[{"innerBlocks":[],"name":"core\/freeform","postId":1285,"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":"Mayo Clinic researchers have developed a prediction tool that can estimate when people who have experienced kidney stones will experience future episodes.

The tool, which is available online or as an app, can help \u201cguide the management of stone formers by individualizing prevention interventions on the basis of the risk of symptomatic recurrence,\u201d according to a new study<\/a> published in the journal Mayo Clinic Proceedings<\/i>.

The risk of symptomatic recurrence is estimated by the tool based on both the number of past episodes and risk factors. One of the study\u2019s findings is that more risk factors for recurrence increased with the number of episodes.

\u201cEach of the risk factors we identified are entered into the model, which then calculates an estimate of the risk of having another kidney stone in the next five or 10 years,\u201d says study co-author John Lieske, MD, director of the Mayo Clinic\u2019s O'Brien Urology Research Center.

Also See<\/b>: Algorithm predicts patients at risk for chronic kidney disease<\/a>

Researchers developed the Recurrence of Kidney Stone model using random sample of incident symptomatic kidney stone formers in Olmsted County, Minn., who were followed for all symptomatic stone episodes resulting in clinical care from January 1984 through January 2017.

\u201cAll data were obtained through the Rochester Epidemiology Project, a resource that provides access to medical records of nearly all medical institutions for residents of the county,\u201d states the study.

What researchers discovered were that patients who had recurrent stone events included younger age, male gender, a higher body mass index, history of pregnancy and a family history of stones.

Lieske contends that by knowing the risk factors for stone recurrence and the likelihood of future kidney stone episodes the tool can be an incentive for patients to modify lifestyle behaviors and help foster their \u201centhusiasm for adopting dietary measures and\/or starting drug regimens to prevent future attacks.\u201d

At the same time, the study notes that \u201cthere are dietary and medication interventions that can be used to help prevent kidney stone formation and growth,^{<\/sup>but these interventions can be burdensome and costly and have potential adverse effects.\u201d

Nonetheless, the study\u2019s co-authors also conclude that the tool can be used to \u201cestimate symptomatic stone episode rates that result in clinical care for use in clinical trials on kidney stone prevention.\u201d","saveContent":"Mayo Clinic researchers have developed a prediction tool that can estimate when people who have experienced kidney stones will experience future episodes.

The tool, which is available online or as an app, can help \u201cguide the management of stone formers by individualizing prevention interventions on the basis of the risk of symptomatic recurrence,\u201d according to a new}study<\/a> published in the journal Mayo Clinic Proceedings<\/i>.

The risk of symptomatic recurrence is estimated by the tool based on both the number of past episodes and risk factors. One of the study\u2019s findings is that more risk factors for recurrence increased with the number of episodes.

\u201cEach of the risk factors we identified are entered into the model, which then calculates an estimate of the risk of having another kidney stone in the next five or 10 years,\u201d says study co-author John Lieske, MD, director of the Mayo Clinic\u2019s O'Brien Urology Research Center.

Also See<\/b>: Algorithm predicts patients at risk for chronic kidney disease<\/a>

Researchers developed the Recurrence of Kidney Stone model using random sample of incident symptomatic kidney stone formers in Olmsted County, Minn., who were followed for all symptomatic stone episodes resulting in clinical care from January 1984 through January 2017.

\u201cAll data were obtained through the Rochester Epidemiology Project, a resource that provides access to medical records of nearly all medical institutions for residents of the county,\u201d states the study.

What researchers discovered were that patients who had recurrent stone events included younger age, male gender, a higher body mass index, history of pregnancy and a family history of stones.

Lieske contends that by knowing the risk factors for stone recurrence and the likelihood of future kidney stone episodes the tool can be an incentive for patients to modify lifestyle behaviors and help foster their \u201centhusiasm for adopting dietary measures and\/or starting drug regimens to prevent future attacks.\u201d

At the same time, the study notes that \u201cthere are dietary and medication interventions that can be used to help prevent kidney stone formation and growth,^{<\/sup>but these interventions can be burdensome and costly and have potential adverse effects.\u201d

Nonetheless, the study\u2019s co-authors also conclude that the tool can be used to \u201cestimate symptomatic stone episode rates that result in clinical care for use in clinical trials on kidney stone prevention.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]}

Kidney stone tool predicts risk of symptomatic recurrence

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