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Background: Scorpion sting is a common medical emergency in rural India. Sparse published data are available regarding scorpion sting envenomation in adults from South India. Methods: We prospectively studied the clinical manifestations and two-dimensional echocardiography findings in fifty adult patients admitted with scorpion sting at a tertiary care teaching hospital in South India. Results: Their mean age was 34.8 ± 13.6 years; there were 28 (56%) males. Forty-one (82%) patients were stung over the limbs and the remaining over the rest of the body. Seventeen (34%) patients received prazosin within 5 h of sting; the remaining 33 (66%) received prazosin thereafter. Salient symptoms were pain over the sting area (n = 18, 36%), dyspnoea (n = 13, 26%), chest pain (n = 9, 18%), vomiting (n = 6, 12%), sweating (n = 5, 10%), nausea (n = 3, 6%), priapism (n = 7, 14%) and piloerection (n = 6, 12%). Common complications were accelerated hypertension (n = 12, 24%), pulmonary oedema (n = 10, 20%), myocarditis (n = 8, 16%), congestive heart failure (n = 6, 12%) and peripheral circulatory failure (n = 3.6%). Common electrocardiogram abnormalities were tachycardia (20%), T-wave inversion (10%), tall T-waves (10%), ST-depression (4%) and atrial fibrillation (2%). Mitral regurgitation was mild (Grade I) in 4 (8%) and moderate (Grade II) in 3 (6%) patients. Decline in left ventricular ejection fraction was noted (n = 14, 28%). Conclusion: Scorpion sting patients present with complaints of pain, palpitation, dyspnoea and paraesthesia. Complications such as hypertension, pulmonary oedema and sinus tachycardia are common. Early administration of prazosin is advocated for better outcome. Therefore, prudent knowledge on cardiovascular manifestations of the disease and timely management is pivotal for physicians.

Introduction: Migraine is a prevalent and disabling primary headache disorder worldwide, causing significant years lost due to disability (YLD) and impacting various aspects of everyday life. Despite its high prevalence and substantial burden, there is a lack of comprehensive data on clinical patterns and management trends, in places like Tamil Nadu, India. This study aims and also fill gaps by investigating and analyzing the clinical characteristics, treatment patterns, and illness burden among patients with episodic migraine (EM) and chronic migraine (CM) in the state of Tamil Nadu., Study: This cross-sectional retrospective study was conducted at the Department of Neurology, Madras Medical College, Chennai, over a three-month period starting from January 2024 to March 2024. The study included migraine patients aged 18 years and above who met the International Classification of Headache Disorders (ICHD)-3 criteria and took treatment at the department. Data were collected using patient interviews, medical records, and counseling sessions and using a pre-designed questionnaire. Patient demographics, clinical characteristics, symptom prevalence, prescription patterns, and illness burden were analyzed accordingly. The Migraine Disability Assessment (MIDAS) questionnaire was used to measure the burden of illness., Results: The analysis involved 400 migraine patients, 92.5% of them having EM and 7.5% of them having CM. The mean age of patients was 37.5 years, with a predominance of females (73.5%). Patients with CM had having significantly higher average number of headache days per month when compared to those with EM. Tension-type headache (TTH) and medication-overuse headache (MOH) were more prevalent in those CM patients. Trigger factors include lack of sleep, bright light exposure, and stress. Comorbidities such as diabetes mellitus, obesity, and depression were significantly higher in CM patients. Acute treatment included NSAIDs and Triptans, while preventive therapy was more commonly used in CM patients. The mean MIDAS score was significantly higher in CM patients, which indicates greater disability., Conclusion: The study provides valuable insights into the clinical characteristics, treatment patterns, and burden of illness among migraine patients in Tamil Nadu, India. Significant differences were observed between EM and CM patients, which highlights the need for comprehensive management strategies. Preventive therapy, lifestyle modifications, and comprehensive assessment of disability are all important in addressing the variable needs of migraine patients and also reducing the burden of illness. Further research is necessary to explore additional factors influencing migraine outcomes in this population., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Irfan et al.)

Objectives: A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic., Methods: The PRC was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the PRC., Results: The consortium developed a general PRC and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures, and controls for a NIST SP 800-53 revision 4 Moderate system. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data., Discussion and Conclusions: Given the various legal and data agreements required to operate a data commons, a PRC is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a PRC can also be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing regional data., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Inappropriate antibiotic use may lead to increased adverse drug events (ADEs). This study assessed whether an antimicrobial stewardship recommendation to discontinue antibiotics in patients with low likelihood for bacterial infection reduced antibiotic duration and antibiotic-associated ADEs. At a 4-hospital system, hospitalized adult patients receiving empiric antibiotics for suspected infection were identified between May 2, 2016 and June 30, 2018. For those patients who were deemed unlikely to have a bacterial infection, a note was left by an infectious diseases physician recommending antibiotic discontinuation. Patient cases were considered "adherent" to recommendations if antibiotics were discontinued within 48 hours of the note and "non-adherent" to recommendations if antibiotics were continued beyond this. Duration of antibiotics and potential antibiotic-associated ADEs were collected retrospectively. Attribution of the adverse event to the antibiotic was decided upon by the investigators. The incidence of ADEs and duration of antibiotics between the adherent and non-adherent groups were compared. Of 253 patients deemed unlikely to have a bacterial infection, 114 (45%) treatment teams stopped antibiotics within 48 hours of the recommendation, and 139 (55%) continued antibiotics. The total number of ADEs was significantly higher in the non-adherent group compared to the adherent group (34 ADEs vs 9 ADEs, P = .001). The median number of total prescribed antibiotic days was higher in the non-adherent group than in the adherent group (5 days vs 1 day, P < .001). This study demonstrates that stewardship programs may prevent ADEs by recommending antibiotic discontinuation in patients with low suspicion for bacterial infection., Competing Interests: All authors report no conflicts of interest relevant to this article., (© The Author(s) 2024.)

Background:  Inefficient electronic health record (EHR) usage increases the documentation burden on physicians and other providers, which increases cognitive load and contributes to provider burnout. Studies show that EHR efficiency sessions, optimization sprints, reduce burnout using a resource-intense five-person team. We implemented sprint-inspired one-on-one post-go-live efficiency training sessions (mini-sprints) as a more economical training option directed at providers., Objectives:  We evaluated a post-go-live mini-sprint intervention to assess provider satisfaction and efficiency., Methods:  NorthShore University HealthSystem implemented one-on-one provider-to-provider mini-sprint sessions to optimize provider workflow within the EHR platform. The physician informaticist completed a 9-point checklist of efficiency tips with physician trainees covering schedule organization, chart review, speed buttons, billing, note personalization/optimization, preference lists, quick actions, and quick tips. We collected postsession survey data assessing for net promoter score (NPS) and open-ended feedback. We conducted financial analysis of pre- and post-mini-sprint efficiency levels and financial data., Results:  Seventy-six sessions were conducted with 32 primary care physicians, 28 specialty physicians, and 16 nonphysician providers within primary care and other areas. Thirty-seven physicians completed the postsession survey. The average NPS for the completed mini-sprint sessions was 97. The proficiency score had a median of 6.12 (Interquartile range (IQR): 4.71-7.64) before training, and a median of 7.10 (IQR: 6.25-8.49) after training. Financial data analysis indicates that higher level billing codes were used at a greater frequency post-mini-sprint. The revenue increase 12 months post-mini-sprint was $213,234, leading to a return of $75,559.50 for 40 providers, or $1,888.98 per provider in a 12-month period., Conclusion:  Our data show that mini-sprint sessions were effective in optimizing efficiency within the EHR platform. Financial analysis demonstrates that this type of training program is sustainable and pays for itself. There was high satisfaction with the mini-sprint training modality, and feedback indicated an interest in further mini-sprint training sessions for physicians and nonphysician staff., Competing Interests: None declared., (Thieme. All rights reserved.)

Objectives: To determine the impact of a machine learning early warning risk score, electronic Cardiac Arrest Risk Triage (eCART), on mortality for elevated-risk adult inpatients., Design: A pragmatic pre- and post-intervention study conducted over the same 10-month period in 2 consecutive years., Setting: Four-hospital community-academic health system., Patients: All adult patients admitted to a medical-surgical ward., Interventions: During the baseline period, clinicians were blinded to eCART scores. During the intervention period, scores were presented to providers. Scores greater than or equal to 95th percentile were designated high risk prompting a physician assessment for ICU admission. Scores between the 89th and 95th percentiles were designated intermediate risk, triggering a nurse-directed workflow that included measuring vital signs every 2 hours and contacting a physician to review the treatment plan., Measurements and Main Results: The primary outcome was all-cause inhospital mortality. Secondary measures included vital sign assessment within 2 hours, ICU transfer rate, and time to ICU transfer. A total of 60,261 patients were admitted during the study period, of which 6,681 (11.1%) met inclusion criteria (baseline period n = 3,191, intervention period n = 3,490). The intervention period was associated with a significant decrease in hospital mortality for the main cohort (8.8% vs 13.9%; p < 0.0001; adjusted odds ratio [OR], 0.60 [95% CI, 0.52-0.71]). A significant decrease in mortality was also seen for the average-risk cohort not subject to the intervention (0.49% vs 0.26%; p < 0.05; adjusted OR, 0.53 [95% CI, 0.41-0.74]). In subgroup analysis, the benefit was seen in both high- (17.9% vs 23.9%; p = 0.001) and intermediate-risk (2.0% vs 4.0 %; p = 0.005) patients. The intervention period was also associated with a significant increase in ICU transfers, decrease in time to ICU transfer, and increase in vital sign reassessment within 2 hours., Conclusions: Implementation of a machine learning early warning score-driven protocol was associated with reduced inhospital mortality, likely driven by earlier and more frequent ICU transfer., Competing Interests: Drs. Edelson and Churpek received funding from EarlySense (Tel Aviv, Israel) and have a patent pending (ARCD. P0535US.P2) through the University of Chicago for risk stratification algorithms for hospitalized patients. Dr. Edelson received funding from Philips Healthcare (Andover, MA) and has ownership interest in AgileMD (San Francisco, CA) that holds the licensing rights to electronic Cardiac Arrest Risk Triage. Dr. Churpek’s institution received funding from the National Institutes of Health (NIH) (R01 GM123193) and EarlySense; he received support for article research from the NIH; and he is also supported by an R01 from National Institute of General Medical Sciences (R01 GM123193). Dr. Ravichandran disclosed that she is funded by the Daniel F. and Ada L. Rice foundation. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

To assess the burden of type 2 diabetes (T2D) and its genetic profile in endogamous populations of India given the paucity of data, we aimed to determine the prevalence of T2D and estimate its heritability using family-based cohorts from three distinct Endogamous Ethnic Groups (EEGs) representing Northern (Rajasthan [Agarwals: AG]) and Southern (Tamil Nadu [Chettiars: CH] and Andhra Pradesh [Reddys: RE]) states of India. For comparison, family-based data collected previously from another North Indian Punjabi Sikh (SI) EEG was used. In addition, we examined various T2D-related cardiometabolic traits and determined their heritabilities. These studies were conducted as part of the Indian Diabetes Genetic Studies in collaboration with US (INDIGENIUS) Consortium. The pedigree, demographic, phenotypic, covariate data and samples were collected from the CH, AG, and RE EEGs. The status of T2D was defined by ADA guidelines (fasting glucose ≥ 126 mg/dl or HbA1c ≥ 6.5% and/or use of diabetes medication/history). The prevalence of T2D in CH (N = 517, families = 21, mean age = 47y, mean BMI = 27), AG (N = 530, Families = 25, mean age = 43y, mean BMI = 27), and RE (N = 500, Families = 22, mean age = 46y, mean BMI = 27) was found to be 33%, 37%, and 36%, respectively, Also, the study participants from these EEGs were found to be at increased cardiometabolic risk (e.g., obesity and prediabetes). Similar characteristics for the SI EEG (N = 1,260, Families = 324, Age = 51y, BMI = 27, T2D = 75%) were obtained previously. We used the variance components approach to carry out genetic analyses after adjusting for covariate effects. The heritability (h 2 ) estimates of T2D in the CH, RE, SI, and AG were found to be 30%, 46%, 54%, and 82% respectively, and statistically significant (P ≤ 0.05). Other T2D related traits (e.g., BMI, lipids, blood pressure) in AG, CH, and RE EEGs exhibited strong additive genetic influences (h 2 range: 17% [triglycerides/AG and hs-CRP/RE] - 86% [glucose/non-T2D/AG]). Our findings highlight the high burden of T2D in Indian EEGs with significant and differential additive genetic influences on T2D and related traits., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Venkatesan, Lopez-Alvarenga, Arya, Ramu, Koshy, Ravichandran, Ponnala, Sharma, Lodha, Sharma, Shaik, Resendez, Venugopal, R, Saju, Ezeilo, Bejar, Wander, Ralhan, Singh, Mehra, Vadlamudi, Almeida, Mummidi, Natesan, Blangero, Medicherla, Thanikachalam, Panchatcharam, Kandregula, Gupta, Sanghera, Duggirala and Paul.)

Objective: We report on our experience of deploying a continuous remote patient monitoring (CRPM) study soft launch with structured cascading and escalation pathways on heart failure (HF) patients post-discharge. The lessons learned from the soft launch are used to modify and fine-tune the workflow process and study protocol., Methods: This soft launch was conducted at NorthShore University HealthSystem's Evanston Hospital from December 2020 to March 2021. Patients were provided with non-invasive wearable biosensors that continuously collect ambulatory physiological data, and a study phone that collects patient-reported outcomes. The physiological data are analyzed by machine learning algorithms, potentially identifying physiological perturbation in HF patients. Alerts from this algorithm may be cascaded with other patient status data to inform home health nurses' (HHNs') management via a structured protocol. HHNs review the monitoring platform daily. If the patient's status meets specific criteria, HHNs perform assessments and escalate patient cases to the HF team for further guidance on early intervention., Results: We enrolled five patients into the soft launch. Four participants adhered to study activities. Two out of five patients were readmitted, one due to HF, one due to infection. Observed miscommunication and protocol gaps were noted for protocol amendment. The study team adopted an organizational development method from change management theory to reconfigure the study protocol., Conclusion: We sought to automate the monitoring aspects of post-discharge care by aligning a new technology that generates streaming data from a wearable device with a complex, multi-provider workflow into a novel protocol using iterative design, implementation, and evaluation methods to monitor post-discharge HF patients. CRPM with structured escalation and telemonitoring protocol shows potential to maintain patients in their home environment and reduce HF-related readmissions. Our results suggest that further education to engage and empower frontline workers using advanced technology is essential to scale up the approach., Competing Interests: The study devices, monitoring platform, and tech support are non-financial support by physIQ. K.L. is an employee of physIQ. All other authors do not have any conflict of interest. K.L. reports employment and equity from physIQ, during the conduct of the study; other from physIQ, outside the submitted work. N.S.S. reports non-financial support from physIQ, during the conduct of the study. J.E. reports and I serve on the ACCMedAxiom Board of Trustees. This is the for-profit arm of the ACC. While I have no direct relationships with vendors, the Board approves partnerships with vendors. U.R. is funded by the Daniel F. and Ada L. Rice Foundation. The foundation is not involved in the research design, implementation, or production of the manuscript in any way. Therefore, there is no conflict of interest. W.N.C. is funded by the Daniel F. and Ada L. Rice Foundation. The foundation is not involved in the research study design, implementation, or production of the manuscript in any way. Therefore, there is no conflict of interest., (Thieme. All rights reserved.)

Background: COVID-19 represents a grave risk to residents in skilled nursing facilities (SNFs)., Objective: To determine whether establishment of an appropriate-use committee was associated with a reduction in SNF utilization., Designs, Setting, and Participants: Retrospective cohort study at NorthShore University HealthSystem, a multihospital integrated health system in northern Illinois. Participants were patients hospitalized from March 19, 2019, to July 16, 2020., Intervention: Creation of a multidisciplinary committee to assess appropriateness of discharge to SNF following hospitalization., Main Outcome and Measures: Primary outcome was total discharges to SNFs. Secondary outcomes were new discharges to SNFs, readmissions, length of stay (LOS), and COVID-19 incidence following discharge., Results: Matched populations pre and post intervention were each 4424 patients. Post intervention, there was a relative reduction in total SNF discharges of 49.7% (odds ratio [OR], 0.42; 95% CI, 0.38-0.47) and in new SNF discharges of 66.9% (OR, 0.29; 95% CI, 0.25-0.34). Differences in readmissions and LOS were not statistically significant. For patients discharged to a SNF, 2.99% (95% CI, 1.59%-4.39%) developed COVID-19 within 30 days, compared with 0.26% (95% CI, 0.29%-0.93%) of patients discharged to other settings (P < .001)., Conclusion: Implementing a review committee to assess for appropriateness of SNF use after a hospitalization during the COVID-19 pandemic is highly effective. There was no negative impact on safety or efficiency of hospital care, and reduced SNF use likely prevented several cases of COVID-19. This model could serve as a template for other hospitals to reduce the risks of COVID-19 in SNFs and as part of a value-based care strategy.

Background: The weighted incidence syndromic combination antibiogram (WISCA) is an antimicrobial stewardship tool that utilizes electronic medical record data to provide real-time clinical decision support regarding empiric antibiotic prescription in the hospital setting. The aim of this study was to determine the impact of WISCA utilization for empiric antibiotic prescription on hospital length of stay (LOS)., Methods: We performed a crossover randomized controlled trial of the WISCA tool at 4 hospitals. Study participants included adult inpatients receiving empiric antibiotics for urinary tract infection (UTI), abdominal-biliary infection (ABI), pneumonia, or nonpurulent cellulitis. Antimicrobial stewardship (ASP) physicians utilized WISCA and clinical guidelines to provide empiric antibiotic recommendations. The primary outcome was LOS. Secondary outcomes included 30-day mortality, 30-day readmission, Clostridioides difficile infection, acquisition of multidrug-resistant gram-negative organism (MDRO), and antibiotics costs., Results: In total, 6849 participants enrolled in the study. There were no overall differences in outcomes among the intervention versus control groups. Participants with cellulitis in the intervention group had significantly shorter mean LOS compared to participants with cellulitis in the control group (coefficient estimate = 0.53 [-0.97, -0.09], P = .0186). For patients with community acquired pneumonia (CAP), the intervention group had significantly lower odds of 30-day mortality compared to the control group (adjusted odds ratio [aOR] .58, 95% confidence interval [CI], .396, .854, P = .02)., Conclusions: Use of WISCA was not associated with improved outcomes for UTI and ABI. Guidelines-based interventions were associated with decreased LOS for cellulitis and decreased mortality for CAP., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Background: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance., Objective: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system., Materials & Methods: We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance., Results: Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85., Discussion: We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation., Competing Interests: The authors have declared that no competing interests exist.