An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy.

BACKGROUND: Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. This retrospective study aimed to create a model predicting AKI through machine learning and identify features that contribute to the development of early and late AKI. METHODS: Patients that underwent major hepatectomy were categorized into the No-AKI, Early-AKI (within 48 h) or Late-AKI group (between 48 h and 7 days). Modeling was done with 20 perioperative features and the performance of prediction models were measured by the area under the receiver operating characteristic curve (AUROCC). Shapley Additive Explanation (SHAP) values were utilized to explain the outcome of the prediction model. RESULTS: Of the 1383 patients included in this study, 1229, 110 and 44 patients were categorized into the No-AKI, Early-AKI and Late-AKI group, respectively. The CatBoost classifier exhibited the greatest AUROCC of 0.758 (95% CI: 0.671-0.847) and was found to differentiate well between Early and Late-AKI. We identified different perioperative features for predicting each outcome and found 1-year mortality to be greater for Early-AKI. CONCLUSIONS: Our results suggest that risk factors are different for Early and Late-AKI after major hepatectomy, and 1-year mortality is greater for Early-AKI.

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

BACKGROUND: Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. OBJECTIVE: The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. METHODS: This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). RESULTS: Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for "no-event," 84% for "apnea," and 51% for "hypopnea." Most misclassifications were made for "hypopnea," with 15% and 34% of "hypopnea" being wrongly predicted as "apnea" and "no-event," respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. CONCLUSIONS: Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

Ethical decision-making climate, moral distress, and intention to leave among ICU professionals in a tertiary academic hospital center.

BACKGROUND: Commentators believe that the ethical decision-making climate is instrumental in enhancing interprofessional collaboration in intensive care units (ICUs). Our aim was twofold: (1) to determine the perception of the ethical climate, levels of moral distress, and intention to leave one's job among nurses and physicians, and between the different ICU types and (2) determine the association between the ethical climate, moral distress, and intention to leave. METHODS: We performed a cross-sectional questionnaire study between May 2021 and August 2021 involving 206 nurses and physicians in a large urban academic hospital. We used the validated Ethical Decision-Making Climate Questionnaire (EDMCQ) and the Measure of Moral Distress for Healthcare Professionals (MMD-HP) tools and asked respondents their intention to leave their jobs. We also made comparisons between the different ICU types. We used Pearson's correlation coefficient to identify statistically significant associations between the Ethical Climate, Moral Distress, and Intention to Leave. RESULTS: Nurses perceived the ethical climate for decision-making as less favorable than physicians (p < 0.05). They also had significantly greater levels of moral distress and higher intention to leave their job rates than physicians. Regarding the ICU types, the Neonatal/Pediatric unit had a significantly higher overall ethical climate score than the Medical and Surgical units (3.54 ± 0.66 vs. 3.43 ± 0.81 vs. 3.30 ± 0.69; respectively; both p ≤ 0.05) and also demonstrated lower moral distress scores (both p < 0.05) and lower "intention to leave" scores compared with both the Medical and Surgical units. The ethical climate and moral distress scores were negatively correlated (r = -0.58, p < 0.001); moral distress and "intention to leave" was positively correlated (r = 0.52, p < 0.001); and ethical climate and "intention to leave" were negatively correlated (r = -0.50, p < 0.001). CONCLUSIONS: Significant differences exist in the perception of the ethical climate, levels of moral distress, and intention to leave between nurses and physicians and between the different ICU types. Inspecting the individual factors of the ethical climate and moral distress tools can help hospital leadership target organizational factors that improve interprofessional collaboration, lessening moral distress, decreasing turnover, and improved patient care.

Disagreement About Surgical Intervention in Trisomy 18.

In this case, we explore physician conflict with performing surgery (tracheostomy) for long-term ventilation in a term infant with trisomy 18 and respiratory failure. Experts in neonatal-perinatal medicine, pediatric bioethics, and pediatric palliative care have provided comments on this case. An additional commentary was written by the parent of another infant with trisomy 18, who is also a medical provider (physical therapist).