Emergency Department Referral of Patients With Chest Pain for Noninvasive Cardiac Testing and 2-Year Clinical Outcomes.

BACKGROUND: Noninvasive cardiac testing (NICT) has been associated with decreased long-term risks of major adverse cardiac events (MACEs) among emergency department patients at high coronary risk. It is unclear whether this association extends to patients without evidence of myocardial injury on initial ECG and cardiac troponin testing. METHODS: A retrospective cohort study was conducted of patients presenting with chest pain between 2013 and 2019 to 21 emergency departments within an integrated health care system in Northern California, excluding patients with ST-segment-elevation myocardial infarction or myocardial injury by serum troponin testing. To account for confounding by indication, we grouped patient encounters by the NICT referral rate of the initially assigned emergency physician relative to local peers within discrete time periods. The primary outcome was MACE within 2 years. Secondary outcomes were coronary revascularization and MACE, inclusive of all-cause mortality. Associations between the NICT referral group (low, intermediate, or high) and outcomes were assessed using risk-adjusted proportional hazards methods with censoring for competing events. RESULTS: Among 144 577 eligible patient encounters, the median age was 58 years (interquartile range, 48-68) and 57% were female. Thirty-day NICT referral was 13.0%, 19.9%, and 27.8% in low, intermediate, and high NICT referral groups, respectively, with a good balance of baseline covariates between groups. Compared with the low NICT referral group, there was no significant decrease in the adjusted hazard ratio of MACE within the intermediate (adjusted hazard ratio, 1.08 [95% CI, 1.02-1.14]) or high (adjusted hazard ratio, 1.05 [95% CI, 0.99-1.11]) NICT referral groups. Results were similar for MACE, inclusive of all-cause mortality, and coronary revascularization, as well as subgroup analyses stratified by estimated risk (history, electrocardiogram, age, risk factors, troponin [HEART] score: percent classified as low risk, 48.2%; moderate risk, 49.2%; and high risk, 2.7%). CONCLUSIONS: Increases in NICT referrals were not associated with changes in the hazard of MACE within 2 years following emergency department visits for chest pain without evidence of acute myocardial injury. These findings further highlight the need for evidence-based guidance regarding the appropriate use of NICT in this population.

An all-inclusive model for predicting invasive bacterial infection in febrile infants age 7-60 days.

BACKGROUND: Invasive bacterial infections (IBIs) in febrile infants are rare but potentially devastating. We aimed to derive and validate a predictive model for IBI among febrile infants age 7-60 days. METHODS: Data were abstracted retrospectively from electronic records of 37 emergency departments (EDs) for infants with a measured temperature >=100.4 F who underwent an ED evaluation with blood and urine cultures. Models to predict IBI were developed and validated respectively using a random 80/20 dataset split, including 10-fold cross-validation. We used precision recall curves as the classification metric. RESULTS: Of 4411 eligible infants with a mean age of 37 days, 29% had characteristics that would likely have excluded them from existing risk stratification protocols. There were 196 patients with IBI (4.4%), including 43 (1.0%) with bacterial meningitis. Analytic approaches varied in performance characteristics (precision recall range 0.04-0.29, area under the curve range 0.5-0.84), with the XGBoost model demonstrating the best performance (0.29, 0.84). The five most important variables were serum white blood count, maximum temperature, absolute neutrophil count, absolute band count, and age in days. CONCLUSION: A machine learning model (XGBoost) demonstrated the best performance in predicting a rare outcome among febrile infants, including those excluded from existing algorithms. IMPACT: Several models for the risk stratification of febrile infants have been developed. There is a need for a preferred comprehensive model free from limitations and algorithm exclusions that accurately predicts IBIs. This is the first study to derive an all-inclusive predictive model for febrile infants aged 7-60 days in a community ED sample with IBI as a primary outcome. This machine learning model demonstrates potential for clinical utility in predicting IBI.