BACKGROUND: Respiratory syncytial virus (RSV) is a widespread respiratory pathogen, and RSV-related acute lower respiratory tract infections are the most common cause of respiratory hospitalization in children <2 years of age. Over the last 2 decades, a number of severity scores have been proposed to quantify disease severity for RSV in children, yet there remains no overall consensus on the most clinically useful score.
METHODS: We conducted a systematic review of English-language publications in peer-reviewed journals published since January 2000 assessing the validity of severity scores for children (≤24 months of age) with RSV and/or bronchiolitis, and identified the most promising scores. For included articles, (1) validity data were extracted, (2) quality of reporting was assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis checklist (TRIPOD), and (3) quality was assessed using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). To guide the assessment of the validity data, standardized cutoffs were employed, and an explicit definition of what we required to determine a score was sufficiently validated.
RESULTS: Our searches identified 8541 results, of which 1779 were excluded as duplicates. After title and abstract screening, 6670 references were excluded. Following full-text screening and snowballing, 32 articles, including 31 scores, were included. The most frequently assessed scores were the modified Tal score and the Wang Bronchiolitis Severity Score; none of the scores were found to be sufficiently validated according to our definition. The reporting and/or design of all the included studies was poor. The best validated score was the Bronchiolitis Score of Sant Joan de Déu, and a number of other promising scores were identified.
CONCLUSIONS: No scores were found to be sufficiently validated. Further work is warranted to validate the existing scores, ideally in much larger datasets.

BACKGROUND: There is no consensus on how to best quantify disease severity in infants with respiratory syncytial virus (RSV) and/or bronchiolitis; this lack of a sufficiently validated score complicates the provision of clinical care and, the evaluation of trials of therapeutics and vaccines. The ReSVinet score appears to be one of the most promising; however, it is too time consuming to be incorporated into routine clinical care. We aimed to develop and externally validate simplified versions of this score.
METHODS: Data from a multinational (the Netherlands, Spain, and United Kingdom) multicenter case-control study of infants with RSV were used to develop simplified versions of the ReSVinet score by conducting a grid search to determine the best combination of equally weighted parameters to maximize for the discriminative ability (measured by area under the receiver operating characteristic curve [AUROC]) across a range of outcomes (hospitalization, intensive care unit admission, ventilation requirement). Subsequently discriminative validity of the score for a range of secondary care outcomes was externally validated by secondary analysis of datasets from Rwanda and Colombia.
RESULTS: Three candidate simplified scores were identified using the development dataset; they were excellent (AUROC >0.9) at discriminating for a range of outcomes, and their performance was not significantly different from the original ReSVinet score despite having fewer parameters. In the external validation datasets, the simplified scores were moderate to excellent (AUROC, 0.7-1) across a range of outcomes. In all outcomes, except in a single dataset for predicting admission to the high-dependency unit, they performed at least as well as the original ReSVinet score.
CONCLUSIONS: The candidate simplified scores developed require further external validation in larger datasets, ideally from resource-limited settings before any recommendation regarding their use.

OBJECTIVE: Although metronidazole is not recommended to treat Clostridioides difficile infection (CDI) in Western countries, it was still to be recommended for the treatment of non-severe CDI among Taiwanese adults in 2020. This controversy in the clinical role of metronidazole therapy for CDI was examined in a prospective clinical study.
METHODS: The study was conducted from January 2015 to December 2016 in three hospitals in Taiwan. Metronidazole treatment failure (MTF) was defined as the persistence of diarrhea after six days of treatment, medication modification (shifting to oral vancomycin), or death after five days of therapy.
RESULTS: Overall, 325 patients receiving metronidazole for CDI were included. The overall MTF rate was 48.6% (158 patients). Leukocyte counts of >15,000 cells/mL in peripheral blood (odd ratio [OR] 1.81; P = 0.04) and congestive heart failure (OR 3.26; P = 0.02) were independently associated with MTF. The MTF rate for patients with leukocyte counts of ≤15,000 cells/mL and no congestive heart failure, leukocyte counts of >15,000 cells/mL and no congestive heart failure, leukocyte counts of ≤15,000 cells/mL and congestive heart failure, and leukocyte counts of >15,000 cells/mL and congestive heart failure were 44.2%, 51.8%, 73.3%, and 66.7%, respectively. Of note, patients who experienced MTF had a higher recurrence rate of CDI than those with metronidazole treatment success (13.9% vs. 6.0%, P = 0.02).
CONCLUSIONS: For Taiwanese adults with CDI, the failure rate of metronidazole therapy approached 50%, which suggests the reappraisal of the therapeutic role of metronidazole therapy, especially for patients with leukocytosis or underlying congestive heart failure.

To validate a vascular severity score as an appropriate output for artificial intelligence (AI) Software as a Medical Device (SaMD) for retinopathy of prematurity (ROP) through comparison with ordinal disease severity labels for stage and plus disease assigned by the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), committee.
Validation study of an AI-based ROP vascular severity score.
A total of 34 ROP experts from the ICROP3 committee.
Two separate datasets of 30 fundus photographs each for stage (0-5) and plus disease (plus, preplus, neither) were labeled by members of the ICROP3 committee using an open-source platform. Averaging these results produced a continuous label for plus (1-9) and stage (1-3) for each image. Experts were also asked to compare each image to each other in terms of relative severity for plus disease. Each image was also labeled with a vascular severity score from the Imaging and Informatics in ROP deep learning system, which was compared with each grader\'s diagnostic labels for correlation, as well as the ophthalmoscopic diagnosis of stage.
Weighted kappa and Pearson correlation coefficients (CCs) were calculated between each pair of grader classification labels for stage and plus disease. The Elo algorithm was also used to convert pairwise comparisons for each expert into an ordered set of images from least to most severe.
The mean weighted kappa and CC for all interobserver pairs for plus disease image comparison were 0.67 and 0.88, respectively. The vascular severity score was found to be highly correlated with both the average plus disease classification (CC = 0.90, P < 0.001) and the ophthalmoscopic diagnosis of stage (P < 0.001 by analysis of variance) among all experts.
The ROP vascular severity score correlates well with the International Classification of Retinopathy of Prematurity committee member\'s labels for plus disease and stage, which had significant intergrader variability. Generation of a consensus for a validated scoring system for ROP SaMD can facilitate global innovation and regulatory authorization of these technologies.

UNASSIGNED: This study aimed to develop and validate a model for mortality risk stratification of intensive care unit (ICU) patients with acute kidney injury (AKI) using the machine learning technique.
UNASSIGNED: Eligible data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Calibration, discrimination, and risk classification for mortality prediction were evaluated using conventional scoring systems and the new algorithm. A 10-fold cross-validation was performed. The predictive models were externally validated using the eICU database and also patients treated at the Second People\'s Hospital of Shenzhen between January 2015 to October 2018.
UNASSIGNED: For the new model, the areas under the receiver operating characteristic curves (AUROCs) for mortality during hospitalization and at 28 and 90 days after discharge were 0.91, 0.87, and 0.87, respectively, which were higher than for the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment (SOFA). For external validation, the AUROC was 0.82 for in-hospital mortality, higher than SOFA, SAPS II, and Acute Physiology and Chronic Health Evaluation (APACHE) IV in the eICU database, but for the 28- and 90-day mortality, the new model had AUROCs (0.79 and 0.80, respectively) similar to that of SAPS II in the SZ2 database. The reclassification indexes were superior for the new model compared with the conventional scoring systems.
UNASSIGNED: The new risk stratification model shows high performance in predicting mortality in ICU patients with AKI.

UNASSIGNED: Coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have explored the role of chest computed tomography (CT) features and severity scores for prognostic prediction. In this study, we aimed to investigate the role of chest CT severity score and imaging features in the prediction of the prognosis of COVID-19 patients.
UNASSIGNED: A total of 134 patients (62 recovered and 72 deceased patients) with confirmed COVID-19 were enrolled. The clinical, laboratory, and chest CT (316 scans) data were retrospectively reviewed. Demographics, symptoms, comorbidities, and temporal changes of laboratory results, CT features, and severity scores were compared between recovered and deceased groups using the Mann-Whitney U test and logistic regression to identify the risk factors for poor prognosis.
UNASSIGNED: Median age was 48 and 58 years for recovered and deceased patients, respectively. More patients had at least one comorbidity in the deceased group than the recovered group (60% vs. 29%). Leukocytes, neutrophil, high-sensitivity C-reactive protein (hsCRP), prothrombin, D-dimer, serum ferritin, interleukin (IL)-2, and IL-6 were significantly elevated in the deceased group than the recovered group at different stages. The total CT score at the peak stage was significantly greater in the deceased group than the recovered group (20 vs. 11 points). The optimal cutoff value of the total CT scores was 16.5 points, achieving 69.4% sensitivity and 82.2% specificity for the prognostic prediction. The crazy-paving pattern and consolidation were more common in the deceased patients than those in the recovered patients. Linear opacities significantly increased with the disease course in the recovered patients. Sex, age, neutrophil, IL-2, IL-6, and total CT scores were independent risk factors for the prognosis with odds ratios of 3.8 to 8.7.
UNASSIGNED: Sex (male), older age (>60 years), elevated neutrophil, IL-2, IL-6 level, and total CT scores (≥16) were independent risk factors for poor prognosis in patients with COVID-19. Temporal changes of chest CT features and severity scores could be valuable for early identification of severe cases and eventually reducing the mortality rate of COVID-19.

Bronchiectasis is a multidimensional lung disease characterized by bronchial dilation, chronic inflammation, and infection. The FACED (Forced expiratory volume in 1 s (FEV1), Age, Chronic colonization, Extension, and Dyspnea) score and Bronchiectasis Severity Index (BSI) are used to stratify disease risk and guide clinical practice. This meta-analysis aimed to quantify the accuracy of these two systems for predicting bronchiectasis outcomes.
PubMed, Embase, and the Cochrane Database of Systematic Reviews were searched for relevant studies. Quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) criteria. Pooled summary estimates, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. Summary receiver operating characteristic curves were constructed, and the area under the curve (AUC) was used to evaluate prognostic performance.
We analyzed 17 unique cohorts (6525 participants) from ten studies. FACED scores with a cut-off value ≥ 5 predicted all-cause mortality better than BSI with a cut-off value ≥ 9, based on pooled sensitivity (0.34 vs 0.7), specificity (0.94 vs 0.66), PLR (4.76 vs 2.05), NLR (0.74 vs 0.48), DOR (6.67 vs 5.01), and AUC (0.87 vs 0.75). Both FACED scores with a cut-off value ≥ 5 (AUC = 0.82) and BSI scores with a cut-off value ≥ 5 or 9 (both AUC = 0.80) help to predict hospitalization.
At a cut-off value ≥ 5, FACED scores can reliably predict all-cause mortality and hospitalization, while BSI scores can reliably predict hospitalization with a cut-off of ≥5 or ≥9. Further studies are essential to validate the prognostic performance of these two scores.

UNASSIGNED: The incidence and severity of Clostridium difficile infection (CDI) have markedly increased over the past decade. However, there is very limited epidemiological data on CDI in China so far, specifically no data in Shandong Province. The aim of this study was to evaluate diagnostic algorithm for CDI and to gain data on molecular epidemiology of CDI in the Shandong Province of China.
UNASSIGNED: Nonrepetitive unformed fecal specimens (n=504) were investigated by the glutamate dehydrogenase (GDH), C. difficile toxin A&B (CDAB) tests and toxigenic culture. Furthermore, 85 isolates were characterized by toxin gene detection, multilocus sequence typing, ribotyping and antimicrobial susceptibility testing.
UNASSIGNED: The algorithm of combining GDH and CDAB tests could define diagnosis of 54.2% CDI cases and excluded 90% of non-CDI. Further adding the toxigenic culture to the algorithm enhanced the detection sensitivity to 100%. Toxigenic strains comprised 84.7% of isolates, including A+B+CDT- (71.8%, 61/85), A-B+CDT- (11.8%, 10/85) and A+B+CDT+ (1.2%, 1/85) isolates. RT046/ST35 (13.9%, 10/72), RT014/ST2 (12.5%, 9/72) and RT017/ST37 (12.5%, 9/72) were the more common genotypes among toxigenic C. difficile strains. The clinical severity score of A-B+CDT- toxin genes genotype (3.50±0.85) was significantly higher than the A+B+CDT- type (2.59±0.93) (P<0.05). RT046/ST35 isolates were highly prevalent and had high clinical severity scores (3.80±0.92). Variations in resistance from different sequence types (STs) were observed. Toxigenic strains showed higher resistance rates to erythromycin, clindamycin and ciprofloxacin compared to nontoxigenic strains (P<0.05).
UNASSIGNED: The epidemiology of C. difficile in Shandong Province differed from other regions in China. Comprehensive optimized diagnosis strategy and continuous surveillance should be established and applied in order to curb the spread of toxigenic C. difficile strains, especially for hospitalized patients.