Safety of Biologic and Small Molecule Therapy for Inflammatory Bowel Disease Among Solid Organ Transplant Recipients: Systematic Review and Meta-Analysis.

BACKGROUND: Patients undergoing organ transplantation are often on immunosuppressing medications to prevent rejection of the transplant. The data on use of concomitant immunosuppression for inflammatory bowel disease (IBD) and organ transplant management are limited. This study sought to evaluate the safety of biologic and small molecule therapy for the treatment of IBD among solid organ transplant recipients. METHODS: Medline, Embase, and Web of Science databases were systematically searched for studies reporting on safety outcomes associated with the use of biologic and small molecule therapy (infliximab, adalimumab, certolizumab, golimumab, vedolizumab, ustekinumab, and tofacitinib) in patients with IBD postsolid organ transplant (eg, liver, kidney, heart, lung, pancreas). The primary outcome was infectious complications. Secondary outcomes included serious infections, colectomy, and discontinuation of biologic therapy. RESULTS: Seven hundred ninety-seven articles were identified for screening, yielding 16 articles for the meta-analyses with information on 163 patients. Antitumor necrosis factor α (Anti-TNFs; infliximab and adalimumab) were used in 8 studies, vedolizumab in 6 studies, and a combination of ustekinumab or vedolizumab and anti-TNFs in 2 studies. Two studies reported outcomes after kidney and cardiac transplant respectively, whereas the rest of the studies included patients with liver transplants. The rates of all infections and serious infections were 20.09 per 100 person-years (100-PY; 95% CI, 12.23-32.99 per 100-PY, I2 = 54%) and 17.39 per 100-PY (95% CI, 11.73-25.78 per 100-PY, I2 = 21%), respectively. The rates of colectomy and biologic medication discontinuation were 12.62 per 100-PY (95% CI, 6.34-25.11 per 100-PY, I2 = 34%) and 19.68 per 100-PY (95% CI, 9.97-38.84 per 100-PY, I2 = 74%), respectively. No cases of venous thromboembolism or death attributable to biologic use were reported. CONCLUSION: Biologic therapy is overall well tolerated in patients with solid organ transplant. Long-term studies are needed to better define the role of specific agents in this patient population.

Efficacy and safety of biologic therapy in microscopic colitis: systematic review and meta-analysis.

BACKGROUND: This systematic review and meta-analysis sought to evaluate the effectiveness and safety of biologic therapy in the treatment of steroid-refractory microscopic colitis (MC). METHODS: We searched MEDLINE, Embase, Web of Science, and Cochrane Central to identify articles and abstracts reporting efficacy or safety data on biologic use (infliximab, adalimumab, certolizumab, golimumab, vedolizumab, ustekinumab, and tofacitinib) for induction and maintenance of remission in MC. We assessed clinical remission and response rates and all reported adverse events (AEs). RESULTS: A total of 376 studies were screened yielding 13 articles (including four abstracts) with a combined information on 78 patients for efficacy and safety outcomes. Most studies were case series. Vedolizumab was used in five studies, adalimumab in three, and a combination of infliximab and adalimumab in five studies. The rates of remission were 66.08% (95% CI, 36.79-95.37%; I2 , 71%) at weeks 3-6 and 54.20% (95% CI, 39.39-69.01%; I2 , 0%) at weeks 12-16. Clinical response rates were 100% (95% CI, 88.04-100%; I2 , 0%) at weeks 3-6 and 67.20% (95% CI, 47.72-86.69%; I2 , 52%) at weeks 12-16. Most frequent AE was medication discontinuation with a pooled incidence of 16.1% (95% CI, 5.9-37.5%). No deaths attributable to biologic use were reported. The overall quality of evidence was very low due to the high risk of biases. CONCLUSION: Low-quality evidence supports the short-term efficacy of biologics in budesonide refractory MC. While our findings represent the most comprehensive evaluation of biologic therapy in severe MC, further research including randomized clinical trials is needed to better define the role of specific agents and long-term therapy.

Predicting open wound mortality in the ICU using machine learning.

BACKGROUND: Open wounds have a significant impact on the health of patients causing pain, loss of function, and death. Labeled as a comorbid condition, open wounds represent a "silent epidemic" that affect a large portion of the US population. Due to their burden of care, open wound patients face an increased risk of ICU stay and mortality. There is a dearth of studies that investigate mortality among wound patients in the ICU. We sought to develop a model that predicts the risk of mortality among wound patients in the ICU. METHODS: Random forest and binomial logistic regression models were developed to predict the risk of mortality among open wound patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. MIMIC-III includes de-identified data for patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. Six variables were used to develop the model (wound location, gender, age, admission type, minimum platelet count and hyperphosphatemia). The Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index were used to assess model strength. RESULTS: A total of 3,937 patients were included with a mean age of 76.57. Of those, 3,372 (85%) survived and 565 (15%) died during their ICU stay. The random forest model achieved an area under the curve (AUC) of 0.924. The CCI and Elixhauser models resulted in AUC of 0.528 and 0.565, respectively. CONCLUSIONS: Machine learning models may allow clinicians to provide better care and management to open wound patients in the ICU.

Predicting Mortality in Diabetic ICU Patients Using Machine Learning and Severity Indices.

Diabetes constitutes a significant health problem that leads to many long term health issues including renal, cardiovascular, and neuropathic complications. Many of these problems can result in increased health care costs, as well risk of ICU stay and mortality. To date, no published study has used predictive modeling to examine the relative influence of diabetes, diabetic health maintenance, and comorbidities on outcomes in ICU patients. Using the MIMIC-III database, machine learning and binomial logistic regression modeling were applied to predict risk of mortality. The final models achieved good fit with AUC values of 0.787 and 0.785 respectively. Additionally, this study demonstrated that robust classification can be done as a combination of five variables (HbA1c, mean glucose during stay, diagnoses upon admission, age, and type of admission) to predict risk as compared with other machine learning models that require nearly 35 variables for similar risk assessment and prediction.

Using Demographic Factors and Comorbidities to Develop a Predictive Model for ICU Mortality in Patients with Acute Exacerbation COPD.

Recognizing factors associated with mortality in patients admitted to the ICU with acute exacerbation of chronic obstructive pulmonary disease could reduce healthcare costs and improve end-of-life care. Previous studies have identified possible predictive variables, but analysis is lacking on the combined effect of demographic factors and comorbidities. Using the MIMIC-III database, this study examined factors associated with mortality in a model incorporating comorbidities, comorbidity indices, and demographic factors. After determining associations between predictive variables and mortality through univariate and multivariate binomial logistic regression, three predictive models were developed: (1) univariate GLM-derived logistic, (2) Mean Gini-derived logistic (MGDL), and (3) random forest. The MGDL model best predicted mortality with an AUROC of 0.778. Variables with the greatest relative importance in determining mortality included the Charlson Comorbidity Index, Elixhauser Index, male, and arrhythmia. The results support the potential of using the MGDL model and need for further work in exploring demographic factors.