Long COVID Characteristics and Experience: A Descriptive Study from the Yale LISTEN Research Cohort.

OBJECTIVE: To describe the experience of people with long COVID symptomatology and characterize the psychological, social, and financial challenges they experience. BACKGROUND: The experience of people with long COVID needs further amplification, especially with a comprehensive focus on symptomatology, treatments, and impact on daily life and finances. METHODS: We collected data from individuals aged 18 and older reporting long COVID as participants in the Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study. The sample population included 441 participants surveyed between May 2022 and July 2023. We evaluated their demographic characteristics, socioeconomic and psychological status, index infection period, health status, quality of life, symptoms, treatments, pre-pandemic comorbidities, and new-onset conditions. RESULTS: Overall, the median age of the participants with long COVID was 46 years (IQR: 38 to 57 years); 74% were women, 86% were Non-Hispanic White, and 93% were from the United States. Participants reported low health status measured by the Euro-QoL visual analogue scale, with a median score of 49 (IQR: 32 to 61). Participants documented a diverse range of symptoms, with all 96 possible symptom choices being reported. Additionally, participants had tried many treatments (median number of treatments: 19, IQR: 12 to 28). They were also experiencing psychological distress, social isolation, and financial stress. CONCLUSIONS: Despite having tried numerous treatments, participants with long COVID continued to experience an array of health and financial challenges-findings that underscore the failure of the healthcare system to address the medical needs of people with long COVID. These insights highlight the need for crucial medical, mental health, financial, and community support services, as well as further scientific investigation, to address the complex impact of long COVID.

Post-Vaccination Syndrome: A Descriptive Analysis of Reported Symptoms and Patient Experiences After Covid-19 Immunization.

Introduction: A chronic post-vaccination syndrome (PVS) after covid-19 vaccination has been reported but has yet to be well characterized. Methods: We included 241 individuals aged 18 and older who self-reported PVS after covid-19 vaccination and who joined the online Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study from May 2022 to July 2023. We summarized their demographics, health status, symptoms, treatments tried, and overall experience. Results: The median age of participants was 46 years (interquartile range [IQR]: 38 to 56), with 192 (80%) identifying as female, 209 (87%) as non-Hispanic White, and 211 (88%) from the United States. Among these participants with PVS, 127 (55%) had received the BNT162b2 [Pfizer-BioNTech] vaccine, and 86 (37%) received the mRNA-1273 [Moderna] vaccine. The median time from the day of index vaccination to symptom onset was three days (IQR: 1 day to 8 days). The time from vaccination to symptom survey completion was 595 days (IQR: 417 to 661 days). The median Euro-QoL visual analogue scale score was 50 (IQR: 39 to 70). The five most common symptoms were exercise intolerance (71%), excessive fatigue (69%), numbness (63%), brain fog (63%), and neuropathy (63%). In the week before survey completion, participants reported feeling unease (93%), fearfulness (82%), and overwhelmed by worries (81%), as well as feelings of helplessness (80%), anxiety (76%), depression (76%), hopelessness (72%), and worthlessness (49%) at least once. Participants reported a median of 20 (IQR: 13 to 30) interventions to treat their condition. Conclusions: In this study, individuals who reported PVS after covid-19 vaccination had low health status, high symptom burden, and high psychosocial stress despite trying many treatments. There is a need for continued investigation to understand and treat this condition.

Internal tremors and vibrations in long COVID: a cross-sectional study.

Importance: Internal tremors and vibrations symptoms have been described as part of neurologic disorders but not fully described as a part of long COVID. Objective: To compare demographics, socioeconomic characteristics, pre-pandemic comorbidities, new-onset conditions, and long COVID symptoms between people with internal tremors and vibrations as part of their long COVID symptoms and people with long COVID but without these symptoms. Design: A cross-sectional study, Listen to Immune, Symptom and Treatment Experiences Now (LISTEN), of adults with and without long COVID and post-vaccination syndrome, defined by self-report. Setting: Hugo Health Kindred, a decentralized digital research platform hosting a network of English-speaking adults interested in contributing to COVID-related research. No geographic limitation applied. Participants: The study population included 423 participants who enrolled in LISTEN between May 2022 and June 2023, completed the initial and the conditions and symptoms surveys, reported long COVID, and did not report post-vaccination syndrome. Exposure: Long COVID symptoms of internal tremors and vibrations. Main outcomes and Measures: Demographics, pre-pandemic comorbidities, and current conditions, other symptoms, and quality of life at the time of surveys. Results: Of the 423 participants (median age, 46 years [IQR, 38-56]), 74% were female, 87% were Non-Hispanic White, 92% lived in the United States, 46% were infected before the Delta wave, and 158 (37%) reported "internal tremors, or buzzing/vibration" as a long COVID symptom. Before long COVID, the groups had similar comorbidities. Participants with internal tremors were different from others in having worse health as measured by the Euro-QoL visual analogue scale (median: 40 points [IQR, 30-60] vs. 50 points [IQR, 35-62], P = 0.007), having financial difficulties caused by the pandemic (very much financial difficulties, 22% [95% CI, 16-30] vs. 11% [7.3-15], P < 0.001), often feeling socially isolated (43% [95% CI, 35-52] vs. 37% [31-43], P = 0.039), and having higher rates of self-reported new-onset mast cell disorders (11% [95% CI, 7.1-18] vs. 2.6% [1.2-5.6], Bonferroni-adjusted P = 0.008) and neurologic conditions (including but not limited to seizures, dementia, multiple sclerosis, Parkinson's disease, neuropathy, etc.; 22% [95% CI, 16-29] vs. 8.3% [5.4-12], Bonferroni-adjusted P = 0.004). Conclusions and Relevance: Among people with long COVID, those with internal tremors and vibrations have several other associated symptoms and worse health status, despite having similar pre-pandemic comorbidities, suggesting it may reflect a severe phenotype of long COVID. KEY POINTS: Question: Do people with long COVID symptoms of internal tremors and vibrations differ from others with long COVID but without these symptoms?Findings: In this cross-sectional study that included 423 adults with long COVID, 158 (37%) reported having "internal tremors, or buzzing/vibration," had worse quality of life, more financial difficulties, and higher rates of new-onset mast cell disorders and neurologic conditions, compared with others with long COVID but without internal tremors and vibrations.Meaning: Internal tremors and vibrations may reflect a severe phenotype of long COVID.

Association between primary or booster COVID-19 mRNA vaccination and Omicron lineage BA.1 SARS-CoV-2 infection in people with a prior SARS-CoV-2 infection: A test-negative case-control analysis.

BACKGROUND: The benefit of primary and booster vaccination in people who experienced a prior Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains unclear. The objective of this study was to estimate the effectiveness of primary (two-dose series) and booster (third dose) mRNA vaccination against Omicron (lineage BA.1) infection among people with a prior documented infection. METHODS AND FINDINGS: We conducted a test-negative case-control study of reverse transcription PCRs (RT-PCRs) analyzed with the TaqPath (Thermo Fisher Scientific) assay and recorded in the Yale New Haven Health system from November 1, 2021, to April 30, 2022. Overall, 11,307 cases (positive TaqPath analyzed RT-PCRs with S-gene target failure [SGTF]) and 130,041 controls (negative TaqPath analyzed RT-PCRs) were included (median age: cases: 35 years, controls: 39 years). Among cases and controls, 5.9% and 8.1% had a documented prior infection (positive SARS-CoV-2 test record ≥90 days prior to the included test), respectively. We estimated the effectiveness of primary and booster vaccination relative to SGTF-defined Omicron (lineage BA.1) variant infection using a logistic regression adjusted for date of test, age, sex, race/ethnicity, insurance, comorbidities, social venerability index, municipality, and healthcare utilization. The effectiveness of primary vaccination 14 to 149 days after the second dose was 41.0% (95% confidence interval (CI): 14.1% to 59.4%, p 0.006) and 27.1% (95% CI: 18.7% to 34.6%, p < 0.001) for people with and without a documented prior infection, respectively. The effectiveness of booster vaccination (≥14 days after booster dose) was 47.1% (95% CI: 22.4% to 63.9%, p 0.001) and 54.1% (95% CI: 49.2% to 58.4%, p < 0.001) in people with and without a documented prior infection, respectively. To test whether booster vaccination reduced the risk of infection beyond that of the primary series, we compared the odds of infection among boosted (≥14 days after booster dose) and booster-eligible people (≥150 days after second dose). The odds ratio (OR) comparing boosted and booster-eligible people with a documented prior infection was 0.79 (95% CI: 0.54 to 1.16, p 0.222), whereas the OR comparing boosted and booster-eligible people without a documented prior infection was 0.54 (95% CI: 0.49 to 0.59, p < 0.001). This study's limitations include the risk of residual confounding, the use of data from a single system, and the reliance on TaqPath analyzed RT-PCR results. CONCLUSIONS: In this study, we observed that primary vaccination provided significant but limited protection against Omicron (lineage BA.1) infection among people with and without a documented prior infection. While booster vaccination was associated with additional protection against Omicron BA.1 infection in people without a documented prior infection, it was not found to be associated with additional protection among people with a documented prior infection. These findings support primary vaccination in people regardless of documented prior infection status but suggest that infection history may impact the relative benefit of booster doses.

Rapid emergence of SARS-CoV-2 Omicron variant is associated with an infection advantage over Delta in vaccinated persons.

Background: The SARS-CoV-2 Omicron variant became a global concern due to its rapid spread and displacement of the dominant Delta variant. We hypothesized that part of Omicron's rapid rise was based on its increased ability to cause infections in persons that are vaccinated compared to Delta. Methods: We analyzed nasal swab PCR tests for samples collected between December 12 and 16, 2021, in Connecticut when the proportion of Delta and Omicron variants was relatively equal. We used the spike gene target failure (SGTF) to classify probable Delta and Omicron infections. We fitted an exponential curve to the estimated infections to determine the doubling times for each variant. We compared the test positivity rates for each variant by vaccination status, number of doses, and vaccine manufacturer. Generalized linear models were used to assess factors associated with odds of infection with each variant among persons testing positive for SARS-CoV-2. Findings: For infections with high virus copies (Ct < 30) among vaccinated persons, we found higher odds that they were infected with Omicron compared to Delta, and that the odds increased with increased number of vaccine doses. Compared to unvaccinated persons, we found significant reduction in Delta positivity rates after two (43.4%-49.1%) and three vaccine doses (81.1%), while we only found a significant reduction in Omicron positivity rates after three doses (62.3%). Conclusion: The rapid rise in Omicron infections was likely driven by Omicron's escape from vaccine-induced immunity. Funding: This work was supported by the Centers for Disease Control and Prevention (CDC).

A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations.

Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020-March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record.

OBJECTIVE: Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations. METHODS: Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations. RESULTS: Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19. CONCLUSIONS: COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection.

OBJECTIVE: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2. DESIGN: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository. SETTING: Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas. POPULATIONS: The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020. MAIN OUTCOME AND PERFORMANCE MEASURES: Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support. RESULTS: Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups. CONCLUSIONS: This observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

First Report of the Hop Cyst Nematode, Heterodera humuli, in Two Counties of the Yakima Valley Region, WA, USA.

Cyst nematodes are ranked as the second most damaging plant-parasitic nematode genus of crops worldwide (Jones et al. 2013). The hop cyst nematode, Heterodera humuli, has been reported to cause up to 38% reduction in dry hops per bine (Hay and Pethybridge 2003). America is the top hop producing country worldwide, with 75% of production occurring in Washington state, with the majority of this production occurring in the Yakima Valley region (USDA, 2019). In late 2019, 30 soil samples from 15 different fields were collected from the hop cvs. HBC 394, HBC 369, and YCR 14. Nematodes were extracted using an adapted centrifugal floatation method (Jenkins 1964) from 100 cc subsamples of soil. Twenty of these samples contained at least one cyst and 23 contained at least one juvenile. Body length of juveniles (n = 5) averaged + standard deviation 377.62 ± 4.76 µm which is consistent with H. humuli juvenile body measurements (Sen 1968). Three samples from Yakima County and two from Benton County were identified to the species level using sequences from the internal transcribed spacer (ITS) region of the 5.8S gene. The sequences (GenBank accession numbers MT840678 to MT840682) were amplified using forward primer 5.8S-F (5'-GTGATTCCATTCACCAHCTACCTG-3'), and reverse primer 5.8S-R (5'-TTCGCACTAATTATCGCAGTTGG-3'). Sequence comparison with available ITS (5.8S) sequences in GenBank using BLAST showed 99.85% identity to H. humuli for all five samples. Because COI sequences of H. humuli are not available, to provide an additional marker for species identification, we amplified the COI sequences by using (forward primer Hete-COI-F (5'-TTTGGDCAYCCHGARGTTTATGTT-3'), and reverse primer Hete-COI-R (5'-AYWGTAAAAAGGRRAATAAAACC-3') for these samples. Four COI sequences (GenBank accession numbers MT840683 to MT840686) were obtained. These COI sequences will be used to identify future H. humuli samples. To confirm pathogenicity, eight 1-gal pots were filled with a 90:10 play sand to potting soil mixture and one hop rhizome cv. 'Centennial' was planted in pots and maintained in a greenhouse. After above ground plant growth was observed, half the pots were inoculated with hand-picked H. humuli cysts from Yakima soil samples at a density of 10 cysts/100 cc of soil. The life cycle of H. humuli in potted experiments is 40 days (McNamara and Mende 1995). Forty-five days after inoculation, plant measurements were recorded and nematodes extracted from five 100 cc soil samples per pot as described above. Soil samples revealed that H. humuli populations had an average Reproductive Factor (RF = final nematode population/initial nematode population) of 2.08. Five cysts were crushed to determine eggs/cyst, which yielded an average of 101 eggs/cyst. Young infected hops lacked vigor, with all replicates stunted both in bine height and leaf length compared to healthy controls. Bine heights were reduced by an average of 40.4% in pots inoculated with H. humuli compared to control plants (P = 0.0016). Distribution of hop cyst within the United States is limited to the top four states for hop production: Washington, Oregon, Idaho and Michigan (Cobb 1962; Sen and Jensen 1967; Hafez et al. 2010, Warner and Bird, 2015). In 1962, Cobb reported H. humuli in Pierce County, Washington, but it had not been reported in Benton County and Yakima County until now. This is a significant finding that has the potential to impact the Washington state hop industry, valued at $475.7 million in 2019 (USDA, 2019). Due to the lack of known effective nematode control measures, the discovery of H. humuli in the major hop-growing region of Washington warrants concern.

Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection.

OBJECTIVE: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2. DESIGN: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository. SETTING: Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas. POPULATIONS: The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020. MAIN OUTCOME AND PERFORMANCE MEASURES: Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support. RESULTS: Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups. CONCLUSIONS: This observational study identified, among people testing positive for SARSCoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

Patient factors associated with SARS-CoV-2 in an admitted emergency department population.

Background: The SARS-CoV-2 (COVID-19) virus has wide community spread. The aim of this study was to describe patient characteristics and to identify factors associated with COVID-19 among emergency department (ED) patients under investigation for COVID-19 who were admitted to the hospital. Methods: This was a retrospective observational study from 8 EDs within a 9-hospital health system. Patients with COVID-19 testing around the time of hospital admission were included. The primary outcome measure was COVID-19 test result. Patient characteristics were described and a multivariable logistic regression model was used to identify factors associated with a positive COVID-19 test. Results: During the study period from March 1, 2020 to April 8, 2020, 2182 admitted patients had a test resulted for COVID-19. Of these patients, 786 (36%) had a positive test result. For COVID-19-positive patients, 63 (8.1%) died during hospitalization. COVID-19-positive patients had lower pulse oximetry (0.91 [95% confidence interval, CI], [0.88-0.94]), higher temperatures (1.36 [1.26-1.47]), and lower leukocyte counts than negative patients (0.78 [0.75-0.82]). Chronic lung disease (odds ratio [OR] 0.68, [0.52-0.90]) and histories of alcohol (0.64 [0.42-0.99]) or substance abuse (0.39 [0.25-0.62]) were less likely to be associated with a positive COVID-19 result. Conclusion: We observed a high percentage of positive results among an admitted ED cohort under investigation for COVID-19. Patient factors may be useful in early differentiation of patients with COVID-19 from similarly presenting respiratory illnesses although no single factor will serve this purpose.

Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data.

Importance: Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models. Objective: To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Design, Setting, and Participants: This comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019. Main Outcomes and Measures: The models' goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2. Results: Among the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions. Conclusions and Relevance: Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.

Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data.

Importance: Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement. Objective: To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures. Design, Setting, and Participants: This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018. Main Outcomes and Measures: The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes. Results: There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers. Conclusions and Relevance: Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

Accurate estimation of cardiovascular risk in a non-diabetic adult: detecting and correcting the error in the reported Framingham Risk Score for the Systolic Blood Pressure Intervention Trial population.

OBJECTIVES: To understand the discrepancy between the published 10-year cardiovascular risk and 10-year cardiovascular risk generated from raw data using the Framingham Risk Score for participants in the Systolic Blood Pressure Intervention Trial (SPRINT). DESIGN: Secondary analysis of SPRINT data published in The New England Journal of Medicine (NEJM) and made available to researchers in late 2016. SETTING: SPRINT clinical trial sites. PARTICIPANTS: Study participants enrolled into SPRINT. RESULTS: The number of SPRINT study participants identified as having ≥15% 10-year cardiovascular risk was not consistent with what was reported in the original publication. Using the data from the trial, the Framingham Risk Score indicated ≥15% 10-year cardiovascular risk for 7089 participants compared with 5737 reported in the paper, a change from 61% to 76% of the total study population. CONCLUSIONS: The analysis of the clinical trial data by independent investigators identified an error in the reporting of the risk of the study population. The SPRINT trial enrolled a higher risk population than was reported in the initial publication, which was brought to light by data sharing.

Quantifying the utilization of medical devices necessary to detect postmarket safety differences: A case study of implantable cardioverter defibrillators.

PURPOSE: To estimate medical device utilization needed to detect safety differences among implantable cardioverter defibrillators (ICDs) generator models and compare these estimates to utilization in practice. METHODS: We conducted repeated sample size estimates to calculate the medical device utilization needed, systematically varying device-specific safety event rate ratios and significance levels while maintaining 80% power, testing 3 average adverse event rates (3.9, 6.1, and 12.6 events per 100 person-years) estimated from the American College of Cardiology's 2006 to 2010 National Cardiovascular Data Registry of ICDs. We then compared with actual medical device utilization. RESULTS: At significance level 0.05 and 80% power, 34% or fewer ICD models accrued sufficient utilization in practice to detect safety differences for rate ratios <1.15 and an average event rate of 12.6 events per 100 person-years. For average event rates of 3.9 and 12.6 events per 100 person-years, 30% and 50% of ICD models, respectively, accrued sufficient utilization for a rate ratio of 1.25, whereas 52% and 67% for a rate ratio of 1.50. Because actual ICD utilization was not uniformly distributed across ICD models, the proportion of individuals receiving any ICD that accrued sufficient utilization in practice was 0% to 21%, 32% to 70%, and 67% to 84% for rate ratios of 1.05, 1.15, and 1.25, respectively, for the range of 3 average adverse event rates. CONCLUSIONS: Small safety differences among ICD generator models are unlikely to be detected through routine surveillance given current ICD utilization in practice, but large safety differences can be detected for most patients at anticipated average adverse event rates.

Systolic Blood Pressure Response in SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD (Action to Control Cardiovascular Risk in Diabetes): A Possible Explanation for Discordant Trial Results.

BACKGROUND: SPRINT (Systolic Blood Pressure Intervention Trial) and the ACCORD (Action to Control Cardiovascular Risk in Diabetes) blood pressure trial used similar interventions but produced discordant results. We investigated whether differences in systolic blood pressure (SBP) response contributed to the discordant trial results. METHODS AND RESULTS: We evaluated the distributions of SBP response during the first year for the intensive and standard treatment groups of SPRINT and ACCORD using growth mixture models. We assessed whether significant differences existed between trials in the distributions of SBP achieved at 1 year and the treatment-independent relationships of achieved SBP with risks of primary outcomes defined in each trial, heart failure, stroke, and all-cause death. We examined whether visit-to-visit variability was associated with heterogeneous treatment effects. Among the included 9027 SPRINT and 4575 ACCORD participants, the difference in mean SBP achieved between treatment groups was 15.7 mm Hg in SPRINT and 14.2 mm Hg in ACCORD, but SPRINT had significantly less between-group overlap in the achieved SBP (standard deviations of intensive and standard groups, respectively: 6.7 and 5.9 mm Hg in SPRINT versus 8.8 and 8.2 mm Hg in ACCORD; P<0.001). The relationship between achieved SBP and outcomes was consistent across trials except for stroke and all-cause death. Higher visit-to-visit variability was more common in SPRINT but without treatment-effect heterogeneity. CONCLUSIONS: SPRINT and ACCORD had different degrees of separation in achieved SBP between treatment groups, even as they had similar mean differences. The greater between-group overlap of achieved SBP may have contributed to the discordant trial results.

Heterogeneity in Early Responses in ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial).

Randomized trials of hypertension have seldom examined heterogeneity in response to treatments over time and the implications for cardiovascular outcomes. Understanding this heterogeneity, however, is a necessary step toward personalizing antihypertensive therapy. We applied trajectory-based modeling to data on 39 763 study participants of the ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) to identify distinct patterns of systolic blood pressure (SBP) response to randomized medications during the first 6 months of the trial. Two trajectory patterns were identified: immediate responders (85.5%), on average, had a decreasing SBP, whereas nonimmediate responders (14.5%), on average, had an initially increasing SBP followed by a decrease. Compared with those randomized to chlorthalidone, participants randomized to amlodipine (odds ratio, 1.20; 95% confidence interval [CI], 1.10-1.31), lisinopril (odds ratio, 1.88; 95% CI, 1.73-2.03), and doxazosin (odds ratio, 1.65; 95% CI, 1.52-1.78) had higher adjusted odds ratios associated with being a nonimmediate responder (versus immediate responder). After multivariable adjustment, nonimmediate responders had a higher hazard ratio of stroke (hazard ratio, 1.49; 95% CI, 1.21-1.84), combined cardiovascular disease (hazard ratio, 1.21; 95% CI, 1.11-1.31), and heart failure (hazard ratio, 1.48; 95% CI, 1.24-1.78) during follow-up between 6 months and 2 years. The SBP response trajectories provided superior discrimination for predicting downstream adverse cardiovascular events than classification based on difference in SBP between the first 2 measurements, SBP at 6 months, and average SBP during the first 6 months. Our findings demonstrate heterogeneity in response to antihypertensive therapies and show that chlorthalidone is associated with more favorable initial response than the other medications.

Discovery of temporal and disease association patterns in condition-specific hospital utilization rates.

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008-2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition's seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.