Clustering of 27,525,663 Death Records from the United States Based on Health Conditions Associated with Death: An Example of big Health Data Exploration

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography

OBJECTIVE

Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.

METHODS

In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50).

RESULTS

Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3).

CONCLUSIONS

The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.

Social determinants of multimorbidity patterns: A systematic review

Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.

Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure

Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently emerged. Clustering is a machine learning (ML) technique that divides the population into distinct subgroups based on the similarity of cases (patients). Given that, we decided to use clustering to find subgroups inside the AHF population that differ in terms of WRF occurrence. We evaluated data from the three hundred and twelve AHF patients hospitalized in our institution who had creatinine assessed four times during hospitalization. Eighty-six variables evaluated at admission were included in the analysis. The k-medoids algorithm was used for clustering, and the quality of the procedure was judged by the Davies–Bouldin index. Three clinically and prognostically different clusters were distinguished. The groups had significantly (p = 0.004) different incidences of WRF. Inside the AHF population, we successfully discovered that three groups varied in renal prognosis. Our results provide novel insight into the AHF and WRF interplay and can be valuable for future trial construction and more tailored treatment.

Short-Term Mortality in Patients with Heart Failure at the End-of-Life Stages: Hades Study

Background: Information regarding short-term vital prognosis in patients with heart failure at advanced stages of the disease is scarce. Objective: To develop a three-month mortality predictive model for patients with advanced heart failure. Methods: Prospective observational study carried out in primary care and a convalescence community facility. Heart failure patients either New York Heart Association (NYHA) III with at least two HF hospitalizations during the previous six months or NYHA IV with/without previous recent hospitalization were included in the study. Multivariable predictive models using Cox regression were performed. Results: Of 271 patients included, 55 (20.3%) died during the first three months of follow-up. Mean age was 84.2 years (SD 8.3) and 59.8% were women. Predictive model including NT-proBNP had a C-index of 0.78 (95% CI 0.71; 0.85) and identified male gender, low body mass index, high potassium and NT-proBNP levels, and moderate-to-severe dependence for daily living activities (Barthel index < 40) as risk factors of mortality. In the model without NT-proBNP, C index was 0.72 (95% CI 0.64; 0.79) and, in addition to gender, body mass index, low Barthel index, and severe reductions in glomerular filtration rate showed the highest predictive hazard ratios for short-term mortality. Conclusions: In addition to age, male gender, potassium levels, low body mass index, and low glomerular filtration, dependence for activities of daily living add strong power to predict mortality at three months in patients with advanced heart failure.

Fatigue in patients with chronic disease: results from the population-based Lifelines Cohort Study

(1) To evaluate the prevalence of severe and chronic fatigue in subjects with and without chronic disease; (2) to assess to which extent multi-morbidity contributes to severe and chronic fatigue; and (3) to identify predisposing and associated factors for severe and chronic fatigue and whether these are disease-specific, trans-diagnostic, or generic. The Dutch Lifelines cohort was used, including 78,363 subjects with (n = 31,039, 53 ± 12 years, 33% male) and without (n = 47,324, 48 ± 12 years, 46% male) ≥ 1 of 23 chronic diseases. Fatigue was assessed with the Checklist Individual Strength-Fatigue. Compared to participants without a chronic disease, a higher proportion of participants with ≥ 1 chronic disease were severely (23% versus 15%, p < 0.001) and chronically (17% versus 10%, p < 0.001) fatigued. The odds of having severe fatigue (OR [95% CI]) increased from 1.6 [1.5–1.7] with one chronic disease to 5.5 [4.5–6.7] with four chronic diseases; for chronic fatigue from 1.5 [1.5–1.6] to 4.9 [3.9–6.1]. Multiple trans-diagnostic predisposing and associated factors of fatigue were found, explaining 26% of variance in fatigue in chronic disease. Severe and chronic fatigue are highly prevalent in chronic diseases. Multi-morbidity increases the odds of having severe and chronic fatigue. Several trans-diagnostic factors were associated with fatigue, providing a rationale for a trans-diagnostic approach.