Semi-Automated Approach to Map Clinical Concepts to SNOMED CT Terms by Using Terminology Server.

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. Snowstorm is a terminology server developed by SNOMED International. In this work, the feasibility of using Snowstorm to automate the data retrieval and mapping has been discussed

Semi-Automated Approach to Retrieve SNOMED CT Hierarchy of Clinical Terms by Using Terminology Server.

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. In addition, hierarchy retrieval of clinical concepts increases the challenges for the user. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. In this work, it is shown that Snowstorm can significantly improve the efficiency of retrieval process if used with semi-automated workflows

Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals.; Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012-2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types.; Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865-0.868) and van Walraven's weights (0.863, 95% CI, 0.862-0.864) had substantial advantage over Charlson's weights (0.850, 95% CI, 0.849-0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights.; All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings

Real-world Health Data and Precision for the Diagnosis of Acute Kidney Injury, Acute-on-Chronic Kidney Disease, and Chronic Kidney Disease: Observational Study.

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

The effect of time-varying capacity utilization on 14-day in-hospital mortality: a retrospective longitudinal study in Swiss general hospitals

High bed-occupancy (capacity utilization) rates are commonly thought to increase in-hospital mortality; however, little evidence supports a causal relationship between the two. This observational study aimed to assess three time-varying covariates-capacity utilization, patient turnover and clinical complexity level- and to estimate causal effect of time-varying high capacity utilization on 14 day in-hospital mortality.; This retrospective population-based analysis was based on routine administrative data (n = 1,152,506 inpatient cases) of 102 Swiss general hospitals. Considering the longitudinal nature of the problem from available literature and expert knowledge, we represented the underlying data generating mechanism as a directed acyclic graph. To adjust for patient turnover and patient clinical complexity levels as time-varying confounders, we fitted a marginal structure model (MSM) that used inverse probability of treatment weights (IPTWs) for high and low capacity utilization. We also adjusted for patient age and sex, weekdays-vs-weekend, comorbidity weight, and hospital type.; For each participating hospital, our analyses evaluated the ≥85th percentile as a threshold for high capacity utilization for the higher risk of mortality. The mean bed-occupancy threshold was 83.1% (SD 8.6) across hospitals and ranged from 42.1 to 95.9% between hospitals. For each additional day of exposure to high capacity utilization, our MSM incorporating IPTWs showed a 2% increase in the odds of 14-day in-hospital mortality (OR 1.02, 95% CI: 1.01 to 1.03).; Exposure to high capacity utilization increases the mortality risk of inpatients. Accurate monitoring of capacity utilization and flexible human resource planning are key strategies for hospitals to lower the exposure to high capacity utilization

Longitudinal Study of the Variation in Patient Turnover and Patient-to-Nurse Ratio: Descriptive Analysis of a Swiss University Hospital

Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that allow high-quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day.; Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of 3 years. We assessed 5 care factors: patient count (care demand), nurse count (care supply), the patient-to-nurse ratio for each nurse group, extreme supply-demand mismatches, and patient turnover (ie, number of admissions, discharges, and transfers).; Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working in their units from January 1, 2015 to December 31, 2017. Two data sources were used. The nurse staffing system (tacs) provided information about nurses and all the care they provided to patients, their working time, and admission, discharge, and transfer dates and times. The medical discharge data included patient demographics, further admission and discharge details, and diagnoses. Based on several identifiers, these two data sources were linked.; Our final dataset included more than 58 million data points for 128,484 patients and 4633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced. The differences mainly coincided with shifts (night, morning, evening). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage within "normal" ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period but was lowest at night.; Based on measurements of patient-to-nurse ratio and patient turnover at 30-minute intervals, our findings indicate that the patient count, which varies considerably throughout the day, is the key driver of changes in the patient-to-nurse ratio. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing patterns in variability such as these are key to appropriate staffing planning. This descriptive analysis was a first step towards identifying time-related variables to be considered for a predictive nurse staffing model

Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism.

OBJECTIVE From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation. METHODS We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach. RESULTS We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3-1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2-1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1-1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5-1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1-1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms. CONCLUSIONS We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered

Improved survival rates of AML patients following admission to the intensive care unit.

Induction chemotherapy in AML patients may have life-threatening side effects requiring intensive care unit (ICU) treatment. We analyzed all AML patients receiving intensive chemotherapy at a single academic center between 01/2006-12/2016. At least one ICU admission was observed in 32% (76/240) patients, and 33% of those died following ICU admission. Whereas the ICU admission proportion remained stable, mortality after ICU admission decreased from 14% (2006-2008) to 3% (2014-2016; p = .056). The number of failing organ systems inversely correlated with surviving ICU admission (p 50% even after 14 days of ICU treatment. Progression-free and overall survival were comparable between ICU surviving patients and patients never needing ICU support. In conclusion, outcome after ICU admission of AML patients has substantially improved in recent years

Sensitivity of ICD coding for sepsis in children-a population-based study.

BACKGROUND International Classification of Diseases 10th edition (ICD-10) is widely used to describe the burden of disease. AIM To describe how well ICD-10 coding captures sepsis in children admitted to the hospital with blood culture-proven bacterial or fungal infection and systemic inflammatory response syndrome. METHODS Secondary analysis of a population-based, multicenter, prospective cohort study on children with blood culture-proven sepsis of nine tertiary pediatric hospitals in Switzerland. We compared the agreement of validated study data on sepsis criteria with ICD-10 coding abstraction obtained at the participating hospitals. RESULTS We analyzed 998 hospital admissions of children with blood culture-proven sepsis. The sensitivity of ICD-10 coding abstraction was 60% (95%-CI 57-63) for sepsis; 35% (95%-CI 31-39) for sepsis with organ dysfunction, using an explicit abstraction strategy; and 65% (95%-CI 61-69) using an implicit abstraction strategy. For septic shock, the sensitivity of ICD-10 coding abstraction was 43% (95%-CI 37-50). Agreement of ICD-10 coding abstraction with validated study data varied by the underlying infection type and disease severity (p < 0.05). The estimated national incidence of sepsis, inferred from ICD-10 coding abstraction, was 12.5 per 100,000 children (95%-CI 11.7-13.5) and 21.0 per 100,000 children (95%-CI 19.8-22.2) using validated study data. CONCLUSIONS In this population-based study, we found a poor representation of sepsis and sepsis with organ dysfunction by ICD-10 coding abstraction in children with blood culture-proven sepsis when compared against a prospective validated research dataset. Sepsis estimates in children based on ICD-10 coding may thus severely underestimate the true prevalence of the disease. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s44253-023-00006-1

Sensitivity of ICD coding for sepsis in children-a population-based study

BACKGROUND International Classification of Diseases 10th edition (ICD-10) is widely used to describe the burden of disease. AIM To describe how well ICD-10 coding captures sepsis in children admitted to the hospital with blood culture-proven bacterial or fungal infection and systemic inflammatory response syndrome. METHODS Secondary analysis of a population-based, multicenter, prospective cohort study on children with blood culture-proven sepsis of nine tertiary pediatric hospitals in Switzerland. We compared the agreement of validated study data on sepsis criteria with ICD-10 coding abstraction obtained at the participating hospitals. RESULTS We analyzed 998 hospital admissions of children with blood culture-proven sepsis. The sensitivity of ICD-10 coding abstraction was 60% (95%-CI 57-63) for sepsis; 35% (95%-CI 31-39) for sepsis with organ dysfunction, using an explicit abstraction strategy; and 65% (95%-CI 61-69) using an implicit abstraction strategy. For septic shock, the sensitivity of ICD-10 coding abstraction was 43% (95%-CI 37-50). Agreement of ICD-10 coding abstraction with validated study data varied by the underlying infection type and disease severity (p < 0.05). The estimated national incidence of sepsis, inferred from ICD-10 coding abstraction, was 12.5 per 100,000 children (95%-CI 11.7-13.5) and 21.0 per 100,000 children (95%-CI 19.8-22.2) using validated study data. CONCLUSIONS In this population-based study, we found a poor representation of sepsis and sepsis with organ dysfunction by ICD-10 coding abstraction in children with blood culture-proven sepsis when compared against a prospective validated research dataset. Sepsis estimates in children based on ICD-10 coding may thus severely underestimate the true prevalence of the disease. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s44253-023-00006-1