This paper presents a paradigm-shifted approach for adverse event (AE) analysis from classic descriptive summary into modern informative statistics to fulfill precision medicine. It defines early AE and derives a series of innovative AE biomarkers to assess grade, treatment relatedness, occurrence, frequency, and duration. Comprehensive data analysis generates opportunities for global discovery of predictive early AE-derived biomarkers. A proof of concept in two lung cancer studies demonstrates the potential clinical utility of early AE-derived biomarkers in patients with advanced non-small cell lung cancer treated with immunotherapy. The methodology also provides a useful tool to help discover predictive AE biomarkers for treatment response and survival outcomes.Rationale: Adverse events (AEs) have been shown to have clinical associations, in addition to patient safety assessments of drugs of interest. However, due to their complex content and associated data structure, AE evaluation has been restricted to descriptive statistics and small AE subset for efficacy analysis, limiting the opportunity for global discovery. This study takes a unique approach to utilize AE-associated parameters to derive a set of innovative AE metrics. Comprehensive analysis of the AE-derived biomarkers enhances the chance of discovering new predictive AE biomarkers of clinical outcomes. Methods. We utilized a set of AE-associated parameters  to derive 24 AE biomarkers. We further innovatively defined early AE biomarkers by landmark analysis at an early time point to assess the predictive value. Statistical methods included the Cox proportional hazards model for progression-free survival (PFS) and overall survival (OS), two-sample t-test for mean difference of AE frequency and duration between disease control (DC: complete response (CR) + partial response (PR) + stable disease (SD)) versus progressive disease (PD), and Pearson correlation analysis for relationship of AE frequency and duration versus treatment duration. Two study cohorts  from two immunotherapy trials in late-stage non-small cell lung cancer were used to test the potential predictiveness of AE-derived biomarkers. Data from over 800 AEs were collected per standard operating procedure in a clinical trial using the Common Terminology Criteria for Adverse Events v5 (CTCAE). Clinical outcomes for statistical analysis included PFS, OS, and DC. Results: An early AE was defined as event occurrence at or prior to day 30 from initial treatment date. The early AEs were then used to calculate the 24 early AE biomarkers to assess overall AE, each toxicity category, and each individual AE. These early AE-derived biomarkers were evaluated for global discovery of clinical association. Both cohorts showed that early AE biomarkers were associated with clinical outcomes. Patients previously experienced with low-grade AEs (including treatment related AEs (TrAE)) had improved PFS, OS, and were associated with DC. The significant early AEs included low-grade TrAE in overall AE, endocrine disorders, hypothyroidism (pembrolizumab’s immune-related adverse event (irAE)), and platelet count decreased (vorinostat related TrAE) for Cohort A and low-grade AE in overall AE, gastrointestinal disorders, and nausea for Cohort B. In contrast, patients with early development of high-grade AEs tended to have poorer PFS, OS, and correlated with PD. The associated early AEs included high-grade TrAE in overall AE, gastrointestinal disorders with two members, diarrhea and vomiting, for Cohort A and high-grade AE in overall AE, three toxicity categories, and five related individual AEs for Cohort B. One low-grade TrAE, alanine aminotransferase increased (vorinostat + pembrolizumab related), was an irAE and correlated with worse OS in Cohort A. Conclusions: The study demonstrated the potential clinical utility of early AE-derived biomarkers in predicting positive and negative clinical outcomes. It could be TrAEs or combination of TrAEs and nonTrAEs from overall AEs, toxicity category AEs, to individual AEs with low-grade event leaning to encouraging effect and high-grade event to undesirable impact. Moreover, the methodology of the AE-derived biomarkers could change current AE analysis practice from a descriptive summary into modern informative statistics. It modernizes AE data analysis by helping clinicians discover novel AE biomarkers to predict clinical outcomes and facilitate the generation of vast clinically meaningful research hypotheses in a new AE content to fulfill the demands of precision medicine. AEs are a critical component of clinical trials for safety data to protect patients from unnecessary risk and to develop a safety profile of the drug for benefit-risk assessment. The National Cancer Institute (NCI) defines an AE as any unfavorable symptom, sign, or disease  temporally associated with the use of a medical treatment or procedure that may or may NOT be considered related to the medical treatment or procedure. NCI has also developed the Common Terminology Criteria for Adverse Events (CTCAE) to standardize toxicity characterization. The CTCAE version 5 lists 26 toxicity categories to cover a total of 837 AE terms. Each AE is recorded from onset time to resolved time with indication of severity defined as grade 1 for mild, grade 2 for moderate, grade 3 for severe, grade 4 for life-threatening, and grade 5 for fatality, as well as status of treatment related cause .While the primary purpose of an AE is to assess patient safety, various studies ,12,13,14Standard AE reporting  has been criticized as sub-optimal and inefficient to properly assess patient safety profiles ,19,20,21Different approaches have been used to improve the characterization of AE profiles by incorporating multiple dimensions of AE, such as the toxicity over time (ToxT) approach , Q-TWiSTThe evaluation of AE association with clinical outcome could easily lead to biased results if it is not properly analyzed. When a patient continues treatment, the chance of experiencing AE events increases. Meanwhile, patients tend to have longer PFS, OS, and treatment response if treatment is continued. On the other hand, patients with treatment discontinuation at early time will likely experience disease progression or death, and therefore reduce AE experiences. This phenomenon is called guarantee-time bias . That isBy taking these considerations with the hypothesis of AE’s clinically predictive value, an integrative approach was developed by utilizing AE-associated parameters and deriving a set of innovative AE biomarkers. Global discovery analysis showed AEs as potential biomarkers in predicting clinical outcomes.Key strategies included the derivation of AE biomarkers, global discovery in all AE data types, early AE as predictive biomarkers, and relevant clinical outcome variables for evaluation. Two study cohorts were used to demonstrate clinical and methodologic utilities.Three key AE elements were used to enrich AE data for biomarker development: treatment relatedness, grade, and measurement. Accordingly, 24 AE-derived biomarkers were formed to evaluate their clinical association  based onAE is one key component to investigate experimental drug agent for safety and efficacy. CTCAE v5 uses five categories  to dictate treatment relatedness to toxicity. Common AE reports are often limited to treatment-related AE (TrAE) which covers three categories . Since nonTrAE (unlikely and unrelated) may be also associated with clinical outcomes, the combination of all the five categories  is often used (AE is used after mentioned to distinguish from TrAE). Thus, both AE types were used for the study: TrAE and AE.CTCAE v5 defines AE severity in five grade levels (1–5) from mild to fatality. Common practice classifies AE severity into low-grade (grade 1 or 2) and high-grade (grade 3 or higher). The use of any-grade  is also often used. Thus, these three grade types were used for evaluation: any-grade, low-grade, and high-grade.AE measurement could be formed in many ways. Here, we considered a set of relevant components for an AE: (i) occurrence to indicate if an AE occurs, (ii) recurrence to indicate if an AE repeatedly occurs, and (iii) duration to indicate if an AE event is temporary or long-term. The inclusion of recurrence and duration is to enrich AE information after an AE occurrence to see if a higher recurrence or longer duration could strengthen the clinical association. Accordingly, for a set of AEs, it forms four informative metrics to characterize the AEs: (a) occurrence of any AE (yes/no): yes for any AE event and no for no AE event at all, (b) frequency of all unique AEs: sum of unique AEs that occurs, (c) frequency of all AEs (including recurrence): sum of all AEs that occurs even they are repeated, and (d) sum of all AE duration (including recurrence). The incorporation of the four metrics could enhance AE contents and help tune up the impact on clinical outcomes in different aspects from occurrence, recurrence, to duration, since they have been reported for their clinical association ,24,35,36All the 837 AE terms (CTCAE v5) in a patient were summarized into the 24 AE-derived biomarkers in The AE-derived biomarkers in The AE-derived biomarkers in Early AE has been shown to have an association with clinical outcomes ,14,33,34Four clinical outcomes were used to evaluate AE-derived biomarkers: progression-free survival (PFS), overall survival (OS), disease control , and duration of treatment (DOT) since most outcomes were reported to be associated with AE ,12,13,14p < 0.05) to determine a clinical association for the corresponding AE in summary of treatment relatedness and grade level. Given the massive analysis results , the strategy efficiently generated summary reports for easy interpretation , each of the 24 AE-derived biomarkers was evaluated in each clinical outcome with Cox model for PFS and OS, t-test for mean difference between treatment response groups , and correlation analysis for DOT e.g., . Analysiretation . For exaTwo study cohorts were used to evaluate the potential of AE-derived biomarkers. Cohort A was the phase I portion  with 32 p = 0.002; median OS: 10.4 vs. 3.7 months with p = 0.007). A higher frequency and longer duration of early low-grade overall TrAE were also associated with improved PFS . Similarly, patients with higher frequency and longer duration of high-grade overall AEs had shorter PFS and OS  were associated with longer PFS and OS compared to patients without the experiences (n = 10)  compared to patients with PD (n = 12)  had a better PFS, OS, or treatment response. In contrast, patients with early development of higher-grade overall AEs tended to have poorer PFS, OS, and with PD.Specifically, Cohort A (treated with pembrolizumab and vorinostat) showed early low-grade TrAEs were associated with longer PFS or OS in overall AE level, in toxicity category level (endocrine disorders), and individual AE . The median survival time was more than double in patients with AE experience compared to the ones without AE event experience A. HypothIn contrast to AEs with improved clinical outcomes, there were different early AEs associated with negative effects. Specifically, patients with early high-grade overall TrAEs had shorter PFS and were associated with PD. Individual TrAEs correlated with worse OS included high-grade AE in gastrointestinal disorders with two members, diarrhea and vomiting, and low-grade AE in alanine aminotransferase increased (irAE) which was considered as a circulating biomarker of liver injury ,45. In ap = 0.12 by Fisher exact test). The insignificant result suggests the early low-grade overall TrAE maintains unique predictive value even under mild association with high-grade overall AE.We further analyzed the association in Cohort A between low- versus high-grade overall AE and found ten patients without experiences of early low-grade overall TrAE and six patients (60%) with early high-grade overall AE, compared to 22 patients experiencing early low-grade overall TrAE with only six patients (14%) with early high-grade overall AE, including one patient with early high-grade overall TrAE (In Cohort B (treated with Taminadenant), early low-grade TrAEs associated with positive clinical outcomes were gastrointestinal disorders and the member, nausea. Patients with DC (n = 25) tended to have longer AE duration compared to PD patients (n = 20). In addition, early low-grade overall AE yielded better OS and PFS and was associated with DC. Other low-grade AEs associated with better clinical outcomes included gastrointestinal disorders and nausea with improved PFS, and anemia, constipation, flatulence, rash maculo-papular with longer treatment duration. On the other hand, early AEs associated with negative survival outcomes included high-grade overall AE, three toxicity categories and five related individual AEs in high-grade AE level, and four toxicity categories and ten individual AEs in low-grade AE level.Overall, the study showed evidence of AE biomarker utility in predicting positive and negative clinical outcomes. It could be TrAEs or a combination of treatment-related and not related AEs. Grade level played a key role in determining the direction of clinical outcomes with low-grade leaning to a positive effect and high-grade to a negative impact. Significant early AEs were identified from overall AEs, toxicity category AEs, down to individual AEs, providing comprehensive view of AE results to improve interpretation.By recognizing the underutilization of AE data due to the complexity of multi-parameters embedded in the data, this study took a novel approach to integrate various AE parameters and derived a set of innovative AE metrics. Specifically, a series of AE-derived biomarkers were developed based on severity level (grade) and treatment relatedness. Each of these biomarkers was further defined based on AE occurrence, frequency, and duration. By taking full consideration of the AE parameters, the AE-derived biomarkers present unique AE profiles for safety and efficacy analysis. The application to both cohorts demonstrated its feasibility by identifying clinical-associated AE biomarkers, such as early low-grade overall AEs and TrAEs with positive survival outcomes in cohort A. Subsequent analysis in toxicity category and individual AE led to discover endocrine disorders and the associated member, hypothyroidism, with improved PFS and longer treatment duration.p < 0.05)  e.g., . Given t5) e.g.,  to efficThis study evaluated interim cohorts from two ongoing immunotherapy trials in NSCLC. Once both trials are completed, the new AE data with large sample size could be naturally served as validation sets to confirm if early AE-derived biomarkers could predict clinical outcomes. Additional AE data from other immunotherapy trials are also needed to make the results more generalizable. Another further direction is to evaluate AE in clinical trials using chemotherapy and targeted therapy. It would provide an opportunity to assess whether different therapy could yield distinctive AE biomarkers.Moreover, the analysis generated multiple meaningful hypotheses worth future research: (1) the value of non-TrAEs. Various TrAEs were shown to have an association with clinical outcomes. Meanwhile, some AEs with combinations of treatment related and non-related events were also correlated with clinical outcomes . The results indicated the potential predictive and prognostic value of non-TrAEs. The separation of TrAE and non-TrAE events could help the assessment, but the process is not straightforward. The same AE could be a treatment related event or non-treatment related event within a patient (e.g., weight-loss in Patient A and fatigue in Patient B; In precision medicine era, this study presents a paradigm-shifted approach for AE analysis from classic descriptive summary into modern informative statistics. It derives a series of innovative AE biomarkers by integrating multiple AE components from grade, treatment relatedness, occurrence, frequency, to duration. Across-the-board AE analysis generates opportunities for global discovery of predictive early AE-derived biomarkers from overall AEs, toxicity category AEs, to individual AEs. A proof of concept in two lung cancer studies demonstrates potential clinical utility of applying this new framework for identifying predictive AE biomarkers for treatment response and survival outcomes, which cannot be identified using standard paradigm. Moreover, the methodology modernizes AE data analysis by helping clinicians discover novel AE biomarkers to predict clinical outcomes and facilitate the generation of vast clinically meaningful research hypotheses in a new AE content to fulfill the demands of precision medicine.


Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level.Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew’s correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%–107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota.https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.PLM-ARG is available for academic purposes at  Global efforts have been initiated to combat the escalating threat of antibiotic resistance, such as the Global Antimicrobial Resistance Surveillance System .Antibiotic resistance is one of the biggest threats to global public health, food safety and security, and environmental sustainability  of ARGs allows bacteria to exchange genetic information among different species, contributing to antibiotic resistance . Also, set al. 2019). Nevertheless, the availability of 3D protein structures of known ARGs is limited. Although, several outstanding protein structure prediction methods, e.g. AlphaFold2 , the proteins could be represented as protein languages. Different protein-language models were applied to develop predictive methods for extracting complex sequence–structure–function relationships  ARG data curation and unification; (ii) protein language model-based ARG classification method development; (iii) model evaluation; and (iv) real-world applications . In PLM-This integrated approach facilitates the concurrent identification of ARGs and the classification of resistance categories. Moreover, we demonstrated the utility of PLM-ARG by expanding the resistance annotation for ARGs in the Uniprot database and exploring the resistance diversity of the Earth's environmental microbiota.The proposed PLM-ARG model architecture encompasses a pretrained extensive protein language model to represent protein sequences. In addition, an inherent classification module is incorporated within this architecture, composed of two consecutive XGBoost models. These models are used for the specific tasks of ARG identification and the subsequent classification of these genes into distinct resistance categories. To establish a benchmark dataset for the development of the PLM-ARG model, we curated a comprehensive Antibiotic Resistance Gene database (PLM-ARGDB). This database was created by amalgamating and standardizing curated ARGs from five public ARG databases, followed by manual correction see .n_estimators, max_depth, learning_rate, subsample, and colsample_bytree, were meticulously optimized using the GridSearchCV procedure, involving 100 iterations of 3-fold cross-validation, as facilitated by Scikit-learn  suggested by both CARD (The comparison was 2-fold: (i) we implemented the three state-of-the-art ARG prediction methodologies with the curated data in PLM-ARGDB with a 5-fold cross-validation strategy. Then, we compared it to the performance of the PLM-ARG model. Briefly, the data was split into five sets. One set is taken for validation and the other four for training. We repeated the process for all five sets. (ii) We compared the optimized state-of-the-art ARG prediction models to the PLM-ARG model with the curated independent validation. Considering the curated independent validation set has no complete “ground-truth” information on the resistance categories, we only limited the comparison to the performance on ARG classification.Receiver operating characteristic (ROC) curves are used to evaluate model performance, generated by plotting sensitivity against specificity at different settings. For PLM-ARG and deepARG, we used the probability to calculate the AUROC. For the Resfam, we used the E-value to calculate AUROC. As the RGI used the “Loose,” “Strict,” and “Perfect” as criteria to determine the ARGs, we used the ratio of “Best_Hit_Bitscore” to “Pass_Bitscore” to calculate AUROC. Besides AUROC, we also evaluated the model using five other performance metrics, including accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC) calculated as follows:TP, TN, FP, and FN denote true positive, true negative, false positive, and false negative. The model performance was calculated for the resistance category classification based on micro-averages for each performance metric.https://www.php.net/) programming. Data interaction was implemented by HTML5, JavaScript, jQuery. All data in PLM-ARG are stored and managed in MySQL database . Data analyses were mainly carried out by the R  or python  script.We utilized the Apache HTTP server as a web server, developed by PHP , Resfams , and DeepARG  by using a 5-fold cross-validation method.For ARG classification, we found the ranking order on the area under the ROC curve (AUROC) in a sequence of PLM-ARG > RGI > DeepARG > Resfams . The AURFor ARG resistance category classification, PLM-ARG outperformed the other three ARG prediction methodologies concerning all the performance metrics and almost all the resistance categories . For exacategory . The resWe also validate the performance of the developed PLM-ARG model on an independent validation set. We first retrieved bacterial ARGs protein sequences with the query “taxonomy: ‘Bacteria [2]’ AND keyword: ‘Antibiotic resistance [KW-0046]’ AND reviewed: yes” against the UniProt database, and then excluded the overlapped ARGs in the PLM-ARGDB, obtaining 2280 ARGs (released on 27 May 2022). Following the strategy outlined in the ARG data curation section, we also curated an equal number of non-ARGs that did not overlap with those in the PLM-ARGDB. As a result, an independent validation consisting of an equal number (i.e. 2280) of ARGs and non-ARGs was constructed to evaluate the PLM-ARG model. Due to the lack of ground truth in resistance category information, the comparison focused on the ARG identification. We observed the same ranking order on all the performance metrics except precision as those of the 5-fold cross-validation, i.e. PLM-ARG > RGI > DeepARG > Resfams . With noFurthermore, we assessed the distribution of annotation quality scores among UniProt-reviewed ARGs predicted by various methods (see Section 2), utilizing the annotation quality score supplied by the UniProt database. In this regard, we included the HMD-ARG method, which provides a web server for ARG prediction, in our analysis. Our findings demonstrate that PLM-ARG could detect more UniProt-reviewed ARGs than other methods, particularly for ARGs with high annotation scores . These rTo investigate whether the proposed PLM-ARG could have better discrimination power for ARGs that are not similar to the ones in the training set, we divided the 2280 UniProt-reviewed ARG in the independent test set into 5 groups according to its similarity with the referenced ARGs in the training set. We summarized the individual rate of the proportion of ARGs identified in the four methodologies. The results showed that PLM-ARG could identify >80% of the ARGs in different similarity groups except for those with <20% similarity . RGI canMoreover, to investigate the decline in performance across all methods within the subset of ARGs sharing <40% identity with the referenced ARGs, we conducted a BLAST analysis of these low-identity ARGs against the non-ARGs present in the training dataset. Our analysis revealed that 35.5% of these ARGs exhibited higher similarity to the non-ARGs present in the training dataset. Notably, our proposed methodology successfully identified 41.54% of these ARGs, surpassing the performance of other contemporary state-of-the-art approaches.A substantial portion of unreviewed bacterial proteins in the UniProt database were categorized as antibiotic resistance, yet lacked specific detailed information about resistance categories. To fill the gap, we utilized our developed PLM-ARG to prioritize unreviewed ARGs in the UNIPROT database and expand their resistance category annotations see . Of 73 9Furthermore, we calculated the identity between the 73 938 unreviewed ARGs and our PLM-ARGDB and found 64.77% of them with a similarity of <40%, which were defined as distant homologous ARGs. We found that 58% of these distant homologous ARGs could be predicted with PLM-ARG. Notably, 29.67% of the ARGs with <20% similarity can also be identified. These results further demonstrated the strong ability of PLM-ARG for ARG identification . The catIn addition to augmenting resistance annotation within the UniProt database, we also demonstrated the practical applicability of the PLM-ARG framework in investigating the diversity of the resistome within Earth's environmental microbiota. This highlights its substantial potential for real-world applications see .Environmental and commensal bacteria harbor a diverse and largely unknown collection of ARGs that may be mobilized and transferred to pathogens, impacting public health over time. Computational approaches using the accumulated omics data offer an effective way for ARG identification and resistance category annotation. Here, we developed a novel Artificial Intelligence (AI)-powered language model framework, PLM-ARG, for ARG and resistance category classification by translating sequence data to protein language. The proposed method outperformed the state-of-the-art approaches with a 9.6%–36% range of improvement over AUC and 40.8%–107.3% over the F1-score based on the independent validation set. Although we demonstrated the superiority of the PLM-ARG on distant homologous ARGs by comparing it with three popular ARG prediction approaches , we have come to recognize that there is no single method that can cater to all real-world applications. Therefore, the pivotal consideration in choosing a “fit-for-purpose” strategy is to meticulously align it with the particular context of the real-world application.It is widely acknowledged that bacteria can develop resistance to specific antibiotics by acquiring ARGs and through point mutations in chromosomal target genes associated with antibiotic resistance. Although this study only focuses on acquired genes, it is crucial to acknowledge that resistance caused by point mutations in genes differs significantly from acquired genes. Thus, identifying resistance caused by point mutations is vital in comprehending the mechanism of bacterial resistance and should receive more attention in future research. In addition, defining resistance categories more precisely would be beneficial, such as grouping beta-lactams into different sub-classes, enhancing their clinical applications.Additional investigations are worth considering to improve the model performance of PLM-ARG further and confirm the findings from this study: (i) PLM-ARG takes the assembled genes/ORF as the input, hampering its identification of low-abundance ARGs that cannot be assembled without enough sequencing depth. Therefore, further investigation on directly modeling the sequence data (e.g. pair-end short-reads sequencing and Nanopore sequencing) may be more desirable and might hold more potential for further improving the performance of ARG identification. (ii) Due to the current constraints in our knowledge of ARGs, we cannot assure a 100% certainty regarding the selection of genes as negative controls or the comprehensiveness of assigned resistance categories for ARGs. Therefore, it is imperative to use a robust strategy when constructing the training dataset to enhance the reliability of most ARG identification methods. (iii) Given the substantial imbalance in the number of genes resistant to various antibiotic categories, there is a risk of performance deterioration, particularly for those resistance categories with fewer protein sequences. Therefore, we recommend adopting an appropriate loss function or preprocessing strategy to rebalance the class prevalence. (iv) In the current study, we showed the great potential of an AI-powered language model for ARG identification. As it needs a long computation time to generate the embedding representation for a protein sequence by the ESM-1b model, which limits the application, a lightweight model is necessary for further improvement (In summary, PLM-ARG offers a robust, one-stop solution for both ARG identification and resistance category classification. Its independence from sequence similarity makes PLM-ARG well-suited for characterizing antibiotic resistance diversity in large-scale microbiome studies.btad690_Supplementary_DataClick here for additional data file.


Pulmonary arterial hypertension is a major cause of death in systemic lupus erythematosus, but there are no tools specialized for predicting survival in systemic lupus erythematosus-associated pulmonary arterial hypertension.To develop a practical model for predicting long-term prognosis in patients with systemic lupus erythematosus-associated pulmonary arterial hypertension.A prognostic model was developed from a multicenter, longitudinal national cohort of consecutively evaluated patients with systemic lupus erythematosus-associated pulmonary arterial hypertension. The study was conducted between November 2006 and February 2020. All-cause death was defined as the endpoint. Cox regression and least absolute shrinkage and selection operators were used to fit the model. Internal validation of the model was assessed by discrimination and calibration using bootstrapping.Of 310 patients included in the study, 81 (26.1%) died within a median follow-up of 5.94 years (interquartile range 4.67–7.46). The final prognostic model included eight variables: modified World Health Organization functional class, 6-min walking distance, pulmonary vascular resistance, estimated glomerular filtration rate, thrombocytopenia, mild interstitial lung disease, N-terminal pro-brain natriuretic peptide/brain natriuretic peptide level, and direct bilirubin level. A 5-year death probability predictive algorithm was established and validated using the C-index (0.77) and a satisfactory calibration curve. Risk stratification was performed based on the predicted probability to improve clinical decision-making.This new risk stratification model for systemic lupus erythematosus-associated pulmonary arterial hypertension may provide individualized prognostic probability using readily obtained clinical risk factors. External validation is required to demonstrate the accuracy of this model's predictions in diverse patient populations.The online version contains supplementary material available at 10.1186/s12931-023-02522-2. Pulmonary arterial hypertension (PAH) is a fatal condition and a leading cause of death among patients with systemic lupus erythematosus (SLE) . The preCurrently, the approach to assessing patients with SLE-PAH relies on the recommendation for group 1 PAH, such as the 2015 and 2022 European Society of Cardiology (ESC) / European Respiratory Society (ERS) guidelines , the risThe Chinese SLE Treatment and Research Group (CSTAR) was established and funded by the Chinese Ministry of Science and Technology in 2009 and was further extended with the formation of the Chinese Rheumatism Data Center, which is directed by the National Health and Family Planning Commission of China , 13. TheThe methods described in this article follow the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement .We developed a prognostic model for all-cause death in the CSTAR-PAH cohort, which included patients with RHC-confirmed SLE-PAH from 14 referral centers participating in the CSTAR between November 2006 and May 2016 . SLE wasBaseline was defined as the time of SLE-PAH diagnosis confirmed by RHC. At baseline, we obtained information related to the following: demographic characteristics, medical history, physical examination findings, transthoracic echocardiography results, pulmonary function test results, hemodynamic measurements from RHC, and serum laboratory results. Planned and recorded comprehensive follow-up evaluations were arranged for patients every 3–12 months or earlier if there was a change in symptoms. Investigators, blinded to both variables and outcomes, reviewed and classified all clinical evaluations in a structured format. Data were collected independently from each participating center.The endpoint of the present analysis was all-cause death during the follow-up period. The causes of death were ascertained by experienced rheumatologists at each referral center based on clinical records, social security data, and death registries. Deaths were assessed without knowledge of the candidate predictor variables.A multidisciplinary team of cardiologists, rheumatologists, and researchers selected the predictors for further evaluation in the prognostic model, based on existing literature and clinical judgment .Continuous variables are presented as medians with interquartile ranges, while categorical variables are presented as frequencies. The follow-up time was calculated from the date of baseline to the date of death from any cause or last follow-up before the end of the study period . For patients who were lost to follow-up, follow-up was censored at the date of last contact. Reverse Kaplan–Meier methods were used to estimate the median follow-up time . All staMissing data were explored to understand the patterns in value gaps, and those were considered to be missing at random. The values for the missing predictors were imputed using multiple imputation techniques based on chained equations (MICE) from R statistical software . All can0(t) is the 5-year average survival probability, and the prognostic index equals the sum of the products of the predictors and their coefficients.To improve model accuracy and reduce model overfitting, we used the least absolute shrinkage and selection operator (LASSO) method  to selecBootstrapping was used to evaluate the performance of the model and permit adjustment for optimism. This is the most efficient internal validation procedure, as all aspects of model development, including variable selection, are validated . For thiThe predictive discrimination of this model was assessed using Harrell’s concordance index (C-index) . EnsemblThe REVEAL tools have powerful clinical applications in patients with PAH . The discriminative power of the REVEAL prognostic Eq. (10) and simplified risk calculator  at 5 yeaThe study cohort comprised 310 patients with RHC-confirmed SLE-PAH. Among them, 25.8% were newly diagnosed with PAH (The duration between onset of symptoms associated with PAH and the performance of RHC is within 3 months). The time from SLE-PAH onset to diagnosis by RHC was 1.5 years. The baseline clinical characteristics are shown in Table Eight variables with nonzero coefficients in the LASSO Cox regression model were selected to predict the risk of all-cause death, which included assessment of functional capacity, hemodynamic variables, organ involvement, and laboratory parameters. The definitions of the predictor variables are listed in Table The entire follow-up data (306 patients with 81 events) were used to develop the prediction model. Hazard ratios and 95% confidence intervals were estimated by fitting the Cox proportional hazards model , where 6MWD is six-minute walking distance, EGFR is estimated glomerular filtration rate, ILD is interstitial lung disease, NT-proBNP/BNP is N-terminal pro-brain natriuretic peptide / brain natriuretic peptide level, and Dbil is direct bilirubin level. The coding of the predictors is presented in Table The model performance of a 5-year predicted risk was assessed using 306 patients with 65 events. The apparent C-index of the model was 0.78. After enhanced bootstrap adjustment for optimism, the prediction model had an optimism-corrected C-index of 0.77 and an optimism-corrected Brier score of 0.04 Table . Sensitie-Fig. The C-index for the prognostic equation and risk score calculator in the SLE-PAH cohort was 0.71 and 0.70, respectively, indicating poorer but modest discriminatory ability compared with that of the SLE-PAH prediction model. However, the Kaplan–Meier survival curves of the five groups classified by the REVEAL risk score calculator showed that the risk groups did not predict risk accurately, especially in the very high risk group Fig. . The REVTo our knowledge, this study provides the first prognostic model for all-cause death in patients with SLE-PAH, based on the largest, prospectively recruited, national SLE-PAH cohort. The model was based on eight objectively measured and readily available variables: modified WHO functional class, 6MWD, PVR, EGFR, thrombocytopenia, mild ILD, NT-proBNP/BNP, and Dbil. Three risk groups were identified based on predicted probability and clinical applicability. The internal validation of the model showed its robustness and adequate performance.Our study had several advantages. First, to provide a comprehensive evaluation of SLE-PAH, the candidate predictors were selected not only based on the known prognostic factors for PAH but also by considering the clinical features and assessment of SLE. The candidate predictors were confirmed by expert opinion to ensure objectivity and stability . SubsequThe new SLE-PAH model we have developed showed good discrimination with satisfactory calibration between the expected and observed risks, whereas the REVEAL model might not particularly suitable for patients with SLE-PAH . REVEAL Previous studies have assessed the performance of various predictors of PAH prognosis. The clinical plausibility, feasibility, and applicability of the final selected predictors were further confirmed by expert opinion. The modified WHO functional class, 6MWD, NT-proBNP/BNP, Dbil, and PVR are well-recognized predictors and have been confirmed to be associated with PAH prognosis , 38–45. Risk stratification is helpful in identifying patients who may benefit from intensive therapy, which would improve clinical decision-making. Both complete case analysis and imputed data analysis showed that the mortality rate of SLE-PAH was > 50% in the high-risk group, and some of the patients were censored during the follow-up. Given the good response to immunosuppressive therapy in patients with SLE-PAH , 46, cliThis study has several limitations. The cohorts consisted of patients with SLE-PAH, recruited on the basis of prespecified criteria, which may limit the use of the model in all patients with SLE-PAH. We did not include patients who had only been examined using transthoracic echocardiography instead of RHC. However, according to the ESC/ERS recommendation, the diagnosis of PAH should be confirmed by definitive RHC . In addiBecause PAH is a relatively rare complication of SLE and the mortality rates have declined significantly in recent years, all samples in the CSTAR-PAH cohort were used to develop a specific SLE-PAH model. We have not yet obtained a separate dataset of patients with SLE-PAH for external validation. The generalizability of the results, especially to other regions and races, should be carefully considered. Further research is required to confirm our proposed models and to measure their performance.This study presents a retrospective analysis of the prospective CSTAR PAH cohort. It is important to note that a limited amount of data is not derived from standardized prospective clinical evaluations. Consequently, we have incorporated a portion of missing data, which can be attributed to challenges in acquiring certain investigations and inconsistencies in specific reports across various centers. We addressed this problem by using a multiple imputation method, which is widely regarded as the best approach. However, the mechanisms underlying the missing pattern may be complicated and may bias the estimates. A study with more complete data is needed in the future to obtain accurate estimates.The risk prediction model proposed in this study provides individualized estimates of risk regarding all-cause death in SLE-PAH, which should be used by physicians experienced in the management of the condition. By obtaining an evidence-based assessment of the patient, clinicians may be better able to individualize and optimize therapeutic strategies to improve survival. External validation will be required to demonstrate the accuracy of this model in different cohorts of SLE-PAH.Additional file 1: Appendix S1. (Summary of candidate predictors), Appendix S2. (Continuous variable transformation), Appendix S3. , Appendix S4. , Fig.S1. , Fig. S2. (Comparison of observed and predicted risk by 5 risk groups of the SLE-PAH prognostic model), Table S1. (Predictors for multiple imputation model and LASSO regression model), Table S2. (Least absolute shrinkage and selection operator (LASSO) coefficient of the five imputed datasets), Table S3. Sensitivity analysis of SLE-PAH prediction model using complete cases only. Supplementary Information of this study.Additional file 2. TRIPOD checklist. TRIPOD checklist: Prediction Model Development and Validation. TRIPOD checklist of this study.


Every Newborn Action Plan (ENAP) coverage target 4 necessitates national scale-up of Level-2 Small and Sick Newborn Care (SSNC) (with Continuous Positive Airway Pressure (CPAP)) in 80% of districts by 2025. Routine neonatal inpatient data is important for improving quality of care, targeting equity gaps, and enabling data-driven decision-making at individual, district, and national-levels. Existing neonatal inpatient datasets vary in purpose, size, definitions, and collection processes. We describe the co-design and operationalisation of a core inpatient dataset for use to track outcomes and improve quality of care for small and sick newborns in high-mortality settings.A priori considerations for variable inclusion were determined by clinicians and policymakers from the four African governments by facilitated group discussions. These included prioritising clinical care and newborn outcomes data, a parsimonious variable list, and electronic data entry. The tool was designed and refined by > 40 implementers and policymakers during a multi-stakeholder workshop and online interactions.A three-step systematic framework was used to review, co-design, and operationalise this novel neonatal inpatient dataset in four countries  implementing with the Newborn Essential Solutions and Technologies (NEST360) Alliance. Existing global and national datasets were identified, and variables were mapped according to categories. n = 6) contained a median of 89 (IQR:61–154) variables, with many relating to research-specific initiatives. Maternal antenatal/intrapartum history was the largest variable category . The Neonatal Inpatient Dataset (NID) includes 60 core variables organised in six categories: (1) birth details/maternal history; (2) admission details/identifiers; (3) clinical complications/observations; (4) interventions/investigations; (5) discharge outcomes; and (6) diagnosis/cause-of-death. Categories were informed through the mapping process. The NID has been implemented at 69 neonatal units in four African countries and links to a facility-level quality improvement (QI) dashboard used in real-time by facility staff.Identified national and international datasets  Implementation Toolkit for SSNC, this adaptable tool enables facility and country-level comparisons to accelerate progress toward ENAP targets. Additional linked modules could include neonatal at-risk follow-up, retinopathy of prematurity, and Level-3 intensive care.The online version contains supplementary material available at 10.1186/s12887-023-04341-2. Every Newborn Action Plan (ENAP), an UN-led multi-country partnership, agreed to targets for 2025, including the scale-up of Level-2 SSNC (with CPAP) [Given that an estimated 2.3 million neonatal deaths occur globally annually, countries are focused on accelerating progress to meet Sustainable Development Goal 3.2, ending preventable deaths of newborns by 2030. The target of < 12 neonatal deaths per 1000 live births requires high coverage of Small and Sick Newborn Care (SSNC), including respiratory support for preterm babies. Hence, the th CPAP) , nationaRoutine Healthcare Information Systems (RHIS) are a building block of a functioning health system . StudiesIn high-income settings, there is a long history of networks and research groups using standardised inpatient datasets – for example, Vermont Oxford Network , 17, a pHowever, many of these existing neonatal inpatient tools were not parsimoniously designed and do not link to national data systems or focus on quality improvement in the Level-2 SSNC (with CPAP) ward. Furthermore, associated publications on these tools provide limited detail on their development, implementation, and institutionalisation.NEST360  is a multi-partner alliance including national governments in four African countries  with engineers, clinicians, and health systems implementation experts. The alliance aims to improve the quality of SSNC and foster learning by implementing a health systems package supporting ENAP targets and the WHO/UNICEF Quality of Care Network . This inThe NEST360 Alliance set out to co-design and operationalise a core neonatal inpatient dataset tool to measure individual-level quality of care to inform quality improvement, measure impact and enable prompt action in neonatal units. This tool was intentionally designed to integrate with existing routine data systems  to enable sustainable use at scale in LMIC settings.1: Review of existing newborn ward data landscape across four countries implementing with NEST360 and describe other available study-specific and national or international neonatal inpatient datasets.a priori considerations, with a focus on variables for quality improvement action, which are included in a small and sick newborn care facility-level dashboard.2: Co-design content of a novel, core dataset based on 3: Pilot, refine and operationalise a novel and core neonatal dataset in Malawi, Kenya, Tanzania and Nigeria, with intentional pathways to institutionalisation.This paper is part of a supplement reporting findings and learnings from NEST360, an alliance of partners, including four African governments , working to reduce neonatal inpatient deaths by improving Level-2 newborn care in hospitals. In this paper, we aim to describe the systematic, evidence-based review, co-design, and operationalisation of a core neonatal inpatient dataset to improve the quality of care and track outcomes of small and sick newborns in high-mortality settings. This paper addresses three objectives:NEST360 has produced a health systems package, including an innovative bundle of devices, data tools and a clinical education package. This has been implemented in 69 newborn units across 65 health facilities in Malawi, Kenya, Tanzania, and Nigeria .As part of this package, we applied a systematic evidence-based developmental approach to the review, co-design, and operationalisation of the NEST360 Neonatal Inpatient Dataset (NID) tool . This waDesign of the NID data tool began with stakeholder consultation. Relevant data forms, clinical case notes or registers used in the newborn ward in countries implementing with NEST360 were identified and described.Identified international, national, and study-specific individual-level datasets used in other LMICs were mapped with each variable  as a row and each dataset as a column in Microsoft Excel . Datasets were described by the number and proportion of total variables by category. The Standards for Improving the Quality of Care for Small and Sick Newborns in Health Facilities were unavailable at this stage of development but were cross-checked after publication in 2020 .Building on the review of pre-existing individual-level data tools described in Objective 1, a face-to-face workshop was conducted in Nairobi, Kenya June 2019). Multi-stakeholder participants contributed, including study implementers, clinicians, nurses, and Ministry of Health officials  from countries implementing with NEST360   and WHO data principles . Data obNEST360 and Ministry of Health data collectors were trained between March 2020 and April 2021  data collection systems). This process used a Training of Trainers (ToT) cascade model, with the NEST360 country data manager being the lead trainer, promoting country leadership and ownership. ToT sessions were mostly given during the COVID-19 pandemic using online videoconferencing software. Training focused on attaining skills, with sessions relating to NEST360 and NID objectives, data collection procedures, data quality assessment and competency-based use of the NID data tool and associated devices. Data entry practice with standardised cases and online pre- and post-training evaluations confirmed skill and knowledge attainment. Neonatologists and neonatal nurses also attended to provide context, acquire knowledge, and supply line-by-line explanations of the data elements in the tool.The NEST360 NID dataset was developed within a REDCap database  hosted in each NEST360 country server using a set of standard operating procedures and data flow scheme  newborn units in the four countries implementing with NEST360 led to the identification of 21 tools  Global Neonatal Database  is incorporated within a series of prepaid service packages with technical support, online data management and educational materials . As an en = 254) designed to focus heavily on a specific intervention, similar to the CPAP Data Summary dataset.Maternal history variables were commonly found within the Indian SNCU dataset  and the Neonatal Nutrition Network (NeoNuNet) (33%). The NeoNuNet and VON datasets held few variables relating to admission details  and newborn complications and observations . WHO iKMC research study dataset was the largest  clinical care pathways and outcomes that are measurable, high-impact and actionable in the newborn ward for quality improvement, and input into a facility-level dashboard.Parsimonious variable list (aiming for ~ 50 variables per baby)Electronic data entry (at the time of discharge)Nairobi Workshop attendees  agreed on a priori considerations. A list of 55 variables was agreed upon using further voting and consensus-building processes. Examples of eliminated variables included maternal history variables , which attendees noted were important, but could not be acted upon in the newborn ward. Maternal variables with a direct impact on the management of sick newborns, including premature rupture of membranes, maternal fever, age, status, cause of death, mode of delivery, antenatal corticosteroids, HIV status and chronic conditions , were included in the NID tool. Variables relating to study-specific initiatives  were also removed due to collection difficulty and the lack of relevance to routine neonatal inpatient care. Finally, some variables  were considered challenging to collect accurately. However, due to their importance to clinical care, they were included in the NID, with the recognition that data quality and utilisation could be improved over time by measuring them.Attendees reviewed all 300 collated variables from the initial mapping of the datasets  and Open Data Kit . Though not open-source, REDCap was chosen due to enhanced data security , ease of use , flexibility , linkage  and in-built validation fields . The NEST360 Alliance agreed to start development with this software but move to an open-source platform in the future. A standardised REDCap data dictionary was formatted with consistent variable naming, module structure, variable formats, and reduced free text to aid user-friendly data collection, support data quality assessments, and enable future adaptation , as demonstrated initially in Malawi. The time delay between data collection and entry was significantly reduced, from a median of 49 days (IQR:6–120) to 6 days (2–23) , data for over 450,000 newborns have been captured. This NID tool can potentially address the inverse data law, where the largest burden contexts have the least data [A standardised national-level neonatal dataset is important for high-quality newborn clinical care, but such datasets are lacking for use in contexts where most neonatal deaths occur . Of the ast data .Four of the six individual-level datasets identified from LMIC settings were primarily designed for research, while two focused on quality monitoring and improvement ) , 31. TheThe NID tool comprises six categories: birth details and maternal history; neonatal admission details and identifiers; complications and observations; interventions/ investigations (including microbiology); discharge outcomes, diagnosis or causes of death. Compared to others, one notable feature of the NID tool is the selective inclusion of maternal variables, such as a history of diabetes and hypertension. Our co-design process emphasised the importance of minimising the data collection burden whilst enabling quality improvement in the newborn ward and individual-level tracking of critical Level-2 (with CPAP) interventions and outcomes.High-quality data entry is crucial for maintaining accurate and reliable information in healthcare settings. Electronic systems with in-built validation fields and branching logic can greatly enhance the accuracy and efficiency of data entry. However, optimal data quality can be challenging to achieve without a dedicated government-employed data entry collector in neonatal wards equipped with a designated desktop computer for real-time data input. An example of this strategy’s effectiveness is the scale-up of India’s national SSNC program . Implementing dedicated data entry collectors within these units has significantly improved data quality and timeliness –48. ThisData quality of the NID is further enhanced by incorporating regular embedded data quality checks, which generate data quality heatmaps in automated reports. These reports facilitate facility and national-level data meetings, promoting informed decision-making within newborn units. In addition, actions are managed locally, with on-site data collectors promptly addressing data queries.Using high-quality data is an important component of supporting quality improvement . HoweverIntegration with national Routine Health Information Systems (RHIS) is crucial for large-scale, sustainable use, a factor consistently considered throughout the NID co-design process. Facility-level count data gathered from labour or newborn ward registers are compiled into monthly tally sheets. These aggregate data are then passed to district and national-level electronic platforms such as DHIS, which is utilised in over 80 LMICs . For exaWithin the NEST360 Alliance, the development of the NID has also supported the improvement of existing routine national-level neonatal care forms. For example, the Malawi government modified their standard paper Neonatal Admission Form (NAP) to align with key variables collected using the NID. Similarly, in Tanzania, newly designed routine forms are being adapted to incorporate NID variables. In addition, the NID data collected in Malawi is also transitioning to being hosted on the government DHIS2 server platform. This interoperable nature of NID with other systems and existing forms has resulted in reduced workloads and intentionally institutionalised the tool within national government-owned information systems, which is critical for its long-term sustainability and fostering local capacity development within the public health sector , 59.A priori NID design considerations set with government colleagues and implementers included: electronic data entry , a parsimonious design , and prioritising data collection on clinical care pathways and outcomes that directly contribute to quality improvement action on the newborn unit. In addition, the co-designed tool undergoes continuous refinement, informed by implementation learning, review, and feedback from facility and country-level users. To date, it has been implemented in 65 facilities across four African countries, reflecting its practical utility and substantial impact in healthcare facilities.A significant strength of the NEST360 NID development lies in its collaborative co-design approach and the adoption of a transparent, evidence-based three-step systematic process. This consensus-driven methodology has proven effective in various applications , 60 and There are limitations to this process. Our review may have missed other datasets if unpublished or not openly accessible. Datasets confined to single facilities or those primarily focused on maternal care aspects were not included –65. CompThe NID is purposefully designed to enhance data-driven action for Level-2 facilities (with CPAP) in LMIC contexts; hence, there is an intentional lack of variables for Level-3 SSNC . Future adaptations could involve the reduction or expansion of the tool, for example, for use in humanitarian settings  or LevelIn conclusion, the NEST360 NID has the potential to be used widely by facilities and countries to improve care and benchmark with others. However, ongoing institutionalisation within government systems is critical for wider use and improving the quality of data and care, which is the focus of the NEST360 Alliance. Therefore, we will continue to use and refine this tool and the linked quality improvement facility dashboards and processes. We welcome adaptation, feedback, and partnerships with others as we share the common aim of improving the quality of clinical neonatal care worldwide and accelerating progress to reduce the annual toll of 2.3 million neonatal deaths.Additional file 1. WHO Levels of Newborn Care with Interventions - NEST360 Informing the ‘How To’ for Implementation.Additional file 2. Summary list of Nairobi 2019 workshop participants by characteristic.Additional file 3. Neonatal Inpatient Dataset paper tool.Additional file 4. Neonatal Inpatient Dataset data flow.Additional file 5. Summary list of 300 potential variables for inclusion into the NEST360 Neonatal Inpatient Dataset, discussed at Nairobi 2019 workshop.Additional file 6. Neonatal Inpatient Dataset REDCap data dictionary.Additional file 7. Evaluating facility-level data entry timeliness in Malawi, Kenya, Tanzania, and Nigeria.Additional file 8. NEST360 Neonatal Inpatient Dataset data collection infrastructure by facility.Additional file 9. Local ethical approval for the complex evaluation of the implementation of a small and sick newborn care package with NEST360.


Dermatomyositis (DM) is an idiopathic inflammatory disorder with a heterogenous presentation. Patients exhibit varying degrees of concomitant cutaneous, muscle or other distinct extracutaneous manifestations that often allow for a clinical diagnosis. Wong-type DM is a rare variant with clinical and pathological overlap between DM and pityriasis rubra pilaris (PRP).A 52-year-old female with a history of poorly controlled psoriasiform plaques over her upper and lower extremities  presenteA and B). However, immunohistochemistry was positive for deposition of intravascular C3 and C5-9, while direct immunofluorescence was negative for IgA, IgG, and IgM, findings consistent with DM (C). Additional laboratory evaluation showed a low-titer positive cyclic citrullinated peptide along with normal creatine kinase and aldolase. This overlap of clinical, histologic and laboratory features of DM and PRP is consistent with Wong-type DM.Physical examination revealed psoriasiform plaques over the bilateral extensor forearms and elbows as well as the anterior, lateral, and posterior thighs. Subtle features consistent with DM were also observed, including heliotrope sign, erythema of the upper chest , and ery with DM , C. AddiGiven the severe and refractory disease, the patient was promptly initiated on monthly IVIg. Given concerns for muscle involvement, methylprednisolone was also initiated at 8 mg daily but quickly tapered to a maintenance dose of 4 mg daily. Several months later, the patient’s cutaneous disease was vastly improved; however, the patient reported dyspnea on exertion. Pulmonary function tests demonstrated a restrictive pattern consistent with ILD, which prompted initiation of mycophenolate mofetil.,Wong-type DM is a rare DM variant in which there is clinical and pathological overlap with PRP. Wong-type DM has been reported in children and adults with unclear consensus on whether this is an overlap syndrome of DM and PRP or a unique presentation of an already protean disease.The pathologic presentation classically reveals compact orthokeratosis alternating with parakeratosis in a checkerboard pattern as in PRP, though can include DM-features of vacuolar interface dermatitis or mucin deposition.As there are fewer than 45 cases of Wong-type DM in the literature, there remains no established connection with myositis-specific autoantibodies or risk of systemic involvement. Myositis-specific autoantibodies are associated with particular DM phenotypes and useful in prognostication of potential pulmonary involvement or association with a visceral malignancy. Our case expands the characterization of Wong-type DM to include anti-SRP and anti-PM/Scl antibodies in the setting of ILD. These autoantibody subtypes and ILD are both uncommon in juvenile-onset DM. Traditionally, anti-SRP and anti-PM/Scl have been associated with necrotizing myopathy and connective tissue disease overlap syndromes with ILD, respectively; these known relationships may provide valuable insights to guide extracutaneous workup in Wong-type DM patients. Further evaluation of myositis-specific autoantibodies in cases of PRP may also expedite diagnosis of unrecognized Wong-type DM. Additionally, although the prevalence of malignancy in Wong-type DM remains unclear,,,Given the paucity of Wong-type DM, management and treatment remains anecdotal. Cutaneous DM has been traditionally treated first-line with various immunosuppressants. More recently, a seminal breakthrough in the treatment of DM was provided in the ProDERM trial, which investigated the use of IVIg in active DM patients. Although the primary clinical endpoint was reduction of myositis disease activity, there was clinically significant cutaneous improvement based on reductions in the Cutaneous DM Disease Area and Severity Index.This case draws attention to a rare clinical entity, Wong-type DM, that often masquerades as PRP. Although seldom encountered, maintaining Wong-type DM in a differential, especially when confronted with severely recalcitrant PRP associated with pulmonary symptoms, could allow for expedited diagnosis. Our case additionally highlights a unique presentation of Wong-type DM with anti-SRP and anti-PM/Scl antibodies associated with ILD and supports the therapeutic potential of IVIg in this rare condition.None disclosed.


Preliminary evidence suggests that people who inject drugs (PWID) may be at an increased risk of developing infective endocarditis (IE), hepatitis C virus (HCV) infection, and/or human immunodeficiency virus (HIV) infection from hydromorphone controlled-release formulation. The hypothesized mechanism is related to insolubility of the drug, which promotes reuse, leading to contamination of injecting equipment. However, this relationship has not been confirmed. We aimed to conduct a systematic review including adult PWID exposed to controlled-release hydromorphone and the risk of acquiring IE, HCV, and HIV.We searched MEDLINE, EMBASE, and Evidence Based Medicine reviews from inception until September 2021. Following pilot testing, two reviewers conducted all screening of citations and full-text articles, as well as abstracted data, and appraised risk of bias using the Newcastle–Ottawa scale and Effective Practice and Organization of Care tool. Equity issues were examined using the PROGRESS-PLUS framework. Discrepancies were resolved consistently by a third reviewer. Meta-analysis was not feasible due to heterogeneity across the studies.After screening 3,231 citations from electronic databases, 722 citations from unpublished sources/reference scanning, and 626 full-text articles, five studies were included. Five were cohort studies, and one was a case–control study. The risk of bias varied across the studies. Two studies reported on gender, as well as other PROGRESS-PLUS criteria . Three studies focused specifically on the controlled-release formulation of hydromorphone, whereas two studies focused on all formulations of hydromorphone. One retrospective cohort study found an association between controlled-release hydromorphone and IE, whereas a case–control study found no evidence of an association. One retrospective cohort study found an association between the number of hydromorphone controlled-release prescriptions and prevalence of HCV. None of the studies specifically reported on associations with HIV.Very few studies have examined the risk of IE, HCV, and HIV infection after exposure to controlled-release hydromorphone. Very low-quality and scant evidence suggests uncertainty around the risks of blood-borne infections, such as HCV and IE to PWID using this medication.The online version contains supplementary material available at 10.1186/s12954-023-00788-9. Hydromorphone is a prescribed semi-synthetic opioid recommended as second-line therapy for mild-to-moderate non-malignant pain and first-line therapy for severe non-malignant pain . HydromoPeople who inject drugs (PWID) are at high risk of hepatitis C virus (HCV) infection  and the It has been suggested that the increasing availability of controlled-release formulation of hydromorphone might be associated with contributing to higher rates of these infections HCV, IE, and HIV due to contamination and reuse of injection drug preparation equipment (IDPE). Some authors have hypothesized that injection of controlled-release hydromorphone elevates the risk of blood-borne infections through multiple mechanism. First, controlled-release hydromorphone does not readily dissolve in water , leadingAs such, we conducted a systematic review to examine the risk of IE, HCV, or HIV in individuals exposed to controlled-release hydromorphone compared with other opioids, as well as to determine the characteristics of individuals exposed to controlled-release hydromorphone experiencing these infections. Our specific research questions were: (1) “What are the rates of IE, HCV infection, and/or HIV infection in adults exposed to controlled-release hydromorphone compared with the rates of the same infections in people using immediate-release  hydromorphone, injectable hydromorphone, and other controlled-release products globally?” and (2) “What are the characteristics of adults exposed to controlled-release hydromorphone who experienced IE, HCV infection and/or HIV infection, including previous treatment or hospitalization for opioid-related harms?”.We were commissioned by Health Canada to conduct this systematic review through the Canadian Institutes of Health Research Drug Safety and Effectiveness Network . A preliThe literature search was developed by an experienced librarian (Dr. McGowan) and peer-reviewed by another librarian (Ms. Rader) using the Peer Review of Electronic Search Strategies (PRESS) checklist . The eleThe population of interest included adults aged 18 years or older. The exposure of interest was controlled-release hydromorphone (HCR) intake through any means or mode of administration, prescription, or illicit use. The comparators were immediate-release  hydromorphone, injectable hydromorphone, or exposure to other opioids. The outcomes were incident cases of HCV, IE, and HIV. Only studies with a valid comparator were considered relevant, including randomized controlled trials, quasi-randomized trials, non-randomized trials, controlled before and after studies, interrupted time series, cohort studies, and case–control studies. No restrictions were applied based on study year, language of dissemination, or study duration.A screening form  hydromorphone, injectable hydromorphone, and other controlled-release products globally?n = 6, 9%) were more likely to inject hydromorphone versus PWID with negative HCV antibodies . Furthermore, PWID with HCV antibodies were more likely to use non-injection routes of administration for hydromorphone  compared with PWID without HCV antibodies . Three PWID seroconverted during the study and two of these reported opioid use (specific use of hydromorphone was not reported). The authors concluded that more PWID with positive HCV antibodies used hydromorphone through injection and non-injection routes than PWID without HCV antibodies and that they were uncertain whether the HCV infection that occurred in the three PWID who seroconverted during the study was due to hydromorphone use. The study did not differentiate between immediate and controlled-release hydromorphone.A prospective cohort study was conducted in Calgary, Alberta, including 257 PWID between 2014 and 2017 . Particip < 0.0001). Furthermore, 21 (1.1%) of the prescriptions were for immediate-release hydromorphone compared with matched PWID who filled prescriptions with non-hydromorphone opioids . For controlled-release hydromorphone, there were 73 (3.9%) hospital admissions compared with 20 (1.1%) admissions amongst matched PWIDs who filled prescriptions for non-hydromorphone opioids . The authors concluded that filling a prescription for controlled-release hydromorphone was associated with a risk of IE that was three times higher than for other opioids.A retrospective cohort study was conducted in Ontario including 60,529 hospital admissions of adult PWID between 2006 and 2015 . OverallA retrospective cohort was conducted in Ottawa, Ontario, between 2017 and 2018 including 26 PWID . At basep = 0.20), which was not statistically significantly different between cases and controls. Heating controlled-release hydromorphone prior to injection was not statistically significantly related to IE. Cases and controls similarly used IDPE (e.g. “cookers”) to prepare the drugs at rates of 50–60%. However, controls were more likely to use IDPE (e.g. Stericup) provided by the provincial government (32%) versus cases , as well as have a greater access to a heating source such as a lighter (58.8%) versus controls . Cases were more likely to use IDPE cookers that were not provided as examples on the survey (48.5%), such as spoon, glass bottle, or shot glass compared with controls . The authors concluded that a significant risk in IE for PWID using controlled-release hydromorphone was not observed and that use of IDPE provided by the government (e.g. Stericup) and lighters to prepare the drugs might be protective against IE.A case–control study was conducted including 33 cases (adult PWID diagnosed with IE) and 102 controls (adult PWID without IE) admitted to addiction clinics in London, Ontario, between 2016 and 2018 . One-on-p < 0.001). The authors concluded that prescription of controlled-release hydromorphone is contributing to HCV transmission in Ontario.A retrospective cohort study was conducted using health unit data on HCV infection and opioid prescription data from Ontario, Canada, in 2016 . There wWhat are the characteristics of adults exposed to controlled-release hydromorphone who experienced IE, HCV infection and/or HIV infection, including previous treatment or hospitalization for opioid-related harms? None of the included studies reported results specific to previous treatment or hospitalization for opioid-related harms.We conducted a comprehensive systematic review on the potential risk of HCV, IE, and HIV infection amongst adults exposed to controlled-release hydromorphone. Only five relevant studies were identified, which indicates a lack of evidence in this area. Although some studies demonstrated a potential association between the use of controlled-release hydromorphone and HCV, IE, and HIV incidence, all but two studies were based on retrospective data . One retInjection risks with controlled-release hydromorphone may be related to sharing of IDPE, which suggests interventions that could decrease this harm. The process for preparing HCR for injection involves removing the substance from the capsule, crushing it in a metal cooker, and adding sterile water. One study suggested that heating controlled-release hydromorphone prior to injection may decrease the risk of IE. Another found that the use of a particular cooker (the Stericup) may be protective against IE.Our systematic review identified several gaps in the literature. All studies were conducted in Canada, indicating a gap in the literature from other countries, especially low- and middle-income countries, as well as in countries with high opioid usage, such as the USA. Furthermore, only two studies reported on the PROGRESS-PLUS criteria , which cThere are limitations to our systematic review that are worth noting. One is the lack of available evidence on this topic, which limits our interpretation of results. We also excluded adolescents from our review and use of controlled-release hydromorphone might occur in this population. Due to the limited evidence in this area, we were more liberal in our inclusion of studies compared with our registered protocol and included studies examining hydromorphone overall and not focused on the controlled-release formulation. We were also unable to conduct GRADE assessments for certainty of evidence. These are deviations from our protocol. Another limitation is the risk of bias of the included studies; particularly regarding the lack of controlling for confounding across the studies. The strengths of our systematic review included following the methodologically rigorous Cochrane handbook and the PRISMA 2020 reporting guidance.Very few studies have examined the risk of IE, HCV, and HIV infection after exposure to controlled-release hydromorphone. Very low-quality and scant evidence suggests uncertainty around the risks of blood-borne infections, such as HCV and IE to PWID using this medication.Additional file 1. Supplementary file and appendices


The role of subjective cognitive concerns (SCC) as a diagnostic criterion for MCI remains uncertain and limits the development of a universally (or widely)-accepted MCI definition. The optimal MCI definition should define an at-risk state and accurately predict the development of incident dementia. Questions remain about operationalization of definitions of self- and informant-reported SCCs and their individual and joint associations with incident dementia.The present study included Einstein Aging Study participants who were non-Hispanic White or Black, free of dementia at enrollment, had follow-up, and completed neuropsychological tests and self-reported SCC at enrollment to determine MCI status. Informant-reported SCC at baseline were assessed via the CERAD clinical history questionnaire. Self-reported SCC were measured using the CERAD, items from the EAS Health Self-Assessment, and the single memory item from the Geriatric Depression Scale. Cox proportional hazards models examined the association of different operationalizations of SCC with Petersen and Jak/Bondi MCI definitions on the risk of dementia, further controlling for age, sex, education, and race/ethnicity. Time-dependent sensitivity and specificity at specific time points for each definition, and Youden’s index were calculated as an accuracy measure. Cox proportional hazards models were also used to evaluate the associations of combinations of self- and informant-reported SCC with the risk of incident dementia.91% of the sample endorsed at least one SCC. Youden’s index showed that not including SCC in either Jak/Bondi or Petersen classifications had the best balance between sensitivity and specificity across follow-up. A subset of individuals with informants, on average, had a lower proportion of non-Hispanic Blacks and 94% endorsed at least one self-reported SCC. Both informant-reported and self-reported SCC were significantly associated with incident dementia.Our findings suggest that the SCC criterion may not improve the predictive validity for dementia when included in widely-employed definitions of MCI. Consistent with some prior research, informant-reported SCC was more related to risk of incident dementia than self-reported SCC. Given that requiring informant report as a diagnostic criterion may unintentionally exclude health disparate groups, additional consideration is needed to determine how best to utilize informant-report in MCI diagnosis. When thThere are a multitude of studies on mild cognitive impairment  with highly variable approaches for (1) how to measure SCC ; (2) optimal sources of the SCC report ; (3) whether corroboration from informant is required; or (4) how to capture and quantify SCC  . FurtherThe original rationale for the inclusion of SCC as a criterion for MCI diagnosis was to identify individuals undergoing cognitive change. Objective cognitive tests measure status, but unless baseline measures are available, they do not capture change. At baseline, cognitive tests may flag individuals with long-standing cognitive difficulties  as impaired . For indRecent reports suggest that SCC can be assessed by an individual or knowledgeable informant, including clinicians, friends, or family members . Some reWhether to include SCC as a criterion for MCI depends critically on its predictive validity for incident dementia. Much of the research on this topic has been carried out in samples of predominantly highly educated, non-Hispanic White older adults, limiting the generalizability of this work to more diverse populations. The current study investigates three operational definitions of self-reported concerns and two definitions of informant-reported concerns about cognition to determine whether their inclusion in two widely-used MCI definitions  add to the predictive validity for incident dementia. Using a more representative cohort of older adults than is typical for aging studies, we additionally investigated discrepancy patterns between self- and informant-reported SCC and their association with incident dementia. Based on prior research , we pred22.1The EAS is a longitudinal study of community-residing individuals from the Bronx NY, which is a racially and ethnically diverse urban setting . Details2.22.2.1Participants completed standardized neuropsychological testing at baseline and all annual follow-up visits . Five co® Dementia Staging Instrument  no significant SCC, measured by no endorsement of any item on three SCC questionnaires (detailed below); (2) unimpaired self-reported ADL as measured by the IADL Lawton Brody Scale ; and (3)nt CDR®, , (ii) a nt CDR®, , or , a yes/no/do not know rating scale of participants’ current cognitive functioning.2.3MCI classifications were made using two widely-used MCI criteria: (1) updated Petersen criteria  requirinAn additional analysis was conducted with individuals with informants to examine the discrepancy between self-reported and informant-reported SCC using the CERAD questionnaire. Using low levels  of self- or informant-reported SCC as reference, the following combinations were used to reflect patterns of discrepancy: (1) high levels of both self- and informant-reported SCC; (2) high levels of informant-reported SCC and low self-reported SCC; and (3) high levels of self-reported SCC and low informant-reported SCC .2.3.1Incident dementia diagnosis at follow-up was the main outcome. Broadly consistent with DSM-IV for major neurocognitive disorder , partici2.4Demographic information from the EAS included self-reported race/ethnicity as defined by the U.S. Census Bureau in 1994 , number of years of education, sex, and age. Subclinical symptoms of depression were assessed using the GDS short form, excluding the single memory item .2.5The time to event was defined as the time between the baseline clinic visit and the date of dementia diagnosis or the final follow-up visit when the participants were known without dementia. To evaluate the associations of five operationalizations of SCC into the MCI definitions at baseline with the risk of incident dementia, Cox proportional hazards models were applied. Because the cumulative dementia disease status is time-dependent and time to dementia can be censored, time-dependent receiver operating characteristic (ROC) based on the Cox model was used to evaluate the discriminative ability of each MCI for dementia incidence within a given time period. Specifically, the time-dependent sensitivity and specificity for cumulative disease incidence within a time interval X, Where Additional models controlling for covariates including age, sex, education, race/ethnicity, and depressive symptoms at baseline were also applied. Cox proportional hazards models were also used to evaluate the associations of combinations of self- and informant-reported SCC with the risk of incident dementia. All statistical analyses were performed using SAS 9.4 .33.1N = 1,097), participants’ age ranged from 70 to 100 (mean = 78.6 ± 5.4) years, the sample was 62.4% female and educational achievement averaged 13.7 ± 3.5 years, with 45.1% obtaining 12 years or fewer years of education. Notably, nearly one fifth of participants (18.9%) did not complete high school, highlighting the educational diversity within the sample. Most participants identified as White (70.6%), though Black participants were well-represented (29.4%). During up to 19.6 years of follow-up , 124 individuals developed incident dementia. As shown in At baseline (3.2p = 0.001) and a higher proportion endorsed at least one self-reported SCC . Among those with informants, high average cutoff for informant-reported SCC  was slightly more strongly related to incident dementia than self-reported SCC using the same questionnaire , but both were significantly associated, controlling for baseline age, sex, education, and race/ethnicity (thnicity .p < 0.0001) and those with high informant-reported SCC but low self-reported SCC . The risk of dementia was also higher, though not significant, in those with high self-reported SCC but low informant-reported SCC .3.3Notably, 91% of the entire sample endorsed at least one self-reported SCC. Comparisons of time-dependent ROC results for various operational definitions of self-reported SCC  as a criterion in both Jak/Bondi and Petersen MCI classifications, at 2-, 3-, 5-, and 7-years of follow-up and numbers of participants at risk at the time are shown in 3.4In the subset of the sample with informants available, 94% endorsed at least one self-reported SCC. Comparisons of time-dependent ROC results for various operational definitions of self-reported SCC  and informant-reported SCC (any SCC and high-average cutoff) as a criterion in both Jak/Bondi and Petersen MCI classifications are shown in 4Whether or not to include SCC as a criterion in the classification of MCI is an uncertainty in the field—but one with important implications. A research-driven approach to the inclusion, exclusion, and/or specific operationalization would be ideal to address this question; however, relatively little attention has been given to standardizing and optimizing the SCC criterion. Moreover, research examining MCI criteria in diverse and/or health disparate populations is limited, which not only restricts the generalizability of the construct, but also upholds health inequities in aging populations . The preResults are consistent with a growing literature that has questioned the utility of SCC as a criterion in MCI classification, primarily on the basis that SCC may contribute to the misdiagnosis of MCI . Self-peFurther contributing to the instability of SCC as a diagnostic criterion, cognitive concerns may evolve as a result of biopsychosocial variables, such as mood, somatic conditions, chronic pain, medication use, sleep disorders, psychosocial stress, personality factors, and/or normal aging  and resoAlthough the association between SCC and dementia risk is complex, our results showed both self- and informant-reported SCC was related to risk of dementia. Additionally, the risk of dementia was significantly higher among those with high levels of informant-reported SCC, regardless of the level of self-reported SCC, but the risk of dementia was not as high in those with high levels of self-reported SCC but low informant-reported SCC. This corroborates previous research showing that informant-reported SCC or mutual report better correlate with an individual’s objective cognitive performance and better predicts progression to dementia than only self-reported SCC . Self-awImportantly, although collateral information about an older adult’s daily functioning can be of great benefit in the clinical setting, requiring the presence of an informant may, in fact, hamper ADRD research efforts. For example, excluding older adults without informants effectively translates to excluding socially isolated older adults, who may be at greater risk for dementia . In addiSeveral study limitations warrant mention. Only a small subset of participants was joined by their informants, and there was a limited number of incident dementia cases. Additional studies in larger samples are needed. Although non-Hispanic Blacks were well represented, the population of Hispanic older adults or those of other racial/ethnic groups were insufficient to include in the analyses. Additionally, the self- and informant-report SCC questionnaires were limited in their coverage of cognitive domains and not all measures were available for both participants and their informants. As EAS has evolved, we have given much more consideration to the measurement of SCC and have newly implemented an expanded version of the widely-used Cognitive Change Index CCI-40, . Future To our knowledge, this was the first longitudinal study directly comparing five specific operational definitions of self- or informant-reported SCC in a diverse community-based sample. Our findings suggest that not including SCC, across different MCI definitions, provides the best balance of sensitivity and specificity in terms of predicting incident dementia in our sample of diverse individuals. Although the original rationale was to avoid prescribing a diagnosis of MCI to individuals with long-standing cognitive difficulties  , the extmindy.katz@einsteinmed.edu.The terms of consent for research participants stipulate that an individual’s data can only be shared outside of the EAS investigators group if the group has reviewed and approved the proposed secondary use of the data. This consent applies regardless of whether data has been de-identified. Access is mediated via a standardized request process via Mindy Katz, All protocols were approved by the Einstein Institutional Review Board (IRB). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent from the participants or the participants’ legal guardians/kin was obtained at the initial clinic visit.KC and LR are the corresponding authors and guarantors of this manuscript and contributed to the conception and the design of the study. MK and CW organized the database. CW performed the statistical analysis and assisted with interpretation of results. KC, in collaboration with the co-authors, wrote the first draft of the manuscript, incorporated all levels of feedback from co-authors, and prepared the manuscript for submission. RL, DB, and CN provided expert guidance and reviewed this manuscript and revisions. The project was funded by successful project grants secured by MK and RL. All authors contributed to manuscript revision, read, and approved the submitted version.