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.

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.

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.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

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.

Serological testing for SARS-CoV-2 antibodies in clinical practice: A comparative diagnostic accuracy study.

BACKGROUND: Serological tests are a powerful tool in the monitoring of infectious diseases and the detection of host immunity. However, manufacturers often provide diagnostic accuracy data generated through biased studies, and the performance in clinical practice is essentially unclear. OBJECTIVES: We aimed to determine the diagnostic accuracy of various serological testing strategies for (a) identification of patients with previous coronavirus disease-2019 (COVID-19) and (b) prediction of neutralizing antibodies against SARS-CoV-2 in real-life clinical settings. METHODS: We prospectively included 2573 consecutive health-care workers and 1085 inpatients with suspected or possible previous COVID-19 at a Swiss University Hospital. Various serological immunoassays based on different analytical techniques (enzyme-linked immunosorbent assays, ELISA; chemiluminescence immunoassay, CLIA; electrochemiluminescence immunoassay, ECLIA; and lateral flow immunoassay, LFI), epitopes of SARS-CoV-2 (nucleocapsid, N; receptor-binding domain, RBD; extended RBD, RBD+; S1 or S2 domain of the spike [S] protein, S1/S2), and antibody subtypes (IgG, pan-Ig) were conducted. A positive real-time PCR test from a nasopharyngeal swab was defined as previous COVID-19. Neutralization assays with live SARS-CoV-2 were performed in a subgroup of patients to assess neutralization activity (n = 201). RESULTS: The sensitivity to detect patients with previous COVID-19 was ≥85% in anti-N ECLIA (86.8%) and anti-S1 ELISA (86.2%). Sensitivity was 84.7% in anti-S1/S2 CLIA, 84.0% in anti-RBD+LFI, 81.0% in anti-N CLIA, 79.2% in anti-RBD ELISA, and 65.6% in anti-N ELISA. The specificity was 98.4% in anti-N ECLIA, 98.3% in anti-N CLIA, 98.2% in anti-S1 ELISA, 97.7% in anti-N ELISA, 97.6% in anti-S1/S2 CLIA, 97.2% in anti-RBD ELISA, and 96.1% in anti-RBD+LFI. The sensitivity to detect neutralizing antibodies was ≥85% in anti-S1 ELISA (92.7%), anti-N ECLIA (91.7%), anti-S1/S2 CLIA (90.3%), anti-RBD+LFI (87.9%), and anti-RBD ELISA (85.8%). Sensitivity was 84.1% in anti-N CLIA and 66.2% in anti-N ELISA. The specificity was ≥97% in anti-N CLIA (100%), anti-S1/S2 CLIA (97.7%), and anti-RBD+LFI (97.9%). Specificity was 95.9% in anti-RBD ELISA, 93.0% in anti-N ECLIA, 92% in anti-S1 ELISA, and 65.3% in anti-N ELISA. Diagnostic accuracy measures were consistent among subgroups. CONCLUSIONS: The diagnostic accuracy of serological tests for SARS-CoV-2 antibodies varied remarkably in clinical practice, and the sensitivity to identify patients with previous COVID-19 deviated substantially from the manufacturer's specifications. The data presented here should be considered when using such tests to estimate the infection burden within a specific population and determine the likelihood of protection against re-infection.

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.

Variation of Daily Care Demand in Swiss General Hospitals: Longitudinal Study on Capacity Utilization, Patient Turnover and Clinical Complexity Levels.

BACKGROUND: Variations in hospitals' care demand relies not only on the patient volume but also on the disease severity. Understanding both daily severity and patient volume in hospitals could help to identify hospital pressure zones to improve hospital-capacity planning and policy-making. OBJECTIVE: This longitudinal study explored daily care demand dynamics in Swiss general hospitals for 3 measures: (1) capacity utilization, (2) patient turnover, and (3) patient clinical complexity level. METHODS: A retrospective population-based analysis was conducted with 1 year of routine data of 1.2 million inpatients from 102 Swiss general hospitals. Capacity utilization was measured as a percentage of the daily maximum number of inpatients. Patient turnover was measured as a percentage of the daily sum of admissions and discharges per hospital. Patient clinical complexity level was measured as the average daily patient disease severity per hospital from the clinical complexity algorithm. RESULTS: There was a pronounced variability of care demand in Swiss general hospitals. Among hospitals, the average daily capacity utilization ranged from 57.8% (95% CI 57.3-58.4) to 87.7% (95% CI 87.3-88.0), patient turnover ranged from 22.5% (95% CI 22.1-22.8) to 34.5% (95% CI 34.3-34.7), and the mean patient clinical complexity level ranged from 1.26 (95% CI 1.25-1.27) to 2.06 (95% CI 2.05-2.07). Moreover, both within and between hospitals, all 3 measures varied distinctly between days of the year, between days of the week, between weekdays and weekends, and between seasons. CONCLUSIONS: While admissions and discharges drive capacity utilization and patient turnover variation, disease severity of each patient drives patient clinical complexity level. Monitoring-and, if possible, anticipating-daily care demand fluctuations is key to managing hospital pressure zones. This study provides a pathway for identifying patients' daily exposure to strained hospital systems for a time-varying causal model.

The association between nurse staffing and inpatient mortality: A shift-level retrospective longitudinal study.

BACKGROUND: Worldwide, hospitals face pressure to reduce costs. Some respond by working with a reduced number of nurses or less qualified nursing staff. OBJECTIVE: This study aims at examining the relationship between mortality and patient exposure to shifts with low or high nurse staffing. METHODS: This longitudinal study used routine shift-, unit-, and patient-level data for three years (2015-2017) from one Swiss university hospital. Data from 55 units, 79,893 adult inpatients and 3646 nurses (2670 registered nurses, 438 licensed practical nurses, and 538 unlicensed and administrative personnel) were analyzed. After developing a staffing model to identify high- and low-staffed shifts, we fitted logistic regression models to explore associations between nurse staffing and mortality. RESULTS: Exposure to shifts with high levels of registered nurses had lower odds of mortality by 8.7% [odds ratio 0.91 95% CI 0.89-0.93]. Conversely, low staffing was associated with higher odds of mortality by 10% [odds ratio 1.10 95% CI 1.07-1.13]. The associations between mortality and staffing by other groups was less clear. For example, both high and low staffing of unlicensed and administrative personnel were associated with higher mortality, respectively 1.03 [95% CI 1.01-1.04] and 1.04 [95% CI 1.03-1.06]. DISCUSSION AND IMPLICATIONS: This patient-level longitudinal study suggests a relationship between registered nurses staffing levels and mortality. Higher levels of registered nurses positively impact patient outcome (i.e. lower odds of mortality) and lower levels negatively (i.e. higher odds of mortality). Contributions of the three other groups to patient safety is unclear from these results. Therefore, substitution of either group for registered nurses is not recommended.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

The Robson classification for caesarean section-A proposed method based on routinely collected health data.

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.

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

BACKGROUND: 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. OBJECTIVE: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Changepoint analysis of gestational age and birth weight: proposing a refinement of Diagnosis Related Groups.

BACKGROUND: Although the complexity and length of treatment is connected to the newborn's maturity and birth weight, most case-mix grouping schemes classify newborns by birth weight alone. The objective of this study was to determine whether the definition of thresholds based on a changepoint analysis of variability of birth weight and gestational age contributes to a more homogenous classification. METHODS: This retrospective observational study was conducted at a Tertiary Care Center with Level III Neonatal Intensive Care and included neonate cases from 2016 through 2018. The institutional database of routinely collected health data was used. The design of this cohort study was explorative. The cases were categorized according to WHO gestational age classes and SwissDRG birth weight classes. A changepoint analysis was conducted. Cut-off values were determined. RESULTS: When grouping the cases according to the calculated changepoints, the variability within the groups with regard to case related costs could be reduced. A refined grouping was achieved especially with cases of >2500 g birth weight. An adjusted Grouping Grid for practical purposes was developed. CONCLUSIONS: A novel method of classification of newborn cases by changepoint analysis was developed, providing the possibility to assign costs or outcome indicators to grouping mechanisms by gestational age and birth weight combined.

Economic Challenges in Nutritional Management.

Disease-related malnutrition (DRM) is a highly prevalent independent risk and cost factor with significant influence on mortality, morbidity, length of hospital stay (LOS), functional impairment and quality of life. The aim of our research was to estimate the economic impact of the introduction of routinely performed nutritional screening (NS) in a tertiary hospital, with subsequent nutritional interventions (NI) in patients with potential or manifest DRM. Economic impact analysis of natural detection of inpatients at risk and estimation of the change in economic activity after the implementation of a systematic NS were performed. The reference population for natural detection of DRM is about 20,000 inpatients per year. Based on current data, DRM prevalence is estimated at 20%, so 4000 patients with potential and manifest DRM should be detected. The NI costs were estimated at CHF 0.693 million, with savings of CHF 1.582 million (LOS reduction) and CHF 0.806 million in additional revenue (SwissDRG system). Thus, the introduction of routine NS generates additional costs of CHF 1.181 million that are compensated by additional savings of CHF 2.043 million and an excess in additional revenue of CHF 2.071 million. NS with subsequent adequate nutritional intervention shows an economic potential for hospitals.

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 < .001). Sepsis and renal, cardiac and pulmonary failure were each associated with higher mortality. With increasing ICU duration, survival probability decreased (p < .001), but remained >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.

Diagnostic value and reliability of the present-on-admission indicator in different diagnosis groups: pilot study at a Swiss tertiary care center.

BACKGROUND: With 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: Coding 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.

Asphyxia in the Newborn: Evaluating the Accuracy of ICD Coding, Clinical Diagnosis and Reimbursement: Observational Study at a Swiss Tertiary Care Center on Routinely Collected Health Data from 2012-2015.

BACKGROUND: The 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. METHODS: Routinely 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. RESULTS: The 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. CONCLUSION: The 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.

Revascularization Treatment of Emergency Patients with Acute ST-Segment Elevation Myocardial Infarction in Switzerland: Results from a Nationwide, Cross-Sectional Study in Switzerland for 2010-2011.

BACKGROUND: Cardiovascular diseases are the leading cause of death worldwide and in Switzerland. When applied, treatment guidelines for patients with acute ST-segment elevation myocardial infarction (STEMI) improve the clinical outcome and should eliminate treatment differences by sex and age for patients whose clinical situations are identical. In Switzerland, the rate at which STEMI patients receive revascularization may vary by patient and hospital characteristics. AIMS: To examine all hospitalizations in Switzerland from 2010-2011 to determine if patient or hospital characteristics affected the rate of revascularization (receiving either a percutaneous coronary intervention or a coronary artery bypass grafting) in acute STEMI patients. DATA AND METHODS: We used national data sets on hospital stays, and on hospital infrastructure and operating characteristics, for the years 2010 and 2011, to identify all emergency patients admitted with the main diagnosis of acute STEMI. We then calculated the proportion of patients who were treated with revascularization. We used multivariable multilevel Poisson regression to determine if receipt of revascularization varied by patient and hospital characteristics. RESULTS: Of the 9,696 cases we identified, 71.6% received revascularization. Patients were less likely to receive revascularization if they were female, and 80 years or older. In the multivariable multilevel Poisson regression analysis, there was a trend for small-volume hospitals performing fewer revascularizations but this was not statistically significant while being female (Relative Proportion = 0.91, 95% CI: 0.86 to 0.97) and being older than 80 years was still associated with less frequent revascularization. CONCLUSION: Female and older patients were less likely to receive revascularization. Further research needs to clarify whether this reflects differential application of treatment guidelines or limitations in this kind of routine data.