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Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda.

ABSTRACT: Mortality rates among hospitalized children in many government hospitals in sub-Saharan Africa are high. Pediatric emergency services in these hospitals are often sub-optimal. Timely recognition of critically ill children on arrival is key to improving service delivery. We present a simple risk score to predict inpatient mortality among hospitalized children. Between April 2010 and June 2011, the Uganda Malaria Surveillance Project (UMSP), in collaboration with the National Malaria Control Program (NMCP), set up an enhanced sentinel site malaria surveillance program for children hospitalized at four public hospitals in different districts: Tororo, Apac, Jinja and Mubende. Clinical data collected through March 2013, representing 50249 admissions were used to develop a mortality risk score (derivation data set). One year of data collected subsequently from the same hospitals, representing 20406 admissions, were used to prospectively validate the performance of the risk score (validation data set). Using a backward selection approach, 13 out of 25 clinical parameters recognizable on initial presentation, were selected for inclusion in a final logistic regression prediction model. The presence of individual parameters was awarded a score of either 1 or 2 based on regression coefficients. For each individual patient, a composite risk score was generated. The risk score was further categorized into three categories; low, medium, and high. Patient characteristics were comparable in both data sets. Measures of performance for the risk score included the receiver operating characteristics curves and the area under the curve (AUC), both demonstrating good and comparable ability to predict deathusing both the derivation (AUC =0.76) and validation dataset (AUC =0.74). Using the derivation and validation datasets, the mortality rates in each risk category were as follows: low risk (0.8% vs. 0.7%), moderate risk (3.5% vs. 3.2%), and high risk (16.5% vs. 12.6%), respectively. Our analysis resulted in development of a risk score that ably predicted mortality risk among hospitalized children. While validation studies are needed, this approach could be used to improve existing triage systems.

SUBMITTER: Mpimbaza A

PROVIDER: S-EPMC4517901 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda.

Mpimbaza Arthur A Sears David D Sserwanga Asadu A Kigozi Ruth R Rubahika Denis D Nadler Adam A Yeka Adoke A Dorsey Grant G

PloS one 20150728 7

Mortality rates among hospitalized children in many government hospitals in sub-Saharan Africa are high. Pediatric emergency services in these hospitals are often sub-optimal. Timely recognition of critically ill children on arrival is key to improving service delivery. We present a simple risk score to predict inpatient mortality among hospitalized children. Between April 2010 and June 2011, the Uganda Malaria Surveillance Project (UMSP), in collaboration with the National Malaria Control Progr ...[more]

PMID: 26218274

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Project description:BackgroundA better understanding of case management practices is required to improve inpatient pediatric care in resource-limited settings. Here we utilize data from a unique health facility-based surveillance system at six Ugandan hospitals to evaluate the quality of pediatric case management and the factors associated with appropriate care.MethodsAll children up to the age of 14 years admitted to six district or regional hospitals over 15 months were included in the study. Four case management categories were defined for analysis: suspected malaria, selected illnesses requiring antibiotics, suspected anemia, and diarrhea. The quality of case management for each category was determined by comparing recorded treatments with evidence-based best practices as defined in national guidelines. Associations between variables of interest and the receipt of appropriate case management were estimated using multivariable logistic regression.ResultsA total of 30,351 admissions were screened for inclusion in the analysis. Ninety-two percent of children met criteria for suspected malaria and 81% received appropriate case management. Thirty-two percent of children had selected illnesses requiring antibiotics and 89% received appropriate antibiotics. Thirty percent of children met criteria for suspected anemia and 38% received appropriate case management. Twelve percent of children had diarrhea and 18% received appropriate case management. Multivariable logistic regression revealed large differences in the quality of care between health facilities. There was also a strong association between a positive malaria diagnostic test result and the odds of receiving appropriate case management for comorbid non-malarial illnesses - children with a positive malaria test were more likely to receive appropriate care for anemia and less likely for illnesses requiring antibiotics and diarrhea.ConclusionsAppropriate management of suspected anemia and diarrhea occurred infrequently. Pediatric quality improvement initiatives should target deficiencies in care unique to each health facility, and interventions should focus on the simultaneous management of multiple diagnoses.

Project description:BackgroundMedication administration error is a medication error that occurs while administering a medication to a patient. A variety of factors make pediatrics more susceptible to medication errors and its consequences. In low-income countries, like Ethiopia, there is no sufficient evidence regarding medication administration error among pediatrics. The aim of this study is, therefore, to determine the magnitude and factors associated with medication administration error among pediatric population.MethodsA prospective observational based cross sectional study design was conducted from January to April 2017. Data collection was done using pre-tested structured questionnaire and blind observation checklist to health professionals in charge of administering selected medications. A total of 1282 medication administrations were obtained using single population proportion formula from patients in the selected public hospitals and the samples were selected using multistage sampling technique. Multivariable logistic regression using odds ratio and 95% confidence interval was used to determine the relationship between the independent and dependent variables. Variables with p-value <?0.05 were considered as independent factors for medication administration error.ResultA total of 1251 medication administrations were observed from 1251 patients. The occurrence of medication administration error was 62.7% with 95% CI (59.6%, 65.0%), wrong dose being the most common type of medication administration error with an occurrence rate of 53.7%. Medications administered for pediatric patients less than 1 month age, administered by bachelor degree holder health professionals, prepared in facilities without medication preparation room, prepared in facilities without medication administration guide and administer for patients who have two or more prescribed medications were more likely to have medication administration error than their counterparts with AOR (95% CI) of 7.54(2.20-25.86), 1.52 (1.07-2.17), 13.45 (8.59-21.06), 4.11 (2.89-5.85), and 2.42 (1.62-3.61), respectively.ConclusionThis study has revealed that there is high occurrence of medication administration error among pediatric inpatients in public hospitals of Tigray, Northern Ethiopia.. Age of patients, educational level of medication administrators, availability of the medication preparation room and guide, and the number of medications given per single patient were statistically significant factors associated with occurrence of medication administration error.

Project description:BackgroundMeasuring the quality of hospital admission care is essential to ensure that standards of practice are met and continuously improved to reduce morbidity and mortality associated with the illnesses most responsible for inpatient deaths. The Paediatric Admission Quality of Care (PAQC) score is a tool for measuring adherence to guidelines for children admitted with acute illnesses in a low-income setting. We aimed to explore the external and criterion-related validity of the PAQC score by investigating its association with mortality using data drawn from a diverse sample of Kenyan hospitals.MethodsWe identified children admitted to Kenyan hospitals for treatment of malaria, pneumonia, diarrhoea, or dehydration from datasets from three sources: an observational study, a clinical trial, and a national cross-sectional survey. We extracted variables describing the process of care provided to patients at admission and their eventual outcomes from these data. We applied the PAQC scoring algorithm to the data to obtain a quality-of-care score for each child. We assessed external validity of the PAQC score by its systematic replication in datasets that had not been previously used to investigate properties of the PAQC score. We assessed criterion-related validity by using hierarchical logistic regression to estimate the association between PAQC score and the outcome of mortality, adjusting for other factors thought to be predictive of the outcome or responsible for heterogeneity in quality of care.FindingsWe found 19?065 eligible admissions in the three validation datasets that covered 27 hospitals, of which 12?969 (68%) were complete cases. Greater guideline adherence, corresponding to higher PAQC scores, was associated with a reduction in odds of death across the three datasets, ranging between 9% (odds ratio 0·91, 95% CI 0·84-0·99; p=0·031) and 30% (0·70, 0·63-0·78; p<0·0001) adjusted reduction per unit increase in the PAQC score, with a pooled estimate of 17% (0·83, 0·78-0·89; p<0·0001). These findings were consistent with a multiple imputation analysis that used information from all observations in the combined dataset.InterpretationThe PAQC score, designed as an index of the technical quality of care for the three commonest causes of admission in children, is also associated with mortality. This finding suggests that it could be a meaningful summary measure of the quality of care for common inpatient conditions and supports a link between process quality and outcome. It might have potential for application in low-income countries with similar disease profiles and in which paediatric practice recommendations are based on WHO guidelines.FundingThe Wellcome Trust.

Project description:BackgroundIdentification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients.MethodsA retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration.ResultsCompared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)).ConclusionsThe COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.

Project description:IntroductionThe last few years have witnessed rapid scaling-up of key malaria interventions in several African countries following increases in development assistance. However, there is only limited country-specific information on the health impact of expanded coverage of these interventions.MethodsPaediatric admission data were assembled from 4 hospitals in Malawi reflecting different malaria ecologies. Trends in monthly clinical malaria admissions between January 2000 and December 2010 were analysed using time-series models controlling for covariates related to climate and service use to establish whether changes in admissions can be related to expanded coverage of interventions aimed at reducing malaria infection.ResultsIn 3 of 4 sites there was an increase in clinical malaria admission rates. Results from time series models indicate a significant month-to-month increase in the mean clinical malaria admission rates at two hospitals (trend P<0.05). At these hospitals clinical malaria admissions had increased from 2000 by 41% to 100%. Comparison of changes in malaria risk and ITN coverage appear to correspond to a lack of disease declines over the period. Changes in intervention coverage within hospital catchments showed minimal increases in ITN coverage from <6% across all sites in 2000 to maximum of 33% at one hospital site by 2010. Additionally, malaria transmission intensity remained unchanged between 2000-2010 across all sites.DiscussionDespite modest increases in coverage of measures to reduce infection there has been minimal changes in paediatric clinical malaria cases in four hospitals in Malawi. Studies across Africa are increasingly showing a mixed set of impact results and it is important to assemble more data from more sites to understand the wider implications of malaria funding investment. We also caution that impact surveillance should continue in areas where intervention coverage is increasing with time, for example Malawi, as decline may become evident within a period when coverage reaches optimal levels.

Project description:BackgroundAlthough the majority of children are hospitalized in nonchildren's hospitals, little is known about the quality and safety of pediatric care in community hospitals.ObjectiveThe aim of this study was to conduct a scoping review and synthesize literature on the quality and safety of pediatric inpatient care in United States community hospitals.MethodsWe performed a systematic literature search in October 2016 to identify pediatric studies that reported on safety, effectiveness, efficiency, timeliness, patient-centeredness, or equity set in general, nonuniversity, or nonchildren's hospitals. We extracted data on study design, patient descriptors, and quality outcomes and assessed the risk of bias using modified Newcastle-Ottawa Scales.ResultsA total of 44 articles met the inclusion criteria. Study designs, patient populations, and quality outcome measures were heterogeneous; only three clinical domains, (1) perinatal regionalization, (2) telemedicine, and (3) imaging radiation, were explored in multiple studies with consistent directionality of results. A total of 30 studies were observational, and 22 studies compared community hospital quality outcomes with other hospital types. The remaining 14 studies reported testing of interventions; 12 showed improved quality of care postintervention. All studies reported an outcome addressing safety, effectiveness, or efficiency, whereas timeliness, patient-centeredness, and equity were infrequently addressed. Risk of bias was moderate or high for 72% of studies.ConclusionsLiterature on the inpatient care of children in community hospitals is limited, making it difficult to evaluate healthcare quality. Measures of timeliness, patient-centeredness, and equity are underrepresented. The field would benefit from more multicenter collaborations to facilitate the application of robust study designs and to enable a systematic assessment of individual interventions and community hospital quality outcomes.

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Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda.

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Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda.

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