Evidence map›Paper›PMID 37936832›Full record

ArticleRisk management and healthcare policy2023

Validation of an ICD-Based Algorithm to Identify Sepsis: A Retrospective Study.

Shi-Tong Diao, Run Dong, Jin-Min Peng, Yan Chen, Shan Li, Shu-Hua He, Yi-Fan Wang, Bin Du, Li Weng, China Critical Care Clinical Trials Group (CCCCTG)

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2023. 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
–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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Shi-Tong DiaoMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.ORCID 0000-0002-4022-2519
Run DongMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Jin-Min PengMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Yan ChenMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Shan LiMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Shu-Hua HeMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Yi-Fan WangMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Bin DuMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
Li WengMedical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
China Critical Care Clinical Trials Group (CCCCTG)

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sepsis surveillance was important for resources allocation, prevention, and development of health policy. Objective: The aim of the study was to validate a modified International Classification of Diseases (ICD)-10 based algorithm for identifying hospitalized patients with sepsis. Methods: We retrospectively analyzed a prospective, single-center cohort of adult patients who were consecutively admitted to one medical ICU ward and ten non-ICU wards with suspected or confirmed infections during a 6-month period. A modified ICD-10 based algorithm was validated against a reference standard of Sequential Organ Failure Assessment (SOFA) score based on Sepsis-3. Sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and areas under the receiver operating characteristic curves (AUROCs) were calculated for modified ICD-10 criteria, eSOFA criteria, Martin's criteria, and Angus's criteria. Results: Of the 547 patients in the cohort, 332 (61%) patients met Sepsis-3 criteria and 274 (50%) met modified ICD-10 criteria. In the ICU setting, modified ICD-10 criteria had SE (84.47%), SP (88.57%), PPV (95.60), and NPV (65.96). In non-ICU settings, modified ICD-10 had SE (64.19%), SP (80.00%), PPV (80.33), and NPV (63.72). In the whole cohort, the AUROCs of modified ICD-10 criteria, eSOFA, Angus's criteria, and Martin's criteria were 0.76, 0.75, 0.62, and 0.62, respectively. Conclusion: This study demonstrated that modified ICD-10 criteria had higher validity compared with Angus's criteria and Martin's criteria. Validity of the modified ICD-10 criteria was similar to eSOFA criteria. Modified ICD-10 algorithm can be used to provide an accurate estimate of population-based sepsis burden of China.

Indexed as

administrative health dataidentificationInternational Classification of Diseasessepsisvalidation

Identifiers

PMID37936832
PMCPMC10627050

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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.