EHR/EMR

Validation of ICD-9-CM and ICD-10-CM Diagnostic Codes for Figuring out

Introduction

Out-of-hospital cardiac arrest (OHCA) is a world public well being difficulty that causes virtually 3.8 million deaths annually.1 The prevalence and outcomes of OHCA range broadly world wide.2,3 The annual incidence of OHCA handled by emergency medical providers (EMS) ranges from 30.0 to 97.1 per 100,000 folks worldwide, whereas the proportion of survival to discharge varies from 3.1% to twenty.4% based on experiences from totally different areas of the world.2

Though a number of national-level OHCA registries have been used to review the epidemiology of OHCA, most research nonetheless depend on regional-level registries or regionally collected information. Nonetheless, information assortment is usually expensive and time-consuming. In accordance with the Utstein-style registry reporting templates for OHCA, the core reporting parts consist primarily of the affected person standing and interventions obtained through the prehospital and in-hospital levels from cardiac arrest to hospital discharge.4,5 Knowledge on survivors’ long-term outcomes after discharge is usually absent. Therefore, healthcare useful resource utilization by OHCA survivors following preliminary hospital discharge can’t be studied through the use of the Utstein-style OHCA registry. In addition to, most OHCA registries solely acquire EMS-treated OHCAs,2 and OHCA sufferers bypassing the EMS system will not be recorded. The incidence of OHCA might thus be underestimated through the use of such registries.

Furthermore, no nationwide OHCA registry presently exists in Taiwan; solely native information are collected by every county or metropolis’s hearth bureau-based EMS system. The info parts of those native registries additionally range throughout areas. These limitations stop researchers from investigating the affect of well being interventions and useful resource utilization on the long-term outcomes of OHCA survivors and large-scale epidemiological research of OHCA in Taiwan. On this regard, Taiwan’s Nationwide Well being Insurance coverage (NHI) claims database might serve in its place information supply for such research if a validated algorithm to determine OHCA is out there.

To this point, OHCA has seldom been studied utilizing Taiwan’s NHI claims database. Wang et al used the NHI database to look at the prevalence and outcomes of OHCA in Taiwan between 2000 and 2012.6 Hsu et al investigated the chance of OHCA amongst sepsis survivors in Taiwan between 2000 and 2013.7 Nevertheless, the validity of the Worldwide Classification of Ailments (ICD) diagnostic codes used to determine OHCA has not been adequately assessed in these research. In addition to, the coding system transition from ICD-9 to ICD-10 in Taiwan in 2016 might result in inconsistent estimation of illness prevalence throughout the transition.8

To make use of Taiwan’s NHI claims database to develop a nationwide OHCA database for illness surveillance and analysis, we aimed to develop and validate case definition algorithms for OHCA in Taiwan’s NHI claims database utilizing information from two hospitals in Taiwan.

Supplies and Strategies

Research Settings

We performed this retrospective research in two NHI-contracted hospitals in Taiwan. Nearly all of the hospitals in Taiwan are contracted with the NHI. All NHI-contracted hospitals should submit claims to the NHI Administration in a normal format. The dataset for growing case definition algorithms was obtained from the Ditmanson Medical Basis Chia-Yi Christian Hospital, a 1000-bed personal tertiary educating hospital with roughly 220 OHCA admissions yearly. The dataset for exterior validation of the algorithms was obtained from the Taichung Veterans Common Hospital, Chiayi department, a 650-bed public veterans’ hospital with roughly 120 OHCA admissions annually. This hospital is right for exterior validation as a result of it belongs to a big system of 15 veterans’ hospitals spanning city and rural areas of Taiwan. Presently, 13 of them, together with the exterior validation hospital, use the identical digital medical report (EMR) system and well being data system. The research protocol was permitted independently by the institutional evaluation boards of Ditmanson Medical Basis Chia-Yi Christian Hospital (IRB2021080) and Taichung Veterans Common Hospital (SE21370A) with a waiver of knowledgeable consent.

Taiwan’s Nationwide Well being Insurance coverage Claims Database

Taiwan’s NHI program, a obligatory single-payer healthcare system, was initiated in 1995 to offer inexpensive healthcare to all residents in Taiwan. It covers practically all providers wanted for illness analysis and therapy, together with inpatient care, outpatient care, laboratory testing, prescriptions, dental providers, residence care, and preventative providers, however not together with EMS. The NHI claims database comprehensively consists of all of the longitudinal claims data of practically 99.8% of Taiwan’s inhabitants (23.5 million), offering helpful real-world information for learning illness prevalence, long-term prognosis, and healthcare insurance policies.9,10 NHI claims information consists of outpatient claims, inpatient claims, prescriptions allotted at pharmacies, and registries for beneficiaries, medical services, and board-certified specialists. These datasets could be linked with encrypted private identification numbers to offer patient-level data on demographic traits for analysis functions.9 The claims data of sufferers who’re discharged immediately from the emergency division (ED) together with those that die in ED are included within the outpatient claims. In distinction, claims data of ED sufferers who’re finally admitted to the intensive care unit or ward are built-in with the hospitalization claims data, that are included within the inpatient claims. The NHI claims database launched for analysis permits a most of three diagnoses on outpatient claims and 5 diagnoses on inpatient claims. Diagnoses have been coded utilizing the Worldwide Classification of Ailments, Ninth Revision, Scientific Modification (ICD-9-CM) till the tip of 2015. From 2016 onwards, diagnoses are coded utilizing the Worldwide Classification of Ailments, Tenth Revision, Scientific Modification (ICD-10-CM).

Knowledge Sources and Contributors

The info used for growing case definition algorithms had been obtained from the Ditmanson Analysis Database, a deidentified research-based database that accommodates all claims information submitted to the NHI and EMRs. It presently holds scientific data of practically 1.4 million sufferers who had been handled on the Ditmanson Medical Basis Chia-Yi Christian Hospital between January 2006 and February 2021, together with 0.6 million inpatient and 21.5 million outpatient data. We extracted each claims information and medical data of all ED visits between January 2010 and December 2020 to develop case definition algorithms for OHCA. The info used for externally validating the developed algorithms had been extracted from the hospital EMRs and claims information of all ED visits to Taichung Veterans Common Hospital, Chiayi department from March 2019 to December 2020.

Case Definition Algorithms

Desk 1 lists the algorithms used to determine OHCA from the NHI claims information. The algorithms included the presence of ICD diagnostic codes associated to cardiac arrest (CA) (Supplementary Desk 1). Variations to the fundamental algorithm included: 1. addition of ICD diagnostic codes associated to deadly ventricular arrhythmia (VA) (Supplementary Desk 1), 2. the place of the ICD codes, that’s, the first analysis subject, the primary three analysis fields, or any analysis subject, 3. presence of a billing code for triage acuity degree 1 (00201B), and 4. presence of a billing code for cardiopulmonary resuscitation (CPR) (47029C).

Desk 1 Case Definition Algorithms for Out-of-Hospital Cardiac Arrest

Ascertainment of Out-of-Hospital Cardiac Arrest

In Taiwan, the prehospital ambulance data of OHCA should be offered to ED physicians for medical report documentation on affected person arrival on the ED. Subsequently, the affected person standing and procedures carried out earlier than arrival, akin to witnessed standing, bystander CPR, use of the automated exterior defibrillator, preliminary cardiac rhythm, and placement of cardiac arrest, and many others., could be obtained from hospital medical data. On this research, we used two strategies to inclusively determine all sufferers with OHCA, that’s, the ahead key phrase search and the backward ICD code search (Determine 1). Within the ahead search, physicians’ notes of all ED visits through the research interval had been screened for key phrases associated to OHCA (Supplementary Desk 2) to determine potential OHCA circumstances. These key phrases had been curated by manually reviewing the physicians’ notes of a random pattern of 100 OHCA sufferers and had been refined iteratively with 4 rounds of preliminary searches. Within the backward search, ICD diagnostic codes associated to CA or VA had been used to determine potential OHCA circumstances from all inpatient and outpatient claims through the research interval. The medical data of the recognized potential OHCA circumstances had been manually reviewed by two of the investigators (MJT and CFH) to find out if every affected person was a real OHCA based on the Utstein standards.9 Discrepancies between the 2 investigators had been resolved by consensus. The consensus analysis was thought of the gold customary for all analyses (Determine 1).

Determine 1 Circulation chart outlining the true out-of-hospital cardiac arrest verification course of.

Abbreviations: ED, emergency division; ICD, Worldwide Classification of Ailments diagnostic codes; OHCA, out-of-hospital cardiac arrest.

Evaluation and Exterior Validation

The efficiency of every algorithm in figuring out true OHCA circumstances was assessed utilizing sensitivity, specificity, constructive predictive worth (PPV), and damaging predictive worth (NPV) based on the confusion matrix (Supplementary Determine 1). Their 95% confidence intervals (CI) had been calculated utilizing the Clopper-Pearson precise technique. The primary analysis metric was the kappa coefficient, which was calculated to look at the settlement between algorithm-detected OHCA circumstances and true OHCA sufferers. The diploma of settlement was interpreted as follows: “slight” (0.00–0.20), “truthful” (0.21–0.40), “average” (0.41–0.60), “substantial” (0.61–0.80), and “excellent” (0.81–1.00).11

The highest two algorithms with the best kappa coefficients had been externally validated on an unbiased dataset from Taichung Veterans Common Hospital, Chiayi department, the place the same course of was undertaken to determine potential OHCA circumstances. One of many investigators (CHT) used the identical standards to find out if every affected person was a real OHCA. As well as, we carried out subgroup evaluation stratified by ICD coding system (ICD-9-CM or ICD-10-CM), yr of ED visits, age (pediatric [<18 years] or grownup [≥18 years]), and kind of claims (outpatient or inpatient claims). Knowledge analyses had been carried out utilizing Stata 17.0 (StataCorp, Faculty Station, Texas). Statistical significance was set at two-tailed p <0.05.

Outcomes

The event cohort consisted of 985,526 ED visits, together with 803,018 visits from the outpatient claims and 182,508 visits from the inpatient claims. The ahead key phrase search recognized 4303 potential OHCA circumstances, of whom 2337 had been adjudicated to be true OHCAs by guide chart evaluation based on Utstein standards (Determine 1). The backward ICD code search recognized 3026 potential OHCA circumstances, and 2234 of them had been adjudicated as true OHCAs by chart evaluation. In complete, 2429 OHCA circumstances had been ascertained through the research interval.

Outcomes for the validity of the perfect two and all algorithms to determine OHCA are proven in Desk 2 and Supplementary Desk 3, respectively. Essentially the most glorious algorithm was algorithm C, which defines OHCA as any CA-related ICD code (ICD-9-CM codes: 798, 798.1, 798.2, 798.9, 799.9, 427.5; ICD-10-CM codes: R99, I46, I46.2, I46.8, I46.9) within the first three analysis fields, yielding a sensitivity, specificity, PPV, NPV, and kappa of 89.5%, 100%, 90.6%, 100%, and 0.900, respectively. The second-best algorithm was algorithm Okay, which defines OHCA as any CA-related ICD code in any analysis subject with a billing code for triage acuity degree 1. Its sensitivity, specificity, PPV, NPV, and kappa had been 85.6%, 100%, 93.6%, 100%, and 0.894, respectively. Basically, including the billing code for triage acuity degree 1 to the algorithms barely elevated the PPV on the expense of a discount within the sensitivity (Supplementary Desk 3, algorithms G to L vs A to F). Nevertheless, including the billing code for CPR largely decreased the sensitivity and kappa (Supplementary Desk 3, algorithms M to R). The explanations for the false-negative and false-positive identification of OHCA by algorithms C and Okay had been summarized in Desk 3. The most typical purpose for false negatives for each algorithms C and Okay was being coded for underlying ailments with out correctly coded with CA-related ICD codes. One other main purpose for false negatives for algorithm Okay was with no billing code for triage acuity degree 1. Then again, in-hospital cardiac arrest (IHCA) occurring through the hospitalization or these taking place on the ED had been the principle causes of false positives for each algorithms C and Okay.

Desk 2 Efficiency of the Finest Two Algorithms for All Sufferers (n = 985,526)

Desk 3 Causes for False-Unfavorable and False-Constructive Identification of OHCA by Algorithms C and Okay

Algorithms C and Okay had been externally validated on the validation cohort, which consisted of 30,849 ED visits. The sensitivity, specificity, PPV, NPV and kappa of algorithm C had been 98.8%, 99.9%, 93.1%, 100%, and 0.958, respectively, whereas algorithm Okay achieved 98.8%, 100%, 94.8%, 100%, and 0.968, respectively. Each algorithms carried out nicely in figuring out OHCA within the exterior validation cohort.

Within the subgroup evaluation stratified by the ICD coding system (Desk 4 and Supplementary Desk 4), the perfect algorithm through the ICD-9 period was algorithm C, yielding a sensitivity, specificity, PPV, NPV and kappa of 90.8%, 100%, 92.3%, 100%, and 0.915, respectively. In the course of the ICD-10 period, algorithm Okay had the best kappa worth (0.898). Its sensitivity, specificity, PPV and NPV had been 88.4%, 100%, 91.2%, 100%, respectively. We in contrast the sensitivity and PPV of algorithms C and Okay by yr to evaluate the affect of coding system transition. As proven in Determine 2, algorithms C and Okay had an obvious drop within the sensitivity (74.0% and 73.2%, respectively) within the yr 2016, indicating decreased coding accuracy for OHCA simply after the transition of the coding system.

Desk 4 Efficiency of the Finest Algorithms Amongst Completely different Subgroups Stratified by ICD Coding System, Age, and Sort of Claims

Determine 2 The sensitivity and constructive predictive worth of algorithms C (A) and Okay (B) to determine OHCA from 2010 to 2020.

Abbreviations: ICD, Worldwide Classification of Ailments diagnostic codes; PPV, constructive predictive worth.

Within the subgroup evaluation stratified by age (Desk 4 and Supplementary Desk 5). Algorithm C and Okay had kappa values of 0.903 and 0.895, making them the highest two algorithms in grownup sufferers. Nevertheless, these algorithms carried out decrease in pediatric sufferers, with kappa values of 0.800 and 0.845, respectively. The very best algorithm for figuring out pediatric OHCAs was algorithm I, which attained a sensitivity, PPV, and kappa of 74.5%, 100% and 0.854, respectively. Within the subgroup evaluation stratified by kind of claims (Desk 4 and Supplementary Desk 5), the algorithms typically carried out decrease within the inpatient claims than within the outpatient claims. Algorithm C continues to be the perfect algorithm to determine OHCAs within the outpatient claims with a kappa worth of 0.935. Nevertheless, the kappa worth of algorithm Okay (0.848) was superior to algorithm C (0.786) in figuring out OHCAs within the inpatient claims. In different phrases, along with utilizing CA-related diagnostic codes, the billing code for triage acuity degree 1 needs to be added to determine OHCA circumstances within the inpatient claims.

Dialogue

This research assessed the validity of OHCA case definitions based mostly on ICD diagnostic codes and billing codes in Taiwan’s NHI claims database. General, the algorithm that defines OHCA as any CA-related ICD code within the first three analysis fields carried out the perfect, adopted by the algorithm that defines OHCA as any CA-related ICD code in any analysis subject with a billing code for triage acuity degree 1. Each algorithms carried out very nicely within the exterior validation cohort. In subgroup analyses, the previous algorithm additionally carried out the perfect in grownup sufferers, the outpatient claims, and through the ICD-9 period. The latter algorithm had the best efficiency within the inpatient claims and through the ICD-10 period. Then again, the perfect algorithm for figuring out pediatric OHCAs was the one which defines OHCA as any CA-related ICD code within the first three analysis fields with a billing code for triage acuity degree 1.

The primary purpose for the false-positive identification of OHCA through the use of the 2 best-performing algorithms (C and Okay) was the misclassification of circumstances of IHCA that occurred both through the hospitalization or on the ED (Desk 3). Given the dearth of particular ICD codes, it’s all the time a problem to distinguish IHCA from OHCA.12 Nonetheless, the variety of false positives could be lowered by including the billing code for triage acuity degree 1 to the algorithm and limiting CA-related ICD codes to the first analysis subject. For instance, algorithm G, which defines OHCA as any CA-related codes within the major analysis subject with a billing code for triage acuity degree 1, yielded a PPV as excessive as 98.0% (95% CI 97.2–98.6%) (Supplementary Desk 3). Such an algorithm is nicely suited to research the place a cohort completely consisting of OHCA sufferers is required, regardless of risking extra false negatives and decreased sensitivity.

Then again, the false-negative identification of OHCA generally occurred due to the failure to code OHCA visits with correct CA-related ICD codes or the position of CA-related codes outdoors the primary three analysis fields (Desk 3). Stress skilled throughout resuscitation is prone to affect the decision-making and efficiency of ED physicians,13 resulting in both faulty scientific analysis or incorrect or insufficient coding of the analysis. That is one downside of utilizing ICD-based algorithms to retrieve OHCA circumstances from declare databases. Nevertheless, by incorporating extra analysis fields or increasing OHCA-related ICD codes to incorporate VA codes, akin to algorithm F, the variety of false negatives could be lowered with improved sensitivity (Supplementary Desk 3).

Notably, the coding system transition harms the validity of OHCA algorithms. Taiwan’s NHI modified the coding system from ICD-9-CM to ICD-10-CM in 2016. The unfamiliarity with the brand new coding system might enhance coding errors, as proven by an obvious drop in algorithm sensitivity in 2016 (Determine 2). Earlier research have additionally demonstrated that coding system transition might result in inconsistent estimation of illness prevalence in some illness situations.8,14 Subsequently, researchers needs to be cautioned about the usage of ICD code-based algorithms within the yr instantly after coding system transition.

Moreover, the validity of OHCA algorithms might range throughout situations akin to totally different age teams and forms of claims. For instance, algorithms utilizing ICD codes alone, akin to algorithm C, carried out higher in adults than in kids (Supplementary Desk 5). The algorithm efficiency may very well be improved by including the billing code for triage acuity degree 1, as proven by algorithm I. In the meantime, algorithms utilizing ICD codes alone typically carried out worse within the inpatient claims than within the outpatient claims (Supplementary Desk 5). Comparable findings have been proven in earlier research. The explanation could also be that algorithms utilizing CA-related ICD codes alone is prone to determine IHCA relatively than OHCA when inpatients claims are used as the information supply.15,16 On this case, an algorithm that mixes CA-related ICD codes with a billing code for triage acuity degree 1 (algorithm Okay) might be extra acceptable. Contemplating the above findings, researchers interested by utilizing Taiwan’s NHI database for OHCA analysis might check with our findings to pick out the optimum algorithm that most accurately fits their analysis questions.

Up till now, solely restricted research have examined the validity of ICD codes for the analysis of OHCA in well being claims databases or nationwide registries. Within the US, Hennessy et al discovered that the first-listed CA and VA-related ICD-9-CM diagnostic codes (427.1, 427.4, 427.41, 427.42, 427.5, 798, 798.1, 798.2) had an general PPV of 85.3% in figuring out OHCA and VA in inpatient and ED claims of Medicaid and Medicare between 1999 and 2002.15 Shelton et al reported a single-center validation of an ICD-9 code (427.5) for figuring out ED sufferers with OHCA in US administrative databases between 2007 and 2012, with a sensitivity, specificity, PPV, and kappa of 86.5%, 99.4%, 92.0%, and 0.895, respectively.17 Grey et al used single-hospital EMRs and the Canadian Resuscitation Consequence Consortium Database to validate the ICD-10-CM codes together with a diagnostic code of cardiac arrest (I46), or a CPR intervention code mixed with codes for sudden toddler demise syndrome (R95, R99), drowning (W65, W67, W69, W74, T75.1), and asphyxiation (T71, R09.0, R09.2). A sensitivity and PPV of 87.3% and 81.4% had been reported for figuring out pediatric OHCA in Canadian administrative information.18 Nonetheless, validation research on OHCA case definitions utilizing ICD codes are scarce in different nations.19

A number of population-based OHCA research have been performed utilizing administrative databases within the US20–24 and out of doors the US7,25,26 based mostly on the above-mentioned validated OHCA-related diagnostic codes. Nevertheless, as a result of coding practices and reimbursement insurance policies might differ throughout healthcare methods, it shouldn’t be taken without any consideration that the validity of case definitions established in a single database could be extrapolated to a different database. To this point, OHCA has seldom been investigated utilizing Taiwan’s NHI database. One potential purpose stands out as the lack of enough evaluation of the validity of OHCA case definitions. In Wang’s research that investigated the prevalence and outcomes of OHCA utilizing Taiwan’s NHI database,6 OHCA was outlined as ED sufferers coded with one of many death-related ICD-9-CM codes (798, 798.1, 798.2 and 798.9) in addition to a billing code for CPR. Nevertheless, as proven on this research (Supplementary Desk 3), the sensitivity of figuring out OHCA was as little as 50% to 60% when a billing code for CPR was added to the algorithm. As well as, the ICD-9-CM codes utilized in Wang’s research weren’t as complete as ours. Comparable points additionally come up in Hsu’s research that investigated the chance of OHCA amongst sepsis survivors in Taiwan, the place the first-listed ICD-9-CM codes of 427.5 and 798 in addition to a billing code for CPR had been used to outline OHCA.7 With out enough information on the validity of OHCA analysis, their research findings could be tougher to interpret.

Nationwide-level medical health insurance databases, which include the longitudinal healthcare information of the entire inhabitants, present helpful real-world information for analysis investigating epidemiology, long-term outcomes, and healthcare utilization, and contribute to healthcare policy-making.9 Nonetheless, earlier than utilizing such claims databases for analysis of a given illness, the validity of illness analysis needs to be examined. To this finish, we consider that this research will present a crucial and significant reference for future OHCA analysis utilizing Taiwan’s NHI claims database.

This research has limitations price noting. First, the algorithms had been developed utilizing information from just one hospital, similar to some earlier research.17,18 It’s thus unclear whether or not the research findings could be generalized to the entire inhabitants lined by Taiwan’s NHI. Nevertheless, at the very least, the developed algorithms carried out nicely on the dataset from one other hospital. Second, the OHCA algorithms can solely be utilized to research utilizing Taiwan’s NHI database in the intervening time. Nonetheless, future research might examine these algorithms utilizing databases from different healthcare methods with related coding practices. Third, claims information solely consists of sufferers who’ve had contact with the healthcare system. OHCAs not transported to the hospital, akin to sufferers whose resuscitation was terminated and declared useless on the scene or these with a do-not-resuscitate directive, is not going to be recognized from the claims database. Fourth, the case definition algorithms developed on this research can not differentiate traumatic from non-traumatic OHCAs. Additional research are wanted to analyze whether or not and the way these two forms of OHCAs could be distinguished utilizing diagnostic or billing codes obtainable in Taiwan’s NHI database.

Conclusion

Validation of case definitions is required earlier than they are often utilized in epidemiological research utilizing claims databases. Taiwan’s NHI claims database, one of many largest complete administrative claims databases on this planet, presently lacks a validated case definition for OHCA. This research developed and assessed numerous case definition algorithms for OHCA based mostly on ICD diagnostic codes and billing codes for NHI reimbursement. The outcomes of this validation research might assist future researchers in making use of acceptable case definitions for OHCA that greatest go well with their analysis targets.

Acknowledgments

This analysis was funded by the Ditmanson Medical Basis Chia-Yi Christian Hospital Analysis Program (grant quantity R110-18) and the Taichung Veteran’s Common Hospital, Chiayi Department (grant quantity RVHCY111005). The funders of the analysis had no function within the design and conduct of the research, interpretation of the information, or determination to submit for publication. The authors thank the assistance from the Scientific Knowledge Heart, Ditmanson Medical Basis Chia-Yi Christian Hospital for offering administrative and technical help. This research relies partially on information from the Ditmanson Analysis Database (DRD) offered by Ditmanson Medical Basis Chia-Yi Christian Hospital. The interpretation and conclusions contained herein don’t characterize the place of Ditmanson Medical Basis Chia-Yi Christian Hospital. The authors additionally thank Ms. Li-Ying Sung for English language modifying.

Disclosure

The authors report no conflicts of curiosity on this work.

References

1. Kudenchuk PJ, Sandroni C, Drinhaus HR, et al. Breakthrough in cardiac arrest: experiences from the 4th Paris Worldwide Convention. Ann Intensive Care. 2015;5(1):22. doi:10.1186/s13613-015-0064-x

2. Kiguchi T, Okubo M, Nishiyama C, et al. Out-of-hospital cardiac arrest throughout the World: first report from the Worldwide Liaison Committee on Resuscitation (ILCOR). Resuscitation. 2020;152:39–49. doi:10.1016/j.resuscitation.2020.02.044

3. Berdowski J, Berg RA, Tijssen JG, Koster RW. International incidences of out-of-hospital cardiac arrest and survival charges: systematic evaluation of 67 potential research. Resuscitation. 2010;81(11):1479–1487. doi:10.1016/j.resuscitation.2010.08.006

4. Perkins GD, Jacobs IG, Nadkarni VM, et al. Cardiac arrest and cardiopulmonary resuscitation consequence experiences: replace of the utstein resuscitation registry templates for out-of-hospital cardiac arrest: a press release for healthcare professionals from a activity power of the Worldwide Liaison Committee on Resuscitation (American Coronary heart Affiliation, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Coronary heart and Stroke Basis of Canada, InterAmerican Coronary heart Basis, Resuscitation Council of Southern Africa, Resuscitation Council of Asia); and the American Coronary heart Affiliation Emergency Cardiovascular Care Committee and the Council on Cardiopulmonary, Crucial Care, Perioperative and Resuscitation. Circulation. 2015;132(13):1286–1300. doi:10.1161/CIR.0000000000000144

5. Jacobs I, Nadkarni V, Bahr J, et al. Cardiac arrest and cardiopulmonary resuscitation consequence experiences: replace and simplification of the Utstein templates for resuscitation registries: a press release for healthcare professionals from a activity power of the Worldwide Liaison Committee on Resuscitation (American Coronary heart Affiliation, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Coronary heart and Stroke Basis of Canada, InterAmerican Coronary heart Basis, Resuscitation Councils of Southern Africa). Circulation. 2004;110(21):3385–3397. doi:10.1161/01.CIR.0000147236.85306.15

6. Wang CY, Wang JY, Teng NC, et al. The secular traits within the incidence fee and outcomes of out-of-hospital cardiac arrest in Taiwan–a nationwide population-based research. PLoS One. 2015;10(4):e0122675. doi:10.1371/journal.pone.0122675

7. Hsu WT, Sherrod CF, Tehrani B, et al. Danger of out-of-hospital cardiac arrest amongst sepsis survivors in Taiwan: evaluation of a nationwide population-based cohort. BMJ Open. 2021;11(9):e051502. doi:10.1136/bmjopen-2021-051502

8. Hsu MC, Wang CC, Huang LY, Lin CY, Lin FJ, Toh S. Impact of ICD-9-CM to ICD-10-CM coding system transition on identification of frequent situations: an interrupted time sequence evaluation. Pharmacoepidemiol Drug Saf. 2021;30(12):1653–1674. doi:10.1002/pds.5330

9. Hsieh CY, Su CC, Shao SC, et al. Taiwan’s Nationwide medical health insurance analysis database: previous and future. Clin Epidemiol. 2019;11:349–358. doi:10.2147/CLEP.S196293

10. Nationwide Well being Insurance coverage Administration. NHI profile. Common well being protection in Taiwan. Avaiable from: https://www.nhi.gov.tw/English/Content_List.aspx?n=4D7051840BF42F52&topn=ED4A30E51A609E49. Accessed February 24, 2022.

11. Landis JR, Koch GG. The measurement of observer settlement for categorical information. Biometrics. 1977;33(1):159–174. doi:10.2307/2529310

12. Khera R, Spertus JA, Starks MA, et al. Administrative codes for capturing in-hospital cardiac arrest. JAMA Cardiol. 2017;2(11):1275–1277. doi:10.1001/jamacardio.2017.2904

13. Groombridge CJ, Maini A, Ayton D, et al. Emergency physicians’ expertise of stress throughout resuscitation and methods for mitigating the results of stress on efficiency. Emerg Med J. 2021;38(1):14. doi:10.1136/emermed-2020-209432

14. Metcalfe A, Sheikh M, Hetherington E. Impression of the ICD-9-CM to ICD-10-CM transition on the incidence of extreme maternal morbidity amongst supply hospitalizations in the US. Am J Obstet Gynecol. 2021;225(4):422e421–422 e411. doi:10.1016/j.ajog.2021.03.036

15. Hennessy S, Leonard CE, Freeman CP, et al. Validation of diagnostic codes for outpatient-originating sudden cardiac demise and ventricular arrhythmia in Medicaid and medicare claims information. Pharmacoepidemiol Drug Saf. 2010;19(6):555–562. doi:10.1002/pds.1869

16. De Bruin ML, van Hemel NM, Leufkens HG, Hoes AW. Hospital discharge diagnoses of ventricular arrhythmias and cardiac arrest had been helpful for epidemiologic analysis. J Clin Epidemiol. 2005;58(12):1325–1329. doi:10.1016/j.jclinepi.2005.04.009

17. Shelton SK, Chukwulebe SB, Gaieski DF, Abella BS, Carr BG, Perman SM. Validation of an ICD code for precisely figuring out emergency division sufferers who are suffering an out-of-hospital cardiac arrest. Resuscitation. 2018;125:8–11. doi:10.1016/j.resuscitation.2018.01.021

18. Grey Okay, Cameron S, McKenzie Okay, Miller M, Odoardi N, Tijssen JA. Validation of ICD-10 codes for the identification of paediatric out-of-hospital cardiac arrest sufferers. Resuscitation. 2021;171:73–79. doi:10.1016/j.resuscitation.2021.12.017

19. Wittwer MR, Ruknuddeen MI, Thorrowgood M, Zeitz C, Beltrame JF, Arstall MA. Overcoming challenges of creating a hospital-based out-of-hospital cardiac arrest registry: accuracy of case identification utilizing administrative information and scientific registries. Resusc Plus. 2021;6:100136. doi:10.1016/j.resplu.2021.100136

20. Dhopeshwarkar N, Brensinger CM, Bilker WB, et al. Danger of sudden cardiac arrest and ventricular arrhythmia with sulfonylureas: an expertise with conceptual replication in two unbiased populations. Sci Rep. 2020;10(1):10070. doi:10.1038/s41598-020-66668-5

21. Fung KW, Baye F, Kapusnik-Uner J, McDonald CJ. Utilizing medicare information to evaluate the proarrhythmic danger of non-cardiac therapy medicine that extend the QT interval in older adults: an observational cohort research. Medication Actual World Outcomes. 2021;8(2):173–185. doi:10.1007/s40801-021-00230-1

22. Khan MZ, Sulaiman S, Agrawal P, et al. Focused temperature administration in cardiac arrest sufferers with a non-shockable rhythm: a nationwide perspective. Am Coronary heart J. 2020;225:129–137. doi:10.1016/j.ahj.2020.04.023

23. Patel H, Calip GS, DiDomenico RJ, Schumock GT, Suda KJ, Lee TA. Comparability of cardiac occasions related to azithromycin vs amoxicillin. JAMA Netw Open. 2020;3(9):e2016864. doi:10.1001/jamanetworkopen.2020.16864

24. Perman SM, Siry BJ, Ginde AA, et al. Intercourse variations in “don’t try resuscitation” orders after out-of-hospital cardiac arrest and the connection to crucial hospital interventions. Clin Ther. 2019;41(6):1029–1037. doi:10.1016/j.clinthera.2019.03.017

25. Kim JS, Web optimization DW, Kim YJ, et al. Extended size of keep within the emergency division and elevated danger of in-hospital cardiac arrest: a nationwide population-based research in South Korea, 2016–2017. J Clin Med. 2020;9(7):2284. doi:10.3390/jcm9072284

26. Cowan A, Garg AX, McArthur E, Muanda Tsobo F, Weir MA. Cardiovascular security of metoclopramide in comparison with domperidone: a population-based cohort research. J Can Assoc Gastroenterol. 2021;4(5):e110–e119. doi:10.1093/jcag/gwaa041

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