Evidence map›Paper›PMID 35962244›Full record

ArticleClinical and experimental nephrology2022

Deep learning analysis of clinical course of primary nephrotic syndrome: Japan Nephrotic Syndrome Cohort Study (JNSCS).

Tomonori Kimura, Ryohei Yamamoto, Mitsuaki Yoshino, Ryuichi Sakate, Enyu Imai, Shoichi Maruyama, Hitoshi Yokoyama, Hitoshi Sugiyama, Kosaku Nitta, Tatsuo Tsukamoto and 39 more

Abstract read
In one paragraph

Article in Clinical and experimental nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

49 authors.

Tomonori KimuraReverse Translational Research Project, Center for Rare Disease Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan. t-kimura@nibiohn.go.jp.ORCID http://orcid.org/0000-0001-8186-094X
Ryohei YamamotoHealth and Counseling Center, Osaka University, Suita, Osaka, Japan.
Mitsuaki YoshinoReverse Translational Research Project, Center for Rare Disease Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan.
Ryuichi SakateReverse Translational Research Project, Center for Rare Disease Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan.
Enyu ImaiNakayamadera Imai Clinic, Takarazuka, Hyogo, Japan.
Shoichi MaruyamaDepartment of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Hitoshi YokoyamaDepartment of Nephrology, Kanazawa Medical University School of Medicine, Kanazawa, Japan.
Hitoshi SugiyamaDepartment of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Kosaku NittaDepartment of Nephrology, Tokyo Women's Medical University, Tokyo, Japan.
Tatsuo TsukamotoDepartment of Nephrology and Dialysis, Kitano Hospital, Tazuke Kofukai Medical Research Institute, Osaka, Japan.
Shunya UchidaDepartment of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan.
Asami TakedaKidney Disease Center, Japanese Red Cross Nagoya Daini Hospital, Nagoya, Aichi, Japan.
Toshinobu SatoDepartment of Nephrology, JCHO Sendai Hospital, Sendai, Miyagi, Japan.
Takashi WadaDepartment of Nephrology and Laboratory Medicine, Kanazawa University, Kanazawa, Japan.
Hiroki HayashiDepartment of Nephrology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
Yasuhiro AkaiFirst Department of Internal Medicine, Nara Medical University, Nara, Japan.
Megumu FukunagaDivision of Nephrology, Department of Internal Medicine, Toyonaka Municipal Hospital, Toyonaka, Osaka, Japan.
Kazuhiko TsuruyaDepartment of Nephrology, Nara Medical University, Kashihara, Nara, Japan.
Kosuke MasutaniDivision of Nephrology and Rheumatology, Department of Internal Medicine, Faculty of Medicine, Fukuoka University, Fukuoka, Japan.
Tsuneo KontaDepartment of Cardiology, Pulmonology, and Nephrology, Yamagata University School of Medicine, Yamagata, Japan.
Tatsuya ShojiDepartment of Kidney Disease and Hypertension, Osaka General Medical Center, Osaka, Japan.
Takeyuki HiramatsuDepartment of Nephrology, Konan Kosei Hospital, Konan, Aichi, Japan.
Shunsuke GotoDivision of Nephrology and Kidney Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
Hirofumi TamaiDepartment of Nephrology, Anjo Kosei hospital, Anjo, Aichi, Japan.
Saori NishioDivision of Rheumatology, Endocrinology and Nephrology, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
Kojiro NagaiDepartment of Nephrology, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan.
Kunihiro YamagataDepartment of Nephrology, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaragi, Japan.
Hideo YasudaInternal Medicine 1, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.
Shizunori IchidaDepartment of Nephrology, Japanese Red Cross Nagoya Daiichi Hospital, Nagoya, Aichi, Japan.
Tomohiko NaruseDepartment of Nephrology, Kasugai Municipal Hospital, Kasugai, Aichi, Japan.
Tomoya NishinoDepartment of Nephrology, Nagasaki University Hospital, Nagasaki, Japan.
Hiroshi SobajimaDepartment of Diabetology and Nephrology, Ogaki Municipal Hospital, Ogaki, Ogagki, Japan.
Toshiyuki AkahoriDepartment of Nephrology, Chutoen General Medical Center, Kakegawa, Shizuoka, Japan.
Takafumi ItoDivision of Nephrology, Shimane University Hospital, Izumo, Shimane, Japan.
Yoshio TeradaDepartment of Endocrinology, Metabolism and Nephrology, Kochi Medical School, Kochi University, Kochi, Japan.
Ritsuko KatafuchiKideny Unit, National Hospital Organization, Fukuoka-Higashi Medical Center, Koga, Fukuoka, Japan.
Shouichi FujimotoDepartment of Hemovascular Medicine and Artificial Organs, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan.
Hirokazu OkadaDepartment of Nephrology, Saitama Medical University, Iruma, Saitama, Japan.
Tetsushi MimuraDepartment of Nephrology, Gifu Prefectural Tajimi Hospital, Tajimi, Gifu, Japan.
Satoshi SuzukiDepartment of Nephrology, Kainan Hospital, Yatomi, Aichi, Japan.
Yosuke SakaDepartment of Nephrology, Yokkaichi Municipal Hospital, Yokkaichi, Mie, Japan.
Tadashi SofueDepartment of Cardiorenal and Cerebrovascular Medicine, Kagawa University, Takamatsu, Kagawa, Japan.
Kiyoki KitagawaDivision of Internal Medicine, National Hospital Organization Kanazawa Medical Center, Kahoku, Kanazawa, Japan.
Yoshiro FujitaDepartment of Nephrology, Chubu Rosai Hospital, Nagoya, Aichi, Japan.
Makoto MizutaniDepartment of Nephrology, Handa City Hospital, Handa, Aichi, Japan.
Naoki KashiharaDepartment of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan.
Hiroshi SatoDepartment of Nephrology, Endocrinology, and Vascular Medicine, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
Ichiei NaritaDivision of Clinical Nephrology and Rheumatology, Kidney Research Center, School of Medical and Dental Sciences, Niigata University Graduate, Niigata, Japan.
Yoshitaka IsakaDepartment of Nephrology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.

Funding

Ministry of Health, Labour and Welfare 2031693
6 · The paper itself

Abstract

backgroundPrognosis of nephrotic syndrome has been evaluated based on pathological diagnosis, whereas its clinical course is monitored using objective items and the treatment strategy is largely the same. We examined whether the entire natural history of nephrotic syndrome could be evaluated using objective common clinical items.

methodsMachine learning clustering was performed on 205 cases from the Japan Nephrotic Syndrome Cohort Study, whose clinical parameters, serum creatinine, serum albumin, dipstick hematuria, and proteinuria were traceable after kidney biopsy at 5 measured points up to 2 years. The clinical patterns of time-series data were learned using long short-term memory (LSTM)-encoder-decoder architecture, an unsupervised machine learning classifier. Clinical clusters were defined as Gaussian mixture distributions in a two-dimensional scatter plot based on the highest log-likelihood.

resultsTime-series data of nephrotic syndrome were classified into four clusters. Patients in the fourth cluster showed the increase in serum creatinine in the later part of the follow-up period. Patients in both the third and fourth clusters were initially high in both hematuria and proteinuria, whereas a lack of decline in the urinary protein level preceded the worsening of kidney function in fourth cluster. The original diseases of fourth cluster included all the disease studied in this cohort.

conclusionsFour kinds of clinical courses were identified in nephrotic syndrome. This classified clinical course may help objectively grasp the actual condition or treatment resistance of individual patients with nephrotic syndrome.

Indexed as

Deep LearningNephrotic SyndromeCohort StudiesCreatinineHematuriaHumansJapanProteinuriaCreatinineClinical courseCreatinineHematuriaMachine learningNephrotic syndromePrognosisProteinuria

Identifiers

PMID35962244
PMCPMC9668942

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