Article in Risk management and healthcare policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 56% 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
1 citing paper in PubMed, 1 citations in OpenAlex.
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
10 authors at 2 institutions in 2 countries.
Mengyue LinHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.ORCID 0000-0002-0389-0331
Mulalibieke HeizhatiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.
Lin GanHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.ORCID 0000-0001-5374-6277
Ling YaoHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.
Wenbo YangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.
Mei LiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.
Jing HongHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.
Zihao WuHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.
Hui WangHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.
Nanfang LiHypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, People's Republic of China.ORCID 0000-0003-1505-8566
Hypertension Institute · USXinjiang Medical University · CN
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
purposePatients with hypertension and glucose metabolism disorder (GMD) are at high risk of developing kidney dysfunction (KD). Therefore, we aimed to develop a nomogram for predicting individuals' 5-year risk of KD in hypertensives with GMD. PATIENTS AND
methodsIn total, 1961 hypertensives with GMD were consecutively included. Baseline data were extracted from medical electronic system, and follow-up data were obtained using annual health check-ups or hospital readmission. KD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m
resultsDuring the follow-up of 5351 person-years with a median follow-up of 32 (range: 3-91) months, 130 patients developed KD. Age, sex, ethnicity, hemoglobin A1c, uric acid, and baseline eGFR were identified as significant predictors for incident KD and used for establishing nomogram. The prediction model displayed good discrimination with C-index of 0.770 (95% CI: 0.712-0.828) and 0.763 (95% CI: 0.704-0.823) in training and validation sets, respectively. Calibration curve indicated good agreement between the predicted and actual probabilities. The decision curve analysis demonstrated that the model was clinically useful.
conclusionThe prediction nomogram, including six common easy-to-obtain factors, shows good performance for predicting 5-year risk of KD in hypertensives with GMD. This quantitative tool could help clinicians, and even primary care providers, recognize potential KD patients early and make strategy for prevention and management.
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.
Development and Validation of a Prediction Model for 5-Year Risk of Kidney Dysfunction in Patients with Hypertension and Glucose Metabolism Disorder. · full record | OpenQuestion