A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support.

OBJECTIVE: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans. MATERIALS AND METHODS: Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods. RESULTS: Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support. Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods. DISCUSSION: The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS. CONCLUSION: We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).

A remote sensing-based three-source energy balance model to improve global estimations of evapotranspiration in semi-arid tree-grass ecosystems.

It is well documented that energy balance and other remote sensing-based evapotranspiration (ET) models face greater uncertainty over water-limited tree-grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass-dominated understory and tree-dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy-limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration (T), along with their different climatic drivers, is still largely unknown nor quantified in TGEs. This study proposes a thermal-based three-source energy balance (3SEB) model, accommodating an additional vegetation source within the well-known two-source energy balance (TSEB) model. The model was implemented at both tower and continental scales using eddy-covariance (EC) TGE sites, with variable tree canopy cover and rainfall (P) regimes and Meteosat Second Generation (MSG) images. 3SEB robustly simulated latent heat (LE) and related energy fluxes in all sites (Tower: LE RMSD ~60 W/m2 ; MSG: LE RMSD ~90 W/m2 ), improving over both TSEB and seasonally changing TSEB (TSEB-2S) models. In addition, 3SEB inherently partitions water fluxes between the tree, grass and soil sources. The modelled T correlated well with EC T estimates (r > .76), derived from a machine learning ET partitioning method. The T/ET was found positively related to both P and leaf area index, especially compared to the decomposed grass understory T/ET. However, trees and grasses had contrasting relations with respect to monthly P. These results demonstrate the importance in decomposing total ET into the different vegetation sources, as they have distinct climatic drivers, and hence, different relations to seasonal water availability. These promising results improved ET and energy flux estimations over complex TGEs, which may contribute to enhance global drought monitoring and understanding, and their responses to climate change feedbacks.

Novel criteria to classify ARDS severity using a machine learning approach.

BACKGROUND: Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO2/(FiO2xPEEP) or P/FPE] for PEEP ≥ 5 to address Berlin's definition gap for ARDS severity by using machine learning (ML) approaches. METHODS: We examined P/FPE values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO2/FiO2 criteria with P/FPE under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014-2015) and extracted data from the first 3 ICU days after ARDS onset (N = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time. RESULTS: P/FPE ratio outperformed PaO2/FiO2 ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711-0.788 and CORR 0.376-0.566; eICU: AUC 0.734-0.873 and CORR 0.511-0.745). CONCLUSIONS: The novel P/FPE ratio to assess ARDS severity after onset over time is markedly better than current PaO2/FiO2 criteria. The use of P/FPE could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.

Methods and measures to quantify ICU patient heterogeneity.

Patients in intensive care units are heterogeneous and the daily prediction of their days to discharge (DTD) a complex task that practitioners and computers are not always able to solve satisfactorily. In order to make more precise DTD predictors, it is necessary to have tools for the analysis of the heterogeneity of the patients. Unfortunately, the number of publications in this field is almost non-existent. In order to alleviate this lack of tools, we propose four methods and their corresponding measures to quantify the heterogeneity of intensive patients in the process of determining the DTD. These new methods and measures have been tested with patients admitted over four years to a tertiary hospital in Spain. The results deepen the understanding of the intensive patient and can serve as a basis for the construction of better DTD predictors.

Computer technologies to integrate medical treatments to manage multimorbidity.

The high prevalence of multimorbid cases is a challenge for Health-Care Systems today. Clinical practice guidelines are the means to register and transmit the available evidence-based medical knowledge concerning concrete diseases. Several computer languages have been defined to represent this knowledge in a way that computers could use to help physicians in the daily practice of medicine. The generation of guidelines for all possible multimorbidities entails several issues that are difficult to address. Consequently, numerous medical informatics technologies have appeared merging computer information structures in a way that the treatment knowledge about single diseases could be combined in order to deliver health-care to patients suffering from multimorbidity. This paper proposes a classification of the most promising current technologies addressing this issue and provides an analysis of their maturity, strengths, and weaknesses. We conclude with an enumeration of ten relevant issues to consider when developing such technologies.

An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients.

Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved. In this paper, we introduce an ontology for the care of chronically ill patients and implement two personalization processes and a decision support tool. The first personalization process adapts the contents of the ontology to the particularities observed in the health-care record of a given concrete patient, automatically providing a personalized ontology containing only the clinical information that is relevant for health-care professionals to manage that patient. The second personalization process uses the personalized ontology of a patient to automatically transform intervention plans describing health-care general treatments into individual intervention plans. For comorbid patients, this process concludes with the semi-automatic integration of several individual plans into a single personalized plan. Finally, the ontology is also used as the knowledge base of a decision support tool that helps health-care professionals to detect anomalous circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases, or preventive actions. Seven health-care centers participating in the K4CARE project, together with the group SAGESA and the Local Health System in the town of Pollenza have served as the validation platform for these two processes and tool. Health-care professionals participating in the evaluation agree about the average quality 84% (5.9/7.0) and utility 90% (6.3/7.0) of the tools and also about the correct reasoning of the decision support tool, according to clinical standards.

Modelling and assessing one- and two-drug dose titrations.

In health-care, there is a need to quantify medical errors. Among these errors, we observe wrong dose prescriptions. Drug dose titration (DT) is the process by which dosage is progressively adjusted to the patient till a steady dose is reached. Depending on the clinical disease, drug, and patient condition, dose titration can follow different procedures. Once modeled, these procedures can serve for clinical homogenization, standardization, decision support and retrospective analysis. Here, we propose a language to model dose titration procedures. The language was used to formalize one- and two-drug titration of chronic and acute cases, and to perform retrospective analysis of the drug titration processes on 253 patients diagnosed of diabetes mellitus type 2 and treated with metformin, 321 patients treated of chonic heart failure with furosemide, 155 patients with hyperuricemia treated with allopurinol as initial drug and febuxostat as alternative drug, and 187 hyperuricemia patients with primary drug allopurinol and supplementary drug probenecid, in order to identify different types of drug titration deviations from standard DT methods.

Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration over time. We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches. Methods: For model description, we extracted data from the first 3 ICU days after ARDS diagnosis from patients included in the publicly available MIMIC-III database. Disease progression was tracked along those 3 ICU days to assess lung severity according to Berlin criteria. Three robust supervised ML techniques were implemented using Python 3.7 (Light Gradient Boosting Machine (LightGBM); Random Forest (RF); and eXtreme Gradient Boosting (XGBoost)) for predicting MV duration. For external validation, we used the publicly available multicenter database eICU. Results: A total of 2466 and 5153 patients in MIMIC-III and eICU databases, respectively, received MV for >48 h. Median MV duration of extracted patients was 6.5 days (IQR 4.4-9.8 days) in MIMIC-III and 5.0 days (IQR 3.0-9.0 days) in eICU. LightGBM was the best model in predicting MV duration after ARDS onset in MIMIC-III with a root mean square error (RMSE) of 6.10-6.41 days, and it was externally validated in eICU with RMSE of 5.87-6.08 days. The best early prediction model was obtained with data captured in the 2nd day. Conclusions: Supervised ML can make early and accurate predictions of MV duration in ARDS after onset over time across ICUs. Supervised ML models might have important implications for optimizing ICU resource utilization and high acute cost reduction of MV.

Persistence of data-driven knowledge to predict breast cancer survival.

BACKGROUND: Machine learning predictive models for breast cancer survival can improve if they are made specific to the stage of the cancer at the time of diagnosis. However, the relevance of the clinical parameters in that prediction, and the predictive quality of these models may change over time. OBJECTIVE: To determine whether the findings on the influence of clinical parameters and the performance of machine learning models in the prediction of breast cancer survival have to be considered temporary or permanent, and if temporary what is the period of validity of the new generated knowledge. METHODS: Fifteen recently published relevant conclusions on the application of machine learning methods to predict breast cancer survival were identified. Then, the data on breast cancer in the SEER database were used to construct several data-driven models over time to predict five-year survival of breast cancer. Three different machine learning methods were used. Stage-specific models and joint models for all the stages were considered. The predictive quality of the models and the importance of clinical parameters were subjected to a persistence analysis over time in order to determine the validity and durability of these fifteen conclusions. RESULTS AND CONCLUSIONS: Only 53% of the conclusions were true for the SEER cases in 1988-2009, and only 75% of these were true over time. Relevant conclusions such as the impossibility to improve survival prediction of the most frequent stages with more data or the importance of the grade of the cancer to predict breast cancer survival of patients with distant metastasis turned to be false when subjected to a temporal analysis. Our study concludes that data-driven knowledge obtained with machine learning methods must be subject to over time validation before it can be clinically and professionally applied.

Ten years of knowledge representation for health care (2009-2018): Topics, trends, and challenges.

BACKGROUND: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide. OBJECTIVES: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future. METHODS: We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future. RESULTS: The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively. CONCLUSIONS: KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patient empowerment and patient-centric care.

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Author Correction: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Knowledge engineering as a support for building an actor profile ontology for integrating Home-Care systems.

One of the tasks towards the definition of a knowledge model for home care is the definition of the different roles of the users involved in the system. The roles determine the actions and services that can or must be performed by each type of user. In this paper the experience of building an ontology to represent the home-care users and their associated information is presented, in a proposal for a standard model of a Home-Care support system to the European Community.

Formalization and acquisition of temporal knowledge for decision support in medical processes.

BACKGROUND: In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge. OBJECTIVE: The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations. METHODS: In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested. RESULTS: A full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1-7 weeks, whereas CPGs recommend 2-4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1-6 weeks, and 2-5 months, respectively. Our algorithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3-6 month range. CONCLUSIONS: Experience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is simple and highly descriptive when dealing with general or specific time constraints' representations, offering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).

Improving resident's skills in the management of circulatory shock with a knowledge-based e-learning tool.

BACKGROUND: Correct clinical management of circulatory shock in emergency departments (ER) and intensive care units (ICU) is critical. In this context, the transmission of professional skills by means of the practical supervision of real cases at the point of care entails important issues that can be widely overcome with the use of computer knowledge-based e-learning tools. OBJECTIVE: Shock-Instructor is a web-based e-learning tool implementing the already tested training program model (TPM) that uses a knowledge base about the evidence found in the clinical practice guidelines about seven types of shock. This tool is expected to reduce the learning times and to improve the skills of hospital residents with regard to both the correct application of the guidelines and patient recovery, suppressing the risks of direct interventions. METHODS: Shock-Instructor has been used to train residents in the Emergency Department of the Hospital Clínic de Barcelona (Spain) in order to reduce the learning cycle without affecting quality. A case-base with the description of 51 cases with shock and a knowledge-base with 137 clinical rules about the treatment of shock were incorporated to the Shock-Instructor system. A group of 33 residents was involved in a randomized controlled trial to check whether the use of Shock-Instructor can significantly improve the skills of clinicians after one week of problem-based training. RESULTS: No significant differences were found in the skill levels of the intervention (IG) and control (CG) groups prior to learning. However, we observed an improvement of the IG clinicians capacity to stabilize patients with shock in better clinical conditions (5% improvement, p = 0.004), and to reduce the risk of death in 19.52% (p = 0.004), after training. First-year residents in IG enhanced 14.3% their sensitivity in the correct application of guidelines (p = 0.01), and 14.9% the mean survival rate of their patients (p = 0.01), after being trained with Shock-Instructor for a week. Residents with specialties different from ER and ICU enhanced 16.1% their application of guidelines (p = 0.04), and 14.5% the survival rate of the cases attended (p = 0.01). CONCLUSIONS: Web-oriented knowledge-based e-learning computer tools such as Shock-Instructor can significantly reduce the learning time of ER and ICU residents, while enhancing their assimilation of evidence-based medicine and both the survival rate and health condition of patients with shock.

Knowledge Formalization to Support Decision-Making in Heart Failure Treatment.

This paper presents medical knowledge representation of data provided within Clinical Practice Guidelines for Heart Failure. The formalization is provided in order to support taking decisions on an appropriate treatment strategy for a specific patient. An intuitive and efficient mechanism of medical knowledge formalization, called extended Timed Transition Diagram (eTTD), is used to represent acquired medical knowledge. The presented models can be used to help students in their training as well as to support physicians with their decision-making tasks.

Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms.

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.

Correlación entre características físicas neonatales y maternas con el recuento total de células nucleadas y de células cd34+ por microlitro en sangre de cordón umbilical / Correlation between neonatal and maternal physical characteristics with total nucleated cell count and cell count per microliter cd34+ in cord blood / Correlação entre características físicas neonatais e maternas com a contagem total de células nucleadas e células cd34 + por microlitro no sangue do cordão umbilical

Introducción: La sangre de cordón umbilical (SCU) ha sido reconocida como una fuente de células madre hematopoyéticas. Múltiples estudios han sido realizados con el propósito de determinar variables maternas y neonatales que afecten el volumen, el recuento total de células nucleadas y de células CD34+. El presente estudio correlaciona variables maternas y neonatales con el recuento total de células nucleadas y de CD34+ medidas en μL (microlitro). Materiales y Métodos: Estudio correlacional en el que se analizaron 50 muestras de sangre de cordón umbilical de gestantes atendidas en dos IPS, una de Bogotá y otra de Ubaté, (Cundinamarca), durante un período de 7 meses del año 2013. La selección de estas muestras fue basada en un muestreo no probabilístico. Se calculó el coeficiente de correlación de Pearson con su respectiva significancia estadística entre las variables cuantitativas de la madre y del neonato, y el volumen, el recuento de células CD34+ y el recuento total de células nucleadas por μL. Resultados: Se encontró correlación positiva entre la longitud del cordón y el recuento total de células nucleadas. Así mismo entre el volumen inicial de la muestra y el recuento total de células nucleadas y el recuento de células CD34+ en μL y en mL (mililitro). Discusión: Múltiples estudios se han desarrollado entorno a la búsqueda de variables maternas y neonatales que afectan la calidad de la muestra. En Colombia no se habían descrito las correlaciones encontradas en el presente estudio. Es por ello, que el desarrollo de nuevos estudios con diseños analíticos será enriquecedor.

Background: The cord blood has been recognized as a source of hematopoietic stem. Multiple studies have been conducted in order to determine maternal and neonatal variables that affect the volume, total nucleated cell count and CD34+ cells. This study correlated maternal and neonatal variables with the total nucleated cell count and CD34+ cells measured by μL. Materials and methods: Correlational study in which 50 samples of umbilical cord blood were analyzed of pregnant women at two health institutions, one at Bogotá and the other one at Ubaté, Cundinamarca, for a period of seven months of 2013. The selection of these samples was based on a non-probability sample. The Pearson correlation coefficient was calculated with their respective statistical significance between quantitative variables of mother and newborn, and the volume, the count of CD34+ cells and the total nucleated cell count measured by μL. Results: Positive correlation between the length of the umbilical cord and the total nucleated cell count was found. Similarly, correlation between total nucleated cell count with the initial volume and count of CD34+ cells in μL and mL was found. Discussion: Several studies have been conducted around to look maternal and neonatal variables that affect the sample quality, however, the Colombian literature has not described the correlation found in this study, so it requires the development of new research with designs of analytical studies to establish associations between the variables described.

Introdução: O sangue do cordão umbilical (SCU) tem sido reconhecido como uma fonte de células-tronco hematopoiéticas. Vários estudos têm sido realizados com a finalidade de determinar as variáveis maternas e neonatais que afetam o volume e a contagem total de células nucleadas e células CD34+. O presente estudo correlaciona as variáveis maternas e neonatais com a contagem total das células nucleadas e células CD34+ medidas em μL (microlitro). Materiais e métodos: Estudo correlacional no que 50 amostras de sangue do cordão umbilical de mulheres grávidas atendidas em dois IPs foram analisadas, uma em Bogota e outra em Ubaté (Cundinamarca), durante um período de 7 meses do ano 2013. A seleção destas amostras foi baseada em uma amostragem não probabilística. O coeficiente de correlação de Pearson foi calculado com a respectiva significância estatística entre variáveis quantitativas da mãe e do recém-nascido, assim como o volume, a contagem de células CD34 + e contagem total de células nucleadas por μL. Resultados: Correlação positiva foi encontrada entre o comprimento do cordão e a contagem total de células nucleadas. Também entre o volume da amostra inicial e contagem total de células nucleadas e contagem de células CD34+ em μL e em mL (mililitro). Discussão: Vários estudos têm sido desenvolvidos em torno da busca de variáveis maternas e neonatais que afetam a qualidade da amostra. Na Colômbia não tinham sido descritas as correlações encontradas no presente estudo. É por esta razão que será gratificante o desenvolvimento de novos estudos com desenhos analíticos.