Interest ◽  
External Validation ◽  
Post Treatment ◽  
Treatment Model ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Abstract STUDY QUESTION What are the best-quality clinical prediction models in IVF (including ICSI) treatment to inform clinicians and their patients of their chance of success? SUMMARY ANSWER The review recommends the McLernon post-treatment model for predicting the cumulative chance of live birth over and up to six complete cycles of IVF. WHAT IS KNOWN ALREADY Prediction models in IVF have not found widespread use in routine clinical practice. This could be due to their limited predictive accuracy and clinical utility. A previous systematic review of IVF prediction models, published a decade ago and which has never been updated, did not assess the methodological quality of existing models nor provided recommendations for the best-quality models for use in clinical practice. STUDY DESIGN, SIZE, DURATION The electronic databases OVID MEDLINE, OVID EMBASE and Cochrane library were searched systematically for primary articles published from 1978 to January 2019 using search terms on the development and/or validation (internal and external) of models in predicting pregnancy or live birth. No language or any other restrictions were applied. PARTICIPANTS/MATERIALS, SETTING, METHODS The PRISMA flowchart was used for the inclusion of studies after screening. All studies reporting on the development and/or validation of IVF prediction models were included. Articles reporting on women who had any treatment elements involving donor eggs or sperm and surrogacy were excluded. The CHARMS checklist was used to extract and critically appraise the methodological quality of the included articles. We evaluated models’ performance by assessing their c-statistics and plots of calibration in studies and assessed correct reporting by calculating the percentage of the TRIPOD 22 checklist items met in each study. MAIN RESULTS AND THE ROLE OF CHANCE We identified 33 publications reporting on 35 prediction models. Seventeen articles had been published since the last systematic review. The quality of models has improved over time with regard to clinical relevance, methodological rigour and utility. The percentage of TRIPOD score for all included studies ranged from 29 to 95%, and the c-statistics of all externally validated studies ranged between 0.55 and 0.77. Most of the models predicted the chance of pregnancy/live birth for a single fresh cycle. Six models aimed to predict the chance of pregnancy/live birth per individual treatment cycle, and three predicted more clinically relevant outcomes such as cumulative pregnancy/live birth. The McLernon (pre- and post-treatment) models predict the cumulative chance of live birth over multiple complete cycles of IVF per woman where a complete cycle includes all fresh and frozen embryo transfers from the same episode of ovarian stimulation. McLernon models were developed using national UK data and had the highest TRIPOD score, and the post-treatment model performed best on external validation. LIMITATIONS, REASONS FOR CAUTION To assess the reporting quality of all included studies, we used the TRIPOD checklist, but many of the earlier IVF prediction models were developed and validated before the formal TRIPOD reporting was published in 2015. It should also be noted that two of the authors of this systematic review are authors of the McLernon model article. However, we feel we have conducted our review and made our recommendations using a fair and transparent systematic approach. WIDER IMPLICATIONS OF THE FINDINGS This study provides a comprehensive picture of the evolving quality of IVF prediction models. Clinicians should use the most appropriate model to suit their patients’ needs. We recommend the McLernon post-treatment model as a counselling tool to inform couples of their predicted chance of success over and up to six complete cycles. However, it requires further external validation to assess applicability in countries with different IVF practices and policies. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the Elphinstone Scholarship Scheme and the Assisted Reproduction Unit, University of Aberdeen. Both D.J.M. and S.B. are authors of the McLernon model article and S.B. is Editor in Chief of Human Reproduction Open. They have completed and submitted the ICMJE forms for Disclosure of potential Conflicts of Interest. The other co-authors have no conflicts of interest to declare. REGISTRATION NUMBER N/A

Abstract 17185: Modest Performance of Heart Failure Clinical Prediction Models: A Systematic External Validation Study

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Jenica N Upshaw ◽  
Jason Nelson ◽  
Benjamin Wessler ◽  
Benjamin Koethe ◽  
Christine Lundquist ◽  
...  
Keyword(s):  
Heart Failure ◽  
Prediction Models ◽  
External Validation ◽  
Significant Heterogeneity ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Derivation Cohort ◽  
C Statistic ◽  
Improved Model ◽  
Relative Change

Introduction: Most heart failure (HF) clinical prediction models (CPMs] have not been independently externally validated. We sought to test the performance of HF models in a diverse population using a systematic approach. Methods: A systematic review identified CPMs predicting outcomes for patients with HF. Individual patient data from 5 large publicaly available clinical trials enrolling patients with chronic HF were matched to published CPMs based on similarity in populations and available outcome and predictor variables in the clinical trial databases. CPM performance was evaluated for discrimination (c-statistic, % relative change in c-statistic) and calibration (Harrell’s E and E 90 , the mean and the 90% quantile of the error distribution from the smoothed loess observed value) for the original and recalibrated models. Results: Out of 135 HF CPMs reviewed, we identified 45 CPM-trial pairs including 13 unique CPMs. The outcome was mortality for all of the models with a trial match. During external validations, median c-statistic was 0.595 (IQR 0.563 to 0.630) with a median relative decrease in the c-statistic of -57 % (IQR, -49% to -71%) compared to the c-statistic reported in the derivation cohort. Overall, the median Harrell’s E was 0.09 (IQR, 0.04 to 0.135) and E 90 was 0.11 (IQR, 0.07 to 0.21). Recalibration of the intercept and slope led to substantially improved calibration with median change in Harrell’s E of -35% [IQR 0 to -75%] for the intercept and -56% [IQR -17% to -75%] for the intercept and slope. Refitting model covariates improved the median c-statistic by 38% to 0.629 [IQR 0.613 to 0.649]. Conclusion: For HF CPMs, independent external validations demonstrate that CPMs perform significantly worse than originally presented; however with significant heterogeneity. Recalibration of the intercept and slope improved model calibration. These results underscore the need to carefully consider the derivation cohort characteristics when using published CPMs.

scholarly journals A review of statistical updating methods for clinical prediction models

2016 ◽  
Vol 27 (1) ◽  
pp. 185-197 ◽  
Author(s):  
Ting-Li Su ◽  
Thomas Jaki ◽  
Graeme L Hickey ◽  
Iain Buchan ◽  
Matthew Sperrin
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Healthcare Outcomes ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Clinical Prediction Model ◽  
The United Kingdom ◽  
Single Strategy ◽  
Using Data ◽  
Dynamic Updating

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.

scholarly journals External validation of six existing multivariable clinical prediction models for short-term mortality in patients undergoing lung resection

2020 ◽  
Author(s):  
Marcus Taylor ◽  
Bartłomiej Szafron ◽  
Glen P Martin ◽  
Udo Abah ◽  
Matthew Smith ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Lung Resection ◽  
External Validation ◽  
Clinical Prediction ◽  
National Guidelines ◽  
Clinical Prediction Models ◽  
Receiver Operating

Abstract OBJECTIVES National guidelines advocate the use of clinical prediction models to estimate perioperative mortality for patients undergoing lung resection. Several models have been developed that may potentially be useful but contemporary external validation studies are lacking. The aim of this study was to validate existing models in a multicentre patient cohort. METHODS The Thoracoscore, Modified Thoracoscore, Eurolung, Modified Eurolung, European Society Objective Score and Brunelli models were validated using a database of 6600 patients who underwent lung resection between 2012 and 2018. Models were validated for in-hospital or 30-day mortality (depending on intended outcome of each model) and also for 90-day mortality. Model calibration (calibration intercept, calibration slope, observed to expected ratio and calibration plots) and discrimination (area under receiver operating characteristic curve) were assessed as measures of model performance. RESULTS Mean age was 66.8 years (±10.9 years) and 49.7% (n = 3281) of patients were male. In-hospital, 30-day, perioperative (in-hospital or 30-day) and 90-day mortality were 1.5% (n = 99), 1.4% (n = 93), 1.8% (n = 121) and 3.1% (n = 204), respectively. Model area under the receiver operating characteristic curves ranged from 0.67 to 0.73. Calibration was inadequate in five models and mortality was significantly overestimated in five models. No model was able to adequately predict 90-day mortality. CONCLUSIONS Five of the validated models were poorly calibrated and had inadequate discriminatory ability. The modified Eurolung model demonstrated adequate statistical performance but lacked clinical validity. Development of accurate models that can be used to estimate the contemporary risk of lung resection is required.

scholarly journals Development and Validation of Clinical Prediction Models for Surgical Success in Patients With Endometriosis: Protocol for a Mixed Methods Study

10.2196/20986 ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. e20986 ◽  
Author(s):  
Nadine Marlin ◽  
Carol Rivas ◽  
John Allotey ◽  
Julie Dodds ◽  
Andrew Horne ◽  
...  
Keyword(s):  
Prediction Models ◽  
Clinical Symptoms ◽  
Treatment Success ◽  
External Validation ◽  
Ease Of Use ◽  
Reproductive Age ◽  
Clinical Prediction ◽  
Management Options ◽  
Clinical Prediction Models ◽  
Chronic Inflammatory Condition

Background Endometriosis is a chronic inflammatory condition affecting 6%-10% of women of reproductive age and is defined by the presence of endometrial-like tissue outside the uterus (lesions), commonly affecting the pelvis and ovaries. It is associated with debilitating pelvic pain, infertility, and fatigue and often has devastating effects on the quality of life (QoL). Although it is as common as back pain, it is poorly understood, and treatment and diagnosis are often delayed, leading to unnecessary suffering. Endometriosis has no cure. Surgery is one of several management options. Quantifying the probability of successful surgery is important for guiding clinical decisions and treatment strategies. Factors predicting success through pain reduction after endometriosis surgery have not yet been adequately identified. Objective This study aims to determine which women with confirmed endometriosis benefit from surgical improvement in pain and QoL and whether these women could be identified from clinical symptoms measured before laparoscopy. Methods First, we will carry out a systematic search and review and, if appropriate, meta-analysis of observational cohort and case-control studies reporting one or more risk factors for endometriosis and postsurgical treatment success. We will search PubMed, Embase, and Cochrane databases from inception without language restrictions and supplement the reference lists by manual searches. Second, we will develop separate clinical prediction models for women with confirmed and suspected diagnoses of endometriosis. A total of three suitable databases have been identified for development and external validation (the MEDAL [ISRCTN13028601] and LUNA [ISRCTN41196151] studies, and the BSGE database), and access has been guaranteed. The models will be developed using a linear regression approach that links candidate factors to outcomes. Third, we will hold 2 stakeholder co-design workshops involving eight clinicians and eight women with endometriosis separately and then bring all 16 participants together. Participants will discuss the implementation, delivery, usefulness, and sustainability of the prediction models. Clinicians will also focus on the ease of use and access to clinical prediction tools. Results This project was funded in March 2018 and approved by the Institutional Research Ethics Board in December 2019. At the time of writing, this study was in the data analysis phase, and the results are expected to be available in April 2021. Conclusions This study is the first to aim to predict who will benefit most from laparoscopic surgery through the reduction of pain or increased QoL. The models will provide clinicians with robustly developed and externally validated support tools, improving decision making in the diagnosis and treatment of women. International Registered Report Identifier (IRRID) DERR1-10.2196/20986

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