Predictors Of Treatment Failure Research Articles

The failure of biological treatment in axial spondyloarthritis is linked to the factors related to increased intestinal permeability and dysbiosis: prospective observational cohort study.

A significant number of patients with axial spondyloarthritis (axSpA) do not respond to biological therapy. Therefore, we decided to investigate the specificity of this group of patients and, in particular, whether haptoglobin (Hp), its polymorphism and zonulin, in addition to other clinical features, are predictors of poor response to biological treatment. 48 patients with axSpA who were unsuccessfully treated with standard drugs were converted to biological treatment, and from this time on, a 12-week follow-up was started to assess the failure of biological treatment (Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) decrease < 2 points). Predictors of treatment failure were identified using logistic regression analysis. 21% of subjects had biological treatment failure. Patients who had a higher zonulin level, a history of frequent infections, were older, had inflammatory bowel disease (IBD), had a lower Hp level at the time of inclusion in biological therapy showed an increased risk of treatment failure. The results of the study support the hypothesis that the effectiveness of biological treatment of axSpA is limited by changed microbiota and intestinal epithelial barrier dysfunction, as an increased risk of biological treatment failure was observed in patients who were older, had higher zonulinlevel, IBD and repeated courses of antibiotics due to frequent infections. Therefore, starting biological treatment should be followed by reducing intestinal permeability and regulating the disturbed gut microbiome.

Second-line anti-retroviral treatment failure and its predictors among patients with HIV in Ethiopia: A systematic review and meta-analysis.

Antiretroviral therapy (ART) treatment failure remains a major public health concern, with multidimensional consequences, including an increased risk of drug resistance, compromised quality of life, and high healthcare costs. However, little is known about the outcomes of second-line ART in Ethiopia. Therefore, this systematic review and meta-analysis aimed to determine the incidence and determinants of second-line ART treatment failure. Articles published in PubMed, Google Scholar, Science Direct, and Scopus databases were systematically searched. All observational studies on the incidence and predictors of treatment failure among patients with HIV on second-line ART were included. A random-effects model was used to estimate the pooled incidence, and subgroup analysis was performed to identify the possible sources of heterogeneity. Publication bias was checked using forest plot, Begg's test, and Egger's test. The pooled odds ratio was also computed for associated factors. Seven studies with 3,962 study participants were included in this study. The pooled incidence of second-line antiretroviral treatment failure was 5.98 (95% CI: 4.32, 7.63) per 100 person-years of observation. Being in the advanced WHO clinical stage at switch (AHR = 2.98, 95% CI: 2.11, 4.25), having a CD4 count <100 cells/mm3 (AHR = 2.14, 95% CI: 1.57, 2.91), poor drug adherence (AHR = 1.78, 95% CI: 1.4, 2.25), and tuberculosis co-infection (AHR = 2.93, 95% CI: 1.93, 4.34) were risk factors for treatment failure. In conclusion, this study revealed that that out of 100 person-years of follow-up, an estimated six patients with HIV who were on second-line antiretroviral therapy experienced treatment failure. The risk of treatment failure was higher in patients who were in an advanced WHO clinical stage, CD4 count <100 cells/mm3, and presence tuberculosis co-infection. Therefore, addressing predictors reduces the risk of treatment failure and maximizes the duration of stay in second-line regimens.

The efficacy of machine learning models in forecasting treatment failure in thoracolumbar burst fractures treated with short-segment posterior spinal fixation

BackgroundAlthough short-segment posterior spinal fixation (SSPSF) has shown promising clinical outcomes in thoracolumbar burst fractures, the treatment may be prone to a relatively high failure rate. This study aimed to assess the effectiveness of machine learning models (MLMs) in predicting factors associated with treatment failure in thoracolumbar burst fractures treated with SSPSF.MethodsA retrospective review of 332 consecutive patients with traumatic thoracolumbar burst fractures who underwent SSPSF at our institution between May 2016 and May 2023 was conducted. Patients were categorized into two groups based on treatment outcome (failure or non-failure). Potential risk factors for treatment failure were compared between the groups. Four MLMs, including random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighborhood (k-NN), were employed to predict treatment failure. Additionally, LR and RF models were used to assess factors associated with treatment failure.ResultsOf the 332 included patients, 61.4% were male (n = 204), and treatment failure was observed in 44 patients (13.3%). Logistic regression analysis identified Load Sharing Classification (LSC) score, lack of index level instrumentation, and interpedicular distance (IPD) as factors associated with treatment failure (P < 0.05). All models demonstrated satisfactory performance. RF exhibited the highest accuracy in predicting treatment failure (accuracy = 0.948), followed by SVM (0.933), k-NN (0.927), and LR (0.917). Moreover, the RF model outperformed other models in terms of sensitivity and specificity (sensitivity = 0.863, specificity = 0.959). The area under the curve (AUC) for RF, LR, SVM, and k-NN was 0.911, 0.823, 0.844, and 0.877, respectively.ConclusionsThis study demonstrated the utility of machine learning models in predicting treatment failure in thoracolumbar burst fractures treated with SSPSF. The findings support the potential of MLMs to predict treatment failure in this patient population, offering valuable prognostic information for early intervention and cost savings.

Patient-Related Predictors of Treatment Failure after Two-Stage Total Hip Arthroplasty Revision for Periprosthetic Joint Infection: A Systematic Review and Meta-Analysis

IntroductionPeriprosthetic joint infection (PJI) treatment has high failure rates even after two-stage revision. Risk factors for treatment failure after staged revision for PJI are not well defined, nor is it well established how they correlate with the risks of developing an index PJI. Identifying modifiable risk factors may allow preoperative optimization, while identifying non-modifiable risk factors can influence surgical options or advise against further surgery. We performed a systematic review and meta-analysis to better define predictors of treatment failure in two-stage revision for PJI. MethodsThe Pubmed, Embase, and Scopus databases were searched from their inception in December 1976 to April 15, 2023. Studies comparing patient-related variables between patients successfully treated who had two-staged revision total hip arthroplasty and patients with persistent infections were included. Studies were screened, and two independent reviewers extracted data, while a third resolved discrepancies. Meta-analysis was performed on these data. There were 10,052 unique studies screened, and 21 studies met the inclusion criteria for data extraction. ResultsThere was good-quality evidence that obesity, liver cirrhosis, and previous failed revisions for PJI are non-modifiable risk factors, while intravenous drug use and smoking are modifiable risk factors for treatment failure after two-stage revision for hip PJI. Reoperation between revision stages was also significantly associated with an increased risk of treatment failure. Interestingly, other risk factors for an index PJI including male gender, American Society of Anaesthesiology score, diabetes mellitus and inflammatory arthropathy did not predict treatment failure. Evidence on Charlson Comorbidity Index was limited. DiscussionPatients with a smoking history, obesity, intravenous drug use, previous failed revision for PJI, reoperation between stages and LC are more likely to experience TF after two-stage revision THA for PJI. Modifiable risk factors include smoking and IVDU and these patients should be referred to services for cessation as early as possible before two-stage revision THA.

Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity

The present study aims to assess the treatment outcome of patients with diabetes and tuberculosis (TB-DM) at an early stage using machine learning (ML) based on electronic medical records (EMRs). A total of 429 patients were included at Chongqing Public Health Medical Center. The random-forest-based Boruta algorithm was employed to select the essential variables, and four models with a fivefold cross-validation scheme were used for modeling and model evaluation. Furthermore, we adopted SHapley additive explanations to interpret results from the tree-based model. 9 features out of 69 candidate features were chosen as predictors. Among these predictors, the type of resistance was the most important feature, followed by activated partial throm-boplastic time (APTT), thrombin time (TT), platelet distribution width (PDW), and prothrombin time (PT). All the models we established performed above an AUC 0.7 with good predictive performance. XGBoost, the optimal performing model, predicts the risk of treatment failure in the test set with an AUC 0.9281. This study suggests that machine learning approach (XGBoost) presented in this study identifies patients with TB-DM at higher risk of treatment failure at an early stage based on EMRs. The application of a convenient and economy EMRs based on machine learning provides new insight into TB-DM treatment strategies in low and middle-income countries.

Predictors of Treatment Failure in Young, Non-Smoking, Non-Drinking Patients with Squamous Cell Carcinoma of the Oral Tongue

Predictors of Treatment Failure in Young, Non-Smoking, Non-Drinking Patients with Squamous Cell Carcinoma of the Oral Tongue

Focal high-intensity focused ultrasound therapy for localized prostate cancer: An interim analysis of the multinational FASST study.

High-intensity focused ultrasound (HIFU) emerged as a novel approach for the treatment of localized prostate cancer (PCa). However, prospective studies on HIFU-related outcomes and predictors of treatment failure (TF) remain scarce. We conducted a multinational prospective cohort study among patients undergoing HIFU therapy for localized, low- to intermediate-risk PCa. Follow-up data on serial prostate specific antigen (PSA), multi-parametric magnetic resonance imaging (mpMRI), targeted/systematic biopsies, adverse events and functional outcomes were collected. The primary endpoint was TF, defined as histologically confirmed PCa requiring whole-gland salvage treatment. Uni- and multi-variable adjusted hazard ratios (HRs) were calculated using Cox proportional hazard regression models. At baseline, mean (standard deviation) age was 64.14 (7.19) years, with the majority of patients showing T-stage 1 (73.9%) and International Society of Urological Pathology grading system Grade 2 (58.8%). PSA nadir (median, 1.70 ng/mL) was reached after 6 months. Of all patients recruited, 16% had clinically significant PCa, as confirmed by biopsy, of which 13.4% had TF. Notably, T-stage and number of positive cores at initial biopsy were independent predictors of TF during follow-up (HR [95% CI] 1.27 [1.02-1.59] and 5.02 [1.80-14.03], respectively). Adverse events were minimal (17% and 8% early and late adverse events, respectively), with stable or improved functional outcomes in the majority of patients. This interim analysis of a multinational study on HIFU therapy for the management of low-to-intermediate-risk PCa reveals good functional outcomes, minimal adverse events and low incidence of TF over the short-term. Data on long-term outcomes, specifically as it relates to oncological outcomes, are awaited eagerly.

Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD.

Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) exhibit heterogeneous responses to corticosteroid treatment. We aimed to determine whether combining eosinophil levels with other routine clinical indicators can enhance the predictability of corticosteroid treatment outcomes and to come up with a scoring system. Consecutive patients admitted with AECOPD receiving corticosteroid treatment between July 2013 and March 2022 at Beijing Chao-Yang Hospital were retrospectively analyzed. Data on patients' demographics, smoking status, hospitalization for AECOPD in the previous year, comorbidities, blood laboratory tests, in-hospital treatment and clinical outcomes were collected. Least absolute shrinkage and selection operator (LASSO) regression and backward logistic regression were used for predictor selection, and predictive nomograms were developed. The discrimination and calibration of the nomograms were assessed using the area under the receiver operating curve (AUC) and calibration plots. Internal validation was performed using the 500-bootstrap method, and clinical utility was evaluated using decision curve analysis (DCA). Among the 3254 patients included, 804 (24.7%) had treatment failure. A nomogram of eosinophils, platelets, C-reactive protein (CRP), low density lipoprotein cholesterol, prognostic nutritional index (PNI), hospitalization for AECOPD in the previous year, ischemic heart diseases and chronic hepatic disease was developed to predict treatment failure for patients with a smoking history. For patients without a smoking history, a nomogram of CRP, PNI, ischemic heart diseases and chronic hepatic disease was developed. Although the AUCs of these two nomograms were only 0.644 and 0.647 respectively, they were significantly superior to predictions based solely on blood eosinophil levels. We developed easy-to-use comprehensive nomograms utilizing readily available clinical biomarkers related to inflammation, nutrition and immunity, offering modestly enhanced predictive value for treatment outcomes in corticosteroid-treated patients with AECOPD. Further investigations into novel biomarkers and additional patient data are imperative to optimize the predictive performance.

Do one-stage indications predict success following two-stage arthroplasty for chronic periprosthetic joint infection?

Introduction: The 2018 International Consensus Meeting (ICM) proposed criteria for one-stage exchange arthroplasty in treating periprosthetic joint infection (PJI). Our study aimed to determine what proportion of PJI patients met the 2018 ICM criteria and how this affected infection-free survivorship for patients. Methods: All chronic PJI patients treated with two-stage exchange within our institution between 2017-2020 were retrospectively reviewed. Included cases met 2011 Musculoskeletal Infection Society (MSIS) criteria for PJI and had a 2-year minimum follow-up. Treatment success was defined as Tier 1A in the 2019 MSIS working group definition. ICM one-stage criteria included non-immunocompromised host, absence of sepsis, adequate soft tissue for closure, known preoperative pathogen, and susceptibility. Immunocompromised host was analyzed as two separate definitions. Kaplan-Meier survivorship, Cox regression, and univariate analyses were performed. Results: A total of 293 chronic PJI patients were included. Overall, treatment failure occurred in (21.8 %) patients. Only 13 % () met ICM criteria definition no.1 for one-stage exchange; 12 % () met definition no.2. In both definitions, infection-free survivorship at 2years did not differ between patients who met and did not meet criteria (). Cox proportional hazard regression analyses demonstrated that the only variable predicting treatment failure was knee joint involvement (). Conclusions: We found that a very limited number of chronic PJI patients were suitable for a one-stage exchange. Furthermore, the supposition that healthier hosts with known pathogens (the basis of the ICM criteria) yield better PJI treatment outcomes was not observed. These results justify the ongoing multicenter randomized control trial comparing one-stage versus two-stage treatment for chronic PJI.

18F-FDG-PET/CT response after first-line treatment as a prognostic factor for survival in peripheral T-cell lymphoma: a Spanish retrospective study

ABSTRACT Background An accurate assessment of tumor viability after first-line treatment is critical for predicting treatment failure in peripheral T-cell lymphomas (PTCLs). 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) has been adopted as the preferred assessment method in clinical trials, but its impact in clinical practice should be examined. This study aims to determine the prognostic significance of18F-FDG-PET/CT for survival following first-line treatment in PTCL patients. Research design and methods Retrospective observational study including 175 patients diagnosed with PTCL between 2008 and 2013 in 13 Spanish sites. Results Fifty patients were evaluated with18F-FDG-PET/CT following first-line therapy: 58% were18F-FDG-PET/CT-negative and 42% were18F-FDG-PET/CT-positive. Disease progression occurred in 37.9% of18F-FDG-PET/CT-negative patients and in 80.9% of18F-FDG-PET/CT-positive patients (p = 0.0037). Median progression-free survival and overall survival were 67 and 74 months for18F-FDG-PET/CT-negative patients, and 5 (p < 0.0001) and 10 months (p < 0.0001), respectively, in18F-FDG-PET/CT-positive patients. After multivariate analysis, only B symptoms emerged as a negative predictive factor of complete response (RR 7.08; 95% CI 1.60–31.31; p = 0.001). Conclusions 18F-FDG-PET/CT identifies high-risk PTCL patients who will have poor prognosis and survival following first-line treatment. However, more research is needed to confirm the best treatment options for PTCL patients.

External validation of the HACOR score and ROX index for predicting treatment failure in patients with coronavirus disease 2019 pneumonia managed on high-flow nasal cannula therapy: a multicenter retrospective observational study in Japan

BackgroundThe HACOR score for predicting treatment failure includes vital signs and acid–base balance factors, whereas the ROX index only considers the respiratory rate, oxygen saturation, and fraction of inspired oxygen (FiO2). We aimed to externally validate the HACOR score and ROX index for predicting treatment failure in patients with coronavirus disease 2019 (COVID-19) on high-flow nasal cannula (HFNC) therapy in Japan.MethodsThis retrospective, observational, multicenter study included patients, aged ≥ 18 years, diagnosed with COVID-19 and treated with HFNC therapy between January 16, 2020, and March 31, 2022. The HACOR score and ROX index were calculated at 2, 6, 12, 24, and 48 h after stating HFNC therapy. The primary outcome was treatment failure (requirement for intubation or occurrence of death within 7 days). We calculated the area under the receiver operating characteristic curve (AUROC) and assessed the diagnostic performance of these indicators. The 2-h time-point prediction was considered the primary analysis and that of other time-points as the secondary analysis. We also assessed 2-h time-point sensitivity and specificity using previously reported cutoff values (HACOR score > 5, ROX index < 2.85).ResultsWe analyzed 300 patients from 9 institutions (median age, 60 years; median SpO2/FiO2 ratio at the start of HFNC therapy, 121). Within 7 days of HFNC therapy, treatment failure occurred in 127 (42%) patients. The HACOR score and ROX index at the 2-h time-point exhibited AUROC discrimination values of 0.63 and 0.57 (P = 0.24), respectively. These values varied with temporal changes—0.58 and 0.62 at 6 h, 0.70 and 0.68 at 12 h, 0.68 and 0.69 at 24 h, and 0.75 and 0.75 at 48 h, respectively. The 2-h time-point sensitivity and specificity were 18% and 91% for the HACOR score, respectively, and 3% and 100% for the ROX index, respectively. Visual calibration assessment revealed well calibrated HACOR score, but not ROX index.ConclusionsIn COVID-19 patients receiving HFNC therapy in Japan, the predictive performance of the HACOR score and ROX index at the 2-h time-point may be inadequate. Furthermore, clinicians should be mindful of time-point scores owing to the variation of the models’ predictive performance with the time-point.Trial registration UMIN (registration number: UMIN000050024, January 13, 2023)

Longitudinal Measurements of Blood Biomarkers in Patients with Crohn's Disease or Ulcerative Colitis Treated with Infliximab: Only the Latest Values in the Induction Period Predict Treatment Failure.

Background: Few studies have incorporated longitudinal assessments or used combinations of blood biomarkers as predictors of loss of response to biologic therapy for patients with Crohn's disease (CD) or ulcerative colitis (UC). Methods: This is a population-based cohort study comprising Danish patients with CD or UC from 2008 to 2018. We used logistic regression to analyze whether levels and changes in levels of C-reactive protein (CRP), serum albumin, and hemoglobin, routinely measured during a 14-week infliximab induction period, predicted a change to another biologic medication or cessation of biologic therapy. Results: During the induction period, 2883 (1626 CD, 1257 UC) patients had 12,730, 12,040, and 13,538 specimens with CRP, serum albumin, and hemoglobin, respectively. In all, 284 patients (9.9%) switched to another biologic medication, and 139 (4.8%) ceased biologic therapy in the follow-up period. Only the most recent CRP and hemoglobin levels predicted the efficacy of infliximab treatment at approximately 14 weeks, a time point when the clinician often determines whether to continue treatment. Conclusion: Measurement of blood biomarkers prior to the clinical assessment does not predict the effectiveness of infliximab.

Predictors of Treatment Failure and Mortality among Patients with Septic Shock Treated with Meropenem in the Intensive Care Unit.

The aim of the study was to determine the predictors of meropenem treatment failure and mortality in the Intensive Care Unit (ICU). This was a retrospective study, involving sepsis and septic shock patients who were admitted to the ICU and received intravenous meropenem. Treatment failure is defined as evidence of non-resolved fever, non-reduced total white cell (TWC), non-reduced C-reactive protein (CRP), subsequent culture negative and death in ICU. An Acute Physiology and Chronic Health Evaluation II (APACHE II) and duration of antibiotic treatment less than 5 days were associated with treatment failure with adjusted OR = 1.24 (95% CI: 1.15, 1.33; P < 0.001), OR = 65.43 (95% CI: 21.70, 197.23; P < 0.001). A higher risk of mortality was observed with higher APACHE and Sequential Organ Failure Assessment (SOFA) scores, initiating antibiotics > 72 h of sepsis, duration of antibiotic treatment less than 5 days and meropenem with renal adjustment dose with an adjusted OR = 1.21 (95% CI: 1.12, 1.30; P < 0.001), adjusted OR = 1.23 (95% CI: 1.08, 1.41; P < 0.001), adjusted OR = 6.38 (95% CI: 1.67, 24.50; P = 0.007), adjusted OR = 0.03 (95% CI: 0.01, 0.14; P < 0.001), adjusted OR = 0.30 (95% CI: 0.14, 0.64; P = 0.002). A total of 50 (14.12%) patients had a treatment failure with meropenem with 120 (48.02%) ICU mortality. The predictors of meropenem failure are higher APACHE score and shorter duration of meropenem treatment. The high APACHE, high SOFA score, initiating antibiotics more than 72 h of sepsis, shorter duration of treatment and meropenem with renal adjustment dose were predictors of mortality.

Alexithymia and depression may predict treatment failure in patients undergoing Gamma Knife radiosurgery for trigeminal neuralgia: the potential contribution of colored pain drawings.

Gamma Knife radiosurgery (GKRS) effectively achieves high rates of pain control in trigeminal neuralgia (TN); however, psychological factors can also impact how individuals perceive pain and respond to treatment. The authors aimed to measure the effects of alexithymia and depression on GKRS outcomes, as well as the potential role of colored pain drawings (CPDs) in identifying patients who require additional psychological support. Seventy-three medically intractable, typical patients with TN were included. Participants completed a visual analog scale (VAS), the Toronto Alexithymia Scale (TAS-20), and the Turkish version of the Beck Depression Inventory-II (BDI-II). Participants used colored pens to draw their pain patterns on standardized face charts and CPDs were categorized as expected or unexpected. Based on the Barrow Neurological Institute pain scale, patients were categorized as responders or nonresponders. Most patients (63%) were female, and the median age was 60 (range 27-88) years. Of 73 patients, 56 (76.7%) were responders and 17 (23.3%) were nonresponders. Forty-nine patients (67.1%) had expected CPDs, whereas 24 (32.9%) had unexpected CPDs. Responder and nonresponder groups had similar ages, genders, education, comorbidities, and initial VAS scores. Both groups preferred the colors red and black to describe intense pain (79.2% vs 67.3%, p = 0.411). Significantly better pain relief was observed in patients with expected CPDs than in those with unexpected CPDs (87.8% vs 54.2%, p = 0.003). The unexpected CPD group had significantly more alexithymia (58.3% vs 32.7%, p = 0.045). The mean BDI-II score of the cohort was 26 (range 15-37) and was significantly higher in the unexpected group (28.3 vs 24.8, p = 0.028). Unexpected CPD (OR 12.540) and higher TAS-20 score (OR 3.22) increased treatment failure risk. The outcomes of TN treatment can be influenced by psychological factors, and patients with TN with higher total TAS-20 and BDI-II scores, along with unexpected CPDs, had an increased likelihood of treatment failure. CPDs can be accessed quickly and may allow the physician to screen out most patients with unfavorable psychometrics and proceed with the necessary treatment with appropriate psychological support.

Application of behavior data to predictive exploratory models of metritis self-cure and treatment failure in dairy cows

The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated- health monitoring device (AHMD) from -21 ± 3 to 60 ± 3 d relative to calving to monitor rumination and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 DIM. Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD) were randomly allocated to one of 2 treatments: Control (CON; n = 62) - no treatment at the time of metritis diagnosis (d 0); Ceftiofur (CEF; n = 70) - subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and days in milk at metritis diagnosis) and behavior data (i.e., daily averages of rumination, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with a P ≤ 0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, sensitivity (Se) 87% and specificity (Sp) 71%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, Se of 93% and Sp of 82%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions.

P337 Proactive therapeutic drug monitoring of Adalimumab, but not HLADQ*A1 determination, predicts inflammatory bowel disease outcome and persistence of treatment

Abstract Background Anti-TNF drugs have proven effective in managing Inflammatory Bowel Disease (IBD), but a significant percentage of patients exhibit primary or secondary non-response. Therapeutic drug monitoring (TDM) is a valuable tool for optimizing patient management. HLA-DQA1*05 gene has been investigated as a potential marker for immunogenicity and loss of response to anti-TNF therapy.This prospective, multicentric study aimed to assess the clinical impact of determining Adalimumab (ADA) levels and anti-adalimumab antibodies (Abs) in IBD patients and the influence of the HLA-DQA1*05 allele on the response and maintenance of ADA treatment over a 24-month follow-up. Methods Patients initiating ADA treatment between October 2020 and October 2022 were enrolled. Demographic data and IBD characteristics were recorded. Clinical Activity Index, C reactive protein (CRP), faecal calprotectin (FC), endoscopy or radiological activity, Global Physician Assesment (GPA) and PRO (by IBDQ-9) were prospectively collected at baseline and at the same time that ADA levels: week 4, 10, 30 and 12 months. HLA-DQA1*05 was genotyped at baseline. Pearson coefficient was utilized to assess correlations between ADA levels and disease activity. Results We included 170 (age 42±17 years, 54% female) IBD patients (74% Crohn`s Disease, 24% Ulcerative Colitis and 2% Indeterminate colitis) who started ADA (85% naïve to anti-TNF). At baseline, 80% had an active disease (GPA=1); CRP and FC were 10.72 ±17.85 mg/L and 568 ±775 µg/g; PRO at baseline was 63±8.6. HLA was determined in 167 patients, of whom 71 (42%) were HLA-DQA1*05 positives. Median follow up was 17 months. No correlation between HLA and drug levels or Abs was founded. ADA levels at the end of induction was correlated with ADA levels during follow up. ADA serum concentration &amp;gt;7 µg/mL at weeks 4 and 10 correlated with improved clinical response at corresponding time points (p&amp;lt;0.001). Patients with ADA levels &amp;gt;7 µg/mL at week 10 demonstrated better disease control at 12 months. 41 patients stopped ADA. It was associated with active disease, low ADA levels, and adverse clinical and laboratory markers. HLA-DQA1*05 positivity did not correlate with adverse reactions or Abs during the observational period. A significative improvement in PROs in patients who started treatment with ADAL (62.73 at baseline and 69.42 at week 30) (p&amp;lt;0.001) was observed. Conclusion Early Drug level monitoring of Adalimumab may improve the management of IBD patients. PCR, CF and GPA at week 10 may predict treatment failure. ADA levels &amp;gt;7 µg/mL is associated with a better clinical response in short and long-term, independent of HLADQA1*05.

Thyroid hormone may predict treatment failure in Kawasaki disease.

In systemic inflammatory conditions, inflammatory cytokines can cause low thyroid hormone levels. There are no reports discussing the relation between thyroid hormone levels and response to treatment for Kawasaki disease. We investigated 67 patients who underwent treatment in the acute phase of Kawasaki disease. We divided patients into two groups based on their response to initial intravenous immunoglobulin (IVIG) treatment: the responder group (n = 40), and the non-responder group (n = 27). The serum levels of the thyroid hormones free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH) were compared before and after treatment in all patients, and between responder and non-responder groups. The FT3, FT4, and TSH levels were low before the initial treatment and increased significantly after treatment (p < 0.05). The FT3, FT4, and TSH levels before treatment were significantly lower in the non-responder group than in the responder group (p < 0.05). Logistic regression analysis suggested that the addition of pre-treatment FT4 values to Gunma score was useful in predicting treatment failure. Thyroid hormone and TSH levels were lower in the non-responder group than in the responder group in the initial IVIG treatment for Kawasaki disease. This study suggests that Kawasaki disease in the acute phase is associated with low thyroid hormone levels and TSH. It is possible that these hormone levels predict response to the initial IVIG.

Machine learning models in predicting failure of Helicobacter pylori treatment: A two country validation study

AbstractBackgroundThe success rate of clarithromycin‐containing Helicobacter pylori treatment had declined globally. This study aims to explore the role of different machine learning algorithms in predicting failure of H. pylori treatment.Materials and MethodsWe included 84,609 adult patients who had received the first course of clarithromycin‐containing triple therapy for H. pylori in Hong Kong from 2003 to 2013 as training set. Results were validated in patients who had received similar triple therapy with 27,736 Hong Kong patients between 2014 and 2017 (internal cohort); and 18,050 UK patients between 2012 and 2017 (external cohort). The performance of 11 available machine learning algorithms were used to predict the failure of triple therapy. The performance was determined by the area under receiver operating characteristic curve (AUC).ResultsThe treatment failure rates in the training, internal and external validation cohort was 5.9%, 9.5%, and 6.1%, respectively. In the internal validation set, Extra‐Tree (ET) Classifier had the best AUC (0.88; 95% CI, 0.87–0.88), sensitivity (79.6%; 95% CI, 79.0–80.2) and specificity (79.4%; 95% CI, 79.0–79.8). In the external validation set, ET Classifier also had the best AUC (0.85; 95% CI, 0.85–0.86), sensitivity (80.1%; 95% CI, 79.5–80.9), and specificity (80.2%; 95% CI, 78.8–81.3). Top features of importance used by ET Classifier in predicting treatment failure included time interval between antibiotic use and triple therapy (48.8%), age (29.1%) and triple therapy regime (6.28%).ConclusionsMachine learning algorithm, based on simple baseline clinical parameters, could help to identify patients at high risk of failure from clarithromycin‐containing triple therapy for H. pylori.

Incidence and Predictors of Treatment Failure among Adult on First-Line Antiretroviral Therapy in Gullele Sub City, Addis Ababa, Ethiopia 2023: A Retrospective Study in Selected Public Health Facilities

Incidence and Predictors of Treatment Failure among Adult on First-Line Antiretroviral Therapy in Gullele Sub City, Addis Ababa, Ethiopia 2023: A Retrospective Study in Selected Public Health Facilities

Plasma miRNAs and Treatment Failure in Participants in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) Study.

To identify plasma miRNAs related to treatment failure in youth with type 2 diabetes (T2D). We examined whether a panel of miRNAs could predict treatment failure in training/test data sets among participants in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study (N = 209). We also examined whether individual miRNAs were associated with treatment failure. Participants were age 14.5 years, and 62% were female. A panel of miRNAs did not predict treatment failure. However, for each doubling, miR-4306 was associated with a 12% decrease (P = 0.040) and miR-483-3p was marginally associated with a 12% increase (P = 0.080) in failure independently of sex, race/ethnicity, BMI, Tanner stage, HbA1c, maternal diabetes, oral disposition index, and treatment arm. The addition of both miRNAs improved model fit (log likelihood without vs. with miRNAs -360.3 vs. -363.5; P = 0.040). miR-483-3p and miR-4306 may be associated with treatment failure in youth with T2D.