{"id":4694,"date":"2023-12-26T17:11:53","date_gmt":"2023-12-26T08:11:53","guid":{"rendered":"https:\/\/noriaki-kurita.jp\/?page_id=4694"},"modified":"2024-01-18T05:14:53","modified_gmt":"2024-01-17T20:14:53","slug":"abc2screener","status":"publish","type":"page","link":"https:\/\/noriaki-kurita.jp\/en\/resources\/abc2screener\/","title":{"rendered":"ABC2-Screener: A model to predict sarcopenia among patients on hemodialysis"},"content":{"rendered":"<style><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><br \/>        body {<br \/>            margin: 0; \/* Reset margin to eliminate extra space *\/<br \/>        }<br \/>    <\/style>\n<p><script>\n        window.onload = function () {\n            function calculate() {\n                \/\/ \u5b9a\u6570\n                var a = 0.2045163;\n                var b = -0.797691;\n                var c = -0.2156561;\n                var d = 0.5999741;\n                var e = 1.490897;\n                var f = -1.336764;\n                \/\/ \u30e6\u30fc\u30b6\u30fc\u304c\u5165\u529b\u3057\u305f\u5024\u3092\u53d6\u5f97\n                var inputX = parseFloat(document.getElementById('inputX').value);\n                var inputY = parseFloat(document.getElementById('inputY').value);\n                \/\/ Y1\u3068Y2\u306e\u5024\u3092\u53d6\u5f97\n                var inputY1 = document.getElementById('inputY1').disabled ? 0 : parseFloat(document.getElementById('inputY1').value);\n                var inputY2 = document.getElementById('inputY2').disabled ? 0 : parseFloat(document.getElementById('inputY2').value);\n                \/\/ X\u3092\u8a08\u7b97\n                var calculatedY;\n                if (document.getElementById('useFormula').checked) {\n                    calculatedY = inputY1 \/ (inputY2 \/ 100) \/ (inputY2 \/ 100);\n                } else {\n                    calculatedY = inputY;\n                }\n                var inputZ = parseFloat(document.getElementById('inputZ').value);\n                var inputV = parseFloat(document.getElementById('inputV').value);\n                var inputW = parseFloat(document.querySelector('input[name=\"inputW\"]:checked').value);\n                \/\/ \u8a08\u7b97\n                var A = a * inputX + b * calculatedY + c * inputZ + d * inputV + e * inputW + f;\n                var expA = Math.exp(A) \/ (Math.exp(A) + 1) * 100;\n                var B = Math.round(expA * 10) \/ 10; \/\/ \u5c0f\u6570\u70b9\u7b2c1\u4f4d\u3067\u56db\u6368\u4e94\u5165\n                \/\/ \u7d50\u679c\u3092\u8868\u793a\n                document.getElementById('result').innerHTML = 'Probability of sarcopenia (P): ' + B + '%';\n            }\n            \/\/ \u30dc\u30bf\u30f3\u306bonclick\u5c5e\u6027\u3092\u8ffd\u52a0\u3057\u3001calculate\u95a2\u6570\u3092\u547c\u3073\u51fa\u3059\n            document.querySelector('button').onclick = calculate;\n            \/\/ \u5f0f\u3092\u4f7f\u7528\u3059\u308b\u30c1\u30a7\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u306e\u72b6\u614b\u306b\u5fdc\u3058\u3066\u5165\u529b\u30d5\u30a3\u30fc\u30eb\u30c9\u306e\u6709\u52b9\/\u7121\u52b9\u3092\u5207\u308a\u66ff\u3048\n            document.getElementById('useFormula').addEventListener('change', function () {\n                var inputY1 = document.getElementById('inputY1');\n                var inputY2 = document.getElementById('inputY2');\n                inputY1.disabled = !this.checked;\n                inputY2.disabled = !this.checked;\n            });\n        }\n    <\/script><\/p>\n<h1><span style=\"color: #c00000;\">ABC<sup>2<\/sup><\/span>&#8211;<span style=\"color: #c00000;\">S<\/span>creener: A model to predict sarcopenia among patients on hemodialysis<\/h1>\n<p>\u65e5\u672c\u8a9e\u7248\u306f\u3053\u3061\u3089\u3092\u30af\u30ea\u30c3\u30af\u3057\u3066\u4e0b\u3055\u3044\uff1a[<a href=\"https:\/\/noriaki-kurita.jp\/resources\/abc2screener\/\" target=\"_blank\" rel=\"noopener\">Japanese version<\/a>]<\/p>\n<h2>Outline<\/h2>\n<p>Population: Adult patients on maintenance hemodialysis<\/p>\n<p>Outcome: Sarcopenia<br \/>\nSarcopenia was defined by the following items proposed by Asian Working Group for Sarcopenia in 2019:<\/p>\n<ul>\n<li>Low muscle strength: Grip strength &lt;28.0 kg in men and &lt;18.0 kg in women<\/li>\n<li>Low skeletal muscle mass: Height-adjusted appendicular skeletal muscle mass &lt;7.0 kg\/m<sup>2<\/sup> in men ; &lt;5.7 kg\/m<sup>2<\/sup> in women<br \/>\n(measured by multi-frequency bioimpedance analysis)<\/li>\n<\/ul>\n<p>For more detailed information, please scroll to the bottom.<\/p>\n<h2>Data entry for risk calculation<\/h2>\n<h4><label for=\"inputX\"><span style=\"color: #c00000;\">A<\/span>ge (in years):<\/label><\/h4>\n<p><input id=\"inputX\" type=\"number\" placeholder=\"Enter age between 25 and 97 years\" \/><\/p>\n<h4><label for=\"inputY\"><span style=\"color: #c00000;\">B<\/span>MI (unit: kg\/m<sup>2<\/sup>):<\/label><\/h4>\n<p><input id=\"inputY\" type=\"number\" placeholder=\"Enter BMI between between 14.5 and 47 kg\/m2\" \/><\/p>\n<p><label for=\"useFormula\">Click BMI formula to enter height and weight instead\uff1a<\/label><br \/>\n<input id=\"useFormula\" type=\"checkbox\" \/> BMI = (weight in kg) \/ (height in cm)<sup>2<\/sup><\/p>\n<p><label for=\"inputY1\">Weight in kg*\uff1a<\/label><br \/>\n*Use the body weight recorded post the first hemodialysis session in a week.<br \/>\n<input id=\"inputY1\" disabled=\"disabled\" type=\"number\" placeholder=\"Enter weight\" \/><\/p>\n<p><label for=\"inputY2\">Height in cm\uff1a<\/label><br \/>\n<input id=\"inputY2\" disabled=\"disabled\" type=\"number\" placeholder=\"Enter height\" \/><\/p>\n<h4><label for=\"inputZ\">Serum <span style=\"color: #c00000;\">C<\/span>reatinine* (unit: mg\/dL):<\/label><\/h4>\n<p>*Use the serum creatinine level obtained at the start of the first hemodialysis session in a week.<br \/>\n<input id=\"inputZ\" type=\"number\" placeholder=\"Enter Creatinine between 2.7 and 17.8 mg\/dl\" \/><\/p>\n<h4><label for=\"inputV\"><span style=\"color: #c00000;\">C<\/span>linical Frailty Scale version 2.0 (choose from 1 to 9):<\/label><\/h4>\n<p><select id=\"inputV\"><!-- \u30d7\u30eb\u30c0\u30a6\u30f3\u306b1\u304b\u30899\u307e\u3067\u306e\u6570\u5b57\u3092\u8ffd\u52a0 --><option value=\"1\">1: Very fit<\/option><option value=\"2\">2: Fit<\/option><option value=\"3\">3: Managing well<\/option><option value=\"4\">4: Living with very mild frailty<\/option><option value=\"5\">5: Living with mild frailty<\/option><option value=\"6\">6: Living with moderate frailty<\/option><option value=\"7\">7: Living with severe frailty<\/option><option value=\"8\">8: Living with very severe frailty<\/option><option value=\"9\">9: Terminally ill<\/option><\/select><br \/>\nTo learn more about the classification, please refer to the following literature: <a href=\"https:\/\/doi.org\/10.5770\/cgj.23.463\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.5770\/cgj.23.463<\/a><\/p>\n<h4><label><span style=\"color: #c00000;\">S<\/span>ex (choose man or woman):<\/label><\/h4>\n<p><label for=\"wOption1\"><input id=\"wOption1\" name=\"inputW\" type=\"radio\" value=\"2\" \/> Man = 2<\/label><br \/>\n<label for=\"wOption2\"><input id=\"wOption2\" name=\"inputW\" type=\"radio\" value=\"1\" \/> Woman = 1<\/label><\/p>\n<h2>Probability of sarcopenia<\/h2>\n<p><button>Calculate risk<\/button><\/p>\n<div id=\"result\"><\/div>\n<h2>Information about the model<\/h2>\n<p>The model was developed in 373 individuals on maintenance hemodialysis therapy at two dialysis centers in 2022-2023 (median age 71 years, 65% male).<br \/>\nThe model was internally validated using bootstrapping.<br \/>\nExternal validation was performed in another two dialysis centers in 2022-2023 (129 patients).<br \/>\nDiscrimination: The model correctly sorted patients who were classified as sarcopenia from those who were not classified as sarcopenia 97% of the time (as measured by the integral area under the receiver operating characteristic curve).<\/p>\n<p>Calibration: The model was well calibrated across all risk ranges.<\/p>\n<p>Citation:<br \/>\n<span style=\"text-decoration: underline;\">Matsunami M<\/span>, <span style=\"text-decoration: underline;\">Aita T<\/span>, Kamitani T, Munakata Y, Kawaji A, Kuji H, <span style=\"text-decoration: underline;\">Suzuki T<\/span>, <span style=\"text-decoration: underline;\">Kurita N<\/span><br \/>\n<a href=\"https:\/\/doi.org\/10.1101\/2024.01.17.24301264\" target=\"_blank\" rel=\"noopener\">Development and external validation of a practical diagnostic support tool, \u2032ABC2-Screener\u2032, to predict sarcopenia among patients on maintenance haemodialysis: A multicentre cross-sectional study<\/a><br \/>\n<em><strong>medRxiv.<\/strong><\/em> 2024: 2024.01.17.24301264. DOI: 10.1101\/2024.01.17.24301264<\/p>\n<h2>Disclaimer<\/h2>\n<p>ABC<sup>2<\/sup>-Screener was created with the support of the Department of Clinical Epidemiology, Graduate School of Medicine, Fukushima Medical University \/ Department of Innovative Research and Education for Clinicians and Trainees, Fukushima Medical University Hospital.<br \/>\nHowever, its content is strictly the work of its authors and has no affiliation with any organization or institution.<br \/>\nThis website does not accept advertisements.<br \/>\nIf you reproduce the material on the website, please cite appropriately.<br \/>\nIf you have feedback or questions regarding the site, please email Noriaki Kurita, MD (kuritan@fmu.ac.jp).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>ABC2&#8211;Screener: A model to predict sarcopenia among patients on hemodialysis \u65e5\u672c\u8a9e\u7248\u306f\u3053\u3061\u3089\u3092\u30af\u30ea\u30c3\u30af\u3057\u3066\u4e0b\u3055\u3044\uff1a[Japanese [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":0,"parent":1934,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_locale":"en_US","_original_post":"https:\/\/noriaki-kurita.jp\/?page_id=4629","vk-ltc-link":"","vk-ltc-target":"0","footnotes":""},"field":[],"class_list":["post-4694","page","type-page","status-publish","hentry","en-US"],"_links":{"self":[{"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/pages\/4694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/comments?post=4694"}],"version-history":[{"count":31,"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/pages\/4694\/revisions"}],"predecessor-version":[{"id":4806,"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/pages\/4694\/revisions\/4806"}],"up":[{"embeddable":true,"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/pages\/1934"}],"wp:attachment":[{"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/media?parent=4694"}],"wp:term":[{"taxonomy":"field","embeddable":true,"href":"https:\/\/noriaki-kurita.jp\/wp-json\/wp\/v2\/field?post=4694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}