Assessing donor-recipient size metrics for heart transplant outcomes: UNOS database analysis.

STUDY: There is no widely accepted donor to recipient size-match metric to predict outcomes in cardiac transplant. The predictive ability of size-match metrics has not been studied when recipients are stratified by heart failure etiology. We sought to assess the performance of commonly used size metrics to predict survival after heart transplant, accounting for restrictive versus non-restrictive pathology. METHODS: The UNOS registry was queried from 2000 to 2017 for all primary isolated heart transplants. Donor-recipient ratios were calculated for commonly used size metrics and their association with survival was assessed using continuous, nonlinear analysis. RESULTS: 29 817 patients were identified. Height (P < .001), predicted heart mass (PHM) (P = .003), ideal body weight (IBW) (P < .001) and body mass index (BMI) (P = .003) ratios were significantly associated with survival, while weight and body surface area (BSA) ratios were not. When stratified, only BMI ratio retained significance for both restrictive (P = .051) and non-restrictive (P = .003) subsets. Recipients with restrictive etiology had increased risk of mortality with both a lower and higher BMI ratio. CONCLUSIONS: While many metrics show association with survival in the non-restrictive subset, BMI is the only metric that retains significance in the restrictive subset. Recipients with restrictive and non-restrictive etiologies of heart failure tolerate size mismatch differently.

Computational sentiment analysis of an online left ventricular assist device support forum: positivity predominates.

BACKGROUND: The impact of left ventricular assist device (LVAD) complications on the individual patient, overall sentiment, and its effect on referral patterns, is not fully understood. We sought to better understand patient attitudes towards LVAD therapy using a computational sentiment analysis approach. METHODS: Posts, comments, and titles were parsed from MyLVAD.com's HTML as a text file using custom Python scripts (version 3.6). Individual word frequency was computed with word classification as 'positive', 'negative', or 'neutral'. Data transformation and cleaning, sentiment determination, and analysis was performed with a binary dictionary package using R software (version 3.6). RESULTS: Sixty-six thousand eight hundred and twenty-one unique words were noted, including 4,623 (6.9%) with positive sentiment and 3,248 (4.8%) with negative sentiment. Net sentiment ratio [(number of positive words - number of negative words)/(number of total words)] was 2.1%. Positive sentiment dominated the 20 most commonly used words. Odds ratio of non-neutral words [(number of positive words/number of negative words)] was 1.42, indicating a less obvious disparity in sentiment when expanding analysis beyond the top 20 words. Word cloud analysis of positive and negative sentiments was performed, indicating common use of "infection" (208 mentions) compared to other complications such as "stroke" (29 mentions), "bleeding" (30 mentions), and "thrombosis" or "clot" (32 mentions). CONCLUSIONS: Positive sentiment dominates the most frequently used words, yet this disparity decreases when considering the totality of words. "Infection" is mentioned a disproportionate number of times compared to other LVAD complications. Further research is required to address analysis limitations, including selection bias.