Browsing by Author "Sigua, Ninotchka L."

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    A Feasibility Study: Testing Whether a Sleep Application Providing Objective Sleep Data to Physicians Improves Patient-Physician Communication Regarding Sleep Experiences, Habits, and Behaviors
    (Springer, 2022) Durrani, Sana; Cao, Sha; Bo, Na; Pai, Jennifer K.; Baker, Jarod; Rawlings, Lori; Qureshi, Zaina P.; Sigua, Ninotchka L.; Manchanda, Shalini; Khan, Babar; Medicine, School of Medicine
    Introduction: Sleep tracker data have not been utilized routinely in sleep-related disorders and their management. Sleep-related disorders are common in primary care practice and incorporating sleep tracker data may help in improving patient care. We conducted a pilot study to assess the feasibility of a sleep program using the Fitbit Charge 2™ device and SleepLife® application. The main aim of the study was to examine whether a program using a commercially available wearable sleep tracker device providing objective sleep data would improve communication in primary care settings between patients and their providers. Secondary aims included whether patient satisfaction with care would improve as result of the program. Methods: A prospective, randomized, parallel group, observational pilot study was conducted in 20 primary care clinics in Indianapolis, IN from June 2018 to February 2019. Inclusion criteria included patients over the age of 18, have a diagnosis of insomnia identified by electronic medical record and/or a validated questionnaire, and were on a prescription sleep aid. The study was not specific to any sleep aid prescription, branded or generic, and was not designed to evaluate a drug or drug class. Each primary care clinic was randomized to either the SleepLife® intervention or the control arm. All patients were provided with a Fitbit Charge 2™ device. Only patients in the intervention arm were educated on how to use the SleepLife® application. Physicians in the intervention arm were set up with the SleepLife® portal on their computers. Results: Forty-nine physicians and 75 patients were enrolled in the study. Patients had a mean age of 57 (SD 12.8) years and 61% were female. Mean age of physicians was 47 (SD 10.6) years. Patients showed high rates of involvement in the program with 83% completing all survey questions. Physician survey completion rate was 55%. Only one physician logged into the SleepLife portal to check their patients' sleep status. At the end of the 6-week intervention, patients' composite general satisfaction scores with sleep health management decreased significantly in the intervention arm when compared to controls (p = 0.03). Patients' satisfaction with communication also decreased significantly in the intervention group (p = 0.01). The sleep outcomes, which were calculated on the basis of study questionnaire answers, improved significantly in the intervention group as compared to the control group (p = 0.04). Physician communication satisfaction scores remained unchanged (p = 0.12). Conclusions: SleepLife® and its related physician portal can facilitate physician-patient communication, and it captures patient sleep outcomes including behaviors and habits. Patients were highly engaged with the program, while physicians did not demonstrate engagement. The study design and questionnaires do not specifically address the reasons behind the decreased patient satisfaction with care and communication, but it was perceived to be a result of physician non-responsiveness. Sleep quality scores on the other hand showed an improvement among SleepLife® users, suggesting that patients may have implemented good sleep practices on their own. Given that it was a feasibility study, and the sample size was small, we were not able to make major inferences regarding the difference between sleep disorder types. Additionally, we excluded patients with a history of alcohol use, substance abuse, or depression because of concerns that they may affect sleep independently. To promote the growth of technology in primary care, further research incorporating results from this study and physician engagement techniques should be included.
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    Hospital outcomes in non-surgical patients identified at risk for OSA
    (Elsevier, 2020) Khan, Sikandar H.; Manchanda, Shalini; Sigua, Ninotchka L.; Green, Erika; Mpofu, Philani B.; Hui, Siu; Khan, Babar A.; Medicine, School of Medicine
    Background: In-hospital respiratory outcomes of non-surgical patients with undiagnosed obstructive sleep apnea (OSA), particularly those with significant comorbidities are not well defined. Undiagnosed and untreated OSA may be associated with increased cardiopulmonary morbidity. Study objectives: Evaluate respiratory failure outcomes in patients identified as at-risk for OSA by the Berlin Questionnaire (BQ). Methods: This was a retrospective study conducted using electronic health records at a large health system. The BQ was administered at admission to screen for OSA to medical-service patients under the age of 80 years old meeting the following health system criteria: (1) BMI greater than 30; (2) any of the following comorbid diagnoses: hypertension, heart failure, acute coronary syndrome, pulmonary hypertension, arrhythmia, cerebrovascular event/stroke, or diabetes. Patients with known OSA or undergoing surgery were excluded. Patients were classified as high-risk or low-risk for OSA based on the BQ score as follows: low-risk (0 or 1 category with a positive score on the BQ); high-risk (2 or more categories with a positive score on BQ). The primary outcome was respiratory failure during index hospital stay defined by any of the following: orders for conventional ventilation or intubation; at least two instances of oxygen saturation less than 88% by pulse oximetry; at least two instances of respiratory rate over 30 breaths per minute; and any orders placed for non-invasive mechanical ventilation without a previous diagnosis of sleep apnea. Propensity scores were used to control for patient characteristics. Results: Records of 15,253 patients were assessed. There were no significant differences in the composite outcome of respiratory failure by risk of OSA (high risk: 11%, low risk: 10%, p = 0.55). When respiratory failure was defined as need for ventilation, more patients in the low-risk group experienced invasive mechanical ventilation (high-risk: 1.8% vs. low-risk: 2.3%, p = 0.041). Mortality was decreased in patients at high-risk for OSA (0.86%) vs. low risk for OSA (1.53%, p < 0.001). Conclusions: Further prospective studies are needed to understand the contribution of undiagnosed OSA to in-hospital respiratory outcomes.
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    Psychiatric symptoms and their association with sleep disturbances in intensive care unit survivors
    (Dovepress, 2019-03-22) Wang, Sophia; Meeker, Jared W.; Perkins, Anthony J.; Gao, Sujuan; Khan, Sikandar H.; Sigua, Ninotchka L.; Manchanda, Shalini; Boustani, Malaz A.; Khan, Babar A.; Psychiatry, School of Medicine
    Background: Sleep disturbances in critically ill patients are associated with poorer long-term clinical outcomes and quality of life. Studies are needed to better characterize associations and risk factors for persistent sleep disturbances after intensive care unit (ICU) discharge. Psychiatric disorders are frequently associated with sleep disturbances, but the role of psychiatric symptoms in sleep disturbances in ICU survivors has not been well-studied. Objective: To examine the association between psychiatric symptoms and sleep disturbances in ICU survivors. Methods: 112 adult ICU survivors seen from July 2011 to August 2016 in the Critical Care Recovery Center, an ICU survivor clinic at the Eskenazi Hospital in Indianapolis, IN, USA, were assessed for sleep disturbances (insomnia, hypersomnia, difficulty with sleep onset, difficulty with sleep maintenance, and excessive daytime sleepiness) and psychiatric symptoms (trauma-related symptoms and moderate to severe depressive symptoms) 3 months after ICU discharge. A multivariate logistic regression model was performed to examine the association between psychiatric symptoms and sleep disturbances. Analyses were controlled for age, hypertension, history of depression, and respiratory failure. Results: ICU survivors with both trauma-related and depression symptoms (OR 16.66, 95% CI 2.89-96.00) and trauma-related symptoms alone (OR 4.59, 95% CI 1.11-18.88) had a higher likelihood of sleep disturbances. Depression symptoms alone were no longer significantly associated with sleep disturbances when analysis was controlled for trauma-related symptoms. Conclusion: Trauma-related symptoms and trauma-related plus moderate to severe depressive symptoms were associated with a higher likelihood of sleep disturbances. Future studies are needed to determine whether psychiatric symptoms are associated with objective changes on polysomnography and actigraphy and whether adequate treatment of psychiatric symptoms can improve sleep disturbances.