Evidence map›Paper›PMID 32423399›Full record

Observational studyBMC health services research2020

Feasibility of coding-based Charlson comorbidity index for hospitalized patients in China, a representative developing country.

Liyi Mo, Zhen Xie, Guohui Liu, Qiang He, Zhiming Mo, Yanhua Wu, Wenji Wang, Feng Ding, Yuanjiang Liao, Li Hao and 11 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Observational
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.

Liyi MoDivision of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China.
Zhen XieDepartment of Dermatology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China.
Guohui LiuDepartment of Nephrology, Dongguan People's Hospital, Dongguan, 523018, China.
Qiang HeDepartment of Nephrology, Zhejiang Provincial People's Hospital (People's Hospital of Hangzhou Medical College), Hangzhou, 310014, China.
Zhiming MoDivision of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China.
Yanhua WuDivision of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China.
Wenji WangDivision of Nephrology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200030, China.
Feng DingDivision of Nephrology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200030, China.
Yuanjiang LiaoDepartment of Nephrology, Chongqing Ninth People's Hospital, Chongqing, 400700, China.
Li HaoDepartment of Nephrology, Second Hospital of Anhui Medical University, Hefei, 230601, China.
Chen LuDepartment of Nephrology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, China.
Jin SunDepartment of Nephrology, Second Hospital of Jilin University, Changchun, 130022, China.
Libin XuDepartment of Nephrology, Inner Mongolia People's Hospital, Hohhot, 010017, China.
Yusheng ZhangSecond Division of Internal Medicine, Wuhua People's Hospital, Meizhou, 514400, China.
Rizwangul GhapparDepartment of Nephrology, First People's Hospital of Kashgar, Kashgar, 844000, China.
Hongwei PengPeople's Hospital of Wanning & The First Affiliated Hospital of Chongqing Medical Univesity, Wanning, 571500, China.
Xiaohong WeiDepartment of Nephrology, Chongzuo People's Hospital, Chongzuo, 844000, China.
Jinglie XieDivision of Nephrology, Guangdong Lufeng People's Hospital, Lufeng, 516500, China.
Yuanhan ChenDivision of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China. chenyuanhan@gdph.org.cn.
Xinling LiangDivision of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China. xinlingliang_ggh@163.com.
China collaborative study on acute kidney injury

Funding

Guangdong Science and Technology Project 2017A070709008Guangzhou Science and Technology Project 201604020037
6 · The paper itself

Abstract

backgroundThe Charlson Comorbidity Index (CCI) can be automatically calculated from the International Classification of Disease (ICD) code. However, the feasibility of this transformation has not been acknowledged, particularly in hospitals without a qualified ICD coding system. Here, we investigated the utility of coding-based CCI in China.

methodsA multi-center, population-based, retrospective observational study was conducted, using a dataset incorporating 2,464,395 adult subjects from 15 hospitals. CCI was calculated using both ICD-10-based and diagnosis-based method, according to the transformation rule reported previously and to the literal description from discharge diagnosis, respectively. A κ coefficient of variation was used as a measure of agreement between the above two methods for each hospital. The discriminative abilities of the two methods were compared using the receiver-of-operating characteristic curve (ROC) for prediction of in-hospital mortality.

resultsTotal agreement between the ICD-based and diagnosis-based CCI for each index ranged from 86.1 to 100%, with κ coefficients from 0.210 [95% confidence interval (CI) 0.208-0.212] to 0.932 (95% CI 0.924-0.940). None of the 19 indices of CCI had a κ coefficient > 0.75 in all the hospitals included for study. The area under the curve of ROC for in-hospital mortality of all 15 hospitals was significantly lower for ICD-based than diagnosis-based CCI [0.735 (0.732, 0.739) vs 0.760 (0.757, 0.764)], indicative of more limited discriminative ability of the ICD-based calculation.

conclusionsCCI calculated using ICD-10 coding did not agree with diagnosis-based CCI. ICD-based CCI displayed diminished discrimination performance in terms of in-hospital mortality, indicating that this method is not promising for CCI scoring in China under the present circumstances.

Indexed as

ComorbidityHospitalizationAdultAgedChinaClinical CodingFeasibility StudiesFemaleHumansInternational Classification of DiseasesMaleMiddle AgedRetrospective StudiesAgreementCharlson comorbidity indexDiagnosisDiscriminationICD-10

Identifiers

PMID32423399
PMCPMC7236530

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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.