Nevin Manimala

JMIRx Med. 2021 May 5;2(2):e20617. doi: 10.2196/20617. eCollection 2021 Apr-Jun.

ABSTRACT

With over 117 million COVID-19-positive cases declared and the death count approaching 3 million, we would expect that the highly digitalized health systems of high-income countries would have collected, processed, and analyzed large quantities of clinical data from patients with COVID-19. Those data should have served to answer important clinical questions such as: what are the risk factors for becoming infected? What are good clinical variables to predict prognosis? What kinds of patients are more likely to survive mechanical ventilation? Are there clinical subphenotypes of the disease? All these, and many more, are crucial questions to improve our clinical strategies against the epidemic and save as many lives as possible. One might assume that in the era of big data and machine learning, there would be an army of scientists crunching petabytes of clinical data to answer these questions. However, nothing could be further from the truth. Our health systems have proven to be completely unprepared to generate, in a timely manner, a flow of clinical data that could feed these analyses. Despite gigabytes of data being generated every day, the vast quantity is locked in secure hospital data servers and is not being made available for analysis. Routinely collected clinical data are, by and large, regarded as a tool to inform decisions about individual patients, and not as a key resource to answer clinical questions through statistical analysis. The initiatives to extract COVID-19 clinical data are often promoted by private groups of individuals and not by health systems, and are uncoordinated and inefficient. The consequence is that we have more clinical data on COVID-19 than on any other epidemic in history, but we have failed to analyze this information quickly enough to make a difference. In this viewpoint, we expose this situation and suggest concrete ideas that health systems could implement to dynamically analyze their routine clinical data, becoming learning health systems and reversing the current situation.

PMID:34042100 | PMC:PMC8104306 | DOI:10.2196/20617

Pathologica. 2021 Apr;113(2):102-114. doi: 10.32074/1591-951X-217.

ABSTRACT

BACKGROUND: Forkhead box (FOX) A1 is a potential therapeutic biomarker that has been investigated in various human cancers. Limited data exist about FOXA1 biologic role in epithelial ovarian cancer (EOC).

AIM: This study assessed FOXA1 immunohistochemical (IHC) expression and evaluated its association with clinico-pathological parameters in EOC including overall and disease-free survivals (OS, DFS) and patient’s outcome.

METHODS: Patient’s socio-epidemiologic, clinical, radiological, laboratory, surgical, and follow-up data were collected. After histopathologic typing, grading and staging, FOXA1 IHC expression was scored in 98 EOC specimens. Clinico-pathological associations were investigated in high-and low-FOXA1 expression groups using appropriate statistical methods. Kaplan-Meier method was used for survival analysis.

RESULTS: FOXA1 tumor cell nuclear staining was detected in 63.3% of EOC with weak, moderate and strong scores (28.6%, 12.2% and 22.5% respectively). Comparing high- and low-expression groups (34.7% and 65.3% respectively), high FOXA1 was associated with larger tumors, low mean serum CA-125, tumor histopathology (mucinous and low-grade serous), type I EOC, limited tumor’s anatomical extent, absence of nodal or distant metastases and omental nodules, earlier FIGO stages, non-recurrent tumors and survival advantage with longer and OS and DFS (all p ≤ 0.05). Independent predictors of high FOXA1 expression included: omental nodules, tumor’s anatomical extent and tumor’s size (p ≤ 0.001, = 0.046 and = 0.023 respectively).

CONCLUSION: FOXA1 is frequently expressed in EOC notably mucinous and low-grade serous carcinomas in association with favorable prognostic clinico-pathological parameters and longer OS and DFS. It likely has a suppressor function in EOC and could be recommended as a prognostic and therapeutic biomarker.

PMID:34042091 | DOI:10.32074/1591-951X-217

Ultrasound Obstet Gynecol. 2021 May 26. doi: 10.1002/uog.23694. Online ahead of print.

ABSTRACT

OBJECTIVES: To estimate the chorionic villus sampling (CVS) related risk of fetal loss after adjustment for chorionicity, nuchal translucency thickness (NT), intertwin discordance in crown-rump length (CRL), maternal demographic characteristics and serum pregnancy associated plasma protein-A (PAPP-A) and free ß-human chorionic gonadotropin (hCG).

METHODS: This was a multicentre study from eight fetal medicine units in which the leadership were trained at the Harris Birthright research centre for fetal medicine in London and the protocols for screening, invasive testing and pregnancy management are similar. The study population of 8581 twin pregnancies undergoing ultrasound examination at 11-13 weeks’ gestation, included 316 dichorionic (DC) and 129 monochorionic (MC) twins that had CVS. Multivariable logistic regression analysis with backward step wise elimination was used to examine whether CVS provided a significant independent contribution in the prediction of risk of fetal loss after adjusting for maternal and pregnancy characteristics, including materal age, racial origin, weight, method of conception, smoking status, parity, chorionicity, intertwin discordance in CRL, fetal NT ≥95^th percentile, free ß-hCG and PAPP-A MoM. Similarly, within the CVS groups multivariable logistic regression analysis was used to investigate the effect of the number of intrauterine needle insertions and size of the needles on the risk of fetal loss.

RESULTS: There are four main findings of this study. First, in twin pregnancies undergoing CVS, compared to those that do not have CVS, there is a 2-fold increased risk of fetal loss at <24 weeks’ gestation and loss at any stage in pregnancy. Second, the factors providing a significant independent contribution in the prediction of miscarriage or fetal loss in twin pregnancies are increased maternal weight, Black racial origin, monochorionicity and more so monoamnionicity, large intertwin discordance in CRL and high fetal NT and in the case of total fetal loss there is also a contribution from low serum PAPP-A. Third, there is a trend for an increased risk of fetal loss from CVS after adjustment for maternal and pregnancy characteristics but this does not reach statistical significance. Fourth, in the twin pregnancies that had CVS there is no significant contribution to fetal loss from the number of intrauterine needle insertions or needle size.

CONCLUSION: The 2-fold increased risk of fetal loss following CVS in twin pregnancies can to a great extent be explained by maternal and pregnancy characteristics rather than the invasive procedure itself. This article is protected by copyright. All rights reserved.

PMID:34038977 | DOI:10.1002/uog.23694

J Athl Train. 2021 May 26. doi: 10.4085/1062-6050-0746.20. Online ahead of print.

ABSTRACT

OBJECTIVE: To determine the effect of electrocardiogram (ECG) screening on prevention of sudden cardiac arrest and death (SCA/D) in young athletes and military members.

DATA SOURCES: MEDLINE, Embase, Cochrane CENTRAL, Web of Science, BIOSIS, Scopus, SPORT discus, PEDro, and clinicaltrials.gov were searched from inception to dates between 2/21/19 and 7/29/19.

STUDY SELECTION: Randomized and non-randomized controlled trials, where pre-participation examination including ECG was the primary intervention used to screen athletes or military 40 years of age or younger. Accepted controls were no screening, usual care, or pre-participation examination without ECG. 3 published studies , and one conference abstract were identified for inclusion.

DATA EXTRACTION: In all four studies, risk of bias was assessed with the Cochrane risk of bias tool, and found to be generally high. Two studies had data extracted for random effects meta-analysis, and the remaining study and conference abstract were included in narrative review. Overall quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Data Synthesis: Four non-randomized studies (11,689,172 participants) were included, all at high risk of bias. Pooled data from two studies (n= 3,869,274; very low quality evidence) observed an inconclusive 42% relative decrease in risk of sudden cardiac death (RR 0.58; 95% CI 0.23, 1.45), equating to an absolute risk reduction of .0016% The findings were consistent with a potential 67% relative decrease to a 45% relative increased risk in participants screened with ECG. Heterogeneity was found to be high as measured with I2 statistic (71%). Data from the remaining study and abstract were similarly inconclusive.

CONCLUSION: Existing evidence for the effect of ECG screening is inconclusive and of very low quality. Our meta-analysis observed that screening ECG may result in considerable benefit or harm to participants. Higher quality studies are needed to reduce this uncertainty. PROSPERO Registration: CRD42019125560.

PMID:34038955 | DOI:10.4085/1062-6050-0746.20

J Clin Neurophysiol. 2021 Apr 30. doi: 10.1097/WNP.0000000000000854. Online ahead of print.

ABSTRACT

PURPOSE: Few and contradictory data are available regarding intraoperative seizures during surgery for low-grade gliomas. Aim of this study was to evaluate possible risk factors for the occurrence of IOS.

METHODS: The authors performed a retrospective analysis of 155 patients affected by low-grade gliomas and tumor-related epilepsy, who underwent surgery in our Department, between 2007 and 2018. A statistical analysis was performed by means of univariate and multivariate regression to evaluate any possible correlation between seizure occurrence and several demographic, clinical, neurophysiological, and histopathological features.

RESULTS: Intraoperative seizure occurred in 39 patients (25.16%) with a total of 62 seizure events recorded. Focal seizures were the prevalent seizure type: among them, 39 seizures did not show motor signs, being those with only electrographic and/or with cognitive features the most represented subtypes. Twenty-six seizures occurring during surgery were not spontaneous: direct cortical stimulation with Penfield paradigm was the most prevalent evoking factor. The univariate analysis showed that the following prognostic factors were statistically associated with the occurrence of intraoperative seizure: the awake technique (P = 0.01) and the interictal epileptiform discharges detected on the baseline electrocorticography (ECoG) (P < 0.001). After controlling for confounding factors with multivariate analysis, the awake surgery and the epileptic ECoG pattern kept statistical significance.

CONCLUSIONS: The awake surgery procedure and the epileptic ECoG pattern are risk factors for intraoperative seizure. ECoG is mandatory to detect electrographic seizures or seizures without motor signs.

PMID:34038932 | DOI:10.1097/WNP.0000000000000854

J Clin Neurophysiol. 2021 Apr 30. doi: 10.1097/WNP.0000000000000853. Online ahead of print.

ABSTRACT

Despite many decades of research, controversy regarding the utility of quantitative EEG (qEEG) for the accurate diagnosis of mild traumatic brain injury (mTBI) remains. This guideline is meant to assist clinicians by providing an expert review of the clinical usefulness of qEEG techniques for the diagnosis of mTBI. This guideline addresses the following primary aim: For patients with or without posttraumatic symptoms (abnormal cognition or behavior), does qEEG either at the time of injury or remote from the injury, as compared with current clinical diagnostic criteria, accurately identify those patients with mTBI (i.e., concussion)? Secondary aims included differentiating between mTBI and other diagnoses, detecting mTBI in the presence of central nervous system medications, and pertinence of statistical methods for measurements of qEEG components. It was found that for patients with or without symptoms of abnormal cognition or behavior, current evidence does not support the clinical use of qEEG either at the time of the injury or remote from the injury to diagnose mTBI (level U). In addition, the evidence does not support the use of qEEG to differentiate mTBI from other diagnoses or detect mTBI in the presence of central nervous system medications, and suitable statistical methods do not exist when using qEEG to identify patients with mTBI. Based upon the current literature review, qEEG remains an investigational tool for mTBI diagnosis (class III evidence).

PMID:34038930 | DOI:10.1097/WNP.0000000000000853

Pediatr Emerg Care. 2021 Jun 1;37(6):e329-e333. doi: 10.1097/PEC.0000000000002447.

ABSTRACT

BACKGROUND: We compared those patients who left without being seen (LWBS) with those who stay for evaluation and determined which subsets were more likely to depart prematurely in the largest pediatric population studied to date.

METHODS: We retrospectively extracted data from the electronic medical records of all pediatric patients who visited the emergency department between January 1, 2013, and December 31, 2015. The demographics and visit characteristics were compared between patients who LWBS and those seen by a provider. Bivariate and multivariate analyses were used to determine the odds for premature departure of specific groups within the population.

RESULTS: Of the 271,364 pediatric patients visiting the emergency department during the 3-year study period, 3835 (1.4%) LWBS by a provider. The mean age of those LWBS was younger, and the odds of leaving slightly decreased as the patient’s age increased (odds ratio [OR], 0.98). Those triaged as having “nonurgent” medical conditions had a statistically significant increase in odds of premature departure when compared with those with “urgent” medical conditions (OR, 1.16). Patients arriving during the evening and overnight hours had a much greater odds of LWBS (OR, 6.7 and 7.3, respectively).

CONCLUSIONS: Our findings demonstrated and confirmed that age, time of arrival, and acuity level upon presentation were predictors of patients leaving before evaluation. This can guide institutions with staffing and flow processes as they attempt to reduce LWBS rates but also raises further questions as to whether these subsets go forward to have worse clinical outcomes after leaving prematurely.

PMID:34038929 | DOI:10.1097/PEC.0000000000002447

Psychother Psychosom. 2021 May 26:1-11. doi: 10.1159/000516599. Online ahead of print.

ABSTRACT

Patient-reported outcome measures (PROMs) are self-rated scales and indices developed to improve the detection of the patients’ subjective experience. Given that a considerable number of PROMs are available, it is important to evaluate their validity and usefulness in a specific research or clinical setting. Published guidelines, based on psychometric criteria, do not fit in with the complexity of clinical challenges, because of their quest for homogeneity of components and inadequate attention to sensitivity. Psychometric theory has stifled the field and led to the routine use of scales widely accepted yet with a history of poor performance. Clinimetrics, the science of clinical measurements, may provide a more suitable conceptual and methodological framework. The aims of this paper are to outline the major limitations of the psychometric model and to provide criteria for clinimetric patient-reported outcome measures (CLIPROMs). The characteristics related to reliability, sensitivity, validity, and clinical utility of instruments are critically reviewed, with particular reference to the differences between clinimetric and psychometric approaches. Of note is the fact that PROMs, rating scales, and indices developed according to psychometric criteria may display relevant clinimetric properties. The present paper underpins the importance of the clini-metric methodology in choosing the appropriate PROMs. CLIPROM criteria may also guide the development of new indices and the validation of existing PROMs to be employed in clinical settings.

PMID:34038901 | DOI:10.1159/000516599

Phys Biol. 2021 May 26. doi: 10.1088/1478-3975/ac059d. Online ahead of print.

ABSTRACT

While the mathematical laws of uncontrolled epidemic spreading are well known, the statistical physics of Coronavirus epidemics with containment measures is currently lacking. The modelling of available data of the first wave of the Covid-19 pandemic in 2020 over 230 days, in different countries representative of different containment policies is relevant to quantify the efficiency of these policies to face the containment of any successive wave. At this aim we have built a 3D phase diagram tracking the simultaneous evolution and the interplay of the doubling time, Td, and the reproductive number, Rt measured using the methodological definition used by the Robert Koch Institute. In this expanded parameter space three different main phases, supercritical, critical and subcritical are identified. Moreover, we have found that in the supercritical regime with Rt>1 the doubling time is smaller than 40 days. In this phase we have established the power law relation between Td and (Rt-1)- with the exponent  depending on the definition of reproductive number. In the subcritical regime where Rt<1 and Td>100 days, we have identified arrested metastable phases where Td is nearly constant.

PMID:34038897 | DOI:10.1088/1478-3975/ac059d

Comput Methods Programs Biomed. 2021 May 9;207:106155. doi: 10.1016/j.cmpb.2021.106155. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as an appropriate outcome measure. However, the RMST only calculates the mean survival time of patients within a period of time after the start of follow-up and may not accurately portray the change in a patient’s life expectancy over time.

METHODS: The life expectancy can be adjusted for the time the patient has already survived and defined as the conditional restricted mean survival time (cRMST). A dynamic RMST model based on the cRMST can be established by incorporating time-dependent covariates and covariates with time-varying effects. We analyzed data from a study of primary biliary cirrhosis (PBC) to illustrate the use of the dynamic RMST model, and a simulation study was designed to test the advantages of the proposed approach. The predictive performance was evaluated using the C-index and the prediction error.

RESULTS: Considering both the example results and the simulation results, the proposed dynamic RMST model, which can explore the dynamic effects of prognostic factors on survival time, has better predictive performance than the RMST model. Three PBC patient examples were used to illustrate how the predicted cRMST changed at different prediction times during follow-up.

CONCLUSIONS: The use of the dynamic RMST model based on the cRMST allows for the optimization of evidence-based decision-making by updating personalized dynamic life expectancy for patients.

PMID:34038865 | DOI:10.1016/j.cmpb.2021.106155