Nevin Manimala

Epilepsia. 2022 Jun 27. doi: 10.1111/epi.17348. Online ahead of print.

ABSTRACT

OBJECTIVE: Hormonal therapy is a standard treatment for children with infantile spasms. However, the high doses given, and long treatment duration expose patients to the risk of adrenal insufficiency (AI). This study aims to quantify the cumulative incidence of AI among children with infantile spasms treated with high-dose corticosteroids and/or adrenocorticotropic hormone.

METHODS: A retrospective chart review of patients treated for infantile spasms was performed between January 2009 and March 2020 in one pediatric specialized hospital. Variables collected include patient and treatment characteristics, risk factors of AI, and adrenal function testing. Analysis included descriptive statistics such as incidence and bivariate analysis.

RESULTS: Thirty-one patients were included and received a total of 33 courses of treatment (17 corticosteroids [prednisone/prednisolone], 12 adrenocorticotropic hormone and four combined). Physiologic hydrocortisone replacement therapy with stress supplementation was received after 32/33 (97%) courses of treatment. Adrenal function was assessed in 32/33 (97%) and AI occurred in 25/33 (76% [95CI 58-89]). No predictive factor of AI was identified after hormonal treatment. No drug regimen was found to be safe. The two patients who developed an acute adrenal crisis presented to the emergency room within the days (between two and seven) following weaning off of hormonal treatment. They were the youngest children of the cohort, and both received prednisolone.

SIGNIFICANCE: Adrenal insufficiency is frequent and can potentially lead to an adrenal crisis in this population. This study highlights the necessity of hydrocortisone replacement therapy until AI has been excluded in a patient who received hormonal therapy to treat infantile spasms. As such, routine laboratory assessment of adrenal function should be done in all patients.

PMID:35759339 | DOI:10.1111/epi.17348

Commun Med (Lond). 2022 Jun 20;2:70. doi: 10.1038/s43856-022-00133-4. eCollection 2022.

ABSTRACT

BACKGROUND: Alzheimer’s disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care.

METHODS: We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called “Alzheimer’s Predictive Vector” (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO).

RESULTS: The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer’s related pathologies (98% and 81% accuracy between ADrp – including the early form, mild cognitive impairment – and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype.

CONCLUSIONS: This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.

PMID:35759330 | PMC:PMC9209493 | DOI:10.1038/s43856-022-00133-4

JMIR Mhealth Uhealth. 2022 Jun 27;10(6):e34273. doi: 10.2196/34273.

ABSTRACT

BACKGROUND: Tobacco addiction is the leading cause of preventable morbidity and mortality worldwide, but only 1 in 20 cessation attempts is supervised by a health professional. The potential advantages of mobile health (mHealth) can circumvent this problem and facilitate tobacco cessation interventions for public health systems. Given its easy scalability to large populations and great potential, chatbots are a potentially useful complement to usual treatment.

OBJECTIVE: This study aims to assess the effectiveness of an evidence-based intervention to quit smoking via a chatbot in smartphones compared with usual clinical practice in primary care.

METHODS: This is a pragmatic, multicenter, controlled, and randomized clinical trial involving 34 primary health care centers within the Madrid Health Service (Spain). Smokers over the age of 18 years who attended on-site consultation and accepted help to quit tobacco were recruited by their doctor or nurse and randomly allocated to receive usual care (control group [CG]) or an evidence-based chatbot intervention (intervention group [IG]). The interventions in both arms were based on the 5A’s (ie, Ask, Advise, Assess, Assist, and Arrange) in the US Clinical Practice Guideline, which combines behavioral and pharmacological treatments and is structured in several follow-up appointments. The primary outcome was continuous abstinence from smoking that was biochemically validated after 6 months by the collaborators. The outcome analysis was blinded to allocation of patients, although participants were unblinded to group assignment. An intention-to-treat analysis, using the baseline-observation-carried-forward approach for missing data, and logistic regression models with robust estimators were employed for assessing the primary outcomes.

RESULTS: The trial was conducted between October 1, 2018, and March 31, 2019. The sample included 513 patients (242 in the IG and 271 in the CG), with an average age of 49.8 (SD 10.82) years and gender ratio of 59.3% (304/513) women and 40.7% (209/513) men. Of them, 232 patients (45.2%) completed the follow-up, 104/242 (42.9%) in the IG and 128/271 (47.2%) in the CG. In the intention-to-treat analysis, the biochemically validated abstinence rate at 6 months was higher in the IG (63/242, 26%) compared with that in the CG (51/271, 18.8%; odds ratio 1.52, 95% CI 1.00-2.31; P=.05). After adjusting for basal CO-oximetry and bupropion intake, no substantial changes were observed (odds ratio 1.52, 95% CI 0.99-2.33; P=.05; pseudo-R²=0.045). In the IG, 61.2% (148/242) of users accessed the chatbot, average chatbot-patient interaction time was 121 (95% CI 121.1-140.0) minutes, and average number of contacts was 45.56 (SD 36.32).

CONCLUSIONS: A treatment including a chatbot for helping with tobacco cessation was more effective than usual clinical practice in primary care. However, this outcome was at the limit of statistical significance, and therefore these promising results must be interpreted with caution.

TRIAL REGISTRATION: Clinicaltrials.gov NCT03445507; https://tinyurl.com/mrnfcmtd.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12911-019-0972-z.

PMID:35759328 | DOI:10.2196/34273

JMIR Form Res. 2022 Jun 27;6(6):e37028. doi: 10.2196/37028.

ABSTRACT

BACKGROUND: Primary care physicians (PCPs) are often limited in their ability to collect detailed medical histories from patients, which can lead to errors or delays in diagnosis. Recent advances in artificial intelligence (AI) show promise in augmenting current human-driven methods of collecting personal and family histories; however, such tools are largely unproven.

OBJECTIVE: The main aim of this pilot study was to evaluate the feasibility and acceptability of a conversational AI medical interviewing system among patients.

METHODS: The study was conducted among adult patients empaneled at a family medicine clinic within a large academic medical center in Northern California. Participants were asked to test an AI medical interviewing system, which uses a conversational avatar and chatbot to capture medical histories and identify patients with risk factors. After completing an interview with the AI system, participants completed a web-based survey inquiring about the performance of the system, the ease of using the system, and attitudes toward the system. Responses on a 7-point Likert scale were collected and evaluated using descriptive statistics.

RESULTS: A total of 20 patients with a mean age of 50 years completed an interview with the AI system, including 12 females (60%) and 8 males (40%); 11 were White (55%), 8 were Asian (40%), and 1 was Black (5%), and 19 had at least a bachelor’s degree (95%). Most participants agreed that using the system to collect histories could help their PCPs have a better understanding of their health (16/20, 80%) and help them stay healthy through identification of their health risks (14/20, 70%). Those who reported that the system was clear and understandable, and that they were able to learn it quickly, tended to be younger; those who reported that the tool could motivate them to share more comprehensive histories with their PCPs tended to be older.

CONCLUSIONS: In this feasibility and acceptability pilot of a conversational AI medical interviewing system, the majority of patients believed that it could help clinicians better understand their health and identify health risks; however, patients were split on the effort required to use the system, and whether AI should be used for medical interviewing. Our findings suggest areas for further research, such as understanding the user interface factors that influence ease of use and adoption, and the reasons behind patients’ attitudes toward AI-assisted history-taking.

PMID:35759326 | DOI:10.2196/37028

JMIR Mhealth Uhealth. 2022 Jun 27;10(6):e32089. doi: 10.2196/32089.

ABSTRACT

BACKGROUND: Despite the availability and accessibility of free Papanicolaou (Pap) smear as a screening tool for cervical cancer, the uptake of Pap smear in Malaysia has not changed in the last 15 years. Previous studies have shown that the high uptake of Pap smear reduces the mortality rate of patients with cervical cancer. The low uptake of Pap smear is multifactorial, and the problem could be minimized through the use of mobile technologies. Nevertheless, most intervention studies focused on individual factors, while other important aspects such as mobile technologies, especially WhatsApp, have not been investigated yet.

OBJECTIVE: This study aims to determine the effects of a theory-based educational intervention and WhatsApp follow-up (Pap smear uptake [PSU] intervention) in improving PSU among postnatal women in Seremban, Negeri Sembilan, Malaysia.

METHODS: A 2-arm, parallel single-blind cluster randomized controlled trial was conducted among postpartum women from the Seremban district. Twelve health clinics were randomly assigned to the intervention and control groups. At baseline, both groups received a self-administered questionnaire. The intervention group received standard care and PSU intervention delivered by a researcher. This 2-stage intervention module was developed based on Social Cognitive Theory, where the first stage was conducted face-to-face and the second stage included a WhatsApp follow-up. The control group received standard care. Participants were observed immediately and at 4, 8, and 12 weeks after the intervention. The primary endpoint was PSU, whereas the secondary endpoints were knowledge, attitude, and self-efficacy scores for Pap smear screening self-assessed using a Google Forms questionnaire. A generalized mixed model was used to determine the effectiveness of the intervention. All data were analyzed using IBM SPSS (version 25), and P value of .05 was considered statistically significant.

RESULTS: We analyzed 401 women, of whom 76 (response rate: 325/401, 81%) had withdrawn because of the COVID-19 pandemic, with a total of 162 respondents in the intervention group and 163 respondents in the control group. The proportion of Pap smears at the 12-week follow-up was 67.9% (110/162) in the intervention group versus 39.8% (65/163) in the control group (P<.001). Significant differences between the intervention and control groups were found for Pap smear use (F_4,1178; P<.001), knowledge scores (F_4,1172=14.946; P<.001), attitude scores (F_4,1172=24.417; P<.001), and self-efficacy scores (F_1,1172=10.432; P<.001).

CONCLUSIONS: This study demonstrated that the PSU intervention is effective in increasing the uptake of Pap smear among postnatal women in Seremban district, Malaysia. This intervention module can be tested in other populations of women.

TRIAL REGISTRATION: Thai Clinical Trials Registry TCTR20200205001; https://www.thaiclinicaltrials.org/show/TCTR20200205001.

PMID:35759319 | DOI:10.2196/32089

J Med Internet Res. 2022 Jun 27;24(6):e36774. doi: 10.2196/36774.

ABSTRACT

BACKGROUND: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security.

OBJECTIVE: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS.

METHODS: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS.

RESULTS: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies.

CONCLUSIONS: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.

PMID:35759315 | DOI:10.2196/36774

Shock. 2022 Jun 1;57(6):268-273. doi: 10.1097/SHK.0000000000001935.

ABSTRACT

INTRODUCTION: The immunobiology defining the clinically apparent differences in response to sepsis remains unclear. We hypothesize that in murine models of sepsis we can identify phenotypes of sepsis using non-invasive physiologic parameters (NIPP) early after infection to distinguish between different inflammatory states.

METHODS: Two murine models of sepsis were used: gram-negative pneumonia (PNA) and cecal ligation and puncture (CLP). All mice were treated with broad spectrum antibiotics and fluid resuscitation. High-risk sepsis responders (pDie) were defined as those predicted to die within 72 h following infection. Low-risk responders (pLive) were expected to survive the initial 72 h of sepsis. Statistical modeling in R was used for statistical analysis and machine learning.

RESULTS: NIPP obtained at 6 and 24 h after infection of 291 mice (85 PNA and 206 CLP) were used to define the sepsis phenotypes. Lasso regression for variable selection with 10-fold cross-validation was used to define the optimal shrinkage parameters. The variables selected to discriminate between phenotypes included 6-h temperature and 24-h pulse distention, heart rate (HR), and temperature. Applying the model to fit test data (n = 55), area under the curve (AUC) for the receiver operating characteristics (ROC) curve was 0.93. Subgroup analysis of 120 CLP mice revealed a HR of <620 bpm at 24 h as a univariate predictor of pDie. (AUC of ROC curve = 0.90). Subgroup analysis of PNA exposed mice (n = 121) did not reveal a single predictive variable highlighting the complex physiological alterations in response to sepsis.

CONCLUSION: In murine models with various etiologies of sepsis, non-invasive vitals assessed just 6 and 24 h after infection can identify different sepsis phenotypes. Stratification by sepsis phenotypes can transform future studies investigating novel therapies for sepsis.

PMID:35759307 | DOI:10.1097/SHK.0000000000001935

Eur J Anaesthesiol. 2022 Jul 1;39(7):591-601. doi: 10.1097/EJA.0000000000001644. Epub 2021 Dec 7.

ABSTRACT

BACKGROUND: Patients undergoing mastectomy surgery experience severe postoperative pain. Several regional techniques have been developed to reduce pain intensity but it is unclear, which of these techniques is most effective.

OBJECTIVES: To synthesise direct and indirect comparisons for the relative efficacy of different regional and local analgesia techniques in the setting of unilateral mastectomy. Postoperative opioid consumption at 24 h, postoperative pain at extubation, 1, 12 and 24 h, postoperative nausea and vomiting were collected.

DESIGN: Systematic review with network meta-analysis (PROSPERO:CRD42021250651).

DATA SOURCE: PubMed, Scopus, the Cochrane Central Register of Controlled Trials (from inception until 7 July 2021).

ELIGIBILITY CRITERIA: All randomised controlled trials investigating single-injection regional and local analgesia techniques in adult patients undergoing unilateral mastectomy were included in our study without any language or publication date restriction.

RESULTS: Sixty-two included studies randomising 4074 patients and investigating nine techniques entered the analysis. All techniques were associated with less opioid consumption compared with controls The greatest mean difference [95% confidence interval (CI)] was associated with deep serratus anterior plane block: mean difference -16.1 mg (95% CI, -20.7 to -11.6). The greatest reduction in pain score was associated with the interpectoral-pecto-serratus plane block (mean difference -1.3, 95% CI, -1.6 to – 1) at 12 h postoperatively, and with superficial serratus anterior plane block (mean difference -1.4, 95% CI, -2.4 to -0.5) at 24 h. Interpectoral-pectoserratus plane block resulted in the greatest statistically significant reduction in postoperative nausea/vomiting when compared with placebo/no intervention with an OR of 0.23 (95% CI, 0.13 to 0.40).

CONCLUSION: All techniques were associated with superior analgesia and less opioid consumption compared with controls. No single technique was identified as superior to others. In comparison, local anaesthetic infiltration does not offer advantages over multimodal analgesia alone.

TRIAL REGISTRATION: PROSPERO (CRD4202125065).

PMID:35759292 | DOI:10.1097/EJA.0000000000001644

JAMA Intern Med. 2022 Jun 27. doi: 10.1001/jamainternmed.2022.2300. Online ahead of print.

ABSTRACT

IMPORTANCE: Smoking cessation interventions for hospitalized patients must continue after discharge to improve long-term tobacco abstinence. How health systems can best deliver postdischarge tobacco treatment is uncertain.

OBJECTIVE: To determine if health system-based tobacco cessation treatment after hospital discharge produces more long-term tobacco abstinence than referral to a community-based quitline.

DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was conducted September 2018 to November 2020 in 3 hospitals in Massachusetts, Pennsylvania, and Tennessee. Cigarette smokers admitted to a study hospital who received brief in-hospital tobacco treatment and wanted to quit smoking were recruited for participation and randomized for postdischarge treatment to health system-based Transitional Tobacco Care Management (TTCM) or electronic referral to a community-based quitline (QL). Both multicomponent interventions offered smoking cessation counseling and nicotine replacement therapy (NRT) for up to 3 months. Data were analyzed from February 1, 2021, to April 25, 2022.

INTERVENTIONS: TTCM provided 8 weeks of NRT at discharge and 7 automated calls with a hospital-based counselor call-back option. The QL intervention sent referrals from the hospital electronic health record to the state quitline, which offered 5 counseling calls and an NRT sample.

MAIN OUTCOMES AND MEASURES: The main outcome was biochemically verified past 7-day tobacco abstinence at 6 months. Self-reported point-prevalence and continuous tobacco abstinence and tobacco treatment utilization were assessed 1, 3, and 6 months after discharge.

RESULTS: A total of 1409 participants (mean [SD] age, 51.7 [12.6] years; 784 [55.6%] women; mean [SD] 16.4 [10.6] cigarettes/day) were recruited, including 706 randomized to TTCM and 703 randomized to QL. Participants were comparable at baseline, including 216 Black participants (15.3%), 82 Hispanic participants (5.8%), and 1089 White participants (77.3%). At 1 and 3 months after discharge, more TTCM participants than QL participants used cessation counseling (1 month: 245 participants [34.7%] vs 154 participants [21.9%]; 3 months: 248 participants [35.1%] vs 123 participants [17.5%]; P < .001) and pharmacotherapy (1 month: 455 participants [64.4%] vs 324 participants [46.1%]; 3 months: 367 participants [52.0%] vs 264 participants [37.6%]; P < .001). More TTCM than QL participants reported continuous abstinence for 3 months (RR, 1.30; 95% CI, 1.06-1.58) and point-prevalence abstinence at 1 month (RR, 1.22; 95% CI, 1.08-1.35) and 3 months (RR, 1.23; 95% CI, 1.09-1.37) but not at 6 months (RR, 1.14; 95% CI, 0.99-1.29). The primary outcome, biochemically verified point-prevalence abstinence at 6 months, was not statistically significantly different between groups (19.9% vs 16.9%; RR, 1.18; 95% CI, 0.92-1.50).

CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, biochemically verified tobacco abstinence rates were not significantly different between groups at the 6-month follow-up. However, the health system-based model was superior to the community-based quitline model throughout the 3 months of active treatment. A longer duration of postdischarge treatment may sustain the superiority of the health system-based model.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03603496.

PMID:35759282 | DOI:10.1001/jamainternmed.2022.2300

JAMA Neurol. 2022 Jun 27. doi: 10.1001/jamaneurol.2022.1567. Online ahead of print.

ABSTRACT

IMPORTANCE: Posttraumatic headache is the most disabling complication of mild traumatic brain injury. Posttraumatic stress disorder (PTSD) symptoms are often comorbid with posttraumatic headache, and there are no established treatments for this comorbidity.

OBJECTIVE: To compare cognitive behavioral therapies (CBTs) for headache and PTSD with treatment per usual (TPU) for posttraumatic headache attributable to mild traumatic brain injury.

DESIGN, SETTING, AND PARTICIPANTS: This was a single-site, 3-parallel group, randomized clinical trial with outcomes at posttreatment, 3-month follow-up, and 6-month follow-up. Participants were enrolled from May 1, 2015, through May 30, 2019; data collection ended on October 10, 2019. Post-9/11 US combat veterans from multiple trauma centers were included in the study. Veterans had comorbid posttraumatic headache and PTSD symptoms. Data were analyzed from January 20, 2020, to February 2, 2022.

INTERVENTIONS: Patients were randomly assigned to 8 sessions of CBT for headache, 12 sessions of cognitive processing therapy for PTSD, or treatment per usual for headache.

MAIN OUTCOMES AND MEASURES: Co-primary outcomes were headache-related disability on the 6-Item Headache Impact Test (HIT-6) and PTSD symptom severity on the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PCL-5) assessed from treatment completion to 6 months posttreatment.

RESULTS: A total of 193 post-9/11 combat veterans (mean [SD] age, 39.7 [8.4] years; 167 male veterans [87%]) were included in the study and reported severe baseline headache-related disability (mean [SD] HIT-6 score, 65.8 [5.6] points) and severe PTSD symptoms (mean [SD] PCL-5 score, 48.4 [14.2] points). For the HIT-6, compared with usual care, patients receiving CBT for headache reported -3.4 (95% CI, -5.4 to -1.4; P < .01) points lower, and patients receiving cognitive processing therapy reported -1.4 (95% CI, -3.7 to 0.8; P = .21) points lower across aggregated posttreatment measurements. For the PCL-5, compared with usual care, patients receiving CBT for headache reported -6.5 (95% CI, -12.7 to -0.3; P = .04) points lower, and patients receiving cognitive processing therapy reported -8.9 (95% CI, -15.9 to -1.9; P = .01) points lower across aggregated posttreatment measurements. Adverse events were minimal and similar across treatment groups.

CONCLUSIONS AND RELEVANCE: This randomized clinical trial demonstrated that CBT for headache was efficacious for disability associated with posttraumatic headache in veterans and provided clinically significant improvement in PTSD symptom severity. Cognitive processing therapy was efficacious for PTSD symptoms but not for headache disability.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02419131.

PMID:35759281 | DOI:10.1001/jamaneurol.2022.1567