Evidence map›Paper›PMID 32306239›Full record

ArticleTranslational stroke research2020

Predicting Functional Outcome Based on Linked Data After Acute Ischemic Stroke: S-SMART Score.

Tae Jung Kim, Ji Sung Lee, Mi-Sun Oh, Ji-Woo Kim, Jae Sun Yoon, Jae-Sung Lim, Chan-Hyuk Lee, Heejung Mo, Han-Yeong Jeong, Yerim Kim and 11 more

Abstract read
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In one paragraph

Article in Translational stroke research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 19% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
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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

21 authors at 6 institutions in 1 country.

Tae Jung KimDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea.
Ji Sung LeeDepartment of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Mi-Sun OhDepartment of Neurology, Hallym University Sacred Heart Hospital, Anyang, South Korea.
Ji-Woo KimHealth Insurance Review and Assessment Service, Wonju, South Korea.
Jae Sun YoonDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea.
Jae-Sung LimDepartment of Neurology, Hallym University Sacred Heart Hospital, Anyang, South Korea.
Chan-Hyuk LeeDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea.
Heejung MoDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea.
Han-Yeong JeongDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea.
Yerim KimDepartment of Neurology, Kangdong Sacred Heart Hospital, Seoul, South Korea.
Sang-Hwa LeeDepartment of Neurology, Hallym University College of Medicine, Chuncheon, South Korea.
Keun-Hwa JungDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea.
Log Young KimHealth Insurance Review and Assessment Service, Wonju, South Korea.
Mi Ra AnHealth Insurance Review and Assessment Service, Wonju, South Korea.
Young Hee ParkHealth Insurance Review and Assessment Service, Wonju, South Korea.
Tae Seon LeeHealth Insurance Review and Assessment Service, Wonju, South Korea.
Yun Jung HeoHealth Insurance Review and Assessment Service, Wonju, South Korea.
Sang-Bae KoDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea.
Kyung-Ho YuDepartment of Neurology, Hallym University Sacred Heart Hospital, Anyang, South Korea.
Byung-Chul LeeDepartment of Neurology, Hallym University Sacred Heart Hospital, Anyang, South Korea.
Byung-Woo YoonDepartment of Neurology, Seoul National University Hospital, 101 Daehakno, Jongno-Gu, Seoul, 03080, South Korea. bwyoon@snu.ac.kr.
Seoul National University Hospital · KRHealth Insurance Review and Assessment Service · KRHallym University Sacred Heart Hospital · KRHallym University · KRKangdong Sacred Heart Hospital · KRUlsan College · KR

Funding

Asan Institute for Life Sciences, Asan Medical Center 2015-9003Ministry of Health and Welfare HI 16C1078
6 · The paper itself

Abstract

Prediction of outcome after stroke may help clinicians provide effective management and plan long-term care. We aimed to develop and validate a score for predicting good functional outcome available for hospitals after ischemic stroke using linked data. A total of 22,005 patients with acute ischemic stroke from the Clinical Research Center for Stroke Registry between July 2007 and December 2014 were included in the derivation group. We assessed functional outcomes using a modified Rankin scale (mRS) score at 3 months after ischemic stroke. We identified predictors related to good 3-month outcome (mRS score ≤ 2) and developed a score. External validations (geographic and temporal validations) of the developed model were performed. The prediction model performance was assessed using the area under the receiver operating characteristic curve (AUC) and the calibration test. Stroke severity, sex, stroke mechanism, age, pre-stroke mRS, and thrombolysis/thrombectomy treatment were identified as predictors for 3-month good functional outcomes in the S-SMART score (total 34 points). Patients with higher S-SMART scores had an increased likelihood of a good outcome. The AUC of the prediction score was 0.805 (0.798-0.811) in the derivation group and 0.812 (0.795-0.830) in the geographic validation group for good functional outcome. The AUC of the model was 0.812 (0.771-0.854) for the temporal validation group. Moreover, they had good calibration. The S-SMART score is a valid and useful tool to predict good functional outcome following ischemic stroke. This prediction model may assist in the estimation of outcomes to determine care plans after stroke.

Indexed as

AgedAged, 80 and overBrain IschemiaFemaleHumansIschemic StrokeMaleMiddle AgedPatient DischargePredictive Value of TestsRecovery of FunctionSemantic WebTreatment OutcomeFunctional outcomeIschemic strokePrediction scoreStroke registry

Identifiers

PMID32306239
OpenAlexW3017323473

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.