[{"innerBlocks":[],"name":"core\/freeform","postId":1371,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Roche and IBM have developed an algorithm based on real-word data, including electronic health records, that predicts patients with early risk of diabetes-related chronic kidney disease.

The Roche-IBM predictive algorithm\u2014trained using half a million electronic health records\u2014outperformed published algorithms derived from clinical trial data in a one-to-one comparison, as well as in cohort studies.

Results of the research, published<\/a> this week in the journal Nature Medicine<\/i>, were based on a year-long collaboration between Roche (the study lead), IBM, Eli Lilly, the Regenstrief Institute and the Indiana Bioscience Research Institute.

\u201cThis study demonstrates the growing importance of real-world data and predictive analytics in diabetes care,\u201d said Mark Davies, chief medical officer (Europe) for IBM Watson Health. \u201cThere is a growing need to improve screening performance and the decision-making processes in diabetes care, and this new data suggest that real-world data and analytics can be applied to help in early recognition of risk of CKD.\u201d

Also See<\/b>: Mount Sinai partners with startup to develop AI for kidney disease<\/a>

The Indiana Bioscience Research Institute, Eli Lilly and Indiana University School of Medicine\u2014through the Regenstrief Institute\u2014provided Roche with another independent real-world data set originating from almost 100,000 patients with diabetes obtained from the Indiana Network of Patient Care database, which confirmed the findings.

\u201cThese extracted datasets constitute the largest RWD basis that has been used thus far to investigate CKD as a long-term complication of diabetes,\u201d according to the study\u2019s authors, who concluded that the \u201cteaching of predictive analytics algorithms using RWD could achieve equivalent or even enhanced accuracy compared with those using clinical trial data.\u201d

At the same time, researchers added that \u201cfurther testing on additional datasets will be necessary before these conclusions can be generalized.\u201d

Nonetheless, the authors speculated that \u201cit is the diversity of the RWD that makes the prediction algorithm better suited for generalization.\u201d","saveContent":"Roche and IBM have developed an algorithm based on real-word data, including electronic health records, that predicts patients with early risk of diabetes-related chronic kidney disease.

The Roche-IBM predictive algorithm\u2014trained using half a million electronic health records\u2014outperformed published algorithms derived from clinical trial data in a one-to-one comparison, as well as in cohort studies.

Results of the research, published<\/a> this week in the journal Nature Medicine<\/i>, were based on a year-long collaboration between Roche (the study lead), IBM, Eli Lilly, the Regenstrief Institute and the Indiana Bioscience Research Institute.

\u201cThis study demonstrates the growing importance of real-world data and predictive analytics in diabetes care,\u201d said Mark Davies, chief medical officer (Europe) for IBM Watson Health. \u201cThere is a growing need to improve screening performance and the decision-making processes in diabetes care, and this new data suggest that real-world data and analytics can be applied to help in early recognition of risk of CKD.\u201d

Also See<\/b>: Mount Sinai partners with startup to develop AI for kidney disease<\/a>

The Indiana Bioscience Research Institute, Eli Lilly and Indiana University School of Medicine\u2014through the Regenstrief Institute\u2014provided Roche with another independent real-world data set originating from almost 100,000 patients with diabetes obtained from the Indiana Network of Patient Care database, which confirmed the findings.

\u201cThese extracted datasets constitute the largest RWD basis that has been used thus far to investigate CKD as a long-term complication of diabetes,\u201d according to the study\u2019s authors, who concluded that the \u201cteaching of predictive analytics algorithms using RWD could achieve equivalent or even enhanced accuracy compared with those using clinical trial data.\u201d

At the same time, researchers added that \u201cfurther testing on additional datasets will be necessary before these conclusions can be generalized.\u201d

Nonetheless, the authors speculated that \u201cit is the diversity of the RWD that makes the prediction algorithm better suited for generalization.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Algorithm predicts patients at risk for chronic kidney disease

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