Factors associated with misdiagnosis of hospitalised patients in general hospitals of Central Uganda

Katongole, Simon Peter; Akweongo, Patricia; DDMO, Robert Anguyo; Kasozi, Daniel Evans; Afari, Augustine Adoma

doi:10.1101/2022.09.16.508252

Cited by 3 publications

(1 citation statement)

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“…Failure to obtain and use accurate patients' weights is associated with increased errors in medication dosing and subsequently suboptimal treatment, antimicrobial resistance, and drug toxicities (12)(13)(14). According to a study conducted in four hospitals in Uganda, 58% of the health workers witnessed medication errors and from a review of documents, medication errors were among the most frequent kind of errors, accounting for 17.2% of all the errors (15). In another study, drug overdose was found to be the most common medication error, accounting for 42.9% of all medication errors (16).…”

Section: Introductionmentioning

confidence: 99%

Validation of anthropometric-based weight prediction equations among Ugandan adults: A Cross-sectional study

Mukasa,

Ntuulo,

Kasirye

et al. 2024

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IntroductionProper patient management often requires accurate weight estimation. However, the appropriate weight-measuring equipment is not always available in resource-limited settings, making clinicians resort to less reliable methods like the visual estimation of weight often with negative consequences. In this study, we assess the accuracy of anthropometric-based equations in predicting weight in Ugandan adults.MethodsA cross-sectional study was conducted at Kira Health Center IV between December 2021 and February 2022. A sample of 240 adult patients, 18 years and above, was selected by quota sampling, stratified by sex and body mass index. Demographic information was obtained and anthropometric measurements including weight, height, knee height, subscapular skin fold thickness, and circumference measurements were taken. The predicted weight was computed using the proposed equations and the accuracy of the different equations was determined using Bland Altman analysis taking the equation with the best agreement as the most accurate.ResultsOut of 240 participants, 50% were females. The mean (standard deviation) age was 32.5(11.8) years; (34.1(12.9) years for males and 30.8(10.3) years for females). The mean (standard deviation) actual weight was 66.43 (16.33) kg while the mean height was 1.64 (0.09) meters. Using Bland-Altman analysis, Rabito equation (Weight = (0.5759xMAC) + (0.5263xAC) + (1.2452xCC) - (4.8689xS)-32.9241) was found to have the best agreement with the actual weight with a mean difference (standard deviation) of 2.55(6.99) kilograms overall.ConclusionEquation R3 was the most accurate equation for predicting weight among Ugandan adults. Therefore, in the absence of appropriate weighing scales to weigh patients, using this anthropometric-based weight estimation equation can act as a relatively accurate alternative.

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Section: Introductionmentioning

confidence: 99%

Validation of anthropometric-based weight prediction equations among Ugandan adults: A Cross-sectional study

Mukasa,

Ntuulo,

Kasirye

et al. 2024

Preprint

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Factors associated with misdiagnosis of hospitalised patients in general hospitals of Central Uganda

Katongole¹,

Akweongo

DDMO

et al. 2022

Preprint

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Misdiagnosis of inpatients is a major public health issue whose scope and causes are unknown in Sub-Saharan African countries. The purpose of this cross-sectional study, which was conducted in five hospitals in central Uganda, was to identify the factors associated with inpatient misdiagnosis in general hospitals in Central Uganda. Records of 2,431 patients admitted between July 1st, 2019 and June 30th, 2020 were specifically reviewed to obtain data on variables thought to be associated with misdiagnosis. The admission diagnosis assigned at the emergency or outpatient department was compared to the discharge diagnosis assigned immediately after the patient’s admission, with any difference considered a misdiagnosis. The disease, patient, health system, and environmental factors associated with misdiagnosis were identified using multivariable logistic regression analysis.Misdiagnosis was found in the records of 223/2431 (9.2%) of the admitted patients. A patient admitted to Nakaseke hospital [aOR=1.95, 95% CI=1.17-3.25, p=0.01], being admitted at night [aOR=3, 95% CI=1.81-5.02, p0.01], male patient [aOR=1.89, 95% CI=1.35-2.64, p0.01], patient’s age groups 10-19 [AOR=2.3, 95% CI=2.3-9.25, p0.01]; 20-29 [AOR=8.15, 95% CI=4.18-15.89], p<0.01; 30-39; and 40-49;; AOR=8.12, 95% CI=3.99-16.54, p<0.01; AOR=7.88, 95% CI=3.71-16.73, p<0.01; and AOR=12.14, 95% CI=6.41-23.01, p<0.0]. Misdiagnosis was also associated with multimorbidity (aOR=4.71, 95% CI=1.91-11.65, p0.01) and patients treated for uncommon diseases (aOR=2.57, 95% CI=1.28-5.18, p0.01). Patients without underlying diseases [aOR=0.63; 95% CI=0.43-0.91, p=0.015] and those who were not referred [aOR=0.51; 95% CI=0.31-0.86, p=0] .011] were less likely to be related to misdiagnosis.To improve diagnostic accuracy, hospitals should reorganize patient admission processes, conducted targeted training, develop policy or guidelines targeting factors predisposing to misdiagnosis, and the adopt a diagnostic error prevention culture.

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Prevalence and Classification of Misdiagnosis Among Hospitalised Patients in Five General Hospitals of Central Uganda

Katongole¹,

Akweongo²,

Anguyo³

et al. 2022

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This study sought to determine the magnitude of patients' misdiagnosis, classify the extent to which patients were misdiagnosed, determine the major diagnostic groupings and the topmost misdiagnosed diseases 11th version of the International Classification of Diseases (ICD-11). Methods: We conducted a retrospective review of 2431 patients' records. A patient was adjudged to have been misdiagnosed if there was a difference between the initial diagnosis and the final diagnoses which had been collectively and collaboratively agreed upon review on admission. Misdiagnosis was classified as Class I, Class II, Class III and Class IV. The Pareto analysis was used to determine the topmost misdiagnosed diseases and ICD-11 major diagnostic groupings. We established the three leading misdiagnosed diseases for each of the topmost ICD-11 major diagnostic groupings. Results:The results indicate that 9.2% (223/2341) of the inpatients were initially misdiagnosed. There were 303 conditions involved in the misdiagnosis. The majority, 70.9% (158/223) of the misdiagnosed patients were classified as Class I. The ICD-11 major diagnostic groupings accounted for at least 80% of the misdiagnosis were certain infectious or parasitic diseases (32%), diseases of the digestive system (12%), diseases of the circulatory system (11%), endocrine, nutritional or metabolic disorders (9%), diseases of the respiratory system (7%), diseases of the genitourinary system 20/303 (7%), and diseases of blood and blood forming organs (5%). The 19 topmost misdiagnosed diseases or conditions included; peptic ulcer disease, severe malaria, hypertension, gastroenteritis, pneumonia, urinary tract infection, enteric fever, tuberculosis, septicemia, bacteremia, anaemia, congestive cardiac failure, diabetes mellitus, stroke, chronic obstructive pulmonary disease, respiratory tract infection, cystitis, cryptococcal meningitis, epilepsy and gastritis. Conclusion:Efforts to improve patient diagnosis should focus on the "vital few" topmost misdiagnosed conditions and ICD-11 major diagnostic groupings that account for at least 80% of the burden of the misdiagnosis.

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Factors associated with misdiagnosis of hospitalised patients in general hospitals of Central Uganda

Cited by 3 publications

References 41 publications

Validation of anthropometric-based weight prediction equations among Ugandan adults: A Cross-sectional study

Validation of anthropometric-based weight prediction equations among Ugandan adults: A Cross-sectional study

Factors associated with misdiagnosis of hospitalised patients in general hospitals of Central Uganda

Prevalence and Classification of Misdiagnosis Among Hospitalised Patients in Five General Hospitals of Central Uganda

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