A comparison of the NIRUDAK models and WHO algorithm for dehydration assessment in older children and adults with acute diarrhoea: a prospective, observational study.

BACKGROUND: Despite the importance of accurate and rapid assessment of hydration status in patients with acute diarrhoea, no validated tools exist to help clinicians assess dehydration severity in older children and adults. The aim of this study is to validate a clinical decision support tool (CDST) and a simplified score for dehydration severity in older children and adults with acute diarrhoea (both developed during the NIRUDAK study) and compare their accuracy and reliability with current WHO guidelines. METHODS: A random sample of patients aged 5 years or older presenting with diarrhoea to the icddr,b Dhaka Hospital in Bangladesh between Jan 30 and Dec 13, 2022 were included in this prospective cohort study. Patients with fewer than three loose stools per day, more than 7 days of symptoms, previous enrolment in the study, or a diagnosis other than acute gastroenteritis were excluded. Patients were weighed on arrival and assessed separately by two nurses using both our novel clinical tools and WHO guidelines. Patients were weighed every 4 h to determine their percent weight change with rehydration, our criterion standard for dehydration. Accuracy for the diagnosis of dehydration category (none, some, or severe) was assessed using the ordinal c-index (ORC). Reliability was assessed by comparing the prediction of severe dehydration from each nurse's independent assessment using the intraclass correlation coefficient (ICC). FINDINGS: 1580 patients were included in our primary analysis, of whom 921 (58·3%) were female and 659 (41·7%) male. The ORC was 0·74 (95% CI 0·71-0·77) for the CDST, 0·75 (0·71-0·78) for the simplified score, and 0·64 (0·61-0·67) for the WHO guidelines. The ICC was 0·98 (95% CI 0·97-0·98) for the CDST, 0·94 (0·93-0·95) for the simplified score, and 0·56 (0·52-0·60) for the WHO guidelines. INTERPRETATION: Use of our CDST or simplified score by clinicians could reduce undertreatment and overtreatment of older children and adults with acute diarrhoea, potentially reducing morbidity and mortality for this common disease. FUNDING: US National Institutes of Health. TRANSLATION: For the Bangla translation of the abstract see Supplementary Materials section.

Origami plot: a novel multivariate data visualization tool that improves radar chart.

OBJECTIVES: We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making. STUDY DESIGN AND SETTING: Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals. RESULTS: We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits. CONCLUSION: The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization.

Correction: Derivation of the first clinical diagnostic models for dehydration severity in patients over five years with acute diarrhea.

[This corrects the article DOI: 10.1371/journal.pntd.0009266.].

Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews.

OBJECTIVES: Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. STUDY DESIGN AND SETTING: We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. RESULTS: Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R2 of 50%). Applied examples are used to illustrate the use of the BoS prediction model. CONCLUSIONS: Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.

Personalized Research on Diet in Ulcerative Colitis and Crohn's Disease: A Series of N-of-1 Diet Trials.

INTRODUCTION: Evidence about specific carbohydrate diet (SCD) for inflammatory bowel disease (IBD) is limited. We conducted 54 single-subject, double-crossover N-of-1 trials comparing SCD with a modified SCD (MSCD) and comparing each with the participant's baseline, usual diet (UD). METHODS: Across 19 sites, we recruited patients aged 7-18 years with IBD and active inflammation. Following a 2-week baseline (UD), patients were randomized to 1 of 2 sequences of 4 alternating 8-week SCD and MSCD periods. Outcomes included fecal calprotectin and patient-reported symptoms. We report posterior probabilities from Bayesian models comparing diets. RESULTS: Twenty-one (39%) participants completed the trial, 9 (17%) completed a single crossover, and 24 (44%) withdrew. Withdrawal or early completion occurred commonly (lack of response [n = 11], adverse events [n = 11], and not desiring to continue [n = 6]). SCD and MSCD performed similarly for most individuals. On average, there was <1% probability of a clinically meaningful difference in IBD symptoms between SCD and MSCD. The average treatment difference was -0.3 (95% credible interval -1.2, 0.75). There was no significant difference in the ratio of fecal calprotectin geometric means comparing SCD and MSCD (0.77, 95% credible interval 0.51, 1.10). Some individuals had improvement in symptoms and fecal calprotectin compared with their UD, whereas others did not. DISCUSSION: SCD and MSCD did not consistently improve symptoms or inflammation, although some individuals may have benefited. However, there are inherent difficulties in examining dietary changes that complicate study design and ultimately conclusions regarding effectiveness.

The effect of race/ethnicity and adversities on smoking cessation among U.S. adult smokers.

INTRODUCTION: Black and Hispanic individuals in the US experience more socioeconomic adversities that are associated with disparities in tobacco use and cessation than White individuals. This study examined if racial/ethnic differences in smoking abstinence were mediated by socioeconomic (SES) adversities. METHODS: Data from 7,101 established smokers were identified in Wave 1 (2013-2014) of the Population Assessment of Tobacco and Health (PATH) and followed to Wave 4 (2016-2018). The study outcome was cigarette abstinence at Wave 4. The main independent variable was race/ethnicity (Non-Hispanic White [White], Non-Hispanic Black [Black] and Hispanic). The mediators were five measures of SES adversities (unemployment, poverty, difficulty with money, lower education level, lack of health insurance). A weighted Generalized Structural Equation Model (GSEM) was used to estimate the total, direct, and indirect effect of race/ethnicity on the odds of quitting mediated by the five SES adversities. This model was adjusted by study covariates, including health and smoking characteristics. RESULTS: The indirect effect of race/ethnicity on cessation showed that differences in quitting between Black and White individuals as well as Hispanic and White individuals were mediated by SES adversities. However, the differences in quitting between Hispanic and Black individuals were not mediated by SES adversities. Black and Hispanic individuals were less likely to quit than White individuals, but Hispanic individuals were more likely to quit than Black individuals. There were no direct effects between Black or Hispanic individuals compared to White individuals. Those with higher SES were more likely to quit compared to those with lower SES. DISCUSSION: Smoking abstinence is higher in White individuals compared to Black and Hispanic individuals and is mediated by SES adversities. However, smoking abstinence is higher among Hispanic individuals compared to Black individuals and it is not mediated by SES adversities. Future studies should consider the role of other factors, such as psychosocial support, racism, discrimination, and stress over the life course in explaining differences in smoking abstinence between Black and Hispanic individuals.

Designing a Novel Clinician Decision Support Tool for the Management of Acute Diarrhea in Bangladesh: Formative Qualitative Study.

BACKGROUND: The availability of mobile clinical decision support (CDS) tools has grown substantially with the increased prevalence of smartphone devices and apps. Although health care providers express interest in integrating mobile health (mHealth) technologies into their clinical settings, concerns have been raised, including perceived disagreements between information provided by mobile CDS tools and standard guidelines. Despite their potential to transform health care delivery, there remains limited literature on the provider's perspective on the clinical utility of mobile CDS tools for improving patient outcomes, especially in low- and middle-income countries. OBJECTIVE: This study aims to describe providers' perceptions about the utility of a mobile CDS tool accessed via a smartphone app for diarrhea management in Bangladesh. In addition, feedback was collected on the preliminary components of the mobile CDS tool to address clinicians' concerns and incorporate their preferences. METHODS: From November to December 2020, qualitative data were gathered through 8 web-based focus group discussions with physicians and nurses from 3 Bangladeshi hospitals. Each discussion was conducted in the local language-Bangla-and audio recorded for transcription and translation by the local research team. Transcripts and codes were entered into NVivo (version 12; QSR International), and applied thematic analysis was used to identify themes that explore the clinical utility of an mHealth app for assessing dehydration severity in patients with acute diarrhea. Summaries of concepts and themes were generated from reviews of the aggregated coded data; thematic memos were written and used for the final analysis. RESULTS: Of the 27 focus group participants, 14 (52%) were nurses and 13 (48%) were physicians; 15 (56%) worked at a diarrhea specialty hospital and 12 (44%) worked in government district or subdistrict hospitals. Participants' experience in their current position ranged from 2 to 14 years, with an average of 10.3 (SD 9.0) years. Key themes from the qualitative data analysis included current experience with CDS, overall perception of the app's utility and its potential role in clinical care, barriers to and facilitators of app use, considerations of overtreatment and undertreatment, and guidelines for the app's clinical recommendations. Participants felt that the tool would initially take time to use, but once learned, it could be useful during epidemic cholera. Some felt that clinical experience remains an important part of treatment that can be supplemented, but not replaced, by a CDS tool. In addition, diagnostic information, including mid-upper arm circumference and blood pressure, might not be available to directly inform programming decisions. CONCLUSIONS: Participants were positive about the mHealth app and its potential to inform diarrhea management. They provided detailed feedback, which developers used to revise the mobile CDS tool. These formative qualitative data provided timely and relevant feedback to improve the utility of a CDS tool for diarrhea treatment in Bangladesh.

Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care.

The term 'data science' usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is of, by, and for an individual, the Per-DS investigator, acting simultaneously as her own investigator, study participant, and beneficiary, and making personalized decisions for study design and implementation. The scope of Per-DS studies may include systematic monitoring of physiological or behavioral patterns, case-crossover studies for symptom triggers, pre-post trials for exposure-outcome relationships, and personalized (N-of-1) trials for effectiveness. Per-DS studies produce personal knowledge generalizable to the individual's future self (thus benefiting herself) rather than knowledge generalizable to an external population (thus benefiting others). This endeavor requires a pivot from data mining or extraction to data gardening, analogous to home gardeners producing food for home consumption-the Per-DS investigator needs to 'cultivate the field' by setting goals, specifying study design, identifying necessary data elements, and assembling instruments and tools for data collection. Then, she can implement the study protocol, harvest her personal data, and mine the data to extract personal knowledge. To facilitate Per-DS studies, Per-DS investigators need support from community-based, scientific, philanthropic, business, and government entities, to develop and deploy resources such as peer forums, mobile apps, 'virtual field guides,' and scientific and regulatory guidance.

Individualized Studies of Triggers of Paroxysmal Atrial Fibrillation: The I-STOP-AFib Randomized Clinical Trial.

Importance: Atrial fibrillation (AF) is the most common arrhythmia. Although patients have reported that various exposures determine when and if an AF event will occur, a prospective evaluation of patient-selected triggers has not been conducted, and the utility of characterizing presumed AF-related triggers for individual patients remains unknown. Objective: To test the hypothesis that n-of-1 trials of self-selected AF triggers would enhance AF-related quality of life. Design, Setting, and Participants: A randomized clinical trial lasting a minimum of 10 weeks tested a smartphone mobile application used by symptomatic patients with paroxysmal AF who owned a smartphone and were interested in testing a presumed AF trigger. Participants were screened between December 22, 2018, and March 29, 2020. Interventions: n-of-1 Participants received instructions to expose or avoid self-selected triggers in random 1-week blocks for 6 weeks, and the probability their trigger influenced AF risk was then communicated. Controls monitored their AF over the same time period. Main Outcomes and Measures: AF was assessed daily by self-report and using a smartphone-based electrocardiogram recording device. The primary outcome comparing n-of-1 and control groups was the Atrial Fibrillation Effect on Quality-of-Life (AFEQT) score at 10 weeks. All participants could subsequently opt for additional trigger testing. Results: Of 446 participants who initiated (mean [SD] age, 58 [14] years; 289 men [58%]; 461 White [92%]), 320 (72%) completed all study activities. Self-selected triggers included caffeine (n = 53), alcohol (n = 43), reduced sleep (n = 31), exercise (n = 30), lying on left side (n = 17), dehydration (n = 10), large meals (n = 7), cold food or drink (n = 5), specific diets (n = 6), and other customized triggers (n = 4). No significant differences in AFEQT scores were observed between the n-of-1 vs AF monitoring-only groups. In the 4-week postintervention follow-up period, significantly fewer daily AF episodes were reported after trigger testing compared with controls over the same time period (adjusted relative risk, 0.60; 95% CI, 0.43- 0.83; P < .001). In a meta-analysis of the individualized trials, only exposure to alcohol was associated with significantly heightened risks of AF events. Conclusions and Relevance: n-of-1 Testing of AF triggers did not improve AF-associated quality of life but was associated with a reduction in AF events. Acute exposure to alcohol increased AF risk, with no evidence that other exposures, including caffeine, more commonly triggered AF. Trial Registration: ClinicalTrials.gov Identifier: NCT03323099.

EMBRACE: An EM-based bias reduction approach through Copas-model estimation for quantifying the evidence of selective publishing in network meta-analysis.

Systematic reviews and meta-analyses synthesize results from well-conducted studies to optimize healthcare decision-making. Network meta-analysis (NMA) is particularly useful for improving precision, drawing new comparisons, and ranking multiple interventions. However, recommendations can be misled if published results are a selective sample of what has been collected by trialists, particularly when publication status is related to the significance of the findings. Unfortunately, the missing-not-at-random nature of this problem and the numerous parameters involved in modeling NMAs pose unique computational challenges to quantifying and correcting for publication bias, such that sensitivity analysis is used in practice. Motivated by this important methodological gap, we developed a novel and stable expectation-maximization (EM) algorithm to correct for publication bias in the network setting. We validate the method through simulation studies and show that it achieves substantial bias reduction in small to moderately sized NMAs. We also calibrate the method against a Bayesian analysis of a published NMA on antiplatlet therapies for maintaining vascular patency.

A Useful and Sustainable Role for N-of-1 Trials in the Healthcare Ecosystem.

Clinicians and patients often try a treatment for an initial period to inform longer-term therapeutic decisions. A more rigorous approach involves N-of-1 trials. In these single-patient crossover trials, typically conducted in patients with chronic conditions, individual patients are given candidate treatments in a double-blinded, random sequence of alternating periods to determine the most effective treatment for that patient. However, to date, these trials are rarely done outside of research settings and have not been integrated into general care where they could offer substantial benefit. Designating this classical, N-of-1 trial design as type 1, there also are new and evolving uses of N-of-1 trials that we designate as type 2. In these, rather than focusing on optimizing treatment for chronic diseases when multiple approved choices are available, as is typical of type 1, a type 2 N-of-1 trial tests treatments designed specifically for a patient with a rare disease, to facilitate personalized medicine. While the aims differ, both types face the challenge of collecting individual-patient evidence using standard, trusted, widely accepted methods. To fulfill their potential for producing both clinical and research benefits, and to be available for wide use, N-of-1 trials will have to fit into the current healthcare ecosystem. This will require generalizable and accepted processes, platforms, methods, and standards. This also will require sustainable value-based arrangements among key stakeholders. In this article, we review opportunities, stakeholders, issues, and possible approaches that could support general use of N-of-1 trials and deliver benefit to patients and the healthcare enterprise. To assess and expand the benefits of N-of-1 trials, we propose multistakeholder meetings, workshops, and the generation of methods, standards, and platforms that would support wider availability and the value of N-of-1 trials.

Bayesian Models for N-of-1 Trials.

We describe Bayesian models for data from N-of-1 trials, reviewing both the basics of Bayesian inference and applications to data from single trials and collections of trials sharing the same research questions and data structures. Bayesian inference is natural for drawing inferences from N-of-1 trials because it can incorporate external and subjective information to supplement trial data as well as give straightforward interpretations of posterior probabilities as an individual's state of knowledge about their own condition after their trial. Bayesian models are also easily augmented to incorporate specific characteristics of N-of-1 data such as trend, carryover, and autocorrelation and offer flexibility of implementation. Combining data from multiple N-of-1 trials using Bayesian multilevel models leads naturally to inferences about population and subgroup parameters such as average treatment effects and treatment effect heterogeneity and to improved inferences about individual parameters. Data from a trial comparing different diets for treating children with inflammatory bowel disease are used to illustrate the models and inferences that may be drawn. The analysis shows that certain diets were better on average at reducing pain, but that benefits were restricted to a subset of patients and that withdrawal from the study was a good marker for lack of benefit.

Clinical, Sociodemographic and Environmental Risk Factors for Acute Bacterial Diarrhea among Adults and Children over Five Years in Bangladesh.

In 2016, diarrheal disease was the eighth leading cause of mortality globally accounting for over 1.6 million deaths with the majority of deaths in adults and children over 5 years. This study aims to investigate the clinical, sociodemographic, and environmental risk factors associated with common bacterial acute diarrhea among adults and children over 5. Data were collected from March 2019 to March 2020 in patients over 5 years presenting with acute gastroenteritis at icddr,b. Stool samples were collected from each patient for culture and polymerase chain reaction (PCR) testing. Bivariate associations between independent variables and stool-testing indicating bacterial etiology were calculated. This analysis included 2,133 diarrheal patients of whom a bacterial enteropathogen was identified in 1,537 (72%). Detection of bacteria was associated with: younger age (OR 0.92; 95% CI: 0.88-0.96), lower mean arterial pressure (OR 0.84; 95% CI: 0.79-0.89), heart rate (OR 1.06; 95% CI: 1.01-1.10), percentage dehydration (OR 1.33; 95% CI: 1.13-1.55), respiration rate (OR 1.23; 95% CI: 1.04-1.46), lower mid-upper arm circumference (OR 0.97; 95% CI: 0.94-0.99), confused/lethargic mental status (OR 1.85; 95% CI: 1.11-3.25), rice watery stool (OR 1.92; 95% CI: 1.54-2.41), and vomiting more than three times in the past 24 hours (OR 1.30; 95% CI: 1.06-1.58). Higher monthly income (OR 0.92; 95% CI: 0.86-0.98), > 8 years of education (OR 0.79; 95% CI: 0.63-1.00), and having more than five people living at home (OR 0.80; 95% CI: 0.66-0.98) were associated with lower odds of bacterial diarrhea. These findings may help guide the development of predictive tools to aid in identifying patients with bacterial diarrhea for timely and appropriate use of antibiotics.

Continuous diagnostic models for volume deficit in patients with acute diarrhea.

BACKGROUND: Episodes of acute diarrhea lead to dehydration, and existing care algorithms base treatment around categorical estimates for fluid resuscitation. This study aims to develop models for the percentage dehydration (fluid deficit) in individuals with acute diarrhea, to better target treatment and avoid the potential sequelae of over or under resuscitation. METHODS: This study utilizes data from two prospective cohort studies of patients with acute diarrhea in Dhaka, Bangladesh. Data were collected on patient arrival, including weight, clinical signs and symptoms, and demographic information. Consecutive weights were obtained to determine the true volume deficit of each patient. Data were entered into two distinct forward stepwise regression logistic models (DHAKA for under 5 years and NIRUDAK for 5 years and over). RESULTS: A total of 782 patients were included in the final analysis of the DHAKA data set, and 2139 were included in the final analysis of the NIRUDAK data set. The best model for the DHAKA data achieved an R2 of 0.27 and a root mean square error (RMSE) of 3.7 (compared to R2 of 0.06 and RMSE of 5.5 with the World Health Organization child care algorithm) and selected 6 predictors. The best performance model for the NIRUDAK data achieved an R2 of 0.28 and a RMSE of 2.6 (compared to R2 of 0.08 and RMSE of 4.3 with the World Health Organization adolescent/adult care algorithm) and selected 7 predictors with 2 interactions. CONCLUSIONS: These are the first mathematical models for patients with acute diarrhea that allow for the calculation of a patient's percentage dehydration (fluid deficit) and subsequent targeted treatment with fluid resuscitation. These findings are an improvement on existing World Health Organization care algorithms.

Assessing the performance of clinical diagnostic models for dehydration among patients with cholera and undernutrition in Bangladesh.

OBJECTIVE: Accurately assessing dehydration severity is a critical step in reducing mortality from diarrhoea, but is complicated by cholera and undernutrition. This study seeks to assess the accuracy of two clinical diagnostic models for dehydration among patients over five years with cholera and undernutrition and compare their respective performance to the World Health Organization (WHO) algorithm. METHODS: In this secondary analysis of data collected from the NIRUDAK study, accuracy of the full and simplified NIRUDAK models for predicting severe and any dehydration was measured using the area under the Receiver Operator Characteristic curve (AUC) among patients over five with/without cholera and with/without wasting. Bootstrap with 1000 iterations was used to compare the m-index for each NIRUDAK model to that of the WHO algorithm. RESULTS: A total of 2,139 and 2,108 patients were included in the nutrition and cholera subgroups respectively with an overall median age of 35 years (IQR = 42) and 49.6% female. All subgroups had acceptable discrimination in diagnosing severe or any dehydration (AUC > 0.60); though the full NIRUDAK model performed best among patients without cholera, with an AUC of 0.82 (95%CI:0.79, 0.85) and among patients without wasting, with an AUC of 0.79 (95%CI:0.76, 0.81). Compared with the WHO's algorithm, both the full and simplified NIRUDAK models performed significantly better in terms of their m-index (p < 0.001) for all comparisons, except for the simplified NIRUDAK model in the wasting group. CONCLUSIONS: Both the full and simplified NIRUDAK models performed less well in patients over five years with cholera and/or wasting; however, both performed better than the WHO algorithm.

Chronic pain treatment preferences change following participation in N-of-1 trials, but not always in the expected direction.

OBJECTIVE: To examine pain treatment preferences before and after participation in an N-of-1 trial. STUDY DESIGN AND SETTING: In this observational study nested within a randomized trial, we examined chronic pain patients' preferences before and after treatment in relation to N-of-1 trial results; assessed the influence of different schemes for defining comparative "superiority" on potential conclusions; and generated classification trees illustrating the relationship between pre-treatment preferences, N-of-1 trial results, and post-treatment preferences. RESULTS: Treatment preferences differed pre- and post-trial for 40% of participants. The proportion of patients whose N-of-1 trials demonstrated "superiority" of one treatment regimen over the other varied depending on how superiority was defined and ranged from 24% (using criteria that required statistically significant differences between regimens) to 62% (when relying only on differences in point estimates). Regardless of criteria for declaring treatment superiority, nearly three-fourths of patients with equivocal N-of-1 trial results nevertheless expressed definite preferences post-trial. CONCLUSION: A large segment of patients undergoing N-of-1 trials for chronic pain altered their treatment preferences. However, the direction of preference change did not necessarily correspond to the N-of-1 results. More research is needed to understand how patients use N-of-1 trial results, why preferences are "sticky" even in the face of personalized data, and how patients and clinicians might be educated to use N-of-1 trial results more informatively.

Derivation and Internal Validation of a Score to Predict Dehydration Severity in Patients over 5 Years with Acute Diarrhea.

Diarrheal disease accounts for more than one million deaths annually in patients over 5 years of age. Although most patients can be managed with oral rehydration solution, patients with severe dehydration require resuscitation with intravenous fluids. Scoring systems to assess dehydration have been empirically derived and validated in children under 5 years, but none have been validated for patients over 5 years. In this study, a prospective cohort of 2,172 patients over 5 years presenting with acute diarrhea to International Centre for Diarrhoeal Disease Research, Dhaka Hospital, Bangladesh, were assessed for clinical signs of dehydration. The percent difference between presentation and posthydration stable weight determined severe (≥ 9%), some (3-9%), or no (< 3%) dehydration. An ordinal regression model was derived using clinical signs and demographics and was then converted to a 13-point score to predict none (score of 0-3), some (4-6), or severe (7-13) dehydration. The Novel, Innovative Research for Understanding Dehydration in Adults and Kids (NIRUDAK) Score developed by our team included age, sex, sunken eyes, radial pulse, respiration depth, skin turgor, and vomiting episodes in 24 hours. Accuracy of the NIRUDAK Score for predicting severe dehydration, as measured by the area under the receiver operating characteristic curve, was 0.76 (95% confidence interval = 0.73-0.78), with a sensitivity of 0.78 and a specificity of 0.61. Reliability was also robust, with an Inter-Class Correlation Coefficient of 0.88 (95% confidence interval = 0.84-0.91). This study represents the first empirically derived and internally validated scoring system for assessing dehydration in children ≥ 5 years and adults with acute diarrhea in a resource-limited setting.