Prediction of Intracranial Hypertension and Brain Tissue Hypoxia Utilizing High-Resolution Data from the BOOST-II Clinical Trial.

The current approach to intracranial hypertension and brain tissue hypoxia is reactive, based on fixed thresholds. We used statistical machine learning on high-frequency intracranial pressure (ICP) and partial brain tissue oxygen tension (PbtO2) data obtained from the BOOST-II trial with the goal of constructing robust quantitative models to predict ICP/PbtO2 crises. We derived the following machine learning models: logistic regression (LR), elastic net, and random forest. We split the data set into 70-30% for training and testing and utilized a discrete-time survival analysis framework and 5-fold hyperparameter optimization strategy for all models. We compared model performances on discrimination between events and non-events of increased ICP or low PbtO2 with the area under the receiver operating characteristic (AUROC) curve. We further analyzed clinical utility through a decision curve analysis (DCA). When considering discrimination, the number of features, and interpretability, we identified the RF model that combined the most recent ICP reading, episode number, and longitudinal trends over the preceding 30 min as the best performing for predicting ICP crisis events within the next 30 min (AUC 0.78). For PbtO2, the LR model utilizing the most recent reading, episode number, and longitudinal trends over the preceding 30 min was the best performing (AUC, 0.84). The DCA showed clinical usefulness for wide risk of thresholds for both ICP and PbtO2 predictions. Acceptable alerting thresholds could range from 20% to 80% depending on a patient-specific assessment of the benefit-risk ratio of a given intervention in response to the alert.

Early Diagnosis of Primary Immunodeficiency Disease Using Clinical Data and Machine Learning.

BACKGROUND: Primary immunodeficiency diseases (PIDD) are a group of immune-related disorders that have a current median delay of diagnosis between 6 and 9 years. Early diagnosis and treatment of PIDD has been associated with improved patient outcomes. OBJECTIVE: To develop a machine learning model using elements within the electronic health record data that are related to prior symptomatic treatment to predict PIDD. METHODS: We conducted a retrospective study of patients with PIDD identified using inclusion criteria of PIDD-related diagnoses, immunodeficiency-specific medications, and low immunoglobulin levels. We constructed a control group of age-, sex-, and race-matched patients with asthma. The primary outcome was the diagnosis of PIDD. We considered comorbidities, laboratory tests, medications, and radiological orders as features, all before diagnosis and indicative of symptom-related treatment. Features were presented sequentially to logistic regression, elastic net, and random forest classifiers, which were trained using a nested cross-validation approach. RESULTS: Our cohort consisted of 6422 patients, of whom 247 (4%) were diagnosed with PIDD. Our logistic regression model with comorbidities demonstrated good discrimination between patients with PIDD and those with asthma (c-statistic: 0.62 [0.58-0.65]). Adding laboratory results, medications, and radiological orders improved discrimination (c-statistic: 0.70 vs 0.62, P < .001), sensitivity, and specificity. Extending to the advanced machine learning models did not improve performance. CONCLUSIONS: We developed a prediction model for early diagnosis of PIDD using historical data that are related to symptomatic care, which has potential to fill an important need in reducing the time to diagnose PIDD, leading to better outcomes for immunodeficient patients.

Multicenter Development and Validation of a Model for Predicting Retention in Care Among People with HIV.

Predictive analytics can be used to identify people with HIV currently retained in care who are at risk for future disengagement from care, allowing for prioritization of retention interventions. We utilized machine learning methods to develop predictive models of retention in care, defined as no more than a 12 month gap between HIV care appointments in the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. Data were split longitudinally into derivation and validation cohorts. We created logistic regression (LR), random forest (RF), and gradient boosted machine (XGB) models within a discrete-time survival analysis framework and compared their performance to a baseline model that included only demographics, viral suppression, and retention history. 21,267 Patients with 507,687 visits from 2007 to 2018 were included. The LR model outperformed the baseline model (AUC 0.68 [0.67-0.70] vs. 0.60 [0.59-0.62], P < 0.001). RF and XGB models had similar performance to the LR model. Top features in the LR model included retention history, age, and viral suppression.

Use of Statins Among Patients Taking Levothyroxine: an Observational Drug Utilization Study Across Sites.

CONTEXT: Treatment with levothyroxine (LT4) that normalize serum thyrotropin (TSH) is expected to restore lipid metabolism. OBJECTIVE: To assess statin utilization in LT4-treated patients through an observational drug utilization study. METHODS: Three sites were involved: (1) 10 723 outpatients placed on LT4 during 2006-2019 identified from the Clinical Research Data Warehouse of the University of Chicago; (2) ~1.4 million LT4 prescriptions prepared by primary care physicians during January-December 2018, identified from the IQVIA™ database of medical prescriptions in Brazil; (30 ~5.4 million patient interviews during 2009-2019, including ~0.32 million patients on LT4, identified from the Fleury Group database in Brazil. RESULTS: On site 1, initiation of therapy with LT4 increased the frequency of statin utilization (19.1% vs 24.6%), which occurred ~1.5 years later (median 76 weeks) and, among those patients that were on statins, increased intensity of treatment by 33%, despite normalization of serum TSH levels; on site 2, after matching for sex and age, the frequency of statins prescription was higher for those patients using LT4: females, 2.1 vs 3.4% (odds ratio [OR] 1.656 [1.639-1.673]); males, 3.1 vs 4.4% (OR 1.435 [1.409-1.462]); and, on site 3, after matching for sex and age, the frequency of statin utilization was higher in those patients using LT4: females, 10 vs 18% (OR 2.02 [2.00-2.04]); males, 15 vs 25% (OR 1.92 [1.88-1.96]); all P values were <.0001. CONCLUSION: Prescription and utilization of statins were higher in patients taking LT4. The reasons for this association should be addressed in future studies.

Abnormal vital signs after laparoscopic colorectal surgery: More common than you think.

BACKGROUND: Anastomotic leak is a feared complication. The presence of abnormal vital signs is often cited as an important overlooked predictive clue in retrospective settings once the diagnosis of leak has already been established. We aimed to determine the prevalence of abnormal vital signs following colorectal resection and assess its predictive value. METHODS: We retrospectively studied patients undergoing colorectal resection. The performance of vital signs in predicting anastomotic leak was assessed using discrete-time survival analysis and receiver operator characteristic curve. RESULTS: 1662 patients (841 laparoscopic, 821 open) were included. Clinical anastomotic leak was diagnosed in 50 patients (3.1%). 96.8% of patients of the entire cohort had at least one abnormal vital sign during their postoperative course. No individual vital sign was a strong predictor of anastomotic leak in either laparoscopic or open cohorts. CONCLUSION: Vital sign abnormalities are extremely common following open and laparoscopic colorectal surgery and alone are poor predictors of anastomotic leak.