Reducing readmissions in the safety net through AI and automation.

Objectives: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.

Study Design: Retrospective interrupted time series analysis between October 2015 and January 2023.

Methods: The readmission reduction initiative standardized inpatient care for patients through a novel, electronic health record-integrated, digitally automated point-of-care decision-support tool.

Heritable Burden of Community Sudden Death by Autopsy and Molecular Phenotyping for Precision Genotype Correlation.

Background: Sudden cardiac death (SCD) genetic studies neglect the majority occurring in older decedents with cardiovascular pathology.

Objectives: This study sought to determine the burden of genetic disease in unselected adult sudden deaths by precision genotype-postmortem phenotype correlation.

Methods: The authors used autopsy, histology, and toxicology to adjudicate cause and identify high-suspicion phenotypes (eg, hypertrophic cardiomyopathy) among presumed SCDs aged 18 to 90 years referred to the county medical examiner from February 2011 to January 2018.

Transcriptomic Profiling of Human Myocardium at Sudden Death to Define Vulnerable Substrate for Lethal Arrhythmias.

Background: While some chronic pathological substrates for sudden cardiac death (SCD) are well known (eg, coronary artery disease and left ventricular [LV] dysfunction), the acute vulnerable myocardial state predisposing to fatal arrhythmia remains a critical barrier to near-term SCD prevention.

Objectives: This study sought to define the distinct myocardial transcriptomic profile of autopsy-defined arrhythmic sudden deaths, compared to nonarrhythmic sudden deaths and trauma deaths, to determine the acute vulnerable state in the hours to days before SCD.

Methods: We used autopsy to adjudicate arrhythmic from nonarrhythmic causes in 1,265 sudden deaths in San Francisco County from 2011 to 2018.

Predictors of All-Cause 30-Day Readmissions in Patients with Heart Failure at an Urban Safety Net Hospital: The Importance of Social Determinants of Health and Mental Health.

Introduction: Heart failure (HF) is a frequent cause of readmissions. Despite caring for underresourced patients and dependence on government funding, safety net hospitals frequently incur penalties for failing to meet pay-for-performance readmission metrics. Limited research exists on the causes of HF readmissions in safety net hospitals.

Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis.

Background: Machine learning (ML) approaches have become increasingly popular in predicting surgical outcomes. However, it is unknown whether they are superior to traditional statistical methods such as logistic regression (LR). This study aimed to perform a systematic review and meta-analysis to compare the performance of ML vs LR models in predicting postoperative outcomes for patients undergoing gastrointestinal (GI) surgery.

Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts.

Importance: Toxic effects of concurrent chemoradiotherapy (CRT) can cause treatment interruptions and hospitalizations, reducing treatment efficacy and increasing health care costs. Physical activity monitoring may enable early identification of patients at high risk for hospitalization who may benefit from proactive intervention.

Objective: To develop and validate machine learning (ML) approaches based on daily step counts collected by wearable devices on prospective trials to predict hospitalizations during CRT.

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study.

Objectives: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes.

Design: Retrospective study.

A Novel Machine Learning Approach to Predict Textbook Outcome in Colectomy.

Background: Several calculators exist to predict risk of postoperative complications. However, in low-risk procedures such as colectomy, a tool to determine the probability of achieving the ideal outcome could better aid clinical decision-making, especially for high-risk patients. A textbook outcome is a composite measure that serves as a surrogate for the ideal surgical outcome.

Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors.

Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this retrospective cohort study, we evaluated 77,428 operative cases at a single academic center between 2016 and 2022.

Multi-institutional Development and External Validation of a Machine Learning Model for the Prediction of Distant Metastasis in Patients Treated by Salvage Radiotherapy for Biochemical Failure After Radical Prostatectomy.

Background: Up to 40% of patients with prostate cancer may develop biochemical recurrence after surgery, with salvage radiation therapy (SRT) being the only curative option. In 2016, Tendulkar et al. (Contemporary update of a multi-institutional predictive nomogram for salvage radiotherapy after radical prostatectomy.

Development and Validation of Prediction Models and Risk Calculators for Posthepatectomy Liver Failure and Postoperative Complications Using a Diverse International Cohort of Major Hepatectomies.

Objective: The study aim was to develop and validate models to predict clinically significant posthepatectomy liver failure (PHLF) and serious complications [a Comprehensive Complication Index (CCI)>40] using preoperative and intraoperative variables.

Background: PHLF is a serious complication after major hepatectomy but does not comprehensively capture a patient's postoperative course. Adding the CCI as an additional metric can account for complications unrelated to liver function.

Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer.

Background: Pelvic lymph node dissection (PLND) is the gold standard for diagnosis of lymph node involvement (LNI) in patients with prostate cancer. The Roach formula, Memorial Sloan Kettering Cancer Center (MSKCC) calculator, and Briganti 2012 nomogram are elegant and simple traditional tools used to estimate the risk of LNI and select patients for PLND.

Objective: To determine whether machine learning (ML) can improve patient selection and outperform currently available tools for predicting LNI using similar readily available clinicopathologic variables.

Hypertension Requiring Medication Use: a Silent Predictor of Poor Outcomes After Pancreaticoduodenectomy.

Background: Although hypertension requiring medication (HTNm) is a well-known cardiovascular comorbidity, its association with postoperative outcomes is understudied. This study aimed to evaluate whether preoperative HTNm is independently associated with specific complications after pancreaticoduodenectomy.

Study Design: Adults undergoing elective pancreaticoduodenectomy were included from the 2014-2019 NSQIP-targeted pancreatectomy dataset.

Hand-Assisted Versus Pure Minimally-Invasive Distal Pancreatectomy: Is There a Downside to Lending a Hand?

Background: Hand-assisted laparoscopic distal pancreatectomy (HALDP) is suggested to offer similar outcomes to pure laparoscopic distal pancreatectomy (LDP). However, given the longer midline incision, it is unclear whether HALDP increases the risk of postoperative hernia. Our aim was to determine the risk of postoperative incisional hernia development after HALDP.

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment.

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.

Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness.

Overcoming barriers in the design and implementation of clinical trials for acute kidney injury: a report from the 2020 Kidney Disease Clinical Trialists meeting.

Acute kidney injury (AKI) is a growing epidemic and is independently associated with increased risk of death, chronic kidney disease (CKD) and cardiovascular events. Randomized-controlled trials (RCTs) in this domain are notoriously challenging and many clinical studies in AKI have yielded inconclusive findings. Underlying this conundrum is the inherent heterogeneity of AKI in its etiology, presentation and course.

Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees.

Objective: After deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees.

Materials And Methods: We introduce 2 procedures for continual recalibration or revision of an underlying prediction model: Bayesian logistic regression (BLR) and a Markov variant that explicitly models distribution shifts (MarBLR).

Selective prediction-set models with coverage rate guarantees.

The current approach to using machine learning (ML) algorithms in healthcare is to either require clinician oversight for every use case or use their predictions without any human oversight. We explore a middle ground that lets ML algorithms abstain from making a prediction to simultaneously improve their reliability and reduce the burden placed on human experts. To this end, we present a general penalized loss minimization framework for training selective prediction-set (SPS) models, which choose to either output a prediction set or abstain.

Efficient nonparametric statistical inference on population feature importance using Shapley values.

The true population-level importance of a variable in a prediction task provides useful knowledge about the underlying data-generating mechanism and can help in deciding which measurements to collect in subsequent experiments. Valid statistical inference on this importance is a key component in understanding the population of interest. We present a computationally efficient procedure for estimating and obtaining valid statistical inference on the hapley opulation ariable mportance easure (SPVIM).

ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS.

CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation.

Rejoinder to Discussions on "Approval policies for modifications to machine learning-based software as a medical device: A study of bio-creep".

We thank the discussants for sharing their unique perspectives on the problem of designing automatic algorithm change protocols (aACPs) for machine learning-based software as a medical device. Both Pennello et al. and Rose highlighted a number of challenges that arise in real-world settings, and we whole-heartedly agree that substantial extensions of our work are needed to understand if and how aACPs can be safely deployed in practice.