Showing papers by "David Prytherch published in 2013"

Patterns in the recording of vital signs and early warning scores: compliance with a clinical escalation protocol

Abstract: Background The recognition of patient deterioration depends largely on identifying abnormal vital signs, yet little is known about the daily pattern of vital signs measurement and charting Methods We compared the pattern of vital signs and VitalPAC Early Warning Score (ViEWS) data collected from admissions to all adult inpatient areas (except high care areas, such as critical care units) of a NHS district general hospital from 1 May 2010 to 30 April 2011, to the hospital's clinical escalation protocol Main outcome measures were hourly and daily patterns of vital signs and ViEWS value documentation; numbers of vital signs in the periods 08:00–11:59 and 20:00–23:59 with subsequent vital signs recorded in the following 6 h; and time to next observation (TTNO) for vital signs recorded in the periods 08:00–11:59 and 20:00–23:59 Results 950 043 vital sign datasets were recorded The daily pattern of observation documentation was not uniform; there were large morning and evening peaks, and lower night-time documentation The pattern was identical on all days 2384% of vital sign datasets with ViEWS ≥ 9 were measured at night compared with 1012–1997% for other ViEWS values 4742% of patients with ViEWS=7–8 and 3122% of those with ViEWS ≥ 9 in the period 20:00–23:59 did not have vital signs recorded in the following 6 h TTNO decreased with increasing ViEWS value, but less than expected by the monitoring protocol Conclusions There was only partial adherence to the vital signs monitoring protocol Sicker patients appear more likely to have vital signs measured overnight, but even their observations were often not followed by timely repeat assessments The observed pattern of monitoring may reflect the impact of competing clinical priorities

Index Blood Tests and National Early Warning Scores within 24 Hours of Emergency Admission Can Predict the Risk of In-Hospital Mortality: A Model Development and Validation Study

Abstract: Background We explored the use of routine blood tests and national early warning scores (NEWS) reported within ±24 hours of admission to predict in-hospital mortality in emergency admissions, using empirical decision Tree models because they are intuitive and may ultimately be used to support clinical decision making. Methodology A retrospective analysis of adult emergency admissions to a large acute hospital during April 2009 to March 2010 in the West Midlands, England, with a full set of index blood tests results (albumin, creatinine, haemoglobin, potassium, sodium, urea, white cell count and an index NEWS undertaken within ±24 hours of admission). We developed a Tree model by randomly splitting the admissions into a training (50%) and validation dataset (50%) and assessed its accuracy using the concordance (c-) statistic. Emergency admissions (about 30%) did not have a full set of index blood tests and/or NEWS and so were not included in our analysis. Results There were 23248 emergency admissions with a full set of blood tests and NEWS with an in-hospital mortality of 5.69%. The Tree model identified age, NEWS, albumin, sodium, white cell count and urea as significant (p<0.001) predictors of death, which described 17 homogeneous subgroups of admissions with mortality ranging from 0.2% to 60%. The c-statistic for the training model was 0.864 (95%CI 0.852 to 0.87) and when applied to the testing data set this was 0.853 (95%CI 0.840 to 0.866). Conclusions An easy to interpret validated risk adjustment Tree model using blood test and NEWS taken within ±24 hours of admission provides good discrimination and offers a novel approach to risk adjustment which may potentially support clinical decision making. Given the nature of the clinical data, the results are likely to be generalisable but further research is required to investigate this promising approach.

Obstetric early warning scores: much more work required

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Combining the National Early Warning Score with an early warning score based on common laboratory test results better discriminates patients at risk of hospital mortality

Abstract: We hypothesised that combining an early warning score (EWS) based exclusively on laboratory tests (LDTEWS) with the National Early Warning Score (NEWS) would improve the discrimination of hospital mortality compared with each system individually. We used a combined electronic database of haematology, biochemistry and vital signs measurements collected routinely soon after admission for 88695 adult admissions for whom the admission specialty was Medicine. The data were divided into 23 sets (Q1-Q23), each corresponding to three months. LDTEWS was generated using decision tree analysis of haematology and biochemistry results for episodes from set Q1. LDTEWS (to haematology and biochemistry results) and NEWS (to vital signs measurements) were then applied in 22 discrete data sets each of three months long (Q2, Q3......Q23) (range of n = 3580 to 4186). A combined EWS was determined for each episode by summing, for each episode, the EWS values for NEWS and LDTEWS. The abilities of NEWS, LDTEWS and the combined EWS to discriminate in-hospital death were assessed using the area under the receiver-operating characteristic (AUROC) curve. The area under the receiver-operating characteristic curve values (95% CI), with in-hospital death as the outcome for the validation sets Q2-Q23: • for LDTEWS, ranged from 0.743 (0.718 to 0.768) (Q10) to 0.799 (0.773 to 0.825) (Q9) • for NEWS, ranged from 0.704 (0.675 to 0.733) (Q7) to 0.759 (0.732 to 0.786) (Q8) • for the combined EWS, ranged from 0.799 (0.776 to 0.822) (Q10) to 0.836 (0.813 to 0.861) (Q21). This study provides evidence that a combined EWS using commonly measured laboratory tests and vital signs better discriminates in-hospital mortality than using either an EWS based on laboratory data or vital signs alone. We hypothesise that, with appropriate modification, the combined EWS could be used for identification of patients at high risk of death in the short term (for example, 24 hour mortality).

Decision tree-early warning scores based on common laboratory test results discriminate patients at risk of hospital mortality

Abstract: We hypothesised that it might be possible to use decision tree (DT) analysis to build an early warning score (EWS) based exclusively on laboratory data to predict patients at risk of in-hospital death early in their hospital stay Using an electronic database of 92354 combined routine haematology and biochemistry tests for adult patients for whom the hospital admission specialty was Medicine, we used DT analysis to generate a laboratory DT EWS (LDTEWS) for each gender DT analysis is a data mining classification technique for building decision trees by recursively splitting or partitioning of datasets into homogenous groups This partitioning is based on derived associations between the chosen outcome – in our case, in-hospital death – and one or more covariates Our tree modelling strategy assessed the following covariates individually: haemoglobin, white cell count, serum urea, serum albumin, serum creatinine, serum sodium, and serum potassium results LDTEWS was developed for a single set (n= 3762) (Q1) and validated in 22 other discrete sets each of three months long (Q2, Q3Q23) (range of n = 3590 to 4341) by testing its ability to discriminate in-hospital death using the area under the receiver-operating characteristic (AUROC) curve As expected, because of different reference ranges for laboratory tests for each gender, the data generated slightly different models for males and females The area under the receiver-operating characteristic curve values (95% CI) for LDTEWS in all patients, irrespective of gender, with in-hospital death as the outcome, ranged from 0748 (0723 to 0772) (Q10) to 0797 (0772 to 0823) (Q9) for the 22 validation sets Q2-Q23 This study provides evidence that the results of commonly measured laboratory tests collected soon after hospital admission can be used in a simple, paper or computer-based early warning score (LDTEWS) to discriminate in-hospital mortality We hypothesise that, with appropriate modification, it might be possible to extend the use of LDTEWS for use on an ongoing basis throughout the patient’s hospital stay

Combining the National Early Warning Score with an early warning score based on Combining the National Early Warning Score with an early warning score based on common laboratory test results better discriminates patients at risk of hospital common laboratory test results better discriminates patients at risk of hospital mortality mortality

Abstract: • LDT-EWS: 0.743 (0.718 to 0.768) (Q10) to 0.799 (0.773 to 0.825) (Q9) • NEWS: 0.704 (0.675 to 0.733) (Q7) to 0.759 (0.732 to 0.786) (Q8) • Combined EWS: 0.799 (0.776 to 0.822) (Q10) to 0.836 (0.813 to 0.861) (Q21) The combined EWS also offered better performance than LDT-EWS or NEWS as measured by an