UNASSIGNED: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. However, there is a lack of comprehensive data from low- and middle-income countries (LMICs) regarding factors influencing COPD outcomes, particularly in regions where biomass exposure is prevalent.
UNASSIGNED: The Factors Affecting Survival in Severe and Very Severe COPD Patients Admitted to Tertiary Centers of India (FAST) study aims to address this gap by evaluating factors impacting survival and exacerbation rates among COPD patients in LMICs like India, with a specific focus on biomass exposure, clinical phenotypes, and nutritional status in patients admitted to the Intensive Care Unit (ICU).
UNASSIGNED: The FAST study is an observational cohort study conducted in university teaching hospitals across India. The study aims to enroll 1000 COPD patients admitted to the ICU meeting specific inclusion criteria, with follow-up assessments conducted every 6 months over a 2-year period. Data collection includes demographic information, clinical manifestations, laboratory investigations, pulmonary function tests, medications, nutritional status, mental health, and health-related quality of life. Adjudication of exacerbations and mortality will also be undertaken. The FAST study seeks to provide crucial insights into COPD outcomes in LMICs, informing more precise management strategies and mitigating the burden of COPD in these settings. By evaluating factors such as biomass exposure, clinical phenotypes, and nutritional status, the study aims to address key knowledge gaps in COPD research.
UNASSIGNED: Arunachala S, Devapal S, Swamy DSN, Greeshma MV, Ul Hussain I, Siddaiah JB, et al. Factors Affecting Survival in Severe and Very Severe COPD after Admission in ICUs of Tertiary Care Centers of India (FAST COPD): Study Protocol for a Multicentric Cohort Study. Indian J Crit Care Med 2024;28(6):552-560.

UNASSIGNED: Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor.
UNASSIGNED: This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status.
UNASSIGNED: Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs).
UNASSIGNED: Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79).
UNASSIGNED: Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

UNASSIGNED: Patients with asthma and low levels of type 2 inflammatory biomarkers (T2 low) have limited effective treatment options. Such biomarkers include eg blood eosinophils (b-eos) and fractional exhaled nitric oxide (FeNO). The healthcare resource utilisation (HCRU) of severe uncontrolled T2 low asthma remains unexplored. Thus, this study aimed to estimate the HCRU of T2 low and non-T2 low severe uncontrolled asthma patients using real-world data in Finland.
UNASSIGNED: Adult patients with an asthma diagnosis during baseline (2012-2017) at the pulmonary department of Turku University Hospital were included and followed during 2018-2021, or until death. Total HCRU costs and respiratory-related HCRU costs were evaluated. The main drivers for the HCRU and costs were assessed with gamma and negative binomial regression models.
UNASSIGNED: Of the severe uncontrolled asthma patients with T2 status available, 40% (N=66) were identified with T2 low and 60% (N=103) with non-T2 low asthma. The average cumulative cost per patient was similar in patients with T2 low compared with non-T2 low, with all-cause costs cumulating in four years of follow-up to 37,524€ (95% CI: 27,160, 47,888) in T2 low compared to 34,712€ (25,484, 43,940) in non-T2 low. The corresponding average cumulative respiratory-related costs were 5178€ (3150, 7205) in T2 low compared to 5209€ (4104, 6313) in non-T2 low. Regression modelling identified no differences between the T2-status groups when assessing all-cause healthcare costs per patient-year (PPY). On the other hand, the regression modelling predicted more inpatient days PPY for severe uncontrolled patients with T2 low status compared to the patients with non-T2 low status.
UNASSIGNED: Patients with uncontrolled severe T2 low asthma use equal healthcare resources as corresponding non-T2 low patients. This study brought new insights into the HCRU of severe uncontrolled asthma patients per T2 status, which has not previously been investigated.

BACKGROUND: Mucosal-associated invariant T (MAIT) cells assume pivotal roles in numerous autoimmune inflammatory maladies. However, scant knowledge exists regarding their involvement in the pathological progression of oral lichen planus (OLP). The focus of our study was to explore whether MAIT cells were altered across distinct clinical types of OLP.
METHODS: The frequency, phenotype, and partial functions of MAIT cells were performed by flow cytometry, using peripheral blood from 18 adults with non-erosive OLP and 22 adults with erosive OLP compared with 15 healthy adults. We also studied the changes in MAIT cells in 15 OLP patients receiving and 10 not receiving corticosteroids. Surface proteins including CD4, CD8, CD69, CD103, CD38, HLA-DR, Tim-3, Programmed Death Molecule-1 (PD-1), and related factors released by MAIT cells such as Granzyme B (GzB), interferon (IFN)-γ, tumour necrosis factor (TNF)-α, interleukin (IL)-17A, and IL-22 were detected.
RESULTS: Within non-erosive OLP patients, MAIT cells manifested an activated phenotype, evident in an elevated frequency of CD69+ CD38+ MAIT cells (p < 0.01). Conversely, erosive OLP patients displayed an activation and depletion phenotype in MAIT cells, typified by elevated CD69 (p < 0.01), CD103 (p < 0.05), and PD-1 expression (p < 0.01). Additionally, MAIT cells exhibited heightened cytokine production, encompassing GzB, IFN-γ, and IL-17A in erosive OLP patients. Notably, the proportion of CD103+ MAIT cells (p < 0.05) and GzB secretion (p < 0.01) by MAIT cells diminished, while the proportion of CD8+ MAIT cells (p < 0.05) rose in OLP patients with corticosteroid therapy.
CONCLUSIONS: MAIT cells exhibit increased pathogenicity and pro-inflammatory capabilities in OLP. Corticosteroid therapy influences the expression of certain phenotypes and functions of MAIT cells in the peripheral blood of OLP patients.

BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learning-based clustering algorithms to identify distinct patient clinical profiles and functional trajectories following surgical intervention.
METHODS: In this study, we applied k-means and latent profile analysis (LPA) to identify patient phenotypes, using aggregated data from three major DCM trials. The combination of Nurick score, NDI (neck disability index), neck pain, as well as motor and sensory scores facilitated clustering. Goodness-of-fit indices were used to determine the optimal cluster number. ANOVA and post hoc Tukey\'s test assessed outcome differences, while multinomial logistic regression identified significant predictors of group membership.
RESULTS: A total of 1047 patients with DCM (mean [SD] age: 56.80 [11.39] years, 411 [39%] females) had complete one year outcome assessment post-surgery. Latent profile analysis identified four DCM phenotypes: \"severe multimodal impairment\" (n = 286), \"minimal impairment\" (n = 116), \"motor-dominant\" (n = 88) and \"pain-dominant\" (n = 557) groups. Each phenotype exhibited a unique symptom profile and distinct functional recovery trajectories. The \"severe multimodal impairment group\", comprising frail elderly patients, demonstrated the worst overall outcomes at one year (SF-36 PCS mean [SD]: 40.01 [9.75]; SF-36 MCS mean [SD], 46.08 [11.50]) but experienced substantial neurological recovery post-surgery (ΔmJOA mean [SD]: 3.83 [2.98]). Applying the k-means algorithm yielded a similar four-class solution. A higher frailty score and positive smoking status predicted membership in the \"severe multimodal impairment\" group (OR 1.47 [95% CI 1.07-2.02] and 1.58 [95% CI 1.25-1.99, respectively]), while undergoing anterior surgery and a longer symptom duration were associated with the \"pain-dominant\" group (OR 2.0 [95% CI 1.06-3.80] and 3.1 [95% CI 1.38-6.89], respectively).
CONCLUSIONS: Unsupervised learning on multiple clinical metrics predicted distinct patient phenotypes. Symptom clustering offers a valuable framework to identify DCM subpopulations, surpassing single patient reported outcome measures like the mJOA.
BACKGROUND: No funding was received for the present work. The original studies were funded by AO Spine North America.

BACKGROUND: In highly multiracial populations with inadequate newborn screening, knowledge of the various phenotypic presentations of Cystic Fibrosis (CF) can help reach an early diagnosis. This study aims to describe phenotypes and genotypes at the time of CF diagnosis in a state in the Northeast Region of Brazil.
METHODS: Retrospective cross-sectional study. Clinical data were extracted from the medical records of CF patients. Clinical, laboratory, and genotypic characteristics were described for patients admitted to a tertiary referral center between 2007 and 2021.
RESULTS: Fifty-eight (58) patients were included in the study, 53.5% of whom were diagnosed through clinical suspicion. The median age at diagnosis was 4.7 months (IQR: 1.5-14.8 months). Five patients had false-negative results in the newborn screening. Faltering growth was the most frequent clinical manifestation. Bronchiectasis and a history of pneumonia predominated in those older than ten, while thinness, underweight, and electrolyte imbalances were more frequent in children under two. Sequencing of the CFTR gene identified 27 genotypes, with at least one class I-III variant in all patients, and nine variants that are rare, previously undescribed, or have uncertain significance (619delA, T12991, K162Q, 3195del6, 1678del > T, 124del123bp, 3121-3113 A > T). The most frequent alleles were p.Phe508del, p.Gly542*, p.Arg334Trp, and p.Ser549Arg.
CONCLUSIONS: Malnutrition and electrolyte imbalances were the most frequent phenotypes for children < 2 years and were associated with genotypes including 2 class I-III variants. Rare and previously undescribed variants were identified. The p.Gly542*, p.Arg334Trp, and p.Ser549Arg alleles were among the most frequent variants in this population.

Cardiogenic shock (CS) is a heterogeneous syndrome reflecting a broad spectrum of shock severity, diverse etiologies, variable cardiac function, different hemodynamic trajectories, and concomitant organ dysfunction. These factors influence the clinical presentation, management, response to therapy, and outcomes of CS patients, necessitating a tailored approach to care. To better understand the variability inherent to CS populations, recent algorithms for staging the severity of CS have been described and validated. This paper is part 1 of a 2-part state-of-the-art review. In this first article, we consider the context for clinical staging and stratification in CS with a focus on established severity staging systems for CS and their use for risk stratification and clinical care. We describe the use of staging for predicting outcomes in populations with or at risk for CS, including risk modifiers that provide more nuanced risk stratification, and highlight how these approaches may allow individualized care.

Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.

Asthma is a chronic lung disease characterized by reversible bronchoconstriction and inflammation of the bronchi. Its increasing prevalence in childhood as well as different triggers make asthma a challenging disease in several ways: defining its phenotype/endotype, the diagnostic approach (especially in younger children), therapeutic options, and systematic follow-up. Considering these problems, this review approaches the current status and limitations of guidelines used for asthma management in children. It also emphasizes the key points which could lead to a better understanding and the direction to take in future studies.

Acinetobacter baumannii is one of the most important pathogens worldwide. The intrinsic and acquired resistance of A. baumannii, coupled with the slow pace of novel antimicrobial drug development, poses an unprecedented and enormous challenge to clinical anti-infective therapy of A. baumannii. Recent studies in the field of pathogenicity, antibiotic resistance, and biofilms of A. baumannii have focused on the model strains, including ATCC 17978, ATCC 19606, and AB5075. However, these model strains represent only a limited portion of the heterogeneity in A. baumannii. Furthermore, variants of these model strains have emerged that show significant diversity not only at the genotypic level but also reflected in differences at the phenotypic levels of capsule, virulence, pathogenicity, and antibiotic resistance. Research on A. baumannii, a key pathogen, would benefit from a standardized approach, which characterizes heterogeneous strains in order to facilitate rapid diagnosis, discovery of new therapeutic targets, and efficacy assessment. Our study provides and describes a standardized, genomically and phenotypically heterogeneous panel of 45 different A. baumannii strains for the research community. In addition, we performed comparative analyses of several phenotypes of this panel. We found that the sequence type 2 (ST2) group showed significantly higher rates of resistance, lower fitness cost for adaptation, and yet less biofilm formation. The Macrocolony type E (MTE, flat center and wavy edge phenotype reported in the literature) group showed a less clear correlation of resistance rates and growth rate, but was observed to produce more biofilms. Our study sheds light on the complex interplay of resistance fitness and biofilm formation within distinct strains, offering insights crucial for combating A. baumannii infection.
OBJECTIVE: Acinetobacter baumannii is globally notorious, and in an effort to combat the spread of such pathogens, several emerging candidate therapies have already surfaced. However, the strains used to test these therapies vary across studies (the sources and numbers of test strains are varied and often very large, with little heterogeneity). The variation complicates the studies. Furthermore, the limited standardized resources of A. baumannii strains have greatly restricted the research on the physiology, pathogenicity, and antibiotic resistance. Therefore, it is crucial for the research community to acquire a standardized and heterogeneous panel of A. baumannii. Our study meticulously selected 45 diverse A. baumannii strains from a total of 2,197 clinical isolates collected from 64 different hospitals across 27 provinces in China, providing a scientific reference for the research community. This assistance will significantly facilitate scientific exchange in academic research.