{"title":"多病测量中的高阶疾病相互作用:与疾病加总法相比的边际效益。","authors":"Melissa Y Wei, Chi-Hong Tseng, Ashley J Kang","doi":"10.1093/gerona/glae282","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Current multimorbidity measures often oversimplify complex disease interactions by assuming a merely additive impact of diseases on health outcomes. This oversimplification neglects clinical observations that certain disease combinations can exhibit synergistic effects. Thus, we aimed to incorporate simultaneous higher-order disease interactions into the validated ICD-coded multimorbidity-weighted index, to assess for model improvement.</p><p><strong>Methods: </strong>Health and Retirement Study participants with linked Medicare data contributed ICD-9-CM claims, 1991-2012. Top 20 most prevalent and impactful conditions (based on associations with decline in physical functioning) were assessed through higher-order interactions (2-way, 3-way). We applied the least absolute shrinkage and selection operator and bootstrapping to identify and retain statistically significant disease interactions. We compared model fit in multimorbidity-weighted index with and without disease interactions in linear models.</p><p><strong>Results: </strong>We analyzed 73 830 observations from 18 212 participants (training set N = 14 570, testing set N = 3 642). Multimorbidity-weighted index without interactions produced an overall R2 = 0.26. Introducing 2-way interactions for the top 10 most prevalent and impactful conditions resulted in a R2 = 0.27, while expanding to top 20 most prevalent and impactful conditions yielded a R2 = 0.26. When adding 3-way interactions, the same top 10 conditions produced a R2 = 0.26, while expanding to top 20 conditions resulted in a R2 = 0.24.</p><p><strong>Conclusions: </strong>We present novel insights into simultaneous higher-order disease interactions for potential integration into multimorbidity measurement. Incorporating 2-way disease interactions for the top 10 most prevalent and impactful conditions showed a minimal improvement in model fit. A more precise multimorbidity index may incorporate both the main effects of diseases and their significant interactions.</p>","PeriodicalId":94243,"journal":{"name":"The journals of gerontology. Series A, Biological sciences and medical sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11701747/pdf/","citationCount":"0","resultStr":"{\"title\":\"Higher-Order Disease Interactions in Multimorbidity Measurement: Marginal Benefit Over Additive Disease Summation.\",\"authors\":\"Melissa Y Wei, Chi-Hong Tseng, Ashley J Kang\",\"doi\":\"10.1093/gerona/glae282\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Current multimorbidity measures often oversimplify complex disease interactions by assuming a merely additive impact of diseases on health outcomes. This oversimplification neglects clinical observations that certain disease combinations can exhibit synergistic effects. Thus, we aimed to incorporate simultaneous higher-order disease interactions into the validated ICD-coded multimorbidity-weighted index, to assess for model improvement.</p><p><strong>Methods: </strong>Health and Retirement Study participants with linked Medicare data contributed ICD-9-CM claims, 1991-2012. Top 20 most prevalent and impactful conditions (based on associations with decline in physical functioning) were assessed through higher-order interactions (2-way, 3-way). We applied the least absolute shrinkage and selection operator and bootstrapping to identify and retain statistically significant disease interactions. We compared model fit in multimorbidity-weighted index with and without disease interactions in linear models.</p><p><strong>Results: </strong>We analyzed 73 830 observations from 18 212 participants (training set N = 14 570, testing set N = 3 642). Multimorbidity-weighted index without interactions produced an overall R2 = 0.26. Introducing 2-way interactions for the top 10 most prevalent and impactful conditions resulted in a R2 = 0.27, while expanding to top 20 most prevalent and impactful conditions yielded a R2 = 0.26. When adding 3-way interactions, the same top 10 conditions produced a R2 = 0.26, while expanding to top 20 conditions resulted in a R2 = 0.24.</p><p><strong>Conclusions: </strong>We present novel insights into simultaneous higher-order disease interactions for potential integration into multimorbidity measurement. Incorporating 2-way disease interactions for the top 10 most prevalent and impactful conditions showed a minimal improvement in model fit. A more precise multimorbidity index may incorporate both the main effects of diseases and their significant interactions.</p>\",\"PeriodicalId\":94243,\"journal\":{\"name\":\"The journals of gerontology. Series A, Biological sciences and medical sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11701747/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The journals of gerontology. Series A, Biological sciences and medical sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/gerona/glae282\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The journals of gerontology. Series A, Biological sciences and medical sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/gerona/glae282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Higher-Order Disease Interactions in Multimorbidity Measurement: Marginal Benefit Over Additive Disease Summation.
Background: Current multimorbidity measures often oversimplify complex disease interactions by assuming a merely additive impact of diseases on health outcomes. This oversimplification neglects clinical observations that certain disease combinations can exhibit synergistic effects. Thus, we aimed to incorporate simultaneous higher-order disease interactions into the validated ICD-coded multimorbidity-weighted index, to assess for model improvement.
Methods: Health and Retirement Study participants with linked Medicare data contributed ICD-9-CM claims, 1991-2012. Top 20 most prevalent and impactful conditions (based on associations with decline in physical functioning) were assessed through higher-order interactions (2-way, 3-way). We applied the least absolute shrinkage and selection operator and bootstrapping to identify and retain statistically significant disease interactions. We compared model fit in multimorbidity-weighted index with and without disease interactions in linear models.
Results: We analyzed 73 830 observations from 18 212 participants (training set N = 14 570, testing set N = 3 642). Multimorbidity-weighted index without interactions produced an overall R2 = 0.26. Introducing 2-way interactions for the top 10 most prevalent and impactful conditions resulted in a R2 = 0.27, while expanding to top 20 most prevalent and impactful conditions yielded a R2 = 0.26. When adding 3-way interactions, the same top 10 conditions produced a R2 = 0.26, while expanding to top 20 conditions resulted in a R2 = 0.24.
Conclusions: We present novel insights into simultaneous higher-order disease interactions for potential integration into multimorbidity measurement. Incorporating 2-way disease interactions for the top 10 most prevalent and impactful conditions showed a minimal improvement in model fit. A more precise multimorbidity index may incorporate both the main effects of diseases and their significant interactions.