Prevalence and risk factors of non-tuberculous mycobacterial pulmonary isolates and infection in interstitial lung disease associated with systemic autoimmune disease.

OBJECTIVES: Non-tuberculous mycobacterial (NTM) lung disease (NTM-LD) prevalence is increasing worldwide. In this study, we aimed to evaluate the clinical significance of NTM pulmonary isolates (NTM-PI) and NTM-LD in patients with systemic autoimmune disease (SAD) who had a concurrent interstitial lung disease (ILD) diagnosis. METHODS: We retrospectively identified patients with SAD who had a concurrent ILD diagnosis (SAD-ILD) and from whom clinically indicated sputum specimens were collected for NTM culture between 2003 and 2018 at a tertiary referral hospital. We analysed the prevalence and risk factors of NTM pulmonary isolates (NTM-PI; ≥1 positive culture) and NTM-LD (≥2 positive cultures). RESULTS: This study included 258 patients. Rheumatoid arthritis and Sjögren's syndrome were the most common SADs (32.2% and 26.7%, respectively). The NTM-negative subgroup had 204 patients (79.1%) and the NTM-PI subgroup had 54 patients (20.9%). In the NTM-PI subgroup, 33 patients had one NTM positive set of specimens (NTM 1+, 12.8% of the entire sample) and 21 had NTM-LD (8.1% of the entire sample). In a multivariable analysis, chronic kidney disease (CKD; adjusted odds ratio [aOR]: 3.10 [1.53, 6.29]) and chronic obstructive pulmonary disease (COPD; aOR: 2.59 [1.16, 5.78]) were significantly associated with NTM-PI. For NTM-LD, CKD (aOR: 2.79 [1.00, 7.76]) and COPD (aOR: 3.70 [1.23, 10.72]) remained significant risk factors. CONCLUSIONS: In patients with SAD-ILD, the NTM-PI and NTM-LD prevalence rates were 20.9% and 8.1%, respectively. COPD and CKD were independent risk factors of both NTM-PI and NTM-LD. Previous use of biological agents was associated with NTM-PI.

Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

BACKGROUND: Bacteremia is a life-threatening complication of infectious diseases. Bacteremia can be predicted using machine learning (ML) models, but these models have not utilized cell population data (CPD). METHODS: The derivation cohort from emergency department (ED) of China Medical University Hospital (CMUH) was used to develop the model and was prospectively validated in the same hospital. External validation was performed using cohorts from ED of Wei-Gong Memorial Hospital (WMH) and Tainan Municipal An-Nan Hospital (ANH). Adult patients who underwent complete blood count (CBC), differential count (DC), and blood culture tests were enrolled in the present study. The ML model was developed using CBC, DC, and CPD to predict bacteremia from positive blood cultures obtained within 4 h before or after the acquisition of CBC/DC blood samples. RESULTS: This study included 20,636 patients from CMUH, 664 from WMH, and 1622 patients from ANH. Another 3143 patients were included in the prospective validation cohort of CMUH. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.844 in the derivation cross-validation, 0.812 in the prospective validation, 0.844 in the WMH external validation, and 0.847 in the ANH external validation. The most valuable predictors of bacteremia in the CatBoost model were the mean conductivity of lymphocytes, nucleated red blood cell count, mean conductivity of monocytes, and neutrophil-to-lymphocyte ratio. CONCLUSIONS: ML model that incorporated CBC, DC, and CPD showed excellent performance in predicting bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

Value of monocyte distribution width for predicting severe cholecystitis: a retrospective cohort study.

OBJECTIVES: Acute cholecystitis is a gallbladder inflammation, and the Tokyo Guidelines 2018 (TG18) can be used to predict its presence and severity with high sensitivity and specificity. However, TG18 grading require the collection of excessive parameters. Monocyte distribution width (MDW) is a parameter used to detect sepsis early. Therefore, we investigated the correlation between MDW and cholecystitis severity. METHODS: We conducted a retrospective study of patients with cholecystitis admitted to our hospital from November 1, 2020, to August 31, 2021. The primary outcome was severe cholecystitis analyzed as a composite of intensive care unit (ICU) admission and mortality. The secondary outcomes were length of hospital stay, ICU stay, and TG18 grade. RESULTS: A total of 331 patients with cholecystitis were enrolled in this study. The average MDWs for TG18 grades 1, 2, and 3 were 20.21 ± 3.99, 20.34 ± 3.68, and 25.77 ± 6.61, respectively. For patients with severe cholecystitis, the average MDW was 25.42 ± 6.83. Using the Youden J statistic, we set a cutoff MDW of 21.6. Multivariate logistic regression revealed that patients with an MDW≥21.6 had a higher risk of severe cholecystitis (odds ratio=4.94; 95 % CI, 1.71-14.21; p=0.003). The Cox model revealed that patients with an MDW≥21.6 were more likely to have a prolonged hospital stay. CONCLUSIONS: MDW is a reliable indicator of severe cholecystitis and prolonged length of stay. Additional MDW testing and a complete blood count may provide simple information for predicting severe cholecystitis early.

Application of machine learning methods for the prediction of true fasting status in patients performing blood tests.

The fasting blood glucose (FBG) values extracted from electronic medical records (EMR) are assumed valid in existing research, which may cause diagnostic bias due to misclassification of fasting status. We proposed a machine learning (ML) algorithm to predict the fasting status of blood samples. This cross-sectional study was conducted using the EMR of a medical center from 2003 to 2018 and a total of 2,196,833 ontological FBGs from the outpatient service were enrolled. The theoretical true fasting status are identified by comparing the values of ontological FBG with average glucose levels derived from concomitant tested HbA1c based on multi-criteria. In addition to multiple logistic regression, we extracted 67 features to predict the fasting status by eXtreme Gradient Boosting (XGBoost). The discrimination and calibration of the prediction models were also assessed. Real-world performance was gauged by the prevalence of ineffective glucose measurement (IGM). Of the 784,340 ontologically labeled fasting samples, 77.1% were considered theoretical FBGs. The median (IQR) glucose and HbA1c level of ontological and theoretical fasting samples in patients without diabetes mellitus (DM) were 94.0 (87.0, 102.0) mg/dL and 5.6 (5.4, 5.9)%, and 92.0 (86.0, 99.0) mg/dL and 5.6 (5.4, 5.9)%, respectively. The XGBoost showed comparable calibration and AUROC of 0.887 than that of 0.868 in multiple logistic regression in the parsimonious approach and identified important predictors of glucose level, home-to-hospital distance, age, and concomitantly serum creatinine and lipid testing. The prevalence of IGM dropped from 27.8% based on ontological FBGs to 0.48% by using algorithm-verified FBGs. The proposed ML algorithm or multiple logistic regression model aids in verification of the fasting status.

Dyslipidemia and coronary artery calcium: From association to development of a risk-prediction nomogram.

BACKGROUND AND AIMS: The associations between dyslipidemia and coronary artery calcium (CAC) are controversial. We investigated their cross-sectional relationships and developed a predictive scoring system for prognostically significant coronary calcification (PSCC). METHODS AND RESULTS: This study evaluated the lipid profiles and the CAC score (CACS) measured through multidetector computed tomography (MDCT) among Taiwanese adult patients in a tertiary hospital between 2011 and 2016. Patients with CACS higher than 100 were classified as having PSCC. Dyslipidemia for each lipid component was defined based on the clinical cutoffs or the use of the lipid-lowering agents. Multivariable logistic regression was used to assess the association between dyslipidemia and PSCC and the model performance was assessed using calibration plot, discrimination, and a decision curve analysis. Of the 3586 eligible patients, 364 (10.2%) had PSCC. Increased age, male sex, higher body mass index (BMI), and higher level of triglyceride (TG) were associated with PSCC. The adjusted odds ratios (95% confidence intervals) of PSCC was 1.15 (0.90-1.47) for dyslipidemia defined by total cholesterol (TC) ≥200 mg/dL, 1.06 (0.83-1.35) for low-density-lipoprotein-cholesterol (LDL-C) ≥130 mg/dL, and 1.36 (1.06-1.75) for TG ≥ 200 mg/dL. The positive association between TG ≥ 200 mg/dL and PSCC was not modified by sex. Incorporating hypertriglyceridemia did not significantly improve the predictive performance of the base model comprising of age, sex, BMI, smoking, hypertension, diabetes, estimated glomerular filtration rate, and fasting glucose. CONCLUSIONS: Hypertriglyceridemia was significantly associated with the prevalent odds of PSCC. Our proposed predictive model may be a useful screening tool for PSCC.

Molecular diagnosis and therapy for Plasmodium ovale infection of a returned traveler from East Africa.

Malaria is an infectious disease caused by Plasmodium parasites that are mainly transmitted through the bites of infected female Anopheles mosquitoes. The average annual number of malaria cases was less than ten in Taiwan in the last five years. Most of the cases were caused by Plasmodium vivax and Plasmodium falciparum, and were primarily diagnosed in travelers who returned from Southeast Asia and Africa. Here, we report the first case of Plasmodium ovale infection within five years that was confirmed by peripheral blood smear examination and molecular identification in a 25-year-old Asian female patient who returned from Uganda.

The ratio and difference of urine protein-to-creatinine ratio and albumin-to-creatinine ratio facilitate risk prediction of all-cause mortality.

The role of the difference and ratio of albuminuria (urine albumin-to-creatinine ratio, uACR) and proteinuria (urine protein-to-creatinine ratio, uPCR) has not been systematically evaluated with all-cause mortality. We retrospectively analyzed 2904 patients with concurrently measured uACR and uPCR from the same urine specimen in a tertiary hospital in Taiwan. The urinary albumin-to-protein ratio (uAPR) was derived by dividing uACR by uPCR, whereas urinary non-albumin protein (uNAP) was calculated by subtracting uACR from uPCR. Conventional severity categories of uACR and uPCR were also used to establish a concordance matrix and develop a corresponding risk matrix. The median age at enrollment was 58.6 years (interquartile range 45.4-70.8). During the 12,391 person-years of follow-up, 657 deaths occurred. For each doubling increase in uPCR, uACR, and uNAP, the adjusted hazard ratios (aHRs) of all-cause mortality were 1.29 (95% confidence interval [CI] 1.24-1.35), 1.12 (1.09-1.16), and 1.41 (1.34-1.49), respectively. For each 10% increase in uAPR, it was 1.02 (95% CI 0.98-1.06). The linear dose-response association with all-cause mortality was only observed with uPCR and uNAP. The 3 × 3 risk matrices revealed that patients with severe proteinuria and normal albuminuria had the highest risk of all-cause mortality (aHR 5.25, 95% CI 1.88, 14.63). uNAP significantly improved the discriminative performance compared to that of uPCR (c statistics: 0.834 vs. 0.828, p-value = 0.032). Our study findings advocate for simultaneous measurements of uPCR and uACR in daily practice to derive uAPR and uNAP, which can provide a better mortality prognostic assessment.

Dialysis timing may be deferred toward very late initiation: An observational study.

The optimal timing to initiate dialysis among patients with an estimated glomerular filtration rate (eGFR) of <5 mL/min/1.73 m2 is unknown. We hypothesized that dialysis initiation time can be deferred in this population even with high uremic burden. A case-crossover study with case (0-30 days before dialysis initiation [DI]) and control (90-120 days before DI) periods was conducted in 1,079 hemodialysis patients aged 18-90 years at China Medical University Hospital between 2006 and 2015. The uremic burden was quantified based on 7 uremic indicators that reached the predefined threshold in case period, namely hemoglobin, serum albumin, blood urea nitrogen, serum creatinine, potassium, phosphorus, and bicarbonate. Dialysis timing was classified as standard (met 0-2 uremic indicators), late (3-5 indicators), and very late (6-7 indicators). Median eGFR-DI of the 1,079 patients was 3.4 mL/min/1.73 m2 and was 2.7 mL/min/1.73 m2 in patients with very late initiation. The median follow-up duration was 2.42 years. Antibiotics, diuretics, antihypertensive medications, and non-steroidal anti-inflammatory drugs (NSAIDs) were more prevalently used during the case period. The fully adjusted hazards ratios of all-cause mortality for the late and very late groups were 0.97 (95% confidence interval 0.76-1.24) and 0.83 (0.61-1.15) compared with the standard group. It is safe to defer dialysis initiation among patients with chronic kidney disease (CKD) having an eGFR of <5 mL/min/1.73 m2 even when patients having multiple biochemical uremic burdens. Coordinated efforts in acute infection prevention, optimal fluid management, and prevention of accidental exposure to NSAIDs are crucial to prolong the dialysis-free survival.

Impact of Enterobius vermicularis infection and mebendazole treatment on intestinal microbiota and host immune response.

BACKGROUND: Previous studies on the association of enterobiasis and chronic inflammatory diseases have revealed contradictory results. The interaction of Enterobius vermicularis infection in particular with gut microbiota and induced immune responses has never been thoroughly examined. METHODOLOGY/FINDINGS: In order to answer the question of whether exposure to pinworm and mebendazole can shift the intestinal microbial composition and immune responses, we recruited 109 (30 pinworm-negative, 79 pinworm-infected) first and fourth grade primary school children in Taichung, Taiwan, for a gut microbiome study and an intestinal cytokine and SIgA analysis. In the pinworm-infected individuals, fecal samples were collected again at 2 weeks after administration of 100 mg mebendazole. Gut microbiota diversity increased after Enterobius infection, and it peaked after administration of mebendazole. At the phylum level, pinworm infection and mebendazole deworming were associated with a decreased relative abundance of Fusobacteria and an increased proportion of Actinobacteria. At the genus level, the relative abundance of the probiotic Bifidobacterium increased after enterobiasis and mebendazole treatment. The intestinal SIgA level was found to be lower in the pinworm-infected group, and was elevated in half of the mebendazole-treated group. A higher proportion of pre-treatment Salmonella spp. was associated with a non-increase in SIgA after mebendazole deworming treatment. CONCLUSIONS/SIGNIFICANCE: Childhood exposure to pinworm plus mebendazole is associated with increased bacterial diversity, an increased abundance of Actinobacteria including the probiotic Bifidobacterium, and a decreased proportion of Fusobacteria. The gut SIgA level was lower in the pinworm-infected group, and was increased in half of the individuals after mebendazole deworming treatment.