Beyond Unplanned ICU Transfers: Linking a Revised Definition of Deterioration to Patient Outcomes.

OBJECTIVES: To develop an electronic descriptor of clinical deterioration for hospitalized patients that predicts short-term mortality and identifies patient deterioration earlier than current standard definitions. DESIGN: A retrospective study using exploratory record review, quantitative analysis, and regression analyses. SETTING: Twelve-hospital community-academic health system. PATIENTS: All adult patients with an acute hospital encounter between January 1, 2018, and December 31, 2022. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical trigger events were selected and used to create a revised electronic definition of deterioration, encompassing signals of respiratory failure, bleeding, and hypotension occurring in proximity to ICU transfer. Patients meeting the revised definition were 12.5 times more likely to die within 7 days (adjusted odds ratio 12.5; 95% CI, 8.9-17.4) and had a 95.3% longer length of stay (95% CI, 88.6-102.3%) compared with those who were transferred to the ICU or died regardless of meeting the revised definition. Among the 1812 patients who met the revised definition of deterioration before ICU transfer (52.4%), the median detection time was 157.0 min earlier (interquartile range 64.0-363.5 min). CONCLUSIONS: The revised definition of deterioration establishes an electronic descriptor of clinical deterioration that is strongly associated with short-term mortality and length of stay and identifies deterioration over 2.5 hours earlier than ICU transfer. Incorporating the revised definition of deterioration into the training and validation of early warning system algorithms may enhance their timeliness and clinical accuracy.

Longitudinal clinical decision support for assessing decisions over time: State-of-the-art and future directions.

Objective: Patients and clinicians rarely experience healthcare decisions as snapshots in time, but clinical decision support (CDS) systems often represent decisions as snapshots. This scoping review systematically maps challenges and facilitators to longitudinal CDS that are applied at two or more timepoints for the same decision made by the same patient or clinician. Methods: We searched Embase, PubMed, and Medline databases for articles describing development, validation, or implementation of patient- or clinician-facing longitudinal CDS. Validated quality assessment tools were used for article selection. Challenges and facilitators to longitudinal CDS are reported according to PRISMA-ScR guidelines. Results: Eight articles met inclusion criteria; each article described a unique CDS. None used entirely automated data entry, none used living guidelines for updating the evidence base or knowledge engine as new evidence emerged during the longitudinal study, and one included formal readiness for change assessments. Seven of eight CDS were implemented and evaluated prospectively. Challenges were primarily related to suboptimal study design (with unique challenges for each study) or user interface. Facilitators included use of randomized trial designs for prospective enrollment, increased CDS uptake during longitudinal exposure, and machine-learning applications that are tailored to the CDS use case. Conclusions: Despite the intuitive advantages of representing healthcare decisions longitudinally, peer-reviewed literature on longitudinal CDS is sparse. Existing reports suggest opportunities to incorporate longitudinal CDS frameworks, automated data entry, living guidelines, and user readiness assessments. Generating best practice guidelines for longitudinal CDS would require a greater depth and breadth of published work and expert opinion.

Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults.

Importance: The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions. Objective: To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration. Design, Setting, and Participants: This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021. Exposure: DTI predictions made every 15 minutes. Main Outcomes and Measures: Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups. Results: A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759 (95% CI, 0.756-0.762) at the observation level and 0.685 (95% CI, 0.671-0.700) at the encounter level. Corresponding AUPRCs were 0.039 (95% CI, 0.037-0.040) at the observation level and 0.248 (95% CI, 0.227-0.273) at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity. Conclusions and Relevance: In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models.

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.

Purpose: To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and Methods: A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results: Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion: AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.

A pilot study demonstrating the impact of surgical bowel preparation on intestinal microbiota composition following colon and rectal surgery.

The intestinal microbiota has been implicated in the pathogenesis of complications following colorectal surgery, yet perioperative changes in gut microbiome composition are poorly understood. The objective of this study was to characterize the perioperative gut microbiome in patients undergoing colonoscopy and colorectal surgery and determine factors influencing its composition. Using Illumina amplicon sequencing coupled with targeted metabolomics, we characterized the fecal microbiota in: (A) patients (n = 15) undergoing colonoscopy who received mechanical bowel preparation, and (B) patients (n = 15) undergoing colorectal surgery who received surgical bowel preparation, composed of mechanical bowel preparation with oral antibiotics, and perioperative intravenous antibiotics. Microbiome composition was characterized before and up to six months following each intervention. Colonoscopy patients had minor shifts in bacterial community composition that recovered to baseline at a mean of 3 (1-13) days. Surgery patients demonstrated substantial shifts in bacterial composition with greater abundances of Enterococcus, Lactobacillus, and Streptococcus. Compositional changes persisted in the early postoperative period with recovery to baseline beginning at a mean of 31 (16-43) days. Our results support surgical bowel preparation as a factor significantly influencing gut microbial composition following colorectal surgery, while mechanical bowel preparation has little impact.

Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions.

Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk for adverse outcomes, resulting in a disproportionate impact on older adults and minorities that fit that profile. However, although there is considerable heterogeneity in the comorbidity profiles of these populations, not much is known about how prior comorbidities co-occur to form COVID-19 patient subgroups, and their implications for targeted care. Here we used bipartite networks to quantitatively and visually analyze heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 hospitals and 60 clinics in the greater Minneapolis region. This approach enabled the analysis and interpretation of heterogeneity at three levels of granularity (cohort, subgroup, and patient), each of which enabled clinicians to rapidly translate the results into the design of clinical interventions. We discuss future extensions of the multigranular heterogeneity framework, and conclude by exploring how the framework could be used to analyze other biomedical phenomena including symptom clusters and molecular phenotypes, with the goal of accelerating translation to targeted clinical care.

Non-alcoholic fatty liver disease (NAFLD) and risk of hospitalization for Covid-19.

Background: Covid-19 disease causes significant morbidity and mortality through increase inflammation and thrombosis. Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis are states of chronic inflammation and indicate advanced metabolic disease. We sought to understand the risk of hospitalization for Covid-19 associated with NAFLD/NASH. Methods: Retrospective analysis of electronic medical record data of 6,700 adults with a positive SARS-CoV-2 PCR from March 1, 2020 to Aug 25, 2020. Logistic regression and competing risk were used to assess odds of being hospitalized. Additional adjustment was added to assess risk of hospitalization among patients with a prescription for metformin use within the 3 months prior to the SARS-CoV-2 PCR result, history of home glucagon-like-peptide 1 receptor agonist (GLP-1 RA) use, and history of metabolic and bariatric surgery (MBS). Interactions were assessed by gender and race. Results: A history of NAFLD/NASH was associated with increased odds of admission for Covid-19: logistic regression OR 2.04 (1.55, 2.96, p<0.01), competing risks OR 1.43 (1.09-1.88, p<0.01); and each additional year of having NAFLD/NASH was associated with a significant increased risk of being hospitalized for Covid-19, OR 1.86 (1.43-2.42, p<0.01). After controlling for NAFLD/NASH, persons with obesity had decreased odds of hospitalization for Covid-19, OR 0.41 (0.34-0.49, p<0.01). NAFLD/NASH increased risk of hospitalization in men and women, and in all racial/ethnic subgroups. Mediation treatments for metabolic syndrome were associated with non-significant reduced risk of admission: OR 0.42 (0.18-1.01, p=0.05) for home metformin use and OR 0.40 (0.14-1.17, p=0.10) for home GLP-1RA use. MBS was associated with a significant decreased risk of admission: OR 0.22 (0.05-0.98, p<0.05). Conclusions: NAFLD/NASH is a significant risk factor for hospitalization for Covid-19, and appears to account for risk attributed to obesity. Treatments for metabolic disease mitigated risks from NAFLD/NASH. More research is needed to confirm risk associated with visceral adiposity, and patients should be screened for and informed of treatments for metabolic syndrome.

The Value of Aggregated High-Resolution Intraoperative Data for Predicting Post-Surgical Infectious Complications at Two Independent Sites.

Surgical procedures carry the risk of postoperative infectious complications, which can be severe, expensive, and morbid. A growing body of evidence indicates that high-resolution intraoperative data can be predictive of these complications. However, these studies are often contradictory in their findings as well as difficult to replicate, suggesting that these predictive models may be capturing institutional artifacts. In this work, data and models from two independent institutions, Mayo Clinic and University of Minnesota-affiliated Fairview Health Services, were directly compared using a common set of definitions for the variables and outcomes. We built perioperative risk models for seven infectious post-surgical complications at each site to assess the value of intraoperative variables. Models were internally validated. We found that including intraoperative variables significantly improved the models' predictive performance at both sites for five out of seven complications. We also found that significant intraoperative variables were similar between the two sites for four of the seven complications. Our results suggest that intraoperative variables can be related to the underlying physiology for some infectious complications.

Single Health System Adherence to 2012 Cervical Cancer Screening Guidelines at Extremes of Age and Posthysterectomy.

OBJECTIVE: To estimate the proportion of guideline nonadherent Pap tests in women aged younger than 21 years and older than 65 years and posthysterectomy in a single large health system. Secondary objectives were to describe temporal trends and patient and health care provider characteristics associated with screening in these groups. METHODS: A retrospective cross-sectional chart review was performed at Fairview Health Services and University of Minnesota Physicians. Reasons for testing and patient and health care provider information were collected. Tests were designated as indicated or nonindicated per the 2012 cervical cancer screening guidelines. Point estimates and descriptive statistics were calculated. Patient and health care provider characteristics were compared between indicated and nonindicated groups using χ and Wilcoxon rank-sum tests. RESULTS: A total of 3,920 Pap tests were performed between September 9, 2012, and August 31, 2014. A total of 257 (51%; 95% confidence interval [CI] 46.1-54.9%) of tests in the younger than 21 years group, 536 (40%; 95% CI 37.7-43.1%) in the older than 65 years group, and 605 (29%; 95% CI 27.1-31.0%) in the posthysterectomy group were not indicated. White race in the older than 65 years group was the only patient characteristic associated with receipt of a nonindicated Pap test (P=.007). Health care provider characteristics associated with nonindicated Pap tests varied by screening group. Temporal trends showed a decrease in the proportion of nonindicated tests in the younger than 21 years group but an increase in the posthysterectomy group. CONCLUSION: For women aged younger than 21 years and older than 65 years and posthysterectomy, 35% of Pap tests performed in our health system were not guideline-adherent. There were no patient or health care provider characteristics associated with guideline nonadherent screening across all groups.