Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review

OBJECTIVES: Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these methods are used in medical research. We aim to find out if and how well prediction model studies using machine learning report on their handling of missing data. STUDY DESIGN AND SETTING: We systematically searched the literature on published papers between 2018 and 2019 about primary studies developing and/or validating clinical prediction models using any supervised ML methodology across medical fields. From the retrieved studies information about the amount and nature (e.g. missing completely at random, potential reasons for missingness) of missing data and the way they were handled were extracted. RESULTS: We identified 152 machine learning-based clinical prediction model studies. A substantial amount of these 152 papers did not report anything on missing data (n = 56/152). A majority (n = 96/152) reported details on the handling of missing data (e.g., methods used), though many of these (n = 46/96) did not report the amount of the missingness in the data. In these 96 papers the authors only sometimes reported possible reasons for missingness (n = 7/96) and information about missing data mechanisms (n = 8/96). The most common approach for handling missing data was deletion (n = 65/96), mostly via complete-case analysis (CCA) (n = 43/96). Very few studies used multiple imputation (n = 8/96) or built-in mechanisms such as surrogate splits (n = 7/96) that directly address missing data during the development, validation, or implementation of the prediction model. CONCLUSION: Though missing values are highly common in any type of medical research and certainly in the research based on routine healthcare data, a majority of the prediction model studies using machine learning does not report sufficient information on the presence and handling of missing data. Strategies in which patient data are simply omitted are unfortunately the most often used methods, even though it is generally advised against and well known that it likely causes bias and loss of analytical power in prediction model development and in the predictive accuracy estimates. Prediction model researchers should be much more aware of alternative methodologies to address missing data

Dynamics in cardiac surgery:trends in population characteristics and the performance of the EuroSCORE II over time

OBJECTIVESThe aim of this study was to investigate the performance of the EuroSCORE II over time and dynamics in values of predictors included in the model.METHODSA cohort study was performed using data from the Netherlands Heart Registration. All cardiothoracic surgical procedures performed between 1 January 2013 and 31 December 2019 were included for analysis. Performance of the EuroSCORE II was assessed across 3-month intervals in terms of calibration and discrimination. For subgroups of major surgical procedures, performance of the EuroSCORE II was assessed across 12-month time intervals. Changes in values of individual EuroSCORE II predictors over time were assessed graphically.RESULTSA total of 103 404 cardiothoracic surgical procedures were included. Observed mortality risk ranged between 1.9% [95% confidence interval (CI) 1.6–2.4] and 3.6% (95% CI 2.6–4.4) across 3-month intervals, while the mean predicted mortality risk ranged between 3.4% (95% CI 3.3–3.6) and 4.2% (95% CI 3.9–4.6). The corresponding observed:expected ratios ranged from 0.50 (95% CI 0.46–0.61) to 0.95 (95% CI 0.74–1.16). Discriminative performance in terms of the c-statistic ranged between 0.82 (95% CI 0.78–0.89) and 0.89 (95% CI 0.87–0.93). The EuroSCORE II consistently overestimated mortality compared to observed mortality. This finding was consistent across all major cardiothoracic surgical procedures. Distributions of values of individual predictors varied broadly across predictors over time. Most notable trends were a decrease in elective surgery from 75% to 54% and a rise in patients with no or New York Heart Association I class heart failure from 27% to 33%.CONCLUSIONSThe EuroSCORE II shows good discriminative performance, but consistently overestimates mortality risks of all types of major cardiothoracic surgical procedures in the Netherlands

Association of sICAM-1 and MCP-1 with coronary artery calcification in families enriched for coronary heart disease or hypertension: the NHLBI Family Heart Study

<p>Abstract</p> <p>Background</p> <p>Data accumulated from mouse studies and in vitro studies of human arteries support the notion that soluble intercellular adhesion molecule-1 (sICAM-1) and monocyte chemoattractant protein-1 (MCP-1) play important roles in the inflammation process involved in atherosclerosis. However, at the population level, the utility of sICAM-1 and MCP-1 as biomarkers for subclinical atherosclerosis is less clear. In the follow-up exam of the NHLBI Family Heart Study, we evaluated whether plasma levels of sICAM-1 and MCP-1 were associated with coronary artery calcification (CAC), a measure of the burden of coronary atherosclerosis.</p> <p>Methods</p> <p>CAC was measured using the Agatston score with multidetector computed tomography. Information on CAC and MCP-1 was obtained in 2246 whites and 470 African Americans (mean age 55 years) without a history of coronary heart disease (CHD). Information on sICAM-1 was obtained for white participants only.</p> <p>Results</p> <p>In whites, after adjustment for age and gender, the odds ratios (ORs) of CAC (CAC > 0) associated with the second, third, fourth, and fifth quintiles of sICAM-1 compared to the first quintile were 1.22 (95% confidence interval [CI]: 0.91–1.63), 1.15 (0.84–1.58), 1.49 (1.09–2.05), and 1.72 (1.26–2.36) (p = 0.0005 for trend test), respectively. The corresponding ORs for the second to fifth quintiles of MCP-1 were 1.26 (0.92–1.73), 0.99 (0.73–1.34), 1.42 (1.03–1.96), and 2.00 (1.43–2.79) (p < 0.0001 for trend test), respectively. In multivariable analysis that additionally adjusted for other CHD risk factors, the association of CAC with sICAM-1 and MCP-1 was attenuated and no longer statistically significant. In African Americans, the age and gender-adjusted ORs of CAC associated with the second and third tertiles of MCP-1 compared to the first tertile were 1.16 (0.64–2.08) and 1.25 (0.70–2.23) (p = 0.44 for trend test), respectively. This result did not change materially after additional adjustment for other CHD risk factors. Test of race interaction showed that the magnitude of association between MCP-1 and CAC did not differ significantly between African Americans and whites. Similar results were obtained when CAC ≥ 10 was analyzed as an outcome for both MCP-1 and sICAM-1.</p> <p>Conclusion</p> <p>This study suggests that sICAM-1 and MCP-1 are biomarkers of coronary atherosclerotic burden and their association with CAC was mainly driven by established CHD risk factors.</p

Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. 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Patterns of inflammation and the use of reversibility testing in smokers with airway complaints.

Contains fulltext : 50193.pdf (publisher's version ) (Open Access)BACKGROUND: Although both smoking and respiratory complaints are very common, tools to improve diagnostic accuracy are scarce in primary care. This study aimed to reveal what inflammatory patterns prevail in clinically established diagnosis groups, and what factors are associated with eosinophilia. METHOD: Induced sputum and blood plasma of 59 primary care patients with COPD (n = 17), asthma (n = 11), chronic bronchitis (CB, n = 14) and smokers with no respiratory complaints ('healthy smokers', n = 17) were collected, as well as lung function, smoking history and clinical work-up. Patterns of inflammatory markers per clinical diagnosis and factors associated with eosinophilia were analyzed by multiple regression analyses, the differences expressed in odds ratios (OR) with 95% confidence intervals. RESULTS: Multivariately, COPD was significantly associated with raised plasma-LBP (OR 1.2 [1.04-1.37]) and sTNF-R55 in sputum (OR 1.01 [1.001-1.01]), while HS expressed significantly lowered plasma-LBP (OR 0.8 [0.72-0.95]). Asthma was characterized by higher sputum eosinophilic counts (OR 1.3 [1.05-1.54]), while CB showed a significantly higher proportion of sputum lymphocytic counts (OR 1.5 [1.12-1.9]). Sputum eosinophilia was significantly associated with reversibility after adjusting for smoking, lung function, age, gender and allergy. CONCLUSION: Patterns of inflammatory markers in a panel of blood plasma and sputum cells and mediators were discernable in clinical diagnosis groups of respiratory disease. COPD and so-called healthy smokers showed consistent opposite associations with plasma LBP, while chronic bronchitics showed relatively predominant lymphocytic inflammation compared to other diagnosis groups. Only sputum eosinophilia remained significantly associated with reversibility across the spectrum of respiratory disease in smokers with airway complaints