Sleep quality and its relationship with school schedules and mental health of Nigerian secondary school adolescents.

STUDY OBJECTIVES: This study aimed to determine the sleep quality of in-school Nigerian adolescents and its association with their schooling and mental health outcomes. METHODS: The study was a descriptive cross-sectional study. It was conducted among adolescents attending public and private secondary schools within the Ife Central Local Government, Osun State, southwestern Nigeria. A multistage sampling technique was used to select study participants. The Pittsburgh Sleep Quality Index, 9-item Patient Health Questionnaire (PHQ-9), and 7-item General Anxiety Disorder questionnaires were used to determine sleep quality, depression, and anxiety, respectively. RESULTS: We studied 448 adolescents aged between 10 and 19 years with a mean age of 15.0 ± 1.8 years. The majority of our respondents (85.0%) had poor sleep quality. More than half of the respondents (55.1%) had insufficient sleep during weekdays, while only 34.8% had insufficient sleep during weekends. The school closing time and school type showed a statistically significant association with sleep quality (P = .039 and .005, respectively). The odds of having poor sleep quality increased by 2-fold among adolescents in private schools when compared with those in public schools (adjusted odds ratio = 1.97, 95% confidence interval = 1.069-3.627). Using multiple linear regression, only depression showed a statistically significant association with sleep quality at 95% confidence interval (CI = 0.073 to 0.219, P < .001), such that for every unit change in depression scores (PHQ-9), there will be a corresponding increase of 0.103 in sleep quality. CONCLUSIONS: Sleep quality is poor in adolescents and adversely associated with their mental health. This should also be addressed in the development of appropriate interventions. CITATION: Olorunmoteni OE, Fehintola FO, Seun-Fadipe C, Komolafe MA, Mosaku KS. Sleep quality and its relationship with school schedules and mental health of Nigerian secondary school adolescents. J Clin Sleep Med. 2023;19(11):1895-1904.

Quality improvement of outpatient clinical chemistry tests through a novel middleware-laboratory information system solution.

OBJECTIVES: Rapid and accurate laboratory tests are essential to support clinical decision-making. Despite the various efforts to control quality in the laboratory, our outpatient chemistry turnaround time (TAT) has deteriorated since 2018. Moreover, these difficulties have accelerated further due to the COVID-19 pandemic. Therefore, we aimed to improve laboratory work efficiency by identifying and eliminating the causes of reduced laboratory work efficiency. DESIGN & METHODS: We surveyed to identify tasks that reduce work efficiency. Based on our survey, a new-concept of work assistance middleware linked to laboratory information system (LIS) was developed. The middleware supports test end-time prediction, automatic real-time TAT monitoring, and urgent test requests so that medical technologists can focus on their chemistry tests. The developed middleware was used for 6 months in laboratory and outpatient clinics, and its effectiveness was evaluated. RESULTS: The median TAT for outpatient chemistry tests was reduced by 6.6 min, from 72.4 min to 65.8 min. And not only did the maximum TAT for the sample decrease from 353 min to 214 min, but the proportion of samples exceeding the TAT target (120 min) also decreased by 77%; from 2.00% in 2010 (1,905 out of 94,989 samples) to 0.46% in 2021 (453 out of 98,117 samples). 2,199 samples were urgently requested through middleware, and they were processed about 15% faster than other samples, effectively performing urgent tests. The test end-time prediction showed an error of 8.6 min in the evaluation using the MAE (Mean Absolute Error) index. CONCLUSIONS: Through this study, the quality and efficiency of the laboratory were improved, and while reducing the workload of medical staff, it contributed to enhancing patient safety and satisfaction.

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models.

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.

The Impact of Surgery duration and Surgery End Time on Postoperative Sleep in Older Adults.

OBJECTIVES/BACKGROUND: Sleep disruption is prevalent in older patients. No previous studies have considered the impact of surgery duration or surgery end time of day on postoperative sleep disruption. Accordingly, we examined the duration of surgery and surgery end times for associations with postoperative sleep disruption. METHODS: Inclusion criteria were patients ≥ 65 years of age undergoing major, non-cardiac surgery. Sleep disruption was measured by wrist actigraphy and defined as wake after sleep onset (WASO) during the night, or inactivity/sleep time during the day. The sleep opportunity window was set from 22:00 to 06:00 which coincided with "lights off and on" in the hospital. WASO during this 8-hour period on the first postoperative day was categorized into one of three groups: ≤ 15%, 15-25%, and > 25%. Daytime sleep (inactivity) during the first postoperative day was categorized as ≤ 20%, 20-40%, and > 40%. Statistical analyses were conducted to test for associations between surgery duration, surgery end time and sleep disruption on the first postoperative day and following night. RESULTS: For this sample of 156 patients, surgery duration ≥ 6 hours and surgery end time after 19:00 were not associated with WASO groups (p = 0.17, p = 0.94, respectively). Furthermore, daytime sleep was also not affected by surgery duration or surgery end time (p = 0.07, p = 0.06 respectively). CONCLUSION: Our hypothesis that patients with longer duration or later-ending operations have increased postoperative sleep disruption was not supported. Our results suggest the pathophysiology of postoperative sleep disruption needs further investigation.

Accuracy of intracoronary ECG parameters for myocardial ischemia detection.

INTRODUCTION: The electrocardiogram (ECG) is a valuable diagnostic tool for the diagnosis of myocardial ischemia during acute coronary syndrome. Aside from the commonly used ST-segment shift indicative of ischemia, several other ECG parameters are pathophysiologically reasonable. Thus, the goal of this study was to assess the accuracy of different ischemia parameters as obtained by the highly susceptible intracoronary ECG (icECG). METHOD: This was a retrospective observational study in 100 patients with chronic coronary syndrome. From each patient, a non-ischemic as well as ischemic icECG at the end of a one-minute proximal coronary balloon occlusion was available, and analysed twice by three different physicians, as well as once together for consensual results. The evaluated parameters were icECG ST-segment shift (mV), ST-integral (mV*sec), T-wave-integral (mV*sec), T-peak (mV), T-peak-to-end time (TPE; msec) and QTc-time (msec). RESULTS: All six icECG parameters showed significant differences between the non-ischemic and the ischemic recording. Using the icECG recording during coronary patency or occlusion as criterion for absent or present myocardial ischemia, ROC-analysis of icECG ST-segment shift showed an area under the curve (AUC) of 0.963 ± 0.029 (p < 0.0001). AUC for ST-integral was 0.899 ± 0.044 (p < 0.0001), for T-wave integral 0.791 ± 0.059 (p < 0.0001), for T-peak 0.811 ± 0.057 (p < 0.0001), for TPE 0.667 ± 0.068 (p < 0.0001), and for QTc-time 0.770 ± 0.061 (p < 0.0001). The best cut-off point for the detection of ischemia by icECG ST-segment shift was 0.365 mV (sensitivity 90%, specificity 95%). CONCLUSION: When tested in a setting with artificially induced absolute myocardial ischemia, icECG ST-segment shift at a threshold of 0.365 mV most accurately distinguishes between absent and present ischemia.

A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India.

We propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 30May 2020. The predictive capability of the model has been validated with real data of infection cases reported during June 1-10, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India, as a whole, is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India, as a whole, could see the peak and end of the epidemic in the month of July 2020 and March 2021 respectively as per the current trend in the data. Active infected cases in India may touch 2 lakhs or little above at the peak time and total infected cases may reach over 19 lakhs as per current trend. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from the national COVID19 dash board on daily basis and updates the model input parameters and predictions instantaneously. This real time application can be accessed from the link: https://docs.google.com/spreadsheets/d/1fCwgnQ-dz4J0YWVDHUcbEW1423wOJjdEXm8TqJDWNAk/edit?usp=sharing and can serve as a practical tool for policy makers to track peak time and maximum active infected cases based on latest trend in data for medical readiness and taking epidemic management decisions.