Transposon insertion in Rothia dentocariosa.

OBJECTIVES: Rothia spp. are emerging as significant bacteria associated with oral health, with Rothia dentocariosa being one of the most prevalent species. However, there is a lack of studies examining these properties at the genetic level. This study aimed to establish a genetic modification platform for R. dentocariosa. METHODS: Rothia spp. were isolated from saliva samples collected from healthy volunteers. Subsequently, R. dentocariosa strains were identified through colony morphology, species-specific polymerase chain reaction (PCR), and 16S ribosomal RNA gene sequencing. The identified strains were then transformed with plasmid pJRD215, and the most efficient strain was selected. Transposon insertion mutagenesis was performed to investigate the possibility of genetic modifications. RESULTS: A strain demonstrating high transforming ability, designated as R. dentocariosa LX16, was identified. This strain underwent transposon insertion mutagenesis and was screened for 5-fluoroorotic acid-resistant transposants. The insertion sites were confirmed using arbitrary primed PCR, gene-specific PCR, and Sanger sequencing. CONCLUSION: This study marks the first successful genetic modification of R. dentocariosa. Investigating R. dentocariosa at the genetic level can provide insights into its role within the oral microbiome.

Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus.

BACKGROUND: Evaluation of new treatment policies is often costly and challenging in complex conditions, such as hepatitis C virus (HCV) treatment, or in limited-resource settings. We sought to identify hypothetical policies for HCV treatment that could best balance the prevention of cirrhosis while preserving resources (financial or otherwise). METHODS: The cohort consisted of 3792 HCV-infected patients without a history of cirrhosis or hepatocellular carcinoma at baseline from the national Veterans Health Administration from 2015 to 2019. To estimate the efficacy of hypothetical treatment policies, we utilized historical data and reinforcement learning to allow for greater flexibility when constructing new HCV treatment strategies. We tested and compared four new treatment policies: a simple stepwise policy based on Aspartate Aminotransferase to Platelet Ratio Index (APRI), a logistic regression based on APRI, a logistic regression on multiple longitudinal and demographic indicators that were prespecified for clinical significance, and a treatment policy based on a risk model developed for HCV infection. RESULTS: The risk-based hypothetical treatment policy achieved the lowest overall risk with a score of 0.016 (90% CI 0.016, 0.019) while treating the most high-risk (346.4 ± 1.4) and the fewest low-risk (361.0 ± 20.1) patients. Compared to hypothetical treatment policies that treated approximately the same number of patients (1843.7 vs. 1914.4 patients), the risk-based policy had more untreated time per patient (7968.4 vs. 7742.9 patient visits), signaling cost reduction for the healthcare system. CONCLUSIONS: Off-policy evaluation strategies are useful to evaluate hypothetical treatment policies without implementation. If a quality risk model is available, risk-based treatment strategies can reduce overall risk and prioritize patients while reducing healthcare system costs.

Adapted time-varying covariates Cox model for predicting future cirrhosis development performs well in a large hepatitis C cohort.

BACKGROUND: Patients with hepatitis C virus (HCV) frequently remain at risk for cirrhosis after sustained virologic response (SVR). Existing cirrhosis predictive models for HCV do not account for dynamic antiviral treatment status and are limited by fixed laboratory covariates and short follow up time. Advanced fibrosis assessment modalities, such as transient elastography, remain inaccessible in many settings. Improved cirrhosis predictive models are needed. METHODS: We developed a laboratory-based model to predict progression of liver disease after SVR. This prediction model used a time-varying covariates Cox model adapted to utilize longitudinal laboratory data and to account for antiretroviral treatment. Individuals were included if they had a history of detectable HCV RNA and at least 2 AST-to-platelet ratio index (APRI) scores available in the national Veterans Health Administration from 2000 to 2015, Observation time extended through January 2019. We excluded individuals with preexisting cirrhosis. Covariates included baseline patient characteristics and 16 time-varying laboratory predictors. SVR, defined as permanently undetectable HCV RNA after antiviral treatment, was modeled as a step function of time. Cirrhosis development was defined as two consecutive APRI scores > 2. We predicted cirrhosis development at 1-, 3-, and 5-years follow-up. RESULTS: In a national sample of HCV patients (n = 182,772) with a mean follow-up of 6.32 years, 42% (n = 76,854) achieved SVR before 2016 and 16.2% (n = 29,566) subsequently developed cirrhosis. The model demonstrated good discrimination for predicting cirrhosis across all combinations of laboratory data windows and cirrhosis prediction intervals. AUROCs ranged from 0.781 to 0.815, with moderate sensitivity 0.703-0.749 and specificity 0.723-0.767. CONCLUSION: A novel adaptation of time-varying covariates Cox modeling technique using longitudinal laboratory values and dynamic antiviral treatment status accurately predicts cirrhosis development at 1-, 3-, and 5-years among patients with HCV, with and without SVR. It improves upon earlier cirrhosis predictive models and has many potential population-based applications, especially in settings without transient elastography available.

Assessing Clinical Disease Recurrence Using Laboratory Data in Surgically Resected Patients From the TOPPIC Trial.

Background: Machine learning methodologies play an important role in predicting progression of disease or responses to medical therapy. We previously derived and validated a machine learning algorithm to predict response to thiopurines in an inflammatory bowel disease population. We aimed to apply a modified algorithm to predict postsurgical treatment response using clinical trial data. Methods: TOPPIC was a multicenter randomized double-blinded placebo-controlled trial of 240 patients, evaluating the effectiveness of 6-mercaptopurine in preventing or delaying postsurgical Crohn disease recurrence. We adapted a well-established machine learning algorithm to predict clinical recurrence postresection using age and multiple laboratory-specific covariates, and compared this to the thiopurine metabolite, 6-thioguanine. Results: The random forest machine learning algorithm demonstrates a mean under the receiver operator curve (AuROC) of 0.62 [95% confidence interval (CI) 0.47, 0.78]. Similar results were evident when adding thiopurine metabolite (6-thioguanine) results. Alanine aminotransferase/mean corpuscular volume (ALT/MCV) and potassium × alkaline phosphatase (POT × ALK) predicted endoscopic and biologic recurrence, respectively, with AuROCs of 0.714 (95% CI 0.601, 0.827) and 0.730 (95% CI 0.618, 0.841). Conclusions: A machine learning algorithm with laboratory data from within the first 3 months postsurgically does not discriminate clinical recurrence well. Alternative noninvasive measures should be considered and further evaluated.

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

Importance: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission. Objective: To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment. Design, Setting, and Participants: This cohort study analyzed data from 3 phase 3 randomized clinical trials (UNITI-1, UNITI-2, and IM-UNITI) conducted from 2011 to 2015. Participants (n = 401) were individuals with active (C-reactive protein [CRP] measurement of ≥5 mg/L at enrollment) Crohn disease who received ustekinumab therapy. Data analysis was performed from November 1, 2017, to June 1, 2018. Exposures: All included patients were exposed to 1 or more dose of ustekinumab for 8 weeks or more. Main Outcomes and Measures: Random forest methods were used in building 2 models for predicting Crohn disease remission, with a CRP level lower than 5 mg/dL as a proxy for biological remission, beyond week 42 of ustekinumab treatment. The first model used only baseline data, and the second used data through week 8. Results: In total, 401 participants, with a mean (SD) age of 36.3 (12.6) years and 170 male (42.4%), were included. The week-8 model had a mean area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI, 0.69-0.87). In the testing data set, 27 of 55 participants (49.1%) classified as likely to have treatment success achieved success with a CRP level lower than 5 mg/L after week 42, and 7 of 65 participants (10.8%) classified as likely to have treatment failure achieved this outcome. In the full cohort, 87 patients (21.7%) attained remission after week 42. A prediction model using the week-6 albumin to CRP ratio had an AUROC of 0.76 (95% CI, 0.71-0.82). Baseline ustekinumab serum levels did not improve the model's prediction performance. Conclusions and Relevance: In patients with active Crohn disease, demographic and laboratory data before week 8 of treatment appeared to allow the prompt identification of likely nonresponders to ustekinumab without the need for costly drug-level monitoring.

Machine learning models to predict disease progression among veterans with hepatitis C virus.

BACKGROUND: Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data. METHODS AND FINDINGS: We used national Veterans Health Administration (VHA) data to identify CHC patients in care between 2000-2016. The primary outcome was cirrhosis development ascertained by two consecutive aspartate aminotransferase (AST)-to-platelet ratio indexes (APRIs) > 2 after time zero given the infrequency of liver biopsy in clinical practice and that APRI is a validated non-invasive biomarker of fibrosis in CHC. We excluded those with initial APRI > 2 or pre-existing diagnosis of cirrhosis, hepatocellular carcinoma or hepatic decompensation. Enrollment was defined as the date of the first APRI. Time zero was defined as 2 years after enrollment. Cross-sectional (CS) models used predictors at or closest before time zero as a comparison. Longitudinal models used CS predictors plus longitudinal summary variables (maximum, minimum, maximum of slope, minimum of slope and total variation) between enrollment and time zero. Covariates included demographics, labs, and body mass index. Model performance was evaluated using concordance and area under the receiver operating curve (AuROC). A total of 72,683 individuals with CHC were analyzed with the cohort having a mean age of 52.8, 96.8% male and 53% white. There are 11,616 individuals (16%) who met the primary outcome over a mean follow-up of 7 years. We found superior predictive performance for the longitudinal Cox model compared to the CS Cox model (concordance 0.764 vs 0.746), and for the longitudinal boosted-survival-tree model compared to the linear Cox model (concordance 0.774 vs 0.764). The accuracy of the longitudinal models at 1,3,5 years after time zero also showed superior performance compared to the CS model, based on AuROC. CONCLUSIONS: Boosted-survival-tree based models using longitudinal information are statistically superior to cross-sectional or linear models for predicting development of cirrhosis in CHC, though all four models were highly accurate. Similar statistical methods could be applied to predict outcomes in other non-linear chronic disease states.

Predicting Corticosteroid-Free Biologic Remission with Vedolizumab in Crohn's Disease.

Background and Aims: Vedolizumab (VDZ) is effective for Crohn's disease (CD) but costly and is slow to produce remission. Early knowledge of whether vedolizumab is likely to succeed is valuable for physicians, patients, and insurers. Methods: Phase 3 clinical trial data on VZD for CD were used to predict outcomes. Random forest modeling on the training cohort was used to predict the outcome of corticosteroid-free biologic remission at week 52 on the testing cohort. Models were constructed using baseline data, or data through week 6 of VDZ therapy. Results: The clinical trial included 594 subjects who received VDZ with baseline active inflammation [elevated C-reactive protein (>5 mg/L)]. Subjects with missing predictor variables (N = 120) or missing outcome data (N = 2) were excluded to produce a modeling dataset of 472 subjects. The Area Under the Receiver Operating Characteristic curve (AuROC) for corticosteroid-free biologic remission at week 52 using baseline data was only 0.65 (95% CI: 0.53 - 0.77), but was 0.75 (95% CI: 0.64 - 0.86) with data through week 6 of VDZ . Patients predicted to be in corticosteroid-free biologic remission at week 52 by the model achieved this endpoint 35.8% of the time, whereas patients predicted to fail only succeeded 6.7% of the time. Conclusions: An algorithm using laboratory data through week 6 of VDZ therapy was able to identify which CD patients with baseline inflammation would achieve corticosteroid-free biologic remission on VDZ at week 52. A majority of patients can be identified by week 6 as very unlikely to achieve remission.

Predicting Hospitalization and Outpatient Corticosteroid Use in Inflammatory Bowel Disease Patients Using Machine Learning.

Background: Inflammatory bowel disease (IBD) is a chronic disease characterized by unpredictable episodes of flares and periods of remission. Tools that accurately predict disease course would substantially aid therapeutic decision-making. This study aims to construct a model that accurately predicts the combined end point of outpatient corticosteroid use and hospitalizations as a surrogate for IBD flare. Methods: Predictors evaluated included age, sex, race, use of corticosteroid-sparing immunosuppressive medications (immunomodulators and/or anti-TNF), longitudinal laboratory data, and number of previous IBD-related hospitalizations and outpatient corticosteroid prescriptions. We constructed models using logistic regression and machine learning methods (random forest [RF]) to predict the combined end point of hospitalization and/or corticosteroid use for IBD within 6 months. Results: We identified 20,368 Veterans Health Administration patients with the first (index) IBD diagnosis between 2002 and 2009. Area under the receiver operating characteristic curve (AuROC) for the baseline logistic regression model was 0.68 (95% confidence interval [CI], 0.67-0.68). AuROC for the RF longitudinal model was 0.85 (95% CI, 0.84-0.85). AuROC for the RF longitudinal model using previous hospitalization or steroid use was 0.87 (95% CI, 0.87-0.88). The 5 leading independent risk factors for future hospitalization or steroid use were age, mean serum albumin, immunosuppressive medication use, and mean and highest platelet counts. Previous hospitalization and corticosteroid use were highly predictive when included in specified models. Conclusions: A novel machine learning model substantially improved our ability to predict IBD-related hospitalization and outpatient steroid use. This model could be used at point of care to distinguish patients at high and low risk for disease flare, allowing individualized therapeutic management.

Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

OBJECTIVE: Assessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort. DESIGN: Adults with CHC with ≥3 years follow-up and without hepatic decompensation, hepatocellular carcinoma (HCC), liver transplant (LT), HBV or HIV co-infection at presentation were analyzed (N = 1007). Outcomes included: 1) fibrosis progression 2) hepatic decompensation 3) HCC and 4) LT-free survival. Predictors included longitudinal clinical and laboratory data. Machine learning methods were used to predict outcomes in 1 and 3 years. RESULTS: The external cohort had a median age of 49.4 years (IQR 44.3-54.3); 61% were male, 80% white, and 79% had genotype 1. At presentation, 73% were treatment naïve and 31% had cirrhosis. Fibrosis progression occurred in 34% over a median of 4.9 years (IQR 3.2-7.6). Clinical outcomes occurred in 22% over a median of 4.4 years (IQR 3.2-7.6). Model performance for fibrosis progression was limited due to small sample size. The area under the receiver operating characteristic curve (AUROC) for 1 and 3-year risk of clinical outcomes was 0.78 (95% CI 0.73-0.83) and 0.76 (95% CI 0.69-0.81). CONCLUSION: Accurate assessments for risk of clinical outcomes can be obtained using routinely collected data across a heterogeneous cohort of patients with CHC. These methods can be applied to predict risk of progression in other chronic liver diseases.

Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines.

BACKGROUND AND AIMS: Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease [IBD] has proved difficult. Current methods using 6-thioguanine nucleotide [6-TGN] metabolites have failed in randomized controlled trials [RCTs], and have not been used to predict objective remission [OR]. Our aims were to: 1) develop machine learning algorithms [MLA] using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted objective remission resulted in fewer clinical events per year. METHODS: Objective remission was defined as the absence of objective evidence of intestinal inflammation. MLAs were developed to predict three outcomes: objective remission, non-adherence, and preferential shunting to 6-methylmercaptopurine [6-MMP]. The performance of the algorithms was evaluated using the area under the receiver operating characteristic curve [AuROC]. Clinical event rates of new steroid prescriptions, hospitalisations, and abdominal surgeries were measured. RESULTS: Retrospective review was performed on medical records of 1080 IBD patients on thiopurines. The AuROC for algorithm-predicted remission in the validation set was 0.79 vs 0.49 for 6-TGN. The mean number of clinical events per year in patients with sustained algorithm-predicted remission [APR] was 1.08 vs 3.95 in those that did not have sustained APR [p < 1 x 10-5]. Reductions in the individual endpoints of steroid prescriptions/year [-1.63, p < 1 x 10-5], hospitalisations/year [-1.05, p < 1 x 10-5], and surgeries/year [-0.19, p = 0.065] were seen with algorithm-predicted remission. CONCLUSIONS: A machine learning algorithm was able to identify IBD patients on thiopurines with algorithm-predicted objective remission, a state associated with significant clinical benefits, including decreased steroid prescriptions, hospitalisations, and surgeries.

Handheld multispectral fluorescence lifetime imaging system for in vivo applications.

There is an increasing interest in the application of fluorescence lifetime imaging (FLIM) for medical diagnosis. Central to the clinical translation of FLIM technology is the development of compact and high-speed clinically compatible systems. We present a handheld probe design consisting of a small maneuverable box fitted with a rigid endoscope, capable of continuous lifetime imaging at multiple emission bands simultaneously. The system was characterized using standard fluorescent dyes. The performance was then further demonstrated by imaging a hamster cheek pouch in vivo, and oral mucosa tissue both ex vivo and in vivo, all using safe and permissible exposure levels. Such a design can greatly facilitate the evaluation of FLIM for oral cancer imaging in vivo.