High leptin levels are present in cord blood at birth and in capillary blood shortly after birth. Since leptin levels in cord blood correlate with birth weight it is tempting to speculate that in the fetus as in later life leptin is signalling expansion of fat stores. Most importantly, we now report that leptin levels are high in the fetus but decline rapidly and dramatically after birth in healthy neonates. This may be important for the stimulation of feeding behaviour and the acquisition of energy homeostasis in the neonate.

BackgroundThe ICD-10 categories of the diagnosis “perinatal asphyxia” are defined by clinical signs and a 1-minute Apgar score value. However, the modern conception is more complex and considers metabolic values related to the clinical state. A lack of consistency between the former clinical and the latter encoded diagnosis poses questions over the validity of the data. Our aim was to establish a refined classification which is able to distinctly separate cases according to clinical criteria and financial resource consumption. The hypothesis of the study is that outdated ICD-10 definitions result in differences between the encoded diagnosis asphyxia and the medical diagnosis referring to the clinical context.MethodsRoutinely collected health data (encoding and financial data) of the University Hospital of Bern were used. The study population was chosen by selected ICD codes, the encoded and the clinical diagnosis were analyzed and each case was reevaluated. The new method categorizes the diagnoses of perinatal asphyxia into the following groups: mild, moderate and severe asphyxia, metabolic acidosis and normal clinical findings. The differences of total costs per case were determined by using one-way analysis of variance.ResultsThe study population included 622 cases (P20 “intrauterine hypoxia” 399, P21 “birth asphyxia” 233). By applying the new method, the diagnosis asphyxia could be ruled out with a high probability in 47% of cases and the variance of case related costs (one-way ANOVA: F (5, 616) = 55.84, p < 0.001, multiple R-squared = 0.312, p < 0.001) could be best explained. The classification of the severity of asphyxia could clearly be linked to the complexity of cases.ConclusionThe refined coding method provides clearly defined diagnoses groups and has the strongest effect on the distribution of costs. It improves the diagnosis accuracy of perinatal asphyxia concerning clinical practice, research and reimbursement.

Background The criteria for the diagnosis of kidney disease outlined in the Kidney Disease: Improving Global Outcomes guidelines are based on a patient’s current, historical, and baseline data. The diagnosis of acute kidney injury, chronic kidney disease, and acute-on-chronic kidney disease requires previous measurements of creatinine, back-calculation, and the interpretation of several laboratory values over a certain period. Diagnoses may be hindered by unclear definitions of the individual creatinine baseline and rough ranges of normal values that are set without adjusting for age, ethnicity, comorbidities, and treatment. The classification of correct diagnoses and sufficient staging improves coding, data quality, reimbursement, the choice of therapeutic approach, and a patient’s outcome. Objective In this study, we aim to apply a data-driven approach to assign diagnoses of acute, chronic, and acute-on-chronic kidney diseases with the help of a complex rule engine. Methods Real-time and retrospective data from the hospital’s clinical data warehouse of inpatient and outpatient cases treated between 2014 and 2019 were used. Delta serum creatinine, baseline values, and admission and discharge data were analyzed. A Kidney Disease: Improving Global Outcomes–based SQL algorithm applied specific diagnosis-based International Classification of Diseases (ICD) codes to inpatient stays. Text mining on discharge documentation was also conducted to measure the effects on diagnosis. Results We show that this approach yielded an increased number of diagnoses (4491 cases in 2014 vs 11,124 cases of ICD-coded kidney disease and injury in 2019) and higher precision in documentation and coding. The percentage of unspecific ICD N19-coded diagnoses of N19 codes generated dropped from 19.71% (1544/7833) in 2016 to 4.38% (416/9501) in 2019. The percentage of specific ICD N18-coded diagnoses of N19 codes generated increased from 50.1% (3924/7833) in 2016 to 62.04% (5894/9501) in 2019. Conclusions Our data-driven method supports the process and reliability of diagnosis and staging and improves the quality of documentation and data. Measuring patient outcomes will be the next step in this project.

Background With an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In this study we set up a novel method of classification by using routinely collected health data. We hypothesize i that routinely collected health data can be used to apply complex medical classifications and ii that the Robson classification is capable of classifying mothers and their corresponding newborn into meaningful groups with regard to outcome. Methods and findings The study was conducted at the coding department and the department of obstetrics and gynecology Inselspital, University Hospital of Bern, Switzerland. The study population contained inpatient cases from 2014 until 2017. Administrative and health data were extracted from the Data Warehouse. Cases were classified by a Structured Query Language code according to the Robson criteria using data from the administrative system, the electronic health record and from the laboratory system. An automated query to classify the cases according to Robson could be implemented and successfully validated. A linkage of the mother’s class to the corresponding newborn could be established. The distribution of clinical indicators was described. It could be shown that the Robson classes are associated to outcome parameters and case related costs. Conclusions With this study it could be demonstrated, that a complex query on routinely collected health data would serve for medical classification and monitoring of quality and outcome. Risk-stratification might be conducted using this data set and should be the next step in order to evaluate the Robson criteria and outcome. This study will enhance the discussion to adopt an automated classification on routinely collected health data for quality assurance purposes.

BackgroundWith few exceptions the International Statistical Classification of Diseases (ICD) codes for diagnoses and official coding guidelines do not distinguish pre-existing conditions from complications or comorbidities which occur during hospitalization. However, information on diagnosis timing is relevant with regard to the case’s severity, resource consumption and quality of care. In this study we analyzed the diagnostic value and reliability of the present-on-admission (POA) indicator using routinely collected health data.MethodsWe included all inpatient cases of the department of medicine during 2016 with a diagnosis of deep vein thrombosis, decubitus ulcer or delirium. Swiss coding guidelines of 2016 and the definitions of the Swiss medical statistics of hospitals were analyzed to evaluate the potential to encode information on diagnosis timing. The diagnoses were revised by applying the information present-on-admission by a coding specialist and by a medical expert, serving as Gold Standard. The diagnostic value and reliability were evaluated.ResultsThe inter-rater reliability for POA of all diagnoses was 0.7133 (Cohen’s kappa), but differed between diagnosis groups (0.558–0.7164). The rate of POA positive of the total applied by the coding specialist versus the expert was similar, but differed between diagnoses. In group “thrombosis” SEN was 0.95, SPE 0.75, PPV 0.97 and NPV 0.60, in group “decubitus ulcer” SEN 0.89, SPE 0.82, PPV 0.89 and NPV 0.82, in group “delirium” SEN 0.91, SPE 0.65, PPV 0.71 and NPV 0.88 For all diagnoses SEN 0.92, SPE 0.73, PPV 0.87, NPV 0.82, summing up the cases of all diagnosis groups.ConclusionsCoding the POA indicator identified diagnoses which were pre-existent with insufficient reliability on individual patient’s level. The overall fair to sufficient diagnostic quality is appropriate for screening and benchmarking performance on population level. As the medical statistics of hospitals carries no variable on pre-existing conditions, the novel approach to apply the POA indicator to diagnoses gives more information on quality of hospital care and complexity of cases. By preparing documentation for POA reporting diagnostic quality must be increased before implementation for risk-assessment or reimbursement on the individual patient’s level.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3858-3) contains supplementary material, which is available to authorized users.