Proportion and predictors of FVC decline in patients with interstitial lung disease.

RATIONALE: The proportion of patients who develop progressive pulmonary fibrosis (PPF), along with risk factors for progression remain poorly understood. OBJECTIVES: To examine factors associated with an increased risk of developing PPF among patients at a referral center. METHODS: We identified patients with a diagnosis of interstitial lung disease (ILD) seen within the Cleveland Clinic Health System. Utilizing a retrospective observational approach we estimated the risk of developing progression by diagnosis group and identified key clinical predictors using the FVC component of both the original progressive fibrotic interstitial lung disease (PFILD) and the proposed PPF (ATS) criteria. RESULTS: We identified 5934 patients with a diagnosis of ILD. The cumulative incidence of progression over the 24 months was similar when assessed with the PFILD and PPF criteria (33.1 % and 37.9 % respectively). Of those who met the ATS criteria, 9.5 % did not meet the PFILD criteria. Conversely, 4.3 % of patients who met PFILD thresholds did not achieve the 5 % absolute FVC decline criteria. Significant differences in the rate of progression were seen based on underlying diagnosis. Steroid therapy (HR 1.46, CI 1.31-1.62) was associated with an increased risk of progressive fibrosis by both PFILD and PPF criteria. CONCLUSION: Regardless of the definition used, the cumulative incidence of progressive disease is high in patients with ILD in the 24 months following diagnosis. Some differences are seen in the risk of progression when assessed by PFILD and PPF criteria. Further work is needed to identify modifiable risk factors for the development of progressive fibrosis.

Erratum: Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool. Diabetes Spectrum 2023;36:161-170 (https://doi.org/10.2337/ds22-0031).

[This corrects the article DOI: 10.2337/ds22-0031.].

A preoperative nomogram incorporating CT to predict the probability of ovarian clear cell carcinoma.

OBJECTIVES: To evaluate clinical, laboratory, and radiological variables from preoperative contrast-enhanced computed tomography (CECT) for their ability to distinguish ovarian clear cell carcinoma (OCCC) from non-OCCC and to develop a nomogram to preoperatively predict the probability of OCCC. METHODS: This IRB-approved, retrospective study included consecutive patients who underwent surgery for an ovarian tumor from 1/1/2000 to 12/31/2016 and CECT of the abdomen and pelvis ≤90 days before primary debulking surgery. Using a standardized form, two experienced oncologic radiologists independently analyzed imaging features and provided a subjective 5-point impression of the probability of the histological diagnosis. Nomogram models incorporating clinical, laboratory, and radiological features were created to predict histological diagnosis of OCCC over non-OCCC. RESULTS: The final analysis included 533 patients with surgically confirmed OCCC (n = 61) and non-OCCC (n = 472); history of endometriosis was more often found in patients with OCCC (20% versus 3.6%; p < 0.001), while CA-125 was significantly higher in patients with non-OCCC (351 ng/mL versus 70 ng/mL; p < 0.001). A nomogram model incorporating clinical (age, history of endometriosis and adenomyosis), laboratory (CA-125) and imaging findings (peritoneal implant distribution, morphology, laterality, and diameter of ovarian lesion and of the largest solid component) had an AUC of 0.9 (95% CI: 0.847, 0.949), which was comparable to the AUCs of the experienced radiologists' subjective impressions [0.8 (95% CI: 0.822, 0.891) and 0.9 (95% CI: 0.865, 0.936)]. CONCLUSIONS: A presurgical nomogram model incorporating readily accessible clinical, laboratory, and CECT variables was a powerful predictor of OCCC, a subtype often requiring a distinctive treatment approach.

Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool.

Objective: To assess whether an electronic health record (EHR)-based diabetes intensification tool can improve the rate of A1C goal attainment among patients with type 2 diabetes and an A1C ≥8%. Methods: An EHR-based tool was developed and sequentially implemented in a large, integrated health system using a four-phase, stepped-wedge design (single pilot site [phase 1] and then three practice site clusters [phases 2-4]; 3 months/phase), with full implementation during phase 4. A1C outcomes, tool usage, and treatment intensification metrics were compared retrospectively at implementation (IMP) sites versus nonimplementation (non-IMP) sites with sites matched on patient population characteristics using overlap propensity score weighting. Results: Overall, tool utilization was low among patient encounters at IMP sites (1,122 of 11,549 [9.7%]). During phases 1-3, the proportions of patients achieving the A1C goal (<8%) were not significantly improved between IMP and non-IMP sites at 6 months (range 42.9-46.5%) or 12 months (range 46.5-53.1%). In phase 3, fewer patients at IMP sites versus non-IMP sites achieved the goal at 12 months (46.7 vs. 52.3%, P = 0.02). In phases 1-3, mean changes in A1C from baseline to 6 and 12 months (range -0.88 to -1.08%) were not significantly different between IMP and non-IMP sites. Times to intensification were similar between IMP and non-IMP sites. Conclusion: Utilization of a diabetes intensification tool was low and did not influence rates of A1C goal attainment or time to treatment intensification. The low level of tool adoption is itself an important finding highlighting the problem of therapeutic inertia in clinical practice. Testing additional strategies to better incorporate, increase acceptance of, and improve proficiency with EHR-based intensification tools is warranted.

Unmet needs and opportunities for optimal management of patients with type 2 diabetes and chronic kidney disease.

This brief report utilizes EHR data from a large US health system to summarize unmet needs in patients with type 2 diabetes and chronic kidney disease and identifies areas of opportunity to optimize management within this patient population from treatment, screening and monitoring, and health care resource use perspectives.

Impact of Vaccination, Prior Infection, and Therapy on Omicron Infection and Mortality.

BACKGROUND: Understanding immunity against Omicron infection and severe outcomes conferred by coronavirus disease 2019 (Covid-19) vaccination, prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and monoclonal antibody therapy will inform intervention strategies. METHODS: We considered 295 691 patients tested for SARS-CoV-2 at Cleveland Clinic between 1 October 2021 and 31 January 2022. We used logistic regression to investigate the association of vaccination and prior infection with the risk of SARS-CoV-2 infection and used Cox regression to investigate the association of vaccination, prior infection, and monoclonal antibody therapy with the risks of intensive care unit (ICU) stay and death. RESULTS: Vaccination and prior infection were less effective against Omicron than Delta infection but provided strong protection against ICU admission and death. Boosting greatly increased vaccine effectiveness against Omicron infection and severe outcomes, although effectiveness waned rapidly over time. Monoclonal antibody therapy considerably reduced risks of ICU admission and death. The relatively low mortality of the Omicron variant was due to both reduced lethality of this variant and increased population immunity acquired from booster vaccination and previous infection. CONCLUSIONS: Booster vaccination and prior SARS-CoV-2 infection provide strong protection against ICU admission and death from Omicron infection. Monoclonal antibody therapy is also beneficial.

Association between first-line monotherapy with metformin and the risk of atrial fibrillation (AMRAF) in patients with type 2 diabetes.

BACKGROUND: Type 2 diabetes (T2D) has a strong association with atrial fibrillation (AF) which increases risk of thromboembolic events, heart failure, and frequent hospitalizations. Metformin is the first-line medication for T2D with proposed anti-inflammatory, pro-metabolic, and cardio-protective benefits. Our objective was to investigate if initial therapy with metformin is associated with reduced incidence of AF in comparison to other non-insulin anti-hyperglycemic agents in patients with newly diagnosed T2D. METHODS: This retrospective cohort analysis included adults with a new diagnosis of T2D who were started on monotherapy (except insulin) between 2007 and 2017, without prior anti-hyperglycemic agent use, history of arrhythmias, or estimated GFR (eGFR) ≤ 30 ml/min. A multivariate analysis was performed using a fine-gray regression competing risk analysis to control for confounding variables after which pooled hazard ratios and 95 % confidence intervals were reported. Patients were followed until the end of study date, development of AF, addition of more anti-hyperglycemic agents, or death, whichever occurred first. RESULTS: Among 4584 metformin initiators compared to 1080 non-metformin monotherapy initiators, 10-year cumulative incidence of AF in metformin group was 5.2 % as compared to 8.1 % with other agents which was not statistically significant. Competing risk analysis did not demonstrate reduced rates of AF with metformin use (HR 0.92, 95 % CI 0.69 to 1.21; P = 0.55). Increased age and the presence of congestive heart failure were associated with significantly higher risk of AF in both groups (HR: 1.29, 95 % CI: 1.21 to 1.37; P ≤ 0.001; HR: 2.73, 95 % CI: 1.62 to 4.61; P ≤ 0.001, respectively). CONCLUSION: Initiation of metformin as a first line monotherapy for T2D, when compared to other non-insulin monotherapies, was not associated with decreased risk of developing AF in this retrospective observational study.

Computable Phenotype of a Crohn's Disease Natural History Model.

BACKGROUND: Analytic tools to study important clinical issues in complex, chronic diseases such as Crohn's disease (CD) include randomized trials, claims database studies, or small longitudinal epidemiologic cohorts. Using natural language processing (NLP), we sought to define the computable phenotype health state of pediatric and adult CD and develop patient-level longitudinal histories for health outcomes. METHODS: We defined 6 health states for CD using a subjective symptom-based assessment (symptomatic/asymptomatic) and an objective disease state assessment (active/inactive/no testing). Gold standard for the 6 health states was derived using an iterative process during review by our CD experts. We calculated the transition probabilities to estimate the time to transitions between the various health states using nonparametric Kaplan-Meier estimation and a Markov model. Finally, we determined a standard utility measure from clinical patients assigned to different health states. RESULTS: The NLP computable phenotype health state model correctly ascertained the objective test results and symptoms 96% and 85% of the time, respectively, based on a blinded chart evaluation. In our model, >25% of patients who begin as asymptomatic/active transition to symptomatic/active over the following year. For both adult and pediatric CD health states, the utility assessments of a symptomatic/inactive health state closely resembled a symptomatic/active health state. CONCLUSIONS: Our methodology for a computable phenotype health state demonstrates the application of real-world data to define progression and optimal management of a chronic disease such as CD. The application of the model has the potential to lead to a better understanding of the true impact of a therapeutic intervention and can provide long-term cost-effectiveness analyses for a new therapy. HIGHLIGHTS: Using natural language processing, we defined the computable phenotype health state of Crohn's disease and developed patient-level longitudinal histories for health outcomes.Our methodology demonstrates the application of real-world data to define the progression of a chronic disease.The application of the model has the potential to provide better understanding of the true impact of a new therapy.

Derivation and Validation of the Critical Bronchiolitis Score for the PICU.

OBJECTIVES: To derive and internally validate a bronchiolitis-specific illness severity score (the Critical Bronchiolitis Score) that out-performs mortality-based illness severity scores (e.g., Pediatric Risk of Mortality) in measuring expected duration of respiratory support and PICU length of stay for critically ill children with bronchiolitis. DESIGN: Retrospective database study using the Virtual Pediatric Systems (VPS, LLC; Los Angeles, CA) database. SETTING: One-hundred twenty-eight North-American PICUs. PATIENTS: Fourteen-thousand four-hundred seven children less than 2 years old admitted to a contributing PICU with primary diagnosis of bronchiolitis and use of ICU-level respiratory support (defined as high-flow nasal cannula, noninvasive ventilation, invasive mechanical ventilation, or negative pressure ventilation) at 12 hours after PICU admission. INTERVENTIONS: Patient-level variables available at 12 hours from PICU admission, duration of ICU-level respiratory support, and PICU length of stay data were extracted for analysis. After randomly dividing the cohort into derivation and validation groups, patient-level variables that were significantly associated with the study outcomes were selected in a stepwise backward fashion for inclusion in the final score. Score performance in the validation cohort was assessed using root mean squared error and mean absolute error, and performance was compared with that of existing PICU illness severity scores. MEASUREMENTS AND MAIN RESULTS: Twelve commonly available patient-level variables were included in the Critical Bronchiolitis Score. Outcomes calculated with the score were similar to actual outcomes in the validation cohort. The Critical Bronchiolitis Score demonstrated a statistically significantly stronger association with duration of ICU-level respiratory support and PICU length of stay than mortality-based scores as measured by root mean squared error and mean absolute error. CONCLUSIONS: The Critical Bronchiolitis Score performed better than PICU mortality-based scores in measuring expected duration of ICU-level respiratory support and ICU length of stay. This score may have utility to enrich interventional trials and adjust for illness severity in observational studies in this very common PICU condition.

Development and validation of pretreatment nomogram for disease-specific mortality in gastric cancer-A competing risk analysis.

BACKGROUND: In several reports, gastric cancer nomograms for predicting overall or disease-specific survival have been described. The American Joint Committee on Cancer (AJCC) introduced the attractiveness of disease-specific mortality (DSM) as an endpoint of risk model. This study aimed to develop the first pretreatment gastric cancer nomogram for predicting DSM that considers competing risks (CRs). METHODS: The prediction model was developed using data for 5231 gastric cancer patients. Fifteen prognosticators, which were registered at diagnosis, were evaluated. The nomogram for DSM was created as visualizations of the multivariable Fine and Gray regression model. An independent cohort for external validation consisted of 389 gastric cancer patients from a different institution. The performance of the model was assessed by discrimination (Harrell's concordance (C)-index), calibration, and decision curve analysis. DSM and CRs were evaluated, paying special attention to host-related factors such as age and Eastern Cooperative Oncology Group performance status (ECOG PS), by using Gray's univariable method. RESULTS: Fourteen prognostic factors were selected to develop the nomogram. The new nomogram for DSM exhibited good discrimination. Its C-index of 0.887 surpassed that of the American Joint Committee on Cancer (AJCC) clinical staging (0.794). The C-index was 0.713 (AJCC, 0.582) for the external validation cohort. The nomogram showed good performance internally and externally, in the calibration and decision curve analysis. Host-related factors including age and ECOG PS, were strongly correlated with competing risks. CONCLUSIONS: The newly developed nomogram accurately predicts DSM, which can be used for patient counseling in clinical practice.

Protective heterologous T cell immunity in COVID-19 induced by the trivalent MMR and Tdap vaccine antigens.

BACKGROUND: T cells control viral infection, promote vaccine durability, and in coronavirus disease 2019 (COVID-19) associate with mild disease. We investigated whether prior measles-mumps-rubella (MMR) or tetanus-diphtheria-pertussis (Tdap) vaccination elicits cross-reactive T cells that mitigate COVID-19. METHODS: Antigen-presenting cells (APC) loaded ex vivo with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), MMR, or Tdap antigens and autologous T cells from COVID-19-convalescent participants, uninfected individuals, and COVID-19 mRNA-vaccinated donors were co-cultured. T cell activation and phenotype were detected by interferon-γ (IFN-γ) enzyme-linked immunospot (ELISpot) assays and flow cytometry. ELISAs (enzyme-linked immunosorbant assays) and validation studies identified the APC-derived cytokine(s) driving T cell activation. TCR clonotyping and single-cell RNA sequencing (scRNA-seq) identified cross-reactive T cells and their transcriptional profile. A propensity-weighted analysis of COVID-19 patients estimated the effects of MMR and Tdap vaccination on COVID-19 outcomes. FINDINGS: High correlation was observed between T cell responses to SARS-CoV-2 (spike-S1 and nucleocapsid) and MMR and Tdap proteins in COVID-19-convalescent and -vaccinated individuals. The overlapping T cell population contained an effector memory T cell subset (effector memory re-expressing CD45RA on T cells [TEMRA]) implicated in protective, anti-viral immunity, and their detection required APC-derived IL-15, known to sensitize T cells to activation. Cross-reactive TCR repertoires detected in antigen-experienced T cells recognizing SARS-CoV-2, MMR, and Tdap epitopes had TEMRA features. Indices of disease severity were reduced in MMR- or Tdap-vaccinated individuals by 32%-38% and 20%-23%, respectively, among COVID-19 patients. CONCLUSIONS: Tdap and MMR memory T cells reactivated by SARS-CoV-2 may provide protection against severe COVID-19. FUNDING: This study was supported by a National Institutes of Health (R01HL065095, R01AI152522, R01NS097719) donation from Barbara and Amos Hostetter and the Chleck Foundation.

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

BACKGROUND: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. METHODS: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. RESULTS: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). CONCLUSIONS: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.

Protective heterologous T cell immunity in COVID-19 induced by MMR and Tdap vaccine antigens.

T cells are critical for control of viral infection and effective vaccination. We investigated whether prior Measles-Mumps-Rubella (MMR) or Tetanus-Diphtheria-pertussis (Tdap) vaccination elicit cross-reactive T cells that mitigate COVID-19. Using co-cultures of antigen presenting cells (APC) loaded with antigens and autologous T cells, we found a high correlation between responses to SARS-CoV-2 (Spike-S1 and Nucleocapsid) and MMR and Tdap vaccine proteins in both SARS-CoV-2 infected individuals and individuals immunized with mRNA-based SARS-CoV-2 vaccines. The overlapping T cell population contained effector memory T cells (TEMRA) previously implicated in anti-viral immunity and their activation required APC-derived IL-15. TCR- and scRNA-sequencing detected cross-reactive clones with TEMRA features among the cells recognizing SARS-CoV-2, MMR and Tdap epitopes. A propensity-weighted analysis of 73,582 COVID-19 patients revealed that severe disease outcomes (hospitalization and transfer to intensive care unit or death) were reduced in MMR or Tdap vaccinated individuals by 38-32% and 23-20% respectively. In summary, SARS-CoV-2 re-activates memory T cells generated by Tdap and MMR vaccines, which may reduce disease severity.

A first step towards a global nomogram to predict disease progression for men on active surveillance.

BACKGROUND: Signs of disease progression (28%) and conversion to active treatment without evidence of disease progression (13%) are the main reasons for discontinuation of active surveillance (AS) in men with localised prostate cancer (PCa). We aimed to develop a nomogram to predict disease progression in these patients. METHODS: As a first step in the development of a nomogram, using data from Movembers' GAP3 Consortium (n=14,380), we assessed heterogeneity between centres in terms of risk of disease progression. We started with assessment of baseline hazards for disease progression based on grouping of centres according to follow-up protocols [high: yearly; intermediate: ~2 yearly; and low: at year 1, 4 & 7 (i.e., PRIAS)]. We conducted cause-specific random effect Cox proportional hazards regression to estimate risk of disease progression by centre in each group. RESULTS: Disease progression rates varied substantially between centres [median hazard ratio (MHR): 2.5]. After adjustment for various clinical factors (age, year of diagnosis, Gleason grade group, number of positive cores and PSA), substantial heterogeneity in disease progression remained between centres. CONCLUSIONS: When combining worldwide data on AS, we noted unexplained differences of disease progression rate even after adjustment for various clinical factors. This suggests that when developing a global nomogram, local adjustments for differences in risk of disease progression and competing outcomes such as conversion to active treatment need to be considered.

Development and Internal Validation of A Prediction Tool To Assist Clinicians Selecting Second-Line Therapy Following Metformin Monotherapy For Type 2 Diabetes.

OBJECTIVE: Adults with type 2 diabetes (T2D) face increased risk of many long-term adverse outcomes. While managing patients with T2D, clinicians are challenged to stay informed regarding all new therapies and must consider potential risks and benefits resultant to their use. Metformin (MET) is typically prescribed as first-line therapy, but a second line is often needed, given MET can be insufficient for maintaining long-term glycemic control. Our objective was to develop a predictive decision-making tool to help clinicians use an outcome-based approach to select second-line therapies for patients when MET monotherapy is insufficient for glycemic control. METHODS: Electronic health records of 19 277 adults with T2D on MET monotherapy and ≥3 months of either GLP-1RA, DPP-4i, Insulin, SGLT-2i, SFU, or TZD therapy were reviewed at Cleveland Clinic from patient visits occurring between 2005 and 2019. Separate models were developed to predict likelihood of each main outcome measure (stroke, myocardial infarction, worsening hypertension, renal failure, and death). Discrimination and calibration were assessed with bootstrapping. RESULTS: The median follow-up time for those without an event was 3.6 years (interquartile range 1.9, 6.3). Model discrimination ability was evaluated by concordance indices (goodness of fit metric with values ranging between 0 and 1: 1 indicates perfect discrimination ability; 0.5 reflects same discrimination ability as chance) demonstrating strong discrimination ability, with concordance index values for outcomes as follows: myocardial infarction (0.786), stroke (0.805), worsening hypertension (0.855), renal failure (0.808), and death (0.827). CONCLUSION: A decision-making tool has been developed that may afford clinicians a more objective and individualized approach to choosing a second-line therapy to control glycemia for persons with T2D.

Phenotypes and Subphenotypes of Patients With COVID-19: A Latent Class Modeling Analysis.

BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts. RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? STUDY DESIGN AND METHODS: We included adult patients (≥ 18 years) positive for laboratory-confirmed SARS-CoV-2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set. RESULTS: We analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91). INTERPRETATION: Hypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.

Healthcare utilization and patient and provider experience with a home visit program for patients discharged from the hospital at high risk for readmission.

BACKGROUND: Home visits after hospital discharge may reduce future healthcare utilization. We assessed the association of home visits by advanced practice registered nurses (APRN) and paramedics with healthcare utilization and mortality, and provider and patient experience. METHODS: We conducted a retrospective cohort study using convergent mixed methods in one health system including adult medical patients discharged to home from November 2017-September 2019. We assessed outcomes for home visit vs. matched comparison patients at 30, 90, and 180 days, including hospital admission, emergency department (ED) use, and death: Phase 1 (APRN or paramedic visits assigned by geographic location) and Phase 2 (APRN and paramedic visit teams assigned to patients). Patients declining home visits and those accepting were also compared. Semi-structured interviews were conducted with home visit patients and providers, primary care providers, and nurse care coordinators. RESULTS: In Phase 1, the 101 home visit matched to 303 comparison patients showed no differences in readmissions, ED visits, or death at 30, 90, and 180 days. In Phase 2, 157 home visit matched to 471 comparison patients had fewer 30-day readmissions (19.1% vs. 28.7%, p 0.024) and no differences in other outcomes. Compared with patients declining home visits, patients accepting had lower odds of 30-day readmission. In 44 interviews, themes of Medication Understanding, Knowledge Gap after Discharge, Patient Medical Complexity, Social Context, and Patient Engagement/Need for Reassurance emerged. CONCLUSION: Post-discharge home visits by APRNs and paramedics working together were associated with reduced 30-day readmissions. Identified themes could inform strategies to improve patient support.

COVID-19 Home Monitoring After Diagnosis and Health Care Utilization in an Integrated Health System.

This cohort study examines health care utilization patterns for patients with COVID-19 who were enrolled vs not enrolled in a home monitoring program.

Differences in Biologic Utilization and Surgery Rates in Pediatric and Adult Crohn's Disease: Results From a Large Electronic Medical Record-derived Cohort.

BACKGROUND AND AIMS: Crohn's disease (CD) is a chronic illness that affects both the pediatric and adult populations with an increasing worldwide prevalence. We aim to identify a large, single-center cohort of patients with CD using natural language processing (NLP) in combination with codified data and extract surgical rates and medication usage from the electronic medical record (EMR). METHODS: Patients with CD were identified from the entire Cleveland Clinic EMR using ICD codes and CD-specific terms identified by NLP to fit a logistic regression model. Cohorts were developed for pediatric-onset (younger than 18 years) and adult-onset (18 years and older) CD. Surgeries were identified using current procedural terminology (CPT) codes and NLP. Crohn's disease-related medications were extracted using physician orders in the EMR. RESULTS: Patients with pediatric-onset (n = 2060) and adult-onset (n = 4973) CD were identified from 2000 to 2017 with a positive predictive value of 98.5%. Rate of CD-related abdominal surgery over time was significantly higher in adult-onset compared with pediatric-onset CD (10-year surgery rate 49.9% vs 37.7%, respectively; P < 0.001). Treatment with biologics was significantly higher in pediatric vs adult-onset CD cohorts (63.6% vs 49.2%; P < 0.001). The overall rate of CD-related abdominal surgery was significantly higher in those who received <6 months of a biologic compared with ≥6 months of a biologic for both cohorts (pediatric 64.1% vs 39.1%, P ≤ 0.001; adult 69.3% vs 56.5%, P ≤ 0.001). Additionally, 60.9% in pediatric-onset CD and 43.5% in adult-onset CD treated with ≥6 months of biologic therapy have not required abdominal surgery. On multivariable analysis, perianal surgery was a significant risk factor for abdominal surgery in both cohorts. CONCLUSION: We used a combination of codified and NLP data to establish the largest, North American, single-center EMR cohort of pediatric- and adult-onset CD patients and determined that biologics are associated with lower rates of surgery over time, potentially altering the natural history of the disease.

3-point major cardiovascular event outcome for patients with T2D treated with dipeptidyl peptidase-4 inhibitor or glucagon-like peptide-1 receptor agonist in addition to metformin monotherapy.

BACKGROUND: The global incidence of type 2 diabetes (T2D) continues to increase annually, and persons with T2D typically require regular changes in pharmacologic invention for achieving glycemic targets. Healthcare providers must consider multiple factors when selecting a 2nd line. This retrospective cohort study evaluates impact of two common anti-diabetes medication classes (dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists) on the well-known composite 3-point major cardiovascular events outcome (3P-MACE, comprised of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke). No significant impact was found. Persons with T2D face increased risks of many adverse cardiovascular outcomes. This study duplicated common inclusion and exclusion criteria to create an observational cohort from a large healthcare system's electronic health records for testing DPP-4i and GLP-1RA against each other to evaluate impact on likelihood to develop 3P-MACE. METHODS: The statistical model and analyses were based on a cohort of 5,518 adult patients with T2D who were prescribed metformin and either DPP-4i or GLP-1RA to control glycemia during clinic visits between January 2005 and September 2019. A Cox proportional hazards model was developed from the cohort to predict the 3P-MACE endpoint. RESULTS: The model did not show a meaningful difference in likelihood of developing the 3P-MACE outcome between patients treated with DPP-4i compared to patients treated with GLP-1RA. CONCLUSIONS: Prior history of cardiovascular disease (CVD) did not impact this small difference between the two classes of drug.