SAFETY AND QUALITY CSIRO PUBLISHING
Australian Health Review, 2014, 38, 454–460 http://dx.doi.org/10.1071/AH13166
Exploring in-hospital adverse drug events using ICD-10 codes Sumit Parikh1,6 PhD, Senior Researcher Donna Christensen2 MBA, Co-ordinator Peter Stuchbery3 MPharm, Director Jenny Peterson2 BNurs, Manager Anastasia Hutchinson1,5 PhD, Associate Professor Terri Jackson1,4 PhD, Associate Professor 1
Northern Clinical Research Centre, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: [email protected] 2 Quality, Safety and Risk Management Unit, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: [email protected]; [email protected] 3 Pharmacy Services, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: [email protected] 4 School of Population Health, The University of Melbourne, 207 Bouverie Street, Parkville, Vic. 3010, Australia. Email: [email protected] 5 School of Nursing & Midwifery, Deakin University, 221 Burwood Highway, Burwood, Vic. 3125, Australia. 6 Corresponding author. Email: [email protected]
Abstract Objective. Adverse drug events (ADEs) during hospital admissions are a widespread problem associated with adverse patient outcomes. The ‘external cause’ codes in the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) provide opportunities for identifying the incidence of ADEs acquired during hospital stays that may assist in targeting interventions to decrease their occurrence. The aim of the present study was to use routine administrative data to identify ADEs acquired during hospital admissions in a suburban healthcare network in Melbourne, Australia. Methods. Thirty-nine secondary diagnosis fields of hospital discharge data for a 1-year period were reviewed for ‘diagnoses not present on admission’ and assigned to the Classification of Hospital Acquired Diagnoses (CHADx) subclasses. Discharges with one or more ADE subclass were extracted for retrospective analysis. Results. From 57 205 hospital discharges, 7891 discharges (13.8%) had at least one CHADx, and 402 discharges (0.7%) had an ADE recorded. The highest proportion of ADEs was due to administration of analgesics (27%) and systemic antibiotics (23%). Other major contributors were anticoagulation (13%), anaesthesia (9%) and medications with cardiovascular side-effects (9%). Conclusion. Hospital data coded in ICD-10 can be used to identify ADEs that occur during hospital stays and also clinical conditions, therapeutic drug classes and treating units where these occur. Using the CHADx algorithm on administrative datasets provides a consistent and economical method for such ADE monitoring. What is known about the topic? Adverse drug events (ADEs) can result in several different physical consequences, ranging from allergic reactions to death, thereby posing a significant burden on patients and the health system. Numerous studies have compared manual, written incident reporting systems used by hospital staff with computerised automated systems to identify ADEs acquired during hospital admissions. Despite various approaches aimed at improving the detection of ADEs, they remain under-reported, as a result of which interventions to mitigate the effect of ADEs cannot be initiated effectively. What does this paper add? This research article demonstrates major methodological advances over comparable published studies looking at the effectiveness of using routine administrative data to monitor rates of ADEs that occur during a hospital stay and reviews the type of ADEs and their frequency patterns during patient admission. It also provides an insight into the effect of ADEs that occur within different hospital treating units. The method implemented in this study is unique because it uses a grouping algorithm developed for the Australian Commission on Safety and Quality in Health Care (ACSQHC) to identify ADEs not present on admission from patient data coded in ICD-10. This algorithm links the coded Journal compilation AHHA 2014
www.publish.csiro.au/journals/ahr
ADEs in ICD-10
Australian Health Review
455
external causes of ADEs with their consequences or manifestations. ADEs identified through the use of programmed code based on this algorithm have not been studied in the past and therefore this paper adds to previous knowledge in this subject area. What are the implications for health professionals? Although not all ADEs can be prevented with current medical knowledge, this study can assist health professionals in targeting interventions that can efficiently reduce the rate of ADEs that occur during a hospital stay, and improve information available for future medication management decisions. Additional keywords: adverse drug reaction reporting systems, drug toxicity classification, drug toxicity diagnosis, international classification of diseases, patient admission, prescriptions, statistics and numerical data. Received 3 September 2013, accepted 2 March 2014, published online 29 May 2014
Introduction Adverse drug events (ADEs) can be defined as any unfavourable medical event that occurs in association with the use of a medication; this includes medication side-effects and complications, as well as prescribing or administration errors.1 ADEs are estimated to be one of the largest causes of hospital-acquired patient harm, and their occurrence during hospital stays has been reported in the range of 2%–4% of admissions to Australian Hospitals.2,3 Quality improvement initiatives typically focus on process improvement activities to mitigate the risks associated with incorrect medication prescribing and administration, with relatively little focus on interventions to decrease the incidence of medication side-effects and complications. One example of this approach is the use of computerised medication order software systems, which have been shown to reduce errors in prescribing and dispensing4,5 but may have little impact on other sources of medication complications. One of the challenges for quality improvement units and hospital administrators is to identify high-risk areas (clinical conditions, diagnosis groups) or pharmaceuticals that are associated with an increased likelihood of ADEs so that interventions can be focused on these areas. The present study was undertaken to explore whether routinely collected hospital administrative data could be used effectively to identify areas at high risk of ADEs during hospital admissions so that preventive interventions can be prioritised. Hospital voluntary adverse event reporting systems rely on clinicians to identify and report ADEs. This approach, although useful for identifying significant ‘errors’, may underestimate the true incidence of ADEs, because there may be under-reporting of incidents that are viewed as ‘expected’ drug reactions or errors with no evident harm. There is some evidence that automatic reporting systems improve ADE detection and may improve hospital monitoring systems.6 The expanded set of codes in the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10 Australian Modification7) and the mandated coding of a ‘condition-onset flag’ (CoF) for all Australian hospital coding in 2008 allow the identification of hospital-acquired diagnoses from all secondary diagnoses coded from the medical record. Development of the Classification of Hospital Acquired Diagnoses (CHADx) was funded by the Australian Commission on Safety and Quality in Health Care (ACSQHC), a government agency leading and co-coordinating
national improvements in safety and quality in healthcare across Australia.8 One of the major grouping classes of the CHADx system is designated ‘MCHADx2’ and refers to ADEs acquired during the hospital stay. Our objective was to explore the potential to apply MCHADx2 to routinely collected hospital data to identify the incidence of ADEs acquired during hospital admissions and to identify correlates of ADEs as a way to prioritise and monitor interventions so as to decrease their occurrence. Methods Setting Northern Health is a suburban health network (including 300 acute care beds and 143 subacute beds (80 geriatric evaluation and management (GEM) beds, 43 rehabilitation beds and 20 inpatient palliative care beds) across four campuses) and is the key provider of public acute and subacute health care in northern metropolitan Melbourne, Australia. This study was considered and approved by the Low Risk Ethics Committee of Northern Health. Definition Following the ACSQHC,9 we defined an adverse medicines event as: . . .an adverse event due to a medicine. This includes the harm that results from the medicine itself (an adverse drug reaction) and the potential or actual patient harm that comes from errors or system failures associated with the preparation, prescribing, dispensing, distribution or administration of medicines.9 Thus, we consider the full range of adverse patient outcomes, but limited to those that occur in an inpatient setting. Although the Commission prefers to term these ‘medicines events’ (and has no definition for an ‘ADE’), we have used these terms because they are more common in hospital usage. Sample Patient-level diagnosis and demographic data were extracted from the data Northern Health submits to the Victorian Admission Episode Dataset (VAED)10 for all acute and subacute discharges between 1 July 2011 and 30 June 2012. Basic sample characteristics are summarised in Table 1 compared with admissions having no recorded ADE.
456
Australian Health Review
S. Parikh et al.
Outcome measures The CHADx groups over 4500 ICD-10 codes into a hierarchy of 17 classes and 145 subclasses that are used to characterise hospital-acquired complications from the coded diagnosis fields for patient admissions.11 The CoF is used to distinguish incident cases (arising after hospital admission) for our analysis. Coding standards mandate coding of both the external cause of a drugrelated problem and the clinical consequences, and the CHADx algorithm makes use of both codes to define an ADE. MCHADx2 comprises 18 subclasses of adverse drug effects grouped according to drug class (see Table 2). The ACSQHC algorithm that mapped the classification to ICD-10 Australian Modification (7th edn)7 and refined the counting rules associated with allocating diagnoses was used to calculate the incidence for each CHADx subclass.12 A software code based on that 7th edition of the ACSQHC algorithm was programmed in SAS (SAS Institute, Cary, NC, USA), and executed on the 39 secondary diagnosis fields with corresponding CoF of the patient hospital discharge data to identify and assign CHADx subclasses. Where a patient admission contained several Table 1. Description of the Northern Health sample (2011–12) ADE, adverse drug event; ALOS, average length of stay; CHADx, Classification of Hospital Acquired Diagnoses With ADE Without ADE No. admissions % Same-day admissions % Men ALOS (days) No. patients with any CHADx (%) Mean no. CHADx per admission
402 10.2% 40.5% 11 402 (100%) 2.55
56 803 41.2% 47.0% 4 7489 (13.2%) 1.86
CHADx subclasses, each was counted and collectively added to its respective MCHADx group. Discharges with one or more of the Major CHADx2 Adverse Drug Effects classes were extracted for further analysis and incidence rates were calculated for each of the 18 subclasses. A new variable, ‘ADE per bed day’, was calculated for each admission as a ratio of the rate of ADEs to length of stay for that admission. A Charlson comorbidity score was also calculated for each admission using the coding algorithm developed and adapted for SAS by Quan et al.13. Adjacent diagnosis related groups (A-DRGs; those belonging to the same diagnosis category but varying in severity) were considered together to identify the top 10 diagnosis groups affected by ADEs during hospital admissions. In determining which was the treating unit, we first identified patients treated in specific hospital units (i.e. obstetrics, emergency department (ED), short stay unit (SSU), aged care (GEM and palliative care units)). In a second step, patients treated in general ward areas were assigned on the basis of whether their DRG was a surgical or medical one. For comparison with the CHADx ADE frequencies, reports of ‘Adverse Drug Events’ recorded on the hospital incident reporting database (RiskMan, RiskMan International, Melbourne, Vic., Australia, http://www.riskman.net.au, verified 15 August 2013) for the same 12-month period were obtained.
Total 57 205 40.1% 45.2% 4 7891 (13.8%) 0.26
Results The Northern Health data extract included details of 57 205 discharges from 614 DRGs across all four campuses, including episodes of care for adult, paediatric and neonatal patients. A CHADx was identified in 7891 (13.8%) hospital admissions including any MCHADx2 recorded in 402 (0.7%) hospital admissions (433 events), as summarised in Table 1. Patients who
Table 2. Adverse drug event by Classification of Hospital Acquired Diagnoses grouped by Drug Class, Northern Health, inpatient episodes, 2011–12 ADE, adverse drug event; CHADx, Classification of Hospital Acquired Diagnoses; CV, cardiovascular; MCHADx, major CHADx Drug class
ADE CHADx
No. events
ADE (%)
MCHADx (%)
Opioids/analgesics
2_7: Nausea and vomiting related to opioids and related analgesics 2_8: Alterations to mental state related to opioids and related analgesics 2_9: Other adverse effects related to opioids and related analgesics
27 33 58
6.2 7.6 13.4
27.3
Antibiotics
2_1: Skin adverse effects related to systemic antibiotics 2_2: Other adverse effects related to systemic antibiotics
33 65
7.6 15.0
22.6
Anticoagulants Blood transfusion Anaesthetics
2_5: Coagulation defect related to drugs affecting blood constituents 2_6: Other adverse effects related to drugs affecting blood constituents 2_10: Adverse effects related to anaesthesia (including misadventure) 2_11: Hypotension related to anaesthesia 2_12: Alterations to mental state related to anaesthesia
54 39 27 8 2
12.5 9.0 6.2 1.9 0.5
12.5 9.0 8.6
Cardiovascular
2_13: Other adverse effects related to drugs affecting CV system 2_14: Hypotension related to drugs affecting CV system
11 26
2.5 6.0
8.5
Antineoplastics
2_3: Nausea and vomiting related to antineoplastic drugs 2_4: Other adverse effects related to antineoplastic drugs
1 13
0.2 3.0
3.2
Drug administration Other Anaphylaxis Endocrine
2_18: Incorrect drug dosage and/or combination administered 2_16: Adverse effects related to other drugs 2_17: Anaphylactic shock related to correct drug properly administered 2_15: Adverse effects related to insulin and oral hypoglycaemic
12 10 8 6
2.8 2.3 1.9 1.4
2.8 2.3 1.9 1.4
433
100
100
Total
ADEs in ICD-10
Australian Health Review
experienced an ADE during their hospital stay were older and more likely to be female than patients with no ADE. Only 12 ADEs (3%) were recorded for patients
Australian Health Review, 2014, 38, 454–460 http://dx.doi.org/10.1071/AH13166
Exploring in-hospital adverse drug events using ICD-10 codes Sumit Parikh1,6 PhD, Senior Researcher Donna Christensen2 MBA, Co-ordinator Peter Stuchbery3 MPharm, Director Jenny Peterson2 BNurs, Manager Anastasia Hutchinson1,5 PhD, Associate Professor Terri Jackson1,4 PhD, Associate Professor 1
Northern Clinical Research Centre, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: [email protected] 2 Quality, Safety and Risk Management Unit, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: [email protected]; [email protected] 3 Pharmacy Services, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: [email protected] 4 School of Population Health, The University of Melbourne, 207 Bouverie Street, Parkville, Vic. 3010, Australia. Email: [email protected] 5 School of Nursing & Midwifery, Deakin University, 221 Burwood Highway, Burwood, Vic. 3125, Australia. 6 Corresponding author. Email: [email protected]
Abstract Objective. Adverse drug events (ADEs) during hospital admissions are a widespread problem associated with adverse patient outcomes. The ‘external cause’ codes in the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) provide opportunities for identifying the incidence of ADEs acquired during hospital stays that may assist in targeting interventions to decrease their occurrence. The aim of the present study was to use routine administrative data to identify ADEs acquired during hospital admissions in a suburban healthcare network in Melbourne, Australia. Methods. Thirty-nine secondary diagnosis fields of hospital discharge data for a 1-year period were reviewed for ‘diagnoses not present on admission’ and assigned to the Classification of Hospital Acquired Diagnoses (CHADx) subclasses. Discharges with one or more ADE subclass were extracted for retrospective analysis. Results. From 57 205 hospital discharges, 7891 discharges (13.8%) had at least one CHADx, and 402 discharges (0.7%) had an ADE recorded. The highest proportion of ADEs was due to administration of analgesics (27%) and systemic antibiotics (23%). Other major contributors were anticoagulation (13%), anaesthesia (9%) and medications with cardiovascular side-effects (9%). Conclusion. Hospital data coded in ICD-10 can be used to identify ADEs that occur during hospital stays and also clinical conditions, therapeutic drug classes and treating units where these occur. Using the CHADx algorithm on administrative datasets provides a consistent and economical method for such ADE monitoring. What is known about the topic? Adverse drug events (ADEs) can result in several different physical consequences, ranging from allergic reactions to death, thereby posing a significant burden on patients and the health system. Numerous studies have compared manual, written incident reporting systems used by hospital staff with computerised automated systems to identify ADEs acquired during hospital admissions. Despite various approaches aimed at improving the detection of ADEs, they remain under-reported, as a result of which interventions to mitigate the effect of ADEs cannot be initiated effectively. What does this paper add? This research article demonstrates major methodological advances over comparable published studies looking at the effectiveness of using routine administrative data to monitor rates of ADEs that occur during a hospital stay and reviews the type of ADEs and their frequency patterns during patient admission. It also provides an insight into the effect of ADEs that occur within different hospital treating units. The method implemented in this study is unique because it uses a grouping algorithm developed for the Australian Commission on Safety and Quality in Health Care (ACSQHC) to identify ADEs not present on admission from patient data coded in ICD-10. This algorithm links the coded Journal compilation AHHA 2014
www.publish.csiro.au/journals/ahr
ADEs in ICD-10
Australian Health Review
455
external causes of ADEs with their consequences or manifestations. ADEs identified through the use of programmed code based on this algorithm have not been studied in the past and therefore this paper adds to previous knowledge in this subject area. What are the implications for health professionals? Although not all ADEs can be prevented with current medical knowledge, this study can assist health professionals in targeting interventions that can efficiently reduce the rate of ADEs that occur during a hospital stay, and improve information available for future medication management decisions. Additional keywords: adverse drug reaction reporting systems, drug toxicity classification, drug toxicity diagnosis, international classification of diseases, patient admission, prescriptions, statistics and numerical data. Received 3 September 2013, accepted 2 March 2014, published online 29 May 2014
Introduction Adverse drug events (ADEs) can be defined as any unfavourable medical event that occurs in association with the use of a medication; this includes medication side-effects and complications, as well as prescribing or administration errors.1 ADEs are estimated to be one of the largest causes of hospital-acquired patient harm, and their occurrence during hospital stays has been reported in the range of 2%–4% of admissions to Australian Hospitals.2,3 Quality improvement initiatives typically focus on process improvement activities to mitigate the risks associated with incorrect medication prescribing and administration, with relatively little focus on interventions to decrease the incidence of medication side-effects and complications. One example of this approach is the use of computerised medication order software systems, which have been shown to reduce errors in prescribing and dispensing4,5 but may have little impact on other sources of medication complications. One of the challenges for quality improvement units and hospital administrators is to identify high-risk areas (clinical conditions, diagnosis groups) or pharmaceuticals that are associated with an increased likelihood of ADEs so that interventions can be focused on these areas. The present study was undertaken to explore whether routinely collected hospital administrative data could be used effectively to identify areas at high risk of ADEs during hospital admissions so that preventive interventions can be prioritised. Hospital voluntary adverse event reporting systems rely on clinicians to identify and report ADEs. This approach, although useful for identifying significant ‘errors’, may underestimate the true incidence of ADEs, because there may be under-reporting of incidents that are viewed as ‘expected’ drug reactions or errors with no evident harm. There is some evidence that automatic reporting systems improve ADE detection and may improve hospital monitoring systems.6 The expanded set of codes in the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10 Australian Modification7) and the mandated coding of a ‘condition-onset flag’ (CoF) for all Australian hospital coding in 2008 allow the identification of hospital-acquired diagnoses from all secondary diagnoses coded from the medical record. Development of the Classification of Hospital Acquired Diagnoses (CHADx) was funded by the Australian Commission on Safety and Quality in Health Care (ACSQHC), a government agency leading and co-coordinating
national improvements in safety and quality in healthcare across Australia.8 One of the major grouping classes of the CHADx system is designated ‘MCHADx2’ and refers to ADEs acquired during the hospital stay. Our objective was to explore the potential to apply MCHADx2 to routinely collected hospital data to identify the incidence of ADEs acquired during hospital admissions and to identify correlates of ADEs as a way to prioritise and monitor interventions so as to decrease their occurrence. Methods Setting Northern Health is a suburban health network (including 300 acute care beds and 143 subacute beds (80 geriatric evaluation and management (GEM) beds, 43 rehabilitation beds and 20 inpatient palliative care beds) across four campuses) and is the key provider of public acute and subacute health care in northern metropolitan Melbourne, Australia. This study was considered and approved by the Low Risk Ethics Committee of Northern Health. Definition Following the ACSQHC,9 we defined an adverse medicines event as: . . .an adverse event due to a medicine. This includes the harm that results from the medicine itself (an adverse drug reaction) and the potential or actual patient harm that comes from errors or system failures associated with the preparation, prescribing, dispensing, distribution or administration of medicines.9 Thus, we consider the full range of adverse patient outcomes, but limited to those that occur in an inpatient setting. Although the Commission prefers to term these ‘medicines events’ (and has no definition for an ‘ADE’), we have used these terms because they are more common in hospital usage. Sample Patient-level diagnosis and demographic data were extracted from the data Northern Health submits to the Victorian Admission Episode Dataset (VAED)10 for all acute and subacute discharges between 1 July 2011 and 30 June 2012. Basic sample characteristics are summarised in Table 1 compared with admissions having no recorded ADE.
456
Australian Health Review
S. Parikh et al.
Outcome measures The CHADx groups over 4500 ICD-10 codes into a hierarchy of 17 classes and 145 subclasses that are used to characterise hospital-acquired complications from the coded diagnosis fields for patient admissions.11 The CoF is used to distinguish incident cases (arising after hospital admission) for our analysis. Coding standards mandate coding of both the external cause of a drugrelated problem and the clinical consequences, and the CHADx algorithm makes use of both codes to define an ADE. MCHADx2 comprises 18 subclasses of adverse drug effects grouped according to drug class (see Table 2). The ACSQHC algorithm that mapped the classification to ICD-10 Australian Modification (7th edn)7 and refined the counting rules associated with allocating diagnoses was used to calculate the incidence for each CHADx subclass.12 A software code based on that 7th edition of the ACSQHC algorithm was programmed in SAS (SAS Institute, Cary, NC, USA), and executed on the 39 secondary diagnosis fields with corresponding CoF of the patient hospital discharge data to identify and assign CHADx subclasses. Where a patient admission contained several Table 1. Description of the Northern Health sample (2011–12) ADE, adverse drug event; ALOS, average length of stay; CHADx, Classification of Hospital Acquired Diagnoses With ADE Without ADE No. admissions % Same-day admissions % Men ALOS (days) No. patients with any CHADx (%) Mean no. CHADx per admission
402 10.2% 40.5% 11 402 (100%) 2.55
56 803 41.2% 47.0% 4 7489 (13.2%) 1.86
CHADx subclasses, each was counted and collectively added to its respective MCHADx group. Discharges with one or more of the Major CHADx2 Adverse Drug Effects classes were extracted for further analysis and incidence rates were calculated for each of the 18 subclasses. A new variable, ‘ADE per bed day’, was calculated for each admission as a ratio of the rate of ADEs to length of stay for that admission. A Charlson comorbidity score was also calculated for each admission using the coding algorithm developed and adapted for SAS by Quan et al.13. Adjacent diagnosis related groups (A-DRGs; those belonging to the same diagnosis category but varying in severity) were considered together to identify the top 10 diagnosis groups affected by ADEs during hospital admissions. In determining which was the treating unit, we first identified patients treated in specific hospital units (i.e. obstetrics, emergency department (ED), short stay unit (SSU), aged care (GEM and palliative care units)). In a second step, patients treated in general ward areas were assigned on the basis of whether their DRG was a surgical or medical one. For comparison with the CHADx ADE frequencies, reports of ‘Adverse Drug Events’ recorded on the hospital incident reporting database (RiskMan, RiskMan International, Melbourne, Vic., Australia, http://www.riskman.net.au, verified 15 August 2013) for the same 12-month period were obtained.
Total 57 205 40.1% 45.2% 4 7891 (13.8%) 0.26
Results The Northern Health data extract included details of 57 205 discharges from 614 DRGs across all four campuses, including episodes of care for adult, paediatric and neonatal patients. A CHADx was identified in 7891 (13.8%) hospital admissions including any MCHADx2 recorded in 402 (0.7%) hospital admissions (433 events), as summarised in Table 1. Patients who
Table 2. Adverse drug event by Classification of Hospital Acquired Diagnoses grouped by Drug Class, Northern Health, inpatient episodes, 2011–12 ADE, adverse drug event; CHADx, Classification of Hospital Acquired Diagnoses; CV, cardiovascular; MCHADx, major CHADx Drug class
ADE CHADx
No. events
ADE (%)
MCHADx (%)
Opioids/analgesics
2_7: Nausea and vomiting related to opioids and related analgesics 2_8: Alterations to mental state related to opioids and related analgesics 2_9: Other adverse effects related to opioids and related analgesics
27 33 58
6.2 7.6 13.4
27.3
Antibiotics
2_1: Skin adverse effects related to systemic antibiotics 2_2: Other adverse effects related to systemic antibiotics
33 65
7.6 15.0
22.6
Anticoagulants Blood transfusion Anaesthetics
2_5: Coagulation defect related to drugs affecting blood constituents 2_6: Other adverse effects related to drugs affecting blood constituents 2_10: Adverse effects related to anaesthesia (including misadventure) 2_11: Hypotension related to anaesthesia 2_12: Alterations to mental state related to anaesthesia
54 39 27 8 2
12.5 9.0 6.2 1.9 0.5
12.5 9.0 8.6
Cardiovascular
2_13: Other adverse effects related to drugs affecting CV system 2_14: Hypotension related to drugs affecting CV system
11 26
2.5 6.0
8.5
Antineoplastics
2_3: Nausea and vomiting related to antineoplastic drugs 2_4: Other adverse effects related to antineoplastic drugs
1 13
0.2 3.0
3.2
Drug administration Other Anaphylaxis Endocrine
2_18: Incorrect drug dosage and/or combination administered 2_16: Adverse effects related to other drugs 2_17: Anaphylactic shock related to correct drug properly administered 2_15: Adverse effects related to insulin and oral hypoglycaemic
12 10 8 6
2.8 2.3 1.9 1.4
2.8 2.3 1.9 1.4
433
100
100
Total
ADEs in ICD-10
Australian Health Review
experienced an ADE during their hospital stay were older and more likely to be female than patients with no ADE. Only 12 ADEs (3%) were recorded for patients
