BackgroundThe development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.MethodsOur objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission.ResultsUtilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224 mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts.ConclusionsExperimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI.

BackgroundFederal and clinical guidelines support integration of reproductive life planning in the care of female patients to aid in the reduction of unplanned pregnancies. A multitude of tools have been created to help in the counseling component, but further research is needed regarding how and whether they facilitate patient-provider communication.ResearchWe performed a randomized controlled trial to evaluate if patients report whether a detailed or simple pregnancy intention screening tool is helpful for communication of reproductive life plans. We compared a novel reproductive counseling aid, the Family Planning Quotient (FPQ), to a simple tool based on the One Key Question® (OKQ). Providers also evaluated whether they thought the tool used at the visit was helpful. We randomized 93 patients to complete a survey including identical demographic questions and either the FPQ or OKQ reproductive counseling tool. We did not provide further instructions to either the patient or provider. Following the visits, we collected 84 subject evaluations and 79 provider evaluations. A similar proportion of subjects using either reproductive counseling tool found it helpful in communicating their reproductive life plans to their providers (approximately 66%), but there was no difference between the two tools studied. Less than half of providers reported that the FPQ tool was helpful (FPQ: 16/43, 37.2% versus OKQ: 18/36, 50%; p = 0.25).ConclusionTwo-thirds of patients reported either a detailed or simple reproductive plan screening tool was helpful to facilitate communication with their provider, but only half of providers found either tool helpful. Use of reproductive screening tools should be followed by patient-centered counseling to help patients meet their reproductive life goals.

In our study, ECC seems more predictive of r/p disease than margin status. Most HIV-positive women with positive margins or ECC have r/p disease, whereas most HIV-negative women do not. One should consider HIV serostatus when deciding whether or not to perform repeat excision.

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The community-based recruitment strategy is a novel, low-touch, clinical trial enrollment method to engage a diverse set of participants. Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. This new strategy for recruitment links data reported from community participants to clinical data in the EHR and allows for eligibility verification and automated follow-up. The workflow has the potential to improve recruitment efficiency and engage traditionally underrepresented individuals in research.