Background: SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)–based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. Methods: A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. Results: The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients’ radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. Conclusions: In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Patients undergoing microvascular reconstruction are often anemic from a combination of iatrogenic hemodilution and acute blood losses. No major clinical study describes the impact of preoperative anemia on free flap morbidity. The plastic surgery service at a high-volume academic center performed 156 free flaps among 147 patients from December 2005 to December 2010. One hundred thirty-two had a preoperative hemoglobin (Hb) or hematocrit (Hct), with mean values of 11.8±2.4 g/dL and 35.2%±7.0%, respectively. The overall failure rate was 9% (12/132), primarily from vascular thrombosis (6/12). Through logistic regression analysis, Hb and Hct were significant predictors of flap failure (P<0.005) and vascular thrombosis (P<0.05). Fisher exact test revealed a significant increase in failure risk at Hct level less than 30% (Hb<10 g/dL) (relative risk, 4.76, P=0.006), and probit analysis demonstrated an exposure-response relationship to decreased Hct level (P<0.005). These findings support that preoperative anemia could significantly impact free flap morbidity.

As the COVID-19 pandemic unfolds, radiology imaging is playing an increasingly vital role in determining therapeutic options, patient management, and research directions. Publicly available data are essential to drive new research into disease etiology, early detection, and response to therapy. In response to the COVID-19 crisis, the National Cancer Institute (NCI) has extended the Cancer Imaging Archive (TCIA) to include COVID-19 related images. Rural populations are one population at risk for underrepresentation in such public repositories. We have published in TCIA a collection of radiographic and CT imaging studies for patients who tested positive for COVID-19 in the state of Arkansas. A set of clinical data describes each patient including demographics, comorbidities, selected lab data and key radiology findings. These data are cross-linked to SARS-COV-2 cDNA sequence data extracted from clinical isolates from the same population, uploaded to the GenBank repository. We believe this collection will help to address population imbalance in COVID-19 data by providing samples from this normally underrepresented population.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.

BACKGROUND: Patients' trauma burdens are a combination of anatomic damage, physiologic derangement, and the resultant depletion of reserve. Typically, Injury Severity Score (ISS) >15 defines major anatomic injury and Revised Trauma Score (RTS) <7.84 defines major physiologic derangement, but there is no standard definition for reserve. The Need For Trauma Intervention (NFTI) identifies severely depleted reserves (NFTI+) with emergent interventions and/or early mortality. We hypothesized NFTI would have stronger associations with outcomes and better model fit than ISS and RTS. METHODS: Thirty-eight adult and pediatric U.S. trauma centers submitted data for 88,488 encounters. Mixed models tested ISS greater than 15, RTS less than 7.84, and NFTI's associations with complications, survivors' discharge to continuing care, and survivors' length of stay (LOS). RESULTS: The NFTI had stronger associations with complications and LOS than ISS and RTS (odds ratios [99.5% confidence interval]: NFTI = 9.44 [8.46–10.53]; ISS = 5.94 [5.36–6.60], RTS = 4.79 [4.29–5.34]; LOS incidence rate ratios (99.5% confidence interval): NFTI = 3.15 [3.08–3.22], ISS = 2.87 [2.80–2.94], RTS = 2.37 [2.30–2.45]). NFTI was more strongly associated with continuing care discharge but not significantly more than ISS (relative risk [99.5% confidence interval]: NFTI = 2.59 [2.52–2.66], ISS = 2.51 [2.44–2.59], RTS = 2.37 [2.28–2.46]). Cross-validation revealed that in all cases NFTI's model provided a much better fit than ISS greater than 15 or RTS less than 7.84. CONCLUSION: In this multicenter study, NFTI had better model fit and stronger associations with the outcomes than ISS and RTS. By determining depletion of reserve via resource consumption, NFTI+ may be a better definition of major trauma than the standard definitions of ISS greater than 15 and RTS less than 7.84. Using NFTI may improve retrospective triage monitoring and statistical risk adjustments. LEVEL OF EVIDENCE: Prognostic, level IV.