Daily Symptoms Research Articles

Relationship between daily rated depression symptom severity and the retrospective self-report on PHQ-9: A prospective ecological momentary assessment study on 80 psychiatric outpatients

BackgroundDepression-related negative bias in emotional processing and memory may bias accuracy of recall of temporally distal symptoms. We tested the hypothesis that when responding to the Patient Health Questionnaire (PHQ-9) the responses reflect more accurately temporally proximal than distal mood states. MethodsCurrently, depressed psychiatric outpatients (N = 80) with depression confirmed in semi-structured interviews had the Aware application installed on their smartphones for ecological momentary assessment (EMA). The severity of “low mood”, “hopelessness”, “low energy”, “anhedonia”, and “wish to die” was assessed on a Likert scale five times daily during a 12-day period, and thereafter, the PHQ-9 questionnaire was completed. We used auto- and cross-correlation analyses and linear mixed-effects multilevel models (LMM) to investigate the effect of time lag on the association between EMA of depression symptoms and the PHQ-9. ResultsAutocorrelations of the EMA of depressive symptom severity at two subsequent days were strong (r varying from 0.7 to 0.9; p < 0.001). “Low mood” was the least and “wish to die” the most temporally stable symptom. The correlations between EMA of depressive symptoms and total scores of the PHQ-9 were temporally stable (r from 0.3 to 0.6; p < 0.001). No effect of assessment time on the association between EMA data and the PHQ-9 emerged in the LMM. LimitationsAltogether 11.5 % of observations were missing. ConclusionsDespite fluctuations in severity of some of the depressive symptoms, patients with depression accurately recollect their most dominant symptoms, without a significant recall bias favouring the most recent days, when responding to the PHQ-9.

High Discovery Rate of Duodenal and Gastric Eosinophilia in Patients With Unexplained Moderate–Severe Abdominal Symptoms: A Prospective US Multisite Study

High Discovery Rate of Duodenal and Gastric Eosinophilia in Patients With Unexplained Moderate–Severe Abdominal Symptoms: A Prospective US Multisite Study

1868. Characterization of COVID-19 Symptoms and Outcomes by Variant Period in the North Carolina COVID-19 Community Research Partnership (NC-CCRP)

Abstract Background We characterize the evolution of symptoms in those with self-reported SARS-CoV-2 infections and the likelihood of seeking treatment or medical care during different waves of the pandemic. Methods The NC-CCRP is a longitudinal observational study of 37,820 participants who completed a daily symptom log from April 2020 through February 2022, during which there were 5,167 self-reported COVID-19 infections. Three variant periods were defined as pre-delta, delta, and omicron, based on the predominant variant in North Carolina. Quasi-Poisson and logistic regression models adjusted for demographics and vaccination were used to assess COVID-19 symptoms and their duration and seeking treatment or hospitalization. Results Cough was the most reported symptom in all waves and increased from 77% pre-delta to 85% during omicron (p=0.001). Sore throat was more common during self-reported infections during omicron (71%), compared with 62% during delta and 54% pre-delta (p&amp;lt; 0.001). The largest change in proportion reporting a symptom was loss of taste or smell which decreased from 55% during pre-delta to 17% during omicron (p&amp;lt; 0.001). Compared with the pre-delta period, delta (incidence risk reduction, IRR 0.86; 95% CI 0.79-0.93) and omicron (IRR 0.67; 95% CI 0.61-0.73) were associated with lower symptom duration. Participants infected during the delta wave were more likely to seek treatment compared with either pre-delta (odds ratio, OR 1.32 95% CI 1.06-1.64) or omicron (OR 1.42; 95% CI 1.21-1.67). Omicron period infections were associated with a lower likelihood of self-reported hospitalization compared with pre-delta (OR 0.26; 95% CI 0.10-0.59) or delta (OR 0.26; 95% CI 0.11-0.60). Vaccination was associated with a reduced likelihood of hospitalization (OR 0.35; 95% CI 0.18-0.70). Proportion and Duration of Symptoms by Variant Wave; Unadjusted by Vaccination Status. Conclusion Our study indicates evolution in symptom presentation and duration by variant period. The omicron wave was associated with shorter duration and lower severity of illness. Longitudinal tracking of symptomology and severity of a novel pathogen provide insights into the evolution of the pathogen in the community and is vital for public health and clinical response. Disclosures All Authors: No reported disclosures.

2204. Pathogen Co-infections and Trends in Influenza-like Illness in PAIVED

Abstract Background The Pragmatic Assessment of Influenza Vaccine Effectiveness in the Department of Defense (DoD) (PAIVED) is a multicenter, multiservice study assessing influenza vaccine effectiveness in active-duty service members, retirees, and dependents. In its fourth season (2021/22), PAIVED offers a unique opportunity to examine influenza-like illness (ILI) trends prior to and during the COVID-19 pandemic in a prospectively followed, well-defined cohort. Methods Over the past 4 influenza seasons, PAIVED has enrolled DoD beneficiaries who were randomized to receive egg-based, cell-based, or recombinant-derived influenza vaccine. Participants provided some basic demographic information and were then sent a weekly text or email that inquired about ILI symptoms, defined as 1) having cough or sore throat, plus 2) feeling feverish/having chills or having body aches/fatigue. Participants with ILI completed a daily symptom diary for one week and submitted a nasal swab for PCR-based pathogen detection. Table 1.Demographic characteristics of PAIVED participants over four seasonsFigure 1.Percent of PAIVED participants with influenza-like illness, SARS-CoV-2, influenza, and rhinovirus identified in swab samples collected over four seasons. Results Over the 4 seasons, 15,449 participants were followed for ILI (Table 1) with 3,407 participants reporting a total of 3,985 ILIs. For the 2021/22 season, ILI reports peaked in January (Figure 1). Overall, 4.7% of episodes had more than one pathogen identified (Table 2). Among the 122 coinfections identified to date, most were coinfections with rhinoviruses (91/122, 75%), including rhinovirus coinfections with seasonal coronaviruses (29, 24%), metapneumovirus (18, 15%), SARS-CoV-2 (17, 14%), and influenza (14, 11%). SARS-CoV-2 and influenza were found together in one sample. The lab data will continue to be processed for the current season (2021/22). Table 2.Pathogens identified in PAIVED nasal swabs over four seasons Conclusion ILI rates were lowest during the third year (2020/21), consistent with national influenza surveillance reports of influenza and outpatient ILI activity, suggesting that measures taken to reduce transmission of SARS-CoV-2 reduced the spread of other respiratory viruses. The emergence of the SARS-CoV-2 omicron variant in December 2021 was associated with higher ILI rates. Among those individuals for whom a sample was collected, coinfections were highest in 2018/19. Data collection and specimen analysis are ongoing for 2021/22. Disclosures Jitendrakumar Modi, MD, GlaxoSmithKline: I am a paid speaker for GSK. I do not speak for their flu brand. Timothy Burgess, MD, MPH, AstraZeneca: The HJF, in support of the USU IDCRP, was funded to conduct or augment unrelated Phase III Mab and vaccine trials as part of US Govt. COVID19 response.

313. Performance of Rapid Diagnostic Testing at Days 4-6 from Diagnosis: Implications for Discharge from Isolation on a University Campus

Abstract Background Omicron rapidly replaced delta as the predominant strain causing COVID-19 related illness in the United States (US) in December 2021, the same month the US CDC reduced the recommended isolation period from 10 to 5 days for asymptomatic individuals or those with resolving symptoms. New evidence suggests some asymptomatic individuals with omicron remain culture positive beyond 5 days from diagnosis. We sought to evaluate the performance of a SARS-CoV-2 antigen rapid diagnostic test (RDT) in predicting persistent potential for transmission at the end of a five-day isolation period among young, fully vaccinated individuals in a university community setting. Methods A subgroup of participants enrolled in a longitudinal COVID-19 cohort were asked to self-perform RDTs on days 4 to 6 from diagnostic test date in addition to a separate self-collected anterior nasal swab used for culture and RT-PCR, and a daily symptom screen (15 COVID-19 symptom questions on a 4-point scale). We calculated the daily and overall sensitivity and specificity of the RDTs in comparison to SARS-CoV-2 culture result. We also compared the N1 cycle threshold (CT) values and symptom score on each day of the study by RDT results. Results Of 23 participants, the mean age was 20 years, all had completed their primary COVID-19 vaccine series, and 13 (65.0%) had received a booster vaccine (Table 1). Compared to culture, sensitivity and specificity of the RDTs were 100% and 62% respectively (Table 2). Compared to participants with negative RDTs, median CT values were lower in those with positive RDTs on each day of the study (Figure 1). Participants who had positive RDTs on all three days had higher symptom scores (Figure 2) than those without. Conclusion RDTs have a high sensitivity in detecting culture positive SARS-CoV-2 on Days 4 to 6 from initial diagnostic test. However, the high false positive rate of 38% means that over a third of culture negative individuals will stay in isolation longer than necessary if RDTs are used in test to release from isolation protocols. Viral loads (CT values) and symptom scores were higher for participants with persistently positive RDT result. An approach that uses a combination of RDTs, CT values and symptom score may prove useful in guiding isolation duration. Disclosures Davidson H. Hamer, MD, Trinity Biotech: Advisor/Consultant Catherine M. Klapperich, Ph.D., BioSens8, LLC: Ownership Interest.

Association between Cough and Ambient Polycyclic Aromatic Hydrocarbons in Patients with Chronic Cough: An Observational Study in Two Regions of Japan

Ambient polycyclic aromatic hydrocarbons’ (PAHs) specific components are likely involved in respiratory disease development and exacerbation in children and adults. Airborne PAH exposure’s effects on cough symptoms in children and adults with chronic coughs in Kanazawa and Fukuoka, Japan, were investigated in this longitudinal study. A total of 98 patients with chronic coughs were enrolled and followed up between 1 April and 31 May 2020. The enrolled patients were non-smoking adults and children aged 3–83 years. Cough diaries were used to record and collect daily cough symptoms. High-performance liquid chromatography coupled with a fluorescence detector was used to determine the particulate PAH content in daily total suspended particles collected on quartz fiber filters. Ambient concentrations of fine particulate matter, nitrogen dioxide, and sulfur dioxide were obtained from local monitoring sites. Generalized estimated equations were used to estimate the association between daily PAHs and cough symptoms. Among nine PAHs measured, benz[a]anthracene (BaA) was significantly associated with cough symptoms for both lag4 and lag5 PAH exposure. These findings suggest that airborne specific PAHs, especially BaA, affect cough symptoms in children and adults with chronic cough. Further studies are needed to develop effective measures to prevent respiratory diseases against specific PAHs.

Perceptions and Attitudes Toward an Interactive Voice Response Tool (Call for Life Uganda) Providing Adherence Support and Health Information to HIV-Positive Ugandans: Qualitative Study

The continuing decline in AIDS-related deaths in the African region is largely driven by the steady scale-up of antiretroviral therapy. However, there are challenges to retaining people living with HIV on treatment. Call for Life Uganda (CFLU) is an interactive voice response tool using simple analogue phones. CFLU supports patients with daily pill reminders, preappointment reminders, symptom reporting and management, and weekly health promotion tips. Mobile health tools are being increasingly used in resource-limited settings but are often adopted without rigorous evaluation. This qualitative study conducted at 12 months after enrollment assessed patients' experiences, perceptions, and attitudes regarding CLFU. We conducted a qualitative substudy within an open-label randomized controlled trial titled "Improving outcomes in HIV patients using mobile phone based interactive software support." Data were collected through 6 focus group discussions with participants sampled based on proportion of calls responded to-<25%, between 25% and 50%, and >50%-conducted at the Infectious Diseases Institute, Mulago, and the Kasangati Health Centre IV. NVivo (version 11; QSR International) was used in the management of the data and in the coding of the emerging themes. The data were then analyzed using content thematic analysis. There was consensus across all groups that they had more positive than negative experiences with the CFLU system. Participants who responded to >50% of the calls reported more frequent use of the specific elements of the CFLU tool and, consequently, experienced more benefits from the system than those who responded to calls less frequently. Irrespective of calls responded to, participants identified pill reminders as the most important aspect in improved quality of life, followed by health promotion tips. The most common challenge faced was difficulty with the secret personal identification number. Findings showed participants' appreciation, high willingness, and interest in the intervention, CFLU, that demonstrated great perceived potential to improve their access to health care; adherence to treatment; health awareness; and, consequently, quality of life. ClinicalTrials.gov NCT02953080; https://clinicaltrials.gov/ct2/show/NCT02953080.

Monitoring daily symptoms and (self-reported) exacerbations in patients with bronchiectasis: a prospective study.

Exacerbations are pivotal events in the natural history of patients with non-cystic fibrosis bronchiectasis (NCFB), since they have a negative impact on the functional evolution of these individuals. The daily symptoms of patients with NCFB show great variability, which negatively affects their self-perception of symptoms and exacerbations. The aim of this study was to identify daily symptoms in patients with NCFB, and to investigate whether there is a correlation between the frequency of self-reported exacerbations and events defined according to the criteria established in the literature to define exacerbation in bronchiectasis. This observational and prospective study was carried out in outpatient clinics of a Brazilian public university hospital. Over 24 weeks, patients completed a diary in which daily symptoms, self-reported exacerbations, and demands for medical care for respiratory symptoms were recorded. The instrument used (diary and symptom scores ranging from 0 to 12) were developed by the researchers. The participants also answered questionnaires mMRC, Leicester's, and St. George's Respiratory (SGRQ). Twenty-eight patients returned the diary, their mean age was 54 years, and 50% out of them were classified as mild by the FACED score. Cough (64%) and expectoration (62%) were the most frequent symptoms. Correlations were found between the stability score and the mMRC (r=0.4727, p=0.011) and SGRQ (r=0.6748, p<0.0001) questionnaires. The number of self-perceived exacerbations (24) was significantly lower than exacerbations using the exacerbation consensus (63) (p<0.01). Additionally, no correlation was found between these two criteria. There was great variability of symptoms among the individuals sampled, and even for the same individual, over time. Patients had low self-perception of exacerbations, which suggests that strategies aimed at improving this self-perception may contribute to the early detection of exacerbations.

Daily Functional Activities in Context After Stroke: A Large-Scale Ecological Momentary Assessment (EMA) Study

<h3>Research Objectives</h3> To describe where, with whom, and how time was spent daily, to characterize these daily activity patterns as a function of employment, mobility device use, and cognitive functioning, and to validate EMA methods of daily activities and symptoms in people with stroke. <h3>Design</h3> Longitudinal observational study using EMA to assess real-time daily functional activities in natural contexts. <h3>Setting</h3> Home and community. <h3>Participants</h3> 212 participants with mild-to-moderate chronic stroke (55% males, 90% ischemic stroke; median age = 60 years). <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measures</h3> Participants responded to a smartphone app five times daily for two weeks. The app queried activities, environmental contexts, and concurrent post-stroke symptoms. Participants also completed standardized assessments of daily activities and symptoms during lab visits. <h3>Results</h3> Participants spent the most time at home. They participated most in passive, unproductive activities (27%) but least in physical activities (4%). Unemployed individuals reported fewer vocational activities but more activities of daily living (ADL) and passive activities than employed counterparts. Mobility device users and those with cognitive problems spent less time on vocational activities and more on ADL than non-users and those without cognitive problems. Our data showed small-to-moderate correlations of EMA with in-lab measures of daily functioning (r=-0.30 to 0.35, p-value < 0.05) and large correlations between EMA and in-lab measures of symptoms (r= -0.64 to 0.79, p-value < 0.0001). <h3>Conclusions</h3> EMA can provide context-dependent information of daily functional activities in people after stroke. Clinicians may consider using EMA to examine post-stroke functional recovery and monitor patients for home- and community-based programs. <h3>Author(s) Disclosures</h3> None disclosed.

PCR197 Psychometric Evaluation of a New Electronic Pediatric Asthma Symptom Diary

We developed an electronic pediatric asthma symptom diary (ePASD) to assess the severity of daily asthma symptoms and proximal impacts in children with asthma. The unique design of the ePASD minimizes the importance of reading skills on children’s ability to self-report accurately and reduces the bias of proxy report. This observational, prospective study evaluated the psychometric properties of the ePASD.

PCR36 Content Validity, Acceptability, and Usability of the Electronic Hidradenitis Suppurativa Symptom Daily Diary and the Electronic Hidradenitis Suppurativa Symptom Questionnaire

An observational, qualitative study was conducted to establish the content validity, including acceptability and usability, of electronic versions of the Hidradenitis Suppurativa (HS) Symptom Daily Diary (eHSSDD) and HS Symptom Questionnaire (eHSSQ). Psychometric validation of these tools is ongoing.

Daily Stressors, Social Support, and Mental Health Experiences of Dementia Family Caregivers

AbstractBackgroundFamily members providing informal care to individuals living with dementia have an increased risk of depression and anxiety. While risk and protective factors of poor mental health outcomes have previously been identified, little is known about the day‐to‐day circumstances in which depression and anxiety‐related symptoms are more likely to appear among dementia caregivers. The purpose of this study was to identify contextual factors associated with daily depression and anxiety symptom experience among dementia family caregivers.MethodA national sample of family caregivers (N = 165) completed daily diary surveys over 21 days (n = 2,841). Mixed‐level models were used to examine relationships between daily behavioral symptoms of dementia (BSDs), emotional and instrumental social support, and depression and anxiety‐related symptoms.ResultDepression and anxiety symptoms were endorsed by 141 (85.45%) and 155 (93.94%) caregivers, respectively. Dementia caregivers were more likely to report depression (OR = 1.15, 95% CI: 1.11‐1.18, p &lt; 0.001) and anxiety (OR = 1.12, 95% CI: 1.09‐1.16, p &lt; 0.001) on a given day when BSDs were present and perceived as more bothersome than usual. Specific BSDs, including restless behaviors and intense emotions, also increased the daily odds of experiencing depression and anxiety symptoms. Further, the daily odds of depression decreased on days when caregivers reported informal instrumental support (OR = 0.67, 95% CI: 0.4‐0.92, p &lt; 0.05), while the daily odds of anxiety increased on days when caregivers reported emotional support (OR = 1.46, 95% CI: 1.07‐1.99, p &lt; 0.05).ConclusionThe risk of experiencing depression and anxiety symptoms varies daily based on the presence of specific contextual factors. The results from this study demonstrate the dynamic nature of mental health in the context of dementia caregiving. These findings support the need for ecologically‐valid interventions to improve the day‐to‐day well‐being of dementia family caregivers.

Ten years after—A follow-up survey on continuing daily symptoms of grief and medical costs in Japan

• One-third of Japanese show daily symptoms of grief even 14 to 24 months after bereavement • Death of emotionally close spouses under age 80 was devastating, especially from cancer • Unemployment, declining income, and deciding on funerals correlated with continuing grief • Mourners spend more time and money on clinical visits, antidepressants, and sleeping medicines • This significant economic burden to national health services requires prevention/intervention

A Coordinated Analysis of Physical Reactivity to Daily Stressors: Age and Proactive Coping Matter

Proactive coping involves efforts to prepare for future stressors and may have implications for physical responses to stress. We examined age differences in physical reactivity to daily stressors moderated by proactive coping in a coordinated analysis across two separate daily diary studies. Study 1 included data from 116 older (age range 60–90) and 107 younger (age range 18–36) adults on daily stressors and physical health symptoms for 8 consecutive days. Study 2 included data from 140 adults (age range 19–86) on daily stressors and self-rated physical health for 29 consecutive days. Participants in both studies reported on their proactive coping on the first day of the study. Physical reactivity was indexed via lagged multilevel models as increases in daily physical symptoms in Study 1 and decreases in daily physical health in Study 2 with corresponding increases in daily stressors. Results indicated that in both studies, younger adults with low proactive coping were more physically reactive to daily stressors compared to younger adults with high proactive coping. Proactive coping was associated with reduced physical reactivity to daily stressors among younger adults, consistent with the characterization of a high degree of control and ample opportunities at earlier phases of adulthood which are critical for accumulating resources to proactively cope.

Introduction Current status of research on social anxiety and relationship with psychoeducational variables: An international perspective

Social anxiety, and its most extreme condition, social anxiety disorder, is a common international problem today. The recent Covid-19 pandemic has brought relief, at least temporarily, to many social anxiety sufferers. The decrease in face-to-face social interactions and the use of face masks have reduced the usually daily discomfort of those interactions. But it has also frozen in time the practice of their social skills and served as a safe refuge from experiencing the daily symptoms of social anxiety so often. We can say, therefore, that the pandemic has served as a reinforcement of social withdrawal behaviors for people with social anxiety, both in children and in adolescents and adults.

Sara Alert: An automated symptom monitoring tool for COVID-19 in 11 jurisdictions in the United States, June - August, 2021.

Health department personnel conduct daily active symptom monitoring for persons potentially exposed to SARS-CoV-2. This can be resource-intensive. Automation and digital tools can improve efficiency. We describe use of a digital tool, Sara Alert, for automated daily symptom monitoring across multiple public health jurisdictions. Eleven of the 20 U.S. public health jurisdictions using Sara Alert provided average daily activity data during June 29 to August 30, 2021. Data elements included demographics, communication preferences, timeliness of symptom monitoring initiation, responsiveness to daily messages, and reports of symptoms. Participating jurisdictions served a U.S. population of over 22 million persons. Health department personnel used this digital tool to monitor more than 12,000 persons per day on average for COVID-19 symptoms. On average, monitoring began 3.9 days following last exposure and was conducted for an average of 5.7 days. Monitored persons were frequently < 18 years old (45%, 5,474/12,450) and preferred communication via text message (47%). Seventy-four percent of monitored persons responded to at least one daily automated symptom message. In our geographically diverse sample, we found that use of an automated digital tool might improve public health capacity for daily symptom monitoring, allowing staff to focus their time on interventions for persons most at risk or in need of support. Future work should include identifying jurisdictional successes and challenges implementing digital tools; the effectiveness of digital tools in identifying symptomatic individuals, ensuring appropriate isolation, and testing to disrupt transmission; and impact on public health staff efficiency and program costs.

Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app

BackgroundThe self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. MethodsWe studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale – “VAS Asthma”) at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma; (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. FindingsWe assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users); (ii) treated and partly-controlled asthma (6.3-9.7%); (iii) treated and controlled asthma (4.6-5.5%); (iv) untreated uncontrolled asthma (18.2-20.5%); (v) untreated partly-controlled asthma (10.1-10.7%); (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. InterpretationWe identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma.

Repetitive transcranial magnetic stimulation: Course and early prediction of response in depression

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression, yet few studies have mapped the trajectories of symptom change over treatment. Tracking clinical response during early treatment may be helpful to predict outcome, particularly non-response. MethodsWe used naturalistic data (N = 117) to examine changes in the Daily Symptom Index (DI-5) scores of adult patients with unipolar or bipolar depression who underwent ≥16 treatment sessions of left dorsolateral prefrontal cortex rTMS at a private psychiatric facility in Western Australia, between 2016 and 2019. ResultsTwo response trajectories were charted: non-response (N = 71, 61%) and response (N = 46, 39%). Both trajectories diverged at 99% confidence interval at session 10, which was used as the point to predict treatment response at session 20. The response group showed a reduction of 4.21 in the mean DI-5 score from baseline at session 10. On this basis, a 4-point reduction in the DI-5 score at session 10 was defined as predictor of responder status at session 20. If the improvement is < 4 points at session 10, the probability of non-response at session 20 is 75%. If the improvement is ≥ 4 points, the probability of response at session 20 is 66%. LimitationsThe DI-5 scores were not examined beyond 20 treatment sessions, which may have shown delayed responders in the non-response group. ConclusionsIn this study of depression response trajectories with rTMS treatment, prediction of response at session 20 can be made at session 10 of treatment. Further research is required to generalise the current findings.

Induction Related Mortality Score in Acute Myeloid Leukemia: Prospective Validation Study (pRISM) of the Hematology Cancer Consortium (HCC)

Treatment of Acute Myeloid Leukemia (AML) should be initiated at the earliest to improve outcomes. The standard induction regimen (3+7) is associated with significant risk of induction mortality, especially in resource limited settings. Hence there is a need to develop risk prediction model for our patient cohorts. Treatment related mortality (TRM) scoring systems developed and used in developed country, mostly for the elderly, may not be directly applicable to population of young adults in developing countries, many of whom have poor general condition and infections at baseline. We developed a multivariate model of induction mortality score in 2019, from a retrospective AML cohort of the Indian acute leukemia research database [INwARD] established by the Hematology Cancer Consortium (HCC). In the present study we have validated the approach and recalibrated the induction mortality score on a multicenter prospective cohort. Prospective data for adult AML, for a period of 20 months, starting July 2020 to February 2022, was received from 17 member institutions in a central online data management system. Potential variables that would predict mortality were selected based on clinical and statistical significance. Eleven variables relating to baseline patient and disease characteristics (age, ECOG performance status, duration of symptoms in days, albumin, creatinine, bilirubin, white cell count, platelet, hemoglobin, peripheral blood blast percentage, and presence of infection requiring intravenous antibiotic within one week prior of starting induction), were considered for the predictive model using machine learning (ML) algorithms: Logistic regression (LR), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGB). Of the various ML algorithms, the best model was chosen based on area under curve (AUC) from the training dataset and validity statistics from the test dataset. We also used the same approach to predict intensive care unit (ICU) admission. R software was used to analyze the data. Of the 779 treated cases during the study period, 438 received intensive induction, '3+7' being the most common regimen in 80%. The median age of this cohort was 37 years (IQR 28, 46), male to female ratio 1.2. European Leukemia Net (ELN) risk group was good in 31.3 % (n= 137), intermediate in 44% (n=192), high in 13.7% (n=60), and unknown in 11% (n=48). Complete remission was attained in 56.3%. Overall induction mortality was 13.0%, ranging from 7.1% to 40% across different centers. Infection was the most common cause of death in 52%. For predicting induction mortality using 11 covariates, SVM provided the best threshold as 0.126, with an AUC of 94.71%, sensitivity (92.50%), and specificity (96.11%). A comparison of the ML algorithms is shown in Fig 1 and statistics for the SVM model in Table 1. ICU admission was observed in 22.8%; a cut-off threshold of 0.231, with an AUC of 93.3% in the SVM model predicted ICU admission with sensitivity of 93.15% and specificity of 89.7%. Induction mortality prediction score developed in a retrospective cohort was effectively applied to contemporary prospective data from major centers across the country, with diverse resources and patient profiles. Thus, we have validated the machine learning approach of predicting induction mortality with variables relevant to regional clinical settings including baseline infection. The SVM model predicted the risk of both induction mortality as well as morbidity with high accuracy. An online calculator is being developed to help clinicians use this score in regular practice for guiding treatment intensity as per an individual patient's risk and in directing appropriate resource utilization. Further, the approach of risk-adapted induction intensity, especially for young adult AML, based on the score adjusted to center-specific prevalence of mortality rates, is under consideration for a prospective clinical trial. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles.

Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex. We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females. Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.