Development and validation of a parsimonious and pragmatic CHARM score to predict mortality in patients with suspected sepsis

Chen, Kuan-Fu; Liu, Su-Hsun; Li, Chih-Huang; Wu, Chin‐Chieh; Chaou, Chung-Hsien; Tzeng, I‐Shiang; Hsieh, Yu‐Hsiang; Blaney, Gerald N.; Liu, Zhen-Ying; Han, Shih-Tsung; Chan, Yi-Lin

doi:10.1016/j.ajem.2016.10.075

Cited by 19 publications

(19 citation statements)

References 39 publications

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“…A total of 3,397 articles were screened by title and abstract in the databases analysed (485 in MEDLINE, 1,254 in Scopus and 1,658 in EMBASE), and 44 articles were studied for full‐text analysis 15‐58 . Of these, 12 were excluded because not all the patients were diagnosed with sepsis, 15‐26 seven did not develop a predictive model, 27‐33 in four the outcome to be predicted was not mortality, 34‐37 in another four they did not use their own data, 38‐41 two evaluated only one specific microorganism and not sepsis in general, 42,43 and one applied Machine Learning techniques, 44 which were not considered in this review. Finally, 14 met all the inclusion criteria and none of the exclusion criteria, 45‐58 from which all the information was extracted according to CHARMS, and the risk of bias and applicability were assessed according to PROBAST.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 3,397 articles were screened by title and abstract in the databases analysed (485 in MEDLINE, 1,254 in Scopus and 1,658 in EMBASE), and 44 articles were studied for full-text analysis. Of these, 12 were excluded because not all the patients were diagnosed with sepsis, [15][16][17][18][19][20][21][22][23][24][25][26] seven did not develop a predictive model, [27][28][29][30][31][32][33] in four the outcome to be predicted was not mortality, [34][35][36][37] in another four they did not use their own data, [38][39][40][41] two evaluated only one specific microorganism and not sepsis in general, 42,43 and one applied Machine Learning techniques, 44 which were not considered in this review.…”

Section: Re Sultsmentioning

confidence: 99%

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A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

et al. 2021

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Background: Sepsis is associated with high mortality and predictive models can help in clinical decision-making. The objective of this study was to carry out a systematic review of these models. Methods:In 2019, we conducted a systematic review in MEDLINE and EMBASE (CDR42018111121:PROSPERO) of articles that developed predictive models for mortality in septic patients (inclusion criteria). We followed the CHARMS recommendations (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), extracting the information from its 11 domains (Source of data, Participants, etc). We determined the risk of bias and applicability (participants, outcome, predictors and analysis) through PROBAST (Prediction model Risk Of Bias ASsessment Tool).Results: A total of 14 studies were included. In the CHARMS extraction, the models found showed great variability in its 11 domains. Regarding the PROBAST checklist, only one article had an unclear risk of bias as it did not indicate how missing data were handled while the others all had a high risk of bias. This was mainly due to the statistical analysis (inadequate sample size, handling of continuous predictors, missing data and selection of predictors), since 13 studies had a high risk of bias. Applicability was satisfactory in six articles. Most of the models integrate predictors from routine clinical practice. Discrimination and calibration were assessed for almost all the models, with the area under the ROC curve ranging from 0.59 to 0.955 and no lack of calibration. Only three models were externally validated and their maximum discrimination values in the derivation were from 0.712 and 0.84. One of them (Osborn) had undergone multiple validation studies.Discussion: Despite most of the studies showing a high risk of bias, we very cautiously recommend applying the Osborn model, as this has been externally validated various times.

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Section: Resultsmentioning

confidence: 99%

“…A total of 3,397 articles were screened by title and abstract in the databases analysed (485 in MEDLINE, 1,254 in Scopus and 1,658 in EMBASE), and 44 articles were studied for full-text analysis. Of these, 12 were excluded because not all the patients were diagnosed with sepsis, [15][16][17][18][19][20][21][22][23][24][25][26] seven did not develop a predictive model, [27][28][29][30][31][32][33] in four the outcome to be predicted was not mortality, [34][35][36][37] in another four they did not use their own data, [38][39][40][41] two evaluated only one specific microorganism and not sepsis in general, 42,43 and one applied Machine Learning techniques, 44 which were not considered in this review.…”

Section: Re Sultsmentioning

confidence: 99%

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

et al. 2021

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“…The nomogram, which included increased age, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and RDW, as well as a decreased lymphocyte-to-monocyte ratio, showed good prognostic accuracy. Additionally, Chen et al developed a clinical prediction rule, namely the CHARM score, based on clinical and laboratory parameters, including RDW [81]. The CHARM score showed good performance for predicting in-hospital mortality in patients with clinically suspected sepsis in the ED.…”

Section: Rdwmentioning

confidence: 99%

The Value of a Complete Blood Count (CBC) for Sepsis Diagnosis and Prognosis

et al. 2021

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Sepsis represents an important global health burden due to its high mortality and morbidity. The rapid detection of sepsis is crucial in order to prevent adverse outcomes and reduce mortality. However, the diagnosis of sepsis is still challenging and many efforts have been made to identify reliable biomarkers. Unfortunately, many investigated biomarkers have several limitations that do not support their introduction in clinical practice, such as moderate diagnostic and prognostic accuracy, long turn-around time, and high-costs. Complete blood count represents instead a precious test that provides a wealth of information on individual health status. It can guide clinicians to early-identify patients at high risk of developing sepsis and to predict adverse outcomes. It has several advantages, being cheap, easy-to-perform, and available in all wards, from the emergency department to the intensive care unit. Noteworthy, it represents a first-level test and an alteration of its parameters must always be considered within the clinical context, and the eventual suspect of sepsis must be confirmed by more specific investigations. In this review, we describe the usefulness of basic and new complete blood count parameters as diagnostic and prognostic biomarkers of sepsis.

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“…Although some progress has been made in the management of sepsis, with morbidity and mortality rates trending downward year by year, reports indicate that the incidence of sepsis still ranges from 30 to 80% annually (2) and remains the leading cause of hospital deaths (3). Accurate prediction of the prognosis of sepsis is vital, which will facilitate early aggressive intervention (4). Unfortunately, although some scoring systems have been shown to correlate with outcomes in patients with sepsis (5)(6)(7)(8), these scoring systems are inconvenient to use due to the numerous indicators involved, and cannot be used as a satisfactory predictive tool in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Association between red blood cell distribution width to albumin ratio and prognosis of patients with sepsis: A retrospective cohort study

Huo

Chen

et al. 2022

Front. Nutr.

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BackgroundRed blood cell distribution width (RDW) to albumin ratio (RAR) is associated with poor prognosis in diabetic comorbidities and cancer. However, the association between RAR and prognosis in patients with sepsis remains unclear, which was investigated in this study.MethodsWe conducted a retrospective cohort study based on the Medical Information Mart for Intensive Care (MIMIC) IV version 2.0 database. The primary outcome of this study was 28-day mortality. Secondary outcomes included 90-day mortality, in-hospital mortality, length of hospital stay, and length of intensive care unit (ICU) stay. Multivariate regression analysis and subgroup analysis were performed to investigate the association between RAR and prognosis in patients with sepsis.ResultsA total of 14,639 participants were included in this study. The mean age of the participants was 65.2 ± 16.3 years and the mean RAR was 5.5 ± 1.9 % /g/dl. For 28-day mortality, after adjusting for covariates, HRs [95% confidence intervals (CIs)] for tertiles 2 (4.4–5.8) and 3 (RAR > 5.8) were 1.33 (1.20, 1.46) and 1.98 (1.79, 2.19), respectively. Similar results were observed for 90-day mortality and in-hospital mortality. According to Kaplan-Meier curve analysis, the higher RAR group had higher 28-day mortality and 90-day mortality.ConclusionOur study shows that RAR is significantly associated with poor clinical prognosis in sepsis. The higher the RAR, the higher the 28-day, 90-day, and in-hospital mortality.

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Development and validation of a parsimonious and pragmatic CHARM score to predict mortality in patients with suspected sepsis

Cited by 19 publications

References 39 publications

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

The Value of a Complete Blood Count (CBC) for Sepsis Diagnosis and Prognosis

Association between red blood cell distribution width to albumin ratio and prognosis of patients with sepsis: A retrospective cohort study

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