David M. Maslove

Barriers to the effective treatment and prevention of malaria in Africa: A systematic review of qualitative studies

BackgroundIn Africa, an estimated 300-500 million cases of malaria occur each year resulting in approximately 1 million deaths. More than 90% of these are in children under 5 years of age. To identify commonly held beliefs about malaria that might present barriers to its successful treatment and prevention, we conducted a systematic review of qualitative studies examining beliefs and practices concerning malaria in sub-Saharan African countries.MethodsWe searched Medline and Scopus (1966-2009) and identified 39 studies that employed qualitative methods (focus groups and interviews) to examine the knowledge, attitudes, and practices of people living in African countries where malaria is endemic. Data were extracted relating to study characteristics, and themes pertaining to barriers to malaria treatment and prevention.ResultsThe majority of studies were conducted in rural areas, and focused mostly or entirely on children. Major barriers to prevention reported included a lack of understanding of the cause and transmission of malaria (29/39), the belief that malaria cannot be prevented (7/39), and the use of ineffective prevention measures (12/39). Thirty-seven of 39 articles identified barriers to malaria treatment, including concerns about the safety and efficacy of conventional medicines (15/39), logistical obstacles, and reliance on traditional remedies. Specific barriers to the treatment of childhood malaria identified included the belief that a child with convulsions could die if given an injection or taken to hospital (10/39).ConclusionThese findings suggest that large-scale malaria prevention and treatment programs must account for the social and cultural contexts in which they are deployed. Further quantitative research should be undertaken to more precisely measure the impact of the themes uncovered by this exploratory analysis.

Electronic Versus Dictated Hospital Discharge Summaries: a Randomized Controlled Trial

ABSTRACTBACKGROUNDPatient care transitions are periods of enhanced risk. Discharge summaries have been used to communicate essential information between hospital-based physicians and primary care physicians (PCPs), and may reduce rates of adverse events after discharge.OBJECTIVETo assess PCP satisfaction with an electronic discharge summary (EDS) program as compared to conventional dictated discharge summaries.DESIGNCluster randomized trial.PARTICIPANTSFour medical teams of an academic general medical service.MEASUREMENTSThe primary endpoint was overall discharge summary quality, as assessed by PCPs using a 100-point visual analogue scale. Other endpoints included housestaff satisfaction (using a 100-point scale), adverse outcomes after discharge (combined endpoint of emergency department visits, readmission, and death), and patient understanding of discharge details as measured by the Care Transition Model (CTM-3) score (ranging from 0 to 100).RESULTS209 patient discharges were included over a 2-month period encompassing 1 housestaff rotation. Surveys were sent out for 188 of these patient discharges, and 119 were returned (63% response rate). No difference in PCP-reported overall quality was observed between the 2 methods (86.4 for EDS vs. 84.3 for dictation; P = 0.53). Housestaff found the EDS significantly easier to use than conventional dictation (86.5 for EDS vs. 49.2 for dictation; P = 0.03), but there was no difference in overall housestaff satisfaction. There was no difference between discharge methods for the combined endpoint for adverse outcomes (22 for EDS [21%] vs. 21 for dictation [20%]; P = 0.89), or for patient understanding of discharge details (CTM-3 score 80.3 for EDS vs. 81.3 for dictation; P = 0.81)CONCLUSIONAn EDS program can be used by housestaff to more easily create hospital discharge summaries, and there was no difference in PCP satisfaction.

Gene expression profiling in sepsis: timing, tissue, and translational considerations

Sepsis is a complex inflammatory response to infection. Microarray-based gene expression studies of sepsis have illuminated the complex pathogen recognition and inflammatory signaling pathways that characterize sepsis. More recently, gene expression profiling has been used to identify diagnostic and prognostic gene signatures, as well as novel therapeutic targets. Studies in pediatric cohorts suggest that transcriptionally distinct subclasses might account for some of the heterogeneity seen in sepsis. Time series analyses have pointed to rapid and dynamic shifts in transcription patterns associated with various phases of sepsis. These findings highlight current challenges in sepsis knowledge translation, including the need to adapt complex and time-consuming whole-genome methods for use in the intensive care unit environment, where rapid diagnosis and treatment are essential.

Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

Background Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. Methods and Findings We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. Conclusions The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to meaningful use of EMR data.

A path to precision in the ICU

Precision medicine is increasingly touted as a groundbreaking new paradigm in biomedicine. In the ICU, the complexity and ambiguity of critical illness syndromes have been identified as fundamental justifications for the adoption of a precision approach to research and practice. Inherently protean diseases states such as sepsis and acute respiratory distress syndrome have manifestations that are physiologically and anatomically diffuse, and that fluctuate over short periods of time. This leads to considerable heterogeneity among patients, and conditions in which a “one size fits all” approach to therapy can lead to widely divergent results. Current ICU therapy can thus be seen as imprecise, with the potential to realize substantial gains from the adoption of precision medicine approaches. A number of challenges still face the development and adoption of precision critical care, a transition that may occur incrementally rather than wholesale. This article describes a few concrete approaches to addressing these challenges.First, novel clinical trial designs, including registry randomized controlled trials and platform trials, suggest ways in which conventional trials can be adapted to better accommodate the physiologic heterogeneity of critical illness. Second, beyond the “omics” technologies already synonymous with precision medicine, the data-rich environment of the ICU can generate complex physiologic signatures that could fuel precision-minded research and practice. Third, the role of computing infrastructure and modern informatics methods will be central to the pursuit of precision medicine in the ICU, necessitating close collaboration with data scientists. As work toward precision critical care continues, small proof-of-concept studies may prove useful in highlighting the potential of this approach.

Brain Tissue Oxygenation in Patients with Septic Shock: a Feasibility Study.

BACKGROUND Delirium is common in critically ill patients and its presence is associated with increased mortality and increased likelihood of poor cognitive function among survivors. However, the cause of delirium is unknown. The purpose of this study was to demonstrate the feasibility of using near-infrared spectroscopy (NIRS) to assess brain tissue oxygenation in patients with septic shock, who are at high risk of developing delirium. METHODS This prospective observational study was conducted in a 33-bed general medical surgical intensive care unit (ICU). Patients with severe sepsis or septic shock were eligible for recruitment. The FORESIGHT NIRS monitor was used to assess brain tissue oxygenation in the frontal lobes for the first 72 hours of ICU admission. Physiological data was also recorded. We used the Confusion Assessment Method-ICU to screen for delirium. RESULTS From March 1st 2014-September 30th 2014, 10 patients with septic shock were recruited. The NIRS monitor captured 81% of the available data. No adverse events were recorded. Brain tissue oxygenation demonstrated significant intra- and inter-individual variability in the way it correlated with physiological parameters, such as mean arterial pressure, heart rate, and peripheral oxygen saturation. Mean brain tissue oxygen levels were significantly lower in patients who were delirious for the majority of their ICU stay. CONCLUSION It is feasible to record brain tissue oxygenation with NIRS in patients with septic shock. This study provides the infrastructure necessary for a larger prospective observational study to further examine the relationship between brain tissue oxygenation, physiological parameters, and acute neurological dysfunction.

Customization of a Severity of Illness Score Using Local Electronic Medical Record Data

Purpose: Severity of illness (SOI) scores are traditionally based on archival data collected from a wide range of clinical settings. Mortality prediction using SOI scores tends to underperform when applied to contemporary cases or those that differ from the case-mix of the original derivation cohorts. We investigated the use of local clinical data captured from hospital electronic medical records (EMRs) to improve the predictive performance of traditional severity of illness scoring. Methods: We conducted a retrospective analysis using data from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database, which contains clinical data from the Beth Israel Deaconess Medical Center in Boston, Massachusetts. A total of 17 490 intensive care unit (ICU) admissions with complete data were included, from 4 different service types: medical ICU, surgical ICU, coronary care unit, and cardiac surgery recovery unit. We developed customized SOI scores trained on data from each service type, using the clinical variables employed in the Simplified Acute Physiology Score (SAPS). In-hospital, 30-day, and 2-year mortality predictions were compared with those obtained from using the original SAPS using the area under the receiver–operating characteristics curve (AUROC) as well as the area under the precision-recall curve (AUPRC). Test performance in different cohorts stratified by severity of organ injury was also evaluated. Results: Most customized scores (30 of 39) significantly outperformed SAPS with respect to both AUROC and AUPRC. Enhancements over SAPS were greatest for patients undergoing cardiovascular surgery and for prediction of 2-year mortality. Conclusions: Custom models based on ICU-specific data provided better mortality prediction than traditional SAPS scoring using the same predictor variables. Our local data approach demonstrates the value of electronic data capture in the ICU, of secondary uses of EMR data, and of local customization of SOI scoring.

Using information theory to identify redundancy in common laboratory tests in the intensive care unit

BackgroundClinical workflow is infused with large quantities of data, particularly in areas with enhanced monitoring such as the Intensive Care Unit (ICU). Information theory can quantify the expected amounts of total and redundant information contained in a given clinical data type, and as such has the potential to inform clinicians on how to manage the vast volumes of data they are required to analyze in their daily practice. The objective of this proof-of-concept study was to quantify the amounts of redundant information associated with common ICU lab tests.MethodsWe analyzed the information content of 11 laboratory test results from 29,149 adult ICU admissions in the MIMIC II database. Information theory was applied to quantify the expected amount of redundant information both between lab values from the same ICU day, and between consecutive ICU days.ResultsMost lab values showed a decreasing trend over time in the expected amount of novel information they contained. Platelet, blood urea nitrogen (BUN), and creatinine measurements exhibited the most amount of redundant information on days 2 and 3 compared to the previous day. The creatinine-BUN and sodium-chloride pairs had the most redundancy.ConclusionsInformation theory can help identify and discourage unnecessary testing and bloodwork, and can in general be a useful data analytic technique for many medical specialties that deal with information overload.

Medical Preprints—A Debate Worth Having

Following a similar movement in other academic fields, most notably the physical sciences and computing, biomedical researchers are increasingly exploring the use of preprint servers to rapidly disseminate their scholarly output.1-3 Preprint servers consist of online repositories that make scientific manuscripts available to view and cite, without prior external peer review. The largest and most popular site for preprints, arXiv.org, began accepting papers in 1991, and now contains more than 1.3 million articles from the physical sciences, with nearly 1 billion downloads as of August 2017.4 More recently, bioRxiv.org has begun to offer preprint services for the biological sciences and is growing rapidly, with nearly 17 000 preprints posted since its inception in 2013, most of them in the last year.5 While some preprints may be or will eventually be submitted to a journal and undergo peer review, others might not be destined to complete this process. These manuscripts may contain early-stage, unfiltered research findings, prompting significant debate in a number of scientific communities about their use. The possible arrival of MedArXiv,6 a recently

Errors, Omissions, and Outliers in Hourly Vital Signs Measurements in Intensive Care.

Objective:To empirically examine the prevalence of errors, omissions, and outliers in hourly vital signs recorded in the ICU. Design:Retrospective analysis of vital signs measurements from a large-scale clinical data warehouse (Multiparameter Intelligent Monitoring in Intensive Care III). Setting:Data were collected from the medical, surgical, cardiac, and cardiac surgery ICUs of a tertiary medical center in the United States. Patients:We analyzed data from approximately 48,000 ICU stays including approximately 28 million vital signs measurements. Interventions:None. Measurements and Main Results:We used the vital sign day as our unit of measurement, defined as all the recordings from a single patient for a specific vital sign over a single 24-hour period. Approximately 30–40% of vital sign days included at least one gap of greater than 70 minutes between measurements. Between 3% and 10% of blood pressure measurements included logical inconsistencies. With the exception of pulse oximetry vital sign days, the readings in most vital sign days were normally distributed. We found that 15–38% of vital sign days contained at least one statistical outlier, of which 6–19% occurred simultaneously with outliers in other vital signs. Conclusions:We found a significant number of missing, erroneous, and outlying vital signs measurements in a large ICU database. Our results provide empirical evidence of the nonrepresentativeness of hourly vital signs. Additional studies should focus on determining optimal sampling frequencies for recording vital signs in the ICU.