Objective When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients. Materials and Methods Machine learning models were trained to predict the common causes of ARF (pneumonia, heart failure, and/or chronic obstructive pulmonary disease [COPD]). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort. Results The internal cohort of 1618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77–0.79), image model AUROC was 0.74 (0.72–0.75), and EHR model AUROC was 0.74 (0.70–0.76). For heart failure, combined: 0.83 (0.77–0.84), image: 0.80 (0.71–0.81), and EHR: 0.79 (0.75–0.82). For COPD, combined: AUROC = 0.88 (0.83–0.91), image: 0.83 (0.77–0.89), and EHR: 0.80 (0.76–0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia. Conclusions Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings.

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Importance When hospitals are at capacity, accurate deterioration indices could help identify low-risk patients as potential candidates for home care programs and alleviate hospital strain. To date, many existing deterioration indices are based entirely on structured data from the electronic health record (EHR) and ignore potentially useful information from other sources. Objective To improve the accuracy of existing deterioration indices by incorporating unstructured imaging data from chest radiographs. Design, setting, and participants Machine learning models were trained to predict deterioration of patients hospitalized with acute dyspnea using existing deterioration index scores and chest radiographs. Models were trained on hospitalized patients without coronavirus disease 2019 (COVID-19) and then subsequently tested on patients with COVID-19 between January 2020 and December 2020 at a single tertiary care center who had at least one radiograph taken within 48 hours of hospital admission. Main outcomes and measures Patient deterioration was defined as the need for invasive or non-invasive mechanical ventilation, heated high flow nasal cannula, IV vasopressor administration or in-hospital mortality at any time following admission. The EPIC deterioration index was augmented with unstructured data from chest radiographs to predict risk of deterioration. We compared discriminative performance of the models with and without incorporating chest radiographs using area under the receiver operating curve (AUROC), focusing on comparing the fraction and total patients identified as low risk at different negative predictive values (NPV). Results Data from 6278 hospitalizations were analyzed, including 5562 hospitalizations without COVID-19 (training cohort) and 716 with COVID-19 (216 in validation, 500 in held-out test cohort). At a NPV of 0.95, the best-performing image-augmented deterioration index identified 49 more (9.8%) individuals as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. At a NPV of 0.9, the EPIC image-augmented deterioration index identified 26 more individuals (5.2%) as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. Conclusion and relevance Augmenting existing deterioration indices with chest radiographs results in better identification of low-risk patients. The model augmentation strategy could be used in the future to incorporate other forms of unstructured data into existing disease models.

Background Acute abdomen is a medical emergency with a wide spectrum of etiologies. Point-of-care ultrasound (POCUS) can help in early identification and management of the causes. The ACUTE–ABDOMEN protocol was created by the authors to aid in the evaluation of acute abdominal pain using a systematic sonographic approach, integrating the same core ultrasound techniques already in use—into one mnemonic. This mnemonic ACUTE means: A: abdominal aortic aneurysm; C: collapsed inferior vena cava; U: ulcer (perforated viscus); T: trauma (free fluid); E: ectopic pregnancy, followed by ABDOMEN which stands: A: appendicitis; B: biliary tract; D: distended bowel loop; O: obstructive uropathy; Men: testicular torsion/Women: ovarian torsion. The article discusses two cases of abdominal pain the diagnosis and management of which were directed and expedited as a result of using the ACUTE–ABDOMEN protocol. The first case was of a 33-year-old male, who presented with a 3-day history of abdominal pain, vomiting and constipation. Physical exam revealed a soft abdomen with generalized tenderness and normal bowel sounds. Laboratory tests were normal. A bedside ultrasound done using the ACUTE–ABDOMEN protocol showed signs of intussusception. This was confirmed by CT-abdomen. The second case was of a 70-year-old female, a known case of diabetes and hypertension, who presented with a 3-hour history of abdominal pain, vomiting and diarrhea. She had a normal physical exam and laboratory studies. Her symptoms mimicking simple gastroenteritis had improved. However, bedside ultrasound, using the ACUTE–ABDOMEN protocol showed localized free fluid with dilated small bowel loop in right lower quadrant with absent peristalsis. A CT abdomen confirmed a diagnosis of intestinal obstruction. These two cases demonstrate that the usefulness of applying POCUS in a systematic method—like the “ACUTE–ABDOMEN” approach—can aid in patient diagnosis and management. Case presentation We are presenting two cases of undifferentiated acute abdomen pain, where ACUTE ABDOMEN sonographic approach was applied and facilitated the accurate patient management and disposition. Conclusion ACUTE ABDOMEN sonographic approach in acute abdomen can play an important role in ruling out critical diagnosis, and can guide emergency physician or any critical care physician in patient management.

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Background Infectious respiratory-track pathogens are a common trigger of healthcare capacity strain, e.g. the COVID19 pandemic. Patient risk stratification models to identify low-risk patients can help improve patient care processes and allocate limited resources. Many existing deterioration indices are based entirely on structured data from the Electronic Health Record (EHR) and ignore important information from other data sources. However, chest radiographs have been demonstrated to be helpful in predicting the progress of respiratory diseases. We developed a joint EHR and chest x-ray (CXR) model method and applied it to identify low-risk COVID19+ patients within the first 48 hours of hospital admission. Methods All COVID19+ patients admitted to a large urban hospital between March 2020 and February 2021 were included. We trained an image model using large public chest radiograph datasets and fine-tuned this model to predict acute dyspnea using a cohort from the same hospital. We then combined this image model with two existing EHR deterioration indices to predict the risk of a COVID19+ patient being intubated, receiving a nasal cannula, or being treated with a vasopressor. We evaluated models’ ability to identify low-risk patients by using the positive predictive value (PPV). Results The image-augmented deterioration index was able to identify 12% of 716 COVID-19+ patients as low risk with 0.95 positive predictive value in the first 48 hours of admission. In contrast, when used individually, the EHR and CXR models each identified roughly 3% of the patients with a PPV of 0.95. Predicting Low Risk Patients Aggregated predictions for COVID19 positive patients within the first 48 hours of admission, shown with exponential weight moving average and 95% CIs. Each plot shows the number of patients flagged as low-risk by lowest aggregated prediction and the resulting accuracy for that fraction of patients. The bottom plot compares the MCURES fused model to the MCURES model. The top plot compares the EDI fused model to the EDI model. Conclusion Our multi-modal models were able to identify far more patients at low-risk of COVID19 deterioration than models trained on either modality alone. This indicates the importance of combining structured data with chest X-rays when creating a deterioration index performance for infectious respiratory-track diseases. Disclosures All Authors: No reported disclosures